Effect of Fear Memory Recall on Interpretation Bias in Rodents · PDF file3.6 Probe paradigms...

Effect of Fear Memory Recall

on Interpretation Bias in

Rodents

by

Nicolas Theodoric

A thesis submitted in conformity with the requirements for the

degree of Master of Science

Graduate Department of Institute of Medical Science

University of Toronto

© Copyright by Nicolas Theodoric 2014

II

Effect of Fear Memory Recall on Interpretation Bias in Rodents

Nicolas Theodoric

Master of Science

2014

Institute of Medical Science

University of Toronto

Abstract

Emotions influence interpretation of information. This psychological phenomenon is

called interpretation bias. Although rodent studies have established negative interpretation

biases due to chronic stressors, the effect of fear memory recall on interpretation bias is still

unclear. Therefore, the goal of this study was to investigate the effect of fear memory recall on

interpretation bias. We trained mice in a novel visual-based touchscreen paradigm to associate

presentation of a large square with a large reward, and small square with a small reward. We

then acutely activated a fearful memory through presentation of a tone previously paired with

a foot-shock. We tested the effect of acute fear memory recall on following interpretation bias

through a paradigm where mice associate ambiguous sized squares with either expectation of a

large or small reward. Recall of fear memory did not induce an interpretation bias, but reduced

overall motivation and reduced speed to interpret ambiguous stimuli.

III

Acknowledgements

This Master’s thesis would not have been completed without the on-going support of

my supervisor, committee members, colleagues, family and friends. I would like to thank my

supervisor Dr. Sheena Josselyn for giving me the wonderful opportunity, resources, and on-

going support to work on and complete this Master’s project for the past two years. The

amount of academic experience I have obtained to this day would not have been possible

without her help.

I would like to thank my committee members Dr. Kaori Takehara-Nishiuchi and Dr.

Steven Prescott for support, advice, and critical evaluation throughout my thesis project.

I would like to thank Dr. Leigh Botly for inheriting this project for my thesis. She had

established much of the technical framework for this project.

I express my deepest gratitude to my colleagues Dr. Dekel Taliaz and Dr. Sophia

González-Salinas, and Chen Yan for their superb academic expertise and support through-out

every step of my project. They have guided me through numerous set-backs, taught me how to

critically evaluate the results of my experiments, and reviewed my thesis.

I would also like to express my gratitude for Dr. David Ehrlich in critically reviewing my

thesis.

I also acknowledge all the technical assistance provided by my colleagues, especially

Peter Mai and Manoel Dias for their help in handling mice for experiments. I would also like to

thank everyone else from the Josselyn/Frankland lab.

IV

Finally, I would like to thank the University of Toronto and the Hospital for Sick Children

for funding this project.

V

Table of Contents

Abstract II Acknowledgement III List of Figures VII List of Abbreviations

IX

Chapter 1. Background and Literature Review 1 1.1 Overview of cognitive bias 2 1.2 Types of cognitive bias 4 1.2.1 Attention bias 5 1.2.2 Memory bias 4 1.2.3 Interpretation bias 7 1.2.4 Relationship between cognitive bias domains 8 1.3 Fear and Anxiety 10 1.4 Animal studies of interpretation bias 12 1.5 Touchscreen operant task

16

Chapter 2. Aims/Hypothesis

18

Chapter 3. Materials and Methods 20 3.1 Animals 21 3.2 Touchscreen apparatus 21 3.3 Touchscreen operant chambers pre-training 23 3.4 Differentially reinforced associative (drAT) task 23 3.5 Auditory Fear conditioning 25 3.6 Probe paradigms 25 3.7 Statistics 29

Chapter 4. Results 31 4.1 Paradigm validation: Stimulus discrimination in outcome prediction 32

4.2 Fear memory recall does not induce negative interpretation bias 39 4.3 Strong fear memories precludes completion of interpretation bias probe tests and foot-shock does not directly influence interpretation bias

48

4.4 Strong fear memory does not affect interpretation bias 57 4.5 Probe stimulus during choice action phase is important for stimulus-reward appraisal

66

4.6 lct-drAT training reverses impaired stimulus interpretation due to removal of stimulus during choice action phase

72

4.7 Stimulus interpretation with limited appraisal is not affected by strong fear memory recall

76

Chapter 5. Discussion 82 5.1 Summary of Results 83 5.2 drAT paradigm 84

VI

5.3 Establishment of interpretation bias probe tests 87 5.4 Fear memory recall slowed stimulus appraisal, but does not affect interpretation bias 5.5 Conclusions

92

98 5.6 Future Directions 100

Chapter 6. References 102

VII

List of Figures

Figure 3.2.1 | Schematic of touchscreen apparatus.

22

Figure 3.6.1 | Square stimuli sizes for probe paradigms 1, 3, and 4.

26

Figure 3.6.2 | Square stimuli sizes for probe paradigm 2.

27

Figure 4.1 | Differentially reinforced associative (drAT) task paradigm.

34

Figure 4.2 | Animals learned to discriminate S+ and S- stimuli with high accuracy.

36

Figure 4.3 | Action latencies during dRAT task suggest discrimination of reward-outcome valence.

38

Figure 4.4 | Experimental outline for fear memory recall and probing of interpretation bias paradigm.

40

Figure 4.5 | Auditory fear conditioning allowed recall of fear memory when the same auditory tone cue was played.

42

Figure 4.6 | Recall of fear memory did not induce significant interpretation bias.

43

Figure 4.7 | Action latencies did not differ between Control and Fear treatment groups during interpretation bias probe paradigm 1.

46

Figure 4.8 | Action latencies for non-ambiguous S+ and S- trials during interpretation bias probe paradigm 1.

47

Figure 4.9 | Method to activate stronger fear memory induced higher freezing levels for subsequent interpretation bias probe.

49

Figure 4.10 | Recall of stronger fear memory impaired completion of all three phases of interpretation bias probe paradigm 2, while shocking animals at 0.7mA did not induce interpretation bias.

51

Figure 4.11 | Action latencies of Strong Fear group differed from Control and Shock groups during interpretation bias probe paradigm 2.

52

Figure 4.12 | Choice accuracy and action latencies for non-ambiguous S+ and S- trials during interpretation bias probe paradigm 2.

55

VIII

Figure 4.13 | Recall of strong fear memory did not induce significant interpretation bias.

59

Figure 4.14 | Action latencies of Strong Fear group differed from Controls during interpretation bias probe paradigm 3.

61

Figure 4.15 | Choice accuracy and action latencies for non-ambiguous S+ and S- trials during interpretation bias probe paradigm 3.

63

Figure 4.16 | Reward expectation shifted towards lesser large reward expectation when animals were probed with probe paradigm 4 (PP4).

67

Figure 4.17 | Action latencies did not differ between animals probed in probe paradigm 3 and probe paradigm 4, but choice accuracies differ between the two paradigms.

69

Figure 4.18 | Transition from drat to lct-drAT training was accompanied by transient drop in choice accuracy and increase in correction trials, while sample and choice touch latencies underwent minimal changes.

74

Figure 4.19 | Recall of strong fear memory did not induce interpretation bias after habituating mice to appraise stimulus-reward outcome without reference square stimulus during choice action phase.

77

Figure 4.20 | Action latencies shared the same profile as previous experiments with PP3 and PP4.

79

Figure 4.21 | Choice accuracy and action latencies for non-ambiguous S+ and S- trials during interpretation bias test 5.

81

IX

List of Abbreviations

Amb ambiguous

ANOVA analysis of variance

CS conditioned stimulus

drAT differentially reinforced associative task

GAD generalized anxiety disorder

IPI inter-probe interval

lct-drAT drAT modified by removal of square stimulus during choice action phase

PP1 probe paradigm 1




S+ stimulus predicting positive outcome

S- stimulus predicting less positive or aversive outcome

US unconditioned stimulus

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Chapter 1

Background and Literature Review

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1.1 Overview of cognitive bias

Imagine for an instance that you walk out of the front door on a Saturday morning to

grab the mail and think to yourself, “Maybe I’ll get something from a long-lost childhood

friend? Or even better… acceptance letter for grad school!” What about on a Monday morning

thinking to yourself, “Oh shoot… it’s probably the bills… or a letter of rejection from a granting

agency”. As you walk into the meeting room to meet your interviewer for your dream job and

look into his or her eyes and think to yourself, “Someone does not look happy… this interview’s

going downhill” or “He/she looks pleasant enough, maybe I’ll score this job.” In either scenario,

we are uncertain about what lies inside the mailbox or what the character of the interviewer is.

However, we try to predict and anticipate the outcome of each interaction. What we predict or

anticipate can be influenced by our emotions. This phenomenon is termed interpretation bias:

when emotions influence our interpretation of uncertainty. Interpretation bias falls under the

umbrella of cognitive bias, which is defined as emotional influence of cognitive processes. In

general, cognitive bias is comprised of three integrated cognitive components that can be

subject to bias: attention, memory, and interpretation. My study seeks to further our

understanding of interpretation bias: how emotions can shift our anticipation from expecting a

positive outcome to a negative outcome or vice versa in situations of uncertainty.

The term cognitive bias was first coined in 1972 within the field of economics by Tversky

and Kahneman when they demonstrated that we often deviate from rational judgement and

decision making (Kahneman & Tversky, 1983). Every rational decision is derived by weighing the

cost of investment to arrive at a particular benefit or reward. However, what is observed is that

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we tend to either overestimate or underestimate costs and rewards. Likewise, our estimated

expectation or anticipation of a positive outcome or a negative outcome deviates from actual

occurrence of said outcomes. The motivation for such deviations from rational choice can be

innate, meaning we have natural tendencies irrespective of mood to deviate positively or

negatively. For example, we are unrealistically optimistic about future life events such as

expecting to live longer than average (Miller, 1996), or having a successful marriage (Baker,

Emery, Bakert, & Emeryt, 2014), despite statistical evidence otherwise. Alternatively, this

motivation can be labile, meaning we can deviate negatively or positively depending on factors

such emotion or affect. For example, depressed and anxious individuals often have a more

negative expectation of uncertain situations or future outcomes (Reviewed in Mathews &

Mackintosh, 1998). Studies in cognitive biases that I will discuss further that began in the 1980’s

have shaped our current definition of cognitive bias as it relates, not to economics, but to

psychology. As such, cognitive bias is currently defined as when emotions affect fundamental

cognitive processes. Briefly, attention bias was first discovered in anxiety patients, and these

findings led to the discovery of the fundamental components of cognitive bias: attention,

memory, and interpretation bias (Everaert, Duyck, & Koster, 2014; Everaert, Tierens, Uzieblo, &

Koster, 2013). I will briefly discuss each type of bias, with interpretation bias as the main focus

of this thesis.

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1.2 Types of cognitive bias

1.2.1 Attention bias

Studies in the 1980’s began to observe bias in attending to threatening versus neutral

information in clinically anxious patients. Notably, the first study on cognitive bias by MacLeod

et al. (1986) used clinically anxious subjects and a visual dot probe task to show an increased

attention bias towards threatening words (C MacLeod, Mathews, & Tata, 1986) compared to

control subjects. This task involves pressing a button as fast as possible when a dot is presented

on a screen. A distractor word is then paired with the dot to test the delay in pressing the

button when the dot appears. MacLeod et al. observed that clinically anxious patients had an

increased latency to press the button when the dot was paired with a threatening word

compared to when paired with a neutral word. This suggests an attention-type bias towards

attending to threatening stimuli. Indeed, these observations stand the test of time as these

initial findings have been replicated by many others (Reviewed in Van Bockstaele et al., 2013).

More importantly, this finding sparked investigations of other components of cognitive bias:

memory and interpretation biases.

1.2.2 Memory Bias

Following findings of attention bias in 1986, studies investigating the effects of anxiety

on biased memory recall began to be conducted. Mogg et al. (1987) tested memory bias in

clinical generalized anxiety disorder (GAD) patients by presenting a series of adjectives and

scoring subsequent recall of these presented words (Mogg, Mathews, & Weinman, 1987).

Adjective words consisted of positive (e.g., “amused”, “secure”), negative threatening (e.g.,

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“humiliated”, “trapped”), or negative non-threatening words (e.g., “bored”, “gloomy”). Their

main finding was that GAD patients exhibited poorer memory recall for threatening words

compared to non-threatening words, which went against their initial predictions that high

anxiety should increase recall of threatening words. Follow-up studies addressing avoidance

behaviour towards threat have obtained mixed results.

A decade later Reidy and Richards (1997) performed similar experiments to Mogg’s in

non-clinical high trait-anxious individuals (Reidy & Richards, 1997). They showed that high trait-

anxiety individuals have a memory bias towards threatening words over non-threatening

words, which was opposite to Mogg’s initial findings. Indeed in 1992, Eysenck proposed that

Mogg’s unexpected results (whereby negative emotionally anxious state leads to a memory

bias of positive words as opposed to negative words) may in part be due to the difference

between clinically diagnosed and non-clinically anxious individuals. It was noted that non-

clinically anxious individuals exhibit memory bias towards negative information but clinical GAD

tends towards avoidance or suppressing of negative and threatening information (Eysenck

1992) such as in the case of Mogg. In effect, this suggests that memory bias towards negative or

non-negative information depends on the severity of anxious states: the more debilitating the

anxious state is (i.e., disorder), the more likely an individual avoids or suppress instead of

biasing recall towards negative information.

More importantly, Reidy and Richards (1997) also noted a greater recall of words that

were presented towards the later part of the word list in the phase of the task where words

were presented to the subjects. As such, if the order of word type (positive, negative

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threatening, negative non-threatening) presentation was not properly controlled, it would limit

interpretative potential of the task. In the case of Mogg’s study (1987), it was unclear whether

order of word type was controlled for. As such, subsequent studies relating anxiety and

memory recall bias have ensured to employ word presentation paradigms to control for such

recency effects (Ebbinghaus, 1913) by dividing total word presentation into multiple blocks,

such that there were equal frequency of word-types (e.g positive, neutral, negative) within

blocks of recall tests (Kverno, 2000; Reidy & Richards, 1997). Additionally, one could also end a

word sequence with a series of neutral words to act as a ‘recency buffer’ (Russo, Fox, Bellinger,

& Nguyen-Van-Tam, 2001). This is important because the design of our study discussed later

used these strategies to control for bias due to recency effects.

In the years that followed, studies involving memory bias have produced mixed results

(see Coles & Heimberg, 2002; Mitte, 2008 for review). The nature of the word learning and

retrieval phase of the task to whether implicit (unconscious memory recollection measured by

way of tasks that test memory without specific command to consciously recall previously

learned words e.g., “Please decide whether the following word is meaningful or not”) or explicit

(memory recall involving direct conscious recollection typically measured by directly requesting

memories e.g., “Please recall as many words as possible from the previous learning phase.”)

memory was tested were proposed to account for the difference in whether the experiment

yields a memory bias towards positive or negative information. The general consensus of

Mitte’s meta-analytic review suggests a preference towards implicit memory recollection of

threatening information in patients with anxiety disorders. Despite a history of mixed findings,

memory bias is a component of cognitive bias as much as attention and interpretation and each

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component may influence the effect of the other. As such, it is important to control for these

components if we are to study a single aspect of cognitive bias in isolation.

1.2.3 Interpretation Bias

Similarly, interpretation bias studies in clinically anxious patients followed and tested

subject’s tendencies to interpret verbal homophone words – words with different spelling and

meaning but the same verbal pronunciation – towards either the neutral interpretation or

negative/threatening interpretation by spelling (die/dye; slay/sleigh). The first study in 1989

showed that clinically anxious individuals tend to favour the threatening homophone

interpretation (Andrew Mathews, Richards, & Eysenck, 1989). A subsequent study showed that

non-clinical, highly trait-anxious individuals also exhibit the same behaviour (Colin MacLeod &

Cohen, 1993). Furthermore, different methods of analyzing ambiguity interpretations showed

similar effects where anxious individuals favoured the negative interpretation of ambiguity,

including interpretation of homographs (e.g., stroke: brain haemorrhage or caress) (Richards &

French, 1992), ambiguous sentences (e.g., the doctor examined little Emma’s growth: growth

meaning height or tumor) (Eysenck, Mogg, May, Richards, & Mathews, 1991), ambiguous social

scenarios (Clark et al., 1995), and series of ambiguous faces morphed in gradients between

different emotional states (e.g., 30% fear and 70% happy) (Gutiérrez-García & Calvo, 2014;

Richards et al., 2002). As such, the mood-congruent effect between anxiety and negative

interpretation bias seems to be robust and translatable to many human interpretation bias

paradigms.

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1.2.4 Relationship between cognitive bias domains

Experiments elucidating attention bias in anxiety disorders have led to findings in

memory and interpretation bias. However, studies in each of these three cognitive bias

domains have primarily focused on each bias type in isolation. Experiments testing interaction

between bias domains in anxiety disorders have been scarce and limited to attention and

memory bias (Watts & Weems, 2006). These studies showed no significant correlation between

attention and memory despite strong correlation with anxiety scores. Studies in depression

have demonstrated that bias in attention to a negative stimulus predicts a greater recall of

negative stimuli presented during the attention task (Koster, De Raedt, Leyman, & De Lissnyder,

2010). In addition, a tendency to interpret ambiguous information negatively also predicts a

greater recall of negative memories (Salemink, Hertel, & Mackintosh, 2010). Likewise, Cognitive

Bias Modification Training results in less negative memory recall. In these studies,

psychologically healthy individuals were trained to attend away from negative information

(Blaut, Paulewicz, Szastok, Prochwicz, & Koster, 2013) or interpret information in a positive

manner (Tran, Hertel, & Joormann, 2011), which subsequently resulted in less negative memory

recall bias. These findings suggest a causative relationship exists between attention or

interpretation to memory bias. Recently, studies in subclinical depression by Everaert et al.

