The Heterogeneity of Category-Specific Semantic Disorders: Evidence from a New Case

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The Heterogeneity of Category-Specific SemanticDisorders: Evidence from a New CaseCristina Rosazza , Emilia Imbornone , Marco Zorzi , Elisabetta Farina , Leonora Chiavari &Stefano F. CappaPublished online: 09 Aug 2010.

To cite this article: Cristina Rosazza , Emilia Imbornone , Marco Zorzi , Elisabetta Farina , Leonora Chiavari & Stefano F.Cappa (2003) The Heterogeneity of Category-Specific Semantic Disorders: Evidence from a New Case, Neurocase: The NeuralBasis of Cognition, 9:3, 189-202

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The Heterogeneity of Category-Specific SemanticDisorders: Evidence from a New Case

Cristina Rosazza1, Emilia Imbornone2, Marco Zorzi1, Elisabetta Farina2, Leonora Chiavari2

and Stefano F. Cappa1

1Department of Psychology, Universita Vita Salute San Raffaele, Milan, Italy and 2Unita di Neurologia Riabilitativa, IRCCS SantaMaria Nascente, Fondazione Don Carlo Gnocchi Onlus, Milan, Italy

Abstract

We report a new case of category-specific semantic impairment, affecting living entities, in a patient with traumatic braindamage. In the present investigation we attempted to replicate as closely as possible the testing procedures whichhave been developed by Caramazza and Shelton (1998) to evaluate EW, a patient with a selective semantic disorder forthe animal category. The results in our patient indicated a different performance profile, characterised by a moreextensive semantic disorder for living entities, and by a more severe loss of specific visual rather than functionalknowledge. These findings concur with other evidence indicating that category-specific semantic disorders areheterogeneous, reflecting different mechanisms of impairment, most likely associated with different neurobiologicalunderpinnings.

Introduction

Category-specific semantic impairments have attracted con-

siderable attention in neuropsychology because of their con-

tribution to the understanding of the organisation, of the

mechanisms and of the neuroanatomical bases of semantic

memory. Various patterns of deficit have been pointed out.

Coltheart et al. (1998) suggested that selective deficits of

semantic memory can be classified according to three distinctclasses: (i) category-specific semantic impairments, (ii) input-

modality specific semantic impairments and (iii) attribute-

specific semantic impairments, with some patients showing

more than one type of selectivity. Among the examples of the

first class there are impairments related to the distinction

between abstract and concrete words (Warrington, 1975),

as well as the most frequently reported dissociation between

knowledge of living things and man-made artefacts(Warrington and Shallice, 1984; Laiacona et al., 1997; Cappa

et al., 1998; Caramazza and Shelton, 1998; Samson et al.,

1998; Gainotti, 2000). In patients belonging to the second

class of selective semantic impairment, the ability to perform

semantic tasks depends on the modality of stimulus input, i.e.

for example, pictures or words (McCarthy and Warrington,

1988). Finally, attribute-specific semantic impairments are

characterised by the patient’s inability to retrieve specificsemantic attributes in semantic memory (for example, visual

information about objects), whereas other semantic attributes

are accessible (Coltheart et al., 1998). Although patients with

an isolated, selective attribute impairment are rare (Coltheart

et al., 1998; Lambon Ralph et al., 1998), there are some cases

of combined attribute-categorical impairments. For example,

Michelangelo (Sartori and Job, 1988), L.A. (Silveri and

Gainotti, 1988), Giulietta (Sartori et al., 1993) and Felicia

(De Renzi and Lucchelli, 1994) represent cases of category-

specific deficit restricted to living things in association withattribute-specific impairments for visual knowledge.

As detailed above, data from the literature agree that dif-

ferent semantic categories can be damaged in isolation, but

several explanations have been suggested to account for them.

These explanations can be divided into two broad categories:

reductionist theories and non-reductionist theories. Among the

reductionist theories, the first to be proposed was the sensory/

functional theory (SFT): Warrington and Shallice (1984)suggested that semantic memory is organised by modality

(visual, olfactory, motor/functional . . . ), i.e. according to the

type of semantic information, rather than category per se.

According to this account, visual (sensory) and functional

features have different weights in the identification of mem-

bers of living and non-living categories, respectively: as a

consequence, damage to visual semantic subsystem results in

impairment of living things, whereas damage to the functionalsubsystem results in impairment of non-living things.

Recently, Moss, Tyler and colleagues proposed another reduc-

tionist model (Durrant-Peatfield et al., 1997; Moss and Tyler,

Neurocase 1355-4794/03/0903–189$16.002003, Vol. 9, No. 3, pp. 189–202 # Swets & Zeitlinger

Correspondence to: Stefano F. Cappa, M.D., Vita Salute San Raffaele S. Raffaele University, DIBIT Via Olgettina 58, 20132 Milan, Italy.Tel: þ39 0226434887 (secr 4784); Fax: þ39 0226434892; e-mail: [email protected]

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2000). They emphasise the concept of intercorrelation between

perceptual and functional features, and the different role of

shared semantic properties versus distinct semantic properties.

A similar proposal had been already put forward by De Renzi

and Lucchelli (1994). Their patient had a deficit in the retrieval

of perceptual attributes. Her performance with artefacts was

better, according to the authors, because non-living items can

access their structural representation, since shape and functionare in a close correspondence. Similarly, Laiacona et al. (1997)

claimed that living entities are more vulnerable than non-living

ones because perceptual and functional properties show a

lesser degree of correlation. Moss and Tyler’s proposal is more

articulated. They suggest that for living things the shared

functional (biological) properties (e.g. can see, can run, can

hear, etc.) and the shared perceptual properties (e.g. has eyes,

has legs, has ears) are highly intercorrelated. On the other hand,the distinctive properties (e.g. has a mane, is pink, chases mice)

tend to be weakly correlated and therefore very vulnerable to

damage. For artefacts, the pattern is reversed: non-living items

have strong correlations between pairs of individual, distinc-

tive form and function properties (e.g. has a serrated edge – can

cut), whereas the shared properties are fewer and less

correlated than those of living things. When the corresponding

neural network model is lesioned by a random removal ofconnections, category-specific impairments can arise: with

mild degrees of damage, non-living entities are less affected,

because of the presence of the strong form-function intercor-

relations among the distinctive features of these items. With

more severe levels of lesioning, artefacts are more affected

because the model can only operate on shared properties and

living items are more resistant because they are supported by a

greater degree of shared, intercorrelated properties.An influential, non-reductionist theory is the domain-specific

knowledge hypothesis proposed by Caramazza and Shelton

(1998). According to these authors, semantic memory is orga-

nised categorically in the brain and separate neural systems may

have become specialised, under evolutionary pressures, for the

recognition of animals (that are potential predators and a source

of food) and plants (that are a source of food and medicine). On

this view the only ‘‘true’’ category-specific deficits will be thosethat involve the category of animals, plant life and by contrast

artefacts. These, and only these, three categories form the basis

for the organisation of conceptual knowledge.

