On the Links between Visual Knowledge and Naming ...

COGNITIVE NEUROPSYCHOLOGY, 1997, 14 (3), 403–458

On the Links between Visual Knowledge and Naming: a Single Case Study of a Patient with a Category-specific Impairment for Living Things E.M.E. Forde, D. Francis, and M.J. Riddoch Cognitive Science Research Centre, University of Birmingham, UK

R.I. Rumiati SISSA, Trieste, Italy

G.W. Humphreys Cognitive Science Research Centre, University of Birmingham, UK Why living things, such as animals, fruit, and vegetables, can pose recognition or naming problems compared to nonliving things for certain patients has intrigued neuropsychologists for a number of years. We report a further case study of a patient (SRB) with a category-specific impairment in naming living things, which occurred in naming from vision, taste, touch, and when auditory definitions stressed the visual properties of objects. In addition, SRB was particularly poor at retrieving the perceptual attributes of living things when asked to draw from memory, make perceptual comparisons, or name associated colours. In contrast to this, SRB’s performance on standard tests of semantic memory was relatively unimpaired, although when asked to give definitions about living things (and faces) he showed interference effects from visually and semantically similar exemplars from the same category. Also, the problem in naming was not necessarily confined to living things, but also occurred with faces and when nonliving things had to be named at a subordinate level. We suggest that SRB was impaired on tasks requiring fine differentiation between the representations of objects with similar perceptual structures, allowing both base-level naming of nonliving things, and semantic categorisation tasks to be Requests for reprints should be addressed to Emer Forde, Cognitive Science Research Centre, Department of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK (e-mail:E.M.E.Forde@ Bham.ac.uk). We would like to offer our sincere thanks to SRB and his wife for the time they spent in our laboratory. This work has been supported by a grant from the Department of Education in Northern Ireland awarded to Emer Forde, an MRC (UK) grant awarded to Glyn Humphreys and Jane Riddoch, and by a grant from the Italian Ministry of University and Scientific Research awarded to Raffaella Rumiati.

Ó 1997 Psychology Press, an imprint of Erlbaum (UK) Taylor & Francis Ltd

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performed relatively well. In a follow-up study, after naming accuracy had recovered, we demonstrated a remaining problem in response latencies to living things. Thus, though performance may recover to some degree in such patients, residual difficulties can still occur.

INTRODUCTION Over the past decade a considerable number of category-specific disorders of visual object recognition have been documented in the neuropsychological literature. Impairments of living things compared to nonliving things have been described most frequently (De Renzi & Lucchelli, 1994; Sartori & Job, 1988; Silveri & Gainotti, 1988; Warrington & Shallice, 1984), although the opposite pattern has also been reported (Hillis & Caramazza, 1991; Sacchett & Humphreys, 1992; Warrington & McCarthy, 1983, 1987). However, it is important to note that a rigid living–nonliving distinction is misleading because the inability to name living items has also co-occurred with an inability to name food items (De Renzi & Lucchelli, 1994; Silveri & Gainotti, 1988; Warrington & Shallice, 1984) and musical instruments (Basso, Capitani, & Laiacona, 1988; Warrington & Shallice, 1984). The characteristics of these disorders, and the factors that generate them, can provide valuable insight into the nature of visual object recognition and naming in the brain. Although category-specific naming disorders have been relatively widely documented, there is still little consensus about the locus (loci) of the impairment in information processing terms. Since a category-specific impairment for living things has been the most widely reported, and is the subject of this case study, we outline a number of the alternative proposals to account for this impairment. Researchers have argued that category-specific recognition impairments reflect: 1. The experimenters’ failure to control for important variables known to affect naming, such as familiarity, frequency, and visual complexity (Funnell & Sheridan, 1992; Stewart, Parkin, & Hunkin, 1992). 2. An impairment in a semantic system that is organised by both modality and category (Farah, Hammond, Metha, & Ratcliff, 1989; Warrington & McCarthy, 1994). 3. An impairment to a visual semantic system that stores information crucial for the identification of living things, but not for nonliving items, which rely more heavily on functional information stored in a separate verbal semantic system (Silveri & Gainotti, 1988; Warrington & Shallice, 1984). 4. Damage to a presemantic structural description system that is categorically organised (Sartori & Job, 1988).

ON THE LINKS BETWEEN VISUAL KNOWLEDGE AND NAMING

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5. The fact that exemplars from categories of living things tend to have similar visual structures to other members of their category, which leaves them particularly vulnerable when there is an impairment in the structural description system (Gaffan & Heywood, 1993; Humphreys, Riddoch, & Quinlan, 1988; Riddoch & Humphreys, 1987a; Sartori, Job, Miozzo, Zago, & Marchiori, 1993). 6. Impaired access from an intact structural description system to stored supramodal semantic knowledge (Riddoch & Humphreys, 1987a). 7. A general impairment in retrieving the visual properties of all classes of item. Nonliving items have close links between their visual attributes and their function, which helps in their identification. Living things can have arbitrary links between their visual and functional attributes, making them vulnerable to the effects of brain damage (De Renzi & Lucchelli, 1994; Sirigu, Duhamel, & Poncet, 1991). Note that this argument does not make any commitment to the underlying architecture of the processing system in terms of, for example, whether visual and functional attributes are stored together (see Farah & McClelland, 1991, on this point). 8. Damage to a categorically organised output lexicon (Hart, Berndt, & Caramazza, 1985). One theme that runs through several of these approaches is that there is an important relationship between loss of visual/perceptual knowledge and naming impairments for living things (Humphreys et al., 1988; Sartori & Job, 1988; Silveri & Gainotti, 1988). A similar argument for the role of perceptual knowledge in identifying living things comes from work with animals (Gaffan & Heywood, 1993) and from computer simulations (Humphreys, Lamonte, & Lloyd-Jones, 1995). Gaffan and Heywood showed that monkeys found it more difficult to learn to distinguish between living things than to learn to distinguish between nonliving things. Since, for the monkeys, this difference cannot be attributed to linguistic factors, Gaffan and Heywood concluded that it must be due to the visual discriminability of the items. They noted that, for living things, there was a relatively steep increase in the difficulty of discrimination as the number of items to be learned in the category increased. However, the same result did not occur for nonliving objects. This is consistent with the idea that living things belong to “visually crowded” categories, making discrimination more difficult as finer within-category judgements are needed to access stored knowledge. This explanation of category-specific effects in terms of “visually crowded” categories is analogous to the notion that living things can be difficult for human patients to identify because these items belong to categories with structurally similar exemplars, and so require relatively fine visual differentiation for stored knowledge to be retrieved (Humphreys et al., 1988). Using a computer simulation, based on an interactive-activation and competition model of object naming, Humphreys et al. (1995) present further

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evidence that the perceptual similarity between neighbours may play a determining role in the performance of patients with a naming impairment for living things. Humphreys et al. (1995) simulated the cascade model of object identification, originally proposed by Humphreys et al. (1988). This model distinguishes between stored structural, semantic, and name representations, and holds that activation is transmitted continuously (in cascade) between different levels of representation. Humphreys et al. (1988) showed that normal subjects find it more difficult to identify living things compared to nonliving things; they also suggested that identification of living things can be more impaired after brain damage because these stimuli have more perceptual neighbours and as a result are more susceptible to competition at each level of representation within the system. Humphreys et al. (1995) used visual similarity ratings between objects to determine the overlap in the simulated activation patterns created by perceptual neighbours in stored perceptual representations for objects. Living things had more overlap in their activation patterns across stored perceptual representations than did nonliving things. Humphreys et al. showed that lesions at different levels in the model led to impaired naming, particularly for living things, including lesions to the structural representations, semantic representations, and to stages of processing involved in mapping from semantics to a name representation. These simulation results suggest that patients can present with category-specific impairments in recognition and naming despite having different functional loci for their lesions within a model of object naming. They also showed that perceptual similarity (and perhaps also impaired stored perceptual knowledge) can have pervasive effects on naming even when other aspects of a patient’s performance (e.g. object recognition and categorisation) seem relatively preserved (e.g. when lesions affect the mapping from semantics to name representations). The simulations of Humphreys et al. (1995) demonstrate how perceptual overlap between objects can combine with semantic similarity to disrupt object naming. For living things, the high level of perceptual similarity within a category leads to the strong activation of other items across the category, and then to semantic competition between category members for name selection. For nonliving things, there tends to be a high level of perceptual overlap only between one or two items, and sometimes the similar items are not even from the same category; consequently there is weaker activation of representations across a category, and less semantic competition for naming. Studies of naming errors produced when normal subjects name objects under deadline conditions are consistent with these proposals. Normal subjects not only make more errors to living relative to nonliving things, but the errors tend to spread more across a broader class of items within the category (Vitkovtich, Humphreys, & Lloyd-Jones, 1993). Furthermore, the errors tend to be both visually and semantically related to target objects. Thus, though perceptual overlap is the cause of the category differences between the naming of living and nonliving


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things in the model, the effects are modulated by semantic similarity between category members. Empirical evidence that effects of structural and semantic overlap can combine in generating recognition deficits in patients comes from a recent study by Arguin, Bub, and Dudek (1996). They reported the case of an agnosic patient who was poor at classifying shapes varying along two visual dimensions (e.g. elongation and degree of tapering), relative to when shapes varied only along one dimension (elongation or tapering). This problem was also more pronounced when classification involved applying labels for fruits and vegetables relative to unrelated artefacts, even though the same shapes were involved in 1 each case. Arguin et al. propose that the difficulty in differentiating between visually similar shapes in memory (for the classification task) is exacerbated when the items are also semantically similar, and so activate overlapping semantic representations. In the present paper, we report a further case of a patient, SRB, with a naming impairment for living things. We show that the category-specific impairment for SRB is not an artefact resulting from uncontrolled variables such as frequency, familiarity, and visual complexity. We explore the nature of his visual knowledge about objects in some detail and show that he is relatively impaired at retrieving stored visual knowledge for living things compared to functional/associative information about the same items. From this we review whether categorical differences between living and nonliving objects are critical to the impairment, and instead suggest that the deficit in stored visual knowledge can precipitate a selective naming impairment when exemplars belong to a “structurally similar” or “visually crowded” category (see Humphreys et al., 1995, for a simulation). We test this hypothesis empirically by examining SRB’s performance on nonliving categories when task performance requires differentiation between a set of structurally similar examples (e.g. subordinate naming of types of car). The implications of these results are reviewed in the General Discussion. Interestingly, we were also able to examine SRB’s performance after his ability to recognise and name living things had improved. Previous studies of patients with category-specific recognition disorders have failed to note significant recovery of function (e.g. Sartori, Miozzo, & Job, 1994). We show that such a recovery can take place, but even when it does there can remain a residual deficit in the recognition of living things, though expressed in measures of the speed rather than accuracy of response. The implications for understanding the nature of category-specific disorders and the underlying structure of semantic memory are reviewed in the General Discussion. 1

Note that this additional problem in learning associations to the names of fruits and vegetables was not due to the unfamiliarity of the names per se; there were no differences between fruits, vegetables, and artefacts when the visual patterns varied along a single dimension.

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CASE STUDY SRB, a 38-year-old man at the time of testing, suffered from what appeared to be a spontaneous intracerebral haemorrhage from an underlying arteriovenous malformation in April 1993. The area of the original haemorrhage was in the left inferior medial region of the temporal lobe extending down to the occipital lobe. An CT scan also showed evidence for a small infarct in the right thalamic region (see Fig. 1). SRB had 12 years of school education and was working as a plumber prior to his brain injury. In addition to the category-specific naming disorder, which will be the subject of this paper, SRB has severe anterograde amnesia and retrograde amnesia spanning 3–4 years prior to the haemorrhage. He is a letter-by-letter reader and his reading problems are discussed in more detail by Mayall and Humphreys (submitted). Following his lesion, SRB’s short-term memory span was relatively low: his digit span was 5 forwards and 2 reverse. His performance IQ (99) on the WAIS was in the 47th per centile (average range), and his verbal IQ (81) was in the 10th percentile (low average). SRB showed no signs of low-level perceptual impairments and on a standard star cancellation test showed no signs of unilateral neglect. He was able to copy simple shapes such as a triangle, circle, and square, overlapping shapes, and pictures of three-dimensional objects (e.g. a cube). He was able to copy more complex items from structurally similar (giraffe, lion) and dissimilar categories (helicopter, chair), see Fig. 2. He was able to match pictures taken from unusual views with errorless performance on both the foreshortened and minimal feature versions of this test taken from BORB (Riddoch & Humphreys, 1993). In these tasks three pictures were presented on each trial. One picture was the target object taken from a standard viewpoint. The second was the same object taken from a different viewpoint, and the third a different object selected to be visually similar to the second picture. In the first version of this task the main identifying feature of the object was present in both standard and unusual views. However, the overall shape of the object differed in the standard and foreshortened view due to the depth rotation of the object. In the second version the main identifying feature of the target object was present in the first picture but obscured in the second. On both tests of unusual views SRB scored 25/25; he performed the tasks effortlessly and in normal time. However, SRB reported that he had difficulties in naming objects, and noticed particular problems with fruit and vegetables when he went shopping, and with the names of friends and relatives. When initially presented with large coloured photographs of objects, he named 10/31 (32%) of fruits and vegetables, 49/51 (96%) of inanimate objects, and 20/20 (100%) of food items. He was significantly impaired with fruit and vegetables compared to nonliving 2 things [c (1) = 38.9, P < .0001]. However, unlike the patients reported by


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Sheridan and Humphreys (1993), Silveri and Gainotti (1988), and Warrington and Shallice (1984), SRB did not have any problems in naming food items in addition to his problem with living things (in this case fruit and vegetables). His performance with food items was similar to that with nonliving things and 2 significantly better than with fruit and vegetables [c (1) = 23.8, P < .0001]. His errors are presented in Appendix 1. When presented with 70 photographs of famous faces he could only name 12/70 (17%), although he could provide some personal information about the person (e.g. occupation) for an additional 21. His wife, an age, education, and interest matched control, named 55/70 (79%) of the faces, which was significantly better than SRB’s naming performance 2 [c (1) = 52.9, P < .0001]. SRB was also impaired in naming photographs of members of his own family and friends, scoring 27/36 (75%), compared to his wife who could of course name them all. These initial tests confirmed that SRB did have a particular difficulty in naming living things and people from their faces, and his impairment was intensively investigated over a period of approximately 4 months and then followed up some time later when his naming had considerably improved.