(2014) showed that the three domains are interconnected in such a way that increased

attentional bias towards negative information over neutral or positive leads to preferential

negative interpretation of ambiguous information. This in turn leads to greater recall/memory

of negatively interpreted information over neutral interpretation. Unfortunately, studies on

interaction between cognitive bias domains in anxiety are currently lacking. Overall, Everaert’s

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study suggests that attention bias may be a major factor that can confound results of studies

that seek only to study interpretation bias. Therefore, it is of importance in the design of an

interpretation bias paradigm to control for factors that may bias attention.

In summary, cognitive bias is considered to be comprised of attention, interpretation,

and memory bias. Each of these components of cognitive bias can influence one another and

account for symptoms commonly observed in some emotional disorders. To understand the

neural correlates behind cognitive bias and certain forms of emotional disorders, it is important

for us to consider specific components of the brain that is affected in these disorders.

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1.3 Fear and Anxiety

The amygdala is largely implicated for modulating affective states and emotional

learning (Anderson & Phelps, 2001; Cardinal, Parkinson, Hall, & Everitt, 2002; Davidson, 2002).

More importantly, human neuroimaging studies have suggested that the amygdala is also

involved in processing of ambiguous information (Blasi et al., 2009; Neta, Kelley, & Whalen,

2013; Whalen, 1998). Specifically, ambiguous neutral facial expressions elicit greater amygdala

activity than non-ambiguous facial expressions such as happy, fearful, or angry (Blasi et al.,

2009). In addition, dysfunctions of the amygdala is involved in emotional disorders such as

anxiety and depression (Davis, 1992; Nestler et al., 2002). Aside from anxiety and depression,

learning and modulation of fear memories is also a crucial function of the amygdala (Erlich,

Bush, & Ledoux, 2012; Johansen, Cain, Ostroff, & LeDoux, 2011; Maren, 2001).

Fear and anxiety function as an adaptive mechanism to protect an individual from

danger (Davis, Walker, Miles, & Grillon, 2010; Steimer, 2002). Although both may share many

similar physiological responses, such as sweating and elevated heart rate, fear and anxiety are

distinct phenomena that hinges on real or perceived threat (Davis, 1992; Davis et al., 2010;

Lang, Davis, & Ohman, 2000). Whereas fear is characterized by a fight-or-flight response due to

real specific threat such as a car speeding towards an individual, anxiety takes in the form of a

generalized flight-or-fight response to an unknown or non-specific threat such as walking in a

dark alley way. It is important to note that despite a clear distinction between fear and anxiety,

both share overlapping circuits within the amygdala. Pharmacological manipulation and

dysfunction of the amygdala can cause irregular modulation of both fear and anxiety (Davis &

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Whalen, 2001). Since the amygdala is important in processing of ambiguous information and

modulating anxiety and fear, we hypothesize that fear can induce negative interpretation bias

similar to what had been shown with anxiety.

As I have previously alluded to, animal models give us a way to test causal neural

pathways that mediate behaviour. The study of interpretation bias had in large been for the

purpose of investigating negative bias symptoms experienced in anxiety patients and

individuals. However, modelling anxiety in animal models had proved to be quite a challenge

due to the highly subjective nature of its symptoms. As such, the study of anxiety in animals has

been reliant upon tests for anxiety-like behaviour such as the elevated plus maze (EPM)

paradigm. In this paradigm a rodent (mice or rat) is placed onto a plus-shaped maze with four

arms, of which two arms provide an enclosure and the other two is exposed (for review: Bourin,

Petit-Demoulière, Dhonnchadha, & Hascöet, 2007). Since the maze is appreciably elevated, the

rodent subject tends to spend more time within the closed arms. It is important to note that

the EPM has its limitations. It this task, it is very difficult to separate increased/decreased time

spent in the arms as a measure of anxious behaviour from pure exploratory behaviour or

increased locomotor activity.

Models of fear learning however, have been more feasible in terms of interpretation of

behavioural output. Specifically, classical Pavlovian fear conditioning has been the predominant

paradigm to model fear and also had served as the bases for our extensive understanding of

fear neurobiology (Maren, 2001). In this paradigm, a rodent is placed in a chamber where a

brief tone is played and paired with a foot-shock. This produces robust encoding of a fear

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memory towards the tone. Reminder of the tone elicits a strong natural freezing behaviour.

This conditioning paradigm will be used as the behavioural treatment to elicit a fear response in

our current study.

1.4 Animal studies of interpretation bias

Unlike attention and memory bias, animal models of interpretation bias have been

developed (Mendl, Burman, Parker, & Paul, 2009). Rodent models of interpretation bias are of

special interest in this study as technological advancements in rodent models of psychiatric

disease have given us the opportunity to probe neural mechanisms underlying behaviour.

The first animal model of interpretation bias was established in 2004 by Harding et al.,

which used an auditory based paradigm where rats associate ambiguous tone frequencies with

either a positive or a negative outcome (Harding, Paul, & Mendl, 2004). They trained rats to

press a lever when a tone (S+) (e.g., 2 kHz) that predicted food reward was played and to

refrain from pressing the lever when tone (S-) (e.g., 4 kHz) that predicted a foot-shock was

played to avoid the foot-shock. Once rats learn the outcomes of each tone at greater than 50%

accuracy, rats in the experimental treatment group were housed in ‘unpredictable’ housing

conditions for nine days. These conditions entailed, for example, tilting of the housing cage or

introducing a stranger rat at random during the day, to induce mild depressive-like symptoms.

They then tested for ambiguous tone interpretation by playing tones of intermediate

frequencies between S+ and S- (2.5, 3, 3.5 kHz) and scored the proportion of tone plays

responded by lever presses and the latency for lever press. They observed a tendency for the

treatment group to perform fewer responses and of those responses, a higher latency,

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indicating a decreased expectation of the food reward and therefore negative interpretation

bias. An important limitation of this study was that the prediction of a negative outcome entails

a no-response and therefore does not exclude confounding possibilities of demotivation or

motor deficits. Nevertheless, subsequent rodent auditory interpretation bias paradigms were

based off of this study and have addressed this confound by requiring an active alternate lever

press response for the S- stimuli to avoid foot-shocks (Enkel et al., 2010). Specifically, Enkel et

al. compared interpretation bias between congenitally helpless rat lines that exhibit

helplessness phenotypes, as a model for depression, and non-helpless rats. They found a

negative interpretation bias with significantly lower proportion of congenitally helpless rats

pressing the lever predicting a food reward and a higher proportion pressing the lever to avoid

foot-shocks during ambiguous tone interpretation, indicating a negative bias. It is important to

note that the interpretation bias test was carried out on 6 consecutive days and each of three

graded ambiguous tones were presented twice. This method allowed multiple testing of each

ambiguous stimuli to allow errors in decision making and generate a reliable bias index with low

sample sizes (6 rats per group). Rygula et al. extended this method to demonstrate a positive

bias in ‘tickled’ rats by using only one ambiguous stimuli (i.e., non-graded) that is repeated ten

times within each test (Rygula, Pluta, & Popik, 2012). Therefore, it is important to incorporate

multiple presentations of ambiguous stimuli in an interpretation bias test to obtain a reliable

measure of bias.

Although auditory based paradigms by Harding et al. and Enkel et al. seems to be

employed the most, it poses a few limitations if our current study seeks to study the effect of

fear on interpretation through classical auditory fear conditioning. Since auditory fear

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conditioning is achieved by pairing a tone (CS) with a foot-shock (US), the tone played during

interpretation bias test can elicit a non-specific fear response (e.g., freezing behaviour)

(Antunes & Moita, 2010; Armony, Servan-Schreiber, Romanski, Cohen, & LeDoux, 1997) and

interfere with the test. Compounded with the fact that animal subjects were initially trained to

avoid foot shocks introduces confounding effects of chronic stress during the interpretation

bias test. Therefore, we also seek to implement an interpretation bias paradigm without

stressful outcomes during the stimulus discrimination task and the interpretation bias probe

test. To do this, instead of reinforcing S+ stimulus with reward and S- stimulus with foot-shock

punishment, we will use different levels of reward as reinforcers: S+ reinforced by a high

reward value and S- by a low reward value.

Aside from the auditory-based paradigms discussed, other rodent paradigms have also

used odour discrimination (Boleij et al., 2012) and spatial location (Oliver H P Burman, Parker,

Paul, & Mendl, 2009; Oliver H.P. Burman, Parker, Paul, & Mendl, 2008) to probe interpretation

bias. Odour discrimination paradigms trained mice to associate one odour with a food reward

and another odour with a quinine soaked food reward. Interpretation bias was tested via

presentation of odour mixtures and measuring the latency to obtain the reward. Spatial

location paradigms trained mice to associate one arm of an eight-arm radial arm maze with a

food reward, and the opposite arm with delivery of a quinine soaked food reward.

Interpretation bias was tested by scoring approach latency when rats were presented with

radial arms intermediate in location between the food associated arms. Interpretation bias had

also been tested in other non-human animals such as dogs (O. Burman et al., 2011), bees

(Bateson, Desire, Gartside, & Wright, 2011), lambs (Destrez, Deiss, Belzung, Lee, & Boissy,

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2012), chicks (Salmeto et al., 2011), and starlings (Brilot, Asher, & Bateson, 2010). The general

design in these studies were similar to rodent interpretation bias paradigms. Animals were

trained to associate one non-ambiguous stimulus with a positive outcome, another with a

negative outcome, and effect of treatment on interpretation bias was tested by scoring positive

or negative outcome expectation in response to presentation of ambiguous stimuli

intermediate in characteristic between the two non-ambiguous stimuli.

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1.5 Touchscreen operant task

Animal models provide us with a framework to study mechanisms mediating cognitive

processes. In light of this, it is important to devise a method in which results can be inferred

towards human subjects. Interpretation paradigms in rodents had mainly used auditory and

odour based tasks where animal subjects discriminate between different auditory tone

frequencies (Amir, Beard, & Bower, 2005; Enkel et al., 2010; Paul, Harding, & Mendl, 2005;

Rygula, Papciak, & Popik, 2014; Rygula et al., 2012) or mixtures of distinct odours (vanilla vs

apple) (Boleij et al., 2012). Interpretation bias paradigms in humans however were generally

visual-based, using computer generated visual stimuli presented on a screen. Designing an

animal interpretation bias paradigm using a computer-based platform similar to humans carries

a number of advantages. Presentation of specific visual ambiguous stimuli is absolute and

without variability. For comparison, ambiguous stimuli in odour based paradigms were

achieved by physical mixing of different concentrations of distinct odours by the experimenter,

which can be subject to bias and variability. In addition, trials within a computer-based test can

be automated, eliminating inter-trial variability. Finally, using a visual-based paradigm in my

study may provide results that are more inferable to human subjects.

For our study, we will adapt a visual-based rodent touchscreen operant platform

developed by Bussey et al. that tests cognitive function in rats and mice (Mar et al., 2013). This

platform allows automated presentation of computer generated stimuli on a screen and

rodents respond by a nose-poke or touch to a visual stimulus. Testing cognition in rodents

through this platform carries several advantages. These advantages are in addition to those

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inherent to computer-based platforms discussed in the previous paragraph. First, the

touchscreen platform provides a means to test cognitive behaviour using visual stimulus much

like in human subjects; second, since the platform allows a high degree of automation, multiple

animal subjects can be tested simultaneously; third, automation and simultaneous testing

prevents experimenter bias and reduces handling variability between animal subjects. Finally,

the automated nature of the platform allows long-term repeated testing with minimal

variability that could arise from experimenter handling.

Conversely, the touchscreen method carries several disadvantages. First, this task

requires long training procedures with multiple phases prior to the actual training paradigm

assessing the executive function of interest. Second, the visual nature of the task precludes use

of mouse lines that are susceptible to visual impairments due to genetic defects. Third,

nutritional reinforcement may introduce confounds related to appetite. Although inability to

introduce aversive reinforcement through this method is touted as an advantage due to stress

factors, it may be a limitation for translating animal interpretation bias paradigms across testing

platforms because rodent studies that have showed an interpretation bias effect have used

aversive reinforcement (e.g., foot-shock). Despite these limitations, used of proper controls

such as assessing visual discrimination capabilities prior and during training can rule out visual

impairments and therefore address some limitations of this method.

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Chapter 2

Aims/Hypothesis

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2.1 Aims/Hypothesis

In summary, the aim of my study was to implement a visual-based paradigm to test for

the first time the effect of acute fear memory recall on interpretation bias. My working

hypothesis is a two-tailed hypothesis that fear memory recall induce negative interpretation

bias in mice. Results obtained from this study will give us important insights into the influence

of fear memories on interpretation. If fear memory recall does influence interpretation, we can

infer that fear circuits may influence brain circuitry involved in processing of ambiguous

information during interpretation. This study sets up a platform for future studies to begin

probing molecular mechanisms behind fear and interpretation under uncertainty.

- 20 -

Chapter 3

Materials and Methods

- 21 -

Materials and Methods

3.1 Animals

Adult male and female eight-week old wild-type F1 hybrids C57Bl/6NTac ×

129S6/SvEvTac mice were used for all experiments. Mice were group-housed (3-4 mice per

cage) in a 12 hour light/dark cycle and all experiments were carried out during the light cycle.

All mice were handled for six days prior to start of training in touchscreen operant chambers.

Mice were food restricted by limiting food access for 2-2.5 hours each day such that weights

were maintained at or slightly above 90% of baseline. Baseline body weight of an individual

mouse was determined by taking the average of weights obtained over 5 days prior to

beginning of food restriction. Training of mice in touchscreen chambers in this lab have

previously been attempted without food restriction but trials have been unsuccessful as mice

would not complete full training sessions. Therefore, food restriction was carried out

throughout every experiment in this study. This provided ample motivation to perform the

tasks as mice are given strawberry milkshake rewards for each correct training trial. All

procedures have been approved by the Hospital for Sick Children Animal Care and Use

Committee.

3.2 Touchscreen apparatus

All behavioural tests excluding auditory fear conditioning were performed in

touchscreen operant chambers (Figure 3.2.1; Bussey-Saksida touch screen chambers for mice,

Lafayette Instruments, Lafayette, IN, USA). Briefly, tests were carried out in triangular chambers

enclosed in sound attenuating walls. The chamber was fitted with an infrared touchscreen and

- 22 -

liquid reward dispensing magazine. Infrared beams detect screen touches and reward collection

in the magazines (not shown on Figure 3.2.1). A Plexiglas mask was placed on the screen to limit

the contact area to regions of the screen where stimulus will be displayed. This reduces

unintended tail or non-specific body part touches to the screen. Reward light turns on during

reward delivery to serve as a delivery cue. A house light was equipped on top of the chamber to

serve as a cue for erroneous touches to a stimulus on the screen.

Figure 3.2.1 | Schematic of touchscreen apparatus.

- 23 -

3.3 Touchscreen operant chambers pre-training

Since mice were required to touch presented stimuli on the screen of touchscreen

chambers in order to obtain a strawberry milkshake during correct trials, mice were first trained

to associate activation of light within the reward chamber with deliver of 10 uL of milkshake

reward. Mice were allotted 15 minutes per training session with unlimited reward chamber

light and milkshake pairing trials. Once all mice in a cohort obtain more than 30 milkshake

rewards from 30 light and milkshake pairings they proceed to learn to associate touch of

randomly presented stimuli on the screen with delivery of a reward. To do this, we allotted 30

minutes each day for mice to complete a maximum of 30 association trials. Once mice complete

all 30 trials within 30 minutes, they proceed to train in the differentially reinforced associative

task.

3.4 Differentially reinforced associative (drAT and lct-drAT) task

Botly et al. (2012; unpublished) have previously established a differentially reinforced

associative task that trained mice to associate a large square (S+) with a large reward and a

small square with a small reward (S-) (Figure 4.1). The goal was to develop a strong S+ large

reward and S- small reward association memory. Mice learned to associate a nose-poke or

touch on a centrally presented sample (sample touch) S+ (large white square) or S- (small white

square) stimulus with the activation of two identical flanking choice stimuli (hollow letter S) to

the left and right of the centre stimuli. In the case of a S+ trial, a nose-poke to the right choice

stimuli delivers a large milkshake reward (15 uL). In the case of an S- trial, a nose-poke to the

left choice stimuli delivers a small milkshake reward (5 uL). This way mice learned to associate a

- 24 -

large square with a large reward, whereas a small square with a small reward. The phase at

which mice nose-poke or touch (choice touch) one of two flanking stimuli will be called choice

action phase. During the choice action phase, the centre square stimulus remained presented.

