Neural systems behind word and concept retrieval...1. Introduction On opening a textbook of...

Neural systems behind word and concept retrieval

H. Damasioa,*, D. Tranela, T. Grabowskia,b,R. Adolphsa, A. Damasioa

aDepartment of Neurology, Division of Behavioral Neurology and Cognitive Neuroscience,

University of Iowa College of Medicine, Iowa City, IA, USAbDepartment of Radiology, University of Iowa College of Medicine, Iowa City, IA, USA

Received 25 July 2001; revised 26 June 2002; accepted 27 July 2002

Abstract

Using both the lesion method and functional imaging (positron emission tomography) in large

cohorts of subjects investigated with the same experimental tasks, we tested the following

hypotheses: (A) that the retrieval of words which denote concrete entities belonging to distinct

conceptual categories depends upon partially segregated regions in higher-order cortices of the left

temporal lobe; and (B) that the retrieval of conceptual knowledge pertaining to the same concrete

entities also depends on partially segregated regions; however, those regions will be different from

those postulated in hypothesis A, and located predominantly in the right hemisphere (the second

hypothesis tested only with the lesion method). The analyses provide support for hypothesis A in that

several regions outside the classical Broca and Wernicke language areas are involved in name

retrieval of concrete entities, and that there is a partial segregation in the temporal lobe with respect

to the conceptual category to which the entities belong, and partial support for hypothesis B in that

retrieval of conceptual knowledge is partially segregated from name retrieval in the lesion study.

Those regions identified here are seen as parts of flexible, multi-component systems serving concept

and word retrieval for concrete entities belonging to different conceptual categories. By comparing

different approaches the article also addresses a number of method issues that have surfaced in recent

studies in this field.

q 2004 Elsevier B.V. All rights reserved.

Keywords: Recognition; Naming; Lesion method; Functional imaging; Conceptual categories

0022-2860/$ - see front matter q 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.cognition.2002.07.001

Cognition 92 (2004) 179–229

www.elsevier.com/locate/COGNIT

* Corresponding author. Neuroimaging Laboratory, Department of Neurology, University of Iowa Hospitals

and Clinics, Iowa City, IA 52242, USA.

E-mail address: [email protected] (H. Damasio).

http://www.elsevier.com/locate/COGNIT

1. Introduction

On opening a textbook of neurology, neuropsychology, or linguistics in the section

concerning the neural basis of language, it is still common to find an account of the

language-related brain map stating that it consists of two regions: the first is located

anteriorly in the left hemisphere, in the left frontal operculum, and is responsible for the

production of words and sentences; the second is located posteriorly in the same left

hemisphere, in the superior temporal gyrus, and is responsible for the comprehension of

spoken words and sentences. These two areas are known, respectively, as Broca and

Wernicke areas, named for their discoverers Paul Broca and Carl Wernicke, and have been

recognized since the second half of the 19th century (Broca, 1861; Wernicke, 1874).

Roughly one century later, Norman Geschwind (Geschwind, 1965) added other

components to this language map. These included parts of the cerebral cortex in the

supramarginal and angular gyri (respectively Brodmann’s areas 40 and 39 (Brodmann,

1909/1999) whose combination forms the human inferior parietal lobule) as well as a

pathway in the white matter subjacent to these areas, the arcuate fasciculus, which was

presumed to connect Wernicke’s to Broca’s area. The pathway was implicit in the

language accounts of Wernicke and other early neurologists of language, and was fleshed

out anatomically by Dejerine (1914), but Geschwind placed it center stage. Geschwind

also called attention to the language-related role of part of Brodmann’s area 37, an area

that is now included in the inferior temporal region. Either the Geschwind updated map or

the simpler 19th century version remains the prevailing anatomical template that informs

discussions on the neural basis of language. We know, however, that the anatomical

situation is not this simple. The problem with the classical anatomical account is not that it

is wrong but that it is quite incomplete. In discussing the macro-architecture of the neural

systems underlying language or the clinical aspects of aphasias, it is certainly appropriate

to refer to Broca’s and Wernicke’s areas, and it is still useful to maintain designations such

as Broca aphasia and Wernicke aphasia, because they predict likely loci of brain damage

in neurological patients and because they help with communication among clinicians and

researchers (see Damasio, 2001). It is no longer reasonable, however, to accept the idea

that these two language-related areas alone, connected by a direct and unidirectional

pathway, translate thoughts into words and vice versa. Any current consideration of the

macrosystems involved in the processing of language requires the involvement of many

other brain regions, connected by bidirectional pathways, forming systems that can

subsequently cross-interact.

What is the origin for the idea that the brain’s language map extends beyond Broca,

Wernicke and Geschwind’s contributions? As is often the case, individual clinical

observations provided important clues, long before systematic experiments could support

the idea. For example, we had good evidence that patients with damage to the anterior

sector of the left temporal lobe, involving the temporal pole, in spite of fluent and non-

aphasic language, were impaired in the retrieval of names for specific persons. The

patients knew who the person was and provided verbal descriptions that allowed an

independent examiner, who did not know what stimulus the patient was looking at, to

guess which person the patient was trying to name. Such patients provided enough

evidence that they recognized the unique entity to be named, but failed to retrieve

H. Damasio et al. / Cognition 92 (2004) 179–229180

the specific name attached to the entity. On the other hand, the patients could name other

objects, natural or manmade, perfectly well, and could retrieve the correct words denoting

actions depicted in visual stimuli. In summary, in such patients, a circumscribed deficit of

word retrieval was associated with a circumscribed area of brain damage in the left

temporal pole, away from the classical language areas, which were not damaged at all

(Fig. 1A). Moreover, when equivalent lesions were located in the right temporal pole

(Fig. 1C), a very different defect ensued: there was defective recognition of previously

known persons, rather than a name retrieval deficit without compromised recognition, i.e.

in these cases the patients displayed a lack of actual knowledge of concepts related to the

entity in question and as a consequence could not name it either. These patients could not

conjure up any pertinent characteristics that might suggest recognition of the stimulus at a

unique level.

Other suggestive observations came from the study of patients with lesions located

posteriorly in the left temporo-occipital junction, again outside the territory of the classical

language areas (Fig. 1B). The patients showed deficits in the retrieval of words denoting

manipulable objects (such as tools), but the retrieval of words for unique persons or for

natural entities, e.g. animals, could be entirely normal. Once again, in such instances the

patients displayed conceptual knowledge of the objects to be named, and could describe

their usage, what they were made of, their size, and so on. Again the description would be

detailed enough for a third person not privy to the image of the object to be able to name

the object. There was patent evidence that the deficient name retrieval was not paralleled

by deficient recognition of the stimulus.

Fig. 1. 3D brain reconstructions of three subjects with lesions in the left temporal lobe (A,B) and the right

temporal lobe (C). All of these lesions are outside the traditional language cortices. Patient A has deficient

retrieval of words denoting unique persons, patient B has deficient retrieval of words denoting manipulable

objects, and patient C has deficient recognition of familiar persons when presented as face photographs.

H. Damasio et al. / Cognition 92 (2004) 179–229 181

Observations such as these, from our own laboratory, as well as work from others

(Chatterjee, Southwood, & Basilco, 1999; Damasio, Grabowski, Tranel, Hichwa, &

Damasio, 1996; Grossman, 1998; Martin, Haxby, Lalonde, Wiggs, & Ungerleider, 1995;

Nobre, Allison, & McCarthy, 1994; Ojemann, 1991; Saffran & Sholl, 1999; Silveri et al.,

1997; Tranel, Damasio, & Damasio, 1997b, 1998), indicate that even when just a single

aspect of language processing is considered, for example word retrieval, the minimally

necessary language map goes well beyond the classical language areas. Leaving aside, for

the moment, aspects of language processing such as phonological and syntactical

processing, the observations prompted the following hypotheses: (A) that the retrieval of

words which denote concrete entities belonging to distinct conceptual categories depends

upon partially segregated regions in higher-order cortices of the left temporal lobe; and

(B) that the retrieval of conceptual knowledge pertaining to the same concrete entities also

depends on partially segregated regions different from those postulated in hypothesis A

and located predominantly in the right hemisphere.

In this article, we report on the testing of these hypotheses, combining previously

published preliminary findings with new and unpublished data. The primary approach

used to test hypotheses A and B is the lesion method. We report on a set of lesion findings,

in a large group of subjects, that provide strong support for hypothesis A and partial

support for hypothesis B. We also report on a series of functional imaging studies using

positron emission tomography (PET), which provide further and convergent evidence in

support of hypothesis A. We acknowledge at the outset that the PET experiments were not

designed, and cannot be used, to provide an unequivocal test of hypothesis B, given that

the processes evoked in the PET experiments comprise both concept retrieval

(recognition) and word retrieval (naming) and cannot be separated. We conclude the

paper by discussing the theoretical significance of the findings.

2. Evidence from lesion studies

2.1. Methods

In testing the two target hypotheses, using the lesion method, the design of the

experiments assumed that naming concrete entities is likely to involve several cortical and

subcortical regions operating at a large-scale systems level. In addition to regions involved

in implementing the actual vocal response, other regions must be involved in processing

the conceptual knowledge behind a given entity, and in retrieving the specific morphemes

required for the correct response. The experiments conducted in these studies aimed at

identifying regions involved in conceptual processing and word retrieval. The experiments

also attempted to address another issue, namely the degree to which conceptual processing

and word retrieval can be functionally separated.

2.1.1. Subjects

Subjects with unilateral brain damage (n ¼ 169) were selected from the Patient

Registry of the University of Iowa’s Division of Cognitive Neuroscience. All had given

informed consent in accordance with the Human Subjects Committee of the University of


Iowa. As a group, the subjects permitted us to probe a large sector of the cortical mantle

and the underlying white matter – their lesions were located in the left (n ¼ 105) or right

(n ¼ 64) hemisphere. Lesions were caused by either cerebrovascular disease (n ¼ 139),

herpes simplex encephalitis (n ¼ 5), or temporal lobectomy (n ¼ 25). Handedness,

measured with the Geschwind–Oldfield Questionnaire, was distributed as follows: 153

were right-handed (þ90 or greater); four were left-handed (290 or lower); 12 were

mixed-handed (,þ90 and .290). Neurological, neuropsychological, and for temporal

lobectomies the WADA test data indicated that all subjects had left hemisphere language

dominance.

All subjects had been fully characterized neuropsychologically and neuroanatomically,

according to the standard protocols of the Benton Neuropsychology Laboratory

(Tranel, 1996) and the Laboratory of Neuroimaging and Human Neuroanatomy (Damasio,

1995; Damasio & Damasio, 1989; Damasio & Frank, 1992). All subjects had normal

intelligence (as measured by the WAIS-R or WAIS-III), and no difficulty attending to and

perceiving visual stimuli. On average, the brain-damaged group had at least 12 years of

formal education. Some subjects in the sample were recovered aphasics, however no

subject had residual aphasia of such severity so as to preclude the production of scorable

responses. Subjects with severe aphasia or with severe visual perceptual deficits were not

included in the brain-damaged sample.

Fifty-five normal controls, who were matched to the brain-damaged subjects on age,

education, and gender distribution, had been studied with the same tasks. The results for

recognition and naming in the five conceptual categories described below for this group of

normal subjects were: (1) unique persons, 75.7 ^ 6.7 for recognition and 92.3 ^ 6.2 for

naming; (2) animals, 91.9 ^ 2.8 for recognition and 95.7 ^ 3.1 for naming; (3) tools and

utensils, 96.2 ^ 3.3 for recognition and 97.2 ^ 3.9 for naming; (4) fruits and vegetables,

92.6 ^ 3.9 for recognition and 94.3 ^ 3.7 for naming; and (5) musical instruments,

96.3 ^ 3.4 for recognition and 96.9 ^ 4.5 for naming.

2.1.2. Stimuli

The unique stimuli were persons (presented as faces) drawn from the Iowa Famous

Faces Test (n ¼ 77) (Tranel, Damasio, & Damasio, 1995) and a modified version of the

Boston Famous Faces Test (n ¼ 56) (Albert, Butter, & Levin, 1979). They were shown

such that all non-face background features were deleted. The non-unique stimuli were

pictures of animals (n ¼ 90), tools and utensils (n ¼ 104), fruits and vegetables (n ¼ 67),

and musical instruments (n ¼ 16) selected from the Snodgrass and Vanderwart (1980) line

drawings and from a set of photographs prepared in our laboratory (Damasio, Damasio,

Tranel, & Brandt, 1990). For all categories, the same entity (e.g. a hammer) was only

shown once (there weren’t different pictures of different hammers). The faces were all

black-and-white photographs. Each of the other categories was comprised of an

approximately equal mix of black-and-white line drawings (65–75% of the items) and

black-and-white photographs (25–35% of the items). No attempt was made to “equate”

the categories on variables such as word length, word frequency, name agreement, image

agreement, familiarity or visual complexity. For the categories of animals, fruits/

vegetables, and tools/utensils (henceforth vegetables and tools for short), we have

previously provided a detailed analysis of such variables (Damasio & Tranel, 1993;


Tranel, Adolphs, Damasio, & Damasio, 2001), and we have not found systematic

differences between categories that might account for the naming and recognition findings.