Problematic findings exist for each of these theories. The

reductionist theories have clear limitations. As for the sen-

sory-functional theory (SFT), patient EW reported by

Caramazza and Shelton (1998) showed a deficit restricted

to the category of animals, whereas she was equally impairedon functional and perceptual notions about animate entities.

There was no evidence that the two patients studied by

Laiacona et al. (1997) with a deficit for living entities showed

a greater loss of visual than functional information. Further,

there is some evidence of cases with defective visual knowl-

edge that do not display a category-specific effect (Coltheart

et al., 1998; Lambon Ralph et al., 1998). These cases run

against the SFT.

Moreover, the model of Moss, Tyler and colleagues

(Durrant-Peatfield et al., 1997; Moss and Tyler, 2000), in

which an initial advantage for non-living entities is replaced

by an advantage for living ones, has not been empirically

supported (Garrard et al., 2001).

On the other hand, the domain-specific knowledge hypoth-

esis does not account for a number of cases of category

specific impairments restricted to living things in whichperceptual information was specifically lost (Sartori and

Job, 1988; Silveri and Gainotti, 1988; Sartori et al., 1993;

De Renzi and Lucchelli, 1994), as well as for the impairment

of categories which do not have evolutionary significance,

such as musical instruments, body parts or gems (but see

Caramazza and Shelton, 1998, for a criticism of the evidence).

Different hypotheses have been proposed in order to account

for category-specific disorders, but only a few efforts havebeen made to capture the heterogeneity of the cases described

in the literature (Humphreys and Forde, 2001). Each hypoth-

esis tends to focus on selected cases, without taking into

account the differences among patients at the psycholinguistic

(categories impaired, task effects) and neural level (aetiology,

localisation of the brain lesion).

At this stage an important question is still open: does a

unique interpretative hypothesis apply to each patient withcategory-specific semantic impairment? It is critically impor-

tant to understand if variability gives us clues to the different

mechanisms of impairment or if it is only the effect of

nuisance variables, which obscure an underlying unique

mechanism. Another important factor to take into account

from this point of view is the way in which the patient is

tested. It is possible that different methodologies affect the

results, because of differences in processing requirements,insufficiently detailed assessment or inadequate control of

nuisance variables.

With this problem in mind, we decided to submit a brain-

damaged patient, MA, with a deficit for living things, to the

same testing procedures employed by Caramazza and Shelton

(1998). The reasons for this choice were twofold. In the first

place, the procedures employed in this case study are excep-

tionally detailed and well-controlled. Second, the results ofthe investigation, which showed a selective disorder for

animals, without any differential impairment in perceptual/

functional knowledge, led the authors to reject reductionist

approaches, in favour of the ‘‘domain-specific’’ account.

The aim of this study is to replicate the methodology used by

Caramazza and Shelton (1998) to another case of category-

specific semantic impairment in order to assess, first, whether

their results could be generalised to our case and second,whether there is theory, either reductionist or non-reductionist,

that could explain the different patterns of category-specific

deficits shown in the literature, included ours.

Case report

In July 1999 MA, a 28-year-old, left-handed student, who was

about to take the final examination for his engineering degree,

190 C. Rosazza et al.

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had a serious motorbike accident and suffered severe facial

and head trauma. A CT scan immediately after admission to

hospital showed multiple facial and right orbital fractures and

a large right frontal haematoma.

He was given to emergency surgery to reconstruct the facial

and orbital fractures; he remained in a coma for eight days andthis was followed by gradual neurological improvement. One

month later he had another operation to further reconstruct the

facial fractures, which had severely affected the right orbital

cavity, and to evacuate a small left frontal hygroma. A follow-

up CT showed a small right frontal hygroma.

At the end of August MA was discharged to a rehabilitation

hospital; there he started to communicate verbally, even if

spontaneous speech was reported to be limited; he did notshow motor deficits.

In November 1999 MA was given to an initial neuropsy-

chological examination at the Neuropsychology Laboratory

of the University of Brescia. He was disoriented in time and

place, and was severely amnesiac for past as well as recent

memories concerning personal and public events. He could

read words and sentences, but not numbers and he made

several mistakes at sums.

In December he went back home; his mother reported

that he was improving continuously, even if his temper

had changed: he was calmer and he always needed to be

stimulated.

In February 2000, he was admitted to Don Gnocchi Day

Hospital, where he was given a complete neuropsychologicalassessment (see below). An EEG was characterised by diffuse

slow activity. A CT scan showed a hypodense right frontal

area, a small bilateral frontal hygroma, and multiple small

frontal bilateral hypodensities (Fig. 1).

On the first of March he began a cognitive rehabilitation

program. A follow-up EEG in June showed a reduction of the

slow activity that was restricted in the right temporal regions.

Single photon emission tomography (SPECT) showed a re-duction of perfusion in the right frontal lobe, as well as in the

left mesial temporal region.

Unfortunately, the etiology of brain damage (severe head

injury) prevents any detailed consideration of the anatomical

locus of damage. While the location of structural lesions and

functional effects appear to be at variance with other patients

with similar neuropsychological profiles, it is likely that

axonal damage may have affected other brain regions.

Fig. 1. CT scan sections showing hypodense right frontal area and multiple small frontal bilateral hypodensities.

Category-specific semantic disorders 191

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Neuropsychological assessment

MA was given two complete general neuropsychological

examinations in February and in June 2000. A summary ofthe neuropsychological tests administered in the two sessions

is reported in Table 1. At the first assessment the patient was

well oriented in time and place: he was given the Mini-Mental

State Examination (Folstein et al., 1975) on which he

obtained a raw score of 27 and a corrected score of 25.03

(cut off 24). He performed well on Raven Progressive

Matrices (Raven, 1962), scoring 26.5 (raw score: 29; cut

off 18). Language was appropriate, although spontaneousspeech was poor; reading and writing were normal, but

slower. MA performed slightly below the normal range on

Token Test (De Renzi and Faglioni, 1978): raw score: 29,

corrected score: 25.75; cut off 26.5. His performance was

normal or marginally defective on oral and written calculation

and number processing examinations. He scored within the

normal range on short-term verbal memory (digit span for-

ward and backward, 5 and 3 respectively) as well as spatialmemory (Corsi raw score: 5, corrected score: 4.5; cut off:

3.75). The patient showed no apraxic deficits and he was able

to copy drawings: on the Rey–Osterreith figure copy

(Osterrieth, 1994) he scored 31.87 (raw score 33; cut off:

28). MA was severely impaired in all the tests that challenged

his long-term memory abilities. He got zero scores on Logical

Memory (De Renzi et al., 1977), on Rey–Osterreith figure

recall and on Rivermead Behavioural Memory Test (Wilson

et al., 1985). His performance on the Naming Test and

Questionnaire (Laiacona et al., 1993b) was severely impaired

with living (37% and 61% correct, respectively) and non-

living items (67% and 87% correct, respectively). This last

result confirmed that his semantic memory was severelyaffected; his impairment was already evident at informal

questioning. MA was not able to define a large set of well-

known living and non-living items, e.g. hippopotamus and

pulley. His categorical fluency was defective as well as his

frontal lobe functions, tested with the Wisconsin Card Sorting

Test (Grant and Berg, 1948) and the Weigl test. Impairment

was also evident on attentional tasks (Attentive Matrices and

Trail Making Test). On Benton Face Recognition test (Bentonet al., 1992) his performance was borderline (39, cut off: 39).