EXPERIMENTAL INVESTIGATIONS The experimental investigations are divided into six sections dealing with: Naming performance, access to structural knowledge, colour knowledge, access to stored semantic knowledge, access to subordinate knowledge for nonliving as well as living things, and performance after naming accuracy had improved.

Naming Performance This section will compare SRB’s ability to name from various modalities including vision, touch, and taste. We explore his performance for different categories and demonstrate that he is significantly impaired with living items such as fruit, vegetables, and animals compared to nonliving items such as tools, furniture, and clothing. Experiment 1: Picture Naming

Method. SRB was presented with 76 pictures from the Snodgrass and Vanderwart (1980) set and given as much time as he needed to name each one. The selected items were taken from Humphreys et al. (1988), who paired items from categories whose exemplars tended to have similar perceptual structures (structurally similar [SS] items; animals, fruit, vegetables) and items from categories whose exemplars tend to have dissimilar perceptual structures (structurally dissimilar [SD] items; clothing, tools, furniture). Structural similarity was determined by measuring the number of rated common parts per category

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FIG. 1. CT scan for SRB.

and the average percentage of contour overlap relative to the other objects from the same category. The mean number of rated parts per category for the SS exemplars was 3.48 and 0.71 for the SD exemplars. The average percentage contour overlap measures were 17.28 and 12.44 for SS and SD exemplars respectively. This distinction between SS and SD maps onto the distinction between living and nonliving with the exception of body parts, which were classed here as objects from a category with SD exemplars. The SS and SD

FIG. 2. SRB’s copies of living and nonliving things.

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objects were pairwise matched for their name frequencies, and name frequencies were divided according to whether they were above (high) or below (low) 10 occurrences per million in the Kucera and Francis (1967) norms.

Results and Discussion. SRB was significantly more impaired at naming items from SS categories (28/38, 71%) compared to items from SD categories 2 (37/38, 95%) [c (1) = 8.6, P = .003]. Within the SS categories SRB was worse with fruit and vegetables (7/12, 58%), compared to animals (21/26, 81%). There was no significant effect of frequency. SRB’s scores are summarised in Table 1 and his errors are documented in Appendix 2. In addition to making more errors to SS items it was apparent that SRB took much longer to name animals, fruit, and vegetables, compared with the nonliving things, and that he was often unsure of the correct response. In contrast his performance with SD items was fast and confident. Although this category-specific impairment for living things has been documented relatively frequently in the literature, Funnell and Sheridan (1992) and Stewart et al. (1992) have raised the possibility that category-specific impairments for living things may result from the failure of experimenters to control for confounding variables such as frequency, familiarity, and visual complexity. Effects of frequency can be eliminated here, because SS and SD items were pairwise matched for name frequency; also there were no frequency effects on SRB’s naming accuracy when this variable was manipulated explicitly. However, the SS and SD items did differ in their familiarity and complexity, SS items being the more complex and less familiar (mean familiarity ratings were 2.73 and 3.58 for SS and SD respectively, and mean complexity ratings 3.5 and 2.7 for SS and SD respectively). In order to assess whether confounding variables such as familiarity and complexity determined SRB’s naming performance, in Experiment 2 he was given 260 Snodgrass and Vanderwart (1980) pictures to name. We performed a multiple regression analysis to investigate which factors significantly influenced his naming performance.

TABLE 1 Number Correct on Picture Naming Task (Experiment 1) Frequency

———————— Category

HF

LF

Living Nonliving

14 19

14 18

Maximum correct = 19.


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Experiment 2: Naming the Full Set of Snodgrass and Vanderwart (1980) Pictures

Method. SRB was presented with 260 pictures from the Snodgrass and Vanderwart (1980) set and asked to name them as quickly and accurately as possible. We submitted his reaction times (RTs) for correct items to a multiple regression with the factors frequency, familiarity, living/nonliving, and visual complexity included to see which (if any) significantly influenced his performance. Results and Discussion. SRB made 23 errors, which are documented in Appendix 3. There were 30 machine errors, 17 items excluded because they were either body parts or musical instruments, which do not empirically fall neatly into the living–nonliving dichotomy, and 13 items excluded because we had no frequency values for them. Thus, 177 items were included in the analysis. The only variable to reach significance was the living/nonliving distinction (P = .005). Frequency (P = .6), familiarity (P = .9), and visual complexity (P = .4) did not significantly affect SRB’s naming times. In terms of naming latencies, SRB’s performance was determined by the category of the item (living/nonliving) and not by the possible confounding factors of name frequency, familiarity, or complexity. The mean naming latencies for different categories of object are given in Table 2. There was also a close relationship between accuracy and naming, in that RTs were slower and performance less accurate for living things (see Table 2). However, although the distinction between living and nonliving things reliably accounted for the majority of the variance in SRB’s naming RTs, we may question whether this factor was the prime determinant or whether the visual similarity between category members was critical. For example, for musical instruments SRB was relatively impaired compared to other nonliving categories: He took a mean of 2717msec to name items from this category compared to a mean of 1710msec for other nonliving things. This pattern has been reported in other patients with category-specific impairments for living things (Basso et al. 1988; Warrington & Shallice, 1984). In addition, SRB was unimpaired with body parts, which may be considered as a category of living things and we note again that this category has been shown to dissociate from other categories of living things such as animals, fruit, and vegetables in patients with a so-called categoryspecific impairment for living things (Warrington & Shallice, 1984). In order to examine the potential influence of other factors on SRB’s object naming, we performed a further analysis on a subset of the pictures. This analysis assessed the effects of one measure of structural similarity between category members (the percentage of shared contour overlap between pictures) and the effects of the rated prototypicality of the items within their categories. One might expect that highly prototypical members would have richer semantic

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FORDE ET AL. TABLE 2 Mean Reaction Times for Specific Categories (Experiment 2) Category

RT

No. correct

Living things Animals Birds Insects Fruit Vegetables

3743 4051 4520 3707 13966

31/35 7/8 8/9 10/13 4/10

Mean

4572

60/75 (80%)

Nonliving things Tools Vehicles Household items Furniture Clothes

1112 1909 1654 1612 1625

10/10 9/9 24/25 6/6 20/20

Mean

1710

69/70 (99%)

Body parts Musical instruments

1629 2712

10/10 7/9

RTs are given in milliseconds.

representations and therefore be more resilient to brain damage. On the other hand it is possible that highly prototypical items that were “central” within their category, and therefore semantically or structurally similar to other category exemplars, might be more difficult for SRB to differentiate relative to other items within the same category, and so atypical items (being less semantically and structurally similar to category members) may be relatively spared. Damasio, Damasio, Tranel, and Brandt (1990) have suggested that patients with posterior inferior temporal lobe damage and occipital-temporal damage may have more problems in naming animals that are prototypical, and share many visual features, compared to atypical animals with unusual shapes, such as a giraffe or an elephant. In contrast, Gainotti and Silveri (1996) have suggested that patients with anterior temporal lesions will be more influenced by familiarity than typicality. They presented a case study of a patient, LA, who had inferior temporal damage and a robust category-specific naming impairment that was most pronounced for low familiarity (atypical) living things. To investigate the effects of prototypicality and contour overlap we submitted SRB’s correct RTs for as many items as possible (59) to a multiple regression with the factors frequency, familiarity, living/nonliving, visual complexity, rated prototypicality within the category (taken from Humphreys


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et al., 1988), and structural similarity (the percentage contour overlap measure taken from Humphreys et al., 1988). The regression analysis showed that, with the measures of prototypicality and contour overlap now included, contour overlap was the only significant predictor of SRB’s naming latency (P = .05). No other factors were significant once the variance due to the contour overlap measure was taken out. This second regression analysis suggests that the distinction between living and nonliving items may not be fundamental in accounting for SRB’s disorder; instead, a measure of the structural similarity between exemplars within a particular category appears to be more important. Note also that it is the shared contour overlap with other category members that predicts SRB’s naming here, not the visual complexity of the individual pictures. In the first two experiments the stimuli were line drawings, which do not convey information about surface properties of objects such as colour or texture. These properties may be important for object recognition, as Price and Humphreys (1989) showed that both colour and surface texture had a significant influence on naming latencies for normal subjects, particularly for objects from categories with SS exemplars. It has also been suggested in the cognitive neuropsychology literature (Sheridan & Humphreys, 1993; Warrington & McCarthy, 1987; Warrington & Shallice, 1984) that perceptual properties such as colour may be particularly important for the identification of living things (e.g. fruit and vegetables), and that impairments to colour knowledge may contribute to impaired naming of SS objects. To assess both the effects of colour and texture on SRB’s object identification we evaluated his naming of photographs (Experiment 3) and real objects (Experiment 4).

Experiment 3: Naming Photographs

Method. SRB was presented with 114 photographs (100 coloured) from a number of categories: 32 common household objects (all coloured), 32 fruits and vegetables (all coloured), and 50 famous faces (5 equal groups of royal family, television personalities, film stars, singers and public figures). The photographs were presented in a random order and SRB was given unlimited time to name each picture. Five age-matched controls (mean age = 38.5 years) provided norms for the faces and his wife (PB) provided norms for the other items. Results. SRB named 28/50 (56%) of the faces (control mean = 47.2/50, SD = 1.3), 23/32 (72%) of the fruit and vegetables (control = 31/32), and 32/32 (100%) of the household objects (control = 32/32). Compared to household 2 objects, SRB was significantly impaired with both faces [ c (1) = 19.2, P < .001] and fruits/vegetables (Fisher exact probability = .001), but there was no

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difference between faces and fruits/vegetables [c (1) = 2.09, P = .15]. There was a significant difference between SRB’s scores and controls on both faces (SRB’s performance here fell more than 3 SDs below the control mean) and fruits/vegetables (relative to control PB, Fisher exact probability = .007), but not for household objects. Interestingly, for 20/28 (71%) of the faces, and 8/23 (35%) of the fruits/vegetables, SRB only produced the correct name after a circumlocutory response (e.g. for Michael Jackson: Singer, loads of brothers . . . the Jackson 5, Mr Jackson . . . Michael Jackson; for cauliflower: Not cabbage, a vegetable . . . the other one, I keep thinking of cucumbers, the only ones I like apart from peas . . . cauliflower; for blackberries: Mushrooms, blackcurrants . . . no . . . blackberries). SRB’s errors for fruit and vegetables are shown in Appendix 4. 2

Experiment 4: Naming Real Objects

Method. SRB was presented with 41 real objects: 20 were nonliving things (household objects and tools) and 21 were fruit or vegetables. The objects were randomly presented and he was given as much time as necessary to name them. Results and Discussion. SRB was significantly better at naming nonliving 2 objects (20/20) compared with fruit and vegetables (12/21, 57%) [c (1) = 11.0, P < .001]. His results are shown in Table 3 and his errors in Appendix 5. SRB’s performance with real fruit and vegetables was no different from that with line drawings (58% in Experiment 1 and 61% in Experiment 2), or with coloured 2 photographs (72%) [c (3) = 1.54]. Riddoch and Humphreys (1987a) suggested that a failure to name real objects any better than line drawings may indicate a relatively central deficit in object identification (e.g. affecting stored knowledge about objects rather than early perceptual processes), and certainly many patients with more peripheral forms of agnosia show an advantage for real objects over photographs, and for photographs over line drawings (Davidoff & De Bleser, 1993; Riddoch & Humphreys, 1987b). The failure to find such an effect for SRB suggests that his problems in naming fruit and vegetables may be relatively central in origin. The experiments to date have demonstrated that SRB is particularly poor at naming items from categories where exemplars are structurally similar (fruit, vegetables, animals, and faces) compared to items from categories with structurally dissimilar exemplars. This pattern is not a result of uncontrolled factors such as visual complexity, frequency, or familiarity and is similar across line drawings, coloured photographs, and real objects. In Experiments 5 and 6 we investigated whether SRB showed a similar impairment when the items were presented in other modalities such as taste and touch (Experiment 5) and from auditory definitions (Experiment 6).


