Post on 11-Aug-2020
NEW BEHAVIORAL PARADIGMS TO STUDY TASTE-QUALITY
GENERALIZATION AND DISCRIMINATION IN RATS
By
CONNIE LYNN GROBE
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2006
Copyright 2006
by
Connie L. Grobe
This dissertation is dedicated to my brother, Robert.
ACKNOWLEDGMENTS
I thank my family, and friends. Their constant support has made it possible to
achieve my goals. At the University of Florida, I have been lucky to meet and interact
with some very talented people, who have each helped to shape my character along the
way. I especially recognize (in chronological order) Laura Tucker, Laura Geran, Cheryl
Vaughan, Shachar Amdur, Mary Clinton, Ginger Blonde, Shawn Dotson, Kathryn
Saulsgiver, Anaya Mitra, and Yada Treesukosol. They have provided me with
encouragement, assistance, advice, and countless other acts of kindness that I will never
fully understand, but always deeply appreciate.
I thank the entire faculty in the Behavioral Neuroscience area for contributing to
my education. I also gratefully acknowledge the help and guidance that I received from
Dr. Neil Rowland, my M.S. advisor and Dr. Alan Spector, my dissertation advisor. They
have each provided me with a perspective on science that I will continue to value and can
only hope to incorporate into my own future scientific approach.
Finally, I cannot thank my husband, Justin Grobe, enough for his unwavering love,
patience, and encouragement, but especially for his exemplary scholarship.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS ................................................................................................. iv
LIST OF TABLES............................................................................................................. ix
LIST OF FIGURES ........................................................................................................... xi
ABSTRACT..................................................................................................................... xiii
CHAPTER
1 LITERATURE REVIEW .............................................................................................1
Introduction...................................................................................................................1 Domains of Taste..........................................................................................................3
Sensory Discriminative Domain............................................................................4 Affective Domain ..................................................................................................4 Physiological Domain ...........................................................................................5
Taste Quality.................................................................................................................5 Animal Models Used to Study Taste Quality ...............................................................6
Discrimination Tasks.............................................................................................6 Generalization Tasks ...........................................................................................10 Ideal Psychophysical Task ..................................................................................13
Importance of Psychophysical Analysis in Animal Models.......................................13 Argument for the Development of Psychophysical Tasks .........................................14
2 RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO SAMPLE TASK USING ONLY TASTE STIMULI .................................................16
Background.................................................................................................................16 Method........................................................................................................................17
Animals................................................................................................................17 Apparatus.............................................................................................................17 Stimuli .................................................................................................................18 Surgery ................................................................................................................19 Training and Testing Phases................................................................................20
Spout training ...............................................................................................20 Side training .................................................................................................21 Alternation....................................................................................................21
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Discrimination training I-II ..........................................................................21 Trial structure (final parameters)..................................................................22
Testing .................................................................................................................23 Adjustments to Testing Parameters .....................................................................23 Statistical Analyses..............................................................................................24
Results.........................................................................................................................24 Overall Performance............................................................................................24 Performance on Same Trials ...............................................................................25 Performance on Different Trials..........................................................................25 Performance on Same Trials versus Different Trials ..........................................25
Discussion...................................................................................................................26
3 A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION IN RATS.....................................................................................................................35
Introduction.................................................................................................................35 Experiment I ...............................................................................................................38
Method.................................................................................................................38 Subjects ........................................................................................................38 Training Stimuli ...........................................................................................38 Procedure......................................................................................................38
Data Analysis.......................................................................................................40 Results .................................................................................................................41 Discussion............................................................................................................42
Experiment II ..............................................................................................................43 Method.................................................................................................................44
Subjects ........................................................................................................44 Apparatus .....................................................................................................44 Task overview ..............................................................................................44 Stimuli ..........................................................................................................45 Groups ..........................................................................................................45 Trial structure ...............................................................................................45 Training ........................................................................................................46 Test compounds............................................................................................48 Retraining water as a comparison stimulus..................................................49 Negative control test.....................................................................................49 Data analysis ................................................................................................49 Generalization score .....................................................................................50
Results .................................................................................................................51 Novel concentrations: NaCl .........................................................................52 Novel concentrations: Sucrose .....................................................................53 Novel concentrations: Quinine.....................................................................53 Novel concentrations: Citric acid.................................................................54 Mixtures between NaCl and sucrose: 1.07 M NaCl + 0.421 M sucrose ......55 Mixtures between NaCl and Sucrose: 1.07 M NaCl + 0.077 M Sucrose.....55 Mixtures between NaCl and Sucrose: 0.376 M NaCl + 0.421 M Sucrose...56 Novel test compound: Water........................................................................56
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Retraining water as a comparison stimulus..................................................57 Negative control session...............................................................................58
Discussion............................................................................................................58
4 APPLICATION OF A NEW BEHAVIORAL PARADIGM TO ASSESS TASTE QUALITY GENERALIZATION...............................................................................76
Introduction.................................................................................................................76 Method........................................................................................................................76
Subjects................................................................................................................76 Apparatus.............................................................................................................77 Task Overview.....................................................................................................77 Stimuli .................................................................................................................77 Trial Structure......................................................................................................78 Training ...............................................................................................................78
Spout training ...............................................................................................79 Side training .................................................................................................79 Alternation....................................................................................................79 Discrimination training I-III.........................................................................80
Test Compounds..................................................................................................81 Data Analysis.......................................................................................................81
Results.........................................................................................................................82 Test Stimulus: Sodium Gluconate .......................................................................82
0.376 M sodium gluconate ...........................................................................82 0.668 M sodium gluconate ...........................................................................83
Test Stimulus: Denatonium .................................................................................83 0.131 mM denatonium .................................................................................83 0.360 mM denatonium .................................................................................84
Test Stimulus: Maltose ........................................................................................84 0.077 M maltose...........................................................................................84 0.148 M maltose...........................................................................................85
Test Stimulus: Potassium Chloride (KCl) ...........................................................86 0.376 M KCl.................................................................................................86 0.668 M KCl.................................................................................................86
Test Stimulus: Monosodium Glutamate ..............................................................87 0.077 M MSG...............................................................................................87 0.148 M MSG...............................................................................................87
Test Stimulus: Fructose .......................................................................................88 0.077 M fructose ..........................................................................................88 0.148 M fructose ..........................................................................................89
Performance of Water Group ..............................................................................89 Discussion...................................................................................................................91
Sodium Gluconate ...............................................................................................91 Denatonium .........................................................................................................92 Maltose ................................................................................................................93 Potassium Chloride..............................................................................................94 Monosodium Glutamate ......................................................................................95
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Fructose ...............................................................................................................97
5 GENERAL DISCUSSION .......................................................................................115
Introduction...............................................................................................................115 Delayed Match/Non-Match to Sample .....................................................................115 Novel Taste Quality Generalization .........................................................................118
Future Validation of the Procedure ...................................................................122 Potential Uses of the New Generalization Procedure........................................124
Neurobiological applications......................................................................124 Behavioral data support analytic processing rather than synthetic ............128
Perspectives ..............................................................................................................129
LIST OF REFERENCES.................................................................................................130
BIOGRAPHICAL SKETCH ...........................................................................................139
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LIST OF TABLES
Table page 3-1. Training compounds selected from Experiment I. ....................................................62
3-2. Experimental groups..................................................................................................62
3-3. Results from one-sample t-tests for a novel concentration of NaCl..........................62
3-4. Performance to training stimuli during novel NaCl testing.......................................62
3-5. Results from one-sample t-tests for a novel concentration of sucrose. .....................63
3-6. Performance to training stimuli during novel sucrose testing ...................................63
3-7. Results from one-sample t-tests for a novel concentration of quinine .......................63
3-8. Performance to training stimuli during novel quinine testing ...................................64
3-9. Results from one-sample t-tests for a novel concentration of citric acid ..................64
3-10. Performance to training stimuli during novel citric acid testing .............................64
3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose ....................65
3-12. Performance to training stimuli during high NaCl + high sucrose testing ..............65
3-13. Results from one-sample t-tests for 1.07 M NaCl + 0.077 M sucrose ....................65
3-14. Performance to training stimuli during high NaCl + low sucrose testing ...............66
3-15. Results from one-sample t-tests for 0.376 M NaCl + 0.421 M sucrose ..................66
3-16. Performance to training stimuli during low NaCl + high sucrose testing ...............67
3-17. Results from separate one-sample t-tests for water .................................................67
3-18. Performance to training stimuli during water testing ..............................................67
4-1. Overview of experimental groups .............................................................................99
4-2. Training schedule for N, S, Q, and C groups ............................................................99
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4-3. Training parameters for W group. ...........................................................................100
4-4. Results from one-sample t-tests for 0.376 M NaGluconate ....................................101
4-5. Results from one-sample t-tests for 0.668 M NaGluconate ....................................101
4-6. Performance to training stimuli during sodium gluconate testing...........................101
4-7. Results from one-sample t-tests for 0.131 mM denatonium ...................................102
4-8. Results from one-sample t-tests for 0.360 mM denatonium ...................................102
4-9. Performance to training stimuli during denatonium testing ....................................102
4-10. Results from one-sample t-tests for 0.077 M maltose ...........................................103
4-11. Results from one-sample t-tests for 0.148 M maltose ...........................................103
4-12. Performance to training stimuli during maltose testing.........................................103
4-13. Table of t-test statistics for 0.376 M KCl ..............................................................104
4-14. Table of t-test statistics for 0.668 M KCl ..............................................................104
4-15. Performance to training stimuli during KCl testing ..............................................104
4-16. Table of t-test statistics for 0.077 M MSG ............................................................105
4-17. Table of t-test statistics for 0.148 M MSG ............................................................105
4-18. Performance to training stimuli during MSG testing ............................................105
4-19. Table of t-test statistics for 0.077 M fructose........................................................106
4-20. Table of t-test statistics for 0.148 M fructose........................................................106
4-21. Performance to training stimuli during fructose testing ........................................106
4-22. Performance to training stimuli for W group during dt3-5 through dt3-8.............107
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LIST OF FIGURES
Figure page 2-1. Trial structure for DMTS/DNMTS (same/different) task. ........................................30
2-2. The mean overall performance to all trial types is shown.........................................31
2-3. Mean performance to same trials...............................................................................32
2-4. Mean overall performance to different trials .............................................................33
2-5. Mean performance on same versus different trials....................................................34
3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test .............................68
3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test..........................68
3-3. Mean (n=8) unconditioned licking to quinine in a brief access test..........................69
3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test ......................69
3-5. An overview of the trial structure..............................................................................70
3-6. The generalization profile obtained when 0.847 M NaCl was used as a test compound .................................................................................................................71
3-7. The generalization profile obtained when 0.068 M sucrose was used as a test compound .................................................................................................................71
3-8. The generalization profile obtained when 0.546 mM quinine was used as a test compound .................................................................................................................72
3-9. The generalization profile obtained when 42.56 mM citric acid was used as a test compound .................................................................................................................72
3-10. The generalization profile obtained when 1.07 M NaCl + 0.421 M sucrose was used as a test stimulus ..............................................................................................73
3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose was used as a test stimulus. .............................................................................................73
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3-12. The generalization profile obtained when 0.376 M NaCl + 0.421 M sucrose was used as a test stimulus ..............................................................................................74
3-13. The generalization profile obtained when water was used as a test stimulus..........74
4-1. Profile for 0.376 M NaGluconate ............................................................................108
4-2. Profile for 0.668 M NaGluconate ............................................................................108
4-3. Profile for 0.131 mM denatonium ...........................................................................109
4-4. Profile for 0.360 mM denatonium ...........................................................................109
4-5. Profile for 0.077 M maltose.....................................................................................110
4-6. Profile for 0.148 M maltose.....................................................................................110
4-7. Profile for 0.376 M KCl ..........................................................................................111
4-8. Profile for 0.668 M KCl ..........................................................................................111
4-9. Profile for 0.077 M MSG ........................................................................................112
4-10. Profile for 0.148 M MSG ......................................................................................112
4-11. Profile for 0.077 M fructose ..................................................................................113
4-12. Profile for 0.148 M fructose ..................................................................................113
4-13. Summary of performance for W group during training with water and quinine...114
4-14. Diagram outlining two possibilities for the level (peripheral or central) at which convergence of taste signal processing leading to the same behavioral output might occur.............................................................................................................114
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
NEW BEHAVIORAL PARADIGMS TO STUDY TASTE-QUALITY GENERALIZATION AND DISCRIMINATION IN RATS
By
Connie Lynn Grobe
August 2006
Chair: Alan C. Spector Major Department: Psychology
Questions regarding the nature of perceivable taste qualities remain: Is taste quality
perception analytic or synthetic? Specifically, are tastes comprised of mixtures of a
discrete number of basic qualities? Currently, there are no appropriate animal models
that allow repeated assessments of the qualitative features of taste stimuli. Because it is
not possible to directly measure taste perception in animals, such sensory experiences
must be inferred on the basis of results from specially designed behavioral tasks.
Here, an operant-conditioning based behavioral paradigm was used to train rats to
taste two samples within a trial and then to make one response if presentations of the taste
stimuli (NaCl or sucrose) matched and another response if they did not match. Rats
performed similarly on matching and non-matching trials. Overall performance reached
an asymptote at ~74%. This approach could provide a means of testing discrimination
and generalization as well as exploring the temporal capacities of short term memory in
the taste system.
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Another study used operant techniques to train four groups of rats to distinguish the
taste quality of a single (standard) compound representing one of the putative four basic
tastes (“salty,” “sweet,” “sour,” “bitter”) from compounds representing the three other
taste qualities (comparisons). Prototypical stimuli were used to represent basic tastes
(NaCl, sucrose, citric acid, quinine). This task was then used to quantify how animals in
each group generalized their responses when presented with novel taste stimuli, providing
a way to assess how NaCl-like, sucrose-like, citric acid-like , and quinine-like the quality
of the solution was. Stimulus control of training compounds was maintained at high
levels, and behavioral responses to test stimuli generalized in predictable ways, providing
a non-invasive method for repeatedly assessing taste quality in the same animals.
Interestingly, the profile of monosodium glutamate is both NaCl-like and sucrose-like.
Overall, results suggest that taste processing is analytic.
Additionally, these paradigms could provide a functional context to interpret the
outcomes of anatomical, pharmacological, and genetic manipulations of the gustatory
system. They are also compatible with existing techniques that are crucial for linking
neural activity with behavior, which is essential for understanding gustatory processing.
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CHAPTER 1 LITERATURE REVIEW
Introduction
Many questions remain concerning the organization of the gustatory system and the
neural mechanisms underlying taste function. How are tastes detected in the mouth and
appropriate signals sent to the brain? Specifically, how are the relevant features of a
chemical stimulus coded by the nervous system? What portions of the gustatory pathway
are necessary for the maintenance of particular functions, like taste intensity
discrimination or taste quality detection and/or discrimination?
Before one can approach these questions, it is important to resolve fundamental
concepts concerning the perceptual characteristics of taste stimuli in the animal models
chosen to study issues pertaining to taste. For example, it is not fully known whether
rats, a commonly used animal model, perceive taste stimuli as categorical or falling along
a continuum of possible qualities. These two possibilities represent theoretically
opposing viewpoints of how gustatory processing occurs: The analytic view and the
synthetic view, respectively. Erickson (1968) stated that color vision is a synthetic
system whereas audition is an analytic system. The difference being that the synthetic
system appears to involve the same set of neurons and the analytic system appears to
involve different sets (Erickson, 1968). Erickson (1968) further pointed out that this key
difference might be at the heart of the debate about whether signals regarding taste
quality are processed through devoted labeled-lines or in an across-fiber pattern.
1
2
Therefore, the purpose of the current experiments was for the development and
application of psychophysical tasks that may yield an answer to the question of whether
there might be a few taste primaries or an indefinite number of them. At issue is whether
a few discrete categories of taste quality are sufficient to encompass all taste experiences
in our animal model (Sprague-Dawley rat) or whether there is a continuum of possible
taste perceptions.
The aim of the first experiment was to design a versatile task that would provide
insight into the ability of rats to discriminate differences between 2 stimuli, whether of
the same compound (intensity discrimination) or between different compounds (quality
discrimination). A second goal of the first experiment was to determine if the same
protocol could be used to investigate the temporal properties of short-term memory for
taste solutions.
The overall goal of the second and third experiments was to examine whether rats
can reliably discriminate taste compounds thought to fall into different qualitative
perceptual classes, and whether they will reliably categorize novel stimuli as possessing
characteristics similar to the training stimuli. The existence of such a paradigm would
offer researchers the opportunity to observe the effects that manipulations made to the
gustatory system have on performance in a behavioral task that was specifically aimed at
measuring taste quality identification. In addition, the task could be used to gain insight
into the gustatory perceptual experience of the animal generated by novel taste
compounds.
In order to conduct these experiments, it was assumed that rats treat taste stimuli as
being composed of (at minimum) the same 4 basic qualitative classes that humans report
3
perceptually: “salty,” “sweet,” “sour,” and “bitter.” Nowlis, Frank, and Pfaffman (1980)
found evidence supporting this assumption using a behavioral approach. Moreover, work
examining the peripheral transduction mechanisms in rodents to prototypical compounds,
those identified by humans as representing the four basic tastes, suggest that animals may
have receptors devoted to the four taste qualities (Chandrashekar et al., 2000; Gilbertson
& Boughter, 2003; Scott & Giza, 1990; Zhang et al., 2003; Zhao et al., 2003). A
controversial fifth taste quality, referred to as “umami” (Yamaguchi, 1991), has been
identified in the literature and is described as the taste quality associated with a savory or
delicious sensation in humans. Support for the existence of “umami” taste in rodents is
mixed; some sources indicate that the taste of monosodium glutamate (MSG) (the
prototypical compound for the “umami” taste) generalizes to sucrose and NaCl, “sweet”
and “salty,” respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data
suggest that rats can nonetheless discriminate MSG from sucrose even when the
contribution of the sodium ion is reduced (Heyer, Taylor-Burds, Mitzelfelt & Delay,
2004). The strategy of using representative compounds from the classic 4 basic tastes
does not detract from the possibility that there could be more taste qualities; in fact, it
could even provide support for such a notion.
How does one measure taste function in non-human animals? To appreciate this, it
is important to understand each of the identified taste domains and how they may be
measured in animals (including humans).
Domains of Taste
The functional aspects of taste can be classified into at least three broad domains:
sensory-discriminative, affective, and physiological (see Spector, 2003b, for review).
The discriminative domain deals with identification of a stimulus, and the affective
4
domain refers to the hedonic aspects of a compound, whereas the physiological domain
consists of the physiological reflexes that a stimulus elicits. Each of the three domains
describes a different facet of taste function, and possibly represents different aspects
related to ingestive behavior.
Sensory Discriminative Domain
Briefly, sensory-discriminative function, the identification of a stimulus, can be
dissociated from the affective/hedonic domain by use of several different operant and
classical conditioning procedures aimed at measuring both detection thresholds and
quality discriminations in animals (Spector, 2003b). These procedures do not rely on the
hedonic aspects of the taste solution to drive responses because the taste serves as a
signal for other reinforcing or punishing events. Consequently, the inherent motivational
properties of the stimulus are irrelevant in the animal’s identification of the stimulus.
Affective Domain
Briefly, the affective domain refers to the hedonic attributes of taste stimuli (i.e.,
the palatability of a compound). The most commonly used methods to describe the
affective responses of animals regarding taste compounds include operant tasks aimed at
assessing appetitive/avoidance behavior and those aimed at measuring consummatory
responses, which are the reflex-like behavior stimulated by a tastant contacting its
sensory receptors (Spector, 2003a). The two-bottle intake test, a variation of it termed
the brief-access task, and various operant response measurements (e.g., progressive ratio
breakpoints, rates of responding) have been used to quantify the reinforcement efficacy
of a taste stimulus (Clark & Bernstein, 2006; Guttman, 1953; Hodos, 1961; Reilly, 1999;
Sclafani, 2006; Sclafani & Ackroff, 2003; Starr & Rowland, 2006). Consummatory
responses, on the other hand, have been measured through use of the taste reactivity
5
paradigm (Grill & Berridge, 1985; Grill et al., 1987), which is a procedure that involves
the quantification of oromotor reflexes elicited by taste stimuli infused directly into the
oral cavity.
Physiological Domain
The physiological domain, often referred to as cephalic phase responses (e.g.,
Berthoud, et al., 1981; Grill, Berridge, & Ganster, 1984; Mattes, 1997; Pavlov, 1902;
Powley, 1977; Spector 2000), consists mainly of salivation and other predigestive
responses that are elicited by taste stimuli. The increased salivation to food/fluids and
other physiological reflexes related to contact with taste receptors are proposed to be
adaptive as they likely contribute both to digestion/assimilation of food and protection of
the oral epithelium (e.g., salivation) (Spector, 2000).
Taste Quality
The quality of a taste falls under the rubric of sensory-discriminative function.
According to Bartoshuk (1978), Aristotle was first to suggest that the taste of all foods
and fluids was a combination of only a few discrete perceptual qualities. He suggested
that there were 7 basic tastes: sweet, bitter, sour, salty, astringent, pungent, and harsh
(Bartoshuk, 1978). It was not until 1927, however, that Hans Henning formally asserted
that the four basic tastes (salty, sweet, sour, and bitter) can be conceived as representing
the corners of a tetrahedron with combinations of two qualities along the edges, and
combinations of three on the face (Bartoshuk, 1978). This idea has been commonly
accepted despite occasional evidence suggesting additional qualities exist; the most
notable is the claim of a fifth quality, the umami taste which is said to arise from
glutamate salts and is described as “savory” by humans (Galindo-Cuspinera, & Breslin,
2006; Schiffman, 2000; Yamaguchi, 1991). Support for the existence of “umami” taste
6
in rodents, however, is mixed with some sources indicating that the taste of MSG (the
prototypical compound for the “umami” taste) generalizes to sucrose and NaCl, “sweet”
and “salty”, respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data
suggest that rodents can nonetheless discriminate MSG from sucrose (Ninomiya &
Funakoshi, 1989a) even when the contribution of the sodium ion is reduced (Heyer,
Taylor-Burds, Mitzelfelt & Delay, 2004).
Animal Models Used to Study Taste Quality
Many researchers assume that the same basic taste qualities that are identified by
humans also extend to other animals. Support for this statement is based on the fact that
animals respond to prototypical compounds putatively representing the 4 basic tastes as
would be expected. That is, animals ingest and avoid taste solutions in a manner that
appears similar to human descriptions of pleasantness and aversion. Suppression of
intake of a solution, however, does not necessarily indicate qualitative similarity to other
avoided compounds in sensory-discriminative terms. In other words, when an animal
avoids two compounds equally, there is no way of knowing whether the animal also
perceives them as possessing the same taste quality. For example, an animal might avoid
drinking very concentrated NaCl to the same extent as it avoids drinking a quinine
solution, but data in animals and humans suggest that the two compounds are
qualitatively dissimilar. Accordingly, other methods are necessary for inferences on taste
quality in nonhuman animals to be established. Indeed, that is a primary theme of this
dissertation.