In June 2000, MAwas given to a second assessment. He had

improved on MMSE (raw score: 29; corrected score: 27.03;

cut off: 24) and on the Raven Test (raw score 34; corrected

score: 30; cut off: 18), and he achieved normal scores on the

Token Test (raw score 34; corrected score: 30.75; cut off:

26.50). His attention improved as well: he scored within the

normal range on Attentional Matrices and on test B and B-A

Table 1. Neuropsychological tests administered in the two sessions (February and June 2000)

First assessment Second assessment

Raw score Corrected (cut off) Raw score Corrected (cut off)

MMSE 27 25.03 (24) 29 27.03 (24)Raven 29 26.15 (18) 34 30 (19)Token test 29 25.75� (26.5) 34 30.75 (26.5)

Digit spanforward 5 6backward 3 4

Corsi’s span 5 4.5 (3.75) 4 3.5� (3.75)

Rey figurecopy 33 31.87 (28)recall 0 0� (6.2)

Logical memory 0 0� (8)Autobiographical memory 16/45�Rivermead 26/93 5/24� (18)

WCSTCorrected R 0 25Categories 0 �1� 0 �1�Persevarative errors 63 < 1� 19 5�

Weigl 5 1.5� (4.5) 8 4.5a (4.5)Categorical fluency 23� 15 (25)Phonemic fluency 35 25 (17)Attentional matrices 34 19.25� (31) 47 32.25 (31)

Trail Making TestA 10900 12500� (9300) 9700 113 (9300)B 41300 47200� (28200) 18500 24400 (28200)A-B 30400 34700� (18600) 8600 12800 (18600)

Benton’s faces 39a

Famous faces 25/45�

�Impaired performanceaBorderline.


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of Trail Making Test. Rey–Osterreith figure copy was perfect

(36/36), however, MA continued to underestimate his condi-

tion and was deeply anosognosic. Frontal lobe functions were

defective on categorical (but not phonemic) fluency. His

performance was borderline on the Weigl Test (raw score:

8, corrected score: 4.5; cut off: 4.5) and still impaired on the

WCST. On short memory tests, he showed a slightly worse

performance on the Corsi test (raw score 4; corrected score:3.5; cut off: 3.75), but an improvement on digit span forward

(6) and backward (4). By and large, his deficits beyond

semantic memory were restricted to long-term episodic mem-

ory. His performance on Logical Memory (0 scores) as well as

on an autobiographical memory questionnaire (Borrini et al.,

1989) (16/45) was severely impaired. He was also severely

defective in naming faces of famous people (25/45). In

summary, at this time there was no evidence of mentaldeterioration or language disturbance. MA showed a severe

amnesia and naming and recognition deficits.

Experimental study

Control group

Five normal male subjects, matched for age (mean age¼ 26

years), gender, education level (18 years of schooling) and

type of studies (one of them had just graduated in engineering

and the others were about to conclude their engineering

degree, as MA) were given the test specifically constructed

for the present study.

Experimental procedures

In this study we carefully replicated the procedures used byCaramazza and Shelton (1998) to investigate MA’s naming

and recognition deficit, through a number of specific tasks,

prepared ad hoc. In particular, for each of the following tasks,

all subjects were asked to decide whether statements about

objects were true or false, and then we selected only those

answers that 4/5 control subjects gave correct, removing

wrong and ambiguous sentences. Only the standardised tests

as the VOSP (Warrington and James, 1991), the BORB(Riddoch and Humphreys, 1993) and the naming test (Lotto

et al., 2001) were not given to the control group. This

experimental study has been carried out in September

2000, during the second assessment.

Task 1 – visuo-perceptual abilitiesGiven the severe ocular damage, we deemed necessary to

perform an in-depth neuro-ophthalmologic examination todetermine the integrity of MA’s basic visual abilities. Visual

acuity was in the normal range and his perceptual abilities

assessed with colour tests (colour naming, recognition and

grouping) were perfect. Lang Test revealed lack of stereopsis.

MA has a restriction of visual field in both eyes, which he

compensates by moving his head and eyes.

The VOSP (Warrington and James, 1991) was administered

to investigate his performance on visual perceptual tasks more

comprehensively: he achieved normal scores on all the subset

except on Silhouettes. Some tests of the BORB battery

(Riddoch and Humphreys, 1993) were administered as well:

his normal performance on test 5 (33/40) ruled out appercep-

tive agnosia. Test 7, 8, 11 and 12 were given to assess his

ability to perform complex visual matching and categorisation

tasks: MA performed within the normal range on all the tasks.

CommentWe can conclude that MA’s impairment is not the result of

defective vision or impaired visual processing of complex

objects.

Task 2 – naming picture and controllingfor nuisance variablesThe patient was given in random order 266 standardisedpictures in black and white from the Lotto et al. (2001)

set, which includes 13 categories i.e. mammals, birds, fruits,

vegetables, flowers, housewares, buildings, vehicles, furni-

ture, clothes, weapons, musical instruments, receptacles and

mix. Examples from mix category are: candle, radio, pipe,

rucksack, alarm etc.; from this group we formed a fourteenth

category of tools. Naming was evaluated again about one

month later.

ResultsThe patient performance is summarised in Table 2. A response

was considered correct if MA provided the Lotto et al.’s

(2001) name or any acceptable response produced by subjects

more frequently than the correct name, according to norma-

tive data.

MA showed a clear naming impairment, affecting mostitem categories. However, he showed an overall category

effect, with living items more affected than non-living items.