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Experiment 5: Naming from Tactile and Taste Modalities

Sheridan and Humphreys (1993) reported that their patient, with a categoryspecific impairment in naming living things and food items, did not benefit when allowed to taste and touch items she was presented with visually, suggesting that her problem extended across all three modalities. In the next experiment we explored SRB’s naming performance with these modalities: Touch, taste, and vision. Experiment 5a: Naming from Tactile and Taste Modalities for Living and Nonliving Things

Method. SRB was blindfolded and asked to name items from touch or taste. On time 1 he was presented with 18 fruit and vegetables and 20 nonliving objects (tools and household items) for naming from the tactile modality, and once the set had been completed he was given a break before naming 9 of the same fruit and vegetable items from taste. In the tactile condition the whole object was given to SRB, but in the taste condition a chunk of the fruit or vegetable was cut and placed in his mouth to minimise cues from the shape or texture of the item. His performance was compared with that of five non-braindamaged control subjects (three male, two female; age range 20–25). Results. SRB performed flawlessly at naming nonliving objects (20/20, 100%) from touch but was significantly worse at naming fruit and vegetables (9/18, 50%) from this modality (Fisher exact probability = .0003). When asked to name fruit and vegetables from taste he scored 3/9 (33%) correct. There was no significant difference between his performance with fruit and vegetables presented from touch or taste (Fisher exact probability = .7). For 6/9 (67%) of these items he was consistent across the two modalities. His errors are recorded in Appendix 6a. The five control subjects scored a mean of 17.8 (SD = 0.45) when asked to name the fruit and vegetables from touch, and a mean of 8.2 (SD = 1.30) correct when asked to name the fruit and vegetables from taste. SRB was significantly worse at naming fruit and vegetables both from touch and from taste relative to the controls, falling more than 3 SDs from the control mean for each modality. Experiment 5b: Naming from Tactile and Taste Modalities for Fruit and Vegetables

Method. The order of modalities was reversed for fruit and vegetables in this experiment. SRB was presented with 14 fruit and vegetables to name from taste and then a larger set of 21 fruit and vegetables to name from touch. These 21 items were the same as those named from vision in Experiment 4. Note that

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the items presented were slightly different exemplars from those used in Experiment 5a. The same controls were tested as for Experiment 5a.

Results. SRB’s overall performance was similar for both modalities: He scored 7/14 (50%) correct from taste and 11/21 (52%) from touch. His errors are recorded in Appendix 6b. For 10/13 (77%) he was consistent across modalities (Fisher exact probability = .09). The five control subjects scored a mean of 20.4 (SD = 0.89) from touch and 12.8 (SD = 1.30) from taste. Again SRB was significantly worse than the controls, scoring more than 3 SDs below the mean. The errors made by control subjects in Experiments 5a and 5b are shown in Appendix 6c. When SRB was presented with the same objects for naming from vision (in Experiment 4) he scored 11/21 (52%), which is again similar to his performance from the taste and tactile modalities. For the items which could be compared across the three modalities he was consistent for 8/13 (62%). Across taste and vision he was consistent for 10/14 (71%) items (Fisher exact probability = .13), and across touch and vision he was consistent for 16/21 (76%) of items (Fisher exact probability = .02). An analysis of SRB’s performance over time (between Experiment 5a and 5b) shows that for taste he was consistent only for 4/7 (57%) items presented in both experiments, but for touch he was consistent for 12/13 (92%) items (Fisher exact probability = .005). His results are summarised in Table 3. Discussion. SRB’s naming performance from taste and touch was similar to his naming from vision (see Table 3), and he showed an impairment for fruit and vegetables across all three modalities. SRB was consistent across time for items named from touch and he was also consistent between vision and touch. One could account for the consistency between tactile and visual naming by arguing that similar knowledge about objects would be important for naming from both modalities. With taste, SRB showed less consistency over time and across the same items when they were presented in other modalities (touch and vision). We return to the implications of these data in the General Discussion. Experiment 6: Naming to Definition

Silveri and Gainotti (1988; Gainotti & Silveri, 1996) reported that their patient, who was poor at identifying living things from vision, was also poor at naming to definition. Interestingly this impairment was more pronounced for definitions that stressed the visual characteristics of objects than for those that stressed verbal/functional knowledge. However, this deficit with visual definitions has not been found in all patients showing category-specific deficits for living things; Sheridan and Humphreys (1993) reported that their patient was poor when definitions stressed verbal/functional attributes about living things.


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TABLE 3 Naming Real Objects (Experiments 4 and 5) Category

Number Correct

Naming from vision (Exp 4.) Inanimate objects Fruit/vegetables

20/20 (100%) 12/21 (57%)

Naming from touch (Exp 5.) Inanimate objects Fruit/vegetables (time 1) Fruit/vegetables (time 2)

20/20 (100%) 9/18 (50%) 12/21 (52%)

Naming from taste (Exp 5.) Fruit/vegetables (time 1) Fruit/vegetables (time 2)

3/9 (33%) 7/14 (50%)

This disparity across patients is consistent with the view that category-specific deficits in identifying living things can arise from impairments at several loci in the object processing system (Humphreys et al., 1995). Here we assessed SRB’s ability to name to definition using definitions that stressed either visual/perceptual or functional/encyclopaedic knowledge. The same items were probed in each type of definition.

Method. SRB was given two auditory definitions for each of the 76 items used as stimuli in Experiment 1. For each item (e.g. squirrel) there was a definition based on visual-perceptual properties (e.g. A small animal with a long fluffy tail. It may be rusty red or grey in colour.) and another on functional-encyclopaedic properties (e.g. This animal stores nuts in winter.). The definitions were presented in an ABBA order across test sessions. The definitions were also given to four age-matched control subjects. Results and Discussion. Overall, SRB was significantly impaired at naming to visual perceptual definitions relative to the controls; he scored 39/76 (51%) compared to a mean of 65/76 (86%), SD = 7. In contrast to his performance on visual/perceptual definitions, SRB was extremely good with the functional-encyclopaedic definitions and did not differ significantly from the controls: SRB scored 73/76 (96%) correct; the control mean was 75/76 (98%) correct, SD = 1. The scores are recorded in Table 4. This experiment suggests that SRB may have impoverished knowledge about the visual-perceptual properties of items. In contrast to this, his performance on definitions stressing functional-encyclopaedic knowledge was at the control level. This difference in naming between the two sets of definitions follows the same direction as that reported by Silveri and Gainotti (1988; Gainotti & Silveri, 1996), and contrasts with that reported by Sheridan and

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FORDE ET AL. TABLE 4 Results from Naming to Definition (Experiment 6) Category

SRB

Control Mean

Control Range

Visual-perceptual definitions Living Nonliving

23 16

32.25 32.50

30–34 21–38

Functional-associative definitions Living Nonliving

35 38

37.00 37.50

36–38 37–38

All scores are out of 38.

Humphreys (1993). SRB’s good performance with functional/encyclopaedic definitions also contrasts with the assertion that there is necessarily some deficit in functional/encyclopaedic knowledge when stored visual knowledge is impaired (Farah & McClelland, 1991). SRB has apparent problems with stored visual knowledge, as indicated by his impaired naming to visual/perceptual definitions. It is possible that problems with stored visual knowledge leads to his naming deficit with living things. This hypothesis will be explored further in the next section, where we examine SRB’s ability to access stored structural information from vision.

Accessing Structural Information In this section we examine SRB’s ability to access stored structural knowledge using a number of tests, such as object decision (Experiment 7), drawing from memory (Experiment 8), explaining the perceptual difference between items (Experiment 9), and an adaption of Paivio’s (1975) size matching test (Experiment 10). Experiment 7: Object Decision

This task has previously been used to assess a patient’s ability to access stored representations of objects in the structural description system (Riddoch & Humphreys, 1987a). The patient is presented with a series of line drawings depicting either real or unreal objects and is asked to indicate whether the picture is of a real object or not. If SRB’s naming deficit is due to impaired processes subsequent to those involved in accessing stored knowledge about objects, he should perform successfully on this task since it neither requires naming nor the accessing of semantic knowledge. Riddoch and Humphreys (1987a) reported that object decision could be intact even when visual access


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to stored semantic and name information was impaired (see also Sheridan & Humphreys, 1993; Stewart et al., 1992).

Method. SRB was shown 16 real objects and 16 nonobjects and asked to say whether each item was real or not. The nonobjects were constructed by taking parts from two line drawings of real objects and putting them together to form a plausible-looking stimulus. The items were taken from BORB (Riddoch & Humphreys, 1993), and included 28/32 animate objects. SRB’s data were compared with those from an age-matched control (his wife, PB) and from 30 older control subjects, mean age 62 years. Results and Discussion. SRB scored 27/32 (84%) correct: 16/16 on the real items and 11/16 on the nonobjects. PB made no errors. The controls had a mean score of 29.8 (SD = 1.2). SRB showed some degree of impairment on this task, performing significantly below the level of PB (Fisher exact probability = .03), and more than 2 SDs below the mean of the older control subjects. The apparent problem in retrieving stored structural knowledge was examined further in Experiments 8 and 9. Experiment 8: Drawing from Memory

In several studies reporting cases of category-specific impairments for living things, the patient has also been impaired at drawing exemplars from the affected category from memory (Sartori & Job, 1988; Sheridan & Humphreys, 1993). In Experiment 8 we evaluated whether the same held for SRB.

Method. SRB was asked to draw 15 animals, 14 fruit and vegetables, and 15 nonliving objects from memory. As a control, SRB’s wife (PB) was asked to draw the same items. Ten independent raters were given all the drawings from SRB and PB in a random order and asked to rate on a scale of 1–9 how accurate the drawings were, with 9 indicating a good representation of the item. Results and Discussion. The ratings for SRB and PB are shown in Table 5 and examples of his drawings in Fig. 3. The mean ratings for each category given by each subject were submitted to a two-way, within-subjects ANOVA, with the factors being subject (SRB vs. PB) and category of object (fruit and vegetables, animals, and nonliving objects). The analysis revealed a significant main effect of both subject [F (1,9) = 79.35, P = .0001] and category [F (2,18) = 33.1, P = .0001]. In general, PB (average rating 6.2) was better at drawing than SRB (3.9), and objects (6.3) were drawn better than animals (4.7) or fruit and vegetables (4.3). However, these main effects were qualified by a significant interaction between subject and category [F (2,18) = 21.8, P = .0001], which is illustrated graphically in Fig. 4. SRB was worse at drawing the living

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FORDE ET AL. TABLE 5 Ratings for Drawings of SRB and PB (Control) (Experiment 8) Category Animals Fruit and vegetables Objects

SRB

PB

3.5 2.5 5.7

5.8 6.0 6.8

things (animals and fruit and vegetables) compared to PB, but there was less difference between their drawings of nonliving things. A post hoc Tukey test showed that there was a significant difference between SRB’s drawings of objects and the two categories of living things (P = .05), but no difference between his drawings of animals and of fruit and vegetables. There were no significant differences between PB’s drawings of items from the three categories. SRB’s impairment for drawing living things compared to nonliving things, and his impairment relative to the control subject, is consistent with an impairment of stored visual knowledge, particularly for living things.

Experiment 9: Perceptual Differences

De Renzi and Lucchelli (1994) described a case study of a patient who was significantly impaired at naming living relative to nonliving things (animals, fruit and vegetables) and who, like SRB, also had difficulty with drawing from memory. In addition, De Renzi and Lucchelli asked their patient to imagine the visual differences between pairs of items that had a similar visual shape but differed in some perceptual attributes. The patient found this task particularly difficult for living things, indicating a deficit in retrieving visual knowledge about exemplars from categories that were also difficult to name (see also Gainotti & Silveri, 1996). In Experiment 9 we gave SRB a modified version of this task, and compared his performance across fruit and vegetables, animals, and nonliving objects.

Method. SRB and PB (as a control) were asked independently to describe the visual differences between two items whose names were presented auditorily. There were 20 pairs of fruit and vegetables (e.g. apricot and plum, apple and peach), 20 pairs of animals (e.g. the tail of a pig and the tail of a horse, a donkey and a zebra), and 20 pairs of nonliving objects (a match and a toothpick, a cigar and a cigarette). Ten raters were given the responses and asked to give 2 points if each was correct, 1 if it was almost correct, and 0 points if it was incorrect.

FIG. 3. SRB’s drawings of living and nonliving things (Experiment 8).

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FIG. 4. Ratings of drawings (Experiment 8).

Results and Discussion. The mean ratings given to SRB’s descriptions were 17/40, 26/40, and 31/40 for fruit and vegetables, animals, and objects, respectively. The control subject scored 40/40 on all categories. A one-way ANOVA on the mean scores given for each category by each rater for SRB’s answers showed a significant effect of category [ F (2,18) = 44.3, P = .0001]. These results again point to SRB having a problem in retrieving stored visual knowledge, particularly about living things. It is interesting that this deficit emerged even in quite a constrained task requiring access to specific perceptual knowledge about objects, as well as in a relatively unconstrained task like drawing from memory (Experiment 8). Similar patterns have also been found by De Renzi and Lucchelli (1994), Gainotti and Silveri (1996), and Sartori and Job (1988), whose patients had difficulty in describing the important differences between pairs of living relative to nonliving things. Sartori and Job proposed that this might be because man-made objects are defined in terms of functional and perceptual properties, but living things more in terms of perceptual properties (see also Farah & McClelland, 1991); they suggested that their patient’s problems specifically reflected a difficulty in retrieving stored perceptual knowledge.


