The breakdown of semantic knowledge: Insights from a statistical model of meaning representation

Brain and Language 86 (2003) 347–365 www.elsevier.com/locate/b&l

The breakdown of semantic knowledge: Insights from a statistical model of meaning representation David P. Vinson,a,* Gabriella Vigliocco,a,1 Stefano Cappa,b and Simona Sirib a

Department of Psychology, University College London, 26 Bedford Way, London WC1H 0AP, UK Departments of Psychology and Neuroscience, Vita-Salute San Raffaele University, Milan, Italy

b

Accepted 8 May 2003

Abstract Investigations of patients with semantic category-specific deficits have revealed a wide range of performance and variability in categories that are impaired or spared; this variability presents a challenge to accounts of category specificity. Accounts based only on impairment to semantic features of a particular type (e.g., visual), as well as accounts based only on featural properties (e.g., feature intercorrelations), are insufficient to explain the variability of patientsÕ performance. A first goal of the paper is to discuss how a hybrid account incorporating both a level of organization according to feature types (a level of nonlinguistic conceptual representations) and a level of organization dictated by featural properties may provide a more comprehensive account of the cases reported in the literature. The second and most novel goal of the study reported here is to derive from our hybrid account a series of novel predictions concerning the representation and impairment of a different domain of knowledge: knowledge of actions and events, a domain of knowledge that has received remarkably little attention to date. Ó 2003 Elsevier Science (USA). All rights reserved. Keywords: Category-specificity; Nouns; Verbs; Semantics; Simulation

1. Introduction The study of patients in whom semantic knowledge has been disrupted has led to a number of important inferences concerning the underlying architecture of the semantic system (Warrington, 1975). Particularly relevant are cases in which focal brain damage creates category-specific deficits (i.e., selective impairment of semantic knowledge along category boundaries). At present there are a substantial number of cases on record [approximately 89, according to Rogers and Plaut (2002)]. Specificity in semantic deficits has particularly been shown along broad boundaries, e.g. selective impairment for objects or actions (see Vinson & Vigliocco, 2002), or within the object domain, for living or nonliving entities. However, narrower category-specific impairments are also well documented. Among the categories of * Corresponding author. Fax: +44-20-7436-4276. E-mail addresses: [email protected] (D.P. Vinson), [email protected] (G. Vigliocco). 1 Also corresponding author.

knowledge reported to be susceptible to selective sparing or impairment are body parts (McKenna & Warrington, 1978); animals (Caramazza & Shelton, 1998); fruits and vegetables (Hart, Berndt, & Caramazza, 1985); and medical terms (Crosson, Moberg, Boone, Gonzalez Rothi, & Raymer, 1997). Some patients with impairments for living things may also show a deficit for other (nonliving) ‘‘sensory’’ categories such as musical instruments, mass nouns, and clothing (Borgo & Shallice, 2001; Siri, Kensinger, Cappa, Hood, & Corkin, 2003; Warrington & Shallice, 1984); also, specific actions that can be distinguished primarily on the basis of sensory properties such as manner of motion (e.g., the difference between ‘‘marching’’ and ‘‘sprinting’’) (Marshall, Chiat, Robson, & Pring, 1996); while for other patients with impairments for living things, these nonliving categories are spared (De Renzi & Lucchelli, 1994). Furthermore, in some patients, the living things impairment co-occurs with problems in tasks involving visual features [e.g., 2 2 Following McRae, de Sa, and Seidenberg (1997), features are presented in the text enclosed in ‘‘<>’’ signs.