Table 2. Picture Naming task. Percent of MA’s correct responses

Categories 18 Naming 28 Naming

Mammals 12/21 (57%) 16/21 (72%)Birds 6/22 (27%) 8/22 (39%)Tot. animals 18/43 (42%) 24/43 (56%)

Fruits 13/21 (62%) 13/21 (62%)Vegetables 6/17 (35%) 5/17 (29%)Flowers 1/14 (7%) 2/14 (14%)Tot. plants 20/52 (38%) 20/52 (38%)Tot. living 38/95 (40%) 44/95 (46%)

Buildings 13/20 (65%) 13/20 (65%)Clothes 25/29 (86%) 26/29 (90%)Tools 3/6 (50%) 4/6 (66%)Vehicles 16/25 (64%) 17/25 (68%)Furniture 7/12 (58%) 9/12 (75%)Houseware 11/11 (100%) 10/11 (91%)Musical Instruments 8/15 (53%) 10/15 (67%)Receptacles 5/11 (45%) 6/11 (54%)Weapons 5/16 (31%) 6/16 (37%)Mix 17/26 (65%) 20/26 (77%)Tot. inanimate 130/223 (58%) 141/223 (63%)Tot. non-living 110/171 (64%) 121/171 (71%)


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In order to assess whether MA was impaired in processing

living things or the more restricted category of animals, data

were entered into a logistic regression analysis with response

accuracy as the dependent variable, and category membership

animate/inanimate and living/non-living as independent vari-

ables. The results revealed that animate did not have a

significant effect on performance in both evaluations. On

the contrary, the living factor had a significant effect in thefirst naming (Wald¼ 9.627, p< 0.002) as well as in the

second naming (Wald¼ 17.653, p< 0.0001), mainly due to

the severely defective performance with items belonging to

the categories of birds, vegetables and flowers.

We also compared the separate sets of animals, plant life

(fruit, vegetable, flowers and plants) and non-living items, by

entering 4 nuisance variables in order to ensure that living/

non-living dissociation with both animals and plants was notdue to uncontrolled stimulus factors. So we performed a

logistic regression analysis by entering response accuracy

as the dependent variable, and animals and plant life together

with other four additional factors i.e. name frequency, concept

familiarity, typicality and age of acquisition, as the indepen-

dent variables. The outcome of this analysis indicated a

significantly inferior performance on both animals and plant

life on the first naming (Wald¼ 12.37, p< 0.0004 andWald¼ 11.126, p< 0.0009, respectively) as well as on the

second naming (Wald¼ 7.509, p< 0.006 and Wald¼ 18.558,

p< 0.0001, respectively).

Age of acquisition has been shown to play an important role

in both naming performances (Wald¼ 21.007, p< 0.0001 and

Wald¼ 19.272, p< 0.0001). Neither familiarity, nor fre-

quency, nor typicality appear to have a significant effect on

both naming performances. The same regression analysis, withanimals, plants and nuisance variables, but without the mix

category belonging to the non-living domain showed the same

results. A significant effect of animals and plants on the first

naming (Wald¼ 11.012, p< 0.0009 and Wald¼ 10.814,

p< 0.001) as well as on the second naming (Wald¼ 6.602,

p< 0.10 and W¼ 17.299, p< 0.0001) and a significant effect

of age of acquisition on both naming performances

(W¼ 16.810, p< 0.0001 and W¼ 19.206, p< 0.001) wereobserved. This further analysis confirmed the living effect

also when the unspecified collection of items, i.e. the mix

category, was excluded.

We can definitely say that naming performance appeared to

be significantly worse for living than non-living entities once

frequency, concept familiarity, typicality and age of acquisi-

tion were accounted for. Thus, whereas age of acquisition had

a powerful influence on naming performance, it cannot byitself account for the category effect.

An analysis of erroneous responses indicated that when

MA is unable to name a stimulus correctly, his verbal

responses are consistently different according to the nature

of the stimulus. When the picture represents a non-living item,

he describes its function, but in presence of living items, he

gives either the name of superordinate category, or a semantic

paraphasia (coordinate), but never a perceptual description.

CommentNaming results show that MA’s deficit concerns the category

of living things (plus the category of weapons), not only the

category of animals, as in the case reported by Caramazza and

Shelton (1998). MA’s naming performance cannot be

explained by nuisance variables such as name frequency,

concept familiarity, typicality and age of acquisition since

MA’s difficulties persist even when these factors are con-trolled. The deficit for the non-living category weapons is

difficult to interpret: items within this group are very different

from each other, both from the perceptual and the functional

point of view. Examples of unrecognised items are: bomb,

crossbow, sword, whip, machine gun, sling and bow. He made

some anomic errors across the two sessions (bomb, sling,

crossbow and whip on both the submissions and bow), as

indicated by his ability to provide detailed information aboutweapons’ function (e.g. sling> ‘you put a stone and you

throw it’; crossbow> ‘it launches spears’; bomb> ‘you fire

it at enemies, you kill a lot of people’).

Task 3 – categorical fluencyMA and control subjects were asked to name in 1 min as many

items as possible from each of 14 categories used in the naming

task (mammals, birds, fruits, vegetables, flowers, kitchenware,buildings, vehicles, furniture, clothes, weapons, musical

instruments, receptacles and tools), plus other 4 categories:

insects, sea animals, animals (in general), and body parts.

ResultsData reported in Table 3 show a general impairment in all the

categories. This result is probably due to the coexistence of a

frontal deficit with a semantic deficit.

Task 4 – discrimination between real and unrealanimals and real and unreal objectsParticipants were asked to discriminate between pictures of

real objects from pictures of non-existing ones. Such an object

Table 3. Categorical fluency task. Number of MA’s and controls’ responses

Categories Controls range MA

Mammals 15–28 6Birds 18–24 4Insects 8–11 2Sea animals 15–27 3Animals (in general) 24–27 14Fruits 20–24 7Vegetables 16–22 4Flowers 8–19 1Buildings 11–25 3Clothes 22–30 5Tools 13–17 3Vehicles 21–24 7Furniture 14–19 3Kitchenware 15–21 3Musical instruments 19–23 5Receptacles 11–22 2Weapons 15–25 3Body parts 30–32 9


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decision task is intended to directly investigate structural

knowledge about objects. A set of 33 pictures was con-

structed, 18 of animals (11 unreal versus 7 real) and 15

objects (9 unreal versus 16 real). Unreal pictures were made

up by joining parts from two different animals or two different

objects to create plausible but non-existent items. In order to

test whether the sets of animals and artefacts were of compa-

rable level of difficulty, the stimuli were initially given to thecontrol subjects: a picture of an animal was then removed.

Moreover MA was submitted to test 10 of the BORB

(Riddoch and Humphreys, 1993), an object decision task,

requiring him to distinguish between drawings of real objects

and drawings of imaginary objects.

ResultsOn the set of pictures we constructed, MA’s performance wasslightly more impaired with animals (13/17) rather than non-

animals (14/15). Although the difference was not significant, a

trend to accept existing items as unreal is present. Controls did

not show any difference. On test 10 of the BORB, he scored 24/

32, 20/32, 22/32 and 22/32, showing a general impairment in

distinguishing real items from unreal ones. Moreover in order

to analyse the difference in MA’s performance between ani-

mals and objects, we combined the 32 pictures we created withBORB stimuli, since BORB items have fewer object pictures

than animal pictures. Results have shown that the difference is

significant (X2¼ 6.828, p< 0.009) and that the patient tends to

reject real animals. Since items were unbalanced to animals’

advantage, we also performed a logistic regression analysis: it

confirmed the previous results, namely, a defective perfor-

mance with animals (Wald¼ 6.123, p< 0.013).