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We also noticed that SRB had difficulty in recalling the correct colour of several items, when colour was needed to differentiate between two items. Given the proposal by Warrington and McCarthy (1987) that sensory properties such as colour may be particularly important for distinguishing between certain fruit and vegetables, we tested SRB’s colour knowledge in more detail (Experiments 11–14). Experiment 10: Memory for Size Test

In a final test of SRB’s stored perceptual knowledge about objects, we required him to make judgements about the real-life size of objects. However, although tests of knowledge of object size putatively demand access to stored perceptual knowledge, previous empirical data are not particularly consistent with this. For instance, Sartori and Job’s (1988) patient scored well on tests requiring the retrieval of size knowledge, even though he was impaired on the previous task and in other tests requiring access to perceptual knowledge for living things, such as completing drawings of incomplete animals. Experiment 10a: Paivio Stimuli

Method. This test was an adaption of Paivio’s (1975) size matching test. SRB was given pictures of a pair of objects with the correct size ratio and a pair with the incorrect size ratio. The task was to choose the pair with the correct size ratio. On a separate occasion, the same items were presented to SRB verbally and he was asked which of the pair was the largest. Results and Discussion. SRB scored 35/35 on both the pictorial and verbal versions of the test, suggesting that he is able to access the size of objects from words and pictures. However, since SRB has a particular problem in naming living things, and only about half (38/70) of the stimuli included in the Paivio (1975) set of pictures were living things, we gave him a second test where items were only from the category fruit and vegetables. In addition, unlike for animals, there may be few physical cues present in fruit and vegetables that can be used to infer the object’s size (e.g. with animals, the relative size of the limbs to the body might be used). Thus, by using fruits and vegetables, we provided a more stringent test of access to stored visual knowledge for SRB. Experiment 10b: Fruit and Vegetables

Method. SRB was given pictures of 20 fruit and vegetables in 10 pairs. As in Experiment 10a, he was presented with a pair of objects with the correct size ratio and a pair with the incorrect size ratio on each trial and asked to choose

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the pair with the correct size ratio. Following this task he was asked to name each item. The stimuli are shown in Appendix 7.

Results. SRB scored 10/10 on size judgements but named only 13/20 of the items correctly (Fisher exact probability = .06). This suggests that even if SRB cannot name an object he is able to access correct information about its relative size. Summary

We found that SRB was impaired on tasks requiring him to access perceptual knowledge about objects (drawing from memory and judgements of perceptual differences) and on these tasks there was also an effect of stimulus category; living things (particularly fruits and vegetables) were worse than nonliving things. In contrast to these results, however, SRB was good at size judgements, a finding similar to that reported by Sartori and Job (1988) in their patient with a category-specific naming impairment. However, in their test of the patient’s ability to access knowledge about object size, the items were not chosen to minimise the physical cues to size in the drawings of the objects. Here, we showed good performance on fruits and vegetables, which should contain few explicit physical cues to object size. Given his impairment in accessing other forms of stored visual knowledge about objects (particularly shape information), how is SRB able to perform size judgements to visually presented exemplars (since we might presume that stored knowledge about object size is accessed through an object’s stored structural description, even if the description itself is coded in a size-independent manner)? Sartori and Job (1988) suggest that this preservation of size information in the absence of knowledge about other visual properties may result from some size information being stored in semantics (e.g. as big as an elephant, as small as a mouse) or from size being stored in the structural description at a level that is “high enough” to be spared (e.g. if only “low-level” knowledge about specific structural properties of objects were affected). Accurate size judgements can thus be made even if the patient is operating with a “noisy” visual recognition system. Such an impaired recognition system may prevent access to the precise structural representation required for naming an object but, nevertheless, the structural knowledge that is activated may still be sufficient to enable the patient to discriminate whether the object was large or small in real life.

Colour Knowledge In this section we assessed whether a particular form of visual-perceptual knowledge, colour knowledge, was impaired for SRB. Tests required simple perceptual matching (Experiment 11), colour naming (Experiment 12), colour


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word matching (Experiment 13) and visual access to stored colour knowledge about particular objects (Experiment 14). Sheridan and Humphreys (1993) suggested that loss of stored knowledge about the colour of objects should precipitate deficits in naming objects that are strongly associated with a particular colour and for which colour strongly determines normal visual recognition, fruit and vegetables being prime examples (see Price & Humphreys, 1989). Gainotti and Silveri (1996) reported such a deficit in their patient, with the problem being most pronounced for living things. Here we evaluated whether SRB’s deficit in naming fruit and vegetables was accompanied by impaired stored colour knowledge, particularly for those items. Experiment 11: Colour Matching

Method. SRB was given 22 colour patches randomly arranged. He was asked to choose the pairs which were shades of the same colour. The colours were black, green, blue, red, yellow, pink, purple, brown, grey, orange, and white. Results. SRB scored 11/11 on this task, indicating that he had no low-level problem in discerning different shades of the same colour. Experiment 12: Naming Colour Patches

Method. SRB was shown one colour patch at a time and asked to name the colour. The colours were the same set of 11 that had been presented in Experiment 11. PB was also tested. Results. SRB scored 8/11. His errors were as follows: orange—“light brown”, pink—“different colour than red . . . can’t think of the name . . . mauve”, purple—“can’t think of the name . . . it’s dark blue.” Although not perfect, SRB was not significantly worse than his wife, who scored 11/11 (Fisher exact probability = .1). Experiment 13: Auditory Word– Colour Patch Matching

Method. SRB was presented with 11 colours, randomly positioned, and asked to point to the target colour in response to the name that was presented auditorily. The stimuli were the same as those in the previous experiment. Results and Discussion. SRB scored 10/11. The error he made was to point to pink in response to the word purple. The data from Experiments 11–13 indicate that SRB had generally preserved colour perception (Experiment 11) and that the ability to link seen colour to a

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verbal label was relatively intact (Experiment 12 and 13). However, preserved colour perception and colour naming can be dissociated from impaired knowledge about object colour. For example, Luzzatti and Davidoff (1994) reported two case studies of patients with no colour anomia who had problems in retrieving object-colour knowledge. In the next experiment we assessed SRB’s object-colour knowledge by asking him to judge if pictures of living and nonliving objects were coloured correctly, and when they were coloured incorrectly, he was asked to name the correct colour of the item. Experiment 14: Colour Object Decision and Retrieval of Object-colour Knowledge

Method. SRB was shown line drawings of 44 living and 33 nonliving objects taken from Price and Humphreys (1989). Each drawing was presented in both its correct and incorrect coloured form to give a total of 88 living and 66 nonliving items. SRB was shown each picture individually and asked to decide whether the item was the correct colour. If it was not correctly coloured, he was asked to say what colour the object should be. Following this SRB was asked to name the object. The entire set of items was presented over two sessions so that during each session each object was seen only once. Results. The results from this test are shown on Table 6. On the objectcolour decision task SRB performed relatively well for both living (76/88, 86%) and nonliving objects (64/66, 97%), although his performance was better with 2 nonliving items (c (1) = 5.1, P < .02). Interestingly, when the picture was coloured incorrectly and SRB was asked to name the correct colour, he was significantly worse for living (22/44, 50%) 2 compared to nonliving (30/33, 91%) items (c (1) = 14.4, P < .0001). SRB was also significantly worse at object naming for living (66/88, 77%) 2 compared to nonliving objects (63/66, 95%) (c (1) = 11.6, P < .0007). In the living category SRB made 22 naming errors in total, and for 8 of these items he misnamed the object in both the correct colour and incorrect colour conditions. For 14 he named the object correctly in one condition, but not in the other. TABLE 6 Results from Colour Decision and Object Colour Naming (Experiment 14) Category

———————————————— Task

Living

Nonliving

Colour decision Object-colour Object naming

76/88 (86%) 22/44 (50%) 66/88 (77%)

64/66 (97%) 30/33 (91%) 63/66 (95%)


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For nonliving items there was no difference in his performance across the tasks, object-colour decision, object-colour naming, and object naming. However, for living items there was a significant difference between the three tasks: Object-colour decision task (86%), object naming (77%), and object-colour 2 naming (50%) [c (2) = 20.5, P < .0001]. The errors in object-colour naming for living things will be discussed in more detail, as there are a number of plausible explanations. For example, SRB made three errors when asked to name patches of colour in Experiment 12. Given this, it would not be surprising if he also failed to produce the correct names for colours he found difficult to name from colour patches (orange, pink, or purple). Three of the living things required the name of a colour that he named incorrectly in Experiment 12 (orange, carrot, and pig), which might explain why he gave the incorrect colour for these items. However, although this is plausible for pig and carrot, it is not for orange, which he misnamed as a lemon and then said it was yellow. Also, even when these three items were discounted, performance was still worse for living (22/41, 54%) relative to nonliving 2 (30/33, 91%) items (c = 12.1, P = .0005). The latter type of error, misnaming the picture and giving the correct colour of the chosen name, occurred for two items, orange and raspberry. However, for five other misnamed items he gave a colour that was neither the correct colour for the target item nor for his response; for example, he misnamed pineapple as coconut and said it was red, which is neither the colour of a pineapple nor of a coconut. For 15 items he was able to give the correct object name but gave an incorrect colour, which indicates that even when SRB can identify the item he may not be able to retrieve the colour. It also seems that there is some relationship between SRB’s ability to retrieve colour information and to name objects, because his performance was significantly worse on both tasks with living things (animals, fruit and vegetables). However, there does not seem to be a direct correspondence between colour retrieval and naming because there were 15 items for which he could retrieve the name but not the correct colour. In addition, he could name colour patches for these colours, demonstrating reasonably intact links between colour knowledge and associated phonological labels. This latter dissociation has been used (Luzzatti & Davidoff, 1994) to propose that object shape information and object colour information are stored separately. Luzzatti and Davidoff suggested that object shape information may be stored in a structural description system and object colour in a semantic store of sensory information. Price and Humphreys (1989) have also suggested that object colour is stored separately from shape information, although they propose that colour is not stored in semantics, but in a functionally distinct perceptual store that can only be accessed to retrieve stored colour information associated with objects after the successful activation of an object’s structural description. For SRB, it may be that failure to activate associated colour knowledge from stored structural descriptions of objects also

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plays a part in his poor naming of living things. This would follow if colour and structural knowledge are reciprocally connected, so that activation of colour knowledge helps the retrieval of structural knowledge. Likewise, weak activation of structural knowledge, though sometimes sufficient for object naming, could lead to problems in retrieving associated colour information. This would be consistent with SRB’s poor naming of colours for living things. The perceptual store for colour knowledge may, however, be directly connected to colour names, and this connection may be intact for SRB, enabling colour patches to be linked to their phonological labels. Summary

We demonstrated that SRB has preserved colour perception (Experiment 11) and relatively intact ability to link colours to verbal labels (Experiments 12 and 13). In addition we have demonstrated (Experiment 14) that SRB has a particular difficulty in retrieving the correct colour of living compared to nonliving things, although there is not a direct one-to-one correspondence between items he cannot name and items for which he cannot retrieve objectcolour information. Both Luzzatti and Davidoff (1994) and Price and Humphreys (1989) have argued that colour information is not stored with other visual properties of objects such as shape or size. Price and Humphreys favour the existence of a separate perceptual store for colour, accessed via the structural description system when object colour knowledge must be retrieved; Luzzatti and Davidoff suggest that colour information may be stored in a sensory semantic system (see Davidoff, 1991, for an extensive description of this model). SRB’s ability to access semantic information is investigated in more detail in the next section.

Accessing Semantic Information The data presented so far have indicated that SRB has a category-specific deficit in naming living things and that this deficit seems to be linked to problems in retrieving stored visual-perceptual information about exemplars from the affected categories. In this section we present data on SRB’s ability to access semantic information about objects from vision. Six experiments are reported, examining: Matching objects on functional similarity (Experiment 15), associative matching (Experiment 16), matching on the basis of semantic relations (Experiments 17 and 18), object categorisation (Experiment 19), and SRB’s verbal definitions of items (Experiment 20). Experiment 15: Function Match

In this experiment, SRB was required to match physically different, but functionally similar, exemplars from vision. Previous investigators have used


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this test to assess access to stored functional knowledge about objects (Riddoch & Humphreys, 1987a; Warrington & Taylor, 1973, 1978).