Discrimination Tasks
Operant discrimination procedures have been useful for determining whether two
compounds are perceptually distinct. If an animal cannot discriminate between two
7
different solutions, then it is plausible that both give rise to a perceptually identical
experience (Spector, 2003a). Alternatively, if an animal can reliably discriminate two
compounds from one another, then there must be some identifiable cue (e.g., differential
neural signals generated by the two stimuli) that can be used by the animal to guide its
behavior accordingly. It is critical in these experimental designs that intensity cues be
minimized so that discriminative responding comes under the explicit control of taste
quality. For example, it is known that a rat can discriminate a relatively lower
concentration of NaCl from a higher concentration of NaCl (Colbert, Garcea, & Spector,
2004), but this does not necessarily imply that the taste quality of the sensation between
strong and weak NaCl solutions is different. Therefore, when conducting studies of
quality discrimination, it is important to use a range of concentrations of the respective
training stimuli so as to render intensity a relatively irrelevant cue (Spector, 2003a;
Spector & Grill, 1992; Spector et al., 1996, 1997; St. John et al., 1995, 1997, 1998).
Spector (2003b) has identified important assumptions associated with this strategy:
the selected range of concentrations must have overlapping intensities and the relevant
taste quality of each compound delivered is assumed to remain constant across the
concentration range tested. If the first assumption were not met and two compounds
were of the same quality but all of the concentrations of one compound were perceived as
weaker than all of the concentrations of the other compound, then the animals would
likely be able to discriminate between the two compounds based on the differences in
intensity. One could identify the basis for such a discrimination, for example, if rats
performed well when lower concentrations of the “weak” compound were added to the
test stimulus array, but performed poorly to additional low concentrations of the “strong”
8
compound. Conversely, the opposite would be true. That is, if greater concentrations
were included in the discrimination task for both compounds, then as the ‘weaker-tasting’
one became more salient, performance would decrease because the rats would incorrectly
respond as if it were the “stronger-tasting” compound. In contrast, performance would be
expected to improve for the “stronger-tasting” compound because a greater concentration
would only serve to distinguish it more from the “weaker-tasting” compound.
A typical stimulus discrimination paradigm involves training an animal to make
one response after tasting one compound and to make a different response after tasting a
different compound. As stated earlier, it is best when the concentration of each
compound is varied to render intensity an irrelevant cue, which should make taste quality
the salient signal. Typically, the animal is water deprived (< 24 h) to encourage
sampling, and correct responses are reinforced with brief access to water.
At least two studies have been published suggesting the occurrence of perceptual
identity as evidenced by rats being unable to discriminate between two different taste
stimuli. In one study, Spector and Kopka (2002) found that rats could not discriminate
quinine hydrochloride (a prototypical “bitter” compound) from denatonium benzoate (a
substance that rats also avoid consuming and that humans report as “bitter”). The same
rats were able, however, to discriminate quinine from KCl (judged to be a complex
“bitter-sour-salt” by humans), and NaCl from KCl. Interestingly, the rats appeared to be
able to substitute denatonium for quinine after being trained to discriminate quinine from
KCl, signifying the two compounds were similar. These results support the claim that
quinine and denatonium likely generate a unitary qualitative percept in rats.
9
The other study which demonstrates perceptual identity between taste compounds
in rodent models is provided by Spector, Guagliardo, and St. John (1996). In that study,
amiloride, an epithelial sodium channel blocker, was used to remove the specific NaCl
taste cues necessary to discriminate NaCl and KCl. With application of 100 µM
amiloride, the remaining gustatory cues were not sufficient for rats to distinguish between
the two salts and they performed at chance levels in a discrimination task. Moreover, an
analysis of the errors in responding showed that mistakes primarily occurred on NaCl +
amiloride trials. This observation suggests that the rats responded as if NaCl + amiloride
was perceptually similar to KCl. Adding support to the hypothesis that amiloride
changes the perceptual taste qualities of NaCl to be more similar to KCl are data from
Hill, Formaker, & White (1990), showing that when NaCl, adulterated with amiloride,
was used as a conditioned stimulus (see below for definition) in a conditioned taste
aversion paradigm, rats generalized their aversion to non-sodium salts (specifically the
halogenated salts tested) including KCl.
Because there are many factors which might potentially serve as cues in a
discrimination task, results from studies using this approach are more compelling when
rats cannot discriminate between two compounds, provided that learning and intensity
effects can be rule-out. For example, the rise and decay time of the signal may differ
between two compounds that share a similar quality. Such temporal cues alone may be
sufficient to allow an animal to distinguish between the stimuli in a discrimination task.
Another possible signal, as mentioned earlier, may be the relative intensity of the tastants
selected. If the experimenter does not know the relevant concentration ranges to include
10
in the test stimulus panel and includes some that do not overlap in intensity, the animal
may be able to use those cues to guide performance.
Generalization Tasks
Guttman & Kalish, (1956) are credited with associating the concept of
discriminability with generalization gradients. A typical study in which a generalization
gradient is obtained consists of a scenario in which appropriate responses are reinforced,
when a specific training stimulus is present. Once stimulus-contingent responding is
established, a generalization test is presented during which no responses are reinforced.
The stimulus is varied on some physical dimension and the rate of responding is
recorded. These experiments generally produce response gradients that decrease as a
function of the difference between the training and test stimuli (Guttman & Kalish,
1956).
This concept has been adapted for use to study similarities between taste
compounds in the conditioned taste aversion (CTA) paradigm. Tapper & Halpern (1968)
innovatively applied the CTA procedure to make inferences on how animals classify taste
stimuli. They exposed experimental animals to radiation (2.5 min exposure of 80 r/min)
20 minutes before a scheduled session in which the rats normally consumed their daily
supply of water; after the radiation, however, a novel taste compound (the conditioned
stimulus; CS) was presented in place of water. This procedure resulted in a robust
avoidance to the CS, evidenced by the fact that after the pairing occurred, rats consumed
less of the CS upon subsequent re-exposure to the tastant. In the procedure, Tapper &
Halpern (1968) assumed: “ i) the [CS] becomes the quality standard against which the
animals compare other solutions; ii) the test solutions will be aversive, that is, associated
with the [CS], as long as their taste to the animal is qualitatively similar to the [CS], iii)
11
the magnitude of rejection indicates the degree of similarity in taste between the test
solution and the [CS].” By means of multiple cross-generalization pairings, they could
construct functions of aversion with which to compare compounds. Their rationale for
inferring that two compounds were of the same quality was based on the assumption that
similar generalization profiles would emerge for the respective stimuli (Tapper &
Halpern, 1968).
This approach was comprehensively extended later by Nowlis, Pfaffmann, and
Frank (1980). They conditioned aversions to a large number of compounds in hamsters
and rats and then measured intake to the four prototypical taste compounds, NaCl,
sucrose, HCl, and quinine, which served as test stimuli. As such, like Tapper and
Halpern (1968), it was assumed that the response profiles obtained related to the
qualitative properties of the prototypical taste compounds, thus allowing them to make
conclusions about the degree to which, a compound was sucrose-like, NaCl-like, HCl-
like, and quinine-like. With some exceptions, these data became the basis for many to
consider that rodents likely share the same perceptual taste experience as humans do.
Later, the same technique was applied to the study of mixtures (Frank, Formaker, &
Hettinger, 2003; Smith & Theodore, 1984), and researchers showed that rats could
identify the CS in a mixture in a concentration-dependent manner.
There are some limitations associated with this approach, which include effects of
stimulus familiarity, extinction, concentration, and stimulus hedonics. First, the CTA
procedure is only successful if the CS is novel to the animal, thus limiting the choice of
CSs to unfamiliar compounds. Additionally, typically only one CS is used per
experimental group, which means that each concentration of the compound included in
12
the study also requires a devoted set of animals. It follows that this substantially
increases the number of animals required for a comprehensive study of taste quality
generalization. In addition, an inclusive design would require the researcher to also test
for cross-generalization of each of the concentrations selected, and therefore, it is
necessary to use a large number of animals.
A second limitation of the CTA approach relates to the strength of conditioning.
Testing occurs in extinction, meaning that the animal does not experience the
unconditioned-stimulus-induced consequences previously paired with intake, and so the
effects of learning can diminish over time. Often the strength of the conditioning is
reassessed periodically, and the avoidance to the CS indeed lessens. This complicates
interpretation of results and limits the number of potential test stimuli (Nowlis, Frank,
and Pfaffmann, 1980).
A third limitation of the CTA procedure is associated with stimulus intensity
dynamism, which must be carefully considered in the interpretation of generalization
profiles. Intensity dynamism refers to the observation that conditioning to a CS will
generalize similarly to all higher concentrations of that compound, rather than as an
inverted-V gradient, peaking at the CS, as might be expected (see Guttman & Kalish,
1968; Hull, 1949). In other words, there is a steep gradient beginning at some
concentration below the CS, but the behavioral profile obtained reveals that increasing
the intensity of the compound results in a greater or at least similar conditioned response.
For example, if conditioning occurred to 0.05 M NaCl it would generalize to higher
concentrations, including 0.5 M NaCl but conditioning to 0.5 M NaCl might not
13
generalize to the lower concentration. That is, cross-generalization does not necessarily
occur between high and low concentrations of the same compound.
Finally, the inherent affective properties of a taste stimulus can influence the
interpretation of CTA results. If a solution is unconditionally aversive, an animal will not
readily consume much, if any, of the compound. This could compromise both the
effectiveness of the pairing and the ability to measure behavior (i.e., floor effect).
Therefore, some compounds, which are preferably ingested, better lend themselves to a
procedure like CTA. In fact, this is a key problem facing those who study taste
classification. Some compounds, especially “bitter” and “sour” solutions are not readily
consumed by rats and attempts to incorporate them into CTA designs can result in
problems associated with floor effects. That is, it is difficult to quantify changes in intake
for an experimental versus control group when intakes of the taste stimuli for both groups
are low.
Ideal Psychophysical Task
An ideal psychophysical task would have the following characteristics: it would 1)
be compatible with assessing discriminability and generalization within the same
animals, 2) allow for repeated test (probe) trials within the same animals, 3) yield clear,
interpretable results, 4) be highly replicable within and between animals (i.e., have little
variance in responses), and 5) circumvent the potential confounding of stimulus intensity.
Importance of Psychophysical Analysis in Animal Models
Advances in our understanding of taste function can be optimally achieved through
a combination of experimental approaches. Arguably, it is the innovation of rigorous
behavioral techniques that facilitates the confirmation or refutation of predictions about
gustatory function that are based on more reduced levels of analysis (i.e.,
14
electrophysiology, molecular biology, etc.). In addition, carefully executed
psychophysical experiments produce results that generate new hypotheses regarding how
the gustatory system is organized. Psychophysical tasks, though time consuming, provide
invaluable data on the sensory capacities of both humans and non-human animals.
Psychophysical analysis of non-verbal subjects is challenging but can be achieved
through the use of operant and classical conditioning procedures.
Chief among the benefits of using a psychophysical approach with non-human
animals is that invasive procedures, in which the gustatory system can be manipulated,
are possible. Taste function is complex; therefore, the design and application of a variety
of psychophysical measures is necessary to obtain a comprehensive assessment of
function.
The failure to develop appropriate tasks can lead to misguided conclusions. For
example, the two-bottle preference test has been, and continues to be, the most common
behavioral measure of taste responsiveness in animals. This measure, however, only
assesses the motivational characteristics of a taste stimulus. Moreover, postingestive
events can influence the behavior. Certainly, the use of this procedure masked for many
years the understanding of the contribution of gustatory nerves in the processing of taste
input (e.g., Pfaffmann, 1952; Richter, 1939; see Spector, 2003a for discussion).
Argument for the Development of Psychophysical Tasks
The use of appropriate behavioral procedures directed at measuring taste function
in animal models has been indispensable in the analysis of the neural organization of the
gustatory system (e.g., Flynn, Grill, Schulkin, & Norgren, 1991; Flynn, Grill, Schwartz,
& Norgren, 1991; Kopka & Spector, 2001; Kopka, Geran, & Spector, 2000; Slotnick,
Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990; Spector & Grill,
15
1992; St. John, Markison, & Spector, 1995; Shimura, Grigson, & Norgren, 1997). It
follows, therefore, that the development of new behavioral paradigms that are aimed at
yet unexplored aspects of gustatory function promise to lead to further important
discoveries.
CHAPTER 2 RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO SAMPLE
TASK USING ONLY TASTE STIMULI
Background
A major goal of this project was to develop a novel behavioral task that would
address whether rats can accurately assess when two samples, tasted in sequence, differ
or whether they are the same. The paradigm combines two procedures, a match to sample
and non-match to sample task. Potentially, such a task could be used to assess the degree
of qualitative discriminability between two taste stimuli. Another possible benefit of this
paradigm is that once the animal has sufficient training in the contingencies of the task,
various compounds or concentrations could be added for testing.
Such a procedure was introduced by Konorski, in 1959, who apparently suggested
it could be used with olfactory or auditory stimuli because they both were sensory
modalities which were incompatible with simultaneous delivery of test stimuli (in Shimp
& Moffit, 1977). The taste system is also incompatible with simultaneous delivery of
two comparison stimuli. This inherent delay between samples, as a consequence of the
rat sequentially sampling two separate stimuli within a single trial, provides the
opportunity to allow one to assess the properties of short-term memory processes
involving taste stimuli – a phenomenon that has not been previously approached. To
date, only long-term memory has been studied in the taste system via conditioned taste
aversion (CTA), which is not optimally designed for multiple trial analyses.
16
17
Method
Animals
Nine adult male Sprague-Dawley rats weighing 555 +/- 20 g at the start of training
were used as subjects. Two of the animals were euthanized within the first two weeks of
training: one demonstrated a response bias within the first few discrimination training
(see below) sessions and was removed from the study to allow for an increase in session
length for the other rats, and the other rat removed his surgically implanted intraoral
cannulae (see below) and thus required immediate euthanization. Therefore, seven rats
served as subjects in the experiment. The rats came from Charles River (Wilmington,
SC) and were maintained on Purina (5001) laboratory rat chow ad libitum (except during
experimental test sessions) in a vivarium that had the lights and temperature
automatically controlled. Lights were programmed to be on a 12:12 hour light:dark cycle
with lights on at 0700 h, but due to an undiscovered timer malfunction the animals were
in constant light during the first 110 days of training and testing. A contingency was in
place so that rats would receive supplemental water if body weight decreased to 85% of
the ad libitum weight calculated each week; this contingency was only necessary for one
of the animals on three separate occasions. All procedures were approved by the
University of Florida Institutional Animal Care and Use Committee.
Apparatus
In the present experiment a gustometer, which is a specially designed stimulus
delivery and response measurement device, was modified from an earlier version
described in detail elsewhere (Spector, Andrews-Labenski, and Letterio, 1990), and was
used in training and testing. Briefly, the test chamber had two response spouts which
flanked either side of a central slot through which the animal could access a sample spout
18
controlled by a stepping motor. There were two cue lights positioned 4.2 cm above the
response spouts which could be activated at the appropriate time in the trial. The
response spouts served as the source for water reinforcement when the animal performed
the appropriate behavior (licked the appropriate response spout after tasting a specific
combination of solutions). Fluid stimuli and the water reinforcer were contained in 11
pressurized reservoirs connected to solenoid valves to regulate the amount of fluid
deposited into the spout. Background masking noise was present during each session, and
the test cage was enclosed in a sound-attenuating chamber housed within a dimly lit room
to minimize possible extraneous cues related to stimulus delivery. A Polyethylene (PE)-
100 tube, covered by a spring, was connected via a swivel to a solenoid valve which was,
in turn, connected to a water reservoir. This tube was inserted through a small hole in the
ceiling of the sound attenuation chamber where it was connected to an intraoral cannula
implanted in the rat. This was used to provide water rinses between stimuli as described
below.
Stimuli
All solutions were prepared daily with purified water (Elix 10; Millipore, Billerica,
MA) and reagent grade chemicals, and were presented at room temperature. Initially, we
attempted to use 0.1 M NaCl and 0.5 M NaCl as training stimuli, but the overall
performance of the rats remained at chance. Consequently, the rats never progressed out
of the training phase and it was deemed necessary to change the training stimuli after two
months (35 sessions). Two solutions were used in the second phase of training: 0.1 M
NaCl and 0.1 M sucrose. We reasoned that a discrimination between two compounds
that are of different qualities might be easier to learn. Although we know that rats can
discriminate NaCl on the basis of concentration (Colbert, Garcea, & Spector, 2004), we
19
believed it might reduce the acquisition time if the two stimuli differed chemically and
were putatively members of different perceptually qualitative classes so as to render them
more distinct from each other. The initial training results with 0.1 M and 0.5 M NaCl
will be ignored for the remainder of this chapter.
Taste stimuli were prepared fresh daily from reagent grade chemicals (NaCl and
sucrose: Fisher Scientific, Atlanta) and purified water (Elix 10; Millipore, Billerica, MA);
they were presented at room temperature
Surgery
Rats were anesthetized with a mixture of 125 mg/kg body wt ketamine, 5 mg/kg
body WT xylazine (injection given intramuscularly) and two intraoral (IO) cannulae were
surgically implanted so that water could be infused directly into the mouth. The rats were
placed in a surgical head holder and an incision was made along the midline of the scalp.
The fascia was cleared and four small machine screws were inserted into holes drilled
into the skull. The rat was then removed from the head holder and placed in a supine
position. The blunt end of a 19g needle shaft was attached to the opposite end of heat-
flared PE-100 tubing. A small Teflon washer was slipped onto the cannulae and placed
against the heat-flared end. The beveled end of the needle was then placed between the
cheek and gum, anterolateral to the first upper molar on either side of the mouth, and the
needle was pushed through the tissue in a trajectory that passed beneath the zygomatic
arch close to the skull until the Teflon washer and heat-flared end of PE tubing rested
against the roof of the mouth lateral to the maxillary molars. The needle was separated
from the PE tubing, the excess was trimmed, and a blunt piece (~10 mm) of 19 gauge
stainless steel tubing, with a bead of solder attached, was securely fitted into the PE
tubing. Both cannulae were placed in the same manner. Once in place, dental acrylic
20
was added so that it created a mound over the screws and secured the cannulae (PE
tubing + 19 G stainless steel tubing with bead of solder for extra anchoring) firmly in
place. All rats were injected with a prophylactic dose of penicillin G Procaine suspension
(30,000 units, s.c.) and the analgesic ketorolac tromethamine (2 mg/kg body mass, s.c.)
immediately before surgery and on the following 3 days. At least three months passed
before animals began training.
The intraoral cannulae were cleaned out every day by passing a smaller diameter
(polyethylene-10) tubing through the cannulae until it exited into the oral cavity. The
intra-oral cannulae were implanted so that water could be infused into the oral cavity
between taste samples in order to reduce the potential for adaptation to occur to the first
stimulus in the pair.
Training and Testing Phases
Training and testing sessions took place Monday through Friday of each week
during the regularly scheduled lights-on phase. Rats were water restricted beginning
Sunday night and received all daily fluid within the session. At the end of the last session
on Friday, water bottles were returned to the home cages until the following Sunday.
Spout training
The rats had access to only one spout (either the sample spout, the left response
spout, or the right response spout) and each spout was connected to a reservoir that
contained water. The purpose of this phase was to train the rats to approach and gain
familiarity with getting fluid from each of the spouts. Eventually, the sample spout
would contain a taste stimulus and only the response spouts would contain water. The
rats had to learn to lick from the sample spout and then select one of the response spouts
21
by licking it. There were a total of 6 days of spout training so that the rats experienced
two sessions with each spout.
Side training
Only one trial type was presented within a given session during side training. If the
rats were trained with same trials then, the trials within the session alternated between 1)
0.1 M NaCl followed by 0.1 M NaCl, and 2) 0.1 M sucrose followed by 0.1 M sucrose.
During the next session, the rats received only different trials in which the first sample
differed from the second (0.1 M NaCl followed by 0.1 M sucrose or 0.1 M sucrose
followed by 0.1 M NaCl). After sampling, rats had 180 s (limited hold period) during
which they were required to respond. If they made the correct response, they had limited
access to water (20 licks or 10 s, whichever occurred first). Side training lasted a total of
4 days.
Alternation
During alternation training, the rats started out with a single trial type (either same
or different). Upon completion of a set criterion of correct responses, the program
automatically switched to delivery of the opposite trial type. The correct responses did
not have to be consecutive. The limited hold was changed from 180 s to 15 s.
Additionally, if the rat failed to initiate the second sample within 15 s of the spout
becoming available, the trial terminated and punishment (timeout) was delivered. During
the decision phase, if a rat failed to make any response, or made the incorrect response, a
10-s timeout was presented.
Discrimination training I-II
Trials were delivered in a block with a random pattern selected by the computer
program. Therefore, the rats had no indication from the prior trial, which solutions would
22
be offered on the current trial. The block size was 8; consequently, every trial was
repeated twice within the block before a new block of randomized presentations
occurred. Reinforcement licks were changed from 20 to 25, and timeout increased to 20
s during this phase. Session length was increased to 50 min.
Trial structure (final parameters)
During the 65-min test session, each rat was allowed to complete as many trials as
possible within the time allotted. Each trial (see Figure 2-1) consisted of six different
phases: sample 1, inter-stimulus interval, sample 2, decision, consequence, and inter-trial
interval. The sample phase began when the rat made contact with the dry sample spout
and initiated licking. The rat was required to lick the dry drinking spout twice within 250
ms, upon which the shaft of the drinking spout was filled with the stimulus and each
subsequent lick resulted in an additional deposit of 5 µl into the fluid column. The rat was
allowed 3 s access to the stimulus or five additional licks, whichever came first. A 6-s
interstimulus delay followed the first sample during which 30µl of fluid was infused into
the mouth through the left intraoral cannula. Additionally, during this inter-stimulus
interval, the sample spout was rotated over a funnel, rinsed with purified water, and air-
dried in preparation for the second sample, which followed the same initiation
requirements as stated above. If the rat failed to initiate the second sample within 2 s of
the spout becoming available, the spout rotated away from the access port and the trial
moved immediately into the consequence phase during which the rat received a timeout.
In a trial in which the rat properly initiated both samples, the houselights in the
gustometer were turned off and the cue lights above each lever were illuminated,
signaling the start of the decision phase. Concurrently, the sample spout was rotated out
of position so that it could no longer be accessed. During the decision phase, rats were
23
allowed a prescribed period of time (5 s during the testing phase, referred to as the
limited hold) to respond by licking the correct response spout. If the correct spout was
licked, the houselights were reactivated and the rat had the opportunity to receive 10 s or
40 licks access to water, whichever came first. If the incorrect spout was selected or no
response was made within the limited hold period, the cue lights were extinguished and
the rat was given a 40-s timeout, during which fluid was unavailable. The trial terminated
with a 48-s intertrial interval, during which all lights were off until the next trial began.
Testing
Testing began 46 sessions after the very first spout training day. The parameters
were the same as those used at the end of Discrimination Training II.
Adjustments to Testing Parameters
Initially, the trial parameters were set during the Discrimination Training II phase.
There were, however, some adjustments made to the trial parameters, during the 21-week
testing phase, in an attempt to increase performance in the rats. In the fifth week, the
magnitude of the reinforcer was increased from 25 licks to 40 licks. In the sixth week,
the timeout was increased from 20 s to 40 s. In the twelfth week, the inter-trial interval
was increased from 10 s to 48 s. In the sixteenth week, the session length was increased
from 60 min to 65 min. During the seventeenth week, session length was increased from
65 min to 70 min, but was reduced again because the rats stopped responding near the
end of the session. Finally, beginning in the nineteenth week, the intraoral rinses were
discontinued because of problems with intraoral cannulae coming loose. Consequently,
two of the rats had to be euthanized because of this problem during the nineteenth and
twentieth weeks of testing.
24
Statistical Analyses
For data analyses, repeated measures analysis of variance (ANOVA), one-sample t-
tests, and paired t-tests were used; Bonferroni adjustment was applied where appropriate.