It is important to note that mice had to touch the centrally presented square before proceeding

to touch a flanking stimulus because this focuses initial attention within a trial to the centre

square. In effect, this controls for any initial bias in attention (see section 1.2.1) and therefore

initial action towards touching to the left or right of the square. If mice chose an incorrect

choice response (e.g., choosing left choice stimuli during an S+/large square trial or choosing

right choice stimuli during an S-/small square trial), no milkshake reward was delivered, and a

white house-light was activated for 5 seconds as a time-out punishment. In the event of an

incorrect response, the same trial was represented to enforce learning. This operant task will be

called drAT task throughout the study. Mice were trained daily in drAT sessions consisting of 60

trials (30 S+ trials and 30 S- trials) plus correction trials or a maximum training time of 30

minutes. Once mice reached an accuracy of >85% for both S+ and S- trials at least two times,

mice will be subjected to fear conditioning followed by an interpretation bias probe.

A modified version of the drAT task (lct-drAT) was also used together with probe

paradigm 4 described below. This modified drAT task was used to train mice to perform the

choice action phase without the reference centre square stimulus. This task is identical to the

drAT task, with the only exception being that the centre square stimulus is removed after

sample touch, leaving only flanking choice stimuli during the choice action phase.

- 25 -

3.5 Auditory Fear conditioning

Mice were placed in a fear conditioning chamber for 2 minutes followed by a

conditioned (CS) and unconditioned stimulus (US) pairing of 30s tone and 2s foot-shock at 0.7

mA. Mice were placed in a different context 24 hours later and the tone was played for 3

minute to induce a fear response. This type of fear conditioning paradigm has been shown to

elicit a strong fear effect as measured by high freezing scores (Maren, 2001). Immediately after

the end of the tone, mice underwent an interpretation bias probe test. Control mice did not

undergo fear conditioning but were exposed to the tone at the same time and context as mice

that were fear conditioned.

At the later part of the study, mice underwent a stronger fear conditioning paradigm to

elicit stronger fear memory recall. In this paradigm, CS/US pairings were repeated 2 more times

at intervals of 2 minutes, giving a total of 3 CS/US pairings. The following day, the tone was

played for 1 minute instead of 3 minutes to reduce extinction of fear response. For all

experiments involving this fear conditioning paradigm, control mice did not undergo fear

conditioning and weren’t exposed to the tone or fear conditioning chambers.

3.6 Probe paradigms

Probe Paradigm 1

Probe paradigms were designed to mimic the differentially rewarded associative task

(drAT). Probe paradigm 1 was adapted from Botly et al. (2012,unpublished). Mice underwent 3

blocks of 11 square association trials, with each block separated by 10 minutes. In each block, 6

- 26 -

of the 11 trials were regular trials similar to drAT to test the effect of fear memory recall on S+

and S- reward association accuracies: 3 trials of S+ square presentations and 3 trials of S- square

presentations. In addition, 5 of the 11 trials presented ambiguous sized squares intermediate

compared to S+ and S- squares. Sizes of the 5 ambiguous squares were measured by

percentage in length smaller than S+ square (Figure 3.6.1). Regular S+ and S- trials were

rewarded with 15 uL and 5 uL of milkshake for correct choices while incorrect choices trigger

houselights the same way as in drAT training. Ambiguous square trials were not reinforced and

subsequent touch to flanking stimulus triggered neither reward nor houselight and was

followed by a 5 second delay towards the next trial. Ambiguous trials were interleaved between

control trials (e.g S+, Amb, S-, Amb, S-) and the order of presentation for each control and

ambiguous trials were fully randomized.

% Length of S+

(S-)

30 45 55 65 75 85

(S+)

100

Actual Length (cm)

5.40 4.59 4.09 3.512.972.431.62

Figure 3.6.1 | Square stimuli sizes for probe paradigms 1, 3, and 4.

- 27 -

Probe Paradigm 2

Probe paradigm 2 introduced three more intermediate-sized squares: 40, 50, and 60 %

length of S+ to increase resolution in the reward expectation profile for interpretation bias.

Mice underwent 3 blocks of 20 trials, with each block separated by 10 minutes. In each block,

mice were presented with 8 unrewarded intermediate square trials (one of each intermediate

size, Figure 3.6.2), and 6 of each S+ and S- regular reinforced trials. To minimize any potential of

learning a pattern of alternating rewarded and unrewarded trial sequence as in probe paradigm

1, we allowed no more than 2 consecutive unrewarded ambiguous trials and 3 consecutive

regular trials. As in probe paradigm 1, the order for ambiguous and regular trial presentations

were randomized within and between blocks.

% Length of S+

(S-)

30 45 55 65 75 85

(S+)

100

Actual Length (cm)

5.40 4.59 4.09 3.512.972.431.62

40 50 60

3.24 2.702.16

Figure 3.6.2 | Square stimuli sizes for probe paradigm 2.

- 28 -

Probe Paradigm 3

Probe paradigm 3 maintained the balance of ambiguous squares (smaller and larger

than ambiguous squares sized 65% of S+ as probe paradigm 1, Figure 3.6.1), but with uniform

percentage increments of length size. In short, the intermediate sized squares used were the

same as probe paradigm 1. As with probe paradigm 3, ambiguous trials could not appear

consecutively. Since interpretation bias was tested over 3 blocks, we took into consideration

that smaller or larger sized intermediate squares could be concentrated in between two blocks

(e.g., first block ends with squares sized 45/55 and second block starts with squares sized

45/55). In effect, this increased frequency of smaller or larger sized squares may bias

subsequent choices to either a small or large reward expectation due to recency effects (see

section 1.1.2).

Probe Paradigm 4

Since we found that fear memory activated the choice latency while maintaining

identical reward prediction outcomes, we designed a probe to interfere reward reappraisal of

reward expectation after touching the centre stimulus. In this way, the final reward expectation

of intermediate-sized square may change in response to fear memory recall. As such, Probe

paradigm 4 was identical to probe paradigm 3 with the only difference being that the centre

stimulus disappeared upon nose-poke, leaving only the flanking stimulus. Ambiguous stimuli

used was the same as probe paradigm 1 and 3 (Figure 3.6.1).

- 29 -

3.7 Statistics

Statistica 8 (a commercially available statistical analysis program) was used for all

statistical analyses. Male and female were pooled for all analyses because there were no

significant differences between sexes for major indices analyzed (e.g drAT accuracy and reward

expectation during interpretation bias tests). Two-way repeated measures Analysis of Variance

(ANOVA) were used for all analyses. Since there were no between-group comparisons in the

drAT task, two-way repeated measures ANOVA on choice accuracy, sample touch latency,

choice touch latency, and reward collection latency was carried out using square size (S+, S-)

and training session (days in drAT training) as within-group factors. Choice accuracy represents

the percentage of trials in a drAT session in which mice chose the correct flanking stimulus

depending on square size (S+, S-). Sample touch latency represents the latency for mice to

nose-poke/touch the centrally presented square at the start of each trial to activate

presentation of flaking stimuli. Choice touch latency represents the latency to nose-poke/touch

either one of two presented flanking stimuli. Reward collection latency represents the latency

to collect the milk shake reward. Freezing levels during tone reminder of shock experience were

also analyzed using two-way repeated measures ANOVA with treatment (e.g., Control, Fear) as

between-group factor and time in fear conditioning chamber as within-group factor. Finally, all

analyses involving the interpretation bias probe (large reward expectation, sample touch

latency, choice touch latency, and reward collection latency) used two-way repeated measures

ANOVA as well, with treatment (e.g., Control, Fear, Strong Fear) as between-group factor and

square size (S-, intermediate ambiguous square sizes, S+) as within-group factor. Large reward

expectation represents mean tendency for mice to choose the right flanking stimulus in

- 30 -

response to a given square stimulus (S+, ambiguous, S-). Newman-Keuls post-hoc tests were

used for further analyses of all statistically significant interactions or main effects. Alpha-value

of 0.05 was used for all analyses.

- 31 -

Chapter 4

Results

- 32 -

4.1 Paradigm validation: Stimulus discrimination in outcome prediction

To study interpretation bias in mice, we designed a touch-screen paradigm to measure

reward outcomes anticipated by the presentation of ambiguous stimuli. For this task, mice

were first trained to distinguish and learn to predict reward outcomes based on non-ambiguous

stimuli. One non-ambiguous stimulus predicted a large reward (S+) and another predicted a

small reward (S-). Once these associations were well-learned, mice could be presented with an

ambiguous stimulus and probed for the expected outcomes, either the large or small reward.

Figure 4.1 outlines the differentially reinforced associative task (drAT) that we used.

Briefly, mice underwent a training session where they were trained with 60 trials of

correct stimulus-reward associations each day. Each trial was divided into three phases. The

first phase was the sample touch phase where mice touched a randomly presented large white

square stimulus, which predicted a large reward (S+), or a small white square stimulus, which

predicted a small reward (S-; Figure 4.1 top). Touch to the centrally presented square stimulus

activated presentation of flanking choice stimuli (hollow letter ‘S’, Figure 4.1 middle). The

second phase was the choice touch phase where mice touched the correct flanking stimulus, as

signified by the size of the square (for instance, right for S+, left for S-, Figure 4.1 middle). The

third and final phase of a trial occurred after a touch to a correct flanking stimulus, which was

followed by a 15 uL or 5 uL strawberry milkshake reward (Figure 4.1 bottom). In contrast, a

touch of the incorrect flanking stimulus resulted in no reward and a brief turning on of the

houselight, to act as an error cue.

- 33 -

Therefore, in this initial training phase, mice were trained that presentation of a large

square (S+) resulted in presentation of a large reward if the right flanking stimulus was touched,

and a small square (S-) resulted in presentation of a small reward if the left flanking stimulus

was touched. When a wrong response was made (large stimulus and touch left, small stimulus

and touch right) was made, no reward was delivered, the houselight turned on, and the trial

was repeated until the correct response was made.

- 34 -

Figure 4.1 | Differentially reinforced associative (drAT) task paradigm. Each trial

of the drAT task was divided into three phases: 1. Sample touch phase, where

mice touch the centre square stimulus; 2. Choice touch / choice action phase,

where mice select a flanking stimulus; and 3. Reward collection phase / time-out

phase, where mice either collect a reward or receive a time-out with houselight

activation, depending on whether the flanking stimulus is chosen.

Sample Touch

Phase

Choice Touch /

Choice Action

Phase

Reward Collection

/ Time-out Phase

- 35 -

Acquisition of the stimulus-reward associations typically occurred over the course of 10-

15 training sessions (Figure 4.2). Figure 4.2A depicts the mean percentage of correct choices

(accuracy) of the 60 trials performed in each training session. Accuracy for correct S+

association trials passed chance levels (50%) 3 days sooner than S- trials. S+ accuracy also

reached 85% eight days faster than S- trials. S+ accuracy reached a plateau above 95% by the

14th day of training while S- accuracy reached a plateau slightly above 85% by the 22nd day of

training. Analysis of variance (ANOVA) of accuracy starting at the 18th training session revealed

a significant effect of training session, square size (S+, S-), and training session and stimulus size

interaction (Training Session: F27,1242 = 115.71, p<0.05; Square Size: F1,26 = 197.87, p<0.05;

Training Session x Square Size: F27,1242 = 5.29). Post-hoc analysis of training session (sessions 18-

28) and square size (S+, S-) interaction revealed significant accuracy differences between S+ and

S- throughout all training sessions in the analysis (p<0.05). Mice were given probe trials to test

interpretation bias only after they reached accuracies of above 85% for both S+ and S- trials. In

addition, Figure 4.2B illustrates the number of correction trials, or the average number of times

an incorrect response was made over each training session regardless of the square size.

ANOVA shows that the number of errors significantly decreased over training sessions (Training

Session: F27,1269 = 29.85, p<0.05) to an average of 5 errors per training session.

- 36 -

0 5 10 15 20 25 30

50

60

70

80

90

100S+

S-

Training Session

%

Accu

racy

Figure 4.2 | Animals learned to discriminate S+ and S- stimuli with high

accuracy. (A) drAT task choice accuracy and (B) correction trials. Mice

learned association of S+ stimulus with touch of right flanking stimulus to

obtain large reward faster than S- stimulus, left flanking stimulus touch

and small reward. Reduction in correction trials coincided with

improvement in accuracy. N = 48.

0 5 10 15 20 25 300

5

10

15

20

25

30

Training Session

# C

orr

ecti

on

Tri

als

A

B

- 37 -

To test for indicators of differing valence between S+ and S- stimuli, we analyzed the

time it took for mice to touch the centre square stimulus, flanking stimulus, and collect reward

(action latencies) during the drAT task. We hypothesized that lower motivation due to lower

reward outcome predicts higher action latencies for S-. In other words, we hypothesized that

action latencies for S- trials would be greater than S+ trials. As predicted, mice had significantly

greater latencies when making choices for smaller predicted rewards (S-) than larger predicted

rewards (S+)(Figure 4.3). The action parameters depicted in Figure 4.3 were sample touch

latency (delay from presentation to touch of the centre square stimulus), choice touch latency

(delay from touch of the centre stimulus until touch of a flanking stimulus), and reward

collection latency (delay from choosing the correct flanking stimulus until retrieving the

reward). Figure 4.3 shows a visible trend of higher latencies for sample touch (Figure 4.3A),

choice touch (Figure 4.3B), and reward collection latency (Figure 4.3C). ANOVA for sample

touch latency during training sessions 18-28, when mice had learned stimulus-reward

associations, revealed a significant effect of square (and therefore reward) size (S+, S-) on

latency (Training Session: F10,190 = 0.81, p>0.05; Square Size: F1,19 = 32.56, p<0.05; Training

Session x Square Size: F10,190 = 1.13, p>0.05). ANOVA also revealed significant square size effect

for choice touch (Training Session: F10,470 = 2.15, p<0.05; Square Size: F1,47 = 25.77, p<0.05;

Training Session x Square Size: F10,470 = 0.022, p>0.05) and reward collection latencies (Training

Session: F10,470 = 1.76, p>0.05; Square Size: F1,47 = 92.16, p<0.05; Training Session x Square Size:

F10,470 = 1.05, p>0.05) as well. These differences in action latencies between S+ and S- trials

suggest that S+ and S- stimuli have differing valence. Faster action latencies for S+ trials suggest

more positive valence compared to S- trials, further validating the paradigm.

- 38 -

0 5 10 15 20 25 300

2

4

6

8

10

Training Session

Ch

oic

e T

ou

ch

Late

ncy (

s)

0 5 10 15 20 25 300.0

0.5

1.0

1.5

2.0

2.5

3.0

S-

S+

Training Session

Rew

ard

C

ollecti

on

L

ate

ncy

(s)

0 5 10 15 20 25 300

20

40

60

80

100

120

Training Session

Sam

ple

To

uch

Late

ncy (

s)

Figure 4.3 | Action latencies during drAT task suggest

discrimination of reward-outcome valence. (A) drAT task touch

sample touch, (B) choice touch, and (C) reward collection

latency. Choice latency and reward collection latency qere

significantly higher in S- trials. N = 20 in (A); N = 48 in (B) and (C).

A B

C

- 39 -

4.2 Fear memory recall does not induce negative interpretation bias

We had previously discussed that there is a degree of overlap between fear and anxiety

circuits (M Davis & Whalen, 2001; Steimer, 2002). Since negative interpretation biases are

associated with anxiety, we hypothesized that acute fear memory recall would affect

interpretation bias as well.

To test the effect of fear memory recall on subsequent interpretation, we performed

fear conditioning using 1 conditioned stimulus (tone) / unconditioned stimulus (foot-shock)

(CS/US) pairing followed by a 3 minute CS reminder the following day. The cohort of mice that

underwent fear conditioning had completed drAT training (Fear group; Figure 4.4). These mice

were required to reach choice accuracies above 85% for S+ and S- at least twice during their

drAT training. Figure 4.5 denotes freezing behaviour of mice when they were placed in the fear

conditioning chambers with a modified context. During the test, fear conditioned mice showed

high levels of freezing (~60%) (Figure 4.5). Control mice in this experiment did not undergo

fear conditioning but were exposed to the tone reminder in the new context the same time as

the Fear group. Following tone presentation, mice were immediately placed into the

touchscreen operant chambers and tested for interpretation bias during a probe test.

- 40 -

Figure 4.4 | Experimental outline for fear memory recall and

probing of interpretation bias.

- 41 -

Interpretation bias test 1

Mice were probed for interpretation bias by a touchscreen paradigm (see Methods 3.6:

Probe Paradigm 1) designed to mimic the drAT task but with the presentation of ambiguous

sized squares intermediate in size between S+ and S-. Control mice showed a gradient of

interpretations, as measured by reward expectation when they were presented with

ambiguous sized squares (Figure 4.6D, Control). Furthermore, they fully expected large or small

rewards during regular S+ and S- trials, respectively. Since ambiguous trials weren’t rewarded, it

was important that mice did not learn that ambiguously sized squares predicted no reward, as

it may have introduce confounds due to reduced motivation to perform the task when no

reward was expected. As described in the methods section, we minimized this possibility by

presenting more non-ambiguous stimuli paired with a reward compared to ambiguous stimuli.

Additionally, sample touch and choice touch latencies (Figure 4.7, Control) were not higher for

ambiguous stimuli compared to non-ambiguous. In addition, behavioural observation of mice

performing the interpretation bias test trials showed that mice consistently orient themselves

towards the reward magazine throughout the test so as to check for delivery of a reward. Taken

together, these findings in control mice depict a successful interpretation bias paradigm.