Method. Three different pictures were presented on each trial; two are of the same class of object (e.g. two different types of chair) and the third an unrelated distracter. SRB was asked to point to the two that were functionally related. The stimuli used in this task were taken from BORB, test 11 (Riddoch & Humphreys, 1993). Results. SRB performed this test quickly and effortlessly and scored 31/32 correct. Experiment 16: Association Match

One difficulty with the function match test used in Experiment 15 is that functionally equivalent objects also typically share visual features. It may be possible for patients to perform well on the test even without recognising objects if they use a physical-match strategy. In Experiment 16 we examined associative matching between objects, since objects that are associatively related are less likely to share visual features. Experiment 16a: Association Matching

Method. This test involved matching a target picture to one of two pictures. The correct choice was an item that was associatively related to the target (e.g. matching a cup with a saucer, rather than a saucepan). The stimuli used in this task were taken from BORB, test 12 (Riddoch & Humphreys, 1993). Results and Discussion. SRB scored 29/30, which is at the control level (Riddoch & Humphreys, 1993). However, one problem with the function and associative matching tasks is that a range of objects were used, nonliving as well as living. To test specifically SRB’s access to semantics from his impaired categories, he was given two associative matching tasks in which the targets were (1) fruit and vegetables and (2) animals. Experiment 16b: Association Matching for Living Things

Method. The procedure was the same as that used in Experiment 16a. SRB was asked to match a target picture to one of two pictures and the correct choice was the item that was associatively related to the target. SRB was presented with 20 targets that were fruit and vegetables (grapes–bottle of wine, coconut–palm tree) and 20 that were animals (horse–saddle, cow–milk). Five age-matched control subjects performed the tests under the same conditions.

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Results. SRB scored 17/20 correct with fruit and vegetables and 20/20 when animals were targets. Control subjects scored a mean of 19.2/20 correct (SD = 0.84) with fruits and vegetables, and all five scored 20/20 with animals as targets. The data indicated that SRB’s ability to match animals to semantically related items was good, but he may be slightly impaired at the same task with fruit and vegetables, falling more than 2 SDs from the mean of the control subjects. Experiment 17: Pyramids and Palm Trees

In Experiment 17 we assessed SRB’s ability to match pictures on the basis of their semantic relations using the Pyramid and Palm Trees test (Howard & Orchard-Lisle, 1984). In this test SRB was asked to match a target picture (pyramid) with a semantically related picture (palm tree) and ignore an unrelated distracter (fir tree). There were 9 living and 41 nonliving targets in this test.

Results. SRB scored 48/50, which is within the normal range (Howard & Orchard-Lisle, 1984, report that control subjects make less than three errors). The errors SRB made were to associate pyramid with fir tree (instead of palm tree), and blackboard with writing desk (instead of school desk). Experiment 18: Picture– Picture Matching

In a further semantic matching task SRB was required to discriminate between close and distant object associates. He was presented with a target picture (e.g. axe) and four surrounding pictures that were respectively strongly related (hammer), more distantly related (scissors), visually related (flag), and unrelated (kite). The task was to match one of the surrounding pictures to the target on the basis of the strongest semantic association.

Method. There were 40 trials; on 5 the target was living and on 35 it was a nonliving object. The test was taken from Rumiati, Humphreys, Riddoch, and Bateman (1994), who used items from the PALPA test battery (Kay, Lesser, & Coltheart, 1992). Results. SRB scored 39/40 with this semantic matching task. Experiment 19: Categorisation

In this experiment we asked SRB to assign pictures or names to their superordinate categories. Some patients with apparent difficulties in naming living things have not been perfect at visual categorisation of these items, although performance was good when the patient had to categorise the corre-


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sponding names (Hart et al., 1985). Here we assessed SRB’s ability to categorise from the two modalities.

Method. SRB was presented with line drawings of 11 fruit, 9 vegetables, 11 animals, and 8 tools. Each picture was presented one at a time and SRB was asked to give the superordinate category name of that item. On a separate occasion he was presented auditorily with the names of these items and again asked to categorise them as fruit, vegetables, animals, or tools. Results. With the pictorial version SRB scored a total of 37/39 (10/11 with fruit, 8/9 with vegetables, 11/11 with animals, and 8/8 with tools). He misclassified a watermelon as a vegetable and an artichoke as a fruit. When presented auditorily with the names of these items he also scored 37/39 correct (11/11 with fruit, 7/9 with vegetables, 11/11 with animals, and 8/8 with tools). He did not know what category a pumpkin or a pepper belonged to. This indicated that his performance with visual categorisation was no worse than his ability to categorise the names of the stimuli.

Experiment 20: Giving Verbal Definitions

Method. SRB was asked to give statements defining the names of 48 of the people, 31 of the fruits/vegetables, and the 32 household objects used in Experiment 3. This is a relatively unconstrained task and we suspected that the quality of the response might be affected by fatigue. In an attempt to ensure that SRB gave as much detail as possible, the items were divided randomly over a number of testing sessions. Responses were scored in the standard way by asking three independent raters to score his definitions (Hillis & Caramazza, 1991; Warrington & Shallice, 1984). Definitions were only accepted as correct if all the raters agreed that SRB demonstrated that he had adequate knowledge about the item. In addition to this, each definition was assessed to see if SRB had included any incorrect information. Results. The raters accepted SRB’s definitions of 44/48 people, 28/31 fruits/vegetables, and 32/32 household errors. The differences across categories were not significant and so SRB performed well on this task superficially. He was able to give definitions for 18/21 of the faces he could not name in Experiment 3, and 6/8 of the fruits and vegetables. However, a closer examination of SRB’s responses revealed that, even for items accepted as correct by the raters, he sometimes suffered interference from visually and semantically similar items within a given category (e.g. for melon: “Full of water, big and round . . . do they make faces out of them on bonfire night?”, and for cucumber: “Big green thing . . . no I was thinking of something

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else, celery. Can’t think what one looks like, I should do—Yes! Got it now. Its a big green thing, you slice it, it’s watery inside. You can have cucumber sandwiches, and you can eat it with salads too.”). Using a stricter criterion, of accepting only those definitions on which SRB gave completely accurate information, he scored 36/48 (75%), 24/31 (77%), and 32/32 (100%) on people, fruits/vegetables, and household objects, respectively. There was a significant difference between household objects and both fruits/vegetables (Fisher exact probability = .005) and people (Fisher exact probability = .001). In addition, using this criterion, SRB’s performance (over items) was consistent between 2 naming (Experiment 3) and giving definitions, for both faces [c (1) = 6.4, P = 2 .01] and fruits/vegetables [ c (1) = 9.8, P = .002]. Summary

Across a range of tests assessing access to semantic information about objects, SRB performed at a normal level. On function matching (Experiment 15), associative matching (Experiment 16), semantic matching (Experiments 17 and 18), superordinate categorisation (Experiment 19), and giving definitions (Experiment 20), his performance was relatively good. However, a closer examination of his definitions revealed that, although he was able to access some correct semantic information about items from his impaired categories, he seemed to suffer interference from visually and semantically similar exemplars within the category. In addition, there was an association between the items that were defined correctly, and those that were named in Experiment 3. We return to consider the implications of these results in the General Discussion.

Within-category Discrimination We have shown that SRB has a category-specific naming impairment for living things and that he is impaired at retrieving structural and perceptual properties of objects, even though he performs relatively well on semantic matching tests. One reason for SRB’s deficit in naming living things may be that more visual differentiation is needed to retrieve name information about these objects relative to nonliving objects. To name a dog requires that activation of the stored structural, semantic, and phonological representations of that stimulus are differentiated from those of other visually similar exemplars from the same category (e.g. sheep, cat, fox). To name a hammer, we propose, requires less differentiation (e.g. a hammer may need to be differentiated from an axe but from few other tools). This contrast between living and nonliving things, in the differentiation required to label the object, may be lessened if more precise labels are required for nonliving things (see Bruce & Humphreys, 1994).


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Object naming is usually performed at the “basic level” (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976), where stimuli have (1) substantial perceptual overlap with other members of their category, (2) a common set of functional properties, (3) a common name, and (4) are grouped together but have no specific referent. For living things, however, more visual differentiation is needed for naming at the basic level compared to nonliving things (see Humphreys et al., 1988, for evidence on naming time differences with normal subjects, and Humphreys et al., 1995, for a simulation of these results). However, the degree of differentiation required for the identification of nonliving things can be increased by demanding that objects be named at the subordinate level rather than with base-level names. If SRB’s difficulty is with visual differentiation within a set of objects that have overlapping visualperceptual properties, then he should have problems with subordinate naming, and this should apply to nonliving as well as to living things. This hypothesis was tested in Experiment 21. Experiment 21: Subordinate Naming

Method. SRB was given two sets of items, one from an animate category (dogs) and the other from an inanimate category (cars). There were 23 dogs and 14 cars. These items were chosen because SRB was particularly interested in both categories and both he and his wife reported that he should have been able to identify all the stimuli presented premorbidly. He was given as much time as he needed and asked to name the picture. All items were coloured photographs. His wife (PB) was tested as a control, along with four male subjects (ages 56–62). Results and Discussion. SRB scored 4/23 (17%) with the set of dogs and 8/14 (57%) with the cars. PB scored 20/23 (87%) and 13/14 (93%) with dogs and cars respectively. Her performance was significantly better than SRB’s for 2 both dogs [c (1) = 22.3, P < .0001] and cars (Fisher exact probability = .04). The four male controls scored a mean 20.25 with the dogs (SD = 2.0) and 13 with the cars (SD = 1.0). SRB’s scores fell more than 4 SDs below the controls, for both dogs and cars. These results indicate that SRB was relatively poor at retrieving subordinate names for nonliving as well as for living things; that is, his difficulties are not completely restricted to living things when more detailed visual differentiation is required to name nonliving things. The data are consistent with (1) SRB’s problems arising when fine-grained visual information is needed to support task performance, irrespective of the category involved; and (2) a deficit in a stored structural description system that is not categorically organised, but for which the efficiency of access depends on the overlap in the structural properties of objects.

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Recovery of Function After a prolonged break (4 months) from testing, SRB returned to our laboratory and reported that although he still had severe memory problems, his naming had improved. To investigate whether his overall performance had improved, and more specifically if his performance with living things was now comparable to his performance with nonliving things, we gave him a number of tests designed to tap different levels of the object recognition system. We compared his naming performance (Experiment 22) with categorisation (Experiment 23) and object decision (Experiment 24). Ten control subjects performed the three tests under the same conditions (age range = 22–45; 5 male and 5 female). Experiment 22: Naming

SRB was presented with line drawings of 20 living items and 20 nonliving items taken from the Snodgrass and Vanderwart (1980) set. The pictures were displayed on a Macintosh Quadra computer and SRB was asked to name them as quickly and accurately as possible.

Results. In terms of accuracy, SRB’s performance had improved since earlier studies: he made only 3/40 errors (orange ® fruit, pineapple® coconut thing, doll ® baby or doll). However, when his RTs were analysed, using a one-way ANOVA (treating each data point as a separate subject), he was still significantly slower with items from living categories (3018msec ) compared to nonliving categories (1625msec ) [ F(1,32) = 4.3, P = .05]. The results are shown in Table 7. There was no significant difference between living (789msec) and nonliving categories (763msec) for 10 control subjects who performed the experiment under the same conditions [F (1,9) = 2.8, P = .13]. Experiment 23: Superordinate Categorisation

SRB was presented with the same 40 items used in Experiment 22 and asked to categorise them into living and nonliving sets, using two designated letters on the keyboard.

Results and Discussion. SRB made no errors in this experiment, although two outliers ( > 3 SD from the mean for the category) were removed from the RT analysis. His RTs were submitted to a one way ANOVA which showed that there was no significant difference in his time to categorise items from living (1080msec ) and nonliving categories (938msec) [F (1,37) = 2.01, P = .17]. His RTs are shown in Table 7. The mean RTs for each category for each control subject were submitted to a one-way, repeated measures ANOVA, which showed a significant difference between living (533msec) and nonliving


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TABLE 7 Recovery of Function (Experiments 21– 23) Living

Nonliving

Naming (Exp. 21) SRB Controls

3018 789

1625 763

* *

Categorisation (Exp. 22) SRB Controls

1080 533

938 605

*

Object Decision (Exp. 23) SRB Controls

1229 701

1047 681

* *

RTs are measured in milliseconds; * indicates a significant difference between living and nonliving categories.

(605msec) categories [ F(1,9) = 13.4, P = .0005]. This advantag e for the categorisation of living things by control subjects replicates prior research (e.g. Riddoch & Humphreys, 1987b), and is likely to be due to subjects using correlated physical features to make categorisation decisions with these stimuli. Experiment 24: Object Decision

SRB was presented with the same 40 real objects used in Experiment 22 and 23 (20 living and 20 nonliving), and 40 nonobjects (20 derived by interchanging parts between living things and 20 derived in the same way from nonliving things). SRB was asked to indicate, using two designated letters on the keyboard, whether each item was a real object or not.