The mean weekly performance score for each trial type for every animal during the first
eighteen weeks of testing were used in the analyses. This time period was chosen for
analysis because it spanned the testing period in which all rats had intraoral rinses and it
also included the weeks for which data were available from all subjects. For each of the
weeks tested, every animal had six performance scores: the two same trial types, the two
different trial types, and an integrated score for both same and different trials.
Results
Overall Performance
Results are shown in Figures 2-2 through 2-5. As shown in Figure 2-2, the mean
performance on the task did not exceed 75%.
A repeated measures ANOVA of overall performance across testing weeks
revealed that the rats performed significantly better over the 18 testing weeks analyzed
(F(17,102) = 10.070, p < 0.001). Multiple one-sample t-test comparisons (null
hypothesis is 50%) of performance during each week, revealed that performance was
better than chance levels initially (t(6) = 2.680, p = 0.037), but a Bonferroni adjustment
eliminated the statistical significance of the comparison (p = 0.658). Beginning at the
third week of testing, however, both the p-value and the Bonferroni adjusted p-value
revealed significant differences (all ps < 0.035), which remained so for the duration of
testing (all Bonferroni adjusted ps < 0.03). Of note is a drop in performance at week 14,
which was attenuated by recalibration of the apparatus to deliver the appropriate volume
per lick.
25
Performance on Same Trials
A graph representing performance on same trials when the trial was NaCl-NaCl or
sucrose-sucrose is shown in Figure 2-3.
A repeated measures ANOVA was used to analyze performance to both types of
same trials. There was a main effect of time (F(17, 102) = 4.52, p < 0.001), but no main
effect of trial type and no interaction was present (both p-values > 0.2). Therefore, these
data could be used to support the claim that rats may have learned to respond to the trial
type regardless of what the chemical compound was.
Performance on Different Trials
Figure 2-4 depicts the performance to different trial types. A repeated measures
ANOVA was used to compare performance on NaCl-sucrose trials to sucrose-NaCl trials.
There was a main effect of time (F(17,102) = 3.290, p < 0.001), but no evidence of a
main effect of trial type (p > 0.34) nor an interaction (p > 0.80). Therefore, when both
compounds are presented within a trial, it does not appear to matter whether the first
sample is NaCl or sucrose.
Performance on Same Trials versus Different Trials
Figure 2-5 shows the performance of same trials collapsed across compounds
versus performance of different trials also combined together. It would appear (Figure 2-
5) that rats perform better on different trials, especially initially, but a statistical analysis
of the performance between same and different trials does not support such a claim. A
repeated measures ANOVA comparing the 18 weeks of testing revealed a main effect of
time (F(17,102) = 3.303, p < 0.001), but no effect of trial type and no interaction was
present (both p-values > 0.50). Additionally, a paired t-test examining the first week of
26
testing did not provide evidence that performance on the two trial types differed (t(6) = -
2.153, p = 0.084).
Discussion
Results from the present study indicate that rats are able to reliably respond to two
taste stimuli, separated by a 6-s delay, and sampled within a single trial, on the basis of
whether they are the same or different. This is the first known report of its kind involving
the taste modality. Below, the performance of the rats in this taste behavioral paradigm is
placed in context with other sensory modalities.
Steckler, Drinkenburg, Sahgal, and Aggleton (1998) published a series of three
articles outlining the ability of rodents at, what they termed, “recognition memory” tasks
and the underlying neuroanatomical substrates mediating such performance. Overall,
they claimed that rodents can acquire these tasks, but do not typically perform at high
levels. Their work, however, focuses on particular tasks using objects or spatial stimuli.
It is interesting that the rats in this experiment did not perform better on the
different trials. Wright and Delius (2005) reported that pigeons performing a matching-
and oddity-to-sample task acquire the oddity-to-sample most rapidly. In fact, there are
published data that suggest a preference for stimuli that do not match (the oddity-
preference effect) (Ginsburg, 1957). There is also a previous account in which matching
performance begins at or below chance (50%) and non-matching performance begins
higher than chance, though these studies used pigeons and differed procedurally from the
task presented here (Zentall Edwards, Moore, & Hogan, 1981).
An experiment by Wallace, Steinart, Scobie, and Spear (1980) might also provide
information worth considering regarding the difficulty the rats had performing at high
levels in this task. In their study, rats performed better in a delayed matching task on
27
trials that contained auditory sample stimuli rather than visual (an illuminated light)
stimuli. The differences in performance between the two modalities disappeared when
the delay was 0 s, but emerged when delays were longer. Perhaps taste stimuli are not as
salient as stimuli from other modalities.
Interestingly, Slotnick and colleagues (1993) reported that rats can learn an odor
matching task and perform at very high levels (>90%) even with a delay of 10 s and
presentation of a masking odor between samples. The reason for the disparity in
performance between their rats and those in the present task are unknown, but there are
procedural differences that may explain some of them. They used a conditional go/no-go
discrimination task, which allowed many more trials and far fewer reinforcers to be
delivered; that difference may have helped acquisition of the task in their case.
Additionally, they used a learning set of stimuli, consisting of several different scents;
thus, it is possible that experience with a variety of training stimuli would improve
acquisition of the task. If such an approach was adopted with taste stimuli, it remains
possible that higher levels of performance would be seen.
Finally, one reason that the mean performance did not surpass 75% might be
related to the ratio between the interstimulus delay and the intertrial interval. One
published study, using pigeons in a visual discrimination paradigm, showed that the
overall correct responding changed when the experimenter varied the ratio of
interstimulus delay to intertrial interval (Roberts & Kraemer, 1982). Specifically, they
tested ratios of 0.5, 2, 8, 16, 32, and 64 and reported that when the delay between trials in
their experiment was the greatest, the highest levels of performance occurred (Roberts &
Kraemer, 1982). In the present study, design limitations of the gustometer restricted the
28
minimum interstimulus interval to 6 s. According to Roberts & Kraemer’s 1982 study,
with a delay this long, it would have been optimal to use an intertrial delay of 386 s. This
was not practical because either the number of trials or the number of sessions possible
per day would have been dramatically reduced. In light of these findings, one might even
conclude that the rats in the present experiment performed as well as would be expected;
this statement is based on the fact that the subjects in Roberts & Kraemer’s (1982) task
performed at 77% when the ISI/ITI ratio was 8, as it was in the present study. Therefore,
reducing the delay or lengthening the intertrial interval would be predicted to improve
performance. Perhaps in contrast to that statement, however, is evidence from Sargisson
and White (2001), who showed that delay appears to become part of the training stimulus
and shares a portion of discriminative control, thus lowering the delay in testing might
actually decrease performance if the animal acquired the task at a higher interstimulus
delay. These are potentially addressable issues empirically.
It might have been insightful to include different test compounds at the end of the
testing period to establish if the rats would be able to apply the concept of sameness or
difference. It is possible that the performance in this test was contingent on prior training
with these compounds, and the learning would not generalize to novel compounds. Thus,
it would have been informative to discern whether such a transfer would have occurred.
If rats acquired high levels of performance to the new set of stimuli more quickly than
with the first set, then it might support the claim that rats could learn to perform the
conceptual task of sameness and/or difference. We felt that the current level of
performance was not sufficiently high to pursue this question. Nevertheless, in the
future, especially if optimal testing parameters can be achieved to increase overall
29
performance levels, adding a variety of test compounds should be included in the
experimental design. Perhaps using multiple training compounds would actually help to
establish higher levels of performance (see Slotnick et al., 1993).
Overall, the results of the present study were encouraging that such a procedure
could be used to study rodent discrimination ability. It certainly seems reasonable that
lowering the delay between stimuli would increase the overall task performance and
allow more options for discrimination (e.g., solutions that vary in intensity).
Additionally, this approach also shows promise for the investigation of short-term
memory in the gustatory neuraxis, which might ultimately provide information about the
properties of the system, the structures involved, and how taste short-term memory
compares with other forms of taste memory and memory processes involving other
sensory modalities. Further development of this task could reveal properties of
neurobiological mechanisms underlying certain forms of behavior.
Unfortunately, because the performance of the rats on the task was not optimal for
continuing in the same research direction, an alternative avenue to assess taste quality in
rats was required. This, however, does not detract from the potential success of the task
outlined above, but because the technical limitations could not be overcome at present, it
was decided to move ahead in a different direction.
30
Figure 2-1. Trial structure for DMTS/DNMTS (same/different) task.
31
TOTAL PERFORMANCE
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)
Per
form
ance
(% C
orre
ct)
Figure 2-2. The mean overall performance to all trial types is shown. Performance on the task increased over the course of the experiment and became significantly different from chance during the 3rd week of testing.
32
NaCl-NaCl vs.
Sucrose-Sucrose
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)
Per
form
ance
(% C
orre
ct)
NaCl Same
Sucrose Same
Incr
ease
d re
inck
s
out off
Incr
ease
ngth
Incr
ease
ngth
Figure 2-3. Mean performance to same trials. The performance of the rats did not differ depending on the stimulus that was included in the same trials. The rats improved over the course of the experiment.
forc
er to
40
li
Incr
ease
d tim
e
Two
wee
ks
d se
ssio
n le
d se
ssio
n le
33
NaCl-Sucrosevs.
Sucrose-NaCl
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)
Per
form
ance
(% C
orre
ct)
NaCl - Sucrose
Sucrose - NaCl
Incr
ease
d re
info
rcer
to 4
0 lic
ks
Incr
ease
d tim
e ou
t
Two
wee
ks o
ff
Incr
ease
d se
ssio
n le
ngth
Incr
ease
d se
ssio
n le
ngth
Figure 2-4. Mean overall performance to different trials. Rats did not perform significantly differently on trials containing both compounds regardless of the order that the stimuli were sampled.
34
SAME vs. DIFFERENT
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time (Weeks)
Per
form
ance
(% C
orre
ct)
Same
Different
Incr
ease
d re
info
rcer
to 4
0 li
Incr
ease
d tim
e
Two
wee
ks
d se
ssio
n le
d se
ssio
n le
cks
out off
Incr
ease
ngth
Incr
ease
ngth
Figure 2-5. Mean performance on same versus different trials. There was no statistical evidence that the performance on different trials was better than performance on same trials.
CHAPTER 3 A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION IN
RATS
Introduction
With some exceptions, the most common method used to assess taste quality in
rodent models is the conditioned taste aversion (CTA) generalization paradigm. In this
procedure an animal is presented with a taste solution, which serves as the conditioned
stimulus (CS), followed by induction of visceral malaise. After such a conditioning trial,
animals will avoid ingesting the CS as well as compounds that are thought to possess a
similar taste quality. Although this procedure has provided useful information to
researchers interested in taste processing, it has some interpretive and methodological
limitations. One constraint is that a novel CS must be used with each group. Thus, a
large number of animals are required to comprehensively assess taste quality
generalization. Another key problem is that some stimuli (e.g., quinine or HCl) are
inherently avoided by rats, hence making it difficult to differentiate conditioned from
unconditioned suppression of intake (e.g., “floor effect”). Additionally, as described in
Chapter 1, stimulus intensity dynamism presents another caveat for data interpretation
that must be considered. Because an animal will show an increased conditioned response
to concentrations higher than the CS, it becomes important to know what the relative
intensity differences elicited by different compounds might be. Finally, given that-testing
occurs in extinction, the number of test stimuli and test sessions possible is restricted.
For the reasons outlined above, a major goal of this experiment was to develop a
35
36
procedure that circumvents the interpretive and methodological limitations associated
with the CTA approach.
Morrison (1967) introduced a unique behavioral procedure that examined taste
generalization in a different manner. He trained a group of rats to press one lever if the
compound sampled was 0.1 M NaCl (the standard), and another lever if the sample was
0.1 M sucrose. He trained another group of rats to discriminate that same concentration
of NaCl from 0.01 M HCl. Finally, he trained a third group of rats to discriminate the 0.1
M NaCl from 0.5 mM quinine. Next, he was able to determine which response each
group made when given a novel test salt. Profiles, based on whether they responded on
the standard (NaCl) lever or the comparison lever, were derived. This design included all
four prototypical taste compounds split across the three groups, so by placing the
proportion of responses made on the comparison lever together on the same graph, it
represented how sucrose-like, quinine-like, and hydrochloric acid-like the test salt was.
If the profile was not any of the three, then the compound was assumed to be entirely
NaCl-like.
Though this approach is clever, it still has some limitations. First, within a single
group, it is not intuitively obvious how to interpret a compound that is similar to neither
of the two compounds. If the basic tastes are indeed different from one another,
presenting a compound from a separate taste quality would not be expected to fall
exclusively on either one of the training stimuli for a given group, yet a score of 0.5
would indicate that the test compound shared similarities with both. Morrison does not
address this possibility (Morrison, 1967). Perhaps a better paradigm would involve
training the rats to discriminate a taste compound putatively representing one quality
37
from taste stimuli thought to represent all other proposed qualities. In this approach, the
rat might learn to focus solely on one taste in order to separate the features of that
compound from all others. If that occurred, then when a rat responded to a novel
compound as if it were the standard, it would indicate that the test compound was similar
to the standard.
Secondly, Morrison (1967) used only a single concentration of each prototypical
stimulus. In that study intensity was not varied to make it an irrelevant cue. Therefore, it
is unknown whether the rats in Morrison’s (1967) experiment were responding on the
basis of intensity differences or quality differences. A better approach would be to
include several concentrations of each training stimulus to decrease the relevance of
intensity making taste quality the only reliable cue.
The present study was undertaken to expand upon Morrison’s (1967) design and to
incorporate improvements to overcome his experimental shortcomings. Namely, the
differences include an attempt to train the rats to focus on discriminating a single
prototypical compound, representing the putative four basic taste qualities, from the
remaining three. Additionally, inclusion of a broader array of concentrations of the
standard stimulus is intended to circumvent problems that might occur with
generalizations based on intensity features.
In order to choose a broad range of concentrations that represent the prototypical
stimuli and include overlapping intensities, a brief-access taste test was conducted with
one prototypical representative from each of the putative 4 basic taste qualities. The goal
of Experiment I was to identify concentrations of NaCl, sucrose, quinine, and citric acid
that span the dynamic range of intensity, which would be used in Experiment II.
38
Experiment I
Method
Subjects
Eight naïve, adult, male Sprague-Dawley (Charles River Breeders; Wilmington,
MA) rats were used. The rats were housed individually in polycarbonate shoe-box style
cages in a room where temperature, humidity, and light cycle (lights on 7am – 7pm) were
controlled automatically. All manipulations were performed during the light phase. The
rats had ad libitum access to Purina Rat Chow (5001) in the home cage. Purified (Elix
10; Millipore, Billerica, MA) water was also available ad libitum except where indicated.
All procedures were approved by the University of Florida Institutional Animal Care and
Use Committee.
Training Stimuli
All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica,
MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli
consisted of six concentrations of sucrose (0.01, 0.03, 0.06, 0.1, 0.3, and 1.0 M; Fisher
Scientific, Atlanta, GA), NaCl (0.03, 0.1, 0.2, 0.3, 0.5, and 1.0 M; Fisher Scientific,
Atlanta, GA), citric acid (0.3, 1, 3, 10, 30, and 100 mM; Fisher Scientific, Atlanta, GA),
quinine (0.01, 0.03, 0.1, 0.3, 1.0, and 3.0 mM; Sigma-Aldrich, St Louis, MO) and
purified water.
Procedure
A brief-access procedure similar to that described by others (e.g., Glendinning,
Gresack, and Spector, 2002; St. John, Garcea, and Spector, 1994; Spector, Redman, and
Garcea, 1996) was used. Testing took place in the gustometer, which was described in
Chapter 2. The sample phase began when the rat made contact with the dry sample spout
39
and initiated licking. The rat was required to lick the drinking spout twice within 250 ms,
upon which the shaft of the drinking spout was filled with the stimulus and each
subsequent lick resulted in an additional deposit of 5 µl into the fluid column. During the
session, the rat was allowed access to a single concentration for a brief period of time (5
s) and then after a 6-s inter-presentation interval during which the sample spout was
rotated over a funnel and rinsed with clean water, a different solution was offered. The
stimulus array for each compound tested included the six different concentrations
detailed above and purified water. A given trial started after the first lick. Trials were
presented in randomized (without replacement) blocks so that every concentration of a
stimulus and water was presented exactly once before the initiation of the subsequent
block. Unconditioned licking responses were recorded for later analysis. Sessions were
30 min in duration during which rats could initiate as many trials as possible. The
animals were first trained to lick a stationary spout delivering water for 30 min in the
gustometer after being placed on 23.5-h restricted water access schedule. For sucrose
testing, animals then received 2 days of testing with six stimulus concentrations and
purified water while maintained on the water-restriction schedule. During this period of
training, the sample spout rotated away from the access slot between trials. The two days
of sucrose training under a water-restriction schedule was done to familiarize the animals
to approaching and licking the spout. Water bottles were then returned to the home cages
for three days, following which, the rats were tested for three days under conditions of
non-deprivation. After the last sucrose session, water bottles were again returned to the
home cages for a rehydration period before the next-testing week. When the test
compound was not sucrose, rats were placed on a water restriction schedule on a Sunday
40
night, placed into the gustometer for two days of testing with water from a spout which
rotated between trials, and then tested for three days under water-restriction. During the
three 3-day test sessions with NaCl, citric acid, and quinine, respectively, water rinses
were presented between each taste stimulus. A rehydration period always occurred
between test compounds.
Data Analysis
A Tastant/Water Lick Ratio was calculated for the data that were collected during
sessions with water-restricted rats. This ratio was computed by taking the average
number of licks per trial for each concentration and dividing it by the average number of
licks per trial when water was delivered as a taste stimulus. This ratio standardizes the
data to control for individual differences in lick rates. In the non-deprived condition, the
average number of licks per trial for each concentration was divided by that animal’s
estimated maximal lick rate (licks/5 s) yielding a Standardized Lick Ratio. The maximal
lick rate was calculated using the reciprocal of the mean of the inter-lick interval (ILI)
distribution (in s) that was measured during training (only inter-lick intervals >50 ms and
<200 ms were used) and multiplying this value by 5. Standardizing the licking response
in this fashion controls for individual differences in lick rates.
These data were used to select concentrations of NaCl, quinine, and citric acid
which elicit similar lick suppression relative to water. The mean lick data for each
concentration were plotted and then a three-parameter logistic equation was used to fit a
curve to the data: f(x) = a/(1+10b(x-c)), where a is the asymptote (note, for NaCl, quinine,
and citric acid, a was a constant set at 1), b is the slope and c is the point of inflection.
The resulting curve was used to guide the choice of concentrations for Experiment II.
41
Results
Results from the brief-access test are shown in Figures 3-1 – 3-4. Table 3-1 lists
the concentrations selected to represent training stimuli for each prototypical compound.
Unfortunately, the incorrect lowest concentration of quinine was included in the proposed
training array through a typographical error. Instead of using the intended concentration
of 0.0827 mM of quinine, 0.027 mM was recorded. Consequently, that low concentration
became incorporated into training array of Experiment II. The lowest training
concentration of quinine, 0.027 mM is only about twice the most conservative measure of
detection threshold for quinine. (0.012 mM , Koh & Teitelbaum, 1961; 0.005 mM, Thaw
& Smith, 1994; 0.003 mM , Shaber, Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St.
John, & Spector).
In order to determine which concentrations resulted in a reduction in licking at a
specific level (i.e., 20%, 40%, and 60% suppression), the equation was rewritten to solve
for x, such that x = (log10((a/y)-1)/b)+c). Following the selection of concentrations
associated with 20%, 40%, and 60% suppression rates, a concentration that was one order
of magnitude (i.e., 1 log10 unit) below the highest concentration of NaCl (which was
associated with a 60% reduction in licking as compared with water) was identified. For
sucrose, the opposite strategy was taken and concentrations that were 40%, 60% and 80%
of their maximal licking rate to water were used along with the concentration that was
approximately 1 log10 unit above the lowest concentration selected. The lowest
concentration for citric acid was selected to be ~1.5 log10 units below the concentration
associated with a 60% reduction in licking because otherwise, there would have been
little difference in behavioral responding for the concentration associated with a 40%
reduction of licking and the intended one that was 1 log unit below the highest
42
concentration. For quinine, it was our intention to choose a concentration that was 1.0
log10 unit lower than highest concentration selected (i.e., 0.0827 mM), but an erroneous
value was selected (0.027 mM) that was actually ~1.5 log10 units lower. Regardless, all
concentrations spanned at least 1 log10 unit and incorporated the dynamic range of
responsiveness measured in this task.
Discussion
The selection of training stimuli suitable for Experiment II was based on the three
isoresponsive concentrations and the additional concentration for each compound that
allowed for the range of concentrations to span at least 1 log10 unit. For the aversive
stimuli (NaCl, quinine, and citric acid), intensities at which rats reduced their licking to
the same benchmark level of performance were selected. The three compounds are
referred to as aversive because the rats decreased their licking monotonically as
concentration was raised. For the appetitive stimulus, sucrose, the concentrations that
resulted in alterations in licking were similarly selected except that the changes in
concentration resulted in increased levels of licking rather than suppression. Thus, we
attempted to match the three highest concentrations of aversive compounds with the three
lowest concentrations of sucrose, with respect to the effect that increasing concentration
has on behavior. Although this procedure likely does not result in exactly matching
intensities between compounds, we assume that it is a good approximation and
importantly provides some confidence that the concentrations chosen at least are
overlapping. Here, the same rats were used to determine the dynamic range of
concentrations for which licking is modulated across four compounds representing the
basic taste qualities.
43
It is plausible that there were order effects associated with the curves obtained for
each compound, considering that sucrose was the first stimulus to be tested, which was
followed by NaCl, citric acid, and then quinine. The nature of the prior experience with
sucrose may have trained the animal to accept stronger concentrations of the taste stimuli,
thus inflating the range of concentrations selected. Perhaps using a naïve set of rats, or
randomizing the order of presentation between the rats, for each of the four compounds
would have yielded different results. An examination of the literature revealed that
comparison of the midpoint of the concentration-dependent curve for quinine obtained
here (approximately 0.4 mM) with those from two published studies examining brief-
access using quinine (approximately 0.3 and 0.2 mM) suggests that these rats did perhaps
accept higher concentrations than naïve rats do (Spector and Kopka, 2002; St. John,
Garcea, and Spector, 1994). Nevertheless, potential parametric influences aside, the
experiment provided some basis upon which to choose a broad range of concentrations
for each stimulus that at the very least overlap in intensities.
Experiment II
The following experiment attempted to adapt Morrison’s (1967) procedure,
described above, but incorporated a broader array of training concentrations and
comparison stimuli in order to test the following two hypotheses: 1) rats can learn to
discriminate prototypical compounds, characteristic of the putative basic taste qualities,
when a variety of concentrations are used to represent each compound, and 2) rats will
generalize the responses learned with training stimuli to novel untrained test stimuli.
44
Method
Subjects
Forty-eight naïve adult male Sprague-Dawley (Charles River Breeders;
Wilmington, MA) rats served as subjects. The rats were housed individually in
polycarbonate shoe-box style cages in a room where temperature, humidity, and light
cycle (lights on 7am – 7pm) were controlled automatically. All manipulations were
performed during the light phase. The rats had ad libitum access to Purina Rat Chow
(5001) in the home cage. Purified (Elix 10; Millipore, Billerica, MA) water was also
available, but was removed approximately 16 hours before (~4:00 pm the night before)
the first behavioral session of the week and was replaced at the completion of the last
session of the week. A contingency was in place that would allow rats to receive
supplemental water if body weight decreased to 85% of the ad libitum weight calculated
each week, but no rat dropped below that criterion in this experiment. One of the animals
was removed before side training (see below) began because it exhibited self-injurious
behavior. All procedures were approved by the Institutional Animal Care and Use
Committee at the University of Florida.