Task 5 – part decision taskPictures from Lotto et al. (2001) and from Snodgrass and

Vanderwart (1980) were used to create a set of 42 stimuli, 20

animals and 22 non-animals. Each ‘‘body’’ with a part missing

was presented to the participants and arranged on a sheet of

paper with two ‘‘heads’’ (one target and one distracter);

participants were asked to match the ‘‘body’’ with the correct

choice. Two trails were performed. Pictures were balanced sothat each ‘‘head’’ was correctly paired with its ‘‘body’’ on one

trail and incorrectly paired with another ‘‘body’’ on the

second trial. The position of the target was varied over the

stimulus chosen; examples are presented in Fig. 2. No pictures

were removed after having given them to the control group.

ResultsMA was only slightly impaired in selecting the correct head;however, his performance was worse with animals (17/20),

than with objects (21/22), although the difference was not

significant. Only one control subject made a mistake with one

animal item.

CommentResults from object decision task, BORB and part deci-

sion task show that MA has defective processing of visual

information about animals and objects. In comparison to EW,

the disorder of recognition appears to be milder, and less

selective.

Assesment of semantic knowledge aboutliving vs. non-living attributes

Up to now we have shown that MA has an impairment in

naming and recognising, which affects more severely living

entities, and is not caused by a general visual processing

deficit, but appears to involve some loss of visual properties

(structural knowledge). Here we provide evidence that MA’sdeficit is due to a disruption of semantic memory and that the

patient is more impaired in judging if a living, as compared to

a non-living, entity has a particular attribute. MA was given

seven extensive questionnaires to evaluate his conceptual

knowledge about living and non-living properties. Question-

naire statements were divided into visual/perceptual versus

associative/functional in order to assess whether MA is more

impaired in retrieving the visual attributes of living entities.

Task 1 – questionnaire on central attributesIn this task 42 animals (20 mammals, 9 birds, 6 seabirds, 5

insects, spider and turtle), 15 fruits, 15 vegetables and 42

artefacts (10 tools, 10 vehicles, 10 furniture, 9 clothes and 3

kitchenware) were selected to test MA’s knowledge about the

most central attributes of the stimuli. By central attribute, we

mean the most representative features of each item, selectedon the basis of a pre-test. In this pre-test, between 4 and 16

attributes for each of 114 stimuli were determined (mean

number of attributes per stimulus¼ 8.3). Twenty normal

subjects (matched as much as possible for age and scholarship

for MA) were asked to rate how much the attributes, divided

into visual and functional, were representative of the stimulus

Fig. 2. Examples of the items used in the Part decision task.


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at issue. From these judgements, we used the attributes that

the largest number of subjects agreed to be ‘‘central’’ in

defining the stimulus.

The questionnaire was given to the 5 control subjects,

after 30 statements were removed; the task consistedof 433 statements, 209 describing visual properties (e.g. a

giraffe has a long neck), 224 functional properties (e.g. wine is

made from grapes), half were false, the other half were true.

Participants were asked to say whether a statement about an

object was true or false.

ResultsA logistic regression was performed, using the responseaccuracy as the dependent variable and the binary variable

living/non-living as the independent factor. Results showed

that MA has difficulties in answering statements about living

entities (Wald¼ 8.924, p< 0.028). Control subjects do not

show any difference. As can be seen in Table 4, MA’s

performance tends to be significantly worse with false ques-

tions than with true ones, namely he tends to accept false

statements as true.Moreover, by entering in the regression analysis living/non-

living, perceptual/functional and true/false as independent

factors, only living (Wald¼ 4.706, p< 0.030) and true/false

(Wald¼ 9.808, p< 0.002) variables were significant.

Although perceptual features were more impaired than func-

tional ones, the difference was not significant (Wald¼ 1.7,

p< 0.19). The results have been confirmed by another regres-

sion analysis: we divided the questions into individual cate-gories, i.e. animals, plant life (fruits and vegetables) and

objects. We entered animals, plants, perceptual/functional

and true/false into a logistic regression and results showed

again that animals (Wald¼ 7.339, p< 0.007), plants

(Wald¼ 4.706, p< 0.03) and true/false (Wald¼ 9.808,

p< 0.0017) were significant.

CommentResults show that MA has a significant deficit of knowledge

for central attributes of animals, fruit and vegetables.

Task 2 – questionnaire on food/non-food animalsIn this task 16 food animals (duck, lobster, horse, deer, rabbit,

mussel, hen, cook, pig, cow, goose, sword-fish, ostrich, turkey,

turtle and sheep) and 26 non-food animals (bee, eagle,

donkey, whale, caterpillar, camel, dog, kangaroo, sea horse,

elephant, butterfly, ant, cat, giraffe, lion, fly, bear, robin,

spider, rhino, squirrel, shark, tiger, mouse, zebra and pigeon)

were selected. After the stimuli were given to the control

subjects, 3 items were removed (sheep, pigeon and turtle).

Participants were asked to tell if the animal or one of its parts

is eaten according to our culture.

ResultsMA had severe difficulties answering questions concerning

whether or not the animal was a food animal (X2¼ 10.363,

p< 0.001). He could recognise food animals, but he made

serious mistakes with non-food ones, saying that dog, ant,

spider, cat, butterfly and mouse can be eaten. This finding

indicates that there is a considerable loss of functional infor-

mation about the animal category.

Task 3 – questionnaire on specific-general attributesThis test was created in order to investigate the level of

knowledge that is damaged in MA. Knowing a general

feature, for instance that something has four legs or breathes,

is sufficient to categorise it as an animal, but it is does not help

in deciding whether it is a dog or an elephant. Following

Caramazza and Shelton (1998), we selected 69 animals (21mammals, 16 birds, 14 insects, 17 sea animals like whale,

octopus, shark, swordfish, mussel) and 45 non-animals (15

fruits, 15 vegetables and 15 buildings) to test MA’s knowledge

about general and specific attributes of stimuli. By general, we

mean those features of a category shared by all or most

members of that category (e.g. cats have eyes); by specific,

we mean distinctive attributes of one or a few members of the

category (e.g. a swimming pool has a springboard). The initialquestionnaire included 789 statements; after having submitted

them to control subjects, the task consists of 724 questions,

419 of which specific (210 perceptual and 209 functional) and

305 general (136 perceptual and 169 functional), matched for

true and false.