Results. In terms of accuracy, SRB performed well on this task: he made only one error to living things (objects and nonobjects) and four to nonliving (objects and nonobjects). The correct RTs were submitted to a two-way, between items ANOVA. There was a significant main effect of category [F (1,66) = 5.64, P = .02]; SRB took significantly longer to make an object decision to living (1229msec ) compared to nonliving items (1047msec). There was no main effect of type of object (real vs. not real) [F (1,66) = 1.6, P = 0.2], and no interaction between category and type of object [F (1,66) = 0.3, P = .6]. SRB’s RTs are shown in Table 7. The RTs of the control subjects were submitted to a two-way, repeated measures ANOVA, which showed a significant main effect of category [F (1,9) = 8.6, P = .02] and of type of object [ F (1,9) = 34.1, P = .0002]. The control subjects were significantly faster (by 20msec) with nonliving things, and they were also faster with real objects than with nonobjects (by 127msec). The interaction also tended towards significance

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[F (1,9) = 4.2, P = .07]. Nonobjects derived from living things tended to be rejected more slowly than nonobjects derived from nonliving things (778msec and 732msec for living and nonliving things, respectively). There was little effect of category on responses to objects (624msec vs. 631msec for living and nonliving things, respectively). These results indicate that SRB found the object decision task for living things much more difficult than that for nonliving things (an overall effect of 182msec). In contrast, controls showed no effect of category on object responses, and only a 46msec effect of category on nonobject responses. The overall effect of category on SRB’s RTs remained outside the normal range (the difference between the RTs for living and nonliving things for normals ranged from 51msec to –14msec). Summary

These results indicate that although SRB’s naming performance had improved in terms of accuracy, he was still significantly slower to name living items compared to nonliving items. In contrast, there was no difference in naming times for living and nonliving items for control subjects. When the task was superordinate categorisation there was a trend for SRB to be slower at categorising living things, which contrasted with the control subjects, who were significantly faster with living things. In object decision, both SRB and control subjects were significantly slower with living compared to nonliving things, although the difference between the two categories appears abnormally large for SRB. It might be argued that the large effects of object category still apparent on SRB’s RTs reflect the general slowness of his responses rather than any residual impairment in the visual recognition of living things. That is, the particularly slow RTs to living things occurred because his RTs were slow overall, and because these items were in any case the more difficult for control subjects. Against this, note that control subjects were faster to categorise living things than nonliving things; SRB showed the opposite trend. Also the sheer magnitude of the category effects with SRB suggest that he remains abnormally affected by this variable. With nonliving things, SRB’s RTs were roughly about twice as slow as the control subjects, presumably reflecting the general effects of brain damage. However, on naming, the effect of category was about fifty four times that found with controls, and on object decision the category effect was about nine times that for controls. We suggest that SRB still has a residual problem in accessing stored structural descriptions for objects, and that this problem is exacerbated for stimuli from categories with structurally similar exemplars (i.e. living things). The cascade model of object recognition outlined by Humphreys and Riddoch (Humphreys et al., 1988; Riddoch, Humphreys, Coltheart, & Funnell, 1988;


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Humphreys & Riddoch, 1988) can accommodate SRB’s pattern of results; an impairment in the early stages of the model affecting access to the structural description system, indicated by poor object decision performance, will produce “knock-on” effects on accessing knowledge for later stages of processing, such as the phonological knowledge required for object naming (see Humphreys et al., 1995, for a simulation). These experiments document one of the first follow-up studies of patients with category-specific naming disorders, and have demonstrated a residual impairment even when recognition accuracy had recovered. We return to discuss the implications of this in the General Discussion.

GENERAL DISCUSSION This paper has documented a case study of a patient (SRB) who shows a category-specific impairment in naming living things. We have shown that this impairment occurs across various modalities including naming from vision, touch, and auditory definitions that stress the visual-perceptual properties of objects. We have also shown that SRB has problems in retrieving some visual-perceptual properties of living things and has difficulty, for example, in drawing from memory, retrieving object-colour knowledge, and describing the visual features that distinguish between pairs of items. In contrast to this, he performed relatively well on tests assessing his ability to access semantic memory, both from pictures and words (although his definitions showed signs of interference between visually and semantically similar living things). Interestingly we found that, although SRB’s problem was most apparent with living things, his impairment was not exclusively confined to living categories and, when asked to perform subordinate naming on nonliving items (types of car), his performance fell below control levels. We also extended previous studies of category-specific naming impairments by examining SRB’s performance on a number of tasks when his naming accuracy had improved substantially. Although in terms of accuracy SRB performed at control level, he remained significantly slower to name, and to perform object decisions to, living things compared to nonliving things. We consider the implications of the evidence for understanding the nature of category-specific effects and for models of object naming and semantic memory.

Is Living–Nonliving a Principal Organising Factor in Semantic Memory? Do patients with impaired recognition for some categories of stimulus provide evidence that living and nonliving things are stored in functionally separate systems within the human brain? Taxonomic category has been proposed to be

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a fundamental organising principle at a number of levels within the object recognition system: the structural description (Sartori & Job, 1988); the semantic system (McCarthy & Warrington, 1994; Sheridan & Humphreys, 1993); and the phonological output lexicon (Hart, Berndt, & Caramazza, 1985). More recently, Damasio, Grabowski, Tranel, Hichwa, and Damasio (1996) have argued that another level mediating between the conceptual and phonological representations of objects might also be categorically organised and (1996, p. 499) that “the retrieval of words denoting entities belonging to three distinct conceptual categories—unique persons, non-unique animals and non-unique tools—depends on separable regions in higher order cortices of the left temporal lobe.” In particular they argued that the names of faces were stored in the left temporal pole, animals in the left anterior inferotemporal region, and tools in the posterolateral inferotemporal region. However, others have maintained that the living/nonliving distinction is not a primary organising principle of semantic memory and that the categoryspecific effects emerge as a result of the different degrees of processing required to identify particular objects (Humphreys et al., 1988), or from differences in the sort of information that is critical for understanding particular categories of item. For example, Farah and McClelland (1991) used a computer simulation to investigate whether semantic memory was organised by category or the properties of the information being represented. Within their model there were three pools of units: name units (verbal input and output), picture units (visual input and output), and semantic units (divided into visual and functional units, to represent respectively the visual and functional properties of objects). Ten living things and 10 nonliving things were represented at all levels but, in accordance with ratings provided by independent subjects, living things were more strongly represented on the visual-semantic units and nonliving things on the functional-semantic units. Farah and McClelland found that, when the visual-semantic units were lesioned, there was a greater effect on living compared to nonliving things, and as the damage increased there was a disproportionately large effect on the living things. In contrast, when the functional-semantic units were damaged only the nonliving things were affected. Consequently they argued that category-specific impairments could arise from a system that was not necessarily organised taxonomically, but by the nature of the information involved (e.g. visual vs. functional). In this paper we found that SRB’s naming impairment was not restricted to living things and that he was also significantly impaired when asked to distinguish at a subordinate level between different exemplars within a nonliving category, such as types of cars (Experiment 20). We propose that the most parsimonious explanation of the data is that SRB does not have an impairment for categories of living things per se, but that his primary difficulty is in accessing sufficient stored visual information to distinguish between items that are visually similar within their category.


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We have also shown that the impairments shown by SRB are not all artefacts arising from covariation in the familiarity, frequency, and visual complexity of stimuli (Funnell & Sheridan, 1992; Stewart et al., 1992). None of these latter factors influenced SRB’s naming performance (Experiment 2). On regression analyses the only factors found to affect SRB’s object naming reliably were category and a measure of the object’s structural similarity relative to other members of its category—the percentage of contour overlap. We suggest that SRB’s deficit is linked to an inability to recruit sufficient stored visual knowledge to identify objects from categories with visually similar exemplars. The fact that this result generalises from living to nonliving things when a finergrained (subordinate) identification response is required also indicates that the problem is not localised to a certain class of item but reflects a more generic impairment in object identification.

The Locus of the Deficit Although interpretations of category-specific impairments have been diverse, there seems to be increasing support for an important relationship between loss of stored visual knowledge and a naming impairment for living things (Sartori & Job, 1988; Silveri & Gainotti, 1988; Warrington & Shallice, 1984). However, the locus of the underlying deficit remains an area of dispute. Warrington and Shallice (1984) suggested that a naming impairment for living things may be due to impaired visual semantic information, which is functionally separate from a semantic store of associative and functional information important for naming nonliving things. In a later paper, Warrington and McCarthy (1987) modified this sensory/functional distinction and outlined an associative network that represented the meaning of an object by the relative contributions from sensory and motor channels of information. These channels of information could be relatively fine-grained so that within the visual modality, for example, colour and shape would have separate channels. They argued (1987, p. 1290) that “the relative importance or ‘weighting’ values of different channels of sensory/motor evidence could possibly form the basis of category specificity in the brain.” For example, the category fruit would have high weightings in the taste and colour channels, so damage to these channels would lead to a selective impairment for identifying fruit. Categories which had low weightings in these channels (e.g. artefacts, which have no characteristic taste or colour) would be spared. This model moves away from a rigid separation of visual and nonvisual semantic information, and is a more distributed, a modal model of semantic memory (for similar accounts see Allport, 1985; Damasio, 1990; and Shallice, 1988). In more recent papers, Warrington and McCarthy have argued for a multiple semantics system in which category specificity runs orthogonal to a visualverbal distinction (based on the modality of input) (McCarthy & Warrington,

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1994; Warrington & McCarthy, 1994). They outlined four functionally independent systems that could break down selectively following brain damage (see Fig. 5). According to this model, SRB would have a number of independent impairments to the representations of living things in (1) the visual semantic system (given his poor visual naming of living things), (2) verbal semantics (given his poor retrieval of the visual attributes of living things from their names), and presumably to (3) tactile semantics and (4) taste semantics (given his poor naming from these touch and taste), if these last two modalities also have corresponding modality-specific semantic representations. If Warrington and McCarthy’s model is extended to include naming from all modalities—vision, words, taste, touch, characteristic sounds (e.g. dog barking, telephone ringing), and characteristic actions (understanding mime)—it becomes an extremely uneconomical way to store semantic information about objects. Further, we suggest that the category-specific nature of SRB’s impairment across a number of modalities provides evidence against this form of multiple semantics. However, some authors have suggested that category-specific impairments for living things may not necessarily be a semantic problem at all. For example, Silveri and Gainotti (1988) suggested that their patient had an impairment to the level of the word and object recognition systems at which stored visual/perceptual attributes about objects were represented. Although this could be at a semantic level (Hillis & Caramazza, 1991; Warrington & Shallice, 1984), it could equally be a presemantic visual store, such as the structural description system (Humphreys et al., 1988; Riddoch & Humphreys, 1987a). Sartori and Job (1988) described a patient, Michelangelo, with a very similar profile to SRB. Michelangelo had a category-specific impairment in naming living things compared to nonliving things, and this occurred both for picture naming and for naming from verbal definitions. He could access superordinate category information, and information about the ferocity, size, noise, and habitat of animals, but had difficulty in retrieving visual-perceptual attributes about living things. Sartori and Job (1988) argued that Michelangelo had a

FIG. 5. Multiple semantics (Warrington & McCarthy, 1994)


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deficit in a categorically organised structural description system. This hypothesis implies that the visual features of animate and inanimate objects would be stored in functionally and/or anatomically distinct brain areas so that a lesion could impair one class of object and not the other. According to Sartori and Job (1988), the structural description system holds the visual attributes about an object, and the spatial relations between them, and should not be considered simply as an access code (Morton, 1979; Seymour, 1973). Sartori and Job proposed that the structural description was not only necessary for naming but it was also involved when tasks demanded verbal production of visual features (e.g. describe what a dog looks like), or the retrieval of visual-perceptual information from words (e.g. are apples and peaches the same shape?) (see also Riddoch et al., 1988). Sartori et al. (1993) extended their concept of a categorically organised structural description by drawing on Marr’s (1982) model of object recognition, which proposed that knowledge about the visual attributes of objects is stored in a hierarchy that becomes progressively more detailed. Sartori et al. suggested that the categorical organisation of the structural description was related to these hierarchies and that living things were represented in “deeper” hierarchies than nonliving things; as a consequence of the differential “depths” of the hierarchies, a categorical breakdow n might occur if the information at the more detailed levels of representation was unavailable. This seems conceptually different from the earlier claim by Sartori and Job (1988), that category was a fundamental organising principle of the structural description system. According to the later hypothesis, category-specific impairments for living things emerge following damage to the structural description system because they require “deeper” levels of processing and not because a specific portion of the system specifying the structural properties of living things has been damaged. The more recent interpretation of category-specific disorders for living things, made by Sartori et al. (1993), is similar to the proposal by Humphreys et al. (1988), that damage to a structural description system may impair the identification of living things more than nonliving things simply because living things share more common visual properties than do nonliving things. If the effects of the damage are generally to impair retrieval from the structural description system, then living things may suffer more than nonliving things because their descriptions require more fine-grained differentiation. Consistent with the proposal for a general deficit in a noncategorical, structural description system is our evidence that SRB was not only impaired at identifying living things but also at naming faces and at subordinate naming of nonliving things (e.g. cars, a category he was previously interested in) (see also Damasio, Damasio & Van Hoesen, 1982). SRB’s problems occurred whenever fine-grained visual differentiation was required for identification, irrespective of the category of the object involved. Note also that SRB showed

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interference between visually and semantically similar exemplars (which would be simultaneously activated, according to the Humphreys et al., 1988, model) when he was trying to isolate information about a target item from categories comprised of visually similar exemplars (e.g. fruit and vegetables). However, he did not suffer similar interference when exemplars were from structurally dissimilar categories (which would not have many simultaneously activated exemplars according to the Humphreys et al., 1988, model). It is interesting to note that SRB did not seem to suffer interference from items that were visually, but not semantically, similar. For example, when asked to describe items in Experiment 19, he confused cucumber and celery, but never cucumber and a cigar, which also have a similar shape. He also confused a pumpkin and a melon, but never a melon and a ball. We propose that “visual” cross-category confusions do not occur because the learned visual properties of real objects from different categories differ greatly (and may include information not only about the global shape of the objects but also the number and spatial arrangements of their parts, possibly also their texture, and so forth). In addition, effects of overlap in such learned visual properties will be exacerbated when activation is mapped onto overlapping semantic representations belonging to other items from the same category (see Arguin et al., 1996). Interestingly, even when SRB’s naming had improved in terms of accuracy, we found that he was significantly slower in naming and in object decision for items from structurally similar categories. Object decision has been used as a standard test of access to the structural description system (Riddoch & Humphreys, 1987a, 1987b, 1993) and SRB’s residual deficit on this task is supportive of our hypothesis that the locus of his impairment is at the level of the stored structural description.