Apparatus
The apparatus was the same as that described in Chapter 2. There was, however,
no cannula lead entering the chamber from the port in the ceiling of the sound attenuation
chamber.
Task overview
The prototypical taste compounds NaCl, sucrose, quinine HCl, and citric acid were
used to represent the putative 4 basic tastes, salty, sweet, bitter, and sour, respectively.
Four groups of rats were trained to respond by licking one response spout after sampling
45
any of the 4 training concentrations of a particular standard, which for each group was
one of the prototypical compounds, and they were trained to lick a different response
spout after sampling any of the comparison stimuli (the remaining three compounds).
Stimuli
All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica,
MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli
consisted of four concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M;
Fisher Scientific, Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M;
Fisher Scientific, Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM;
Fisher Scientific, Atlanta, GA), quinine (0.027 mM, 0.131 mM, 0.360 mM, and 0.827
mM; Sigma-Aldrich, St Louis, MO) and purified water.
Groups
For overview of the four groups (N, S, Q, and C) and their associated standard and
comparison stimuli, see Table 3-2. Each of the groups was named for their standard
stimulus and was trained to discriminate four concentrations of that compound from four
concentrations each of the comparison stimuli (those from the remaining three
prototypical compounds).
Trial structure
On any given trial (see Figure 3-5), rats were trained to lick a centrally positioned
stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two
times with an inter-lick interval < 250 ms, then the predetermined solution filled the shaft
of the spout, after which the rat could receive up to 5 licks (~5µl was deposited into the
fluid column upon each lick) before the spout was rotated out of position. Next, a
decision phase was initiated, during which the rat was required to lick one response spout
46
after sampling the standard stimulus or the other response spout after sampling a
comparison stimulus. During the consequence phase, if the rat responded correctly to the
stimulus, water reinforcement was delivered directly through the response spout (20 licks
@ ~5µl per lick or a total of 10 s access, whichever occurred first). If the rat failed to
respond, or responded on the incorrect response spout, then the rat was punished with a
20-s timeout. After either consequence of the decision phase, the trial moved into an
intertrial interval that lasted 6 s.
Training
Spout training. The rats had access to only one spout (either the sample spout, the
left response spout, or the right response spout) and each spout was connected to a
reservoir that contained water. The purpose was to train the rats to approach and gain
familiarity with obtaining fluid from each of the spouts. Eventually, the sample spout
would contain a taste stimulus and only the response spouts would contain water.
Side training. Only one trial type was presented within a given session during side
training so that the solutions available alternated with each session. That is, if the rats
were trained with their standard compound in the first session, then during the next
session, the rats received only comparison compounds. After sampling, rats had 180 s
(limited hold period) during which they were required to respond. Side training lasted a
total of 4 days. Only the third highest concentration of each stimulus was presented. The
rats were required to lick the sample spout to obtain a small volume of the stimulus and
then select one of the response spouts by licking it. If the rat responded correctly, then
water reinforcement was available (10 s access or 20 licks, whichever came first). The
intertrial interval during this phase was 6 s.
47
Alternation. During alternation training, the rats started out with either a standard
or one of the comparison stimuli. Upon completion of a set criterion of correct
responses, the program switched to the opposite trial type. Each time the rat completed
the criterion of correct responses, the program automatically switched to delivery of the
other trial type. When the trial type consisted of comparison stimuli, the computer
randomly selected (without replacement) the solution to deliver. The correct responses
did not have to be consecutive. The limited hold was changed from 180 s to 15 s.
During the decision phase, if a rat failed to make any response, or made an incorrect
response, a 10-s timeout was initiated.
Discrimination training I-II. Stimuli were delivered in a block with a random
pattern selected by the computer program. Therefore, the rats had no indication from the
prior trial, which solutions would be offered on the current trial. All four training
concentrations were used in this phase, but because the gustometer had a limited number
of fluid reservoirs, only two concentrations (always one of the highest two and one of the
lowest two) of each prototypical compound were included per session. The block size
was 12; consequently, every standard concentration for a given session was repeated
three times within the block so that the number of standard stimuli matched the number
of comparison stimuli available (which were each only presented once per block). The
timeout period was increased to 20 s during this phase. After 12 days of discrimination
training, a partial schedule of reinforcement was introduced. During the session, two
trials (one standard and one comparison) from each block of 12 trials were randomly
selected to have neither reinforcement nor punishment delivered contingent on the
animal’s response. That is, the animal did not receive reinforcement if it made the
48
correct response and it did not receive punishment if it made the incorrect response on
those selected trials. There was, however, a punishment contingency in place if the rat
failed to make a response. The partial schedule of reinforcement was introduced in
anticipation of the eventual inclusion of test stimuli, which would make up approximately
16% of the total trials in a session. The limited hold period (the time the animal was
allowed to make a response after sampling) was 5 s for this phase.
Test compounds
There was no correct response associated with a test stimulus, so the animal would
not receive reinforcement, but it also did not receive punishment for a response, unless it
failed to make the response before the limited hold period expired. In order to validate
whether rats would generalize untrained test stimuli to the standard compound, novel
concentrations of the training stimuli were presented. The following novel
concentrations of the training compounds and mixtures of NaCl and sucrose compounds
served as test stimuli:
• 0.847 M NaCl
• 0.068 M Sucrose
• 0.546 mM Quinine
• 42.56 Mm Citric acid
• 1.07 M (high) NaCl + 0.421 M (high) Sucrose
• 1.07 M (high) NaCl + 0.077 M (low) Sucrose
• 0.376 M (low) NaCl + 0.421 M (high) Sucrose
• Water
49
Retraining water as a comparison stimulus
Water was selected as a test compound because it has an interesting history in the
literature. Conceptually, water should represent the absence of a taste. The literature,
however, reveals that some humans (Anderson, 1959) report water as having a “bitter”
taste and animals respond to water as if it were quinine-like (Bartoshuk, 1977; Morrison,
1967).
Here, the profile for water as a test stimulus showed that water appeared to
generalize to quinine (see Results). It was not clear whether this was a result of the
erroneous inclusion of the very weak concentration of quinine in the training array, or if
water indeed has a quinine-like taste (note, these are not mutually exclusive).
Consequently, we attempted to train the rats to identify the difference between water and
quinine by adding water to the comparison group.
Negative control test
A water control session was included at the end of the experiment, in which all of
the reservoirs were filled with water. Two reservoirs were arbitrarily assigned to the
“standard” spout, and another six were designated as the “comparison” spout. This was
done to examine whether the rats were using non-chemical cues to guide their behavior.
Data analysis
A 1-way analysis of variance (ANOVA) was conducted for each test stimulus to
determine the presence of differences among groups followed by more detailed
Bonferroni-adjusted paired comparisons. Separate one-sample t-tests against both of the
null hypotheses 1.0 (i.e., the test compound was similar to the standard stimuli) and 0
(i.e., the test compound was similar to the comparison stimuli) were performed. The
conventional p ≤ 0.05 value was used as the statistical rejection criteria.
50
Data for the negative control test were analyzed using a one-sample Binomial
analysis with null hypothesis = 0.5, which corresponds with the chance level of
performance.
Generalization score
A Generalization Score was calculated for each animal, which essentially
quantified the degree to which the test compound was similar to the standard stimulus.
The following equation was used to calculate the Generalization Score: [P(T)-P(C)] /
[P(S)-P(C)]; where, P(T) = proportion of times the rats responded on the standard
response spout when presented with a test stimulus; P(C) = proportion of times the rat
responded on the standard response spout when presented with a comparison stimulus;
and P(S) = proportion of times the rat responded on the standard response spout when
presented with a standard stimulus. Performance (reported as errors) to the comparison
stimuli was included in the equation in an attempt to account for response bias that may
have developed for individual animals, thus the Generalization Score serves to
standardize performance scores for each animal.
The data are presented as Generalization Scores for each group. Each vertical bar,
represents a different group and shows the degree to which the test compound was
behaviorally treated like the standard. A Generalization Score of 0 indicates that the rat
responded to the test compound as if it were a comparison stimulus. A score of 1.0
indicates that the rat responded to the test compound as if it were a standard stimulus. A
Generalization Score of 0.5 indicates that the compound was no more like the standard
than it was the comparison. A score of 0.5, therefore, could indicate that the test
compound shares some similarities with both the standard and one (or more) of the
comparison compounds. Alternatively, a score of 0.5 could indicate that the test
51
compound is completely unlike any of the trained stimuli (standard and comparison) and
the score is obtained because the rat is randomly placing its behavior between the trained
responses.
Results
The generalization profiles for each test compound are shown in Figures 3-6
through 3-13. These figures are used to reveal the proportion of responding to the test
stimulus as compared with the standard stimulus. This format is similar to that used by
Morrison (1967), except that the Generalization Score is plotted on the ordinate instead of
proportion of responses to the standard; the group names are listed along the horizontal
axis. Tables 3-4, 3-6, 3-8, 3-10, 3-12, 3-14, 3-16, and 3-18 list the performance for each
group to individual concentrations of the training stimuli for each test compound. Data
reported in these tables can be used to support the conclusion that rats in this experiment
were reliably able to discriminate between training compounds and that stimulus control
was maintained during the testing period. Each table reflects the data for those particular
sessions that contained the test stimulus. It is noteworthy that these scores were generally
high and the variance was low. Interestingly, the scores for the lowest concentration of
citric acid in the quinine group for many of the test stimuli were lower than the other
concentrations, which implies that the group had more trouble discriminating that
concentration of citric acid from their standard (quinine concentrations). Indeed, results
from studies using electrophysiological and CTA approaches suggest that the signals for
“bitter” and “sour” stimuli may overlap to some extent (e.g., Frank, Contreras, and
Hettinger, 1983; Lemon and Smith, 2005; Nowlis, Frank, and Pfaffmann, 1980), but
clearly the generally high levels of behavioral performance seen here would argue against
that. Besides, similarly poor performance to the lowest sucrose concentration can be seen
52
during some of the same test weeks, which might suggest a problem with an overall
ability to maintain stimulus control for the weakest solutions in that group.
Novel concentrations: NaCl
Figure 3-6 depicts the untrained responses to the novel concentration of NaCl,
0.847 M. An ANOVA comparing performance in the 4 groups revealed that there was a
significant difference between one or more of the groups (F(3, 43) = 2607.5, p < 0.01).
Subsequent post-hoc analysis with Bonferroni adjustment revealed that the
Generalization Scores for the different groups could be ordered in the following way: N>
S > Q > C. Separate one-sample t-test tests (see Table 3-3) showed that the
Generalization Scores for the N group were actually greater than 1.0, indicating that
novel NaCl is more standard-like than the standard concentrations used to maintain
stimulus control, but the actual value was indeed very close to unity. Conversely, the
Generalization Scores from the S and C groups were significantly less than 0, indicating
that those groups treated the novel NaCl as more comparison-like than their actual
comparison stimuli. Both of these types of findings can probably be explained as
statistical artifacts.
In general, it is fair to say that the N group responded as if novel NaCl was
standard-like and rats in the S, Q, and C groups treated the test compound as if it were
comparison-like; this was expected given that NaCl is one of the comparison compounds
for each of these latter three groups. The overall performance on training stimuli, which
were used to maintain stimulus control during testing sessions, is listed in table 3-4; the
performance values during the sessions with the test compound present are shown.
53
Novel concentrations: Sucrose
Figure 3-7 depicts the untrained responses to the novel concentration of sucrose,
0.068 M. An ANOVA comparing Generalization Scores obtained for the 4 groups
revealed that there was a significant difference between one or more of the groups (F(3,
43) = 1587.9, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that
the Generalization Scores for the different groups could be ordered in the following way:
S> N > Q > C. Separate one-sample t-test analyses of the Generalization Scores (see
Table 3-5) revealed that in the S group, novel sucrose was not different from the standard
(sucrose) training stimuli and that the N and Q groups were statistically not different from
comparison training stimuli. The C group did, however, treat the novel sucrose as more
comparison-like than their comparison training compounds. Again, this can likely be
explained by statistical artifact. Overall, there is statistical evidence to support the claim
that the novel concentration of sucrose generalizes to sucrose in the S group, and not at
all to the standards for the N, Q, and C groups. Table 3-6 includes the performance data
for all of the animals during this phase of testing.
Novel concentrations: Quinine
Figure 3-8 describes the untrained responses to the novel concentration of quinine,
0.546 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups
revealed that there was a significant difference between one or more of the groups (F(3,
43) = 2329.181, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that
the Generalization Scores for the different groups could be ordered in the following way:
Q> C > N > S. Of interest, separate one-sample t-test tests of the Generalization Scores
(see Table 3-7) showed that the Generalization Scores to novel quinine in the Q group
were not statistically different from their standard stimulus and that performance in the N
54
and S groups was not different from comparison stimuli. Analysis of the C group
revealed that Generalization Scores were statistically greater than 0, but this difference
did not survive a Bonferroni correction and it was minor in magnitude. Therefore,
performance to the novel concentration of quinine appears to generalize completely to the
trained concentrations of quinine in the Q group and all other groups respond as if the
stimulus were comparison-like. Table 3-8 lists the performance data for all of the
animals during this phase of testing.
Novel concentrations: Citric acid
Figure 3-9 shows the untrained responses to a novel concentration of citric acid,
42.56 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups
revealed that there was a significant difference between one or more of the groups (F(3,
43) = 2734.3, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that
the Generalization Scores for the different groups could be ordered in the following way:
C> N > Q > S. Of interest, separate one-sample t-tests of the Generalization Scores (see
Table 3-9) showed that the novel citric acid test stimulus was statistically more standard-
like for the C group than the training concentrations used, though that effect disappeared
with Bonferroni correction. Also of note is that the S and Q groups responded as if the
novel concentration of citric acid was more comparison-like than the training compounds,
though Bonferroni adjustment resulted in the Q group failing to reach significance. The
N group responded as if the test stimulus was not different from the comparison training
stimuli. Overall, the rats in the C group responded as if the novel concentration of citric
acid were similar to the training concentrations, while the rats in the other groups
responded as if it were a comparison stimulus. Table 3-10 contains the performance to
all concentrations of training stimulus for all of the rats.
55
Mixtures between NaCl and sucrose: 1.07 M NaCl + 0.421 M sucrose
Figure 3-10 shows the untrained responses to a mixture of 1.07 M NaCl and 0.421
M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups
revealed that there was a significant difference between one or more of the groups (F(3,
43) = 89.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the
Generalization Scores for the different groups could be ordered in the following way: N =
S > Q > C. Of interest, separate one-sample t-tests of the Generalization Scores (see
Table 3-11) showed that all of the groups differ statistically from 1.0 (the test compound
is standard-like), and only the C group responds as if the test stimulus is not statistically
different than the comparison stimuli. Consequently, the N and S groups report that the
mixture is also not comparison-like, while the Q group responded as if the mixture was
more comparison-like than the training compounds. The performance in the N and S
groups showed similar levels of responding (ANOVA post hoc between N and S p =
1.000), which suggests that both qualities (NaCl-like and sucrose-like) contributed to the
overall experience of the solution. Table 3-12 contains performance data for the training
stimuli.
Mixtures between NaCl and Sucrose: 1.07 M NaCl + 0.077 M Sucrose
Figure 3-11 shows the untrained responses to a mixture of 1.07 M NaCl and 0.077
M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups
revealed that there was a significant difference between one or more of the groups (F(3,
43) = 1122.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that
the Generalization Scores for the different groups could be ordered in the following way:
N > = C = S =Q. Of interest, separate one-sample t-test analyses of the Generalization
Scores (see Table 3-13) showed that all groups were statistically different from 1.0 (i.e.,
56
the test stimulus was standard-like) and only the N group differed significantly from 0
(the test stimulus is comparison-like). Taken together, these data indicate that a NaCl-
like taste appears to be the only quality present in the mixture. It would seem that the
relatively strong concentration of NaCl overshadows the relatively weak concentration of
sucrose. Table 3-14 contains data for performance to training stimuli.
Mixtures between NaCl and Sucrose: 0.376 M NaCl + 0.421 M Sucrose
Figure 3-12 shows the untrained responses to a mixture of 0.376 M NaCl and 0.421
M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups
revealed that there was a significant difference between one or more of the groups (F(3,
43) = 78.0, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the
Generalization Scores for the different groups could be ordered in the following way: S >
N > C > Q. Of interest, separate one-sample t-test analyses of the Generalization Scores
(see Table 3-15) showed that all groups differed significantly from 1.0 (i.e., that the test
stimulus was standard-like) and that the S and N groups also differed significantly from 0
(the test stimulus is comparison-like). Both the Q and C groups responded as if the test
compound was comparison-like. The post hoc test of the ANOVA indicated that the S
component was statistically greater than the N component. This suggests that rats can
distribute their behavior according to the relative contribution of each compound that is
present in a mixture. The overall performance to training stimuli, which were used to
maintain stimulus control during testing sessions, is listed in Table 3-16.
Novel test compound: Water
Figure 3-13 shows the untrained responses to water as a test compound. An
ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there
was a significant difference between one or more of the groups (F(3, 43) = 386.4, p <
57
0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization
Scores for the different groups could be ordered in the following way: Q > C > S > N. Of
interest, separate one-sample t-tests of the Generalization Scores (see Table 3-17) showed
that all groups were statistically different from 1.0 (the test compound is standard-like)
and only the N group was not statistically different from 0 (the test stimulus is
comparison-like). Taken together, these results show that, under these testing conditions,
there is a strong quinine-like component, followed by citric acid-like and sucrose-like
components to water. The significance of this will be addressed in the discussion section,
but briefly it may have occurred because the lowest training concentration of quinine was
only about twice the most conservative measure of detection threshold reported and also
because water might actually possess a weak quinine taste. Performance to all stimulus
control concentrations are shown in Table 3-18.
Retraining water as a comparison stimulus
The results of the phase in which we attempted to retrain the rats include water with
the comparison stimuli are shown in figure 3-14. The results for the overall performance
when all training compounds were present were poor for the water stimulus in the Q
group (data not shown). Because the percentage of the trials in the session that were
water was very low when all training stimuli were presented, we reasoned that to increase
the ability of the rats to specifically learn the discrimination, it was necessary to limit the
types of training stimuli encountered to only water and quinine. Consequently, to
increase the animals overall experience with discriminating water from quinine, only
water and 0.360 mM & 0.827 mM quinine were present in training sessions shown in
Figure 3-14; for reference, the performance during the first day of retraining is included.
Clearly, the rats were unable to perform this discrimination well. Although it can be seen
58
that their performance to water improved over the course of training, the ability of the
rats to correctly detect the presence of quinine worsened, indicating that the stimuli were
unable to maintain the high levels of stimulus control previously seen with the other
training compounds.
Negative control session
The results for the negative (water) control session are shown in Figure 3-15.
When all of the testing reservoirs which were normally filled with chemical stimuli were
filled instead with water, performance dropped to chance levels for most of the animals.
There were 8 rats that performed significantly worse than chance. If a Bonferroni
correction is applied to control for multiple tests, then the same rats fail to reach
significance. These data support the claim that rats used only chemical cues to guide
their behavior.
Discussion
The rats in this experiment readily learned to discriminate several concentrations of
one prototypical compound representing one of the putative basic taste qualities from
various concentrations of prototypical compounds representing the three remaining taste
qualities. Moreover, the results from test stimuli support the claim that responses to
training stimuli generalized to novel compounds that likely shared similar taste qualities
with the training compounds. The fact that these trials were presented without
consequence allows the assertion that the behavior generalized on the basis of the training
history of the animal. Additionally, other evidence to support that claim is based on the
performance to the novel concentrations of training compounds; the rats performed as if
the novel concentrations were similar to the training compounds.
59
The novel concentrations of training stimuli were taken from the midpoint of each
of the curves obtained during the brief-access experiment. Because the concentrations
included were within the range of training stimuli, it remains possible that more intense
compounds would not have generalized as well, but given what is known about stimulus
intensity dynamism, it is likely that higher concentrations would be identified
appropriately. Nevertheless, it remains an empirical question which could be addressed
by further experiments.
The data on the mixtures of NaCl and sucrose were insightful. These data showed
that rats do not fully generalize to their standard concentration just because it is present
within the mixture. When the standard compound is included in a solution with another
compound to which the rat has been trained to make a competing response, a
Generalization Score of 0.5 may result. Therefore, depending on the relative
concentrations of the standard and comparison solutions used, the behavior of the rats
seems to reflect which compound(s) is/are dominant in the solution. It suggests, then,
that profiles of this type would be helpful in the identification of the components of
complex stimuli (either naturally complex, or through mixtures).
Overall, the data from this novel paradigm suggest that this testing method has the
potential to provide information similar to that obtainable using the conditioned taste
aversion approach with respect to the way rats categorize taste stimuli, presumably on the
basis of their qualitative feature. The most obvious benefit of this procedure over the
CTA approach, however, is that the same test animals can be used repeatedly to report on
essentially an unlimited number of test compounds. The initial training that is required
60
could be described by some as rather lengthy, but the potential for information return is
quite large, and arguably worthwhile.
This paradigm could serve as a more efficient method of obtaining information
about the taste quality of several compounds. It is fair to state that the procedure has
promise as an alternative or complementary testing protocol to the study of taste quality
in animal models. Clearly, more test compounds should be used to extend previous
findings and to identify similarities between this method and other existing procedures.
While it is feasible that this paradigm would yield different findings (e.g., because of
different-testing parameters), it is also possible that this method would provide
converging lines of evidence for results obtained using the conditioned taste aversion
approach and taste discrimination tasks. Such an outcome would increase the confidence
that these different approaches are tapping into similar principles.
Even if this paradigm, however, resulted in conflicting findings from those
generated with other procedures such as CTA, it still does not undermine the information
that this method could potentially provide. As long as the results are reproducible some
aspect of taste behavior is being measured. Perhaps the unique strengths of this
procedure will be realized with further development. One possible avenue which sets this
approach apart from the CTA method is that extinction of learning is not a factor.
Theoretically, the same compound could be tested weeks apart and the animal would
respond to it in the same manner, provided the training stimuli still maintained stimulus
control. The usefulness of this aspect of the task is that it is compatible with
manipulations of the gustatory system in which subsequent re-testing in the same animal
61
subjects is required by design. This feature (i.e., the strength of within subject designs
for data interpretation) is a benefit that the CTA approach does not offer.
When water served as a test compound, the profile generated was unexpected. In
the planning stages of the experiment, the wrong concentration of quinine was included
in the proposed training array through an unfortunate typographical error. Because the
lowest training concentration of quinine, 0.027 mM is only about twice the most
conservative measure of detection threshold for quinine reported in the literature (0.012
mM , Koh & Teitelbaum, 1961; 0.005 mM , Thaw & Smith, 1994; 0.003 mM , Shaber,
Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St. John, & Spector), it remains possible
that animals generalized water responses to quinine because, of all the stimuli, quinine
had the weakest of the low concentrations. It is also possible that water might have a
quinine-like taste as has been reported for both humans (Anderson, 1959), and rats
(Bartoshuk, 1977; Morrison, 1967). These two possibilities are not mutually exclusive,
but as the next experiment will suggest, however, the latter explanation seems to have
some merit.