In addition to validating our probe paradigm, we also found that fear memory recall did

not induce negative interpretation bias compared to controls. Five intermediate squares were

sized based on the length of square of the S+ square (Methods Figure 3.1). Each unrewarded

intermediate sized square was presented once during a probe, interleaved between

presentations of regular rewarded drAT task S+ and S- stimuli trials. Order of intermediate sized

- 42 -

0 60 120 180 240 3000

20

40

60

80 Control (N = 6)

Fear (N = 6)

Time(s)

% F

reezin

g

squares was completely randomized and mice were probed three times (3 probe phases) with

10 minute inter-probe interval (IPI). During IPI, mice were removed from the chamber after

completing a probe session. Cue size correlated with reward expectation, such that larger

intermediate cues more often elicited choices that would be correct for the largest stimulus

(S+; Figure 4.6). The proportion of mice choosing the right flanking stimulus was plotted as a

function of intermediate stimulus size. Figure 4.6A-C shows the proportions for the first (Figure

4.6A), second (Figure 4.6B), and third (Figure 4.6C) probe phases. Together, they demonstrate

that proportions of mice expecting the large reward fluctuated between probe

Figure 4.5 | Auditory fear conditioning allowed recall of fear memory when the

same auditory tone cue was played. Mice were fear conditioned by pairing a tone

with a 0.7 mA footshock. Freezing behaviour was scored in a new context the

following day when the same tone was played (arrow, vertical dotted line) for 3

minutes.

- 43 -

30 45 55 65 75 85 1000.0

0.2

0.4

0.6

0.8

1.0

Ambiguous Cue Size (% length of S+)

Larg

e R

ew

ard

E

xp

ecta

tio

n

35 45 55 65 75 85 950.0

0.2

0.4

0.6

0.8

1.0


Larg

e R

ew

ard

E

xp

ecta

tio

n

30 45 55 65 75 85 1000.0

0.2

0.4

0.6

0.8

1.0


Larg

e R

ew

ard

E

xp

ecta

tio

n

30 45 55 65 75 85 1000.0

0.2

0.4

0.6

0.8

1.0

Control (N = 6)

Fear (N = 6)


Larg

e R

ew

ard

E

xp

ecta

tio

n

Figure 4.6 | Recall of fear memory did not induce significant interpretation bias.

Control and Fear treatment groups were probed for interpretation bias using

probe paradigm 1 in three repeated phases: (A) phase 1, (B) phase 2, (C) phase 3.

To account for choice variability across three phases, average response for each

mice were scored (D). Sizes 30 and 100 denotes non-ambiguous cues S- and S+.

A B

D C

- 44 -

phases. To account for these fluctuations, we calculated the average response for individual

mice across three probe trials and plotted the mean of response across mice in Figure 4.6D.

Again, Figure 4.6D suggests there was no effect of fear on interpretation. ANOVA revealed no

significant treatment (Control vs Fear group) effect differences (Treatment: F1,10 = 1.86, p>0.05;

Square Size: F6,60 = 66.67, p<0.05; Treatment x Square Size: F6,60 = 1.88, p>0.05).

Since fear memory recall did not influence reward expectations in the interpretation

bias probe, we analyzed action latencies to see whether there were any changes in physical

activity that may be indicative of changes in information processing or motivational factors. As

shown in Figure 4.7, there was no effect of fear conditioning on action latencies. ANOVA

revealed no significant differences between groups for sample touch (Treatment: F1,10 = 1.76,

p>0.05; Square Size: F6,60 = 7.35, p<0.05; Treatment x Square Size: F6,60 = 1.47, p>0.05) or choice

touch latency (Treatment: F1,10 = 1.07, p>0.05; Square Size: F6,60 = 4.23, p<0.05; Treatment x

Square Size: F6,60 = 1.22, p>0.05).

Analysis of choice accuracy and action latencies of regular S+ and S- trials also showed

that fear memory recall did not affect behaviour associated with performing a regular drAT task

(Figure 4.8A-D). Regular S+ and S- trials were interleaved between ambiguous stimulus trials in

the probe paradigm. Figure 4.8A shows that accuracies for both S+ and S- were maintained

above 85% during the probe test, regardless of fear memory recall. High accuracies for regular

trials allow us to infer validity of reward expectations for ambiguous stimuli.

Figure 4.8B-D shows that fear memory did not cause any effect on action latency

parameters. ANOVA revealed no significant treatment effect differences between groups for all

- 45 -

parameters (sample touch latency: F1,10 = 0.67; p>0.05, choice touch latency: F1,10 = 0.69;

p>0.05, reward collection latency: F1,10 = 0.20; p>0.05). However, the same ANOVA revealed

significant square size (S+ vs S-) effect for all parameters (sample touch latency: F1,10 = 8.42;

p<0.05, choice touch latency: F1,10 = 24.3; p<0.05, reward collection latency: F1,10 = 68.5;

p<0.05). Overall, these results show that fear memory did not cause any effect in interpretation

or action behaviours compared to controls. Additionally, difference in valence between S+ and

S- stimuli was preserved due to higher action latencies associated with S- compared to S+.

- 46 -

30 45 55 65 75 85 1000

2

4

6

8

Fear (N = 6)

Control (N = 6)


Ch

oic

e T

ou

ch

Late

ncy (

s)

30 45 55 65 75 85 1000

10

20

30


Sam

ple

To

uch

Late

ncy (

s)

Figure 4.7 | Action latencies did not differ between Control and Fear

treatment groups during interpretation bias probe paradigm 1. (A)

Sample touch latency, (B) choice touch latency. Sizes 30 and 100 denotes

non-ambiguous cues S- and S+.

A

B

- 47 -

S-

S+

0

20

40

60

80

100

Accu

racy (

%)

S-

S+

0.0

0.5

1.0

1.5

2.0

2.5

Control (N = 6)

Fear (N = 6)

Ch

oic

e T

ou

ch

Late

ncy (

s)

S-

S+

0.0

0.5

1.0

1.5

2.0

Rew

ard

Co

llecti

on

Late

ncy (

s)

S-

S+

0

10

20

30

Sam

ple

To

uch

Late

ncy (

s)

Figure 4.8 | Action latencies for non-ambiguous S+ and S- trials during interpretation

bias probe paradigm 1. (A) Choice accuracies, (B) sample touch latency, (C) choice

touch latency, and (D) reward collection latency did not differ between Control and

Fear group. Sample touch latency for S+ and S- trials differed significantly for Fear, but

not Control group. In both Control and Fear group, choice touch latency and reward

collection latency were higher for S- trials than S+ trials

A B

D C

- 48 -

4.3 Strong fear memories precludes completion of interpretation bias probe tests and foot-

shock does not directly influence interpretation.

Since the previous experiment showed minimal effects of fear memory recall on

interpretation bias, we tested whether there were any immediate effects of a single foot-shock

used during fear conditioning paradigms on interpretation bias. To do this, we probed mice

immediately after they were given a single foot-shock (Shock group). Additionally, we also

tested whether stronger fear memory recall can induce interpretation bias. To induce a

stronger fear memory, animals were fear conditioned with 3 CS/US pairings instead of 1 pairing.

The following day, CS was played for 1 minute instead of 3 minutes to probe the animals

immediately after they exhibited a very high level of freezing (Figure 4.9). Comparison of

freezing levels between mice that underwent this fear conditioning paradigm (Strong Fear)

(Figure 4.9: Strong Fear group) and previous paradigm (Figure 4.5; Figure 4.9: Fear group)

showed stronger fear response in mice from this paradigm. This was confirmed by two-way

repeated measures ANOVA with treatment (Control, Fear, Strong Fear) as between group

factor, and time as within-group factor revealing significant effect of treatment (F2,76 = 27.30;

p<0.05) and time (F5,380 = 33.53; p<0.05), with significant treatment and time interaction (F10,380

= 9.05; p < 0.05). Post-hoc analysis showed that freezing levels in Strong Fear treatment group

were higher than Fear treatment group during all time points except for 30 and 150 seconds.

Mice depicted in Figure 4.9 Control group is the same as Figure 4.5 Control group. Since the

freezing level of Strong Fear group at its last time point was significantly higher than Fear group

(180 seconds), stronger fear memory was induced using 3 CS/US pairings during fear

conditioning compared to 1 CS/US pairing from the previous experiment.

- 49 -

0 60 120 180 240 3000

20

40

60

80

Control (N = 6)

Fear (N=6)

Strong Fear (N = 67)

Time(s)

% F

reezin

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Figure 4.9 | Method to activate stronger fear memory induced higher freezing levels

for subsequent interpretation bias probe. Stronger auditory fear memory (cross) was

induced by fear conditioning mice with three tone-shock pairings at 0.7 mA per shock.

- 50 -


Since interpretation bias test 1 (Figure 4.6D) showed a slight but not significant increase

in large reward expectation at ambiguous square size 55, we introduced three additional

ambiguous intermediate sized squares: sizes 40, 50, and 60. The goal was to provide a higher

resolution of reward expectation surrounding those ambiguous stimuli. As described in the

methods section (Methods 2.5: Probe Paradigm 2), we had also pseudo randomized the order

between ambiguous and regular S+/S- trials in such a way that mice did not encounter more

than two consecutive unrewarded ambiguous trials and three consecutive regular trials. This

way, rewarded (if correct) S+ / S- and unrewarded ambiguous square trials were not interleaved

to prevent any effects of learning a pattern of rewarded/unrewarded trials. We additionally

introduced six regular trials (3 S+ and 3 S-) for a total of 8 ambiguous trials and 12 non-

ambiguous regular trials. These additional regular trials were introduced to reinforce the overall

paradigm and minimize any demotivating effects due to unrewarded trials.

Elicitation of a stronger fear memory prevented mice from completing the

interpretation bias probe test. As observed in Figure 4.10, interpretation bias data for the

Strong Fear group were only available for the first phase out of three phases of the

interpretation bias probe. This was because animals in the Strong Fear group did not complete

the second and third probe phase. Paired with the increase in action latencies with high

variability (Figure 4.11A-B), stronger fear memory impaired completion of the interpretation

bias probe task.

- 51 -

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Figure 4.10 | Recall of stronger fear memory impaired completion of all three phases of

interpretation bias probe paradigm 2, while shocking animals at 0.7mA did not induce

interpretation bias. Control, Strong Fear, and Shock treatment groups were probed for

interpretation bias using probe paradigm 2 in three repeated phases: (A) phase 1, (B) phase 2,

(C) phase 3. Strong Fear group only completed phase 1in (A). (D) Average response for animals

in Control and Shock group did not differ. Sizes 30 and 100 denotes non-ambiguous cue S- and

S+.

A B

D C

- 52 -

35 45 55 65 75 85 950

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35 45 55 65 75 85 950

100

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400


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Figure 4.11 | Action latencies of Strong Fear group differed from Control and Shock groups

during interpretation bias probe paradigm 2. (A) Sample touch latency of all three groups. Sample

touch latencies were significantly higher in the Strong Fear group at square sizes 30, 40, and 55

compared to Controls. (B) Choice touch latency of all three groups. Choice touch latencies were

significantly higher in the Strong Fear group at square sizes 40 and 55 compared to Controls. (C)

Graph of sample touch latency in (A) without Strong Fear group and re-scaled Y-axis. (D) Graph of

choice touch latency in (B) without Strong Fear group and re-scaled Y-axis. There were no

significant differences in (C) sample touch and (D) choice touch latency between Control and

Shock group. Sizes 30 and 100 denotes non-ambiguous cue S- and S+.

A B

D C

- 53 -

Additionally, shocking mice had no effect on interpretation bias. It is important to

reiterate that mice in the Shock group were given a foot-shock similar to the fear conditioning

paradigm in interpretation bias test 1 and immediately probed for interpretation bias. In

essence, this tested the effect of a shock stressor on subsequent interpretation bias. Similar to

interpretation bias test 1 (Figure 4.6), Figure 4.10A-C shows that large reward expectations

fluctuated across the three probe phases. To account for these fluctuations, Figure 4.10D shows

the mean response over the three probe phases. Reward expectation did not differ between

Control and Shock groups. This was further evidenced by two-way repeated measures ANOVA,

which revealed no difference in reward expectation between treatment (Control, Shock) groups

(Treatment: F1,14 = 0.0006, p>0.05; Square Size: F9,126 = 61.73, p<0.05; Treatment x Square

Size: F9,126 = 0.87, p>0.05).

Although mice in the Strong Fear group did not complete all three phases of the

interpretation bias probes, behavioural analysis during the first probe phase showed that action

latencies were drastically increased. ANOVA investigation of action latencies in Figures 4.11

revealed significant delays in sample touch (Treatment:F2,20 = 41.47, p<0.05; Square Size: F9,180 =

4.35, p<0.05; Treatment x Square Size: F18,180 = 2.89, p<0.05) and choice touch latency

(Treatment: F2,20 = 18.71, p<0.05; Square Size: F9,180 = 2.50, p<0.05; Treatment x Square Size:

F18,180 = 2.03, p<0.05) between groups. Sample touch latencies (Figure 4.11A) were significantly

increased in the Strong Fear group, with post-hoc analysis revealing a significant difference

between this group and both control and Shock groups at ambiguous square sizes 40, 55, and

non-ambiguous S- (size 30) (p<0.05). In contrast, subsequent choice touch latencies (Figure

4.11B) were significantly higher in Strong Fear group compared to Control and Shock groups at

- 54 -

ambiguous square sizes 40 and 55 (p<0.05) but not at S- square trials (p>0.05). There were no

statistically significant differences between the control and SHOCK group for sample (Figure

4.11A,C) and choice (Figure 4.11B,D) touch latencies (p>0.05).

Analyses of S+ and S- trials within the interpretation bias probe showed that recall of a

strong fear memory compromised behaviour associated with non-ambiguous stimuli

discrimination (Figure 4.12). This was especially evident through the decrease in choice

accuracy during S- trials (Figure 4.12A). ANOVA revealed significant between-group factor

differences (control, Shock, Strong Fear) for choice accuracy (Treatment: F2,20 = 8.60, p<0.05;

Square Size: F1,20 = 5.59, p<0.05; Treatment x Square Size: F2,20 = 1.33, p>0.05) (Figure 4.12A).

Post-hoc analysis showed significantly lower S- accuracy in the Strong Fear group compared to

all other conditions (control and Shock) (p<0.05). In addition, S- accuracy was significantly lower

than S+ in the Strong Fear group (p<0.05).

Strong fear memory also affected motivation because sample touch and reward

collection latencies were drastically increased. ANOVA showed a significant effect of treatment

for sample touch latency (Treatment: F2,20 = 27.4, p<0.05; Square Size: F1,20 = 39.76, p<0.05;

Treatment x Square Size:F2,20 = 14.26, p<0.05) (Figure 4.12B), reward collection latency

(Treatment: F2,20 = 6.74, p<0.05; Square Size: F1,20 = 7.20, p<0.05; Treatment x Square Size: F2,20

= 0.77, p>0.05) (Figure 4.12D), but not choice touch latency (Treatment: F2,20 = 2.00, p>0.05;

Square Size: F1,20 = 4.68, p<0.05; Treatment x Square Size: F2,20 = 1.75, p>0.05) (Figure 4.12C).

Post-hoc analysis showed that sample touch and reward collection latencies for the Strong Fear

group were significantly higher compared to Control and Shock groups.

- 55 -

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Figure 4.12 | Choice accuracy and action latencies for non-ambiguous S+ and S- trials during interpretation

bias probe paradigm 2. (A) Choice accuracy for S- trials in the Strong Fear group was significantly lower

compared S+ trials and to Control S- trials. (B) Sample touch latency for S- trials in the Strong Fear group was

significantly higher compared S+ trials and to Control S- trials. (C) Choice touch latency did not statistically differ

between groups, but choice touch latency for S- trials pooled over groups was significantly higher than S trials.

(D) Reward collection latency for S- trials in the Strong Fear group was significantly higher compared S+ trials

and to Control S- trials. Sizes 30 and 100 denotes non-ambiguous cue S- and S+.

A B

D C

- 56 -

On the other hand, foot-shock treatment did not affect non-ambiguous stimuli

discrimination and action latencies. Post hoc analysis of the ANOVA on the previous paragraph

showed no significant difference between control and Shock for sample touch, choice touch, or

reward collection latencies. Finally, it is important to note that there was an overall square size

(S+, S-) effect for all parameters, confirming that difference in valence between S+ and S- were

preserved.

In summary, we observed no effect of strengthening fear memory on interpretation.

Strong fear memory also prevented mice from completing the interpretation bias probe test.

Delivering a foot shock also did not affect interpretation bias or action latencies. Animals that

were subjected to recall of a strong fear memory only completed one out of three phases of the

interpretation bias probe, precluding consideration of fluctuation in reward expectation across

phases and construction of an averaged interpretation bias curve. Careful analysis of action

latency data however showed stronger fear memory recall reduced motivation when square

stimulus either predicted a small reward (S-, size 30 square), or during select ambiguous

squares close in size to S- (40, 55). Interestingly, time to touch subsequent flanking stimuli

(choice touch latency) was only delayed during same select ambiguous square sizes but not

during S-.