Category-specific Naming Deficits and Loss of Visual-perceptual Knowledge As we have established, SRB’s problem in naming living things was coincident with poor visual-perceptual knowledge about these items. Though a causative relationship between impaired visual-perceptual knowledge and poor naming of living things cannot be proved from the present data, our findings, like those of other investigators, suggest that impaired visual-perceptual knowledge lies at the heart of the poor naming of living things. We have suggested that an impairment in accessing visual-perceptual properties might lead to particular problems for living things because they belong to structurally similar (visually crowded) categories. However, others have emphasised different aspects of the relationship between living things and visual perceptual knowledge.


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Farah and McClelland (1991) stressed that the core of the semantic representations for living and nonliving things might be qualitatively different: Living things would be defined in terms of visual-perceptual properties and nonliving things in terms of their functional properties (see also Warrington and Shallice, 1984). If living things are defined primarily in terms of their visual-perceptual properties, loss of knowledge about these properties could precipitate a selective impairment for these items compared to nonliving things. This hypothesis would also predict that loss of functional information would lead to selective impairments for man-made artefacts. To date, there have only been a handful of reports of category-specific impairments for nonliving things and, as far as we know, none of these have specifically tested sensory versus functional knowledge in these patients (Hillis & Caramazza, 1991; Sacchett & Humphreys, 1992; Warrington & McCarthy, 1983, 1987, 1994). To simulate semantic memory impairments, Farah and McClelland (1991) used a distributed associative memory system, in which nodes representing perceptual and functional knowledge about objects were fully interconnected. One emergent consequence of this was that the loss of perceptual knowledge about objects also led to some problems in retrieving functional knowledge. Interestingly, SRB performed at control levels on tests requiring naming from verbal definitions of objects (Experiment 6), contrary to Farah and McClelland’s account. These results fit better with a model in which verbal information is represented in a store functionally independent of a store (or stores) of sensory information. De Renzi and Lucchelli (1994) have also suggested that, if patients have a general problem in retrieving visual-perceptual knowledge, intact functional information may be able to compensate for the loss of perceptual knowledge when nonliving things are identified. For instance, even if the stored perceptual information accessed from an object is insufficient to specify whether (say) the object is a cup or a bowl, functional information may be used in a top-down way to select the object with the most appropriate features for the function (e.g. the hypothesis could take the form: If a cup, it must have a handle for lifting). This use of top-down functional knowledge may not be possible for many living things that tend not to have specific functions and for which there are many competitors with the appropriate features for a given function (e.g. running fast).

A Top-down Account of Object Naming One interesting aspect of SRB’s case was that he appeared to have relatively good access to semantic knowledge from vision. For example, he performed well on tests designed to test access to semantic knowledge, even for items from his impaired categories (Experiment 6, naming from functional definitions of living things; Experiment 16b, associative matching for living things; Experi-

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ment 19, superordinate categorisation). Relatively good access to semantics, but poor naming, has been used in previous studies to argue that the patient had a specific impairment in name retrieval, as opposed to a recognition impairment for living things (Farah & Wallace, 1992; Hart et al., 1985). However, we are reluctant to conclude that SRB’s impairment was confined to name retrieval. First, in the superordinate categorisation task (Experiment 24) SRB tended to take longer to classify living relative to nonliving things, whereas controls were significantly faster with living items. Second, in both the associative matching task (Experiment 16b) and the categorisation task (Experiment 19), for which SRB’s performance was not grossly impaired, some form of constraining information was present. In the associative matching of living things, SRB was presented with three pictures, two of which were related, and it is possible that the semantic knowledge activated from the objects was enough to enable matching to take place, even if it was insufficient to support naming. Similarly, in the categorisation task SRB knew the categories into which the pictures had to be sorted. Again, partial activation of semantic knowledge may have been sufficient to generate a reasonable standard of task performance. In this sense, the semantic knowledge activated by the stimuli, combined with the constraints of the task, may have the same facilitation effect as functional knowledge does for identification of nonliving things (cf. De Renzi & Lucchelli, 1994). Such top-down constraints could help overcome a deficit in accessing semantic knowledge consequent on impaired visual-perceptual knowledge. Consistent with this, SRB’s own verbal definitions of living things tended to be conflated by interference from visually and semantically related items. The task of providing verbal definitions is much less constrained than tasks involving forced-choice semantic discriminations. Definitions may also be problematic even if a patient has intact semantic knowledge if he/she retrieves incorrect visual information (from the structural description system) when giving the definition. In addition, we suggest that in the previous case studies of patients with problems apparently confined to name retrieval, good access to semantic knowledge was not convincingly demonstrated. For example, the patient reported by Hart et al. (1985) made errors on categorising seen fruits and vegetables. Farah and Wallace (1992) did not test visual access to semantics directly, but relied on the patients’ own verbal definitions (which were reported to be “generally quite adequate” by the authors, although no independent ratings were provided) and on a demonstration of positive effects of phonemic cueing to support their argument that the problem was one of name retrieval rather than impaired semantic knowledge. Consequently, we propose that there is little evidence to support their claim that post-semantic name retrieval stages may be organised categorically. One other possibility, however, is that the functional locus of SRB’s deficit was in an impaired structural description system, and that this primarily


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prevented successful name retrieval but not the retrieval of semantic knowledge. It may be that, specifically for object naming but not for semantic access, there needs to be protracted visual processing (activation of the structural description system) in order for activation across objects to be differentiated sufficiently for naming to take place. The degree of differentiation required may be greater for living than for nonliving things, so that living things suffer more when structural knowledge is degenerate. Nevertheless, the activation derived even from impaired structural representations may be sufficient to support semantic access, especially under constrained task conditions (see earlier). This view holds that object recognition and naming do not operate as a series of discrete processing stages, but rather that “late” task requirements (e.g. for name retrieval) may impose demands on earlier processing stages (activation at the structural description level). It may even be that there is enhanced top-down processing of structural information when naming rather than semantic classification is needed. Recent studies of object naming using functional imaging (PET) techniques support this last account. For example, Martin, Wiggs, Ungerleider, and Haxbey (1995) and Price, Moore, Humphreys, Frackowiak, and Friston (1996) have both found that object naming led to enhanced activation at what are presumed to be relatively early stages of visual processing, including the inferior occipital and lingual gyri (particularly in the left hemisphere). Martin et al. found that this enhancement of activation was most pronounced for the naming of living things. Such PET data suggest that, under naming conditions, there is increased activation in brain areas concerned with encoding and visual matching to memory of seen objects, consistent with either prolonged visual processing or top-down activation effects.

Category Specificity and Colour There have been relatively few cases where patients have been good at naming colours but poor at retrieving knowledge about the colour of the object (Farah, Levine, & Calvanio, 1988; Schnider, Landis, Regard, & Benson, 1988), and even with these cases, colour naming was not perfect. Since this pattern is relatively rare it has been suggested that impaired object-colour retrieval may result from interference from the colour naming system. Beauvois and Saillant (1985) presented some evidence to support this hypothesis by showing that performance at identifying the colour of objects improved when the patient’s mouth was covered with a plaster. However, Davidoff and colleagues (Davidoff, 1991; Luzzatti & Davidoff, 1994) have suggested that colour anomia and object-colour retrieval may be separable processes. Luzzatti and Davidoff presented two case studies of patients who had no colour anomia but had difficulty in retrieving the colour of objects from verbal or visual modalities. They argued for functionally separate stores of object colour knowledge and

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object shape knowledge since the inability to name the colour did not correlate with an inability to name the object. They argued that there was no evidence to support the suggestion that colour was more important for the identification of living relative to nonliving things (Sheridan & Humphreys, 1993; Warrington & McCarthy, 1987), because their patients did not have category-specific naming deficits when items were matched for variables such as familiarity, frequency, and visual complexity. Note, however, that one of their patients did have a significant category-specific impairment for living things when tested on drawing from memory, which was reliable even when the experimenters controlled for familiarity and visual complexity. We have demonstrated that SRB has a category-specific naming impairment for living things and that his object-colour knowledge for these items is also more impaired than it is for nonliving objects. This is despite the fact that living things will tend to have more reliably associated colours than many nonliving things. Price and Humphreys (1989) have suggested that colour information is stored separately from structural knowledge about objects, but that in tasks requiring the retrieval of colour information about objects, the colour store is accessed via linked representations in the structural description system. The data from SRB fit with this proposal of linked representations because he was impaired at retrieving information about both types of knowledge specific to certain categories of item. Also, weak activation of colour knowledge may in turn affect access to stored object knowledge for living things, producing reciprocal problems both in identifying and retrieving colour knowledge for these items (cf. Sheridan & Humphreys, 1993).

Identification across Modalities Other than Vision SRB was impaired at naming from definitions stressing the visual-perceptual properties of items (particularly living things), although his retrieval of verbal/functional knowledge was at the control level (Experiment 6). This is consistent with the structural description system being used even when stimuli are presented verbally, when visual-perceptual information must be retrieved for task performance (Riddoch et al., 1988). We also showed that SRB had problems in naming fruit and vegetables when they were presented via the tactile and taste modalities. The items that were impaired for visual identification also tended to be impaired when SRB was tested on tactile identification. In addition, the items that were affected by visually similar, categorically related exemplars when SRB was asked to give definitions (Experiment 19) tended to be impaired in naming from visual presentation (Experiment 3). This item-specific consistency suggests that there is a common functional impairment, affecting performance in all modalities. For example, both visual and tactile identification may depend on access to a common structural description system wherein the nature and spatial relations


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between the parts of objects are specified. Damage to this system could affect tactile as well as visual object identification. When fruit and vegetables were presented to SRB for identification from taste, his score was again outside the normal range. In addition, SRB showed less item-consistency in naming from taste and either touch or vision than he did between the latter two modalities. One possibility, then, is that SRB had a second mild problem specific to sensory knowledge about the tastes of stimuli. This may be a general deficit, rather than being category-specific in nature; unfortunately, we were unable to assess SRB’s ability to identify stimuli from taste, other than fruits and vegetables. However, one could also argue that, if fruit and vegetables are defined in terms of their visual-perceptual properties (Farah & McClelland, 1991; Sheridan & Humphreys, 1993; Warrington & Shallice, 1984), and if these properties are degraded (or inaccessible), then activation of all sensory knowledge relating to these items could be below normal resting levels, due to reciprocal connections between sensory systems. Perhaps on some occasions when he eats a strawberry SRB can recognise that the taste is familiar, and he can activate some semantic properties such as “eaten in summer”, but he cannot always identify the object as a strawberry since the stored visual-perceptual properties (such as small, red, round with seeds on the outside) necessary for object identification are degraded. This explanation fits particularly neatly with suggestions that semantic knowledge is organised in a multimodal associative network (Allport, 1985; Damasio, 1990; Warrington & McCarthy, 1987; Shallice, 1988).