62
Table 3-1. Training compounds selected from Experiment I. Compound 1 2 3 4 NaCl (M) 0.107 0.376 0.668 1.07 Sucrose (M) 0.042 0.077 0.148 0.421 Quinine (mM) 0.027 0.131 0.360 0.827 CitricAcid (mM) 2.04 10.4 28.2 64.3
Table 3-2. Experimental groups Group Standard Comparison Solutions 1) N NaCl Sucrose, Quinine, Citric Acid 2) S Sucrose NaCl, Quinine, Citric Acid 3) Q Quinine NaCl, Sucrose, Citric Acid 4) C Citric Acid NaCl, Sucrose, Quinine
Table 3-3. Results from one-sample t-tests for a novel concentration of NaCl. Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 5.0 < 0.01 < 0.01 228.62 < 0.01 < 0.01S 10 -148.2 < 0.01 < 0.01 -4.85 < 0.01 < 0.01Q 11 -67.3 < 0.01 < 0.01 -1.77 0.11 0.84C 11 -93.5 < 0.01 < 0.01 -7.12 < 0.01 < 0.01
Table 3-4. Performance to training stimuli during novel NaCl testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 0.107 92.1 1.4 96.0 1.6 88.9 1.9 97.5 0.376 0.376 97.8 0.7 96.7 1.3 96.0 2.2 98.9 0.668 0.668 97.9 0.6 97.0 0.9 97.9 1.1 97.6
NaCl (M)
1.07 1.07 97.9 0.6 97.6 1.3 98.1 1.6 97.4 0.042 0.042 98.7 0.9 90.5 2.8 87.6 3.3 94.7 0.077 0.077 98.0 1.1 96.3 1.1 93.5 1.7 98.9 0.148 0.148 100.0 0.0 96.8 0.7 96.7 2.1 98.2
Sucrose (M)
0.421 0.421 99.0 0.5 98.8 0.8 96.8 1.4 97.6 0.027 0.027 96.1 1.3 87.4 3.3 94.8 1.0 79.3 0.131 0.131 97.9 0.9 92.4 1.5 94.3 1.2 80.7 0.360 0.360 96.2 0.7 94.4 0.9 94.7 1.0 86.2
Quinine (mM)
0.827 0.827 98.4 0.7 93.1 1.3 95.3 0.6 85.6 2.04 2.04 98.6 0.8 96.1 1.4 79.8 3.6 89.2 10.4 10.4 96.3 1.2 97.9 0.8 90.9 1.6 94.4 28.2 28.2 97.3 1.1 99.1 0.9 96.9 1.2 97.7
Citric Acid (mM)
64.3 64.3 98.2 1.0 99.0 0.7 95.6 2.5 99.1
63
Table 3-5. Results from one-sample t-tests for a novel concentration of sucrose. Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 -112.36 < 0.01 < 0.01 0.19 0.85 1.00S 10 -0.25 0.81 1.00 77.92 < 0.01 < 0.01Q 11 -54.94 < 0.01 < 0.01 0.51 0.62 1.00C 11 -146.99 < 0.01 < 0.01 -7.88 < 0.01 < 0.01
Table 3-6. Performance to training stimuli during novel sucrose testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6 0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6
NaCl (M)
1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9 0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1
Sucrose (M)
0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7 0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1
Quinine (mM)
0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3
Citric Acid (mM)
64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9
Table 3-7. Results from one-sample t-tests for a novel concentration of quinine Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 -153.10 < 0.01 < 0.01 -0.62 0.55 1.00S 10 -122.15 < 0.01 < 0.01 -1.80 0.10 0.82Q 11 2.66 0.02 0.16 109.65 < 0.01 < 0.01C 11 -61.17 < 0.01 < 0.01 3.11 0.01 0.08
64
Table 3-8. Performance to training stimuli during novel quinine testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 92.1 1.4 96.0 1.6 88.9 1.9 97.5 1.1 0.376 97.8 0.7 96.7 1.3 96.0 2.2 98.9 0.6 0.668 97.9 0.6 97.0 0.9 97.9 1.1 97.6 1.0
NaCl (M)
1.07 97.9 0.6 97.6 1.3 98.1 1.6 97.4 1.0 0.042 98.7 0.9 90.5 2.8 87.6 3.3 94.7 1.6 0.077 98.0 1.1 96.3 1.1 93.5 1.7 98.9 0.6 0.148 100.0 0.0 96.8 0.7 96.7 2.1 98.2 1.0
Sucrose (M)
0.421 99.0 0.5 98.8 0.8 96.8 1.4 97.6 0.8 0.027 96.1 1.3 87.4 3.3 94.8 1.0 79.3 3.6 0.131 97.9 0.9 92.4 1.5 94.3 1.2 80.7 3.2 0.360 96.2 0.7 94.4 0.9 94.7 1.0 86.2 2.9
Quinine (mM)
0.827 98.4 0.7 93.1 1.3 95.3 0.6 85.6 2.6 2.04 98.6 0.8 96.1 1.4 79.8 3.6 89.2 1.3 10.4 96.3 1.2 97.9 0.8 90.9 1.6 94.4 1.1 28.2 97.3 1.1 99.1 0.9 96.9 1.2 97.7 0.4
Citric Acid (mM)
64.3 98.2 1.0 99.0 0.7 95.6 2.5 99.1 0.2
Table 3-9. Results from one-sample t-tests for a novel concentration of citric acid Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 -178.99 < 0.01 < 0.01 -0.21 0.84 1.00S 10 -145.84 < 0.01 < 0.01 -6.11 < 0.01 < 0.01Q 11 -68.01 < 0.01 < 0.01 -32.85 0.01 0.08C 11 3.50 0.01 0.08 109.77 < 0.01 < 0.01
Table 3-10. Performance to training stimuli during novel citric acid testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6 0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6
NaCl (M)
1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9 0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1
Sucrose (M)
0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7 0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1
Quinine (mM)
0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3
Citric Acid (mM)
64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9
65
Table 3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 -6.89 < 0.01 < 0.01 12.88 < 0.01 < 0.01S 10 -5.72 < 0.01 < 0.01 9.47 < 0.01 < 0.01Q 11 -104.61 < 0.01 < 0.01 -6.29 < 0.01 < 0.01C 11 -62.61 < 0.01 < 0.01 -0.56 0.59 1.00
Table 3-12. Performance to training stimuli during high NaCl + high sucrose testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 89.9 2.2 94.6 1.4 78.2 3.2 96.1 1.3 0.376 96.6 1.4 95.8 1.2 93.6 1.6 95.9 2.6 0.668 97.0 1.3 96.1 1.1 96.2 2.7 88.2 8.1
NaCl (M)
1.07 98.8 0.4 100.0 0.0 98.0 1.0 95.2 1.6 0.042 98.7 0.5 86.9 1.2 80.0 3.0 89.6 2.7 0.077 99.6 0.4 94.7 1.8 91.3 2.6 97.7 0.9 0.148 99.3 0.5 98.4 0.4 97.2 1.2 97.2 1.4
Sucrose (M)
0.421 97.5 1.2 99.0 0.5 98.0 1.2 97.0 1.5 0.027 98.0 0.9 88.1 2.5 94.9 1.0 81.5 1.9 0.131 99.0 0.5 94.7 1.6 95.2 1.0 87.7 2.4 0.360 98.5 0.6 95.7 1.6 97.1 0.8 89.7 2.3
Quinine (mM)
0.827 97.5 1.0 99.0 0.5 97.0 0.9 89.3 3.8 2.04 99.0 0.7 97.3 1.3 80.2 2.5 87.0 1.4 10.4 99.2 0.5 97.8 0.8 88.7 2.6 94.0 1.1 28.2 99.2 0.5 98.6 0.8 96.4 0.8 98.4 0.6
Citric Acid (mM)
64.3 98.6 1.1 97.8 1.1 97.9 0.6 99.5 0.3
Table 3-13. Results from one-sample t-tests for 1.07 M NaCl + 0.077 M sucrose Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 -4.91 < 0.01 < 0.01 98.04 < 0.01 < 0.01S 10 -110.81 < 0.01 < 0.01 0.48 0.63 1.00Q 11 -63.78 < 0.01 < 0.01 -0.68 0.51 1.00C 11 -51.85 < 0.01 < 0.01 -0.95 0.36 1.00
66
Table 3-14. Performance to training stimuli during high NaCl + low sucrose testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 94.6 1.6 95.9 1.3 89.0 2.6 95.2 1.9 0.376 97.4 1.0 98.4 0.7 98.1 1.0 98.6 0.5 0.668 98.6 0.4 98.3 0.9 99.7 0.3 98.8 0.7
NaCl (M)
1.07 98.6 0.3 98.2 0.8 97.7 0.6 98.0 0.8 0.042 99.7 0.3 92.2 1.9 87.2 2.3 96.5 2.0 0.077 100.0 0.0 97.9 0.8 95.1 1.6 98.1 0.7 0.148 98.6 0.8 97.7 1.3 99.1 0.5 97.4 1.1
Sucrose (M)
0.421 99.4 0.4 98.5 1.0 97.3 1.1 96.4 1.2 0.027 99.0 0.5 87.5 3.9 93.7 1.3 79.7 4.0 0.131 99.1 0.5 94.2 1.0 96.8 0.5 84.8 3.3 0.360 98.9 0.6 94.3 1.4 97.5 0.5 90.1 2.5
Quinine (mM)
0.827 98.0 0.8 96.0 2.1 96.1 0.8 92.3 1.8 2.04 99.7 0.3 94.3 1.7 71.9 2.6 88.9 1.6 10.4 99.6 0.4 96.8 1.2 89.8 1.8 94.3 0.9 28.2 99.0 0.7 98.3 1.0 97.2 1.4 98.3 0.5
Citric Acid (mM)
64.3 99.0 0.5 99.7 0.3 98.8 0.7 99.1 0.3
Table 3-15. Results from one-sample t-tests for 0.376 M NaCl + 0.421 M sucrose Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 -17.58 < 0.01 < 0.01 15.07 < 0.01 < 0.01S 10 -4.43 < 0.01 < 0.01 14.87 < 0.01 < 0.01Q 11 -51.13 < 0.01 < 0.01 -1.17 0.27 1.00C 11 -18.29 < 0.01 < 0.01 3.18 0.01 0.08
67
Table 3-16. Performance to training stimuli during low NaCl + high sucrose testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 92.2 1.5 95.0 1.5 86.2 2.2 98.1 0.6 NaCl 0.376 99.2 0.3 98.5 0.6 98.6 0.6 98.7 0.5 (M) 0.668 98.9 0.5 99.3 0.5 98.1 1.1 96.7 1.6 1.07 98.7 0.4 99.2 0.5 90.6 8.3 97.6 0.7 0.042 98.1 0.6 88.9 1.9 81.7 3.4 91.2 2.3 Sucrose 0.077 99.7 0.3 93.0 1.7 87.0 2.2 93.3 3.1 (M) 0.148 99.6 0.4 98.7 0.7 98.0 1.0 97.6 1.0 0.421 99.6 0.4 98.9 0.5 97.8 0.8 98.9 0.6 0.027 97.5 1.2 87.5 2.0 93.1 1.0 78.8 3.4 Quinine 0.131 97.5 1.2 95.1 1.6 95.1 1.0 85.0 3.5 (mM) 0.360 98.9 0.6 96.8 1.2 88.4 8.0 91.4 1.9 0.827 98.3 0.6 98.4 0.7 96.3 0.8 90.1 2.3 2.04 98.3 0.8 93.9 1.4 57.3 4.2 79.3 2.5 Citric
Acid (mM)
10.4 98.9 0.8 98.7 0.7 81.4 7.2 91.8 1.4 28.2 99.7 0.3 99.5 0.5 99.3 0.7 99.0 0.4 64.3 97.7 1.0 98.5 0.9 97.1 1.0 99.4 0.3
Table 3-17. Results from separate one-sample t-tests for water Test against 1.0 Test against 0
Grp df t p-
value Adjusted p-value t p-value
Adjusted p-value
N 11 -130.33 < 0.01 < 0.01 3.01 0.01 0.08S 10 -30.60 < 0.01 < 0.01 7.19 < 0.01 < 0.01Q 11 -4.73 < 0.01 < 0.01 90.67 < 0.01 < 0.01C 11 -24.67 < 0.01 < 0.01 7.880 < 0.01 < 0.01
Table 3-18. Performance to training stimuli during water testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 97.3 0.6 99.2 0.8 94.5 1.0 97.9 0.8 NaCl 0.376 98.3 0.5 98.9 0.6 98.6 0.6 98.4 0.8 (M) 0.668 98.7 0.4 96.7 0.9 99.2 0.5 98.5 0.6 1.07 99.0 0.3 98.3 0.9 99.3 0.5 99.7 0.3 0.042 96.7 1.0 90.7 2.4 87.3 2.5 94.2 1.9 0.077 98.3 1.4 95.2 1.3 96.7 1.1 98.0 1.0 0.148 99.2 0.5 98.4 0.4 98.4 0.7 97.9 1.2
Sucrose (M)
0.421 99.3 0.5 98.1 0.8 97.4 0.8 96.9 1.2 0.027 97.2 1.6 89.1 2.7 92.3 1.2 81.6 2.2 0.131 96.9 1.2 94.0 2.3 94.7 0.9 88.7 2.5 0.360 97.5 1.0 92.4 2.3 94.3 1.2 91.0 2.6
Quinine (mM)
0.827 98.0 0.7 97.1 1.1 94.0 1.2 90.1 1.6 2.04 98.3 0.7 97.2 1.1 90.9 1.6 93.4 1.0 10.4 97.4 1.1 98.8 0.8 95.8 1.1 93.9 1.6 28.2 97.2 1.1 99.2 0.5 92.8 1.6 93.4 1.2
Citric Acid (mM)
64.3 98.6 0.6 99.5 0.5 97.9 1.0 98.6 0.6
68
NaCl
Concentration (M)0.01 0.1 1
0.0
0.2
0.4
0.6
1.0
0.8
Figure 3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test. Rats monotonically decreased licking as concentration increased.
Sucrose
Concentration (M)0.001 0.01 0.1 1 10
0.0
0.2
0.4
1.0
0.8
0.6
Figure 3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test. Rats monotonically increased licking as concentration increased.
69
Quinine
Concentration (mM)0.001 0.01 0.1 1 10
0.0
0.2
0.4
0.6
0.8
1.0
Figure 3-3. Mean (n=8) unconditioned licking to quinine in a brief access test. Rats monotonically decreased licking as concentration increased.
Citric Acid
Concentration (mM)0.01 0.1 1 10 100 1000
0.0
0.2
0.4
0.6
0.8
1.0
Figure 3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test. Rats monotonically decreased licking as concentration increased.
70
Figure 3-5. An overview of the trial structure.
71
0.847 M NaCl
GroupsN S Q C
-0.20.00.20.40.60.81.01.2
Figure 3-6. The generalization profile obtained when 0.847 M NaCl was used as a test compound. The novel concentration generalized to the trained standard.
0.068 M Sucrose
GroupsN S Q C
-0.20.00.20.40.6
1.2
0.81.0
Figure 3-7. The generalization profile obtained when 0.068 M sucrose was used as a test compound. The novel concentration generalized to the trained standard.
72
0.546 mM Quinine
GroupsN S Q C
-0.20.00.20.40.60.81.01.2
Figure 3-8. The generalization profile obtained when 0.546 mM quinine was used as a test compound. The novel concentration generalized to the trained standard.
42.56 mM Citric Acid
GroupsN S Q C
-0.20.00.20.40.60.81.01.2
Figure 3-9. The generalization profile obtained when 42.56 mM citric acid was used as a test compound. The novel concentration generalized to the trained standard.
73
1.07 M NaCl + 0.421 M Sucrose
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 3-10. The generalization profile obtained when 1.07 M NaCl + 0.421 M sucrose was used as a test stimulus. The profile obtained was equally NaCl- and sucrose-like.
1.07 M NaCl + 0.077 M Sucrose
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose was used as a test stimulus. The profile obtained was NaCl-like.
74
0.376 M NaCl + 0.421 M Sucrose
Groups
Gen
eral
izat
ion
Scor
e
N S Q C-0.2
0.20.0
0.40.6
1.01.2
0.8
Figure 3-12. The generalization profile obtained when 0.376 M NaCl + 0.421 M sucrose was used as a test stimulus. The profile obtained was more sucrose-like than NaCl-like, but there was no statistical evidence for quinine-like or citric acid-like components.
Water
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.2
0.20.40.6
1.01.2
0.0
0.8
3. The generalization profile obtained when water was used as a test stimulus. The profile obtained was predominantly quinine-like, indicating that either
quinine had the weakest of the low concentrations.
Figure 3-1
water has a quinine-like taste quality and/or because, of all of the stimuli,
75
Retraining 0.360 mM & 0.827 mM Concentrations of Quinine Versus Water
0.4
0.5
0.6
0.8
0.9
1
% C
orre
c
01
0
0.3
0.7
14 15 16 17 18 19 20 21 22 23 24 25 26 27
t
.1
0.2 Quinine
Wa
Days
ter
f water Figure 3-14. Performance of the Q group during retraining for discrimination o
from the two mid-range concentrations of quinine.
Overall Performance During Negative Control Test
70
80
90
100
% C
orre
ct
0
10
0
1 2 3 4 5 6 7 8 9
Rat Number
2
40
50
60
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
* * * * * ** *
30
Figure 3-15. Performance on water control test. Individual performance scores for all
rats indicate that taste did not serve as a cue to guide behavior.
CHAPTER 4 APPLICATION OF A NEW BEHAVIORAL PA
QUALITY GENERALIZATION RADIGM TO ASSESS TASTE
Introduction
s,
d
s that were completely novel to the rats.
s. The rats were housed individually in polycarbonate shoe-
Chapter 3 explored whether rats would be able to perform a task in which they
were required to discriminate one prototypical taste compound, thought to be
representative of one of the putative four basic taste qualities, from the other three
prototypical taste compounds. Furthermore, we wanted to determine whether we could
use the untrained responses of the animals, when presented with novel taste compound
to generate profiles which would indicate how NaCl-like, sucrose-like, quinine-like, and
citric acid-like each novel test compound is. A few questions and confounds were not
addressed in that particular paradigm. Therefore, the following experiment was modifie
by 1) increasing the lowest concentration of quinine from 0.027 mM to 0.083 mM, and 2)
including a water (W) group specifically trained to discriminate the four prototypical
stimuli (comparison stimuli) from their water standard in an attempt to overcome the
pitfalls encountered in Experiment II of Chapter 3. Additionally, the current experiment
was also designed to extend the findings of the previous chapter by including test
compound
Method
Subjects
Thirty naïve adult male Sprague-Dawley (Charles River Breeders; Wilmington,
MA) rats served as subject
76
77
box style cages in a room where temperature, humidity, and light cycle (lights on 7am –
7pm)
e HCl, and citric acid,
present the putative 4 basic tastes, salty, sweet, bitter, and sour,
respe
out
g
ere
were controlled automatically. All manipulations were performed during the light
phase. The rats had ad libitum access to Purina Rat Chow (5001) in the home cage.
Purified (Elix 10; Millipore, Billerica, MA) water was also available, but was removed
approximately 16 hours before (4:00 pm the day before) the first behavioral session of the
week and was replaced at the completion of the last session of the week.
Apparatus
The apparatus was the same as that described in Experiment II of Chapter 3.
Task Overview
The prototypical taste compounds, NaCl, sucrose, quinin
were used to re
ctively. Five groups of rats were trained in a manner similar to that in Chapter 3.
Briefly, they were trained to respond by licking one response spout after sampling any of
the 4 training concentrations of a particular standard, which for each group was one of the
prototypical compounds or water, and they were trained to lick a different response sp
after tasting any of the comparison stimuli, which included the remaining compounds
(see Table 4-1).
Stimuli
The same concentrations of each of the prototypical compounds were used durin
training and were based on the results of Experiment I in Chapter 3. All solutions w
prepared daily with purified water (Elix 10, Millipore, Billerica, MA) and reagent grade
chemicals, and were presented at room temperature. Test stimuli consisted of four
concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M; Fisher Scientific,
Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M; Fisher Scientific,
78
Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM; Fisher Scien
Atlanta, GA), quinine (0.083 mM, 0.131 mM, 0.360 mM, and 0.827 mM; Sigma-A
St Louis, MO) and purified water. Note the originally intended lowest
tific,
ldrich,
concentration of
d in this experiment.
Trial
t
parison
s,
If the rat failed to respond, or
respo out .
al
he
,
r
quinine, 0.083 mM, was include
Structure
On any given trial (see flow chart), rats were trained to lick a centrally positioned
stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two
times with an interlick interval < 250 ms, then the shaft of the spout was filled with the
stimulus solution, after which the rat could sample up to 5 licks (~5µl was deposited into
the fluid column upon each lick) before the spout was rotated out of position. Next, a
decision phase was initiated, during which the rat was required to lick one response spou
after tasting the standard stimulus or the other response spout after tasting a com
stimulus. During the consequence phase, if the rat responded correctly to the stimulu
water reinforcement was delivered directly through the response spout (20 licks @ ~5µl
per lick or a total of 10 s access, whichever occurred first).
nded on the incorrect response spout, then the rat was punished with a 20-s time
After either consequence of the decision phase, the trial moved into an inter-trial interv
that lasted 6 s. See Figure 3-5 in Chapter 3 for an overview of the trial structure.
Training
Table 4-2 contains the training parameters associated with this experiment. T
inclusion of water as a comparison had to be abandoned in order to proceed with training
but the water group was maintained, albeit on a different training schedule than the othe
4 groups (see Table 4-3).
79
Spout training
This phase was the same as that described in Experiment II Chapter 3. The rats had
pout (either the sample spout, the left response spout, or the right
respo
ty with getting fluid
m each of the spouts. Eventually, the sample spout would contain a taste stimulus and
re ntain water. The rats had to learn to lick from the
mp ponse spouts by licking it. If the rat responded
ment was available (10 s access or 20 licks, whichever
me ial interval during this phase was 6 s.
s the same as that described in Experiment II Chapter 3. Briefly,
ly o as presented within a given session during side training. If the rats
r standard in the first session, then during the next session, the rats
ison trials. After sampling, rats had 180 s (limited hold period)
rin required to respond. Side training lasted a total of 4 days. Only
th ration of each stimulus was presented.
access to only one s
nse spout) and each spout was connected to a reservoir that contained water. The
point of this phase was to train the rats to approach and gain familiari
fro
the sponse spouts would only co
sa le spout and then select one of the res
correctly, then water reinforce
ca first). The inter-tr
Side training
This phase wa
on ne trial type w
were trained with thei
received only compar
du g which they were
the ird highest concent
Alternation
This phase was the same as that described in Experiment II Chapter 3. Briefly,
during alternation training, the rats started out with either a standard or one of the
comparison stimuli. Upon completion of a set criterion of correct responses, the program
switched to the opposite trial type. The criterion was set at 6 the first day, 4 the second
day, and 2 the third day of alternation training. Each time the rat completed the criterion
of correct responses, the program automatically switched to delivery of the other trial
80
type. The correct responses did not have to be consecutive. The limited hold was
changed from 180 s to 15 s. During the decision phase, if a rat failed to make any
respo
ere delivered in a block with a random pattern selected by the computer
program. Therefore, the rats had no indi m the prior trial, which solutions would
be off
reservoirs, only two concentrations
e of the lowest two) of each prototypical compound
he block size was 16 to accommodate the water standard;
conse
shment
e
t
owever, a punishment contingency if the rat
failed to make a response. There was no correct response associated with a test stimulus,
nse, or made the incorrect response, a 10-s timeout was initiated.