- 57 -

4.4 Strong fear memory does not affect interpretation bias


Since recall of a strong fear memory precluded completion of three interpretation bias

probes, we devised a new interpretation bias probe to promote completion of all three probe

tests (see Methods 2.5: Probe Paradigm 3). We removed the 3 additional ambiguous square

sizes (40, 50, 60), leaving five ambiguous square sizes as in the first experiment. This increased

the rewarded to unrewarded trial ratio from experiment 2 and should reduce any demotivating

effects of encountering more ambiguous trials interpreted to give small reward as opposed to

large reward. We pseudo-randomized the order between rewarded and unrewarded

ambiguous trials such that unrewarded ambiguous trials could not appear consecutively. This

should have further reduced any demotivating effects of encountering consecutive trials

without reward. As we have previously alluded to in section 3.6 Probe Paradigm 3 of the

Materials and Methods, increased frequency of smaller sized squares between probe phases

(e.g., phase 1 ends with square sizes 55, 45, 30 and phase 2 starts with square sizes 30, 45, 55)

may bias reward expectation of ambiguous square tests that follows. As such, the order of

stimuli that appeared in the first probe was repeated exactly for the second and third probe.

Since the Strong Fear group would have taken a much longer time to complete all three probes

compared to Control group, we have removed the 10 minute inter-probe interval to minimize

any diminishing effects of the fear memory recall that may occur immediately during the probe

tests.

- 58 -

Utilizing the new probe paradigm that we designed, we again tested the effect of strong

fear memory recall on interpretation bias. The experimental design was identical to the

previous experiment with the only difference being the interpretation bias probe paradigm.

Briefly, mice were given three CS/US pairings followed by a 1 minute exposure to the same tone

CS the following day in a new context. Immediately after the end of 1 minute tone exposure,

mice were probed for interpretation bias using the new paradigm.

Animals in the Strong Fear group completed all three probes using the new

interpretation bias probe paradigm 3, but again there was no effect of fear on interpretation

(Figure 4.13). Figure 4.13D shows the average large reward expectation response across three

probes. There was no statistically significant difference between control and Strong Fear groups

(Treatment: F1,31 = 1.46, p>0.05; Square Size: F6,186 = 112.99, p<0.05; Treatment x Square Size:

F6,186 = 0.25, p>0.05).

- 59 -

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Figure 4.13 | Recall of strong fear memory did not induce significant interpretation

bias. Control and Strong Fear treatment groups were probed for interpretation bias

using probe paradigm 3 in three repeated phases: (A) phase 1, (B) phase 2, (C) phase 3.

To account for choice variability across three phases, average response for each mice

were scored (D). Sizes 30 and 100 denotes non-ambiguous cue S- and S+.

A B

D C

- 60 -

Interestingly however, recall of a strong fear memory induced drastic demotivation

effects and a specific increase in time to choose final reward expectations when animals were

presented with ambiguous information. Reduced motivation in the Strong Fear group was

evidenced by the trend of increased sample touch latencies for all square sizes (Figure 4.14A).

Statistically significant difference between groups was observed via ANOVA (Treatment: F1,31 =

10.32, p<0.05; Square Size: F6,186 = 10.34, p<0.05; Treatment x Square Size: F6,186 = 5.53,

p<0.05). Post-hoc analysis showed that increase in sample touch latency in the Strong Fear

group is only significant at S- trials (p<0.05). The Strong Fear group also showed a specific

increase in choice touch latency only for ambiguous trials (Figure 4.14B). ANOVA showed

significant difference between groups (Treatment: F1,31 = 8.80, p<0.05; Square Size: F6,186 = 4.72,

p<0.05; Treatment x Square Size: F6,186 = 2.2, p<0.05). In contrast to sample touch latency, post-

hoc analysis of choice touch latency showed significant increase in latency for ambiguous

square size 65 (p=0.05). Figure 4.14C and 4.14D are both representations of action latencies

with ambiguous trial action latencies averaged across all ambiguous square sizes. ANOVA of

sample touch latency in Figure 4.14C showed similar results as 4.14A (Treatment: F1,31 = 9.92,

p<0.05; Square Size: F2,62 = 10.83, p<0.05; Treatment x Square Size: F2,62 = 6.27, p<0.05) with

post-hoc analysis showing that sample touch latency for S- trials in the Strong Fear group is

significantly higher than all other conditions (p<0.05). Likewise, ANOVA of choice touch latency

in Figure 4.14D showed significant between-group differences (Treatment: F1,31 = 9.17, p<0.05;

Square Size: F2,62 = 17.05, p<0.05; Treatment x Square Size: F2,62 = 7.22, p<0.05) with post-hoc

analysis showing significant increase in choice touch latency in the Strong Fear group for

ambiguous trials compared to all other conditions.

- 61 -

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Figure 4.14 | Action latencies of Strong Fear group differed from Controls during interpretation bias

probe paradigm 3. There was a trend in increased (A) sample touch latency in the Strong Fear group.

Sample touch latency was increased significantly only during S- trials in Strong Fear group. There was

also a trend in increased (B) choice touch latency in the Strong Fear group only during ambiguous

trials. Choice touch latency in Strong Fear group was increased significantly compared to Controls at

ambiguous square size 65. Modified representation of (A) and (B) by pooling all ambiguous sized

square into one variable was depicted in (C) for sample touch latency and (D) for choice touch

latency. Sizes 30 and 100 denotes non-ambiguous cue S- and S+.

A B

D C

- 62 -

To test whether strong fear memory recall effects regular stimulus discrimination as in

the drAT task, we also analyzed choice accuracy and action latencies only for S+ and S- trials

during the interpretation bias probe test. In contrast to interpretation bias test 2 where choice

accuracy was impaired due to fear, choice accuracies for regular S+ and S- trials (Figure 4.15A)

were not impaired during this test (Treatment: F1,31 = 0.73, p>0.05; Square Size: F1,31 = 0.41,

p>0.05; Treatment x Square Size: F1,31 = 0.11, p>0.05). ANOVA of sample touch latency revealed

significant difference between Control and Strong Fear (Treatment: F1,31 = 9.20, p<0.05; Square

Size: F1,31 = 9.84, p<0.05; Treatment x Square Size: F1,31 = 6.29, p<0.05), with post-hoc analysis

showing significant increase in S- sample touch latency for Strong Fear group compared to

controls indicating impairment of motivation for S- trials. There were no significant between-

group differences for choice touch latency (Treatment: F1,31 = 1.33, p>0.05; Square Size: F1,31 =

14.10, p<0.05; Treatment x Square Size: F1,31 = 1.57, p>0.05) and reward collection latency

(Treatment: F1,31 = 2.90, p>0.05; Square Size: F1,31 = 17.16, p<0.05; Treatment x Square Size: F1,31

= 0.49, p>0.05). Overall, there was an overall square size (S+, S-) effect on sample, choice, and

reward collection latency, suggesting that S+ and S- still differ in valence during the

interpretation bias probe test.

- 63 -

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Figure 4.15 | Choice accuracy and action latencies for non-ambiguous S+ and S- trials during

interpretation bias probe paradigm 3. (A) Choice accuracy did not differ between Strong Fear and

Controls. (B) Sample touch latency for S- trials in the Strong Fear group was significantly higher

compared S+ trials and to Control S- trials. S+ and S- sample touch latencies did not differ in

Controls. (C) Choice touch latency did not statistically differ between groups, but choice touch

latency for S- trials pooled over groups was significantly higher than S+ trials. (D) Reward collection

latency did not statistically differ between groups, but reward latency for S- trials pooled over

groups was significantly higher than S+ trials

A B

D C

- 64 -

To summarize, implementation of interpretation bias probe paradigm 3 allowed animals

subjected to strong fear memory recall to complete the test. We showed that strong fear

memory did not induce subsequent interpretation bias. Furthermore, fear activated mice

completed the test despite reduced motivation, as observed from increased sample touch

latencies. Interestingly, strong fear memory drastically increased the time to choose flanking

stimuli only during ambiguous square trials and non-ambiguous S+ or S- square trials. It is

unlikely that the increase in choice latency for ambiguous stimuli suggests reduced motivation

because choice latency for non-ambiguous S- trials remained unaffected. One explanation that

could account for this observation is that increased choice latency signifies increased time

required to appraise the centre square stimulus. Therefore, we hypothesized that specific

increase of choice latency only for ambiguous trials prevented change in interpretation bias

that we may observe due to recall of a strong fear memory. As such, we aimed to specifically

manipulate the conditions during the choice action phase of the task.

As observed in the previous experiment (interpretation bias test 3), time to touch the

centre square stimulus did not differ substantially during ambiguous square presentation

between control and fear activated mice. But after touching the square, time to choose flanking

stimuli was increased in fear activated mice. In interpretation bias probe paradigm 3 (PP3) used

in the previous experiment, mice could hypothetically appraise stimulus-reward value at two

phases of the task paradigm: before touching the centre square stimulus (sample touch phase)

and after, when flanking stimuli are activated (choice action phase). Therefore, the choice

latency increase in the Strong Fear group suggests that appraisal in the choice action phase is

more important in the Strong Fear group compared to controls. We hypothesized that fear

- 65 -

memory activated mice need more time to appraise square stimulus during choice action

phase. Given this further appraisal during the choice action phase, fear memory activated mice

arrived at same interpretation bias profile as controls. Of course, this was under the assumption

that the action phase was indeed crucial for stimulus appraisal. To test both the hypothesis and

assumption, we implemented a modified interpretation bias probe (probe paradigm 4, or PP4)

where after touching the centre square stimuli, the centre stimuli disappeared simultaneously

as the flanking stimuli appeared (see Materials and Methods 3.6 Probe Paradigm 4). This

alteration changed the context during the choice action phase of stimuli-reward appraisal such

that the reference square stimulus was absent during this phase for effective appraisal. The

experimental outline was identical as the previous experiment, with the only difference being

during the interpretation bias probe test where the centre square stimulus disappeared on

touch.

- 66 -

4.5 Probe stimulus during choice action phase is important for stimulus-reward appraisal.


When both control group (PP4-Control) and fear activated group (PP4- Strong Fear)

were probed in probe paradigm 4, both groups showed an identical shift of choices towards

lesser expectation of large reward during ambiguous trials compared to corresponding groups

(PP3-Control, PP3-Strong Fear) in the previous probe paradigm (Figure 4.16). ANOVA revealed

significant between- and within-group factor differences (Treatment: F3,47 = 11.78, p<0.05;

Square Size: F6,282 = 92.85, p<0.05; Treatment x Square Size: F18,282 = 3.69, p<0.05). Post-hoc

analysis showed that large reward expectation between PP4-Control and PP4-Strong Fear are

both significantly different from PP3-Control and PP3-Strong Fear, specifically at square sizes 65

and 75 (p<0.05). Additionally, large reward expectation was also significantly different between

PP4-Strong Fear and both PP3-Control and PP3-Strong Fear at square size 85 (p<0.05).

- 67 -

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Figure 4.16 | Reward expectation shifted towards lesser large reward expectation

when animals were probed with probe paradigm 4 (PP4). Reference square stimulus

was removed during choice action phase in trials of PP4. There were no differences

between Strong Fear group and Controls in PP4. There was a trend towards lower large

reward expectation in Controls of PP4 compared to Controls of PP3. Large reward

expectation was significantly lower at square size 65 for Controls of PP4 compared to

PP3. Sizes 30 and 100 denotes non-ambiguous cue S- and S+.

- 68 -

Although probe paradigm 4 (PP4) changed the large reward expectation profile in the

probe, action latency profiles (Figure 4.17A and B) did not differ from PP3. ANOVA revealed

significant between- and within-group factor differences for sample touch latencies (Treatment:

F3,47 = 4.36, p<0.05; Square Size: F2,94 = 16.50, p<0.05; Treatment x Square Size: F6,94 = 3.75,

p<0.05). Post-hoc analysis however, showed no differences between the same treatment

conditions with different probe paradigms (p>0.05), i.e., PP3-Strong Fear did not differ from

PP4-Strong Fear and PP3-Control did not differ from PP4-Control. Difference between Control

and Strong Fear groups were the same as before with sample touch latency for PP4-Strong Fear

and PP3-Strong Fear significantly different from PP4-Control an PP3-Control groups only during

S- (square size 30) trials (p<0.05). ANOVA also showed significant between- and within-group

factor differences for choice touch latencies (Treatment: F3,47 = 3.34, p<0.05; Square Size: F2,94 =

18.58, p<0.05; Treatment x Square Size: F6,94 = 2.69, p<0.05) with post-hoc analysis again

showing no difference between probe paradigms (p>0.05) and significant choice touch latency

differences between treatment conditions only for ambiguous trials (p<0.05)

It is also important to emphasize that the shift towards lesser expectation of large

reward outcomes was not specific to ambiguous square trials (Figure 4.16). Closer analysis of

non-ambiguous S+ and S- trials within the probe showed that accuracy for S+ trials dropped

significantly for mice probed in PP4 (Figure 4.17C). ANOVA revealed significant between- and

within-group factor differences (Treatment: F3,47 = 10.70, p<0.05; Square Size: F1,47 = 19.26,

p<0.05; Treatment x Square Size: F3,47 = 8.83, p<0.05) with post-hoc analysis showing significant

difference between PP3 groups and PP4 groups only during S+ trials (p<0.05).

- 69 -

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1525

45

65

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ple

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s)

S- Ambiguous S+0

2

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Figure 4.17 | Action latencies did not differ between animals probed in probe paradigm 3 and

probe paradigm 4, but choice accuracies differed between the two paradigms. (A) Sample touch

latency and (B) choice touch latency did not differ between PP3 and PP4. Sample touch latency in

PP4 maintained the same trend, as in PP3, of increase in the Strong Fear group, with significance

during S- trials. Choice touch latency in PP4 also maintained the same profile of specific significant

increase choice touch latency during ambiguous trials but not S+ or S- trials for Strong Fear group.

(C) Choice accuracy for S+ trials was impaired for both Strong Fear group and Control probed in

PP4, compared to accuracies of PP3. Sizes 30 and 100 denotes non-ambiguous cue S- and S+.

A B

C

- 70 -

In summary, we have developed a probe paradigm to examine the effect of fear

memory recall on interpretation of ambiguous stimuli. This test confirms the assumption that

the square stimulus during choice action phase was crucial for stimulus appraisal, since choice

accuracy for non-ambiguous stimuli was impaired in control mice. Specifically, implementation

of this new probe paradigm caused both control and fear activated group to perform flanking

stimulus choices that represented expectation of lower reward outcome for ambiguous squares

and non-ambiguous S+. Since choice accuracies were impaired in PP4 however, we couldn’t test

the hypothesis that fear activated mice needd more time to appraise square stimulus during

choice action phase compared to controls. This was because the interpretation bias profile for

control mice have changed, suggesting impairment of non-ambiguous stimuli interpretation.

We hypothesized that the control group’s impairment of square stimuli interpretation

was due the animals not being habituated to perform a task where the centre square stimulus

is removed during the choice action phase. To test this hypothesis and retest the previous

hypothesis in the most recent experiment, we added a new phase to the drAT task, whereby

the centre square stimulus disappears during the choice action phase similar to PP4. This new

phase was called lct-drAT. We observed that in the previous interpretation bias test using PP4,

choice accuracy for control mice decreased. This suggests impairment of non-ambiguous stimuli

interpretation due to removal of square stimulus during choice action phase. Therefore if our

hypothesis was correct, when animals transition from drAT training (square stimulus persists

during choice action phase) to lct-drAT training (square stimulus fades during choice action

phase), their S+ accuracies and/or S- accuracies should decrease. Subsequently, further training

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in lct-drAT should increase S+/S-accuracies such that the accuracies are maintained during the

interpretation bias probe for the control group.

- 72 -

4.6 lct-drAT training reverses impaired stimulus interpretation due to removal of stimulus

during choice action phase.

Mice trained in lct-drAT learned to discriminate S+ and S- with high choice accuracies

without square stimulus during the choice action phase. As before, mice were trained in drAT

until they reached accuracies greater than 85% for both S+ and S- trials. The following day, they

started lct-drAT training. Figure 4.18A shows a decrease in S+ accuracy but not S- accuracy the

day mice began lct-drAT (dotted vertical line on Figure 4.18). ANOVA performed beginning at

training session day 15 when accuracies for drAT had begun to plateau revealed significant

within group differences of square size and training session factors (Square Size: F1,30 = 11.76,

p<0.05; Training Session: F21,630 = 1.86, p<0.05; Square Size x Training Session: F21,630 = 1.23,

p>0.05). Post-hoc analysis revealed a significant drop in S+ but not S- accuracy from session 19

to 20. S+ accuracy continued to drop the following day and began to improve by the third day

of lct-drAT. S- accuracy decreased the day after the start of lct-drAT (p>0.05). This decreased S-

accuracy level was maintained for 7 days before slowly improving back to greater than 85%

accuracy. At the end of lct-drAT training, S+ accuracy sustained a plateau at 90% and S-

accuracy returned to levels above 85%, similar to the last day of drAT.

In addition to changes in choice accuracy following the start of lct-drAT, increase in

correction trials coincided with the drop in accuracy (Figure 4.18B). ANOVA of correction trials

(Training Session: F21,630 = 5.98, p<0.05) showed significant increase at the second day (21st day

of overall training) of lct-drAT training compared to the average number of correction trials at

the last day of drAT (p<0.05). This elevated level of correction trials gradually decreased

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whereby correction trial level at the last day of lct-drAT was significantly lower (p<0.05) than

the second day of lct-drAT and not significantly different from the last day of drAT (p>0.05).