0093-934X/$ - see front matter Ó 2003 Elsevier Science (USA). All rights reserved. doi:10.1016/S0093-934X(03)00144-5

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for ‘‘tiger’’; for ‘‘truck’’] for both living and nonliving entities, although this is not always the case. Patients have been described who are equally impaired in tasks involving knowledge of visual and other features for both living and nonliving entities (Barbarotto, Capitani, Spinnler, & Trivelli, 1995); and patients have been described who have a selective impairment for visual features but only for living entities (Forde, Francis, Riddoch, Rumiati, & Humphreys, 1997; Hart & Gordon, 1992). Finally, although in the majority of cases the patientsÕ deficit spans different tasks, there is at least one case on record showing a specific impairment (for nonliving entities) which is limited to naming tasks (Cappa, Frugoni, Pasquali, Perani, & Zorat, 1998a). This great variability among different patients can be considered as one of the fundamental challenges in developing inferences regarding the organization of semantic memory. A number of hypotheses have been put forward to account for the patterns of spared and impaired semantic knowledge shown by the patients that attempt to explain the variability in the patientsÕ performance, at least to some extent. A basic assumption common to all proposals is the fact that semantic knowledge is based upon featural representations to some extent, an assumption well represented in theories of semantic memory developed within the cognitive tradition. Recently, a number of researchers have attempted to render this basic assumption testable by using speakergenerated features (i.e., using multiple speakersÕ judgement of those semantic properties that are important in a wordÕs meaning) to gain insight into the dimensions that are important to differentiate one category from another and to assess specific claims made by hypotheses to account for semantic deficits and theories of semantic organization (e.g., Garrard, Lambon Ralph, Hodges, & Patterson, 2001; McRae & Cree, 2002). The present study represents a further step in this direction that crucially extends the investigation beyond concepts referring to objects. We have collected speakergenerated features for a large number of concepts, both object concepts as in previous work and action concepts. Properties emerging from the feature norms have been used to account for noun and verb specific impairments (Vinson & Vigliocco, 2002) and for patterns of semantic effects in behavioral tasks (Vigliocco, Vinson, Lewis, & Garrett, 2003; Vigliocco, Vinson, Damian, & Levelt, 2002). Here, analyses of the speaker-generated feature norms are conducted to develop inferences concerning selective impairments of semantic memory. More precisely, we have three goals. First, to extend the logic underlying featural accounts of semantic impairments from the well-studied domain of object concepts to the largely unexplored domain of action concepts, providing new predictions to be tested in this latter domain; second, to evaluate the assumptions of previous accounts of category-specificity that attempt to explain the deficit

on the basis of disruption of feature knowledge (see Garrard et al., 2001; for a similar approach); finally, to present an account for the variability observed in patientsÕ performance. Feature-based accounts of semantic memory for object concepts, also referred to as ‘‘reductionist’’ accounts (Caramazza, 1998), are common within both cognitive and neuropsychological traditions. Within the cognitive psychology tradition, feature-based representations have been assumed by many theories (see Smith & Medin, 1981 for a review) and have been successfully used in a number of models to account for semantic effects such as semantic priming (e.g., Masson, 1995; McRae et al., 1997; Plaut, 1995; Vigliocco et al., 2003). Within the neuropsychological tradition, a number of different feature-based hypotheses concerning categoryspecificity have been developed, sharing the idea that distributed featural networks in the brain correspond to concepts referring to entities in the world (e.g., Martin & Chao, 2001). These accounts can be divided into two types: accounts that assume a feature type3 organization of semantic memory (Allport, 1985; Farah & McClelland, 1991; Martin & Chao, 2001; Saffran, 2000; Warrington & McCarthy, 1987; Warrington & Shallice, 1984); and accounts that stress the importance of featural properties (such as shared features, distinctiveness of features, and correlation among features) in the structure of concepts without making the claim (or, in fact, rejecting the claim) of modality-specific organization (Caramazza, Hillis, Rapp, & Romani, 1990; Rapp, Hillis, & Caramazza, 1993; Tyler, Moss, Durrant-Peatfield, & Levy, 2000). According to accounts based on feature type, featural representations encompass a variety of types in order to underscore the representation of multitudes of concepts from concrete to abstract, from referential to relational. However, at least for concepts referring to concrete entities (and this can be extended to events) in the world, these features are grounded in our interactions with the environment (in perception and action) and are organized in the brain in a manner that closely follows the organization of sensorimotor systems (Allport, 1985; Farah & McClelland, 1991; Martin & Chao, 2001; Saffran, 2000; Warrington & McCarthy, 1987; Warrington & Shallice, 1984). Category-specific deficits 3