ResultsPerformance is summarised in Table 5. The results of thelogistic regression analysis revealed that MA has greater

difficulties with animals (Wald¼ 4.835, p< 0.028), while

his performance with plants was not significant (Wald¼0.322, p> 0.685). This difference in comparison with the

results of other tests is most likely due to the fact that, in order

to follow the procedures used by Caramazza and Shelton

(1998), that were directed only to test conceptual knowledge

about animals versus non-animals, we included a smallnumber of non-living items (15) compared to living ones

(99). This might have resulted in a reduced sensitivity of the

task in the case of plant stimuli. Questions about animals,

plants and objects were heavily unbalanced (433, 196, 95,

respectively). We also performed a logistic regression analysis

by entering, as independent variables, animals, plants, percep-

tual/associative and true/false; results revealed that animals

have a significant effect (Wald¼ 5.01, p< 0.025) and that a

Table 4. Central Attributes Questionnaire: proportion of MA’s correct

responses

Animals Inanimate Living Non-living

PerceptualTrue 26/37 (0.70) 69/79 (0.87) 53/70 (0.76) 42/46 (0.91)False 26/43 (0.60) 33/50 (0.66) 40/66 (0.61) 19/27 (0.70)

FunctionalTrue 28/39 (0.72) 68/76 (0.89) 56/70 (0.80) 40/45 (0.89)False 34/43 (0.79) 47/66 (0.71) 46/66 (0.70) 35/43 (0.81)


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trend for perceptual properties is present (Wald¼ 3.45,

p< 0.063). The results of a logistic regression performed

with animals, perceptual/functional and specific/generalshowed that MA’s performance is much worse with specific

attributes than general attributes (Wald¼ 16.086, p< 0.0001)

and with animals (Wald¼ 8.948, p< 0.0028); a trend for

perceptual properties is still present (Wald¼ 2.756,

p< 0.097). Control subjects did not show any significant

differences.

Looking at the only general statements, no significant

effects exist. Looking at the only specific statements, onecan note that MA has greater difficulties in answering ques-

tions about animals (Wald¼ 3.59, p< 0.058). His perfor-

mance is significantly worse with perceptual attributes as

compared with functional attributes (Wald¼ 8.345,

p< 0.004) when we enter into logistic regression animals,

perceptual/functional, plants and true/false. The last two

factors were not significant. By entering only plants and

perceptual/associative, results show that plants are not sig-nificant, but perceptual properties are more impaired than

functional ones (Wald¼ 8.09, p< 0.004). Multinomial logis-

tic regression did not show any interactions. Again, control

subjects did not show any significant difference.

CommentSince the living group was about 7 times more numerous than

non-living group, in this task it was not possible to show

a significant impairment for living entities. MA is more

impaired in answering statements about animals, and his

performance appeared to be basically worse with perceptual

attributes. Moreover MA has no difficulty with questions

concerning general attributes, while his knowledge of specific

attributes is significantly damaged. When we considerjust specific statements, MA appeared to be significantly

more impaired in retrieving visual attributes than functional

attributes. However, MA has difficulties with perceptual

properties of all the stimuli, not only with those of animate

entities.

Task 4 – questionnaire on shared attributesCaramazza and Shelton (1998) created this task to rule out the

possibility that their patient’s deficit for animals reflected a

selective deficit in processing certain properties that may be

differentially distributed across semantic categories. Namely,

they wanted to rule out the possibility that the problem

concerns attributes themselves and not the semantic category

of animals.

Task 4a – questionnaire on size judgementsWe selected 21 pairs of animals, 20 pairs of fruits and

vegetables and 20 pairs of objects. For each pair a precise

discrimination was required to judge item sizes (e.g. sheep-deer, carrot-cob, spoon-ladle). No items were removed after

the submission to control subjects. Participants were asked to

indicate which item of the pair was bigger.

ResultsMA made 5 mistakes, all with fruits and vegetables. Fruits and

vegetables are significantly more impaired than animals and

objects, categories without mistakes.

Task 4b – questionnaire on other shared attributesWe selected 7 attributes (big, small, number of legs, surface

texture, short, tall, colour) that are shared between animals

and non-animals, and 12 items, 6 of which animals and 6

artefacts. Each item that had one of these attributes was paired

with another item not having this attribute (e.g. an item was

rhino: 1. is it bigger than a sheep? 2. is it smaller than anelephant? 3. does it have smoother skin than a rabbit? etc.).

Two questions were removed because they were not appli-

cable to each object (e.g. numbers of legs) and two questions

were removed after having been given to the control group. So

the task consisted of 80 questions, 40 on animals and 40 on

non-animals. Participants were asked to answer questions in

the affirmative or not.

ResultsMA made 4 mistakes with animals and 3 mistakes with non-

animals. The difference is not significant.

CommentMA’s preserved ability to indicate the bigger animal of

the pair seems unusual, but other cases are described in the

literature. Michelangelo, the patient reported by Sartori andJob (1988), showed a deficit affecting living entities, a greater

impairment for perceptual attributes, an inability to distin-

guish real animals from unreal ones, but his performance on

size judgements was perfect (100%). A case of a patient

(Jennifer) with a deficit for living things, reported by Samson

et al. (1998), did not have difficulties on this kind of task.

The authors proposed that size might be a general physical

attribute that is not very discriminating. Coltheart et al.(1998) suggest that size is a semantic attribute that is not

perceptual. The patient AC they studied had a selective deficit

for visual attributes of stimuli, but he performed very well on

size judgement task (20/24). According to the authors, when

one forms a visual image of an animal, that image does not

have a size. Other possibilities should also be taken into

account. In the first place, the comparison between pairs of

items could be less demanding then a verification task.

Table 5. Specific-General Attributes Questionnaire: proportion of MA’s

correct responses

General Specific

Perceptual Functional Perceptual Functional

Animals 74/88 (0.84) 70/94 (0.74) 68/123 (0.55) 95/128 (0.74)Inanimate 42/48 (0.87) 66/75 (0.88) 64/87 (0.73) 63/81 (0.78)

Living 99/117 (0.85) 120/151 (0.79) 112/181 (0.62) 134/181 (0.74)Non-living 17/19 (0.89) 16/18 (0.89) 20/29 (0.69) 24/28 (0.86)


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Namely, asking whether the dolphin is grey may place a

greater demand than asking whether the dolphin has the

same colour as the shark’s. In the second case you may just

think whether the two animals are similar, whereas in the first

case you are required to access its right colour. Moreover,

when a subject is asked to make a size judgement task, some

encyclopaedic information could be retrieved: for example,

if you are asked to say whether the horse is bigger thanthe donkey or the spoon than the ladle, you may just rely

on learned notions, instead of thinking about their visual

properties.