In slight contrast, analysis of sample (Figure 4.18C) and choice (Figure 4.18D) latencies

showed less drastic changes during the transition from drAT to lct-drAT. First, ANOVA of sample

touch revealed significant training session and square size effect (Square Size: F1,30 = 11.67,

p<0.05; Training Session: F21,630 = 1.86, p<0.05; Square Size x Training Session: F21,630 = 1.23,

p>0.05). During the last five days of drAT and first three days of lct-drAT there were no

significant differences between S+ and S- sample touch latencies. On the fourth day however, S-

sample touch latency was significantly longer than S+ (p<0.05). On the following days, S+ and S-

sample touch latencies weren’t significantly different until the thirty third, thirty fourth, and

last day of lct-drAT (p<0.05). In contrast, analysis of choice touch latencies (Square Size: F1,30 =

33.74, p<0.05; Training Session: F21,630 = 1.11, p>0.05; Square Size x Training Session: F21,630 =

0.50, p>0.05) with post hoc showed general differences of higher choice touch latency for S-

trials than S+ trials.

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5 10 15 20 25 30 35 400

10

20

30

Session

# C

orr

ecti

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Tri

als

10 20 30 40

30

40

50

60

70

80

90

100

Session

Ac

cu

rac

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%)

10 20 30 400

10

20

30

40

50

60

100

S+

S-

Session

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s)

10 20 30 400

1

2

3

4

8

12

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Late

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Figure 4.18 | Transition from drat to lct-drAT training was accompanied by transient drop in

choice accuracy and increase in correction trials, while sample and choice touch latencies

underwent minimal changes. (A) Choice accuracy. S+ accuracy significantly decreased on the

first day of lct-drAT compared to the last day of drat. A trend in decreased S- accuracy also

accompanied the transition, but both S+ and S- improved to above 85% within an enxtra 16

training sessions. Increase in (B) correction trials coincided with drops in S+ and S- accuracy

during drAT to lct-drAT transition. (C) Difference in sample touch latency between S+ and S-

occurred on the third day following first day of lct-drAT. (D) Choice touch latency steadily

increased in S- trials compared to S+ trials throughout the experiment. N = 31. Sizes 30 and

100 denotes non-ambiguous cue S- and S+.

A B

D C

- 75 -

Figure 4.18 demonstrates that transitioning from drAT to lct-drAT training by removal of

square stimulus during choice action phase impaired S+ and S- accuracy in the short term. At

the end of lct-drAT training behavioural parameters of choice accuracy, correction trials, sample

and choice touch returned to levels similar to the end of drAT training. Such behavioural

parameters at the end of lct-drAT indicated strong ability to discriminate, with high accuracy,

between non-ambiguous S+ and S- stimuli without square stimulus during choice action phase.

Higher sample and choice touch latencies for S- confirmed that S- stimulus had a less positive

valence. Next, we probed for interpretation bias in these animals habituated to appraise square

stimulus without the stimulus during the choice action phase.

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4.7 Stimulus interpretation with limited appraisal is not affected by strong fear memory

recall.


Following the last day of lct-drAT, mice were divided into two groups: Control and

Strong Fear, in which the Strong Fear group were fear conditioned as before with three tone-

shock pairings. The day after fear conditioning, mice in the Fear group were presented with the

tone briefly for 1 minute and then probed for interpretation bias using probe paradigm 4 (PP4).

Interpretation bias as measured by large reward expectation for ambiguous stimuli was not

affected by recall of a strong fear memory even after mice learn to appraise without the square

stimulus during choice action phase(Figure 4.19) (Treatment: F1,26 = 0.64, p>0.05; Square Size:

F6,156 = 54.3, p<0.05; Treatment x Square Size: F6,156 = 0.8, p>0.05).

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35 45 55 65 75 85 950.0

0.2

0.4

0.6

0.8

1.0

Control (N = 15)



Larg

e R

ew

ard

E

xp

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tio

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Figure 4.19 | Recall of strong fear memory did not induce interpretation bias after

habituating mice to appraise stimulus-reward outcome without reference square

stimulus during choice action phase. After training mice in lct-drAT, subsequent fear

memory recall and probe using the probe paradigm 4 did not reveal any significant

changes in large reward expectation and therefore interpretation bias. Sizes 30 and

100 denotes non-ambiguous cue S- and S+.

- 78 -

Fear memory recall reduced motivation, while increase in time to make a final choice

during ambiguous stimuli presentation persists. Sample touch latency was significantly higher in

the Fear group compared to the Control group (Figure 4.20A) (Treatment: F1,26 = 18.47, p<0.05;

Square Size: F2,52 = 15.4, p<0.05; Treatment x Square Size: F2,52 = 10.52 p<0.05). Post-hoc

analysis revealed that significant difference between groups occured during S- trials (p<0.05).

Sample touch latencies in all other square sizes including S+ did not differ between groups, but

showed a trend of increase in the Strong Fear group. Figure 4.20C pools all ambiguous sized

squares into one variable. ANOVA gave similar results (Treatment: F1,26 = 6.87, p<0.05; Square

Size: F2,52 = 15.32, p<0.05; Treatment x Square Size: F2,52 = 2.10, p>0.05) where post-hoc showed

that sample touch latency was increased significantly for S- trials. ANOVA across all square sizes

for choice touch latency showed significantly higher choice touch latency in the Fear group

(Figure 4.20B) (Treatment: F1,26 = 4.76, p<0.05; Square Size: F6,156 = 3.02, p<0.05; Treatment x

Square Size: F6,156 = 0.89, p>0.05). Post-hoc analysis did not show differences between Control

and Fear at specific square sizes. However, ANOVA of pooled ambiguous square sizes into one

variable (Treatment: F1,26 = 6.87, p<0.05; Square Size: F2,52 = 15.33, p<0.05; Treatment x Square

Size: F2,52 = 2.10, p>0.05) showed that choice touch latency was significantly increased in the

Fear group only for ambiguous trials (Figure 4.20D).

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35 45 55 65 75 85 950

10

20

3040

80

Control (N = 15)



Sam

ple

To

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Late

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s)

S- Ambiguous S+0

20

40

60

80

100

Control (N = 15)


Square Size

Sam

ple

To

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Late

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s)

S- Ambiguous S+0

2

4

6

8

10

Square Size

Ch

oic

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Late

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s)

35 45 55 65 75 85 950

5

10

15

20


Ch

oic

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ou

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Late

ncy (

s)

Figure 4.20 | Action latencies shared the same profile as previous experiments with PP3 and

PP4. (A) Sample touch latency was increased significantly during S- trials, with a trend of

increase for other square sizes in the Strong Fear group. (B) Choice touch latency had a trend

of increase in the Strong Fear group only during ambiguous trials. Modified representation of

(A) and (B) by pooling all ambiguous sized square into one variable is depicted in (C) for

sample touch latency and (D) for choice touch latency. (C) and (D) depicts significant increase

in sample touch latency for S- trials, and significant increase in choice latency for ambiguous

trials. Sizes 30 and 100 denotes non-ambiguous cue S- and S+.

A B

D C

- 80 -

Stimulus discrimination of regular S+ and S- stimulus during this interpretation bias test

was not impaired and difference in valence was preserved. Although S- accuracy (Figure 4.21A)

was significantly higher than S+ (Treatment: F1,26 = 1.61, p>0.05; Square Size: F1,26 = 5.92,

p<0.05; Treatment x Square Size: F1,26 = 0.57, p>0.05), accuracies of ~85% suggested strong

ability to discriminate between S+ and S- stimuli. Sample touch latency (Figure 4.21B) analysis

again showed that sample touch latency were different between treatment groups (Treatment:

F1,26 = 15.31, p<0.05; Square Size: F1,26 = 20.48, p<0.05; Treatment x Square Size: F1,26 = 13.37,

p<0.05), with post-hoc showing that S- sample touch latency significantly higher than all other

conditions. This confirms our finding that fear memory recall reduced motivation during S-

trials. Analysis of choice touch latency revealed no difference between treatment groups

(Treatment: F1,26 = 2.04, p>0.05; Square Size: F1,26 = 6.50, p<0.05; Treatment x Square Size: F1,26

= 2.84, p>0.05) and post-hoc of Square Size showed significant difference between S+ and S-

trials. Finally, reward latencies do not differ between groups (Treatment: F1,26 = 3.44, p>0.05;

Square Size: F1,26 = 21.61, p<0.05; Treatment x Square Size: F1,26 = 0.12, p>0.05) and post-hoc of

Square Size showed significant difference between S+ and S- trials. Together, higher overall

action latencies for S- trials indicated a less positive valence compared to S+ trials.

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S- S+0

20

40

60

80

100

Accu

racy (

%)

S- S+0

20

40

60

80

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Figure 4.21 | Choice accuracy and action latencies for non-ambiguous S+ and S- trials

during interpretation bias test 5. (A) Choice accuracy did not differ between Strong Fear

group and Controls and accuracies were at or above 85%. (B) Sample touch latency for S-

trials in the Strong Fear group was significantly higher compared S+ trials and to Control S-

trials. S+ and S- sample touch latencies did not differ in Controls. (C) Choice touch latency

did not statistically differ between groups, but choice touch latency for S- trials pooled over

groups was significantly higher than S+ trials. (D) Reward collection latency did not

statistically differ between groups, but reward latency for S- trials was significantly higher

than S+ in both Control and Strong Fear group.

A B

D C

- 82 -

Chapter 5

Discussion

- 83 -

Chapter 5. Discussion

5.1 Summary of Results

In this study, we implemented a novel visual-based paradigm to test the effect of fear

on interpretation bias. We demonstrated that mice were able to discriminate between two

distinct stimuli, predicting either a positive or less positive outcome. When presented with a

range of ambiguous stimuli intermediate in size between the two distinct, non-ambiguous

stimuli, animals’ interpretation of outcome expectation varied on a gradient depending on

whether ambiguous stimulus closely resembled stimulus predicting positive or less positive

outcome. In effect, we were able to demonstrate an interpretation bias profile from outcome

predictions of ambiguous stimuli in mice. Subsequently, we observed through response

latencies and final reward expectation that strong fear memory recall reduced motivation to

perform interpretation bias tests, reduced speed to interpret ambiguous information, but did

not affect final interpretation of ambiguous information. We also showed that mice can learn to

interpret ambiguous information effectively without sustained presentation of stimulus. Finally,

we did not find evidence that fear memory recall affected the ability to interpret when stimulus

presentation was not sustained during interpretation process.

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5.2 drAT paradigm

The first aim of this study was to establish a novel, visual-based murine paradigm to test

interpretation bias. Our current design relied on the ability of mice to discriminate between

two stimuli (S+ vs S-) and their respective rewards (high vs low). Test of interpretation bias then

followed by presenting stimuli intermediate of S+ and S- and scoring its association with either

the high reward or low reward. Indeed, this type of design where animals first learned to

discriminate between two stimuli based on the stimuli itself (e.g., size, smell, or sound) and its

association with an outcome (e.g., different reward values or punishment) was typically used in

previous non-visual based interpretation bias studies done in rat or mouse systems (Boleij et

al., 2012; Enkel et al., 2010; Harding et al., 2004; Papciak, Popik, Fuchs, & Rygula, 2013). To

establish measurable discrimination ability, odour- (Boleij et al., 2012) and auditory-based

paradigms (Enkel et al., 2010; Harding et al., 2004) trained rodents to discriminate between two

different odours (e.g., vanilla vs apple) or frequencies of tones (2 vs 9 kHz) and their associated

appetitive (positive) or aversive (negative) outcomes. This was then followed by tests of

interpretation bias through presentation of odour mixtures (e.g., 1:1 vanilla to apple

concentration mixture) or intermediate tone frequencies (3, 5, or 7 kHz) and scoring

associations with appetitive (food) or aversive outcomes (footshock or bad tasting food).

To this extent, I have used a previously established visual-based discrimination paradigm

in our lab (Botly et al. 2012; unpublished) where mice discriminated between large (S+) and

small (S+) singly presented white square with the correct touch of flanking stimuli (Figure 4.1)

and corresponding reward outcomes (S+: high milkshake reward; S-: low milkshake reward). We

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showed that mice successfully learn to discriminate between S+ and S- stimuli based on reward

outcomes. Auditory paradigms have used criteria of 50% (Harding et al., 2004) to 70% (Enkel et

al., 2010) choice accuracy, odour paradigms have used statistically different reward collection

times (Boleij et al., 2012), and tactile paradigms (different textures of sandpaper) have used

75% choice accuracy over consecutive days (Brydges, Leach, Nicol, Wright, & Bateson, 2011).

We demonstrated that mice trained in our visual drAT paradigm exceeded performance of

these other studies through higher discrimination accuracies sustained over consecutive days.

In effect, choice of flanking stimulus made by animals used in our studies at the end of drAT

discrimination training had very low error rates. This allows us to minimize error in choosing

reward expectation during ambiguous stimuli interpretation. Our high accuracy rate may well

be due to our longer training duration compared to other studies in terms of days of training.

Nevertheless, it is striking that mice were able to achieve high competency in this visual task of

attending to a visual stimulus and responding via nose-pokes or touch on a computer generated

touch-screen.

Response latencies in our visual discrimination paradigm served as an indicator of

stimulus valence. We demonstrated difference in valence for S+ stimulus compared to S-

during drAT through faster response latencies during S+ trials compared to S-. This is in

accordance with previous studies in rodents using auditory-based paradigms where time to

press the lever to obtain a food pellet in response to hearing the S+ tone was faster than to

press the lever to avoid a foot-shock after an S- tone (Enkel et al., 2010; Harding et al., 2004). In

odour-based paradigms, experimenters have used the time to collect the food reward in

response to smelling S+ (e.g., vanilla) or S- (e.g., apple) odour as measure for the valence of

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odour stimuli (Boleij et al., 2012). In spatial judgement discrimination paradigm for

interpretation bias, experimenters used the time to arrive at rewarded location as a measure of

valence for the spatial location (Oliver H.P. Burman et al., 2008). Indeed, time to collect food

reward was faster for S+ odour and time to arrive at a specific spatial location was faster if

animals were rewarded versus not rewarded. It is also important to note that the response

latency difference in our visual discrimination paradigm was sustained throughout each action

phase of the task: touch of the centre square, flanking stimulus, and reward collection. This

suggests that the different values of S+ and S- stimulus were fully reflected through all phases

of a trial within our training paradigm.

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5.3 Establishment of interpretation bias probe tests

Throughout the study, we developed four slight variations of interpretation bias probe

paradigms to ultimately test the effect of fear on interpretation bias. The first modification of

the probe paradigm was designed to accommodate completion of the probe task when mice

were subjected to stronger fear training. The second modification was to minimize potential

confounds due to unrewarded ambiguous trials. The final modification involved balancing the

number of more positive and less positive ambiguous stimuli. Since interpretation bias probes

were carried out after fear memory recall, we removed inter-probe intervals as part of the final

probe modification to reduce potential diminishing effects of fear after the recall. Modifications

aside however, our general design that encompasses all variations of our interpretation bias

tests carries a number of distinct advantages compared to designs in other studies.

First, our design is the first visual-based rodent touchscreen interpretation bias

paradigm. As discussed in the Background and Literature Review, the automated nature of the

touchscreen platform reduces experimental variability, and allows high volume of long-term

animal testing. Additionally, the visual nature of the task provides us with results that are more

readily inferable to human subjects. To date, only one study has used mice in an odour-based

interpretation bias paradigm (Boleij et al., 2012). The majority of rodent interpretation bias

studies have been in rats, implementing auditory (Enkel et al., 2010; Harding et al., 2004;

Rygula, Papciak, et al., 2014; Rygula et al., 2012) and spatial (Oliver H P Burman et al., 2009;

Oliver H.P. Burman et al., 2008) based paradigms. However, our study is not the first non-

human visual interpretation bias study. Studies in avian systems using chicks (Salmeto et al.,

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2011) and starlings (Brilot et al., 2010) have used interpretation of ambiguous visual stimuli to

probe interpretation bias.

Second, our paradigm directly measures reward expectation in addition to multiple

response latencies (sample touch, choice touch, reward collection latencies). Measurement of

reward expectation by scoring interaction with a choice stimulus allowed us to dissociate

interpretation of ambiguous stimuli from motivation to perform the task. Previous non-human

interpretation bias studies have often used response latencies as a final measurement of

interpretation. These response latencies included time to collect reward (Boleij et al., 2012),

time to approach a rewarded location (Oliver H P Burman et al., 2009; Oliver H.P. Burman et al.,

2008), time to approach a visual stimulus (Salmeto et al., 2011). Since response latencies in

these studies typically measured the first physical action performed by the animal subject, our

sample touch response latency parameter would be the most analogous. In previously

mentioned examples of studies that used response latencies as a measurement of

interpretation, shorter response latencies indicated positive bias and longer latencies indicate

negative bias. However, these designs using response latency as the final measurement of

interpretation bias lacks the ability to completely dissociate between interpretation and

motivation to respond or perform the task (Mendl et al., 2009). The modified auditory based

paradigm by Enkel et al. (2010) in rats seem to currently be the most popular method as it

measures both response latency to respond and active choice via choice lever presses. As such,

our design improves from designs that do not employ active choice action during ambiguous

interpretation.

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We were able to show in control animals during our interpretation bias tests that all

three response latencies (sample touch, choice touch, and reward collection latency) were

slower for non-ambiguous stimulus predicting a low reward compared to large reward. This

suggests that differing valence of non-ambiguous stimuli was reflected by response latencies.