It is important to note here that our discussion strictly concerns only the format of the featural representations, not the input modality (feature source). Hence a feature such as ‘‘red’’ for strawberry is considered to be a visual feature and part of the conceptual representation of ‘‘strawberry’’ regardless of whether we see a strawberry, we taste a strawberry or we hear the word ‘‘strawberry.’’ The hypothesis that the semantic system may honor not only the format of featural information but also the input modality has been proposed in terms of a distinction between visual and verbal semantics to account for, among other things, the performance of optic aphasics. This issue is, however, beyond the scope of the current paper.

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would arise because concepts in different domains would differ with respect to how important a given feature type is in that specific domain. Evidence compatible with this view comes from a number of patients with categoryspecific deficits for living things who also show a deficit in appreciating perceptual features of concepts (e.g., Basso & Capitani, 1988; De Renzi & Lucchelli, 1994; Farah, McMullen, & Meyer, 1991; Forde et al., 1997; Gainotti & Silveri, 1996; Moss, Tyler, & Jennings, 1997; Sartori & Job, 1988; Silveri & Gainotti, 1988). Additional evidence comes from the finding that, in some patients, the selective impairment of living things can also encompass exemplars from other categories, such as foods and musical instruments, arguably related to living entities on the basis of the weight of perceptual features (Borgo & Shallice, 2001; Siri et al., 2003). Important for our purposes, for example, is patient RG described by Marshall et al. (1996) who showed a living thing deficit coupled with a problem with concepts for manner of motion (i.e., he was found to have problems with concepts such as ‘‘sprinting,’’ ‘‘tip-toeing’’ etc.).4 The association between living things and manner of motion was taken by the authors to reflect the importance of perceptual features in both domains. The finding that semantic impairments can cross the category boundary between living and nonliving things, and at least in patient RG, between object and action concepts, can be taken as evidence compatible with an organization of conceptual knowledge in terms of different types of critical features which are frequently associated with particular categories but are not limited to one. In contrast to proposals based upon feature types, other proposals have stressed the differential distribution across concepts of feature properties such as distinctive, shared, and correlated features to account for category-specificity. For example, a general account along these lines is the Organized Unitary Content Hypothesis (OUCH) (Caramazza et al., 1990; Rapp et al., 1993) which assumes that differences in feature intercorrelations and shared features among members of the same category can give rise to categorical organization of knowledge. Hence, without assuming that featural representations have a different format, in this view, category-specific deficits may arise because similar things would cluster together in a semantic space organized in the basis of shared features and feature intercorrelations. Another proposal recently put forward by Tyler et al. (2000), like OUCH, recognizes the importance of shared features and correlation among features, but provides 4

Breedin, Saffran, and Coslett (1994) also report a patient, DM, who suffers from semantic dementia and is impaired for concrete objects, relative to abstract nouns. Notably this impairment includes both living and nonliving exemplars for which perceptual properties are important.