Discrimination training I-III
Trials w
cation fro
ered on the current trial. All 4 training concentrations were used in this phase, but
because the gustometer had a limited number of fluid
(always one of the highest two and on
were included per session. T
quently, every standard concentration for a given session was repeated three times
within the block so that the number of standard stimuli matched the number of
comparison stimuli available (which were each only presented once per block). The
timeout period was increased to 20 s during this phase. The training schedules differed
for the W group and the N, S, Q, & C groups at this point.
Once performance reached an asymptote for all animals in the N, S, Q, and C
groups (85% or better two consecutive days), a partial schedule of reinforcement was
introduced. During the session, 2 trials (one standard and one comparison) from each
block of 12 trials were randomly selected to have neither reinforcement nor puni
delivered contingent on the animal’s response. That is, the animal did not receiv
reinforcement if it made the correct response but it also did not receive punishment if i
made the incorrect response. There was, h
81
so the animal would not receive reinforcement, but it also did not receive punishment for
a response, unless it failed to make the response before the limited hold (5 s) timed out.
Test Compounds
In order to extend the results from the last experiment, only novel taste compounds
were tested. The following novel compounds served as test stimuli:
• 0.376 M sodium gluconate
• 0.668 M sodium gluconate
• 0.131 mM denatonium
• 0.360 mM denatonium
• 0.077 M maltose
• 0.148 M maltose
• 0.376 M KCl
• 0.668 M KCl
• 0.077 M MSG
• 0.148 M MSG
• 0.077 M fructose
• 0.148 M fructose
Data Analysis
The same calculation and interpretation for the Generalization Score was used as
described in Experiment II of Chapter 3. One-way analyses of variance (ANOVAs) were
conducted for each test stimulus to determine the presence of differences among groups
followed by detailed Bonferroni-adjusted paired comparisons. Separate one-sample t-
tests testing group means against both of the null hypotheses 1.0 (the test compound was
similar to the standard stimuli) and 0 (the test compound was similar to the comparison
82
stimuli) were performed. The conventional p < 0.05 value was used as the statistical
rejection criteria.
Data for the negative control test were analyzed using a one-sample Binomial
analysis with null hypothesis = 0.5, which corresponds with the chance level of
performance.
Results
, S, Q, and C for the two concentrations of each of the 6 test
comp
C.
standard stimulus, the profile was still
In addition, Bonferroni post hoc comparisons of the
re revealed that S, Q, and C groups did not differ from each other,
while
Results for groups N
ounds can be seen in Figures 4-1 through 4-10.
Test Stimulus: Sodium Gluconate
0.376 M sodium gluconate
Figure 4-1 shows the behavioral profile obtained for 0.376 M sodium gluconate.
An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that
there was a significant difference between one or more of the groups (F(3, 20) = 68.7, p <
0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization
Scores for the different groups could be ordered in the following way: N > S = Q =
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-4) showed
that all groups were statistically different than 1.0. In addition, the N and S groups were
statistically different than 0. These results show that although the N group did not treat
0.376 M sodium gluconate exactly like a
predominantly NaCl-like.
Generalization Sco
the N group differed from all three.
83
0.668 M sodium gluconate
Figure 4-2 shows the behavioral profile obtained for 0.668 M sodium gluconate.
An ANOVA comparing Generalization Scores obtained from the 4 groups revealed
there was a significant difference between one or more of the groups (F(3, 20) = 44.4, p
0.01). A post-hoc analysis with Bonferroni adjustment indicated that the Generalization
Scores for the different groups could be ordered in the following way: N > Q = S = C.
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-5) showed
that all groups were statistically different than 1.0, although with a Bonferroni adjustme
for multiple t-tests, the N group failed to reach statistical significance. Thus, statistical
evidence exists to support the claim th
that
<
nt
at the N group was standard-like. After Bonferroni
lied to the results from the t-test aimed at discerning which groups
differ sis
-
there
Separate one-sample t-tests of the Generalization Scores (see Table 4-7) determined that
correction was app
ed statistically from 0 (that the test stimulus was comparison-like), the analy
revealed that only the N and Q groups differed from 0. Thus, there is a predominant
NaCl-like component in 0.668 M sodium gluconate and possibly also a slight quinine
like component. Performance to the training stimuli used during testing for 0.376 M and
0.668 M sodium gluconate is shown in Table 4-6.
Test Stimulus: Denatonium
0.131 mM denatonium
Figure 4-3 shows the behavioral profile obtained for 0.131 mM denatonium. An
ANOVA comparing Generalization Scores obtained from the 4 groups revealed that
was a significant difference between one or more of the groups (F(3, 20) = 508.9, p <
0.01). A post-hoc analysis with Bonferroni adjustment showed that the Generalization
Scores for the different groups could be ordered in the following way: Q > S > C = N.
84
all groups except the Q group differed significantly from 1.0 (the test compound was
standard-like). Thus, denatonium is statistically not different than the training
, the t-test comparing the Generalization
ealed that both the Q and S groups were statistically greater than 0,
indica
tion
S.
led
different than 0, indicating that the N, S, and C groups had performance that
n-like. This test compound, 0.360 mM denatonium, was clearly quinine-
like.
there
concentrations of quinine. On the other hand
Scores to 0 rev
ting that 0.131 mM denatonium is treated behaviorally as predominantly quinine-
like, and very slightly sucrose-like.
0.360 mM denatonium
Figure 4-4 shows the behavioral profile obtained for 0.360 mM denatonium. An
ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there
was a significant difference between one or more of the groups (F(3, 20) = 258.1, p <
0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generaliza
Scores for the different groups could be ordered in the following way: Q > C = N =
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-8) revea
that the Q group is not statistically different than 1.0 (i.e., the test compound is standard-
like), while all of the other groups are different. Furthermore, only the Q group is
statistically
was compariso
Performance to the stimulus control concentrations for both 0.131 mM and 0.360
mM denatonium is shown in Table 4-9.
Test Stimulus: Maltose
0.077 M maltose
Figure 4-5 shows the behavioral profile obtained for 0.077 M maltose. An
ANOVA comparing Generalization Scores obtained from the 4 groups revealed that
was a significant difference between one or more of the groups (F(3, 20) = 25.7, p <
85
0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization
Scores for the different groups could be ordered in the following way: Q > S > C = N.
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-10)
revealed that all of the groups differed significantly from 1.0 but that only the S and Q
groups differed from 0. This indicates that there was both a sucrose-like and quinine-like
component to
the maltose. Since the post hoc analyses of the ANOVA revealed that Q >
r Q component to the compound than an S
hould be stated again that the Generalization Score does not reflect the
intens
ere
N.
that the post hoc analysis of the ANOVA showed no differences
d Q group means. These results, taken together, indicate that there is an
equal
n
S, it can be concluded that there is a stronge
component. It s
ity of the taste quality, but it is an indicator of how similar the test compound is to
the standard stimulus concentrations.
0.148 M maltose
Figure 4-6 shows the behavioral profile obtained for 0.077 M maltose. An
ANOVA comparing Generalization Scores obtained from the 4 groups revealed that th
was a significant difference between one or more of the groups (F(3, 20) = 28.9, p <
0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization
Scores for the different groups could be ordered in the following way: S = Q > C >
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-11)
revealed that all of groups differed statistically from a hypothesized mean of 1.0.
Additionally, the S and Q groups also differed significantly from a hypothesized mean of
0. It is interesting
between the S an
sucrose-like and quinine-like component arising from 0.148 M maltose. These data
might reveal the basis of taste cues which allow discrimination of maltose and sucrose i
86
rats. Performance to the training stimuli for both concentrations of maltose can be seen
in Table 4-12.
Test Stimulus: Potassium Chloride (KCl)
0.376 M KCl
Figure 4-7 shows the behavioral profile obtained for 0.376 M KCl. An ANOV
comparing Generalization Scores obtained from the 4 groups revealed that there was a
significant difference between one or more of the groups (F(3, 20) = 6.2, p < 0.01). A
post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for
the different groups could be ordered in the following way: Q
A
> C = N = S (also Q>N=S)
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-13) show
that all groups are statistically different than 1.0 (standard-like). After Bonferroni
adjustment for multiple comparisons, only the performance of the Q and N groups
differed from 0 (comparison-like). Collectively, these data indicate that while KCl is
pred
.
ominantly quinine-like there is also a NaCl-like component. The profile is that of a
alities contributing at least some portion to the overall
0.668
a
r
complex taste, with two qu
experience
M KCl
Figure 4-8 shows the behavioral profile obtained for 0.668 M KCl. An ANOVA
comparing Generalization Scores obtained from the 4 groups revealed that there was
significant difference between one or more of the groups (F(3, 20) = 5.4, p < 0.01). A
post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores fo
the different groups could be ordered in the following way: Q > C = N = S. Separa
one-sample t-test analyses of the Generalization Scores (see Table 4-14) show that
performance in all groups was statistically different than it was for their respective
te
87
standards. Performance for each of the groups, N, Q, and C, was significantly different
than 0, indicating that portions of those three qualities contributed to the taste of KCl.
The predominant aspects are quinine and citric acid followed by NaCl, which coincid
nicely with data from previous studies, suggesting KCl has a ‘bitter’, ‘sour’, ‘salty
(Morrison, 1967). Data concerning the performance to the training stimuli are shown i
es
’ taste
n
Test
A
es for
te
y
s result
o a
pect.
A
Table 4-14.
Stimulus: Monosodium Glutamate
0.077 M MSG
Figure 4-9 shows the behavioral profile obtained for 0.077 M MSG. An ANOV
comparing Generalization Scores obtained from the 4 groups revealed that there was a
significant difference between one or more of the groups (F(3, 20) = 33.5, p < 0.01). A
post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scor
the different groups could be ordered in the following way: S > N > Q = C. Separa
one-sample t-test analyses of the Generalization Scores (see Table 4-16) show that
performance for all groups differed from the standard (hypothesized mean of 1.0). Onl
the S and N groups differed from 0, indicating that there was both a NaCl-like and
sucrose-like component to the 0.077 M MSG. The post hoc analysis of the ANOVA
indicated that there was a greater sucrose-like component than NaCl-like. Thi
suggests that MSG, at this concentration, is predominantly sucrose-like but there is als
NaCl-like as
0.148 M MSG
Figure 4-10 shows the behavioral profile obtained for 0.148 M MSG. An ANOV
comparing Generalization Scores obtained from the 4 groups revealed that there was a
significant difference between one or more of the groups (F(3, 20) = 32.2, p < 0.01). A
88
post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scor
the different groups could be ordered in the following way: N > S > C = Q. Separate
one-sample t-test analyses of the Generalization Scores (see Table 4-17) show that
performance for all groups differed from the standard (hypothesized mean of 1.0). O
the S and N groups differed from 0, indicating that there was both a NaCl-like and
sucrose-like component to the 0.077 M MSG. The post hoc analysis of the ANOVA
indicated that there was a greater NaCl-like component than sucrose-like. This res
suggests that MSG, at this concentration, is predominantly NaCl-like but there is also a
sucrose-like aspect. Taken together, both profiles for MSG would suggest there is good
evidence to postulate that the taste of MSG is more sucrose-like or NaCl-like, depen
on the concentration, than anything else, but that it is definitely a combination of the two
compounds and there is little evidence to support the claim that MSG is representative of
a fifth taste quality in rats. The performance to the training stimuli is shown in Table 4-
18.
Test Stimulus: Fructose
es for
nly
ult
ding
0.077
ion
the S, Q, and C groups differed from 0. This indicates that there was a sucrose-like,
M fructose
Figure 4-11 shows the behavioral profile obtained for 0.077 M fructose. An
ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there
was a significant difference between one or more of the groups (F(3, 20) = 30.1, p <
0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalizat
Scores for the different groups could be ordered in the following way: Q > S > C = N.
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-19)
revealed that all of the groups differed significantly from 1.0 except the Q group, but that
89
quinine-like, and citric acid-like component to the fructose. Since the post hoc analyses
of the ANOVA revealed that Q > S > C, it can be concluded that ther
e is a stronger Q
component to the compound than an S It should be stated again that the
Gene
.
re
alization
from a hypothesized mean
that the post hoc analysis of the ANOVA showed no differences
betwe s an
e
component.
ralization Score does not reflect the intensity of the taste quality, but it is an
indicator of how similar the test compound is to the standard stimulus concentrations
0.148 M fructose
Figure 4-12 shows the behavioral profile obtained for 0.077 M fructose. An
ANOVA comparing Generalization Scores obtained from the 4 groups revealed that the
was a significant difference between one or more of the groups (F(3, 20) = 36.8, p <
0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Gener
Scores for the different groups could be ordered in the following way: S = Q > C = N.
Separate one-sample t-test analyses of the Generalization Scores (see Table 4-20)
revealed that all of groups differed statistically from a hypothesized mean of 1.0.
Additionally, the S, Q and C groups also differed significantly
of 0. It is interesting
en the S and Q group means. These results, taken together, indicate that there i
equal sucrose-like and quinine-like component arising from 0.148 M fructose. Thes
data might indicate that fructose and sucrose would be discriminable to rats.
Performance to the training stimuli for both concentrations of fructose can be seen in
Table 4-21.
Performance of Water Group
The water (W) group was on a different schedule than the other groups because
discrimination of water from quinine proved a difficult task. Results for the different
phases of training are shown in Figure 4-13. First, the W group was performing poorly
90
overall when only the third-highest concentrations of all compounds were used in
training. When comparing their performance to all compounds, it was clear that the
problem primarily occurred between water and quinine (data not shown). Only the
highest concentration of quinine and water were next used to help rats discriminat
between the compounds. See summary of training schedule (Table 4-3) for number of
days at each manipulation. Next, the number of sample licks and reinforcement licks was
increased and appeared to improve the performance slightly (see Figure 4-13). A
different water source (Publix purified water) was used, and that appeared also to help th
rats learn the discrimination (see Figure 4-13), but we cannot rule-out that this could ha
been based on potential chemical cues arising from the storage container (Song, Al-
Taher, & Sadler, 2003). In fact, high levels of discriminability remained when the
source was switched back to the in-house Millipore-purified water. A drop in the nu
e
e
ve
water
mber
of sam ee
e
g that
ows that there was
iability in the group (Figure 4-13). The overall performance to training
comp
ple licks and reinforcement licks resulted in a decrease in performance levels (s
point dt1-27 on Figure 4-13). Finally, returning to the 10 sample licks and 40
reinforcement licks appeared to increase levels of performance.
Next, one of the two highest concentrations of each prototypical compound was
used to retrain the rats to discriminate water from all 4 prototypical compounds; th
concentration present depended on which concentrations the other groups were usin
session. Finally, all of the concentrations were rotated through the training sessions and
overall performance was better than chance, though the graph sh
substantial var
ounds is listed in Table 4-22; from the table, it can be seen that the rats did not
perform as well as the other rats on many of the compounds, suggesting that maintaining
91
stimulus control under these conditions is difficult. The training history of the ani
cannot be ruled out as a contributing factor to the poor performance.
Discussion
The results from the test compounds reveal that the rats can generalize the behavi
learned from the prototypical training compounds to completely novel compounds.
Presumably, if the behavior generalizes to untrained (i.e., no consequence was delivered
for responses on these trials) compounds, then it supports the conclusion that the anima
is responding on the basis of a shared feature between the standard stimuli and the test
stimulus, most likely related to taste quality. The fact that performance to the tra
stimuli remained high and the profiles obtained were distinct for the different
compounds, suggests the animals were not just arbitrarily responding to test compounds.
Some of the profiles were complex, which further
mals
or
l
ining
demonstrates the strength of this
parad
ach
size of
ate anion size limiting passage of sodium through tight junctions; NaCl, with
the re ons
igm to capture not only pure taste qualities, but also compounds which appear to
possess two or more distinct taste qualities (e.g., MSG, KCl).
Sodium Gluconate
The profiles for both concentrations of sodium gluconate were similar to e
other, although there was more of a quinine-like component present for the highest
concentration tested. These data may help to explain differences found between NaCl
and sodium gluconate in other behavioral data in the literature. Sodium gluconate and
NaCl are thought to activate different salt transduction pathways due to the large
the glucon
latively smaller anion is thought to be capable of passing through the tight juncti
resulting in activation of paracellular receptor sites (Elliot & Simon, 1990; Formaker &
Hill, 1988; Ye et al., 1991, 1993, 1994). Therefore, it was assumed that when amiloride,
92
an epithelial sodium channel (ENaC) blocker, was applied to the oral cavity, the
transduction of both salts would be impaired differentially because sodium gluconat
theoretically would not have a pathway to activate. Geran and Spector (2000), howe
showed that although amiloride treatment significantly shifted sodium gluconate
detection thresholds more than it did for NaCl thresholds, rats were still able to pe
e
ver,
rform
above ter
m
l.,
e and high concentrations of sodium gluconate, that is responsible for
onent present in the higher concentration.
Dena
.
chance at higher concentrations (0.1, 0.2 and 0.4 M) of the organic salt. They la
concluded that it did not seem likely that the higher levels of performance to sodiu
gluconate was related to Na+ reaching the transcellular receptors (see Geran & Spector,
2000, 2004 for further discussion). Their conclusion leaves open the possibility that a
non-sodium cue was being detected at the high sodium gluconate concentrations. The
data in this experiment, showing that there is a quinine-like component to sodium
gluconate, however slight, might serve as a basis for the difference in performance.
It would be interesting to know which gustatory receptors sodium gluconate, at
various concentrations, activates. Perhaps it additionally activates taste receptors
belonging to the T2R family, which have been shown to be involved in “bitter” taste
transduction (Chandrashekar et al., 2000; Gilbertson & Boughter, 2003; Zhang, et a
2003). If not, then perhaps there is some convergence that occurs downstream of
receptor signaling, such as a shared component in the signal transduction pathway
activated by quinin
the quinine-like comp
tonium
That denatonium was treated as similar to the Q group standard was not surprising
In fact, this was predicted based on the work of Spector and Kopka (2002) that showed
Sprague-Dawley rats cannot discriminate between the two purported “bitter” tasting
93
compounds. Their results were controversial in light of other published findings
suggesting that rats could discriminate between the two compounds (Caicedo & Roper,
2001) because their application to taste receptor cells in situ resulted in differential
calcium responses in separate subpopulations of cells, which they interpreted to imply
discrimination at the cellular level. The data presented here together with the data from
Spector and Kopka (2002) could argue that even if different populations of receptor cells
are activated with exposure to the two ligands, the signal that is used by the animal to
guide
The
ped
rences
th
e two compounds,
havioral data here and in Spector and Kopka (2002).
Malto
of
ther
, &
behavior is apparently the same for both quinine and denatonium. Alternatively,
activation of potentially separate signal processing pathways results in the same
behavioral outcome. See Figure 4-14 for a diagram outlining both possibilities.
designs of the current experiment, and that of Spector and Kopka (2002) are not equip
to determine which of the two might be the case.
These two findings could be reconciled if one considers that there were diffe
between levels of investigation. That is, the behavior of the rats represents the output of
the entire gustatory system, whereas the findings from measurement of calcium
responding (Caicedo & Roper, 2001) are based on the initial stages of stimulus
processing, which occur in a discrete subpopulation of receptor cells. Therefore, it is
possible that convergence of information somewhere in the gustatory neuraxis from bo
denatonium and quinine plays a role in the perceptual similarity of th
which was supported by be
se
The fact that maltose did not fully generalize to sucrose makes sense in light
previous studies showing that maltose and sucrose are discriminable from one ano
(Nissenbaum and Sclafani, 1987; Spector and Grill, 1988; Spector, Markison, St. John
94
Garcea, 1997). The results from this paradigm, along with those from previous work
suggests that the discrimination of maltose from sucrose is not based merely using
intensity cues, but is likely guided by other “sideband” tastes, described here. This
finding, above all others, might demonstrate the true strength of this approach. It gives
insight into how similar the test compound is to each of the prototypical stimuli.
It is interesting, however, that there is a substantial quinine-like (assumed to be
inherently aversive) component to the maltose profiles because maltose has been
established as a preferred stimulus in rats (Davis, & Smith, 1992; Richter & Campbell,
1940; Sclafani, & Clyne, 1987; and Sclafani, & Mann, 1987; Sc
,
lafani & Nissenbaum,
1987) s
r
at
ulus is related to the extensive use of this
salt a
times in rats that NaCl is behaviorally discriminable from KCl (St. John, Markison, &
. It has also been shown to cross-generalize to other sugars, like sucrose, in studie
employing the conditioned taste aversion approach in the rat (Sako, Shimura, Komure,
Mochizuki, Matsuo, & Yamamoto, 1994; Spector & Grill, 1988), but not in the hamste
(MacKinnon, Frank, Hettinger, & Rehnberg, 1999). The issue at hand highlights an
interpretive requirement concerning the profiles obtained here. It must be stressed th
the height of the bar does not imply intensity of the compound, but merely indicates the
presence of the component. That is, when a test compound fully generalizes to the
standard of a given group, it says nothing of the intensity of that signal, but only that the
taste arising from the test compound fits into the range that was trained to define the
standard stimulus.
Potassium Chloride
The choice to include KCl as a taste stim
s a taste stimulus in other studies. Morrison (1967) showed in his study with rats
that KCl produced a profile that was distinct from NaCl and it has also been shown many
95
Spector, 1997; Kopka, Geran, & Spector, 2000; Spector, Guagliardo, & St. John, 1996
St. John, Markison, Guagliardo, Hackenberg, & Spector, 1995). Potassium chloride
salt that tastes “salty-bitter” to humans (e.g., van der Klaauw & Smith, 1995). In
employing behavioral generalization (CTA), non-sodium salts and acids are catego
similarly by rats (Nowlis, Frank, & Pfaffmann, 1980). One of the goals here was to use
the present paradigm to obtain a behavioral description of KCl in rats, which mi
to determine the qualitative characteristics used by rats to identify the taste of KCl.
The profiles obtained for both concentrations of KCl were indicative of a complex
taste. There were components of quinine-like, citric acid-like, and NaCl-like tastes,
which may pr
;
is a
studies
rized
ght help
ovide insight into the differential taste cues that a rat might use to
te the salt from NaCl. It would be interesting to know if adulteration with
amilo ted
hn,
this
discrimina
ride would cause NaCl to yield a profile that looked like KCl, as would be predic
from behavioral work showing the two compounds are treated similarly with oral
amiloride application (Hill, Formaker, & White, 1990; Spector, Guagliardo, & St. Jo
1996). Technically, this would be difficult because it would be important to maintain
stimulus control of the NaCl training stimuli, and if everything was adulterated with
amiloride (as is commonly done to assure constant exposure to the ENaC blocker),
would be impossible. It might be feasible, however to present the animals with a few
trials at the end of the session in which amiloride is added to NaCl.
Monosodium Glutamate
The inclusion of MSG as a test compound was in response to the growing
acceptance for “umami” taste as a distinct fifth quality. As stated previously, the
evidence for a separate MSG-like taste quality is mixed for rodents, but there are
examples supporting the existence of this taste quality where MSG is distinguishable
96
from sucrose and NaCl (Heyer, Taylor-Burds, Mitzelfelt, & Delay, 2004; Stapleton,
Luellig, Roper, & Delay, 2002). The NaCl-like aspect of MSG is often controlled for
using amiloride to suppress the taste of NaCl or adding NaCl to sucrose solutions to
account for the salt taste present in MSG (e.g., Heyer, Taylor-Burds, Mitzelfelt, & Delay,
2004). Heyer, Taylor-Burds, Mitzelfelt, and Delay (2004) conclude that “sweet”
(sucrose) and “umami” (MSG) afferent signaling may share a similar signaling pathway
either through a common taste receptor with high affinity for both prototypical
compounds, some similar downstream transduction mechanism, or possibly through cell-
cell interactions (e.g., see Figure 4-14 for similar explanation).