Moreover, we have noted that “matching” categories on such factors can produce

categories comprised of highly unrepresentative members, and we decided to prevent such

a bias (cf. Tranel et al., 2001; see also Dixon, Piskopos, & Schweizer, 2000; Pulvermuller,

Harle, & Hummel, 2000).

The rationale for selecting the categories of persons, animals, tools, vegetables, and

musical instruments was both practical and theoretical. On the practical side, these are the

categories that have received the most attention in the literature, and it was important to

include them so that we could compare our findings to those of other authors. From a

theoretical perspective, the choice of these types of items was dictated by the fact that the

sensory and motor characteristics that define the entities in each of these categories are

remarkably different (e.g. the combination of motor, somatosensory and visual

characteristics that define a tool is not necessary for the definition of a unique face),

and that so is the context in which the entities of the categories are normally used (e.g. high

contextual complexity in the case of unique faces; usually low complexity in the case of

non-unique items) (Damasio & Damasio, 1994; Tranel, Logan, Frank, & Damasio, 1997).

The typical example of a unique entity is a person you know well, including his name: my

friend Michael. My dog Wilson is also a unique entity, but a dog, or even a Labrador

Retriever, is a non-unique (living) entity. My Stradivarius is a unique non-living entity;

a violin is not.

2.1.3. Procedures

All subjects were tested in the chronic epoch at least 3 months post onset of lesion.

The stimuli were shown in random order one-by-one on a Caramate 4000 slide

projector in free field. All 410 stimuli were administered to all the subjects. In most cases,

subjects were tested over two sessions, in order to avoid confounding effects of fatigue and

inattention. Typically, each session lasted about two hours. In a few cases, more than two

sessions were necessary, especially for subjects who had difficulty with many of the items

and who required frequent prompting. The primary consideration was that subjects were

not tested beyond the point that they could cooperate fully with the procedures. In all

cases, administration of the entire set of stimuli was completed at least within a several-

day period. For each stimulus, the subject’s task was to tell the experimenter what (or who)

the entity was. If the subject gave a vague or superordinate-level response (e.g. ‘a

politician’ or ‘something you can work with’), the subject was prompted to “be more

specific; tell me exactly who [what] you think that [thing] is.” Prompting was repeated if

the experimenter sensed that the subject could generate a more specific answer, or if the

subject produced a paraphasic response that might be difficult to score. Time limits were

not imposed. Responses were audiotaped and prepared as typewritten transcriptions,

which were scored by two independent raters who were blind to the experimental

hypotheses, following procedures specified below.

2.1.4. Neuropsychological data quantification and analysis

The dependent measures were a recognition score and a naming score. For each

stimulus, the subject’s recognition/naming response was scored as follows. First, if


the stimulus was named correctly, the item was scored as a correct recognition and

naming. In other words, we accepted correct naming as unequivocal evidence of correct

recognition. Our rationale for this approach is that we have never found a subject who

would produce a correct name, and then fail to recognize the stimulus that was named. In

background work for this set of studies we explored in a subset of subjects with correct

naming responses whether they had retrieved the concept for an item prior to retrieving its

name, and, as we expected, they had. In other words, we never encountered a patient who

would, for example, name Jane Fonda when presented with her picture and then say “who

is Jane Fonda?”, or see a broom, call it a broom, and not know what a broom is, or what it

is made of, or what it is used for. In fact, we do not believe it is possible for someone to

name, accurately and reliably, an unrecognized item, even in the extreme instance of

patients with Alzheimer’s disease who may on occasion appear to do just that. A severely

inattentive or demented patient may produce a correct naming response and, by the time

the response comes under scrutiny, may have lost from working memory the material

recalled during concept retrieval. It may appear that the patient has ‘named but not

recognized’, but this is an artifact of the attentional/working memory defect, and we

remain convinced that concept retrieval is a prerequisite for accurate naming.

Second, for items that were not named correctly, the subject’s responses were presented

(as typewritten transcriptions) to two raters who were asked to determine what the

stimulus was from the description alone, without having in front of them either the

stimulus or its name. The raters were blind to the experimental hypotheses, and had not

participated in the testing of the subjects. We scored as correct any recognition response

that either or both raters were able to use to generate the correct name of the entity, as

described above. Inter-rater reliability was not assessed. Although this procedure does not

provide a direct probe of the extent of conceptual knowledge of entities the subject may

access, the procedure demands substantial conceptual knowledge. For a rater to identify an

entity from reading a subject’s description, the description must contain specific

information about the entity. We believe this approach places more stringent demands on

the subjects’ ability to access conceptual information than specifically probing the subject

with prepared questions about size, weight, nature, and so on. Since subjects are given a

completely open-ended opportunity to generate whatever information they can about all

entities they cannot name, we create a situation that encourages subjects to substitute

detailed descriptions for naming failures.

Thus, when the subject had provided a description of the entity (e.g. “that’s the

president who was killed…his brother was also killed, later…he had an affair with that

movie star who killed herself…”, or “that’s an animal that you find on farms; it makes an

oinking sound and likes to roll in the mud”) specific enough for raters to identify the entity

from the description alone, the response was scored as correct recognition but as failure in

naming. When it was not possible for the rater to come up with the correct name of the

stimulus, the item was classified as failure in recognition, which precluded correct

naming, i.e. the item was not counted as a failure in naming, only as a failure in

recognition.

For each subject and each category, the number of correct recognition responses was

divided by the number of stimuli in the category and multiplied by 100 to yield a percent

correct recognition score. The naming score was calculated by summing the number of


correct naming responses using only those stimuli for which the subject had produced a

correct recognition response. Thus, the number of items was always the same, across

subjects, when the recognition score was determined, but varied across subjects when the

naming score was determined. All subjects in whom a word retrieval score was obtained

had to achieve a correct recognition of at least 50% of the test items. This eliminated

subjects in whom the recognition deficit was so profound that it would have left only a few

items on which the classification of intact or defective naming might be based.

Classification of brain-damaged subjects as normal or abnormal on each of the five

tasks was conducted in two different ways:

2.1.4.1. The standard deviation approach (SD). We calculated for each subject the extent

to which their scores differed from the means of normal controls. In this first approach we

considered scores that were two or more SDs below that mean as abnormal, the usual

approach in classical neuropsychology. But we established a gray zone of borderline

performances, intended to separate normal from abnormal subjects; thus, we considered

normal all individuals whose scores deviated no more than 1.5 SD from the normal mean.

Scores falling between 1.5 and 2.0 SD from the normal mean were considered borderline

and were omitted from further analysis.

2.1.4.2. The distribution analysis approach (DA). We looked at the entire sample of brain-

damaged individuals and asked if this group of subjects, with brain damage in varied

regions, represented a single, or multiple populations. To do this, we plotted a histogram of

all the subjects’ scores and fitted this histogram with Gaussians. This procedure is easiest

to perform, and to visualize, if we first plot each subject’s score to the score that would be

expected if all the scores were drawn from a Gaussian normal distribution. Fig. 2 provides

an example of this approach: if there were a single Gaussian-distributed population, we

should see a straight line when we plot subjects’ actual scores against the scores expected

from a single Gaussian distribution. Instead, as Fig. 2A shows, we obtained two lines with

different slopes, indicating that there are two distinct Gaussian-distributed populations,

with different means. This procedure usually defined two populations, one with lower

scores than the other, for each of the five conceptual categories. To separate the subjects

that fell into each of these two Gaussian populations, we fitted a least-squares regression

line to each of the two populations so as to maximize the least-squares error fit to each

population. In most instances, there were some subjects that ended up omitted from either

of the two populations (their addition would have decreased the fit of either regression

line), or there were some subjects that ended up included in both of the two populations

(their addition would have increased the fit of both regression lines); in such cases, those

subjects were considered to form a borderline group intermediate to the two populations,

and we omitted them from the final analysis.

One reason for this dual approach was to preempt the criticism that the classification

into normal/abnormal categories based on 2 SD from the mean of a normal matched

control group provides an arbitrary dichotomy (Caramazza & Shelton, 1998; Saffran &

Sholl, 1999). It has been argued that such a procedure may classify two subjects as

belonging to either side of the divide, when their scores differ only marginally. The SD

approach we used does address some of these concerns, as an intermediary group is


created as a buffer between abnormal and normal. The DA approach addresses the

criticism even more stringently. By using both approaches we will be in a position to

compare the results obtained with each of the analyses in the same data set.

2.1.5. Neuroanatomical data quantification and analysis

The neuroanatomical analysis was based on magnetic resonance (MR) data obtained in

a 1.5 Tesla General Electric Signa scanner with a 3D SPGR sequence yielding 1.5 mm

contiguous T1 weighted coronal cuts, or else, in those subjects in whom an MR could not

Fig. 2. Example of the DA approach for naming of unique entities. (A) To determine whether our entire sample of

subject scores consisted of a single, Gaussian-distributed population, or consisted of more than one such

population, we plotted subjects’ scores (y-axis) vs. the scores that would be expected if there were a single

Gaussian distribution (x-axis). If there were only a single population, we would have obtained a single straight

line. Instead we obtained two lines with different slopes, corresponding to two Gaussian-distributed populations

with different means (shown in red and blue). We used a least-squares regression to determine the separation

between the two populations (red and blue); there were also a few subjects who did not fit into either population

because their scores were intermediate (indicated in yellow). (B) The same data as depicted in (A) shown as a

histogram of the raw performance scores. The blue and red lines shown in (A) and the red and blue populations

shown in (B) comprise two separate Gaussian-distributed populations: one with a high performance score and one

with a low performance score. The blue data points correspond to normal performance, and the red to defective

performance.


be obtained, analysis was based on computerized axial tomography (CT) data. All

neuroimaging data were obtained in the chronic epoch, at the time of the neuropsycho-

logical evaluation. Each subject’s lesion was reconstructed in three dimensions using

Brainvox (Damasio & Frank, 1992; Frank, Damasio, & Grabowski, 1997).

In order to analyze the placement of lesions and the overlap of lesions in specific brain

areas in a group of subjects of this size, it is necessary to place all lesions in a common

space. Transformation of structural images from their native space into a standard space

has several problems. Talairach’s linear transformation (Talairach & Szikla, 1967;

Talairach & Tournoux, 1988) is not adequate for the detailed neuroanatomical analysis we

wanted to perform, on two grounds. First, the linear transformation achieved by

Talairach’s method does not assure superposition of identical anatomical structures at the

gyri level. Such discrepancies were pointed out by Talairach himself in his 1967

presentation. The use of more recent non-linear warping methods, as for instance

automated image registration (AIR) developed by Woods in 1992 (Woods, Cherry,

& Mazziotta, 1992), is better than the piecewise linear Talairach transformation, but still

does not allow a satisfactory superposition of sulci and gyri of different brains in one single

space (H. Damasio, 2000; Woods, Grafton, Watson, Sicotte, & Mazziotta, 1998). Second,

in the case of brain damage another source of error is introduced: changes in tissue

intensity and distortion of normal anatomical relations due to atrophy or retraction of

surrounding tissue (for an example see Fiez, Damasio, & Grabowski, 2000). In the future,

new developments such as voxel-based morphometry (VBM) (Ashburner & Friston, 2000)

may help circumvent some of the problems. However, to date, VBM has not been

convincingly validated for focal lesions so as to be used as a reliable tool for group

analysis in patients with focal brain damage.

We circumvented these problems with the development of a manual warping technique.

The lesion contour from the lesioned brains is manually warped into a normal template

brain, taking into account gyral and sulcal landmarks. We named the technique the MAP-3

technique. The principles of the technique have been described previously (H. Damasio,

2000; Frank et al., 1997). In summary they entail the following: (1) a normal brain, the

template brain, is reconstructed in three dimensions from thin contiguous MR slices using

Brainvox; (2) all major sulci are identified and color-coded in this template brain as well as

in the lesion brain; (3) the template brain volume is resliced so as to match the MR slices

(or CT slices) of the lesioned brain; (4) the slices in the template brain are matched in

orientation and thickness to the lesioned brain taking into consideration the intersection of

the slices with the color-coded sulci (a good match can be assured both by inspection of the

2D images as well as by the positioning of the slices seen in the 3D images); (5) once the

matching slices have been obtained, the lesion contour on each slice is manually

transferred onto the template brain taking into consideration the distance of the lesion

contour to the identifiable landmarks, such as gyri, and subcortical structures; (6) the

collection of transferred traces, the ROIs, defines a volume that can be corendered with the

template brain; (7) the volumes of several lesions so transferred into the template brain

intersect in space and create a complex volume, which can also be corendered with the

template brain (Fig. 3A). The overlaps of lesions in this volume, calculated by the sum of

lesions overlapping on any single voxel, can be color-coded. Thus, the final overlap of N

lesions can be analyzed in the template brain for areas (collections of adjoining voxels)


corresponding to maximal overlaps. Unfortunately, so far, no automated procedure

proposed has been shown to (a) improve on the human knowledge-based biases used in

MAP-3 and (b) assure accurate transfer of lesions into a common space.