Task 5 – questionnaire on visible and not visibleattributesThis task was created to investigate MA’s knowledge about

properties of pictured animals, both when the properties werevisible and when they were not visible in the picture. Twenty

animals were selected and one to three pictures of these

animals were developed. A different view of the animal

was represented in each picture, so that only experimentally

relevant visual attributes were shown. For example in one

picture just a horse muzzle appeared and in another one, just

the tail was shown. Some pictures were coloured; others were

in black and white. We developed two to six questions foreach picture; 45 questions could be answered based on the

information in the picture and 77 attribute questions were not

answerable based on the information in the picture. The

aim was to test whether MA was able to answer the same

question both when the information was available in the

picture and when it was not given in the picture. Different

pictures of the same animal were spaced out throughout the

submission such that subjects could not rely on informationpresent in the previous picture. After having administered the

task to control subjects, 3 questions had been removed.

Participants were shown a picture of the animal (e.g. an

elephant) and told. ‘‘This is a picture of an elephant; I will

ask you some questions about elephants and you can use the

information available in the picture to answer them’’. Ques-

tions were closed.

ResultsMA had no difficulties in answering when the attribute was

visible (39/42), but if the information required was not

available in the picture, his performance worsened signifi-

cantly: 55/73 (X2¼ 5.479, p< 0.019).

CommentsThese results confirm that the disorder is specific for notvisible attributes, as in the case of EW.

Discussion

In the present paper we report a further case of a patient

showing a category-specific naming impairment affecting

living things. We show that in this case the living/non-living

dissociation is a semantic deficit that is independent of the

modality in which information is provided and the patient’s

response is expressed. The main results of this case study can

be summarised as follows (see Table 6):

1. MA’s deficit is not due to defective visual abilities or

impaired visual processing of complex objects, as testified

by his performance on tests such as the VOSP and the

BORB, and is characterised by the absence of considerable

language impairment.

2. MA has a category-specific deficit concerning the category

of living entities, and not only the category of animals: the

patient is more impaired in naming animals, fruits, vege-tables, flowers and plants; the category of weapons is

damaged as well. Overall, non-living entities are more

preserved compared to living things (on average 67%

versus 41%). The category-specific nature of the deficit

remains significant when potential confounding variables

such as name frequency, concept familiarity, typicality and

age of acquisition are controlled for. In particular, the

living (not the animate) effect persists, even when all fourfactors are entered in a logistic regression analysis at the

same time.

3. We replicated carefully the procedures employed by

Caramazza and Shelton (1998) to assess the possibility

to generalise their findings; the tasks were thus closely

comparable, but different results emerged.

4. MA has a category-specific deficit for living things that,

only in the case of specific features, is associated with a lossof perceptual more than functional attributes. The informa-

tion about perceptual features available to MA is unlikely to

be sufficient to discriminate between real and unreal ani-

mals, but may be sufficient to distinguish a real artefact

from an unreal one. Moreover MA had no difficulties with

the general properties of the stimuli: he knew what, for

example, a vegetable is, but he got confused with detailed

distinctions among category members.

Some authors have suggested that a number of reported

cases of deficits for living things may be accounted for by

nuisance variables such as low familiarity or low frequency

(Funnel and Sheridan, 1992); however, this explanation can beruled out in the case of our patient’s performance. Neither

familiarity nor frequency has significant effects; only age of

acquisition influenced MA’s naming performance, although

the patient still showed a highly reliable category effect. The

results of this study reinforce the importance of age of

acquisition in semantic impairments (Morrison et al., 1992;

Lambon-Ralph et al., 1998; Moss and Tyler, 2000, Garrard

et al., 2001). Age of acquisition is a strong predictor becauseearly-acquired words are more likely to be named than late

acquired ones. However, it cannot be claimed that MA’s

deficit is due to an age-to-acquisition effect, since it cannot

account by itself for the category effect.

Once we have ruled out the possibility that name frequency,

concept familiarity, typicality and age of acquisition were

responsible for MA’s decrement in performance for living

items, we can move on to compare our results with Caramazza


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and Shelton’s (1998). EW, who had been tested using the same

procedure.

The patient reported by these authors showed a category-specific deficit only for animate entities: on naming, on object

decision task, on sound identification, and on the several

questionnaires that tested her semantic knowledge, EW was

never impaired with fruits, vegetables, buildings or vehicles,

but only with animals. On the basis of this case, Caramazza and

Shelton (1998) proposed that there are selective neural

mechanisms responsible for the recognition of three broad

semantic domains: animals, plant life and artefacts.This hypothesis assumes and predicts a strong correlation

between cerebral substrates that have been damaged and the

impaired domains of knowledge. Studies on brain-damaged

patients and on normal subjects have provided evidence for

distinct brain substrates of knowledge concerning different

categories (Gainotti, 2000). Many patients with category-

specific disorders affecting living items have bilateral damage

to the antero-medial and inferior parts of the temporal lobes;patients with deficit for non-living things often have left

fronto-parietal damage (Gainotti et al., 1995; Gainotti

2000). However, there are exceptions to this pattern. Tranel

et al. (1997) found that defective knowledge about animals

was associated with medial occipito-temporal lesions bilat-

erally, while deficit for tools was associated with the left

occipito-parietal-temporal junction. Finally, data from PET

and fMRI studies have provided other evidence about corre-lation between activated regions and categories. The most

consistent finding is that tools elicit greater activity in the left

posterior middle temporal gyrus across different tasks (Chao

et al., 1999; Perani et al., 1999; Martin and Chao, 2001). On

the other hand, animals activated different areas less consis-

tently, in particular the left fusiform gyrus and the more lateral

aspect of the fusiform gyrus. Moreover, neuroimaging studies

have shown that the response to an object category is not

limited to the region that responds maximally to that category,but it involves other regions that respond maximally to other

categories. What emerges is a continuous representation of

object features, instead of a collection of category-specific

modules (Ishai et al., 1999).

Another prediction made by Caramazza and Shelton (1998)

is that category specific deficits exist only for those categories

that have an evident evolutionary value (animals, plant life

and artefacts): consequently other finer-grained distinctionsare considered to be artefactual. This theory cannot account

for some impairments that do not respect this tripartition: L.A.

(Silveri and Gainotti, 1988) showed a deficit for animals,

fruits, vegetables and also food, while her performance was

better with tools and body parts. Patient JP studied by Siri et al.

(submitted) has a deficit restricted to fruits, vegetables, birds

and musical instruments, while his performance with animals

was spared. It is difficult to understand how the neural systemspecialised for animals is preserved in presence of an impair-

ment of the category of birds. In the present study as well, MA

is more impaired with living things and with weapons within

non-living categories on naming; unfortunately we did not

include this category in our questionnaires.