Some of those possibilities have been supported by work using the mouse model
that indicates that the two transduction processes do share similar components (Zhang et
al., 2003; Zhao et al., 2003). Taken together, it is not surprising that we found both a
NaCl-like and sucrose-like profile for the concentrations of MSG tested. The data from
the present experiment extend previous findings suggesting that, in the rat, the taste
quality associated with MSG is not uniquely different than that arising from sucrose and
NaCl, but is likely a combination of the two. It would be interesting to test more amino
acids to uncover whether they are similarly categorized by rats to be a combination of the
putative four basic tastes, or if they will yield a profile as yet unseen. Moreover, perhaps
different amino acids would fall into categories, based on similarity of responding, that
would match those interpreted to be “sweet” tasting and “bitter” tasting (Iwasaki,
Kasahari, & Sato, 1985; Nelson et al., 2002).
In summary, the findings from the present study have provided evidence for the
usefulness of this paradigm to examine the perceptual taste qualities of novel compounds
97
for which the animals have never received explicit training. That rats will respond to
novel stimuli the level to w stimulus (e.g.,
q and d aton aC ts that the two
. Additionally, the behavioral
paradigms can potentially be used to indicate other taste qualities that might play a part in
the overall perceptual experience gen pound. This is especially
rmati hemical stimuli that have comp any
g a rch to pursu n the finding ent study, but first the
tion e must be lly defined. Chapter 5 provides a discussion
po
ked nearly identical to maltose
was unexpected. Again, it is interesting that there is a substantial quinine-like (assumed
to be inherently aversive) component to the fructose profiles because fructose, like
maltose, is a preferred stimulus and it is a component of the sucrose molecule. It has
been shown to cross-generalize to other sugars, like sucrose, in studies employing the
conditioned taste aversion approach in the rat (e.g., Nissenbaum & Sclafani, 1987;
Nowlis, Frank, & Pfaffmann, 1980). On the other hand, Ramirez (1994) showed that rats
differentially avoided consuming sucrose and fructose following aversion training of
each, indicating that the two sugars differ in some aspect of quality after he attempted to
control for intensity. Additionally, experimental evidence from a human psychophysical
task claims that with many sugars (including fructose), the bitterness of sweeteners
decreases as concentration increases (Sciffman et al., 1995). Finally, it stands repeating
that the height of the bar does not imply intensity of the compound, but merely indicates
at same hich they respond to the standard
uinine en ium, and N l and sodium gluconate) sugges
compounds likely share similar qualitative features
erated by a com
info ve regarding c lex tastes. There are m
excitin venues of resea e give s of the pres
limita s of the procedur carefu
to that int.
Fructose
The fact that fructose generated a profile that loo
98
the presence of the component. That is, when a test compound fully generalizes to the
of a giv up, it says the in al, that the
ta ising f compound fits into the range that was trained to define the
s d stimu
standard en gro nothing of tensity of that sign but only
ste ar rom the test
tandar lus.
99
Table 4-1. Overview of experimental groups Group N St omparison Stimuli andard C1) N 6 NaCl Su nine, Citric Acid, Water* crose, Qui2) S 6 Sucro Na e, Citric Acid, Water) Q 6 inine NaCl, S , Citric A ater* ) C 6 ric Aci NaCl, , Quinine, * ) W 6 ter NaCl, , Quinine, Acid ate as ultim ndoned mparis
se Cl, Quinin * 3 Qu ucrose cid, W4 Cit d Sucrose Water5 Wa Sucrose Citric
*W r w ately aba as a co on stimulus
Table 4-2. Training schedule for N, S, Q, and C groups
Sessions Phase Limited hold (s),
t (s) Schedule timeou1-6 Spout ning N N/A trai /A 7-10 Side trai 180, tant
-13 Altern 15, ating 25 Discrim I 10, dom37 Only Q s 10, dom
38-40 Disc. I. (N,S,Q,C) 10, 20 Semi-random 41-47 Discrimination II 5, 20 Semi-random
Lim d hold i he maximu ount of time al or ns alternatited ly u ertain numb ct respons ade. This d w the ssi the secon , the es muli were
p d in ize ks dur sem om s ule
ning 0 Cons11 ation 10 Altern14- ination 20 Semi-ran 26- , W grp 20 Semi-ran
48-64 Partial Reinforcement 5, 20 Semi-random 65-90 Testing 5, 20 Semi-random
ite s t m amntil a c
lotted fer of corre
a respo e. Duringes were m
on, a stimulus predeterminewas presen
alternation repeated
criterion as 6 in first seing a
on, 4 in dched
and 2 in final s sion. Stiresente random d bloc i-rand
100
Table 4-3. Training parameters for W group.
Sessio Phase ple licks,
RLimited hold
tim out (s) ns Sam
einf. licks (s),
e Stimuli 1-6 Spout training 5, N/A Millipore wa 20 ter
7-10 S ng 5, 20 1 3rd conc., e wate
1-1 ion 5, 20 1 conM e water
14-3 Discrimination I 5, 20 10, 20 3rd highest conc., Millipore water
re water & 0.827 nine
53-61 Discrimin 5, 20 rified water & 0.827 M q
Di tion 5, ore water & 0.827 m ne
0-7 Di tion I 10, 4 M e water & 0.827 nine
77-8 Discrimination II 10, 40 5, 20 M e water &0.827 mM or 0.360 mM quinine
re water, all trations all
compounds
ide traini 80, 0 highestMillipor r
1 3 Alternat 5, 10 3rd highest c., illipor
7
38-52 Discrimination I 10, 40 10, 20 MillipomM qui
ation I 10, 40
20
Publix puuinine
62-69 scrimina I 5, 20 MillipM quini
7 6 scrimina 0 5, 20 illipormM qui
illipor either 1
82-90 Discrimination III 10, 40 5, 20 Millipoconcen
101
Table 4-4. Results from one-sample t-tests for 0.376 M NaGluconate Test against 1.0 Test against 0
Grp df t p-value Adjusted p-value
-t p-value
Adjusted pvalue
N 5 -3.34 0.02 0.17 14.01 - -
< 0.01 < 0.01S 5 58.84 < 0.01 < 0.01 6.62 < 0.01 < 0.01Q 5 15.48 < 0.01 < 0.01 0.49 0.64 1.00C 5 -26.81 < 0.01 < 0.01 -1.06 0.34 1.00
Table 4-5. Results from one-sample t-tests for 0.668 M NaGluconate Test against 1.0 Test against 0
Grp df t p-value Adjusted p-value
-t p-value
Adjusted pvalue
N 5 -3.06 0.03 <
0.225 10.06 < 0.01 0.01S 5 -19.13 < 0.01 < 0.01 4.38 < 0.01 0.06Q 5 -32.01 < 0.01 < 0.01 5.71 < 0.01 0.02C 5 -28.43 < 0.01 < 0.01 1.53 0.19 1.00
Table 4-6. Performance to training stimuli during sodium gluconate testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 86.5 2.5 89.5 4.3 88.2 3.1 96.4 1.5 0.376 96.0 0.7 97.0 1.5 96.4 0.9 92.6 1.3 0.668 98.0 0.4 99.1 0.9 98.6 0.6 94.0 2.0
NaCl (M)
1.07 93.2 1.5 92.6 3.0 96.0 1.0 93.5 2.4 0.042 97.4 0.7 87.3 1.7 84.8 2.7 88.6 1.7 0.077 96.8 1.4 94.6 2.2 92.2 3.0 85.7 3.2 0.148 96.5 2.4 97.2 0.9 98.1 0.6 95.6 1.4
Sucrose (M)
0.421 97.8 1.0 96.0 0.9 81.3 4.8 83.0 3.5 0.027 91.5 1.6 86.1 3.0 90.6 2.1 83.8 2.5 0.131 95.9 1.2 84.0 1.3 89.2 1.0 78.2 5.1 0.360 91.6 3.0 86.3 1.9 91.6 2.1 83.0 3.3
Quinine (mM)
0.827 95.1 0.9 92.3 2.3 95.8 0.8 78.9 4.6 2.04 97.6 1.6 96.6 1.7 70.5 4.8 78.3 3.3 10.4 93.6 2.8 95.4 1.8 84.3 2.7 88.9 2.0 28.2 96.7 1.0 95.2 3.2 93.3 2.2 94.9 1.0
Citric Acid (mM)
64.3 98.8 0.8 93.5 5.4 99.3 0.5 93.5 2.4
102
Table 4-7. Results from one-sample t-tests for 0.131 mM denatonium Test against 1.0 Test against 0 Grp df t p-value t p-value Adjusted p-
value Adjusted p-
value N 5 -68.14 < 0.01 < 0.01 0.26 0.80 1.00 S 5 -38.43 < 0.01 < 0.01 7.06 < <
4 <-
0.01 0.01 Q 5 -3.32 0.02 0.17 5.38 < 0.01 0.01 C 5 48.70 < 0.01 < 0.01 1.71 0.15 1.00
Table 4-8. Results from one-sample t-tests for 0.360 mM denatonium Test against 1.0 Test against 0
Grp df t p-value Adjusted p-
value t p-value Adjusted p-
value N 5 -69.45 < 0.01 < 0.01 1.10 0.32 1.00 S 5 -34.50 < 0.01 < 0.01 0.69 0.52 1.00 Q 5 0.29 0.79 1.00 3 <
- <5.38 < 0.01 0.01
C 5 21.55 < 0.01 0.01 1.70 0.15 1.00
Table 4-9. Performance to training stimuli during denatonium testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 86.5 2.5 89.5 4.3 88.2 3.1 96.4 1.5 0.376 96.0 0.7 97.0 1.5 96.4 0.9 92.6 1.3 0.668 98.0 0.4 99.1 0.9 98.6 0.6 94.0 2.0
NaCl (M)
1.07 93.2 1.5 92.6 3.0 96.0 1.0 93.5 2.4 0.042 97.4 0.7 87.3 1.7 84.8 2.7 88.6 1.7 0.077 96.8 1.4 94.6 2.2 92.2 3.0 85.7 3.2 0.148 96.5 2.4 97.2 0.9 98.1 0.6 95.6 1.4
Sucrose (M)
0.421 97.8 1.0 96.0 0.9 81.3 4.8 83.0 3.5 0.027 91.5 1.6 86.1 3.0 90.6 2.1 83.8 2.5 0.131 95.9 1.2 84.0 1.3 89.2 1.0 78.2 5.1 0.360 91.6 3.0 86.3 1.9 91.6 2.1 83.0 3.3
Quinine (mM)
0.827 95.1 0.9 92.3 2.3 95.8 0.8 78.9 4.6 2.04 97.6 1.6 96.6 1.7 70.5 4.8 78.3 3.3 10.4 93.6 2.8 95.4 1.8 84.3 2.7 88.9 2.0 28.2 96.7 1.0 95.2 3.2 93.3 2.2 94.9 1.0
Citric Acid (mM)
64.3 98.8 0.8 93.5 5.4 99.3 0.5 93.5 2.4
103
Table 4-10. Results from one-sample t-tests for 0.077 M maltose T Test against 1.0 est against 0
Grp df t p-value A
pA
vdjusted p-
value t -value djusted p-
alue N 5 -42.68 < 0.01 < 0.01 0.
5 <11 < 0. < 0.
2 0. 0.
158 0.88 1.00 S 5 -9.89 < 0.01 < 0.01 .79 0.01 0.02 Q 5 -6.98 < 0.01 < 0.01 .37 01 01 C 5 -14.34 < 0.01 < 0.01 .98 03 25
Table 4-11. Results from one-sample t-tests for 0.148 M maltose T Test against 1.0 est against 0
Grp df t p-value A
pA
vdjusted p-
value t -value djusted p-
alue N 5 - < 0
1 < <6 < 0. < 0.4 < 0. 0.
56.27 < 0.01 0.01 .69 0.52 1.00 S 5 -11.23 < 0.01 < 0.01 3.75 0.01 0.01 Q 5 -10.01 < 0.01 < 0.01 .93 01 01 C 5 -16.59 < 0.01 < 0.01 .21 01 07
Table 4-12. Performance to training stimuli during maltose testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc.
1 1
10.4 98.7 0.9 98.2 1.2 97.2 1.0 95.0 1.1 28.2 99.0 0.7 96.9 1.5 97.7 1.1 96.5 0.6 64.3 98.0 0.8 98.4 1.0 97.8 1.5 98.3 0.7
Mean SE Mean SE Mean SE Mean SE 0.107 92.5 1.4 96.3 2.3 91.6 2.2 91.9 1.7 0.376 96.6 1.4 97.4 0.8 94.9 1.1 94.5 1.9 0.668 98.8 0.6 98.9 1.1 99.3 0.5 96.5 1.6
NaCl (M)
1.07 94.4 2.3 96.6 1.1 93.6 1.8 92.8 1.6 0.042 94.6 1.9 89.7 2.7 89.7 3.0 91.9 3.3 0.077 93.3 1.2 94.6 1.8 95.4 0.8 95.3 2.6 0.148 94.9 1.5 97.9 0.8 00.0 0.0 00.0 0.0
Sucrose (M)
0.421 94.4 1.3 96.8 1.0 96.5 2.5 94.4 1.1 0.027 92.5 3.8 93.4 2.3 89.0 2.0 90.7 1.5 0.131 92.8 0.9 93.6 3.3 94.3 0.7 89.2 3.0 0.360 91.7 2.8 95.1 1.7 97.1 0.5 86.9 3.5
Quinine (mM)
0.827 93.3 1.8 96.1 2.0 93.2 2.2 77.2 3.2 2.04 99.2 0.5 97.5 1.1 82.5 1.6 90.4 1.5 Citric
Acid (mM)
104
Table 4-13. Table of t-test statistics for 0.376 M KCl Test against 1.0 Test against 0
Grp df t p-value adjusted p-
value t p-value Adjusted p-
value N 5 -24.52 < 0.01 < 0.01 8.70 < 0.01 < 0.01 S 5 -14.24 < 0.01 < 0.01 2.58 0.05 0.40 Q 5 -4.99 < 0.01 0.03 6.06 < 0.01 0.01 C 5 -9.56 < 0.01 < 0.01 4.20 < 0.01 0.07
Table 4-14. Table of t-test statistics for 0.668 M KCl Test against 1.0 Test against 0
Grp df t p-value Adjusted p-
value t p-value Adjusted p-
value N 5 -18.08 < 0.01 < 0.01 6.38 < 0.01 0.01 S 5 -13.37 < 0.01 < 0.01 2.98 0.03 0.25 Q 5 -4.88 < 0.01 0.04 6.35 < 0.01 0.01 C 5 -6.37 < 0.01 0.01 4.67 < 0.01 0.04
Table 4-15. Performance to training stimuli during KCl testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 93.5 1.3 95.2 1.8 87.2 1.6 97.5 1.2 0.376 95.9 1.3 96.5 1.8 99.4 0.4 97.6 0.8 0.668 99.7 0.2 93.7 3.5 96.7 1.3 94.7 1.3
NaCl (M)
1.07 96.8 1.2 97.3 1.8 91.3 4.5 92.8 2.0 0.042 95.4 1.8 87.4 2.2 93.0 1.4 95.8 1.2 0.077 88.5 1.7 93.6 1.6 95.5 2.5 98.2 0.8 0.148 94.7 1.6 96.9 1.4 96.0 1.5 99.0 0.7
Sucrose (M)
0.421 94.0 1.7 93.6 3.2 90.7 3.6 94.7 1.1 0.027 98.1 0.8 90.8 3.1 93.1 1.7 86.2 2.4 0.131 96.5 0.6 87.7 2.6 93.4 0.7 84.0 4.7 0.360 98.9 0.7 92.6 4.2 93.8 1.5 89.2 1.9
Quinine (mM)
0.827 95.2 1.0 92.8 3.0 95.8 0.5 86.3 1.6 2.04 97.2 0.8 95.2 2.2 87.0 1.3 84.7 2.2 10.4 94.3 1.3 87.8 6.1 87.5 4.6 92.6 2.0 28.2 97.8 0.9 91.6 7.7 99.0 0.7 99.4 0.4
Citric Acid (mM)
64.3 98.1 0.8 96.9 2.3 98.7 0.6 95.4 2.0
105
Table 4-16. Table of t-test statistics for 0.077 M MSG Test against 1.0 Test against 0
Grp df t p-value Adjusted p-value t p-value
Adjusted p-value
N 5 -25.21 < 0.01 < 0.01 9.31 < 0.01 < 0.01 S 5 -6.85 < 0.01 < 0.01 8.91 < 0.01 < 0.01 Q 5 -28.00 < 0.01 < 0.01 0.86 0.43 1.00 C 5 -22.09 < 0.01 < 0.01 0.16 0.88 1.00
Table 4-17. Table of t-test statistics for 0.148 M MSG Test against 1.0 Test against 0
Grp df t p-value Adjusted p-value t p-value
Adjusted p-value
N 5 -5.00 < 0.01 0.03 7.84 < 0.01 < 0.01 S 5 -18.56 < 0.01 < 0.01 12.66 < 0.01 < 0.01 Q 5 -38.35 < 0.01 < 0.01 0.87 0.43 1.00 C 5 -21.62 < 0.01 < 0.01 1.93 0.11 0.89
Table 4-18. Performance to training stimuli during MSG testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 87.9 2.5 91.3 3.5 90.9 1.7 92.7 1.9 0.376 94.5 2.2 95.8 1.8 96.3 0.9 91.9 1.4 0.668 98.6 0.5 94.9 5.1 100.0 0.0 90.7 2.6
NaCl (M)
1.07 95.7 1.0 97.0 3.0 95.7 1.1 96.7 1.2 0.042 94.1 1.7 85.6 1.9 83.9 3.0 87.9 2.3 0.077 93.7 1.2 95.0 0.8 86.7 2.4 94.4 1.4 0.148 98.1 0.8 96.8 1.1 95.4 1.2 92.2 2.1
Sucrose (M)
0.421 98.7 0.9 97.6 1.2 95.2 1.8 96.6 1.5 0.027 95.3 1.5 92.9 2.5 94.1 1.2 80.0 2.5 0.131 93.4 1.4 92.7 2.2 93.6 1.5 85.7 2.9 0.360 100.0 0.0 95.3 1.8 96.5 1.2 82.5 1.2
Quinine (mM)
0.827 96.1 1.6 94.4 3.5 96.0 1.0 82.8 2.7 2.04 91.4 2.4 99.3 0.7 69.1 5.8 83.3 1.7 10.4 97.0 0.9 95.5 1.7 81.1 2.5 90.2 1.7 28.2 97.6 1.6 98.2 1.1 91.0 3.6 93.9 1.9
Citric Acid (mM)
64.3 94.3 1.9 98.3 1.7 98.2 1.2 97.7 0.5
106
Table 4-19. Table of t-test statistics for 0.077 M fructose Test against 1.0 Test against 0
Grp df t p-value Adjusted p-value t p-value
Adjusted p-value
N 5 -68.25 > 0.01 > 0.01 1.28 0.26 1.00 S 5 -15.90 > 0.01 > 0.01 9.59 > 0.01 > 0.01 Q 5 -4.00 0.01 0.08 7.52 > 0.01 > 0.01 C 5 -30.18 > 0.01 > 0.01 6.08 > 0.01 0.01
Table 4-20. Table of t-test statistics for 0.148 M fructose Test against 1.0 Test against 0
Grp df t p-value Adjusted p-value t p-value
Adjusted p-value
N 5 -44.44 > 0.01 > 0.01 0.01 0.99 1.00 S 5 -11.08 > 0.01 > 0.01 17.41 > 0.01 > 0.01 Q 5 -7.88 > 0.01 > 0.01 6.85 > 0.01 > 0.01 C 5 -17.51 > 0.01 > 0.01 2.58 0.05 0.40
Table 4-21. Performance to training stimuli during fructose testing Group
NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE
0.107 92.8 1.3 97.0 1.0 89.9 2.4 95.9 1.2 0.376 95.4 1.8 97.4 1.2 97.7 0.7 94.3 2.2 0.668 95.5 1.9 97.7 1.0 99.2 0.5 94.2 2.3
NaCl (M)
1.07 97.4 0.5 98.3 1.1 98.6 1.0 93.3 1.4 0.042 96.9 0.6 90.3 2.2 80.9 2.3 92.0 3.2 0.077 92.7 1.6 94.0 2.0 93.3 2.0 95.2 1.2 0.148 96.1 1.8 98.1 1.2 96.5 1.9 95.2 2.0
Sucrose (M)
0.421 96.2 1.1 87.6 4.5 89.2 5.5 98.9 0.7 0.027 92.3 2.4 92.0 2.4 94.2 0.8 86.4 4.2 0.131 96.0 2.2 90.8 3.2 92.2 1.2 82.8 2.2 0.360 93.8 1.4 82.5 6.2 93.5 1.2 78.3 2.6
Quinine (mM)
0.827 96.0 2.7 100.0 0.0 96.0 0.8 93.6 1.3 2.04 97.5 1.7 77.9 7.8 28.3 2.7 60.0 4.4 10.4 97.1 1.9 98.5 0.9 94.1 1.9 90.5 2.6 28.2 100.0 0.0 100.0 0.0 95.3 2.8 99.0 0.3
Citric Acid (mM)
64.3 98.8 0.8 100.0 0.0 99.1 0.7 99.0 0.5
107
Table 4-22. Performance to training stimuli for W group during dt3-5 through dt3-8.
Group Water Solution Conc. Mean SE
0.107 69.3 5.9 0.376 82.1 2.8 0.668 96.6 1.4
NaCl (M)
1.07 85.8 6.2 0.042 97.4 0.7 0.077 77.4 5.1 0.148 92.9 2.1
Sucrose (M)
0.421 86.1 7.6 0.083 53.4 6.0 0.131 70.1 4.4 0.360 73.5 6.2
Quinine (mM)
0.827 100.0 0.0 2.04 89.0 3.6 10.4 80.7 6.0 28.2 87.1 5.7
Citric Acid (mM)
64.3 98.0 1.3 Water Water 86.1 2.0
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0.376 M NaGLUCONATE
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-1. Profile for 0.376 M NaGluconate. Mean Generalization Scores for each group are plotted. The novel concentration of NaCl generalized to NaCl training concentrations
0.668 M NaGLUCONATE
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-2. Profile for 0.668 M NaGluconate. Mean Generalization Scores for each group are plotted. The novel concentration of NaCl generalized to NaCl training concentrations
109
0.131 mM DENATONIUM
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-3. Profile for 0.131 mM denatonium. Mean Generalization Scores for each group are plotted. The novel concentration of denatonium generalized to quinine training concentrations.
0.360 mM DENATONIUM
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-4. Profile for 0.360 mM denatonium. Mean Generalization Scores for each group are plotted. The novel concentration of denatonium generalized to quinine training concentrations.
110
0.077 M MALTOSE
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-5. Profile for 0.077 M maltose. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to sucrose and quinine training concentrations.
0.148 M MALTOSE
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-6. Profile for 0.148 M maltose. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to sucrose and quinine training concentrations.
111
0.376 M KCl
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-7. Profile for 0.376 M KCl. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, quinine, and citric acid training concentrations.
0.668 M KCl
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-8. Profile for 0.668 M KCl. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, quinine, and citric acid training concentrations.
112
0.077 M MSG
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-9. Profile for 0.077 M MSG. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, and sucrose training concentrations.