Once an overlap map is created for the subjects with abnormal performances (Fig. 3B)

and another for the subjects with normal performances (Fig. 3C), the overlap volume for

Fig. 3. (A) MAP-3 of the 139 subjects in the present study, seen both in the lateral and mesial views. The color bar

indicates the number of overlapping lesions at each voxel. (B) MAP-3 of the 76 subjects who had naming defects

(assessed with SD analysis) in at least one conceptual category. (C) MAP-3 of the 49 subjects who had normal

naming in all conceptual categories (color bar as in (A)). (D) Lesion-difference map: image of the subtraction of

the volume of the normal subjects from the volume of the abnormal subjects shown in (C) and (B). The color bar

indicates the number of residual overlaps on a voxel by voxel basis (right side for the abnormal group, left side for

the normal group).


the normal group is subtracted from the overlap volume of the abnormal group, again on a

voxel by voxel basis. In this way a volume is created that shows the regions in which there

is an excess of lesions of the abnormal group (or, on the other extreme, of the normal

group). This is the lesion-difference map (Fig. 3D). An area is considered as related to a

given defect when at least five impaired subjects are included in the lesion-difference map.

Unless stated otherwise, all results we present reflect overlaps of five or more subjects,

meaning that the overlap map is due to at least five abnormal subjects and to no normal

subjects, or that there is an excess of at least five abnormal subjects, over and above

whatever number of normal subjects might overlap in the region (e.g. 5 vs. 0, or 7 vs. 2, or

8 vs. 3, etc.).

The choice of five as a cutoff for “significant” residual overlap is supported by direct

testing for the possibility that such a residual overlap might occur by chance. We have

used empirically this cutoff in several previous studies. But we entertained the possibility

that it could possibly occur by chance alone. To investigate this possibility we created ten

random lists of the final sample of 139 subjects used in this study. The choice of ten groups

reflects the actual number of analyses run on the data. We took the first 20 subjects to stand

in for the random “abnormal” group and the next 90 to stand in for the random “normal”

group (a typical proportion between abnormal and normal groups in our studies). For eight

combinations no overlap of five subjects was detected; for the remaining two, there were

no more than a few clusters of contiguous voxels well below any of the regions described

as maximal lesion-difference maps. The largest volume of such contiguous cluster of

voxels was at least 12 times smaller than the smallest volume of contiguous clusters in the

lesion-difference maps in our results.

We also addressed this possibility of chance occurrence of five overlaps by calculating

this probability for each voxel, directly from the binomial distribution, as

P ¼ ðN!=ðN2kÞ!k!ÞpkqðN2kÞ, in which N is the total number of subjects with lesions at that

voxel, k is the number of impaired subjects found at that voxel, and p and q are the

probabilities that a subject randomly sampled would or would not be impaired. On

average, across all categories and types of analyses, a difference of five corresponds to

probabilities that are smaller than 0.001 (without correction for multiple comparisons). We

thus feel that our choice of five subjects as residual overlap is a meaningful threshold given

that it does not occur in random subtractions and that it is supported by conservative

probabilities that preclude false positives (see Table 1 for details). It also is important to

note that we are not producing statistical voxel-based maps but rather descriptive maps.

The scoring method, as described above, allowed for subjects to be classified as normal

or impaired in either naming or recognition. It also allowed for impairment to be found in

both naming and recognition, as for example in someone whose score for recognition

would be below 22 SD, and therefore abnormal in SD terms (but the subject still

recognized correctly at least 50% of the stimuli in the category under scrutiny), and whose

naming score was also below 22 SD (in those stimuli that were correctly recognized). On

the other hand, for that same conceptual category there would be subjects with exclusively

a naming defect (e.g. subjects with scores for recognition at 21.5 SD or better, but with

naming scores at 22 SD or worse). In order to detect possible underlying anatomical

differences between these two groups (those that for a given conceptual category showed a

naming defect only, and those that combined a naming defect with a recognition defect),


we created overlap maps for each of them, and subtracted those with the combined defect

from those with a naming defect only. These subtractions were conducted for all

conceptual categories and for the two types of analyses, SD and DA.

2.2. Results

The results discussed here pertain to the sub-group of 139 subjects (out of the initial

group of 169), who were tested in all five categories of concrete entities (unique persons,

and non-unique animals, tools/utensils, fruits/vegetables, and musical instruments). This

sub-group consisted of 84 subjects with left hemisphere lesions and 55 with

right hemisphere lesions; 126 were right-handers (þ90 or greater), four were left-handers

(290 or lower), and nine were of mixed-handedness (,þ90 and .290). The cause of

brain damage was cerebrovascular disease in 113, herpes simplex encephalitis in five, and

temporal lobectomy in 21. The number of subjects for each category of concrete entities

studied was thus 139.

2.2.1. Overall results

Using the SD approach the number of subjects with defects in at least one conceptual

category was 76 for word retrieval defects, with 58 in the left hemisphere (LH) and 18 in

the right hemisphere (RH), and 100 (58 LH and 42 RH) for concept retrieval defects. There

were 49 subjects who had normal naming across all five conceptual categories (19 LH and

30 RH), and 32 had normal recognition (21 LH and 11 RH). In brief, 55% of the total

group of subjects showed some naming defect (69% of those with left hemisphere lesions

and 35% of those with right hemisphere lesions) while 35% were normal, and 72% had

some recognition defect (70% LH and 75% RH) while 23% were normal. Respectively

10% and 4% were in the intermediary group, between 22.0 and 21.5 SD. Among the

subjects with either naming or recognition defects there was a group of 55 (43 LH

Table 1

Probability for a lesion-difference map overlap of five

N k 4 5 6 7 8

(A) Global mean (29 abnormal, 99 normal)

5 0.01018937 0.000597

6 0.02364252 0.00277024 0.0001352

7 0.04266737 0.00749911 0.0007322 0.00003054

8 0.06600108 0.01546692 0.00226536 0.0001896 0.000006942

(B) Recognition of fruits/vegetables analyzed in SD (worst ratio) (62 abnormal, 74 normal)

5 0.11750976 0.0196908

6 0.1918174 0.06428475 0.0089767

7 0.24353288 0.12242464 0.0341907 0.0040923

8 0.26502107 0.17763575 0.07441498 0.0178136 0.0018656

Calculation of the binomial probability for various lesion overlaps in the lesion-difference maps (A) for the

average number of abnormal subjects, 29 across categories and mode of analysis, and the average number of

normal subjects, 99, also across categories and modes of analysis, and (B) for the worst group, recognition of

fruits/vegetables, where there were 62 abnormal subjects for only 74 normal. Overlaps $5 are in bold.


and 12 RH) that showed both recognition and naming defects albeit often for different

categories, as will be shown below, leaving 20 (14%) with exclusively naming defects

(14 LH and 6 RH) and 42 (31%) with exclusively recognition defects (13 LH and 29 RH).

See Table 2 for more details.

Using the DA approach (Table 2) we limited our overall analysis to four conceptual

categories (we found that with respect to musical instruments there were so many subjects

at ceiling, both for recognition and for naming, that no meaningful separation into different

populations could be achieved). For DA the results show that 42% of all subjects have a

naming defect (62% of the left hemisphere lesions and 13% of the right) and 46% have a

recognition deficit (50% of the left hemisphere lesions and 40% of the right). Fifty percent

and 36% had respectively normal naming and normal recognition. The percentage of

subjects that showed a combination of recognition and naming defects was 26%, and those

with naming defect only were 14% and with recognition defect only 19%. Because the DA

approach could only be performed convincingly on four conceptual categories, we also

decided to analyze with the SD approach those four categories alone so as to be able to

compare better the results of the two approaches as shown in Table 2.

The assignment of subjects to normal and abnormal groups by SD and DA is not equal.

The SD lists actually contain nine and 19 subjects (for naming and recognition,

respectively) that are not part of the corresponding DA lists, and in the normal naming

group the DA list contains 11 subjects that are not in the normal SD list. Further analysis

showed that the non-overlapping subjects are all part of the borderline group in the other

type of analysis, with the exception of six subjects in the abnormal naming list of SD who

are in the normal list of DA. In these subjects the Z scores are between 22.16 and 22.48,

and the cutoff point in DA fell at 22.35 and 22.48.

Failures in naming occurred in a single conceptual category (in 37 and 38 cases for SD

and DA, respectively), in two categories (19 and 13, respectively), in three categories

(11 and 6, respectively), in four categories (8 and 5, respectively), and in all five categories

(7 in SD). Failures in recognition followed a similar pattern in that for a single conceptual

category there were 29 subjects in SD and 33 in DA, for two categories 34 SD and 19 DA,

for three categories 27 SD and 8 DA, for four categories 8 SD and 0 DA, and only one

subject had deficits in all five categories in the SD analysis (Table 3).

2.2.2. Results per conceptual category

The anatomical identification of the brain regions most often related to defects in each

of the conceptual categories was addressed creating lesion-difference maps for the groups

mentioned above in each conceptual category. Table 4 shows the distribution of subjects,

Table 5 shows the anatomical description of the areas of maximal lesion-difference

overlap, and Figs. 4 and 5 show the 3D brain images depicting the difference-overlap for

each conceptual category, for naming and for recognition.

Regardless of approach and of conceptual category, abnormal naming is seen most

often in subjects with left hemisphere lesions (see Tables 2 and 4). In other words, 69% of

all subjects with left hemisphere lesions showed naming defects in SD and 62% in DA.

Even so, defective naming occurred in some subjects with lesions in the right hemisphere;

33% (or 24% in the analysis using only four categories) of the subjects with naming

defects in the SD analysis had right hemisphere lesions (18 of 76, or 13 of 69), and 12% in


Table 2

Overall results for the two types of analysis

Four conceptual categories (excluding musical instruments) (%) All five conceptual categories (%)

Standard deviation analysis Distribution analysis Standard deviation analysis

Total of 139 Left

hemisphere

Right

hemisphere

Total of 139 Left

hemisphere

Right

hemisphere

Total of 139 Left

hemisphere

Right

hemisphere

Normal naming 69 (50) 29 (35) 40 (73) 59 (43) 23 (27) 36 (65) 49 (35) 19 (23) 30 (55)

Normal recognition 50 (36) 29 (35) 21 (38) 51 (37) 30 (36) 21 (38) 32 (23) 21 (25) 11 (20)

Normal naming

and recognition

36 (24) 16 (19) 20 (36) 28 (20) 14 (17) 14 (25) 15 (11) 9 (11) 6 (11)

Abnormal naming 59 (42) 52 (62) 7 (13) 69 (50) 56 (67) 13 (24) 76 (55) 58 (69) 18 (33)

Abnormal recognition 64 (46) 42 (50) 22 (40) 85 (61) 52 (62) 33 (60) 100 (72) 59 (70) 41 (75)

Abnormal

naming and recognition

36 (26) 34 (40) 2 (4) 46 (33) 38 (45) 8 (15) 55 (40) 43 (51) 12 (22)

Pure abnormal naming 19 (14) 15 (18) 4 (7) 23 (17 19 (23) 4 (7) 20 (14) 14 (17) 6 (11)

Pure abnormal recognition 27 (19) 8 (10) 19 (35) 38 (27) 13 (15) 25 (45) 42 (30) 13 (15) 29 (53)

Overall distribution of subjects with respect to their performance in naming and recognition. SD and DA analyses on the left were calculated for four conceptual

categories only (missing musical instruments, see text for details). On the right are calculations for all five conceptual categories using SD. The percentages are

calculated in relation to the whole group of 139 subjects (84 with left hemisphere lesions, 55 with right hemisphere lesions).

H.

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DA (7 of 59). With respect to abnormal recognition the distribution of lesions between the

hemispheres is more even (see Tables 2 and 4). There is one remarkable exception,

however: impairment of recognition of non-unique tools occurs with left hemisphere

lesions in all but two subjects (out of 14) in the SD analysis and in all with the DA

approach (see Table 4).

2.2.3. Anatomical description of lesion-difference maps

Although the lesions associated with abnormal naming overlap maximally in the left

hemisphere regardless of conceptual category, there are remarkable differences within the

left hemisphere relative to the conceptual category (see Table 5 and Fig. 4). Independently

of the type of analysis used, naming of unique persons fails mostly with lesions in the left

temporal pole, and naming of animals with lesions in left anterior IT, anterior insula, and

dorsal temporo-occipital junction, close to area MT. Failure to name tools occurs with

lesions in the posterior lateral temporo-occipito-parietal junction (classical area MT), the

inferior pre- and post-central gyri and the insula. Failure to name vegetables is associated

with lesions in inferior pre- and post-central gyri and anterior insula, and for musical

instruments with lesions in the temporal polar region and anterior IT, the posterolateral

temporal region (close to MT), the insula and the inferior pre- and post-central gyri.

Concerning recognition, the differences between the two approaches are also minimal.