Finally, Caramazza and Shelton’s hypothesis (1998) pre-

dicts that perceptual attributes are damaged just as much as

functional ones. As already said, there are a number of casesof patients with a different pattern of impairment (Basso et al.,

1988; Sartori and Job, 1988; Silveri and Gainotti, 1988;

Sartori et al., 1993; De Renzi and Lucchelli, 1994) to which

we can add MA, who, at least for specific features, is more

impaired in perceptual than functional features. Caramazza

and Shelton (1998) have questioned the evidence supporting

Table 6. Comparison of the most important findings in MA and EW (Caramazza and Shelton, 1998)

Naming EW MA

Animals Plants Objects Animals Plants Objects

16/47 (34%) 24/24 (100%) 162/179 (90%) 21/43 (49%) 20/52 (38%) 115/171 (67%)

Object decision task 36/60 (60%) 55/60 (92%) 86/129 (67%) 28/31 (90%)

Central attributes questionnairePerceptual 64/98 (65%) ? 91/98 (93%) 52/80 (65%) 41/56 (73%) 61/73 (83%)Functional 37/57 (65%) ? 56/57 (98%) 62/82 (76%) 40/54 (44%) 75/88 (85%)

Specific-general questionnaireGeneral

Perceptual 130/130 (100%) 60/60 (100%) 74/88 (84%) 42/48 (87%)Functional 129/130 (99%) 75/75 (100%) 70/94 (74%) 66/75 (88%)

SpecificPerceptual 74/100 (74%) 100/100 (100%) 68/123 (55%) 64/87 (73%)Functional 115/150 (77%) 149/150 (99%) 95/128 (74%) 63/81 (78%)

Size judgement 13/18 (72%) ? 17/18 (94%) 21/21 (100%) 15/20 (75%) 20/20 (100%)

Other shared attributes 32/42 (76%) 42/42 (100%) 36/40 (90%) 37/40 (92%)


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these cases on the basis of defective matching of the items in

terms of familiarity, frequency and visual complexity. It is

noteworthy that a similar criticism has been formulated by

Borgo and Shallice (2001) about a case study showing

defective visual knowledge in the absence of a category effect

(Lambon Ralph et al., 1998). This criticism should not apply

to the present case, insofar as the replication of the procedure

used by Caramazza and Shelton (1998) resulted in a highlyreliable living effect when familiarity, name frequency, typi-

cality and age of acquisition were jointly controlled for.

Caramazza and Shelton (1998) do not take into account the

frequent, although non-constant association between

impaired category and defective type of knowledge.

Contrary to other reports (for example, Sartori and Job,

1988; Silveri and Gainotti, 1988) the impairment for visual

properties in MA was not specific for living items, i.e. it failedto show an interaction with category. It is noteworthy that the

presence of interaction is not deemed to be necessary by some

computational models, such as Farah and McClelland (1991),

in which damage to visual properties results in a category-

specific disorder because of the differential weight of visual to

functional information between living and non living entities.

Our original idea was to replicate the testing procedures

developed by Caramazza and Shelton (1998) to assess whetherthe domain-specificity theory could be applied to our patient.

This was not the case. The presence of a more severe loss of

specific visual rather than functional knowledge could support

an interpretation according to the SFT. It must be however

underlined that there is evidence that a deficit in perceptual

knowledge is neither necessary (Caramazza and Shelton, 1998)

nor sufficient (Coltheart et al., 1998; Lambon Ralph et al.,

1998) to bring about a selective disorder for the living category.At the moment no theory can account for our case com-

pletely, and more importantly, no theory can account for all

the cases described in the literature.

What can be concluded from the insufficient explanatory

power of current theories about category-specific disorders? A

possible interpretation is that different mechanisms may be

responsible for category-specific deficits in individual

patients. Patients with category-specificity are very hetero-geneous, and each patient represents a ‘‘nature experiment’’

that permits verification of a specific hypothesis about normal

cognitive processing structures. As observed by Gainotti and

Silveri (1996), although patients with a category specific

deficit for living things have some characteristics in common

from the clinical, anatomical and pathophysiological point of

view, this does not necessarily mean that they constitute a

homogeneous entity.Heterogeneity is an interesting aspect to consider, because

it may reflect, in addition to different patterns of impairments

of distributed conceptual representations, different mecha-

nisms of disruption of information processing likely to be

related to lesion site.

It must be underlined that most of the theories of knowledge

organisation do not include any treatment of processing

aspects. As has already been mentioned, it is clear that

retrieving the name of a specific feature is different from

verifying a sentence or comparing two statements. Further,

the role of specific features may differ among tasks: for

example, there is evidence that visual features are central

in the definition of conceptual representations of living enti-

ties, while functional features may be more important for

name representations (Marques, 2002). In the absence of a

careful consideration of the requirement of any individualtask, the variability of patients’ performance may in the end

represent a confounding factor from the point of view of the

contribution of category-specific disorders to the understand-

ing of how semantic memory is organised. Finally, different

aetiologies and different lesion locations may affect not only

conceptual representations, but also the mechanisms of access

and retrieval of semantic knowledge (consider, for example,

the contrast between the involvement of the semantic orlexical level – see Gainotti 2000 for a review). What is clearly

lacking is a comprehensive theory which can take all these

different factors into account.

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Received on 30 July, 2002; resubmitted on 26 November, 2002;accepted on 29 November, 2002


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The heterogeneity of category-specificsemantic disorders: evidencefrom a new case

C. Rosazza, E. Imbornone, M. Zorzi, E. Farina,L. Chiavari and S. F. Cappa

AbstractWe report a new case of category-specific semantic impairment, affecting livingentities, in a patient with traumatic brain damage. In the present investigationwe attempted to replicate as closely as possible the testing procedures whichhave been developed by Caramazza and Shelton (1998) to evaluate EW, apatient with a selective semantic disorder for the animal category. The results inour patient indicated a different performance profile, characterised by a moreextensive semantic disorder for living entities, and by a more severe loss ofspecific visual rather than functional knowledge. These findings concur withother evidence indicating that category-specific semantic disorders are hetero-geneous, reflecting different mechanisms of impairment, most likely associatedwith different neurobiological underpinnings.

JournalNeurocase 2003; 9: 189–202

Neurocase Reference Number:542/02

Primary diagnosis of interestCategory-specific semantic disorder

Author’s designation of caseMA

Key theoretical issue* Status of visual and functional knowledge in a patient with semantic disorderfor living entities

Key words: category-specific semantic impairments; reductionist and non-reductionist theories; heterogeneity; task repairments; different mechanismsof disruption of information processing

Scan, EEG and related measuresComputerised tomography, single photon emission tomography

Standardized assessmentMMSE, Raven PMC, Token test, digit span, Corsi span, Rey figure copy andrecall, Logical Memory, Rivermead Behavioural Memory Test, Naming Testand Questionnaire, Wisconsin Card Sorting, Weigl, Benton Face Recognition,BORB

Other assessmentPicture naming, object and part decision tasks, questionnaires semanticknowledge

Lesion location* Hypodense right frontal area, multiple small frontal bilateral hypodensities,

reduction of perfusion in the left mesial temporal region

Lesion typeClosed head injury

LanguageEnglish


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Documents

The Heterogeneity of Category-Specific Semantic Disorders: Evidence from a New Case