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further constraints by specifically stressing the type of associations between perceptual and functional features in the representation of both living and nonliving entities. In particular living things are described in terms of ‘‘biological function’’ (i.e., how living things interact with the environment). The association between perceptual and functional features would differ for living and nonliving concepts. While (shared) correlated perceptual features of living things would become associated with different biological functions (e.g., eyes for seeing, legs for moving, etc.), distinctive features would not (e.g., for ‘‘tiger’’; for ‘‘lion’’). For artifacts, instead, the association between perceptual and functional features would involve distinctive perceptual features (e.g., the perceptual feature and the functional feature for ‘‘knife’’). These latter assumptions lead to the following predictions. First, brain damage should tend to involve living things (because their distinctive features would be only weakly correlated with other properties). Second, loss of visual knowledge should be heterogeneous, with distinctive features more vulnerable than correlated features when the deficit concerns living entities. Finally, deficits for artifacts should arise only when the damage to the semantic system is severe, because of the high correlation in this semantic field between distinctive perceptual features and function, rendering the field more resistant to damage. The presence of different patterns of semantic dissociation observed in brain damage patients has been supported by neuroanatomical considerations based on lesion site. The majority of patients showing a living impairment suffer from herpes simplex encephalitis (HSE), a pathology that results in bilateral damage to the medial and inferior temporal lobes (Gainotti, 2000; Gainotti, Silveri, Daniele, & Giustolisi, 1995). In a group study on 79 patients, Strauss et al. (2000) found that anterior temporal lobectomy has a greater effect on naming ability for living things compared to nonliving things (see also Luckhurst & Lloyd-Jones, 2001). On the other hand, defective knowledge about tools has been associated with lateral temporo–parietal–occipital lesions (Tranel, Damasio, & Damasio, 1997), and defective tool naming with posterolateral inferior temporal cortex and temporo–parietal junction damage (Damasio, Grabowski, Tranel, Hichwa, & Damasio, 1996). Imaging studies on control subjects confirm the involvement of left middle temporal gyrus and the dorsolateral frontal cortex in processing artifacts (Damasio et al., 1996; Martin, Wiggs, Ungerleider, & Haxby, 1996; Perani et al., 1995). More fine-grained distinctions than the living/nonliving dichotomy have been tested using fMRI. For example, two investigations of ventral temporal cortex (Chao, Haxby, & Martin, 1999; Ishai, Ungerleider, Martin, Schouten, & Haxby, 1999) indicated that different

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ventral cortical regions responded preferentially to pictorial stimuli from specific categories. Biological entities (faces and animals) were associated with a greater activation in the lateral fusiform gyrus. Activation for tools and houses was more medial, while the inferior temporal gyrus responded maximally for chairs. However, these studies showed that response to a specific object category is not restricted to the region which responds maximally for that category; but that all categories activated, to different degrees, a broad region of the ventral temporal cortex. According to these results, the representation of objects within the ventral temporal cortex appears to be organized by object features clustering together, rather than into semantic categories corresponding to specific and anatomically segregated modules. Chao et al. (1999) observed two other differences in activation on the lateral temporal surface: superior temporal sulcus activation for biological entities and middle temporal gyrus activation for tools. The former may be related to motion processing, which is specific for animate entities, while the latter to the manipulation activity related to tools (see also Perani et al., 1995). Recently, Martin and Chao (2001) have proposed a general model of the representation of semantic knowledge in the brain in which object features are represented in networks of cortical regions paralleling the sensorimotor organization of the brain, while other regions in the left frontal and temporal lobe are responsible for the retrieval, maintenance, and selection of these representations. Comparable patterns of activations for picture and word stimuli were also observed in a PET study investigating animals and tools (Perani et al., 1999), consistent with a system in which object features are activated by both linguistic and nonlinguistic input. Moving from the object to the action domain, lesion site, and imaging data converge in indicating a role for left premotor and middle temporal regions for tasks concerning actions, interpreted in terms of the salience of motor-related features for these concepts (e.g., Damasio et al., 2001; Grabowski, Damasio, & Damasio, 1998; Silveri & Di Betta, 1997; Tranel, Adolph, Damasio, & Damasio, 2001). Interestingly, these same areas have also been shown to be involved in knowledge of artifacts (Gainotti et al., 1995), for which motor-related properties are also important; some authors have argued that, in fact, the noun–verb dissociation observed in some patients may be accounted for by specific deficits for either perceptual features (selective impairment for nouns, especially those referring to living things) or functional features (selectively impairing verbs and artifacts, once imageability is taken into account; Bird, Lambon Ralph, Patterson, & Hodges, 2000). Cappa et al. (1998b) found a dissociation between action and object naming in patients with degenerative disease. Patients with frontotemporal de-