Impaired recognition of unique faces, animals, tools, and vegetables is associated with

similar overlaps in the two types of analysis (see Table 5 and Fig. 5 for details). In short,

recognition of unique persons fails in association with lesion-difference overlaps in the

right temporal pole, angular gyrus, and lateral occipital cortices. Defective recognition of

animals is seen both with right and left hemisphere lesions in the mesial occipital regions

Table 3

Distribution of subjects with respect to failure in naming and recognition based on the number of conceptual

categories involved (SD and DA analyses)

No. of categories SD DA

LH RH Total LH RH Total

All s MI All s MI All s MI

(A) Recognition abnormal

1 16 20 13 14 29 34 20 13 33

2 23 17 11 13 34 30 13 6 19

3 14 7 13 6 27 13 5 3 8

4 5 1 3 0 8 1 0 0 0

5 1 NA 0 NA 1 NA NA NA NA

(B) Naming abnormal

1 16 22 12 9 28 31 31 7 38

2 16 16 5 3 21 19 13 0 13

3 10 9 1 0 11 0 6 0 6

4 8 6 0 0 14 0 5 0 5

5 6 NA 0 NA 6 NA NA NA NA

sMI: Without Musical Instruments


(mostly infracalcarine) and in the posterior IT regions. Defective recognition of vegetables

is also associated with lesions in both hemispheres but asymmetrically. Maximal lesion-

difference maps can be found in the left and right anterior temporal regions and in the right

latero-inferior IT and angular gyrus. Tools provide an exception to the rule that the right

hemisphere is involved when there are recognition defects. Tool recognition defects are

associated with lesion-difference overlaps only in the left hemisphere, in the MT region,

close to the location of the lesion-difference overlap seen for defective naming of tools.

Table 4

Results for naming and recognition deficits per conceptual category and type of analysis

Standard deviation analysis (%) Distribution analysis (%)

(A) Defective naming Total of 76 LH ¼ 58 RH ¼ 18 Total of 59 LH ¼ 52 RH ¼ 7

For persons 39 (51) 32 (55) 7 (39) 40 (67) 33 (63) 7 (87)

Persons only 10 8 2 23 17 6

For animals 32 (42) 29 (50) 3 (17) 22 (37) 22 (42) 0

Animals only 4 3 1 4 4 0

For tools/utensils 30 (39) 27 (47) 3 (17) 11 (18) 11 (21) 0

Tools/utensils only 5 3 2 1 1 0

For fruits/vegetables 26 (34) 23 (40) 3 (17) 18 (30) 18 (35) 0

Fruits/vegetables only 2 0 2 4 4 0

For musical instruments 45 (59) 36 (62) 9 (50) NA NA NA

Musical instruments only 7 3 5 NA NA NA

(B) Defective recognition Total of 100 LH ¼ 59 RH ¼ 41 Total of 64 LH ¼ 42 RH ¼ 22

For persons 25 (25) 9 (15) 16 (39) 30 (67) 14 (33) 16 (73)

Persons only 5 1 4 23 17 8

For animals 46 (46) 30 (51) 16 (39) 30 (47) 22 (52) 8 (36)

Animals only 7 6 1 11 8 3

For tools/utensils 14 (14) 12 (20) 2 (5) 9 (14) 9 (41) 0

Tools/utensils only 5 4 1 3 3 0

For fruits/vegetables 62 (62) 37 (63) 25 (61) 30 (47) 20 (35) 10 (45)

Fruits/vegetables only 20 12 8 10 8 0


Musical instruments only 16 7 9 NA NA NA

(C) Defective recognition and naming Total of 55 LH ¼ 43 RH ¼ 12 Total of 36 LH ¼ 34 RH ¼ 2

For persons 8 (14) 6 (14) 2 (5) 13 (36) 11 (32) 2 (100)

For animals 12 (22) 12 (28) 0 10 (28) 10 (29) 0

For tools/utensils 11 (20) 11 (26) 0 7 (19) 7 (19) 0

For fruits/vegetables 20 (36) 17 (39) 3 (7) 7 (19) 7 (21) 0


Distribution of subjects with respect to their performance in naming and recognition in each of the conceptual

categories (SD and DA analyses). (A) Naming defects. For each conceptual category the bold (e.g. persons

defective) is used when considering subjects with defect in that particular category regardless of other associated

naming defects. The line immediately below (e.g. persons only) conveys the number of subjects with a naming

defect in that category alone. (B) Recognition defects. The same principle used for (A) is applied here.

(C) Recognition and naming defects for each category, regardless of defects in other categories. The top line in

each group shows the total number of subjects with naming defect, recognition defect, or naming and recognition

defect combined. The total number is also given for each hemisphere separately.


Table 5

Maximal overlap of lesions using SD

Category Concept retrieval Word retrieval

Left

hemisphere

Right

hemisphere

Left

hemisphere

Right

hemisphere

(A) SD analysis

Persons – Temporal pole into

anterior IT

Temporal pole

into anterior IT

–

Angular G./lateral

occipital

Anterior

parahippocampal G.

Mesial infracalcarine Mesial occipital,

mostly infracalcarine

Anterior IT

extending into

parahip. G.

Mes. þ inf. Temporo-

occipital junction

Frontal operculum

(pars orbitalis)

Animals Mes. þ inf. Posterior IT Inferior pre-central G. –

Lateral

occipito-temporal

Anterior insula

Fruits/vegetables Temporal pole Latero-inferior IT

into temporal pole

Inferior pre- and

post-central gyrus

–

Lower sector

of frontal operculum

Angular gyrus Anterior insula

Tools/utensils MT (temporo-occipito

-parietal junction)

– MT (temporo-occipito-

parietal junction)

Inferior pre- and

post-central G.

Insula

Musical instruments Insula Angular G. Temporal pole –

Post.-inf. Frontal

operculum

MT (temporo-occipito-

parietal junction)

Anterior IT extending

into parahip. G. Post.-

lateral mid-temporal

gyrus (MT)

Interior pre- and

post central G.

Infero-lateral

temporo-occipital junc.

Insula

Infero-lateral

temporo-occipital junc.

Inferior pre- and

post-central G.

(B) DA analyses

Persons – Temporal pole into

anterior IT

Temporal pole

into anterior IT

–

Extending into ant

parahippoc. gyrus

Ant half of

parahip. gyrus

Angular G./lateral

occipital

Animals Mesial infracalcarine Mes. þ inf. Posterior IT Latero-inferior

anterior IT

–

Infero-lateral occipital Anterior insula

Lateral

occipito-temporal

(continued on next page)


2.2.4. Special analyses

We used a special analysis to address the finding that all groups of subjects with

abnormal naming contained two populations, one that showed a naming-only defect and

the other a combined naming and recognition defect. We obtained a lesion-difference

overlap by subtracting subjects with a combined naming and recognition defect from those

with a naming-only defect, for each of the conceptual categories. The results are as

follows: (1) for tools, naming-only defect occurs with lesion-difference overlap in the left

frontal operculum and inferior motor region, while for combined naming and recognition

defect the lesion-difference overlap is in posterior left temporal lobe, in the MT region;

(2) for musical instruments, the lesion-difference overlap for naming-only defect is in the

left auditory cortices (primary and immediately adjacent association cortices) (Fig. 6A,B);

and (3) for the other conceptual categories the lesion-difference overlaps did not differ

from the lesion-difference overlaps mentioned in the previous section.

Because the lesion-difference overlaps for naming defects for unique persons and for

animals shared the anterior IT region, and because some subjects had naming defect for

both unique persons and animals, while others showed abnormal naming of unique persons

but not animals, we compared these two groups to each other, subtracting the latter from

the former. The lesion-difference overlap shows a band of five or more overlaps in anterior

IT, immediately behind the polar region, corresponding to the lesions seen in the subjects

with the dual deficit (Fig. 6C).

2.2.5. Exceptions

We investigated the possibility that subjects with abnormal naming or recognition might

have lesions outside of the maximal lesion-difference map. We analyzed this possibility for

naming and recognition of persons, animals and tools using SD. The results are as follows.

1. Abnormal performance with non-overlapping lesions is found: (a) in 7% of subjects with

recognition defects for persons, 7% for animals, and 4% for tools; (b) in 12% of subjects

with naming defects for persons, 6% for animals, and another 6% for tools.

Table 5 (continued)

Category Concept retrieval Word retrieval

Left

hemisphere

Right

hemisphere

Left

hemisphere

Right

hemisphere

Fruits/vegetables Temporal pole Latero-inferior IT into

temporal pole

Inferior pre- and

post-central gyrus

–

Angular gyrus Anterior insula

Tools/utensils MT (temporo-occipito-

parietal junction)

– MT (temporor-occipito-

parietal junction)

Inferior pre- and

post-central G.

Musical instruments NA NA NA NA

Anatomical distribution of areas of maximal lesion-difference overlaps (five subjects or more) for naming

(word retrieval) and for recognition concept retrieval defects in each conceptual category.


2. Normal performance in spite of overlapping lesion is found in: (a) 5% of subjects

with normal recognition of persons, 2% of animals, and 4% of tools; (b) in 4% of

subjects with normal naming of persons, 7% of animals, and 4% of tools.

Abnormal recognition or naming with non-overlapping lesions occurred more often

with lesions of the left hemisphere for all the categories. In the future, we plan to

investigate the reasons behind these exceptions.

3. Evidence from functional neuroimaging studies

Hypothesis A, that naming of concrete entities belonging to different conceptual

categories depends on partially segregated brain regions, has also been addressed with

functional imaging techniques, specifically with [15O]water PET experiments. We have

performed experiments addressing the correlates of naming of entities in several

conceptual categories, including unique persons, and non-unique animals, tools,

vegetables, and musical instruments (Damasio et al., 1996, 2001; Eichhorn, Grabowski,

& Damasio, 2001; Grabowski et al., 2000, 2001). Because in normal subjects it is difficult,

if not impossible, to separate recognition and naming, the previously published results

have to be interpreted as possibly representing both processes, at least in part. Here, using

data from the original studies, we report on a cross-cohort random effects analysis of

findings for three primary categories (persons, animals, and tools). We also include a

comparison of the PET findings with the lesion results (for defective naming and for

defective recognition) described in the previous section.

3.1. Methods

3.1.1. Subjects

The subjects were normal, right-handed, English speaking adults with no history of

neurological or psychiatric disease who had 12 or more years of formal education. They

were studied in several cohorts of eight to ten subjects, most of which were balanced for

Fig. 4. Lesion-difference map for naming in all five conceptual categories of concrete entities in (A) using the SD

approach, and for four conceptual categories in (B) using the DA approach (see Section 2.1.4 and 2.5 for details).

Note that the images depicted in (A) and in (B) are very similar. The maximal overlap (defined as five subjects or

more) is consistently seen in the left hemisphere, although its position within the hemisphere varies with the

conceptual category. The overlap is located in the temporal pole for failure in naming unique persons; in anterior

lateral and ventral IT, posterior lateral IT, and lower frontal operculum/precentral gyrus for failure in naming

animals; in lateral posterior IT (MT area), and in the lower sector of the fronto-parietal operculum for failure in

naming tools/utensils; in the same lower sector of fronto-parietal operculum and in anterior IT for failure in

naming fruits/vegetables; and in temporal pole and anterior ventral IT, posterior lateral IT, and inferior fronto-

parietal operculum for failure in naming musical instruments. The numbers under each subtraction correspond to

the number of subjects in the abnormal and normal groups, respectively. The white lines on the lateral surface of

the 3D reconstructed brains indicate the position of the coronal slices to the right. The color bar at the bottom is to

be used as a reference for the numerical overlap corresponding to each color. The maximal overlap for abnormal

naming is in reds, and the maximal overlap of normal naming is in blue and violet.

R


gender. All subjects gave informed consent in accordance with the Human Subjects

Committee of the University of Iowa. Identical PET imaging techniques were used for all

subjects. Experimental tasks varied across cohorts.

3.1.2. Stimuli

Stimuli from the test batteries used in the lesion study were used for the naming tasks,

and subjects were asked to name the stimuli at the basic object level, and, for the case of

faces of known persons, at the unique level. A set of unknown human faces presented

either right-side up or upside down was used as a baseline task, and subjects were asked to

say aloud “up” or “down”. All stimuli were presented on a video screen suspended 15

inches from the subject, using a computer-driven video laser disk. The rate of presentation

for each set of stimuli that would produce naming success rates of approximately 90% was

determined in a pilot session with other normal subjects. The aim was to obtain high

performance, but not at ceiling, and to equate difficulty (i.e. success rate) across tasks

involving stimuli from different conceptual categories. In order to achieve similar levels of

performance, the rate of stimulus presentation had to be different among the tasks.