This is in accordance with other interpretation bias studies that suggested lower latencies

reflected increased expectation of reward and therefore drive to perform the task (Mendl et al.,

2009; Paul et al., 2005). Accordingly, studies in rats and non-rodent systems repeatedly showed

a gradient of decreasing (faster) response latency as ambiguous stimuli resembles closer to S+.

In contrast, in our study there were no differences in either sample or choice touch response

latencies among ambiguous stimuli. This tells us that in our case, although response latencies

between non-ambiguous stimuli reflected the valence of stimulus, response latencies for

ambiguous stimuli did not.

The discrepancy between our ambiguous stimuli response latency behaviour compared

to other studies could be due to the outcome paired with S- stimulus. S- stimulus in most non-

human/rodent systems had aversive outcomes (e.g., foot shock or quinine soaked food),

whereas ours had a less positive and non-aversive outcome (lesser reward amount). Based on

the common result of gradient decrease in response latency as ambiguous stimulus approaches

S+, we can infer that the more closely ambiguous stimuli resembled S+, the more motivated

animals’ were to choose in order to obtain a reward as opposed to avoid possible S- outcome.

In other words, animals were driven by reward reinforcers, and not by fear of punishment. This

was especially the case for the auditory paradigm by Enkel et al. (2010), in which one of two

lever presses was a response to avoid impending foot-shock after S-. If animals in Enkel’s study

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were motivated by avoidance of aversive outcome, response latencies would have been faster

for S- stimulus than S+ (food reward).

In contrast, our system did not use aversive outcomes since S+ and S- were both

reinforced by reward. Therefore, the graded decrease in response latency was not required

because both stimuli predicted a reward. Indeed, this suggests that animals were motivated to

perform the task, independent of reward outcome, to the same degree for all ambiguous

stimuli. As such, our paradigm does not determine expected outcome of ambiguous stimuli

from response latencies.

Although our design does not determine expected outcome of ambiguous stimuli from

response latencies, it does so by scoring animal subjects’ choice association between

ambiguous stimuli and expected reward. Indeed, as ambiguous stimuli shift from closely

resembling S- towards S+, expectation for positive outcome gradually increases. In other words,

reward expectation, but not response latency, is correlated with degree of ambiguity. It is also

important to note that the reward expectations of mice were observed to follow a

psychometric function (Wichmann & Hill, 2001). This type of sigmoid function was commonly

observed in human and non-human primate studies of visual acuity using the two-alternative

forced choice (2AFC) task, where subjects choose the direction (left or right) of a synchronously

moving subset of dots within a random dot motion field (Gold & Shadlen, 2000; Mckee, Klein, &

Teller, 1985). Interestingly, studies of discrimination of auditory tone frequency (Stevens &

Volkmann, 2014; Traunmuller & Eriksson, 2014), as well as visual quantity (Dehaene, Izard,

Spelke, & Pica, 2008) in humans have suggested a logarithmic function in stimulus

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discrimination. Indeed, rodent auditory interpretation bias paradigms have differed ambiguous

stimuli by a logarithmic change (Enkel et al., 2010) in auditory tone frequencies. We showed

however that, for the set of square sizes used in this study, discrimination of square stimuli

varying in size based on a linear change in length did not follow a logarithmic function.

Therefore, future visual interpretation bias test designs using shapes such as squares may

consider differing the size of the ambiguous stimuli based on a simpler linear change. We

also acknowledge that pairing of square size with subsequent correct flanking stimulus to

deliver reward was not counterbalanced in this study. Therefore, future implementation of our

experimental design should consider training a cohort of mice to associate S+ square stimulus

with subsequent touch of left flanking stimulus to activate large reward, and S- stimulus with

subsequent touch of right flanking stimulus for a small reward.

Overall, our design measured the animals’ reward expectation through choice, while still

measuring motivation to perform the task under situations of uncertainty through response

latencies. Therefore, we have established a viable visual-based interpretation bias test in which

we can measure an animal subject’s expectation of positive or less positive outcomes under

uncertainty.

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5.4 Fear memory recall slowed stimulus appraisal, but does not affect interpretation bias

The second aim of this study was to test the effect of fear memory recall on

interpretation bias using the probe tests discussed in the previous section. In this study we

observed that two levels of fear memory recall did not induce changes in final choice of reward

expectation for ambiguous stimuli. We found however, that strong fear memory recall induced

an increase in sample touch latency within probe tests for both non-ambiguous and ambiguous

stimuli, suggesting reduced motivation to perform the task. More interestingly, fear memory

recall increased choice latency only for ambiguous sized square stimuli.

Fear reduced motivation and increased time to choose reward expectation

We have previously mentioned that our interpretation bias paradigm was designed in

such a way that we can measure the final choice of reward expectation in addition to action

latencies to dissociate between final interpretation bias and motivation or drive to perform the

task. Overall we found that fear did not induce choice bias but instead induced increases in

sample touch latency for ambiguous and non-ambiguous stimuli, and choice touch latency

specifically for ambiguous stimuli. These observations were sustained even after we removed

the square stimulus during the choice action phase of the interpretation bias probe.

Interpretation bias studies in non-human species have often used approach/action

latency as the measure of interpretation in such a way that shorter latencies indicated

interpretation of a relatively positive outcome compared to longer latencies and negative

outcomes (Boleij et al., 2012; Oliver H P Burman et al., 2009; Harding et al., 2004). More

specifically, authors of these studies argued that action latencies were reflected reward

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expectation. The argument that action latency differences reflect interpretation bias for these

studies may have been justifiable because when animals in treatment groups were presented

with ambiguous stimuli, actions latencies were intermediate between S+ and S- stimulus.

Response latencies were typically higher when responding to S- stimulus compared to S+ for

control and treatment groups. Treatment group latencies being higher compared to controls in

at least one ambiguous stimulus indicated a negative interpretation bias. It is important to note

however that fear memory recall treatment was not used in these studies. Instead, these

studies used animal strains that exhibit heightened anxiety-like behaviour, induced anxiety-like

behaviour through lighting conditions, or induced depressive-like symptoms via unpredictable

housing conditions.

The measurement in our paradigm that would be most analogous to action latencies

used in the studies discussed in the previous paragraph is the sample touch latency. This is

because it is the first approach behaviour that our mice must perform within a probe trial.

Unlike the results of aforementioned studies, sample touch latencies in our studies were

indicative of motivation as opposed to interpretation bias. This is because sample touch

latencies did not correlate with size of ambiguous stimuli even though latencies increased

within our treatment group.

In our study, we observed that fear recall induced a general increase in sample touch

latency for all stimuli, with a more pronounced effect during S- stimulus which predicted low

reward. More importantly, sample touch latencies for ambiguous stimuli did not correlate with

size of stimuli. This pattern of results suggests that again, sample touch latencies were not

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indicative of interpretation. Instead, a plausible explanation for the non-gradient increase in

sample touch latency would be that fear activated mice experienced a general anhedonia

effect, which is a decrease in motivation to perform the task. Another plausible and simpler

explanation could be that the freezing effects of the tone reminder persisted throughout

performance of the interpretation bias test. If this was the case, sample touch latency would be

predicted to be at similarly elevated levels independent of the type of square stimulus

presented (ambiguous or non-ambiguous). However, fear memory recall was demonstrated to

induce an exceptionally high sample touch latency when the square stimulus non-ambiguously

predicts a low reward outcome. In this way, the milkshake reward reinforcers may have been

less motivating for the animals due to fear memory recall, but even more so when stimulus

non-ambiguously predicted a low reward outcome (S-). Taken together, sample touch latency in

our study was a measure of motivation to perform the task, but was not an effective parameter

to measure interpretation of ambiguous stimuli and their reward outcomes.

In addition to sample touch latency, which is analogous to approach behaviour

commonly measured in other studies, we measured choice latency: time between touching the

centre square stimulus and touching a flanking stimulus. We found a striking effect of fear recall

in increasing choice latency specifically for ambiguous stimuli. Since choice latency for non-

ambiguous stimuli was unaffected, it suggests that anhedonia did not affect general drive or

motivation to perform the action of choosing reward expectation. Furthermore, it is unlikely to

be the case that it was avoidance behaviour since non-ambiguous stimuli choice latencies were

not affected. Additionally, the finding that increases in choice latency for ambiguous stimuli did

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not differ from one another further suggests that choice latency for ambiguous stimuli did not

depend on the size of the stimuli either.

We demonstrated that through our choice latency data, fear memory recall ultimately

slowed the speed at which mice arrive at their final choice, but appraisal ability and therefore

final choices made were not affected by the recall. First, we found that the reference square

stimulus during the choice action phase was necessary for effective appraisal. This was because

discrimination accuracy in controls was impaired without the stimulus during choice action

phase. However, discrimination accuracy and interpretation was no different in the fear

treatment group. Furthermore, fear memory recall also did not induce interpretation bias when

mice had learned to discriminate non-ambiguous stimuli effectively without the reference

square stimulus during the choice action phase.

There are a few explanations that could account for our observed reduction in speed to

make the final choice in mice which were reminded of a fearful event. Mice may be impaired in

attention capabilities due to stress effects of fear conditioning leading to slower reaction time

(Bondi, Rodriguez, Gould, Frazer, & Morilak, 2008). For example, mice chose from one of two

flanking stimuli during the choice action phase. Attention from one targeted flanking stimulus

could be distracted by attention towards the other flanking stimulus, thus increasing choice

latency. However, this explanation does not account for absence of speed impairment for non-

ambiguous stimuli. A trait that is common to ambiguous stimuli, independent of square size, is

that they were novel in comparison to non-ambiguous stimuli during the interpretation bias

test. As such, fear memory recall could have induced heightened attention towards to the

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novelty of ambiguous stimuli. However, this explanation does not account for the same

increase of choice latency even when stimulus was removed during the choice action phase.

Therefore, another explanation could be the notion that in a decision making task, such as the

one we employed in this study, increased reaction time is required for accuracy when a

problem difficulty is increased (Uchida, Kepecs, & Mainen, 2006; Uchida & Mainen, 2003).

Assuming that presentation of ambiguous stimuli posed a choice task of higher ‘difficulty’, what

we may have observed was that fear memory recall increased the reaction time such that their

final decisions would be ‘accurate’, i.e. similar to controls. To test this, we can limit the duration

of the choice action phase to the average time it took for controls to choose. Consequently, we

can then hypothesize that limiting duration of choice action phase would cause fear memory

recall to impair interpretation of ambiguous stimuli and therefore induce a change in

interpretation bias.

Final choice selection

Despite fear-induced alterations in action latencies to perform the interpretation bias

probe test, fear did not induce changes in final reward expectations compared to controls

suggesting no effect on interpretation bias. Since we have tested two levels of fear conditioning

protocols, one being significantly stronger, it is unlikely that a stronger fear conditioning

protocol (i.e., more tone-shock pairings and/or higher shock intensity) would be needed to

induce interpretation bias. Given the exhaustive approach employed here, with varying

intensities of fearful stimuli and designs of probe tests, I conclude that acute states of

fearfulness do not confer change in interpretation bias for reinforcing stimuli. My studies

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provide the first evidence that acute fear does not change interpretation bias. These results are

particularly compelling, given the consistent effect of anxiety states to induce negative biases.

There are a few alternative explanations for the lack of interpretation bias observed

here. First, our fear treatment protocol was an acute protocol. Although other animal

interpretation bias studies did not test fear, those that showed a change in interpretation bias

used chronic stress protocols (Enkel et al., 2010; Papciak et al., 2013). In the same way as long-

term stress may cause the required changes in brain regions important in decision making

(Ernst & Paulus, 2005), we may need to implement longer-term fear conditioning protocols

such as repeated fear conditioning. Long-term treatment however may induce multiple

psychological effects due to stress including chronic anxiety (Bondi et al., 2008) and/or

depressive-like states (Anisman & Matheson, 2005), as well as heightened anhedonia

associated with depression. Our paradigm controls for anhedonia since mice have to make a

final choice despite the reward outcome of the stimulus. However, a chronic treatment will

impede us from dissociating any changes in interpretation as being due to anxiety, fear, or

depression.

Second, rodent studies that showed interpretation bias via choice association have used

an aversive outcome to reinforce the S- stimulus (Enkel et al., 2010; Papciak et al., 2013; Rygula,

Szczech, Papciak, Nikiforuk, & Popik, 2014). Our paradigm used a lesser reward outcome for S-

and therefore does not have an aversive element overall. As such, animals were driven

primarily by reward-seeking behaviour as opposed to harm avoidance behaviour (Mendl et al.,

2009). Since fear is an adaptive response to threat, it would be favourable to increase

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anticipation of a harmful outcome and therefore respond in a way to avoid the outcome under

situations of uncertainty. Indeed, animals trained in a discrimination task with appetitive and

aversive outcomes are more likely to respond negatively to ambiguous stimuli than animals

trained with two levels of appetitive outcome (high vs low reward) (Hales & Stuart, 2014). As

such, we can hypothesize that fear may exacerbate this likelihood of expecting an aversive

outcome during ambiguous stimulus interpretation. To test this, we can modify our paradigm

such that S- predicts an aversive outcome such as a bad tasting milkshake reward, a high

intensity strobe light activity, or even mild foot-shocks.

It is also important to ensure that future experiments testing the effects of acute fear

memory recall on interpretation bias incorporate consistent comparison of outcome

expectation between fear memory recall group and a fear conditioning group with no fear

memory recall. This would be crucial to dissociate the effects of fear memory recall on

interpretation bias from the effects of shocking mice in a fear conditioning chamber.

Additionally, a positive control group would also be advisable to further validate the paradigm.

Such a group would demonstrate negative interpretation bias. For example, a cohort of mice in

depressive-like states induced by chronic stress should in theory, exhibit negative interpretation

bias in our paradigm.

5.5 Conclusions

My study devised a novel visual-based interpretation bias paradigm and demonstrated

that recall of an acute fear memory did not influence interpretation bias. Therefore, my findings

refute my original hypothesis that acute fear memory recall affects interpretation bias. My

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study also showed that recall of a fear memory reduced motivation to perform and slowed

response times during interpretation of ambiguous information.

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5.6 Future Directions

We have discussed that in our study, fear memory recall did not induce change in

interpretation bias given our experimental designs. However, we posed a number of

explanations to account for a number of behaviours observed as a result of implementing our

novel visual paradigm and testing the effect of fear memory recall. From these explanations, we

proposed potential changes to our design for future experiments to further our understanding

of interpretation bias.

First, we aim to test whether fear affects interpretation bias if animals have to predict

between a reward and a punishment outcome. This experiment will allow us to explore the

difference between appetitive/appetitive outcome paradigms versus appetitive/aversive

outcome paradigms. If fear affects interpretation bias in the later paradigm, it would imply

modulation of stimulus interpretation between appetitive and aversive outcomes by fear.

Second, we aim to test whether chronic treatment of conditioned fear can affect

interpretation bias. Studies have suggested that long-term stress was able to induce changes in

normal decision making (Ernst & Paulus, 2005). Moreover, interpretation bias studies that have

demonstrated a treatment effect have used chronic treatments to show changes in

interpretation bias (Enkel et al., 2010; Paul et al., 2005; Rygula et al., 2005). Therefore, we

hypothesize that chronic fear treatment may induce changes in interpretation bias. Since

conditioned fear commonly involved conditioning a neutral stimulus (CS) such as a tone with a

foot-shock (US), repeated fear treatment would require repeated exposure to the CS. This

subjects the treatment design to extinction effects where animals learn to dissociate

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conditioned stimulus (tone) from the fearful event (foot-shock) (Falls, Miserendino, & Davis,

1992). We can circumvent this by performing repeated CS/US pairing. However, this would

introduce confounding effects of chronic anxiety and depressive-like states to our

interpretation bias measurements. As such, chronic fear treatments involving repeated long-

term tone and foot-shock shock (CS/US) pairings would require proper controls to dissociate

effect of fear from anxiety/depression. One example of such control would be a treatment

group tested in conjunction that involves chronic foot-shock treatment, without pairing with a

neutral tone stimulus.

Finally, we can limit the duration at which mice choose one of two flanking stimuli to

indicate prediction of outcome expectation. This way, we can test whether fear modulates

reaction time to predict an outcome under uncertainty in mice.

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Chapter 6

References

- 103 -

Amir, N., Beard, C., & Bower, E. (2005). Interpretation Bias and Social Anxiety. Cognitive Therapy and Research, 29(4), 433–443. doi:10.1007/s10608-005-2834-5

Anderson, a K., & Phelps, E. a. (2001). Lesions of the human amygdala impair enhanced perception of emotionally salient events. Nature, 411(6835), 305–9. doi:10.1038/35077083

Anisman, H., & Matheson, K. (2005). Stress, depression, and anhedonia: caveats concerning animal models. Neuroscience and Biobehavioral Reviews, 29(4-5), 525–46. doi:10.1016/j.neubiorev.2005.03.007

Antunes, R., & Moita, M. a. (2010). Discriminative auditory fear learning requires both tuned and nontuned auditory pathways to the amygdala. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 30(29), 9782–7. doi:10.1523/JNEUROSCI.1037-10.2010

Armony, J. L., Servan-Schreiber, D., Romanski, L. M., Cohen, J. D., & LeDoux, J. E. (1997). Stimulus generalization of fear responses: effects of auditory cortex lesions in a computational model and in rats. Cerebral Cortex (New York, N.Y. : 1991), 7(2), 157–65. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9087823

Baker, L. A., Emery, R. E., Bakert, L. A., & Emeryt, R. E. (2014). Perceptions and Expectations of Divorce at the Time of Marriage *, 17(4), 439–450.