0.148 M MSG
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-10. Profile for 0.148 M MSG. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, and sucrose training concentrations
113
0.077 M FRUCTOSE
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-11. Profile for 0.077 M fructose. Mean Generalization Scores for each group are plotted. The novel concentration of fructose generalized to sucrose training concentrations.
0.148 M FRUCTOSE
GroupsN S Q C
Gen
eral
izat
ion
Scor
e
-0.20.00.20.40.60.81.01.2
Figure 4-12. Profile for 0.148 M fructose. Mean Generalization Scores for each group are plotted. The novel concentration of fructose generalized to sucrose training concentrations.
114
Water Group Performance
0
0.1
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7
Training Days
% C
orre
ct
QUININE WATER
0.82
7 m
M
Qui
nine
0.82
7 m
M Q
uini
ne &
Wat
er/
10
sam
ple
licks
(Wee
k of
f)
(wee
k of
f)/P
ublix
W
t
Wee
k of
f/Cal
ibra
tion
chec
ked
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pore
vs
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27 m
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uini
ne
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pore
vs
Hig
hest
co
ncen
tratio
ns o
f all
stim
uli
Milli
pore
vs
all
stim
uli5 licks 10 licks
Figure 4-13. Summary of performance for W group during training with water and quinine.
Figure 4-14. Diagram outlining two possibilities for the level (peripheral or central) at which convergence of taste signal processing leading to the same behavioral output might occur.
CHAPTER 5 GENERAL DISCUSSION
Introduction
The findings presented in the current collection of studies are novel to the field of
taste quality research. Chapters 2 and 3 presented data from two experimental procedures
that have not been previously used with taste stimuli, and the results suggest that they
could be applied as an alternative method to examine taste quality discrimination and
generalization. These paradigms could provide a functional context to interpret the
outcomes of anatomical, pharmacological, and genetic manipulations of the gustatory
system. Additionally, they may afford a means of testing hypotheses proposing how
signals generated from taste stimuli give rise through some central process to the
perception of taste quality. Moreover, these paradigms extend existing techniques that
are crucial for linking neural activity with behavior, which is essential for understanding
gustatory processing. Perhaps of most theoretical interest, however, is that the data
presented in Chapters 3 and 4 provide evidence that the perception of taste quality is
analytic. In other words, the behavior of the rats in this task was unambiguously
categorical, and at least one of the four putative taste qualities, respectively represented
by four prototypical stimuli, or a combination of them, was sufficient to describe each of
the novel test compounds, including MSG.
Delayed Match/Non-Match to Sample
The experiment employing the delayed matching and non-matching
(DMTS/DNMTS) approaches (Chapter 2) was successful in that rats indeed learned to
115
116
respond to all types of trials presented at above chance levels. Although performance of
the rats was statistically better than chance, applying the paradigm, as it currently stands,
to address questions such as the temporal capacity of taste memory in the same/different
discrimination would be complicated by the fact that the range between asymptotic
performance and chance levels of responding is too limited to adequately measure
changes in behavior. A strategy to lower the interstimulus delay would be the most
plausible solution. Reliably higher levels of correct responding would be necessary to
pursue any aims focused on attributing changes in performance to specific gustatory
components.
As mentioned in Chapter 2, asymptotic performance could potentially be improved
by decreasing the delay period between the two sample stimuli. There have been
accounts showing that animals acquire a similar task at a faster rate when the delay
between the sample and a comparison stimulus is shorter, with a 0 s delay often yielding
the best performance (e.g., Sargisson & White, 2001). Currently, the design of the
gustometer prevents a delay shorter than 6 s. A substantial modification would be
necessary to enable the delivery of two taste compounds in a shorter time period. In
recent weeks, an adapter was constructed offsite to allow two sample spouts to be
controlled by the same stepping motor; consequently, a reduced delay between samples
would be achievable. There are still some technical aspects to overcome in order to use
such an adaptor, however, and therefore empirical testing is not yet possible. Further
development of this paradigm is almost certain to provide results indicating higher levels
of performance.
117
Another change that could be made to the design of the experiment, as it was
presented, would be to increase the number of trials the rats were able to experience in a
given session. Reducing both the interstimulus interval and the intertrial interval would
help to achieve this; with shorter delays rats would be able to initiate more trials in a
session. Another potential problem that may have decreased the performance of the rats
in the present experiment was the likely loss of motivation through satiation. A water-
restriction schedule was used to potentiate the reinforcer efficacy of water, but as animals
sampled fluid in the trials, received fluid during intraoral rinses, and had access to water
after correct responses, they became sated. Perhaps introducing a partial schedule of
reinforcement might help improve performance while reducing the amount of fluid
received during each trial. One problem with this suggestion, however, is that it would
interfere with a benefit of the design. Typically, the water obtained during the
reinforcement phase serves to rinse the oral cavity between trials so that adaptation to a
particular compound does not occur. There have been prior accounts in the literature
showing that adaptation to a stimulus can affect subsequent responses to taste stimuli in
both rodents and humans (e.g., Bartoshuk, 1977; Galindo-Cuspinera et al., 2006).
An alternative, though not mutually exclusive, explanation for the overall
performance levels not surpassing 75% in this task might be related to the malfunction
that occurred in the light timer during the first 110 days of training and testing. Published
studies have used continuous exposure to lighted conditions as a chronic mild stressor
(CMS) (Grippo & Johnson, 2002; Grippo, Beltz, & Johnson, 2003; Grippo, Moffitt, &
Johnson, 2002), which results in various physiological changes in the animal, including
an increase in circulating corticosterone and decreased behavioral responsiveness to
118
sucrose (experimentally induced anhedonia). This might lead some research groups to
speculate that hypothalamic–pituitary–adrenal dysregulation following CMS may result
in altered cognitive function. This remains an untested hypothesis.
The development of the DMTS/DNMTS paradigm with taste stimuli has potential
benefits for those interested in studying cognitive processes in animal models. For
example, this task could be used to address the concepts of working memory, or short-
term memory processing, as has been attempted in other sensory modalities. Evidence
exists in audition and olfaction that shows that differential neuronal activity can be found
during tasks requiring the animal to compare one stimulus to a second before making a
response (Sakurai, 1990; Wiebe & Staubli, 2001). A process, that some have termed
olfactory recognition memory, has been shown to have neural activity correlates in
hippocampal theta cells (Wiebe & Staubli, 2001). Therefore, the potential for identifying
similar underlying explanations and neural structures for taste behavior exists, especially
using a task such as the DMTS/DNMTS task outlined here.
Further development of the DMTS/DNMTS task could also facilitate efforts to
assess intensity discrimination in animal models. In the same way that tasks similar to
the one outlined here helped understanding of hue discrimination (e.g., see Wright,
1972), this task could help gustatory researchers realize the limits of taste quality and
intensity discrimination of their animal models.
Novel Taste Quality Generalization
In Chapter 3, it was demonstrated that rats can learn to discriminate between
stimuli thought to typify the four classic basic tastes. Further, they are capable of
responding to test stimuli that have not been explicitly trained, in ways that would be
predicted. When mixtures of two of the prototypical stimuli are presented, the behavioral
119
responses of the rats can be used to generate behavioral profiles that indicate which of the
compounds was present in the highest overall concentration. The fact that a rat does not
respond entirely on the standard response spout when a familiar concentration of the
standard has been mixed with a familiar concentration of the comparison reveals that the
animal can detect that there is more than one qualitative taste component. This is a
remarkable aspect of this paradigm because it gives insight into the relative features of a
complex stimulus. The results from the mixtures indicate that rats will distribute their
behavior according to how prevalent a taste component is within a mixture.
There was also a major caveat of the paradigm that was highlighted when water
was used as a test compound. Together with the findings in Chapter 4 which examined
the extent that water can be discriminated from characteristic stimuli of the putative four
basic tastes, we now know that the number of sample licks is likely a critical factor in the
ability of the rats to make some discriminations in this task. Additionally, data from
Chapter 4 support the view that this paradigm can be used to obtain behavioral profiles
which may describe the qualitative features of novel taste compounds. Moreover, rats do
not need to be trained explicitly with these novel compounds, the training received using
the prototypical stimuli appears to generalize to new taste compounds. Presenting the
data from all groups reveals the degree to which the four basic taste qualities are
generated by a given test stimulus. The extent to which this holds true should (and could)
be examined by varying the relative concentrations of different pairings so that mixtures
for all possible combinations at various concentrations are thoroughly explored.
If one wanted the animals to respond completely on their standard response spout
when the taste was present within a compound, it might be possible to train the rats to
120
identify the standard in mixtures during training. The concentration in the mixture could
be varied in much the same way that the training concentrations were varied to render
intensity an irrelevant cue, which should result in better discriminatory control.
Conceivably, the rats would be able to perform quite well with feedback encountered
during training. If the groups of rats could learn to identify, at high levels of
performance, the presence of the standard in many different combinations of the four
prototypical taste compounds, then it would increase the confidence that responses on the
standard response spout after presentation of unknown test compounds indicate detection
of a standard-like taste. This would be an alternative strategy for using behavioral
profiles to describe a compound’s qualitative features. Unfortunately, the current design
of the gustometer delivery system prevents such a strategy due to a limited number of
fluid reservoirs, but if such technical limitations could be overcome it would be useful in
the future to explore the use of complex mixtures as standards.
It is interesting that in the experiment where water was used as a test stimulus, a
quinine-like profile was obtained. Additionally, it is remarkable that the rats in the Q
group of the experiment in Chapter 3 were unable to learn to discriminate quinine from
water. Studies measuring absolute detection threshold for quinine are possible, and
thresholds have even been obtained using the same equipment, albeit with a
methodologically different task. Additionally, Experiment I in Chapter 3 (brief-access
test) showed that rats will alter their licking behavior in a concentration dependent
manner to quinine, though in that experiment the rats could initiate as many licks in a 5-s
period as possible (which could result in as many as 35 licks) compared to the 5 licks
they were allowed during sampling in Experiment II. In fact, the average (+/- SE) licks
121
to the concentration closest to 0.027 mM quinine (0.03 mM) presented during brief-
access testing was 27.93 (+/-1.18). The highest concentration used as a training stimulus
in the generalization experiment (0.827 mM) falls between two of the concentrations
used in the brief-access test, 1 mM and 0.3 mM. At the higher concentration, 1 mM
quinine, rats in the brief access test licked an average of 9.5 (+/- 1.2) and at the next
lowest concentration, 0.3 mM quinine, rats licked an average of 21.7 (+/- 1.1).
Therefore, it is plausible that the number of sample licks associated with the testing
parameters were too low in the original design of this task. Clearly they were sufficient
to maintain high levels of performance with the other compounds, but apparently water
and/or quinine are different from the other three compounds. The experiment in Chapter
4 helped to clarify the ability of rats to discriminate quinine and water under these
conditions, but interpretation of those results are complicated by the different training
histories encountered by animals between these two experiments.
Surprisingly, the naïve rats in the W and Q groups from Chapter 4 had difficulty
discriminating 0.360 mM quinine and water also. After 22 days of discrimination
training, the average performance was near the mid-to-low 60% range. This is in stark
contrast with the rate at which the N, S, and C groups learned to discriminate their
training compounds. It was decided at that point in the experiment to remove water from
the comparison stimuli for the N, S, Q, and C groups. The W group received more
discrimination training with the water and 0.827 mM quinine so that eventually the group
might be useful for assessing whether a test compound would generate a water-like
profile. It was clear, however, that when all of the training concentrations were added to
the training array, there was evidence of loss of stimulus control. It is possible that with
122
more explicit training using all of the training compounds, high levels of performance
should be achievable. The explanation for the impaired performance is elusive, unless
one considers that water may have a quinine-like taste in rats (Bartoshuk, 1977,
Morrison, 1967).
Future Validation of the Procedure
For this paradigm to be useful to researchers, it should be further validated with
respect to understanding the limitations of the information provided by the profiles. For
example, it would be instructive to examine how the rats would respond if they were
given a concentration completely unrelated to the range of the training compounds
encountered. This could most easily be accomplished by using very high concentrations
of the standards and comparisons. Not only would this provide information about the
ability for the training to generalize to concentrations completely outside the range of
training compounds, but it might also provide information about the constancy of a
quality at high concentrations.
Another important issue would be to understand what would happen if a compound
from a new distinct taste quality was encountered during testing. One way to approach
that issue would be to train three groups of rats to discriminate only three of the
prototypical compounds and use the fourth as a test compound. If the generalization
profile obtained did not resemble any of the standard stimuli (or it resembled all of them
equally, i.e., Generalization Score = 0.5), then it would provide evidence that rats were
capable of indicating when a stimulus was unlike any of the familiar training stimuli. All
possible combinations of comparisons should be tested to identify which, if any, qualities
might generalize most to others.
123
It would not be surprising if the rats generalized one of the prototypical compounds
to another. Morrison (1967) showed this in his study, for example, when he used quinine
as a test compound so that the rats in the HCl and sucrose groups had to distinguish
whether it was more NaCl-like or more like their comparison stimulus (HCl or sucrose,
respectively). He showed that when rats were forced to choose between HCl and NaCl to
“behaviorally describe” quinine taste, that the responses were more HCl-like than NaCl-
like. This has been the basis for some to interpret that rats have difficulty discriminating
between quinine and HCl (Lemon & Smith, 2005). It would be interesting to know what
the generalization profile might reveal using this paradigm if we trained rats in the citric
acid group to discriminate citric acid from sucrose and NaCl (in this example, the only
two comparison stimuli) and then presented quinine as a test stimulus. It might look like
the profile obtained using Morrison’s (1967) procedure, or this paradigm might offer
more flexibility for responding. Either way, it is an interpretably important piece of
information to consider.
Another recommended validation procedure would be to understand what happens
to responding when the animal is made to no longer experience a specific taste quality.
For example, if specific gustatory nerve transections were performed, and they resulted in
the complete inability to detect one (or more) training compound(s), would responding to
the other stimuli then be normal? What would the profile for that taste compound look
like? Perhaps such an outcome would result in loss of stimulus control, especially if the
quality is the standard. This is an interesting and important interpretive issue that is
revisited below.
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It would also be important to extend the test stimulus array to include many
examples of compounds, especially those with complex tastes. The limited number of
test compounds incorporated into these studies may bias the conclusions drawn about the
utility of the profiles. Each of the test stimuli were carefully chosen because of a certain
expectation about what the outcome should look like as we anticipated that it would
probably be most informative to first include compounds that have been tested using
other behavioral methodology (e.g., the conditioned taste aversion approach) to provide
an external validation. The only unanticipated results came from the maltose profile
where we found evidence of a more dominant quinine-like component than a sucrose-like
component for the putative sweetener. This was surprising because rats are known to
prefer maltose, so one would not imagine that it would contain a dominant quinine-like
quality. If the results from this paradigm and others are not in agreement, however, it
would not necessarily suggest that this paradigm (or another) is flawed, but it certainly
would warrant further investigation.
This list of suggested means to further validate the procedure is likely not
exhaustive, but it is meant to indicate that the interpretation of profiles should be done
with these caveats in mind. Addressing each of the issues would only serve to strengthen
any conclusions about the taste quality of a test compound determined using this
procedure.
Potential Uses of the New Generalization Procedure
Neurobiological applications
The behavioral testing paradigm presented in Chapters 3 and 4 has the potential to
provide great insight into the study of the peripheral gustatory system. For example, it
would be possible to employ gustatory nerve transections in order to understand the
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necessity and sufficiency of specific nerves to maintain gustatory function. Such studies
might reveal that a particular nerve (or combination of nerves) is necessary for the
transduction of specific taste qualities. Alternatively, it might be the case that a single
nerve is important for all quality discrimination, indicating that the signal for taste quality
is channeled through a specific pathway. Currently, such studies have not been
attempted, likely because the conditioned taste aversion approach is not compatible with
such a design, or at least not one that would yield such straightforward results.
The peripheral gustatory system. The known presence of narrowly tuned N-
fibers, in the chorda tympani (CT) nerve, which respond to sodium salts (and LiCl),
suggested that the anterior tongue taste receptor cells (which are innervated by the CT)
were critical in NaCl sensibility (Frank, Contreras, & Hettinger, 1983). Researchers were
surprised, however, when transection of the CT did not affect NaCl intake or preference
in an overnight test as compared with intact rats (Akaike, Hiji, & Yamada, 1965;
Pfaffmann, 1952; Richter, 1939; Vance, 1967). It was not until later, when the application
of more detailed and rigorous behavioral testing was used, that severe consequences of
CT transection on salt taste perception were revealed (Slotnick, Sheelar, & Rentmeister-
Bryant, 1991; Spector, Schwartz, & Grill, 1990). Transection of the CT increases the
detection threshold for NaCl by at least 1–2 orders of magnitude (Kopka & Spector,
2001; Slotnick, Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990),
resulting in decreased sensitivity to Na+ salts. Chorda tympani nerve transection also
attenuates salt discrimination performance (Kopka, Geran, & Spector, 2000; Spector &
Grill, 1992; St. John, Markison, & Spector, 1995). These results suggest that the CT is
126
necessary to maintain normal sodium detectability and recognition even though its
transection denervates only 15% of taste buds in the oral cavity.
Conversely, when the glossopharyngeal (GL) nerve is severed, thereby removing
input from roughly 60% of the total taste bud complement (Miller, 1977), salt
discrimination and sodium recognition remain normal (Markison, St. John, & Spector,
1995; Spector, Schwartz, & Grill, 1990). Thus, it appears that the GL, which innervates
taste buds on the posterior 1/3 of the tongue, is not necessary for the maintenance of these
particular functions (St. John, & Spector, 1998).
Given what is known about the importance of the CT in taste discrimination
behavior, it would be enlightening to design an experiment in which half of the animals
in each group receive bilateral transection of the CT nerve and their subsequent ability to
discriminate between the training compounds is assessed. If, they were still able to
perform at high levels, then it might be equally exciting to see whether the generalization
profiles for test compounds (either novel and/or those experienced prior to surgery)
compare to the other half of each group, which would receive sham surgery.
Likewise, it would be interesting to perform the same experiment with GL
transection as the surgical manipulation because although the GL has not been shown to
be highly involved with any behavioral tasks assessing the discrimination of compounds,
it is responsive to all four prototypical stimuli. The GL is highly responsive to quinine,
responds well to acids, and also has a somewhat weaker response to salts and sugars
(Oakley, 1967; Boudreau, et al., 1987; Frank, 1991; Dahl et al., 1997). Therefore, it is
possible that the GL would be involved in carrying information specifically about
127
quinine-like taste qualities, or possibly even all four putative basic tastes. It remains a
conceptually interesting question that can now be addressed.
As pointed out earlier, it may first be important to understand what happens to
intact rats if one of their training stimuli suddenly disappears. The loss of a specific taste
quality could be mimicked through providing sham licks. If the gustometer was
programmed to proceed normally allowing a rat to lick the dry sample spout, but not
deliver a taste sample contingent on the licks, and then otherwise treat the trial normally,
it is conceivable that that condition might mimic loss of a specific taste quality.
Unfortunately, it would not mimic the other sensory cues associated with sampling (e.g.,
somatosensory) and so is not the most ideal; although it would be a better alternative than
presenting water, which has been associated with a quinine-like taste in this paradigm.
Additionally, the current generalization paradigm would be well-suited for a within
subject design that could assess function before, during, and after recovery from specific
nerve transection. Following regeneration of the nerve after a surgical lesion, it would be
interesting to know if function returns to the same levels seen prior to the insult. Such a
procedure might not be possible given the likelihood of loss of stimulus control that
would occur in those animals. This point could be addressed, however, by including
other groups that do not receive testing during the period of time that regeneration occurs.
Perhaps findings of this ilk would be useful to predicting recovery of function after
human injury.
Finally, another exciting avenue of study that is possible with this paradigm would
be to use an inducible knockout preparation. The rationale for such a statement is that the
knockout technology could be key in understanding whether specific taste receptors are
128
necessary for a specific taste quality (or just specific compounds). For example, the
experimenter could train the animal to discriminate the prototypical compounds as used
here, and then they could “knockout” function of a specific receptor and observe the level
of discrimination behavior. Next, the experimenter could restore the function of the
receptor and note the effects on performance. Likely, the animal would perform poorly
without the proper signal transduction machinery, but would be able to perform the task
once the receptor was restored. It might be more interesting, however, to find out that the
animal can compensate for non-functional receptors, suggesting redundancy in the
system. Obviously the success of the proposed study depends on a lot of technical factors
working properly, but theoretically, the behavioral testing paradigm opens the doors to a
lot of currently unachievable inquiries.
Behavioral data support analytic processing rather than synthetic
Results from the novel taste quality generalization experiments suggest that taste
quality signals may undergo analytic, rather than synthetic, processing in the gustatory
system. The fact that rats could learn to discriminate between the four prototypical taste
compounds, representing the four putative basic taste qualities, and then respond to novel
test compounds in terms of how NaCl-like, sucrose-like, quinine-like, or citric acid-like
they were provides support for this claim. The profiles generated from sodium gluconate
and quinine looked like the profiles generated from the novel concentration of NaCl and
quinine, respectively, used in Chapter 3. Although maltose and fructose did not generate
a strictly sucrose-like profile, it was still consistent with an analytic viewpoint because
there was also a quinine-like component to the response profiles. Even KCl, which has
been argued as a complex taste which is distinct from NaCl gave rise to a profile that
consisted of a combination of the 4 prototypical stimuli. Moreover, when MSG was
129
presented, the profile generated appeared to be a combination of two of the training
compounds, NaCl and sucrose, which is also consistent with the assertion that taste
quality is analytic. This is especially remarkable given that some researchers argue that
the taste of MSG is representative of a distinct fifth taste quality referred to as “umami.”
If all of the novel test compounds had generated profiles indicative of a separate
taste quality then it would have refuted the analytic claim. For a test compound to have
generated a profile suggestive of a separate taste quality, either all of the groups
Generalization Scores would have been 0.5 or they would all have been 0. Therefore, if
the rats did not recognize distinct components comprising the novel test compounds but
instead responded as if the test compounds were novel qualities, then it would have
suggested synthetic processing (that continua of taste qualities exist rather than a few
discrete qualities) (see Erickson, 1968).
Perspectives
The present collection of studies introduced and utilized two novel behavioral
paradigms to study aspects of taste processing in rats. Each paradigm has unique
strengths that attempt to circumvent shortcomings associated with the commonly used
conditioned taste aversion technique. This dissertation provides significant groundwork
towards the characterization of these new behavioral paradigms for assessing taste quality
in rodents, and indicates future lines of investigation necessary to fully elucidate the
strengths and limitations of these paradigms. The two approaches will likely prove useful
in future investigations of taste quality coding in rodents, especially with respect to
answering questions about whether taste coding is governed by analytic or synthetic
processing. Thus far, results support analytic processing, but further testing is
recommended.
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BIOGRAPHICAL SKETCH
Connie Lynn Colbert was born in Miami, Florida, on May 5, 1977, to Robert and
Debora Colbert. She has a twin brother and had an older brother (deceased), both of
whom fostered a healthy competitive spirit regarding school. Connie always enjoyed
school and knew at a very young age that she would enjoy a career in the sciences. She
graduated high school in 1995 with an AA degree from Miami Dade Community College
and then attended Florida International University in Miami and received her Bachelor of
Science in psychology in 1998. She spent the next year continuing her research at F.I.U.,
and in 1999, Connie started graduate school in psychobiology at the University of
Florida, and received her M.S. degree in August 2002. She also met her husband, Justin
L. Grobe, while obtaining her Ph.D. After obtaining her Ph.D., Connie will move to
Iowa for postdoctoral training with her husband.