Familiar faces were presented every 2.5 s, tools and musical instruments every 1.8 s, and

animals and vegetables every 1.5 s. Stimuli for the face orientation judgement task were

presented every 1.0 s. Each stimulus was on screen for 25% of the stimulus duty cycle;

otherwise the screen was black. Faces were selected during a pilot session 24–48 h before

PET by having the subjects view a collection of famous faces (from the Iowa and Boston

Famous Faces tests). Subjects were told not to name any of the stimuli, and the

experimenter did not provide any names; subjects were asked to say if they knew the

person represented in each image, and to rate the degree of certainty with which they were

identifying the person in the picture. For each subject, the set of faces chosen for the PET

experiment was composed from those that the subject was certain to recognize. The reason

for customizing these stimuli sets was that we had found, in normative studies, enough

variability in the knowledge of persons, at the unique level, to make a universal stimulus

set incompatible with 90% performance success. On the other hand, no such problem was

noted for non-unique entities, for which we used universal sets of stimuli retrieved from

the group of stimuli used in the lesion studies, as described earlier. In the PET scanning

Fig. 5. Lesion-difference map for recognition in all five conceptual categories of concrete entities in (A) using the

SD approach, and for four conceptual categories in (B) using the DA approach (see Section 2.1.4 and 2.5 for details).

As in Fig. 4, the images generated with the two analyses are fairly comparable. However, here, with the exception of

the categories of persons and of tools/utensils, the areas of maximal overlap for the abnormal recognition groups are

located in both hemispheres. For failure in recognition of unique persons the maximal overlap is in the right

temporal pole and in the right angular gyrus. For tools/utensils the overlap is in the left hemisphere in the same

posterior IT region in which we saw the overlap for naming deficits (Fig. 4). For animals the overlap is clearly

bilateral and mostly in mesial occipital (infracalcarine) regions, on the right extending into the mesial and ventral

occipito-temporal junction. For fruits/vegetables the maximal overlap is also bilateral but mostly in the lateral and

ventral regions of anterior IT, involving on the right most of lateral IT and the angular gyrus. For musical

instruments the overlap seems less well defined spatially. It is bilateral, with angular gyrus involvement on the right,

and somewhat less focal on the left (a spotted appearance in the lateral and ventral posterior IT region, the lateral

occipital region, and the fronto-parietal operculum). Numbers, white lines and color bar as in Fig. 4.

R


session, subjects performed each task twice, in random order. During the PET scan,

subjects produced names to approximately 85% of stimuli in each category (slightly less

than determined during the pilot testing), and task performance success was similar among

the naming tasks (e.g. Table 6).

3.1.3. Data acquisition

PET data were acquired with a General Electric 4096 Plus whole body tomograph,

yielding 15 transaxial slices with a nominal interslice interval of 6.5 mm. For each

injection, 50 mCi of [15O]water was administered as a bolus through a venous catheter

(Herscovitch, Markham, & Raichle, 1983).

Subjects performed the task from 5 s after injection until 40 s after injection of

[15O]water. The bolus of labeled water reached the brain 12–15 s after injection (Hichwa,

Ponto, & Watkins, 1995). Thus, subjects performed the requested tasks for 35 s beginning

7–10 s before bolus arrival. Subjects then viewed a fixation cross for an additional 60 s

after injection (Cherry, Woods, Doshi, Bannerjee, & Mazziotta, 1995; Hurtig et al., 1994).

The spoken responses given by the subjects during each scan were audiotaped and later

digitized. Latencies to voice onset were determined for each item using custom software.

Overall performance during each injection was indexed by the median latency to voice

onset during the 30 s period beginning 5 s before bolus arrival in the brain and ending 25 s

after arrival.

All subjects also underwent a 3D MR scan using the protocol described in the section

on lesion studies (Section 2). The 3D reconstructions of the MR images were used to orient

the PET slices parallel to the long axis of the temporal lobe, with the lowest plane

intercepting the most ventral points in the temporal and the occipital poles (Grabowski

et al., 1995). The MR data were also used to define the a priori search volumes.

3.1.4. Data analysis

In each of the original experiments, reconstructed images of the distribution of

radioactive counts from each injection were coregistered with each other using AIR

(Woods et al., 1992). PET and MR data were coregistered using PET-Brainvox fiducials

(Damasio et al., 1993; Grabowski et al., 1995) and AIR (Woods, Mazziotta, & Cherry,

1993). Talairach space was constructed directly for each subject via user-identification of

the anterior and posterior commissures and the midsagittal plane in Brainvox. An

automated planar search routine defined the bounding box and a piecewise linear

Fig. 6. (A,B) Lesion-difference maps for the difference between naming-only defect (reds) and combined naming

and recognition defect (blue/magenta) for tools/utensils (N ¼ 18 and 12, respectively) in (A) and for musical

instruments (N ¼ 16 and 23, respectively) in (B). In (A) the anterior, frontal operculum overlap occurs for those

subjects who have naming-only defect, while the overlap in posterior lateral IT occurs when there are both

recognition and naming defects. In (B), naming-only defects show an overlap in the posterior sector of the

superior temporal gyrus, involving Heschl’s gyrus (first coronal slice), while a combined failure in recognition

and naming shows a strong overlap in the inferior sector of the temporal pole. (C) Lesion-difference map for the

subtraction of subjects with abnormal naming of persons and normal naming of animals (N ¼ 18) from those

with abnormal naming of both persons and animals (N ¼ 13).

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transformation was used (Frank et al., 1997) as defined in the Talairach atlas (Talairach &

Tournoux, 1988). After Talairach transformation, the MR data sets were warped (AIR 5th

order nonlinear algorithm) to an atlas space constructed by averaging 50 normal Talairach-

transformed brains, rewarping each brain to the average, and finally averaging them again

(analogous to the procedure described in Woods, Dapretto, Sicotte, Toga, & Mazziotta, 1999).

The PET data were warped to the atlas space using the AIR warping parameters

generated from the registration of the structural MR images to the atlas space. The

coregistered MR images were used to mask away extracerebral voxels from the PET

images; subsequently the PET data were smoothed with an isotropic 16 mm Gaussian

kernel by Fourier transformation, complex multiplication, and reverse Fourier

transformation. The final calculated image resolution was 18 £ 18 £ 18 mm.

Each of the individual experiments reported earlier focused on the contrast of a few specific

categories with the face orientation judgement task and used study cohorts of modest size,

typically ten subjects. In such small samples, contrasts of the categories with each other, which

entail smaller differences in activity, were not very powerful (Damasio et al., 1996). In order to

compare results of naming in the three main categories of entities – persons, animals and tools

– in a larger group of subjects we had to draw on subjects from several different cohorts. Since

the intended analysis could not be performed within-subject we used random effects analysis,

in which a function of between-subject, within-task variability is used in the test statistics. This

form of analysis entails lower degrees of freedom and lower power, a problem that can be

overcome by increasing the sample size.

Table 6

Composition and performance of the group of subjects used in the random effects analysis

Cohort N and gender Mean age Source Stimulus delivery Performance

ISI

(ms)

Integrated

latency/ISI

% Correct

(SD)

Latency

(ms)

(SD)

Naming

persons

8 men/8 women 29.5/28.4 Damasio

et al. (1996)

and Grabowski

et al. (2001)

2500 0.62 82 (11) 1332 (206)

Naming

animals

13 men/13 women 29.6/27.5 Eichhorn

et al. (2001)

and Grabowski

et al. (2000)

1500 0.72 93 (7) 1048 (193)

Naming

tools/utensils

12 men/13 women 33.6/33.8 Damasio

et al. (2001)

and

unpublished data

1800 0.59 95 (3) 1018 (134)

Source and scan-time performance for the subjects in the random effects analysis. The performance measures

are the mean latency for successful responses and the mean of the rate of performance. Integrated latency refers to

the proportion of the image acquisition time window during which subjects were attempting to retrieve words

(i.e. the acquisition time window minus the interval between successful response and the delivery of the next

stimulus). Note: for technical reasons no latency data were available for five subjects (two in person naming, one

in animal naming, and two in tool naming).


The following conditions were considered in the selection of subjects: (1) each subject

was included for only one task (e.g. for the subjects of Damasio et al. (1996) only the data

for person naming were included); (2) for each category, an equal number of men and

women were included; (3) mean age was balanced across categories. The final group

comprised 68 subjects. Table 6 shows the origin and performance measures for this cohort

of subjects.

The strategy was to compare the target tasks between groups of subjects by standardizing

activity in the target tasks (naming of stimuli) against the baseline (face orientation

judgement task) which was common to all studies, and therefore to all subjects. One

standardized image was computed per subject, as follows. As described above, the PET data

were warped to the standard space by using the AIR parameters generated by fitting

individual structural MR images to the standard space. In addition, each PET image was

normalized to a global mean of 1000 counts per voxel, by determining the mean activity

over all voxels within the brain (defined on coregistered MR images) and multiplying by the

appropriate scalar. In a final standardization step, the mean face orientation judgement task

image was subtracted from the mean naming task image. The reason this indirect approach

is necessary is that the anatomical standardization of the 2D PET images (i.e. utilizing the

warping fields that coregister the corresponding structural MR images) does not fully

account for interindividual anatomical differences, notably those arising from local

differences in macroscopic anatomy (i.e. the distribution of gray and white matter) and those

arising as artifacts of undersampling due to the relatively large PET slice thickness. In fixed

effects analyses with a general linear model, these subject-specific effects can be modeled

and accounted for across all images generated by the same subject. In random effects

analyses such as the one implemented here, with only one image allowed per subject, the

variance due to these effects cannot be modeled, with the result that it either contaminates

the task effect or else increases residual error and reduces sensitivity. Since these residual

anatomical differences are expected to be the same in the naming and face orientation

judgement tasks, they can effectively be removed by preliminary image subtraction and the

use of the subtraction images as the dependent variable in the random effects analysis. Thus,

the comparison of one naming task [N1] to another [N2] was actually implemented as

[N1 2 face orientation judgement] 2 [N2 2 face orientation judgement]. We refer to this

contrast as the contrast of standardized target task images. We stress that in this application,

the face orientation judgement task does not serve as a cognitive baseline, but as an arbitrary

activity standard that incorporates the same subject-specific anatomic effects (at the voxel

level) as the naming tasks. Note that the face orientation judgement task terms effectively

cancel each other (i.e. [N1 2 face orientation judgement] 2 [N2 2 face orientation

judgement] reduces to [N1 2 N2]), and therefore differences between the face orientation

baseline task requirements and the naming task requirements are not pertinent. This

approach does make the assumption that the correlates of the face orientation judgement

task are not systematically influenced by the experimental context, i.e. that its performance

and the brain activity correlating with performance are the same for the groups of subjects

who performed person, tool, and animal naming. One might argue that there might have

been some differential indirect effect of the various naming tasks on the face orientation

judgement task. There are several reasons why we think that such context effects are

unlikely: first, the face orientation judgement task performances were separated by at least


Table 7

Maxima from the PET analysis

x y z t x y z t x y z t

(A) Persons/FOJ Persons/animals Persons/tools

Temporal lobe

Pole L 2 40 7 2 24 4.10 2 39 3 2 18 3.35 2 39 2 2 18 3.27

R *38 *15 * 2 15 4.66 38 14 2 21 3.79

Sup. temp. sulc. L 2 53 2 14 2 2 4.55

R 53 2 9 2 13 4.38

61 2 33 5 4.50

Frontal lobe

Operculum L 234 23 21 7.44

Mes. orbital L 23 18 212 5.49

Cingulate

Anterior L 23 37 29 7.07 21 38 29 6.29

213 42 6 6.37 29 44 14 5.62

R 1 23 15 5.27 10 44 21 6.49

Posterior L 211 242 20 4.68

21 255 18 5.61

R 2 2 48 27 4.11

Brainstem L 23 232 216 5.75 22 230 217 4.29

(B) Animals/FOJ Animals/persons Animals/tools

Temporal lobe

Infero-temporal L 2 47 2 49 2 15 7.96 2 44 2 59 2 10 6.69 2 39 2 57 2 15 3.28

2 28 2 31 2 18 6.66 2 20 66 3 5.79

R 27 236 217 5.27 26 254 24 5.86 42 244 218 4.28

Frontal lobe

Operculum L 235 23 21 7.78

235 29 0 5.86

Occipital lobe

Dorso-lateral L 241 286 13 5.53 239 287 11 8.33 243 285 9 4.75

H.

Da

ma

sioet

al.

/C

og

nitio

n9

2(2

00

4)

17

9–

22

92

06

39 276 24 7.72

R 46 278 21 5.01

Mesial L 27 263 24 9.62 210 266 29 5.03 210 288 25 8.79

7 268 210 5.00

R 9 269 2 11.48 25 281 4 7.63 21 281 2 7.28

(C) Tools/FOJ Tools/persons Tools/animals

Temporal lobe

Infero-temporal L 251 250 212 7.03 249 260 26 7.05

222 214 224 4.74 228 243 213 4.64

226 235 213 6.78

R 27 253 26 4.89

Frontal lobe

Operculum L 235 27 0 7.66

227 10 31 6.13

Cingulate

Anterior L 23 33 32 5.21

211 24 34 4.71

Parietal lobe

Supramarg. G. L 258 214 32 4.02

R 55 228 39 5.18

46 214 12 4.52

Occipital lobe

Dorso-lateral L 223 280 27 5.26 226 280 24 6.10

Mesial L 219 271 12 6.19

25 265 11 5.90

Results from the PET study. The first column on the left contains the maxima for the contrasts between each target task and the face orientation judgement task (FOJ). The

two next columns show the maxima found for the standardized contrast between each category and the other two. The threshold, at P , 0:05, for the search volume (bilateral

temporal pole and left IT) is t ¼ 3:56, and for the whole brain search it is 4.62. Numbers in bold denote maxima in the search volume. Numbers in italic denote maxima that did

not reach the significance level at P , 0:05. *This maximum falls between the temporal pole and the orbital frontal region.