Category Specificity and Rehabilitation We have documented that, over time, SRB’s naming performance improved considerably so that, 12 months post lesion, he could usually access the names of living things when given enough time. Nevertheless, even when naming accuracy improved, SRB remained abnormally slow at naming living compared to nonliving things. He was also abnormally slow with living compared to nonliving things in an object decision task, which suggests that he had a residual difficulty in accessing a detailed structural description for visually presented objects. There have only been a few reports of rehabilitation programmes for patients with category-specific naming impairments. Sartori et al. (1994) documented the rehabilitation programmes of two patients, Michelangelo (Sartori & Job, 1988) and Giuliette, who both had a similar pattern of impairments to SRB. All three patients had category-specific impairments for living things and had difficulty on tasks requiring the retrieval of visual-perceptual knowledge (such as drawing from memory). In addition, Michelangelo and Giuliette, like SRB, suffered from retrograde and anterograde amnesia. Sartori et al. (1994) aimed to improve the patients’ perceptual knowledge of living things and to improve

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their episodic memory. Their programme consisted of giving the patients practice twice a week for 8 months and included a wide range of tasks such as categorisation, description of perceptual attributes, word–picture matching, and drawing from memory. However, despite this intensive programme, both patients had a marked residual category-specific naming impairment when retested. Sartori et al. argued that the information stored in the structural description system was lost since the patients remained impaired on naming, object decision, on retrieving the perceptual properties of objects, and on drawing from memory. They argued that the failure of the rehabilitation programme may be due to the anterograde amnesia of the two patients and suggested (Sartori et al., 1994, p. 122) that “treatment of specific semantic memory loss should be considered impossible if accompanied by a severe anterograde amnesia.” Nevertheless, we have demonstrated that, in terms of accuracy, the category-specific naming problem in a patient with severe anterograde and retrograde amnesia may recover over time. Our aim throughout the study was to assess the underlying deficits causing the category-specific disorder and we were not primarily interested in rehabilitation. However, SRB was tested intensively for at least 2 hours a week over a period of months. It is impossible to assess to what extent his improvement was spontaneous or a result of this intensive practice with various tests involving living things and probing visual-perceptual knowledge. Nevertheless, it is relevant to note that, clinically, SRB’s amnesia made very little recovery during the total time of testing and remained the most intrusive problem in his day-to-day life. At the very least, SRB’s improvement suggests that stored perceptual knowledge may recover independently of any general improvement in episodic memory. It may be too soon to conclude that the presence of severe amnesia necessarily prevents recovery of other cognitive functions such as naming. Manuscript received 26 September 1995 Revised manuscript received 12 February 1997 Manuscript accepted 12 February 1997

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Damasio, A.R. (1990). Category-related recognition defects as a clue to the neural substrates of knowledge. Trends in Neuroscience, 13 (3), 95–98. Damasio, A.R., Damasio, H., & Van Hoesen, G.W. (1982). Prosopagnosia: Anatomical basis and behavioural mechanisms. Neurology, 32 , 331–341. Damasio, A.R., Damasio, H., Tranel, D., & Brandt, J.P. (1990). Neural regionalisation of knowledge access: Preliminary evidence. Cold Spring Harbor Symposia on Quantitative Biology, 55, 1039–1047. Damasio, H., Grabowski, T.J., Tranel, D., Hichwa, R.D., & Damasio, A.R. (1996). A neural basis for lexical retrieval. Nature, 380, 499–505. Davidoff, J. (1991). Cognition through colour. Cambridge, MA: MIT Press. Davidoff, J., & De Bleser, R. (1993). Impaired picture recognition with preserved object naming and reading. Brain and Cognition, 24, 1–23. De Renzi, E., & Lucchelli, F. (1994). Are semantic systems separately represented in the brain? The case of living category impairment. Cortex, 30, 3–25. Farah, M.J., Hammond, K.M., Metha, Z., & Ratcliff, G. (1989). Category-specificity and modality-specificity in semantic memory. Neuropsychologia, 27 (2), 193–200. Farah, M.J., Levine, D.N., & Calvanio, R. (1988). A case study of mental imagery deficit. Brain and Cognition, 8, 147–164. Farah, M.J., & McClelland, J.L. (1991). A computational model of semantic memory impairment: Modality specificity and emergent category specificity. Psychological Review, 120, 339–357. Farah, M.J., & Wallace, M.A. (1992). Semantically bounded anomia: Implications for the neural implementation of naming. Neuropsychologia, 30,(7), 609–621. Funnell, E., & Sheridan, J. (1992). Categories of knowledge? Unfamiliar aspects of living and nonliving things. Cognitive Neuropsychology, 9(2), 135–153. Gaffan, D., & Heywood, C.A. (1993). A spurious category-specifi c visual agnosia for living things in normal human and nonhuman primates. Journal of Cognitive Neuroscience, 5(1), 118–128. Gainotti, G., & Silveri, M.C. (1996). Cognitive and anatomical locus of a lesion in a patient with a category-specifi c semantic impairment for living beings. Cognitive Neuropsychology, 13,(3), 357–389. Hart, J., Berndt, R.S., & Caramazza, A. (1985). Category-specific naming deficit following cerebral infarction. Nature, 316 , 439–440. Hillis, A.E., & Caramazza, A. (1991). Category-specific naming and comprehension impairment: A double dissociation. Brain, 114 , 2081–2094. Howard, D., & Orchard-Lisle, V.M. (1984). On the origin of semantic errors in naming: Evidence from the case of a global aphasic. Cognitive Neuropsychology, 1 , 163–190. Humphreys, G.W., & Riddoch, M.J. (1988). On the case for multiple semantics: A Reply to Shallice. Cognitive Neuropsychology, 5 (1), 143–150. Humphreys, G.W., Riddoch, M.J., & Quinlan, P. (1988). Cascade processes in picture identification. Cognitive Neuropsychology, 5, 67–103. Humphreys, G.W., Lamonte, C., & Lloyd-Jones, T.J. (1995). An interactive activation approach to object processing: Effects of structural similarity, name frequency and task in normality and pathology. Memory, 3 , 535–586. Kay, J., Lesser, R., & Coltheart, M. (1992). PALPA. Hove, UK: Lawrence Erlbaum Associates Ltd. Kucera, H., & Francis, W.N. (1967). Computational analysis of present-day American English. Providence, RI: Brown University Press. Luzzatti, C., & Davidoff, J. (1994). Impaired retrieval of object-colour knowledge with preserved colour naming. Neuropsychologia, 32 (6), 1–18. Marr, D. (1982). Vision. San Francisco, CA: Freeman.

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Martin, A., Wiggs, C., Ungerleider, L., & Haxbey, J. (1995). Neural correlates of category-specific knowledge. Nature, 379, 649–652. Mayall, K.A., & Humphreys, G.W. (submitted). Impaired lexical output in letter-by-letter reading: Evidence for a third locus of deficits. Manuscript submitted for publication. McCarthy, R., & Warrington, E.K. (1994). Disorders of semantic memory. Philosophical Transactions of the Royal Society of London, 346, 89–96. Morton, J. (1979). Facilitation in word recognition: Experiments causing change in the logogen model. In P.A., Kolers, M. Wrolstad, & H. Bouma (Eds.), Processing of visible language. New York: Plenum Press. Paivio, A. (1975). Perceptual comparisons through the mind’s eye. Memory and Cognition, 3(6), 635–647. Price, C.J., & Humphreys, G.W. (1989). The effects of surface detail on object categorisation and naming. Quarterly Journal of Experimental Psychology, 41A, 797–828. Price, C.J., Moore, C.J., Humphreys, G.W., Frackowiak, R.S.J., & Friston, K.J. (1996). The neural pathways sustaining object and colour naming. Proceedings of the Royal Society, Series B, 263, 1501–1507. Riddoch, M.J., & Humphreys, G.W. (1987a). Visual object processing in optic aphasia: A case study of semantic access agnosia. Cognitive Neuropsychology, 4 , 131–185. Riddoch, M.J., & Humphreys, G.W. (1987b). Picture naming. In G.W. Humphreys & M.J. Riddoch (Eds.), Visual object processing: A cognitive neuropsychological approach (pp. 107–143). London: Lawrence Erlbaum Associates Ltd. Riddoch, M.J., Humphreys, G.W., Coltheart, M., & Funnell, E. (1988). Semantic systems or semantic system? Neuropsychological evidence re-examined. Cognitive Neuropsychology, 5 , 3–25. Riddoch, M.J., & Humphreys, G.W. (1993). BORB: The Birmingham Object Recognition Battery. Hove, UK: Lawrence Erlbaum Associates Ltd. Rosch, E., Mervis, C., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8 , 382–439. Rumiati, R., Humphreys, G.W., Riddoch, M.J., & Bateman, A. (1994). Pure visual agnosia without prosopagnosia or alexia: Evidence for hierarchical theories of visual recognition. Visual Cognition, 1(2/3), 181–225. Sacchett, C., & Humphreys, G.W. (1992). Calling a squirrel a squirrel but a canoe a wigwam: A category-specifi c deficit for artefactual objects and body parts. Cognitive Neuropsychology, 9, 73–86. Sartori, G., & Job, R. (1988). The oyster with four legs: A neuropsychological study on the interaction of visual and semantic information. Cognitive Neuropsychology, 5 (1), 105–132. Sartori, G., Job, R., Miozzo, M., Zago, S., & Marchiori, G. (1993). Category-specific formknowledge deficit in a patient with Herpes Simplex Virus Encephalitis. Journal of Clinical and Experimental Neuropsychology, 15(2), 280–299. Sartori, G., Miozzo, M., & Job, R. (1994). Rehabilitation of semantic memory impairments. In M.J. Riddoch & G.W. Humphreys (Eds.), Cognitive neuropsychology and cognitive rehabilitation. Hove, UK: Lawrence Erlbaum Associates Ltd. Schnider, A., Landis, T., Regard, M., & Benson, F. (1992). Dissociation of colour from object in amnesia. Archives Neurology, 49, 982–985. Seymour, P.H.K. (1973). A model for reading, naming and comparison. British Journal of Psychology, 64, 35–49. Shallice, T. (1988). From neuropsychology to mental structure. Cambridge: Cambridge University Press. Sheridan, J., & Humphreys, G.W. (1993). A verbal-semantic category-specifi c recognition impairment. Cognitive Neuropsychology, 10(2), 143–184. Silveri, M.C., & Gainotti, G. (1988). Interaction between vision and language in categoryspecific semantic impairment. Cognitive Neuropsychology, 5 (6), 677–709.


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APPENDIX 1 Errors in Photograph Naming Target

Response

Fruit and vegetables lemon pineapple avocado grapefruit kiwi watermelon peach raspberries cherries grapes garlic tomato courgettes cabbage pepper radish cauliflower scallions beans cucumber potatoes

bitter . . . an orange . . . no . . . DK DK orange DK DK it’ s furry on the outside we have them in the garden have them in drinks . . . we’ve got a tree and the birds get them have them in hospital cloves look like nuts mushroom can’t get it I don’t like them courgette DK DK DK DK DK I eat them every day

Objects cushions rolling pin

pillows (makes correct action) . . . for rolling . . . for cooking . . .

DK refers to responses where SRB said that he didn’t know what the item was.

APPENDIX 2 Errors of SB in Picture Naming Task (Experiment 1) Targe t

Response

Living things bear not a bear . . . no . . . swims in the sea in cold countries . . . eats fish corn can’t think of the name . . . (what colour is it?) red (when would you have it?) as a main course . . . it’s foreign fly wasp lemon an orange . . . (when would you have it?) . . . as a dessert and put it on top of food orange onion

ON THE LINKS BETWEEN VISUAL KNOWLEDGE AND NAMING beetle celery giraffe lettuce rhino

455

fly can’t get name . . . (tell me about it?) . . . it’s green and you have it as a main course from hot countries cabbage . . . no it isn’t . . . can’t get name . . . (tell me about it?) . . . live in the zoo and hot countries . . . (is it bigger or smaller than a cow?) . . . as big as a cow . . . large . . . dark

Nonliving things thimble . . . can’t get the name . . . wear it on your finger or thumb Questions in parentheses were asked by the experimenter.

APPENDIX 3 Errors from Naming Snodgrass and Vanderwart (1980) Pictures (Experiment 2) Target

Response

Living things artichoke pepper sheep peach pumpkin peacock asparagus beetle pineapple celery leopard onion cherry racoon tiger

acorn avocado bull damson DK fan opens out . . . at castle flute or a plant fly foreign fruit I like it . . . dip it in salt . . . eat it panther . . . jaguar plant plum American . . . cat . . . big cat

Nonliving things accordion frenchhorn nailfile refridgerator cloud

squeeze box . . . an harmonica instrument knife cigarette lighter or fridge? DK

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APPENDIX 4 Errors in Photograph Naming (Experiment 3) Target

Response

lime kiwi raspberry peach melon sweet corn rhubarb pineapple cucumber

like an orange massive big thing, something narine pick them from bushes, nan has some DK DK DK used to have them in garden I was looking at it in Sainsburys I should know that, slicing . . . it’s gone . . . I know what it is, big long thing

APPENDIX 5 Errors in Naming Real Objects (Experiment 4) Target

Response

plum celery cucumber apricot peach pineapple cauliflower garlic leek

grows in the garden . . . can’t get the name dip it in salt and eat it put in sandwiches DK DK DK DK DK . . . got cloves DK

APPENDIX 6 (a) Errors in Naming from Touch and Taste (Experiment 5a) Target

Response

Naming from touch raspberry spring onion cucumber celery grapes

strawberry plant have it in sandwiches or salad, green white at the bottom, green at the top put them in drinks

ON THE LINKS BETWEEN VISUAL KNOWLEDGE AND NAMING pepper cauliflower lemon peach

foreign, greenish sunday dinner, green on outside, white inside DK same shape as an apple, but it’s not an apple

Naming from taste cucumber celery peach raspberry mushroom pear

DK white and long, put salt on it and eat it tastes like an apple, but it’s not red, grows on little bushes eat it cooked with eggs DK

APPENDIX 6 (b) Errors in Naming from Touch and Taste (Experiment 5b) Target

Response

Naming from taste grapes tomato cherry plum cucumber apricot peach

little round things DK fruit like an orange veg it has furry skin DK

Naming from touch grape tomato plum celery apricot peach pineapple cauliflower spring onion leek

cherry DK DK cucumber . . . no . . . you eat it with salt DK DK DK have it for sunday lunch in salads . . . you eat the end of it it grows under the ground

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APPENDIX 6 (c) Errors Made by Control Subjects in Naming Fruit and Vegetables from Touch and Taste (Experiment 5) Target

Response

Naming from touch apricot apricot plum raspberry

small peach nectarine tomato DK

Naming from taste apricot carrot cherry pear pear raspberry

peach (2) radish plum peach plum gooseberry

(2) indicates that two subjects made this response.

APPENDIX 7 Stimuli Used in Memory for Size Test with Fruit and Vegetables (Experiment 10b) coconut and apple sweetcorn and mushroom watermelon and orange banana and strawberry potato and cauliflower

onion and cherry lettuce and tomato pumpkin and lemon pineapple and raspberry celery and carrot

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