Bateson, M., Desire, S., Gartside, S. E., & Wright, G. a. (2011). Agitated honeybees exhibit pessimistic cognitive biases. Current Biology : CB, 21(12), 1070–3. doi:10.1016/j.cub.2011.05.017

Blasi, G., Hariri, A. R., Alce, G., Taurisano, P., Sambataro, F., Das, S., … Mattay, V. S. (2009). Preferential amygdala reactivity to the negative assessment of neutral faces. Biological Psychiatry, 66(9), 847–53. doi:10.1016/j.biopsych.2009.06.017

Blaut, A., Paulewicz, B., Szastok, M., Prochwicz, K., & Koster, E. (2013). Are attentional bias and memory bias for negative words causally related? Journal of Behavior Therapy and Experimental Psychiatry, 44(3), 293–9. doi:10.1016/j.jbtep.2013.01.002

Boleij, H., van’t Klooster, J., Lavrijsen, M., Kirchhoff, S., Arndt, S. S., & Ohl, F. (2012). A test to identify judgement bias in mice. Behavioural Brain Research, 233(1), 45–54. doi:10.1016/j.bbr.2012.04.039

Bondi, C. O., Rodriguez, G., Gould, G. G., Frazer, A., & Morilak, D. a. (2008). Chronic unpredictable stress induces a cognitive deficit and anxiety-like behavior in rats that is prevented by chronic antidepressant drug treatment. Neuropsychopharmacology : Official

- 104 -

Publication of the American College of Neuropsychopharmacology, 33(2), 320–31. doi:10.1038/sj.npp.1301410

Bourin, M., Petit-Demoulière, B., Dhonnchadha, B. N., & Hascöet, M. (2007). Animal models of anxiety in mice. Fundamental & Clinical Pharmacology, 21(6), 567–74. doi:10.1111/j.1472-8206.2007.00526.x

Brilot, B. O., Asher, L., & Bateson, M. (2010). Stereotyping starlings are more “pessimistic”. Animal Cognition, 13(5), 721–31. doi:10.1007/s10071-010-0323-z

Brydges, N. M., Leach, M., Nicol, K., Wright, R., & Bateson, M. (2011). Environmental enrichment induces optimistic cognitive bias in rats. Animal Behaviour, 81(1), 169–175. doi:10.1016/j.anbehav.2010.09.030

Burman, O. H. P., Parker, R. M. a, Paul, E. S., & Mendl, M. T. (2009). Anxiety-induced cognitive bias in non-human animals. Physiology & Behavior, 98(3), 345–50. doi:10.1016/j.physbeh.2009.06.012

Burman, O. H. P., Parker, R., Paul, E. S., & Mendl, M. (2008). A spatial judgement task to determine background emotional state in laboratory rats, Rattus norvegicus. Animal Behaviour, 76(3), 801–809. doi:10.1016/j.anbehav.2008.02.014

Burman, O., McGowan, R., Mendl, M., Norling, Y., Paul, E., Rehn, T., & Keeling, L. (2011). Using judgement bias to measure positive affective state in dogs. Applied Animal Behaviour Science, 132(3-4), 160–168. doi:10.1016/j.applanim.2011.04.001

Cardinal, R. N., Parkinson, J. a, Hall, J., & Everitt, B. J. (2002). Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience and Biobehavioral Reviews, 26(3), 321–52. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12034134

Clark, D. M., Wells, A., Heimberg, R. G., Liebowitz, M. R., Hope, D. A., & Schneier, F. R. (1995). A cognitive model of social phobia. In Social phobia diagnosis assessment and treatment (Vol. xii, pp. 69–93). Guilford Press. Retrieved from http://www.il.proquest.com/umi/

Coles, M. E., & Heimberg, R. G. (2002). Memory biases in the anxiety disorders: current status. Clinical Psychology Review, 22(4), 587–627. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12094512

Davidson, R. J. (2002). Anxiety and Affective Style : Role of Prefrontal Cortex and Amygdala. Society of Biological Psychiatry, 51(1), 68-80. doi: 10.1016/S0006-3223(01)01328-2

Davis, M. (1992). The role of the amygdala in fear and anxiety. Annual Review of Neuroscience, 15, 353–75. doi:10.1146/annurev.ne.15.030192.002033

- 105 -

Davis, M., Walker, D. L., Miles, L., & Grillon, C. (2010). Phasic vs sustained fear in rats and humans: role of the extended amygdala in fear vs anxiety. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 35(1), 105–35. doi:10.1038/npp.2009.109

Davis, M., & Whalen, P. J. (2001). The amygdala: vigilance and emotion. Molecular Psychiatry, 6(1), 13–34. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11244481

Dehaene, S., Izard, V., Spelke, E., & Pica, P. (2008). Log or linear? Distinct intuitions of the number scale in Western and Amazonian indigene cultures. Science (New York, N.Y.), 320(5880), 1217–20. doi:10.1126/science.1156540

Destrez, A., Deiss, V., Belzung, C., Lee, C., & Boissy, A. (2012). Does reduction of fearfulness tend to reduce pessimistic-like judgment in lambs? Applied Animal Behaviour Science, 139(3-4), 233–241. doi:10.1016/j.applanim.2012.04.006

Ebbinghaus, H. (1913). (Ruger, H., & Bussenius, C., Trans.) New York, NY: Teachers College.

Enkel, T., Gholizadeh, D., von Bohlen Und Halbach, O., Sanchis-Segura, C., Hurlemann, R., Spanagel, R., … Vollmayr, B. (2010). Ambiguous-cue interpretation is biased under stress- and depression-like states in rats. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 35(4), 1008–15. doi:10.1038/npp.2009.204

Erlich, J. C., Bush, D. E. a, & Ledoux, J. E. (2012). The role of the lateral amygdala in the retrieval and maintenance of fear-memories formed by repeated probabilistic reinforcement. Frontiers in Behavioral Neuroscience, 6(April), 16. doi:10.3389/fnbeh.2012.00016

Ernst, M., & Paulus, M. P. (2005). Neurobiology of decision making: a selective review from a neurocognitive and clinical perspective. Biological Psychiatry, 58(8), 597–604. doi:10.1016/j.biopsych.2005.06.004

Everaert, J., Duyck, W., & Koster, E. H. W. (2014). Attention, Interpretation, and Memory Biases in Subclinical Depression: A Proof-of-Principle Test of the Combined Cognitive Biases Hypothesis. Emotion (Washington, D.C.). doi:10.1037/a0035250

Everaert, J., Tierens, M., Uzieblo, K., & Koster, E. H. W. (2013). The indirect effect of attention bias on memory via interpretation bias: evidence for the combined cognitive bias hypothesis in subclinical depression. Cognition & Emotion, 27(8), 1450–9. doi:10.1080/02699931.2013.787972

Eysenck, M. W. (1992). Anxiety: the cognitive perspective. Hillsdale, NJ: Lawrence Erlbaum Associates

- 106 -

Eysenck, M. W., Mogg, K., May, J., Richards, A., & Mathews, A. (1991). Bias in Interpretation of Ambiguous Sentences Related to Threat in Anxiety. Journal of Abnormal Psychology, 100(2), 144–150. doi:10.1037/0021-843X.100.2.144

Falls, W. a, Miserendino, M. J., & Davis, M. (1992). Extinction of fear-potentiated startle: blockade by infusion of an NMDA antagonist into the amygdala. The Journal of Neuroscience, 12(3), 854–63. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1347562

Gold, J. I., & Shadlen, M. N. (2000). Representation of a perceptual decision in developing oculomotor commands. Nature, 404(6776), 390–4. doi:10.1038/35006062

Gutiérrez-García, A., & Calvo, M. G. (2014). Social anxiety and interpretation of ambiguous smiles. Anxiety, Stress, and Coping, 27(1), 74–89. doi:10.1080/10615806.2013.794941

Hales, C., & Stuart, S. (2014). Modelling Cognitive Affective Biases in Major Depressive Disorder using Rodents. British Journal of Pharmacology, 44(0). doi:10.1111/bph.12603

Harding, E. J., Paul, E. S., & Mendl, M. (2004). Cognitive bias and affective state. Nature, 427(6972), 312.

Johansen, J. P., Cain, C. K., Ostroff, L. E., & LeDoux, J. E. (2011). Molecular mechanisms of fear learning and memory. Cell, 147(3), 509–24. doi:10.1016/j.cell.2011.10.009

Kahneman, D., & Tversky, A. (1983). Choices , Values , and Frames. American Psychologist, 39(4), 341–350.

Koster, E. H. W., De Raedt, R., Leyman, L., & De Lissnyder, E. (2010). Mood-congruent attention and memory bias in dysphoria: Exploring the coherence among information-processing biases. Behaviour Research and Therapy, 48(3), 219–25. doi:10.1016/j.brat.2009.11.004

Kverno, K. (2000). Trait anxiety influences on judgments of frequency and recall. Personality and Individual Differences, 29, 395–404. Retrieved from http://www.sciencedirect.com/science/article/pii/S0191886999002019

Lang, P. J., Davis, M., & Ohman, a. (2000). Fear and anxiety: animal models and human cognitive psychophysiology. Journal of Affective Disorders, 61(3), 137–59. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11163418

MacLeod, C., & Cohen, I. (1993). Anxiety and the Interpretation of Ambiguity: A Text Comprehension Study. Journal of Abnormal Psychology, 102(2), 238–247. doi:10.1037/0021-843X.102.2.238

- 107 -

MacLeod, C., Mathews, a, & Tata, P. (1986). Attentional bias in emotional disorders. Journal of Abnormal Psychology, 95(1), 15–20. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/3700842

Mar, A. C., Horner, A. E., Nilsson, S. R. O., Alsiö, J., Kent, B. a, Kim, C. H., … Bussey, T. J. (2013). The touchscreen operant platform for assessing executive function in rats and mice. Nature Protocols, 8(10), 1985–2005. doi:10.1038/nprot.2013.123

Maren, S. (2001). Neurobiology of Pavlovian fear conditioning. Annual Review of Neuroscience, 24, 897–931. doi:10.1146/annurev.neuro.24.1.897

Mathews, A., & Mackintosh, B. (1998). A cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22(6), 539–560. Retrieved from http://link.springer.com/article/10.1023/A:1018738019346

Mathews, A., Richards, A., & Eysenck, M. W. (1989). Interpretation of homophones related to threat in anxiety states. Journal of Abnormal Psychology, 98(1), 31–34. doi:10.1037/0021-843X.98.1.31

Mckee, S. P., Klein, S. A., & Teller, D. Y. (1985). Statistical properties of forced-choice psychometric functions: Implications of probit analysis. Perception & Psychophysics, 37(4), 286–298.

Mendl, M., Burman, O. H. P., Parker, R. M. a., & Paul, E. S. (2009). Cognitive bias as an indicator of animal emotion and welfare: Emerging evidence and underlying mechanisms. Applied Animal Behaviour Science, 118(3-4), 161–181. doi:10.1016/j.applanim.2009.02.023

Miller, B. A. K. (1996). Unrealistic optimism about aging. Dissertation Abstracts International: Section B: The Sciences and Engineering, 56(10-B), 5837.

Mitte, K. (2008). Memory bias for threatening information in anxiety and anxiety disorders: a meta-analytic review. Psychological Bulletin, 134(6), 886–911. doi:10.1037/a0013343

Mogg, K., Mathews, a, & Weinman, J. (1987). Memory bias in clinical anxiety. Journal of Abnormal Psychology, 96(2), 94–8. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12573928

Nestler, E. J., Barrot, M., Dileone, R. J., Eisch, A. J., Gold, S. J., & Monteggia, L. M. (2002). Neurobiology of Depression Review, 34, 13–25.

Neta, M., Kelley, W. M., & Whalen, P. J. (2013). Neural responses to ambiguity involve domain-general and domain-specific emotion processing systems. Journal of Cognitive Neuroscience, 25(4), 547–57. doi:10.1162/jocn_a_00363

- 108 -

Papciak, J., Popik, P., Fuchs, E., & Rygula, R. (2013). Chronic psychosocial stress makes rats more “pessimistic” in the ambiguous-cue interpretation paradigm. Behavioural Brain Research, 256, 305–10. doi:10.1016/j.bbr.2013.08.036

Paul, E. S., Harding, E. J., & Mendl, M. (2005). Measuring emotional processes in animals: the utility of a cognitive approach. Neuroscience and Biobehavioral Reviews, 29(3), 469–91. doi:10.1016/j.neubiorev.2005.01.002

Reidy, J., & Richards, A. (1997). Anxiety and memory: A recall bias for threatening words in high anxiety. Behaviour Research and Therapy, 35(6), 531–542. Retrieved from http://www.sciencedirect.com/science/article/pii/S0005796797000016

Richards, A., & French, C. C. (1992). An anxiety-related bias in semantic activation when processing threat/neutral homographs. The Quarterly Journal of Experimental Psychology Section A, 45(3), 503–525. doi:10.1080/02724989208250625

Richards, A., French, C. C., Calder, A. J., Webb, B., Fox, R., & Young, A. W. (2002). Anxiety-related bias in the classification of emotionally ambiguous facial expressions. Emotion, 2(3), 273–287. doi:10.1037//1528-3542.2.3.273

Russo, R., Fox, E., Bellinger, L., & Nguyen-Van-Tam, D. P. (2001). Mood-congruent free recall bias in anxiety. Cognition and Emotion, 15(4), 419–433. doi:10.1080/0269993004200259

Rygula, R., Abumaria, N., Fl, G., Fuchs, E., Eckart, R., & Havemann-reinecke, U. (2005). Anhedonia and motivational deficits in rats : Impact of chronic social stress, 162, 127–134. doi:10.1016/j.bbr.2005.03.009

Rygula, R., Papciak, J., & Popik, P. (2014). The effects of acute pharmacological stimulation of the 5-HT, NA and DA systems on the cognitive judgement bias of rats in the ambiguous-cue interpretation paradigm. European Neuropsychopharmacology, 1–9. doi:10.1016/j.euroneuro.2014.01.012

Rygula, R., Pluta, H., & Popik, P. (2012). Laughing rats are optimistic. PloS One, 7(12), e51959. doi:10.1371/journal.pone.0051959

Rygula, R., Szczech, E., Papciak, J., Nikiforuk, A., & Popik, P. (2014). The effects of cocaine and mazindol on the cognitive judgement bias of rats in the ambiguous-cue interpretation paradigm. Behavioural Brain Research, 270, 206–212. doi:10.1016/j.bbr.2014.05.026

Salemink, E., Hertel, P., & Mackintosh, B. (2010). Interpretation training influences memory for prior interpretations. Emotion, 10(6), 903–7. doi:10.1037/a0020232

- 109 -

Salmeto, A. L., Hymel, K. a, Carpenter, E. C., Brilot, B. O., Bateson, M., & Sufka, K. J. (2011). Cognitive bias in the chick anxiety-depression model. Brain Research, 1373, 124–30. doi:10.1016/j.brainres.2010.12.007

Steimer, T. (2002). The biology of fear- and anxiety-related behaviors. Dialogues in Clinical Neuroscience, 4(3), 231–249.

Stevens, S. S., & Volkmann, J. (2014). The Relation of Pitch to Frequency: A Revised Scale. The American Journal of Psychology, 53(3), 329–353.

Tran, T. B., Hertel, P. T., & Joormann, J. (2011). Cognitive bias modification: induced interpretive biases affect memory. Emotion, 11(1), 145–52. doi:10.1037/a0021754

Traunmuller, H., & Eriksson, A. (2014). The perceptual evaluation of F0 excursions in speech as evidenced in liveliness estimations. The Journal of the Acoustical Society of America, 97(3), 1905–1915.

Uchida, N., Kepecs, A., & Mainen, Z. F. (2006). Seeing at a glance, smelling in a whiff: rapid forms of perceptual decision making. Nature Reviews, Neuroscience, 7(June), 485–492.

Uchida, N., & Mainen, Z. F. (2003). Speed and accuracy of olfactory discrimination in the rat. Nature Neuroscience, 6(11), 1224–9. doi:10.1038/nn1142

Van Bockstaele, B., Verschuere, B., Tibboel, H., De Houwer, J., Crombez, G., & Koster, E. H. W. (2013). A Review of Current Evidence for the Causal Impact of Attentional Bias on Fear and Anxiety. Psychological Bulletin. doi:10.1037/a0034834

Watts, S. E., & Weems, C. F. (2006). Associations among selective attention, memory bias, cognitive errors and symptoms of anxiety in youth. Journal of Abnormal Child Psychology, 34(6), 841–52. doi:10.1007/s10802-006-9066-3

Whalen, P. J. (1998). Fear, vigilance, and ambiguity: Initial neuroimaging studies of the human amygdala. Current Directions in Psychological Science, 7(6), 177–188. Retrieved from http://www.jstor.org/stable/20182537

Wichmann, F. a, & Hill, N. J. (2001). The psychometric function: I. Fitting, sampling, and goodness of fit. Perception & Psychophysics, 63(8), 1293–313. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11800458

Effect of Fear Memory Recall on Interpretation Bias in Rodents · PDF file3.6 Probe paradigms...

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