H.

Da

ma

sioet

al.

/C

og

nitio

n9

2(2

00

4)

17

9–

22

92

07

15 minutes from any preceding task; second, for any given subject the remaining tasks were

not just the ones chosen for this analysis, but several other naming tasks, in some cases even

overlapping with other target categories used in the present analysis (but excluded from this

analysis, as indicated above, so as to avoid mixing between- and within-subject effects);

third, scan-time behavioral indices (latency and performance success) were not significantly

different among the groups of subjects who contributed the images corresponding to the

three target categories. In summary, comparing standardized images of the target tasks

avoids the confound of subject-specific (mainly anatomical) effects; any differences

between the contrasts of the standardized target tasks should reflect differences between the

target tasks themselves.

The standardized target task data were analyzed with a general linear model (Friston

et al., 1995; Grabowski et al., 1996), with effects of task and gender included in the model.

We balanced gender within category, and included gender in the model because we have

data that show a significant contribution of gender to between-subjects functional

variability (Eichhorn et al., 2001).

Even if the purpose of this report is to analyze the PET data using an approach that

does not depend on the content of the baseline task, we took the opportunity to compare

the results of the new approach with the results obtained in the contrast of each of the

naming tasks with the face judgement task (our previous approach) in this larger cohort

of subjects.

For the comparison between lesion overlaps for defective word retrieval and

defective concept retrieval and the PET activations, the following procedures were used

on the data from the earlier reports mentioned before. For each of the conceptual

categories, an image of the volume of the region of significant activation, in the search

volume as well as in the whole brain, was created using Brainvox, and converted to a

binary (255/0) representation. A procedure using AIR3, analogous to that outlined

above, determined the parameters necessary to transform data in Talairach space to the

native space of the normal template brain used in the MAP-3 analyses. Using these

parameters, the binary Talairach space volumes were transformed into the MAP-3

space, permitting direct anatomical comparison of MAP-3 and PET results for each

conceptual category.

Fig. 7. PET results for the standardized comparisons between naming of persons, animals, and tools. (A) Persons

minus animals; (B) persons minus tools; (C) tools minus animals. Areas in red and blue represent significant

activations for each of the two categories being compared set at t ¼ 3:56. See Table 7 for the individual t values

for each of the regions depicted here. In the original search volume (bilateral temporal pole and left IT) there is

bilateral temporal pole activation for persons (seen in both A2, 5, 7, 8, and B2, 7), bilateral posterior IT activation

for animals (seen in A1, 2, 5, 6, 7, 9; and mostly on the right in C2, 7, 8), and mostly left posterior IT activation for

tools in B2, 5, 7 9. At whole brain search (t ¼ 4:62) there is activation in mesial frontal regions bilaterally and in

the posterior cingulated/precuneus region, also bilaterally, for persons as seen in A3, 4, 7, 8, 9, and in B3, 4, 8, 9.

Activation for animals is seen in mesial occipital cortices (A3, 4, 8, and C3, 4, 8). For tools there is activation in

supramarginal gyrus, bilaterally, particularly evident in the comparison between tools and animals (C1, 2, 5, 8,

but also evident in B1, 5, 6, 9).

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3.2. Results

3.2.1. Standardized comparison of results of naming experiments in three main categories

The standardized comparisons of naming persons, animals and tools among each other

reveal the following results, at whole brain level, with a significance threshold of 4.62 at

P , 0:05 (Table 7 and Fig. 7). The significance threshold for the search volume,

constituted by right and left temporal poles, and left IT, is 3.56 also at P , 0:05.

1. For persons minus animals areas of significant activation are: (1) right temporal pole;

(2) left anterior sector of the superior temporal sulcus; (3) mesial orbito-frontal sector;

and (4) anterior cingulate gyrus. The left temporal pole activation only reached a

marginal level of statistical significance (P ¼ 0:1).

2. For persons minus tools: (1) right temporal pole; and (2) mesial anterior sector of the

superior frontal gyrus and anterior cingulate gyrus. As in (1) the left temporal pole

activation only reached a marginal level of statistical significance (P ¼ 0:1).

3. For animals minus persons: (1) left and right posterior inferior IT; and (2) activation of

the occipital cortices bilaterally.

4. For animals minus tools: (1) no significant activation in posterior IT; and (2) activation

of occipital cortices bilaterally, more marked mesially than dorsolaterally.

5. For tools minus persons: (1) left and right posterior ventral IT sector; (2) left and right

dorsolateral occipital lobe, but not the mesial aspect; and (3) anterior sector of the

supramarginal gyrus is active but does not reach significance.

6. For tools minus animals: right supramarginal gyrus; the left supramarginal gyrus

activation does not reach significance.

3.2.2. Results from naming experiments using the old approach

The results from the analysis of naming persons, animals and tools minus the face

orientation judgement task can be seen in Table 7.

1. The maxima are extremely similar to those found in the standardized naming task

comparisons between target tasks with two exceptions. First, in all three baseline

subtractions there is a maximum in the left frontal operculum, which is not seen, in the

standardized naming task comparisons target. Second, the maxima seen in left IT for

the naming of animals and tools are seen when persons are subtracted from each of

them, but not when animals and tools are compared to each other using random effects

analysis (see Table 7 for details).

2. The maxima found in IT/temporal pole are similar to those reported earlier. The naming

persons minus face orientation judgement task showed a maximum in the left temporal

pole at 240, 7, 224 while previous analyses had shown maxima at 237, 3, 233

(Damasio et al., 1996), and at239, 15,217 (Grabowski et al., 2001), maxima that are at

10 and 10.6 mm distance. The naming animals minus face orientation judgement

task showed maxima in left IT at 247, 249, 215 and at 228, 231, 218, while the

previously reported maximum was at237,235,215 (Damasio et al., 1996), a distance

of 17 and 10 mm. The naming tools minus face orientation judgement task showed

maxima in left IT at 251, 250, 212, at 222, 214, 224, and at 226, 235, 213,


while the previously reported maxima were at 252, 246, 212 and at 231, 227, 219

(Damasio et al., 1996), and at 231, 239, 214 (Damasio et al., 2001). The first two

maxima found here are at a distance of 4 and 6.5 mm, respectively, from the first

maximum reported in 1996 and the one reported in 2001.

3.2.3. Comparison of lesion overlap volumes and PET activation volumes

Fig. 8 shows the comparison of regions activated in the earlier PET naming studies

(Damasio et al., 1996; Eichhorn et al., 2001; Grabowski et al., 2000, 2001) and regions of

maximal lesion overlap, associated with name retrieval defects as seen in Fig. 4A, for the same

conceptual categories, all rendered as volumes. Considering the naming of unique persons, the

region of maximal lesion overlap contains the region of left temporal pole activation seen in

the PET experiments. However, the PET experiment also identifies an area of frontal

activation, which has no counterpart in the lesion map. Considering the naming of animals,

both lesion studies and PET point to the left IT region, but the lesion overlap is mostly anterior

and lateral to the PET activation site. In both instances there is left frontal lobe involvement

but the PET activation site is anterior to the lesion overlap site. With respect to naming of tools,

there is overlap for lesion and PET, both in posterior lateral IT and in the fronto-parietal

operculum. As with naming animals, the activation site in the frontal lobe is anterior to the

lesion overlap site. For naming fruits and vegetables, the activation sites and the lesion sites

are close to each other and they partially overlap with those related to naming tools and

utensils. With respect to naming musical instruments, the lesion overlap is mostly in left

temporal pole and anterior ventral IT, while the activation site is immediately posterior to it.

The same PET results were also compared to the lesion data pertaining to defective

concept retrieval in the five conceptual categories as seen in Fig. 5A. As is suggested by

the inspection of the difference overlap maps in Fig. 5 and the activation images in Fig. 8,

apart from finding a close relation between the right temporal pole activation in naming

unique persons and one of the maximal overlaps found for the defective concept retrieval

of unique persons, and a relative closeness of the posterior temporal activation during

name retrieval for tools and the maximal different overlap region seen in defective concept

retrieval for tools, there is no strong relation, and even less overlap of brain regions,

between activation sites during the naming tasks and maximal difference overlaps for

concept retrieval defects related to the same conceptual categories of concrete entities.

4. Discussion

We begin our discussion by drawing conclusions from each set of results and from the

studies as a whole. We then comment on conclusions in the perspective of other pertinent

work, and conclude with an interpretation of the evidence in the perspective of the

theoretical framework that guided this investigation.

4.1. Lesion studies

From the evidence on lesion studies we can conclude as follows. First, the SD and DA

types of analysis generate slightly different lists of normal and abnormal subjects. In spite


of the difference, the maximal overlap maps are remarkably concordant (Figs. 4 and 5, and

Table 5). In the categories of animals (both for recognition and naming) and tools (naming

only) the cutoff point in DA for normal performance actually includes a few subjects who

were considered abnormal in SD analysis. In the remaining cases the discrepancy pertains

to subjects who are considered normal or abnormal in one approach, and, in the other, fall

in the intermediary group. The DA seems to detect differences that the more rigid SD

approach cannot detect. Even so, the two approaches yield comparable results. Of note, the

SD approach may allow the analysis of groups in which DA is impossible to apply because

a large number of subjects achieve perfect scores (as, for example, recognition and naming

of musical instruments).

Second, in general agreement with the hypothesis, naming is more often impaired with

lesions of the left hemisphere than the right. However, recognition defects are associated

with lesions that are more evenly distributed across the two hemispheres, against our

prediction that lesions causing defective recognition would be located predominantly in

the right hemisphere.

Third, the combination of naming and recognition defects, for which we did not have a

specific hypothesis, was found mainly with lesions of the left hemisphere, regardless of the

type of analysis. Recognition-only defects or naming-only defects were associated with

mostly right or mostly left lesions, respectively, as we would have expected (Table 2).

Fourth, there is ample support for the prediction that defects in naming or recognition in

different conceptual categories of concrete entities are related to spatially separable

lesions. The failure in naming unique persons, non-unique animals and tools is associated

predominantly with lesions in left IT. However, in a previous and more limited analysis

(Damasio et al., 1996), the lesion overlap associated with the failure to name animals did

not involve the frontal operculum nor the posterior lateral IT region, as we are reporting

here. The frontal opercular region had however been found to be related to naming of

animals in normal subjects studied with PET (Grabowski, Damasio, & Damasio, 1998). A

possible interpretation of the posterior lateral IT region of overlap, which is close to MT,

may be that it is related to the recall of the characteristic movement of different animals.

The failure to name stimuli in two other categories of non-unique entities not reported

previously, one natural (fruits/vegetables) and one man-made (musical instruments), is

associated with lesions in regions that overlap to some extent with those found to be

associated with the first three categories, e.g. the anterior IT region, as in the case of

animals, the inferior motor region, as in the case of tools, and the anterior IT/TP region, as

in the case of unique entities. This is as expected, since the two additional categories

contain entities that are as manipulable as tools, making it likely that some of the crucial

areas are shared. Finally, it is possible that the similarity of shapes noted within groups of

musical instruments (e.g. string instruments, woodwind instruments) requires more

disambiguation among the different yet similar exemplars, something that can be achieved

Fig. 8. Comparison of the location of the volumes corresponding to the maximal overlaps of lesions for deficits in

naming, and the location of the volumes corresponding to the areas of significant activation seen for the same

categories of entities. The volumes represented in red correspond to PET activations, those in blue to lesion

overlaps, and the areas in yellow to the regions shared by the two. The lesion sites and the PET activation sites are

in the same region at the large-scale systems level.

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by the engagement of more anterior temporal regions, as needed in the processing of

unique persons, thus explaining the overlap of musical instrument defects in the anterior

temporal sector.

For concept retrieval defects, the spatial separation among different conceptual

categories is less clear. The finding pertaining to recognition of tools, which is consonant

with our preliminary finding (Tranel, Logan et al., 1997), defies the traditional notion that

recognition is the province of the right hemisphere. The maximal overlap of lesions

associated with tool recognition defects is in the left hemisphere, close to the site of maximal

overlap associated with defects for naming tools. On the other hand, failure to recognize

unique persons is associated with a maximal overlap in the expected right temporal pole (as

previously found in Tranel, Logan et al., 1997), and yields an additional site in the right

angular gyrus and lateral occipital region. Lesions at the latter site have been associated with

prosopagnosia (Damasio, Damasio, & Van Hoesen, 1982; Damasio, Tranel, & Damasio,

1990; Tranel et al., 1995). The overlap of lesions related to the defective recognition of

animals is concordant with our previous findings (Tranel, Logan et al., 1997). The lesion

overlap associated with defects in recognition of fruits/vegetables and musical instruments

is less crisp. Once again, they fall in sectors related to the three original categories.

Fifth, the comparison of lesion overlaps for subjects with naming-only defects and with

combined naming and recognition defects, in the same conceptual category, revealed two

intriguing results. For the category of tools the subtraction of the combined naming and

recognition defect from the naming-only defect separated the two sites of maximal lesion-

difference seen in association with naming defects. The anterior locus seems to be

associated with the naming defect while the posterior locus seems to be associated with the

combined defects of recognition and naming. This result goes together with the fact that

the recognition defect overlap map only identified the posterior IT region, coinciding with

the MT region. This region has been reliably associated with the detection of movement

(Corbetta, Miezin, Dobmeyer, Shulman, & Petersen, 1990; Damasio et al., 2001; David &

Senior, 2000; Kable, Lease-Spellmeyer, & Chatterjee, 2002; Kourtzi & Kanwisher, 2000;

Senior et al., 2000; Watson et al., 1993; Zeki et al., 1991). The other interesting finding

pertains to musical instruments: when there is a naming-only defect, the maximal overlap

is in left auditory cortices.

4.2. Functional imaging studies

The PET results of the standardized naming task comparisons for persons, animals and

tools reveal segregated neuroanatomical correlates in agreement with the hypothesis. As

expected the comparisons also support previously published results from our laboratory,

even if the activation in the anterior temporal lobe region is less strong. When we restrict

the analysis to a temporal lobe search volume, as used in our previous PET reports, naming

animals contrasted with naming persons shows activation in posterior IT bilaterally, as

does the contrast of tools with persons. Naming persons contrasted with naming animals

and tools shows activation in both temporal poles, even if the left activation is of a lower

significance level.

Our PET results are generally comparable to results that have been reported from other

laboratories, in regard to the neural correlates of knowledge retrieval for various categories


of concrete entities. For example, a number of investigators have shown, based on PET or

fMRI techniques, that there is relative regional separation for categories such as persons,

animals, and artifacts (Cappa, Perani, Schnur, Tettamanti, & Fazio, 1998; Chao, Haxby, &

Martin, 1999; Chao & Martin, 2000; Chao, Weisberg, & Martin, 2002; Grafton, Fadiga,

Arbib, & Rizzolati, 1997; Grossman et al., 2002; Ishai, Ungeleider, Martin, Schouten, &

Haxby, 1999; Martin, Wiggs, Ungerleider, & Haxby, 1996; Moore & Price, 1999;

Mummery, Patterson, Hodges, & Price, 1998; Okada et al., 2000; Perani et al., 1995, 1999;

Whatmough, Chertkow, Murtha, & Hanratty, 2002). It has been pointed out (e.g. Devlin

et al., 2002; Joseph & Gathers, 2002; Moore & Price, 1999) that these studies have yielded

somewhat inconsistent results insofar as the details of the activation patterns are

concerned. Such discrepancies have been attributed to differences in stimuli, task

demands, baseline tasks, and subtraction approaches. Nonetheless, the general picture that

emerges from this body of work is that there is relative neural segregation associated with

different categories of concrete entities, and this is compatible with the idea that objects

are represented neurally according to features that have systematic category-related

differences (cf. Vinson & Vigliocco, 2002).

The results presented here, at the whole brain search level, show that naming persons,

regardless of the contrast, caused significant activations in the mesial orbital frontal

cortices. On the other hand, naming animals, regardless of the contrast, always showed

increased activation in the mesial aspect of the occipital lobes (early visual cortices), but

not in the frontal regions. Naming tools showed activation of the anterior region of the

supramarginal gyrus, mostly on the right. These results had not been mentioned in earlier

reports but, with the exception of the supramarginal gyrus activation, are seen in the

subtraction of the face orientation judgement task from each of the naming tasks. The fact

that they are present regardless of which subtraction is performed increases their

significance.

We interpret these results as follows:

1. Naming unique faces: in addition to requiring the participation of anterior temporal

lobe cortices (which we have related to the degree of complexity of the necessary

search), this task also requires regions related to the processing of emotions (e.g.

ventromedial prefrontal cortices and anterior cingulated), because unique familiar

faces, for most subjects, are emotionally competent stimuli, more so than the other two

categories. Earlier experiments in subjects with damage to these sectors have shown

that such patients fail to produce the discriminatory Skin Conductance Responses

(SCR) normally associated with known persons (Tranel et al., 1995), although they can

recognize and name familiar faces, suggesting that they fail to conjure up the emotional

tags associated with those familiar faces.

2. Naming animals shows a strong and systematic activation in occipital cortices

(particularly the mesial cortices) suggesting that, in addition to requiring the

participation of IT, naming animals involves visual association cortices extensively.

We had noted this effect before, but had not thought we could make a strong case for it

because the rate of presentation was not equal across categories, by design, and because

the stimuli had not been equated for psychophysical properties. However, the rate at

which animals and tools are presented is close, if not identical, and when we subtract


tools from animals a significant mesial occipital activation is still present. Moreover, we

have unpublished evidence showing that when subjects name animals from auditorily

presented stimuli, and in the absence of specific visual stimulation, there is a significant

activation in mesial occipital lobe (Tranel et al., under review). Finally, this finding

conforms well with the results of the lesion studies presented earlier, namely that there

are deficits in the recognition of animals with lesions in either left or right mesial occipital

regions while no such association is found for recognition of persons or tools/utensils.

3. Naming tools: in addition to requiring posterior IT, this suggests involvement of anterior

regions of the supramarginal gyrus, a somatosensory region, possibly related to the

particular characteristic of tools, namely their manipulability.

We do not mean to suggest that these studies reveal all the areas involved in the

recognition and naming of each category. It is likely that only the more salient effects

and/or those that are less variable across subjects reach significance.

Overall, the comparisons between lesion overlap data and PET activation data, whether

based on formal volume comparisons or mere inspection of the anatomical results, reveal a

substantial but by no means complete concordance. PET activations and lesion

“deactivations” fall in the same sectors of association cortices.

4.3. General conclusions

From the evidence presented above we can draw the following general conclusions.

1. The neural support for word retrieval does not depend on the two classic language areas

alone. Both lesion and functional imaging results indicate that a number of other areas

are required to support normal language processing.

2. Given the same entity, damage to certain language-related areas impairs naming but

does not preclude satisfactory recognition, while damage to other areas impairs concept

retrieval and thus precludes naming. At least in some instances, it is possible to separate

system components primarily dedicated to word retrieval or to concept retrieval.

3. The optimal retrieval of words for entities belonging to varied conceptual categories

depends on the integrity of anatomically separable regions. This suggests that there is

not one single system supporting word retrieval for all entities of all categories, but

rather several systems, segregated at least in part.

4. The same applies to the retrieval of concepts themselves. Some regions are consistently

associated with the retrieval of concepts for entities of certain categories, suggesting a

partial segregation.

5. At the large-scale systems level the anatomical regions identified by lesions, e.g.

dysfunction sites, are consistent with the anatomical regions identified in PET, e.g.

activation sites.

4.4. The results in the perspective of pertinent work

The investigation of category-related deficits in recognition and naming of various

concrete entities has received considerable attention over the past couple of decades.


Beginning with the studies of Warrington and her colleagues (Warrington & McCarthy,

1987, 1994; Warrington & Shallice, 1984), there have been a number of case studies of

subjects who had defective knowledge retrieval related to some conceptual categories but

not others (e.g. De Renzi & Lucchelli, 1994; Hart & Gordon, 1992; Laiacona, Barbarotto,

& Capitani, 1993; Laiacona & Capitani, 2001; Moss, Tyler, Durrant-Peatfield, & Bunn,

1998; Sartori, Job, Miozzo, Zago, & Marchiori, 1993; Sheridan & Humphreys, 1993;

Small, Hart, Nguyen, & Gordon, 1995; Tranel, Damasio, & Damasio, 1997a). Functional

imaging studies have also begun to contribute convergent data (Cappa et al., 1998; Chao

et al., 1999; Gerlach, Law, Gade, & Paulson, 1999; Gorno-Tempini et al., 1998; Martin

et al., 1996; Perani et al., 1999). A number of authors have recently reviewed such

data (Caramazza, 1998, 2000; Forde & Humphreys, 1999; Gainotti, 2000; Gainotti,

Silveri, Daniele, & Giustolisi, 1995; Humphreys & Forde, 2001; Martin, Ungerleider,

& Haxby, 2000).

There has been also a significant effort devoted to studying category-related effects in

retrieving words for concrete entities. Various case studies have been reported in which

patients had defects in retrieving words for some categories but not others (Goodglass,

Wingfield, & Ward, 1997; Hodges, Patterson, Oxbury, & Funnell, 1992; Luckhurst &

Lloyd-Jones, 2001; Sacchett & Humphreys, 1992; Silveri et al., 1997; Vitkovitch,

Humphreys, & Lloyd-Jones, 1993; Warrington & McCarthy, 1983). Electrophysiological

and functional imaging approaches have lent further support to the notion of category-

related word retrieval effects (see Dehaene, 1995; Grossman, 1998; Martin et al., 1995;

Nobre et al., 1994; Ojemann, 1991; Pulvermuller, 1999; Raichle et al., 1994; Saffran &

Schwartz, 1994; Saffran & Sholl, 1999). A recent study using single unit recording even

reported category-related effects at the level of single neurons (Kreiman, Koch, & Fried,

2000).

Nonetheless, as Gainotti (2000) pointed out, most cases of category-related recognition

or naming impairments have incomplete descriptions of the underlying brain lesions. Also,

recognition and naming have frequently been confounded. Many investigators have failed

to distinguish adequately between the capacity to know what an entity is (testable by

recognition), and to know what it is called (testable by naming). The results of studies that

do not make this distinction carefully are difficult to interpret. The problem is especially

salient when it comes to functional imaging studies because the distinction is more

difficult to make in normal subjects, although some investigators have tried to address the

question (e.g. Friston et al., 1996).

Be that as it may, it appears well established that the performances of brain-damaged

patients can reveal both category-related concept retrieval defects and category-related

word retrieval defects, consonant with the results reported here. However, some authors

have expressed doubts that in patients with category-related defects the separation of

concept and word retrieval defects has been convincingly established (Caramazza, 1998,

2000). That is a different issue, which we also address in this study. We conclude that in

certain circumstances the separation is both possible and convincing.

Another issue that our studies address regards the anatomical correlates of category-

related defects. Judging from both the lesion and functional imaging studies cited above,

there is considerable agreement on a number of facts that are also supported by our current

results, namely that category-related defects for different conceptual categories are


associated with different lesion sites, and that normal retrieval of concepts and words for

different categories is associated with different sites of activation.

Other facts, however, have not received the same support. For example, based on prior

observations and on the theoretical framework that guides our work (see next section), not

only should lesion sites and activation sites differ, relative to conceptual categories, but

there should be a principled distribution of those sites along the brain’s postero-anterior

axis, relative to category (Damasio & Damasio, 1994). We have proposed that concept and

name retrieval for non-unique items (items which can be recognized and named with little

need for disambiguation based on either intrinsic features or the context of which they are

normally a part) require structures located posteriorly along that axis (e.g. occipital

cortices), and that unique items (which do require substantial disambiguation based on

distinctions of physical structure and context) depend, for their concept retrieval and

naming, on anteriorly located brain structures, e.g. rostral temporal and frontal higher

order cortices; finally, we also proposed that the sensory channels and motor patterns

associated with the learning and utilization of an entity play a role in the location of the

structures required to recognize and name it (e.g. a manipulable tool vs. an entity that is

only processed by vision or smell).

The evidence presented here is supportive of these predictions. For example, naming of

unique faces (the entities whose recognition and naming require the highest disambigua-

tion of physical structures and the highest recall of pertinent context) is associated with

rostral temporal and frontal regions as shown by both the lesions that impair performance

and by the activation sites correlated with normal performance. The naming and

recognition of non-unique animals is also supportive of the principle, in that their

performance is associated with infero-temporal and posterior occipital regions. Moreover,

the recognition and naming of tools again supports a prediction: the brain regions critically

associated with these performances include an area known to be specialized for visual

movement (MT), and a contiguous territory located in the proximity of somatosensory

regions involved in manipulation.

The previously published lesion-based results are consonant with the results presented

here, even if the number of patients is usually small by comparison. However, we must

note some exceptions reported by Semenza (1997) of patients in whom defects in naming

of unique persons were not associated with rostral left temporal lesions. This is of great

interest because we too have found such instances of exception, as reported in the current

study. The point, however, is the frequency of such exceptions. The frequency is low in

our large sample and, in accord to our expectation, even lower for non-unique than unique

entities. The report of such exceptions does not alter the conclusions presented earlier. The

reasons why such exceptions arise are another matter and are the focus of an ongoing

investigation in our laboratory.

Finally, we should address an issue of interpretation of the results presented here. We

have suggested that the defects identified in our patients may be the result of a breakdown

in intermediary processes (see next section). In the case of defective word retrieval, the

mediation would break down between conceptual processing and word retrieval; in the

case of defective recognition the mediation would break down among different

components whose recall is necessary for the retrieval of the concept. Likewise, for

functional imaging experiments, we have suggested that the activation sites may be related


to intermediary processes. We should point out that the PET studies were not designed to

separate the processes involved in concept and name retrieval. However, the results are

compatible with the notion that some of the sites we identified in the left temporal lobe

may play a mediational role between concept retrieval and word retrieval because the

congruence between the PET results and lesion results is considerably greater for the

naming comparison than for the concept comparison. To resolve the remaining questions

further functional imaging experiments are needed to distinguish specific correlates of

concept retrieval and of name retrieval.

We must also note that there is evidence from other laboratories that would not be in

agreement with our interpretation. For example, Indefrey and Levelt (2000) produced a

meta-analysis of published naming data from functional imaging studies (which included

some of our previous data), and concluded, “the core process of word production is

subserved by a left lateralized perisylvian/thalamic language production network”. If the

authors encompass in their word production core process the sort of function which we are

designating as intermediary, their results are in conflict with ours because they do not

include the higher-order cortices of the temporal lobe, outside of the perisylvian area, that

we regard as necessary for the intermediary process. Also we believe that some of “the

core processes of word production” the authors have in mind are compromised in our

patients with naming difficulties, and that what the authors call “lead-in processes” are

shown to be intact in those patients. There would be no disagreement, however, if the

authors are simply identifying a system resembling our implementation system. Moreover,

the apparent discrepancy might simply indicate the mismatch between Levelt’s detailed

cognitive/linguistic model on the one hand (Levelt, Roelofs, & Meyer, 1999), and the

anatomical scale at which lesion experiments and functional imaging experiments must be

performed for the time being.

4.5. Recognition and naming in the perspective of our theoretical framework

To interpret the evidence presented above we use a theoretical framework discussed

elsewhere (A. Damasio, 1989a,b, 2000; Damasio & Damasio, 1994; Damasio, Damasio

et al., 1990). A brief sketch of the framework is needed at this point.

As far as mental processes are concerned, the system operates on images, by which we

mean explicit, on-line mental patterns of any sensory type (e.g. visual, auditory,

somatosensory), some of which constitute the manifest mental contents that we experience

consciously, while others remain nonconscious. The principal neural substrates for images

are the explicit neural patterns (or maps) formed in areas of cerebral cortex located in and

around the points of entry of sensory signals in the cerebral cortex (the early sensory

cortices). These neural patterns continuously change under the influence of external and

internal inputs involved, for example, in perception or recall. The system’s factual

knowledge base, as well as the know-how mechanisms for image and action processing,

are contained in dispositions, with which (a) images can be constructed from recall, (b) the

processing of images can be facilitated, (c) movements can be generated, and (d) biological

processes can be regulated. The “contents” of dispositions are implicit, i.e. they exist in

latent form and are nonconscious. The engagement of dispositions produces a wide variety

of explicit outcomes, for example, the attempt to reconstruct from memory an image that


was actually perceived on a previous occasion, or the deployment of a learned action. All

knowledge, that which was accumulated in evolution and is innately available, and that

which has been acquired through learning, exists in dispositional form (implicitly,

covertly, nonconsciously), with the potential to become an explicit image or action.

The neural substrates for dispositions are distributed in higher-order cortices, parts of

the limbic cortices, and numerous subcortical nuclei (e.g. basal ganglia, amygdala). When

disposition circuits are activated, they signal to other circuits and cause either images or

actions to be generated elsewhere in the brain. The framework posits that dispositions are

held in neuron ensembles called convergence zones. Convergence zones are made of

microcircuits and can not be resolved individually by current neuroimaging techniques,

although it is presumed that aggregates of many activated convergence zones constitute,

temporarily, anatomically macroscopic sets that can be visualized by current techniques.

Convergence zones of comparable functional level are distributed within convergence

regions which correspond to large-scale sectors of the brain, e.g. temporal pole, anterior

IT, frontal operculum. The hypotheses and evidence discussed in this study pertain

entirely to the convergence region level, not the convergence zone level, as do the

examples discussed around Fig. 9.

The basic neuroanatomical design of convergence regions and convergence zones is

available prior to individual experience, in higher-order cortices, and is then shaped by

individual learning – the process of learning selects the input–output projections that

optimally perform certain tasks in certain circumstances. The records of learning are held

in the form of cellular transformations in the microcircuitry, on the basis of which the re-

activation of the microcircuits is facilitated, along with their subsequent firing towards

connected microcircuit nodes along a multi-component system.

Because of the constraints of the brain’s anatomical design, convergence zones

involved in certain classes of tasks are likely to be found, in most individuals, in the same

large-scale convergence region of the brain, e.g. posterior IT, frontal operculum. There is

no reason to expect, however, that micro scale convergence zones would be found in equal

sites across individuals. On the contrary, we expect that, in the very same individual, the

same task will engage different convergence zones, depending on task demand, exact

stimulus, etc., and that the same task will require the engagement of different convergence

zones across comparable individuals. However, both in single subjects and across subjects,

we expect all of those different convergence zones to be activated within the same

convergence region. Thus, we should not expect to find nearly equal lesions associated

with precisely the same defects; likewise, we should not expect, in functional imaging

experiments, that the same tasks activate or deactivate sites with precisely the same

coordinates. The process of anatomical selection of convergence zones both during

learning and during subsequent operation is probability-driven, flexible, and individua-

lized. Only at the large-scale should the spatial placement of convergence zones be

predictable in single subjects and across individuals.

We begin the interpretation by addressing the process of concept retrieval. When a

stimulus depicting a concrete entity (the cup in Fig. 9A) is shown to a subject, the visual

properties of the stimulus prompt activity in primary and early visual association cortices

of both hemispheres (#1 in Fig. 9A). This is the sector in which explicit, visually-related

neural patterns give rise to visual images. Next, engagement of intermediary regions


occurs in higher order association cortices, which are the basis for dispositions necessary

for concept retrieval (#2 in Fig. 9A). In our example, the engagement of dispositions

results in a reconstruction of explicit sensorimotor patterns pertaining to the object, which

occur in the appropriate early sensory cortices and motor structures. The evocation of

some parts of the large number of such patterns and in varied sensorimotor cortices, over a

brief lapse of time, constitutes the conceptual evocation for the object (#3 in Fig. 9A).

When a concept from another category is evoked, for example that of a familiar person

(Fig. 9B), different intermediary regions are engaged and a wider array of concept-related

cortices becomes involved (#3 in Fig. 9B). The results presented here speak to the

possibility that postero-lateral temporal regions (more so in the left hemisphere) are

necessary to recognize tools normally, given that damage in this region impairs

recognition. Neither lesions nor PET suggest that anterior IT cortices are required for the

recognition of tools. This is not the case, however, for the recognition process for familiar

faces. Lesions in right IT and temporo-polar regions and also posterior temporo-occipital

regions preclude the normal recognition of familiar persons. Also, during naming of

familiar persons, the PET results show activation in right temporal lobe which we regard

as related to the recognition phase preceding name retrieval. The orbito-frontal and

anterior cingulate activation also appear related to the recognition of familiar persons,

probably providing pertinent emotional knowledge capable of facilitating the recognition

process (lesions in this region do not impair overt recognition of familiar faces but

interfere with recall of emotion-related knowledge for those faces as mentioned earlier).

Obviously, we are oversimplifying the number of steps that these processes entail, in the

hope that the skeletal mechanism we are describing here may be appropriate to match with

the level of system analysis that lesion and functional imaging data currently permit.

Naming a stimulus from a particular conceptual category is dependent on three kinds of

neural structures: (1) structures which support conceptual knowledge that were just

mentioned; (2) structures which support the implementation of word-forms in eventual

vocalization (the classical language areas located in the left perisylvian region, including

Broca and Wernicke areas); and (3) intermediary structures for “words”, which are

anatomically separable from the other two kinds of structures and from the intermediary

structures for the retrieval of the concept, at least in part. In the naming process,

the intermediary structures are engaged by the structures in (1) to trigger and guide the

implementation process executed by the structures in (2).

Consider again Fig. 9. When a subject is shown a picture of a cup or a picture of a familiar

person (e.g. John F. Kennedy) and is asked to name the item, there will be activation of the

appropriate “concept” intermediary regions, which will promote concept retrieval as

outlined above. Activation in the concept intermediary regions leads to activation in the

corresponding “word” intermediary region, and in turn to the activation of the “dispositions

for naming” (#4 in Fig. 9). We note again that the operation is flexible, probabilistic,

context-dependent, and conceptual-category-dependent. The dispositions are not words;

they are abstract records of potentialities. Words (or signs as in American Sign Language),

just as any other item of knowledge, exist in dispositional form, but can be expressed as

images (the mental representations of words) and as actions (speech and written patterns).

The areas engaged in the two examples are quite different from each other. In the case

of naming a cup, the critical regions are largely in left posterior temporal occipital cortices


Fig. 9. (A) Schematic representation of the brain structures involved in the retrieval of concept and production of

a word denoting a visually presented non-unique concrete entity, a cup. The image processing begins in primary

visual cortices of both hemispheres and continues along a cascade of divergent and later convergent association

cortices. Signals related to this process eventually reach motor-related cortices. In this particular example we

propose the following: (1) bilateral engagement of early visual cortices is followed by engagement of (2) left


(#4 in Fig. 9A) while in the case of naming John F. Kennedy they are in left anterior

temporal regions (#4 in Fig. 9B). The lesion data suggest that the convergence regions

containing dispositions for naming in the case of tools or utensils are found in postero-

lateral temporal regions and in inferior sensorimotor regions, mostly in the left

hemisphere, while PET data suggest that bilateral sensorimotor regions and posterior IT

are involved. For naming of familiar persons, both PET and lesion data point to the left

anterior temporal regions as the likely convergence regions holding dispositions for proper

name retrieval.

Activation of the “word” intermediary region promotes the retrieval of lexical

knowledge required for word production (e.g. the phoneme maps of the appropriate

morphemes as in #5 of Fig. 9A,B). This is likely to require auditory structures (areas

41/42; 22), which overlap in part with the classic Wernicke’s area. In another step,

somatomotor (3/1/2; 40; 44/45) structures are engaged for the phonetic sequencing and

production of the actual spoken name (#6 in Fig. 9A,B); these structures overlap in part

with the classical Broca’s area. These two last steps are also supported by lesion data.

Lesions in superior temporal gyrus involving the auditory cortices just mentioned cause

defects in the proper sequencing of phonemes and morphemes that are part of words

related to any conceptual category. The defect is pervasive, as we might expect, and not

category-related. Our PET studies never show activation in this region, and again, this may

well be due to the fact that any task involving name retrieval engages this area and the

subtraction techniques mask the possible activation. Lesions in the left inferior frontal

gyrus also have a tendency to cause more pervasive and non-category-related defects of

language output. Again the PET results do not reveal activations in left frontal operculum.

However, when the baseline task is subtracted from the target tasks, there is a consistent

activation of the left frontal operculum suggesting its involvement in the vocalization of

the actual names.

We propose that the entire system operates in reverse when the stimulus is a word and

concept retrieval is the goal of the task. We note, again, that we are oversimplifying, in the

sense that the retrieval of lexical knowledge is likely to entail many intermediate steps, as

suggested for example in the work of Levelt (Levelt et al., 1999), all or most of which will

parieto-occipital cortices (which hold dispositions for concept retrieval); (3) engagement of cortices which hold

dispositions related to the concept; (4) bi-directional connections among these areas probably allow for iterative

activations and eventually lead to engagement of (5) regions which hold dispositions for naming; (6) engagement

of areas capable of phonemic map retrieval, e.g. in the left superior temporal gyrus, necessary to guide (7) the

regions in charge of word production to construct the phonetic sequence of the actual spoken word. (B) Schematic

representation of the brain structures involved in the retrieval of concept and production of a word denoting a

visually presented unique concrete entity, a picture of John F. Kennedy. As in (A), the visual presentation of the

face triggers activity in early visual cortices of both hemispheres (1), and (2 and 3) engages regions necessary for

concept retrieval (which are assumed to be bilateral and skewed to the temporal cortices in the case of human

faces, and also in bilateral orbito-frontal cortices and anterior cingulate gyrus). (4) The same iterative engagement

referred to in (A) leads to (5) engagement of regions necessary for assembly of phonemic maps, and for (6) word

production. (For depiction of schematic representation of the course of producing the name of a concrete entity

when the stimulus is the characteristic sound of the entity rather than the image see Eichhorn et al. (2001) and

Tranel, Damasio, Eichhorn, Grabowski, Ponto and Hichwa, (2003).

R


need to be correlated with microanatomical loci, something that is not possible at the

moment.

Acknowledgements

This study was supported in part by NINDS grant PO1 NS19632 and NIDCD P50

DC03189. We thank Jon Spradling, Denise Krutzfeldt, Kathy Jones, Jocelyn Cole, Ken

Manzel, Emily Ives and Patricia Cassidy for their help in the coordination of subjects,

preparation and execution of experiments, gathering of neuropsychological and

neuroimaging data, and preparation of the manuscript.

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