mentia (FTD) were more impaired in action naming than patients with AlzheimerÕs disease (AD), while the pattern was reversed for object naming. This result can be interpreted as evidence of the role of the frontal areas in processing motor-related features of concepts that, in the case of actions, are assumed to be predominant. To our knowledge, accounts based on neuropsychological data have not attempted to explain dissociations in performance for objects and actions in terms of different featural properties in the two domains in a manner parallel to the living/nonliving dissociation.5 However, in the cognitive psychology literature, some authors have argued for differences in interconceptual organization for objects and actions (Graesser, Hopkinson, & Schmid, 1987; Huttenlocher & Lui, 1979). Compared to actions, objects would tend to possess more features that are shared only among members of a semantic field (e.g., vs. for animals) and that do not cut across fields. Actions, instead, would tend to possess more features applicable to members of diverse semantic fields (e.g., ). Furthermore, features would tend to be more strongly correlated within semantic fields for objects (e.g., , and for mammals) than for actions. Because of these differences, it has been proposed (Huttenlocher & Lui, 1979) that these two domains are differentially organized: words referring to objects would be organized hierarchically, whereas words referring to actions would have a matrix-like organization without well-defined levels of structure. It is in fact intuitively clear that while distinguishing between different levels (superordinate, basic, and subordinate) (Rosch & Mervis, 1975) is relatively simple and fruitful for object concepts (especially natural kinds); this is far more difficult for actions. Nonetheless, featural properties may differ between the object and action domains, giving rise to differences in their interconceptual organization. We assess these differences by providing detailed analyses of speaker-generated feature norms. Following McRae et al. (1997), we asked native English speakers to generate semantic features to define and describe words belonging to various semantic fields, crucially including a variety of objects and actions (detailed in Vinson & Vigliocco, 2002). As in previous work, these speakergenerated features are not taken to provide a literal record of semantic representations, but to provide valid information because conceptual representations are systematically used by the speakers when generating the features (Barsalou, 1993; McRae et al., 1997; Rosch & 5 In this paper we use the term ‘‘domain’’ to refer to broad conceptual distinctions, in particular between objects and actions. We use the term ‘‘semantic field’’ to refer to semantic distinctions within the domains of object and action concepts.

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Mervis, 1975; Smith, Osherson, Rips, & Keane, 1988). They therefore provide a window into important aspects of meaning without being definitive (Medin, 1989). In contrast to previous studies that have used feature norms for object concepts to investigate conceptual organization, we extend the same logic to the investigation of actions.6 Our previous work provides us with first evidence that such an extension is informative. We have shown that measures of semantic similarity between concepts obtained starting from the speaker-generated features are good predictors of semantic similarity effects in on-line tasks (e.g., picture-word interference tasks) in both the object and action domain (Vigliocco et al., 2002, 2003). Given our norms, we first assess whether in fact concepts in different domains (objects and actions) and within different fields within these domains (animals, tools, communication, manner of motion, etc.) differ in terms of feature types. Three broad differences we expect to observe are: (a) overall, motion features should be more common among the actions than among the objects; (b) living things should be more defined in terms of perceptual properties than artifacts; (c) objects should exhibit more perceptual properties than actions, overall (otherwise we would expect patients with difficulty for sensory features to also exhibit problems naming actions). Note that we have already demonstrated that objects have more perceptual features than actions (Vinson & Vigliocco, 2002) using a very conservative feature classification criterion (features were classified as sensory only if they had no cognitive component); the present analysis is carried out using a more liberal classification of sensory features (sensory features are permitted to include some cognitive component, e.g., Garrard et al., 2001; Warrington & Shallice, 1984). Second, we assess some claims that have been made about featural properties, particularly the patterns of correlation among features. In particular, we expect to observe that living things have more strongly intercorrelated features than nonliving things and that, also, objects have more strongly intercorrelated features than actions. On the basis of the account put forward by Tyler et al. (2000), we further expect that living things will also show a larger number of features and of shared features than nonliving things. Finally, this account predicts that sensory features of living things should tend to be less distinctive and that the strongest correlations between features should be between those shared among many exemplars, whereas sensory features for artifacts should be distinctive, and the strongest corre6 We use the term ‘‘action’’ in a general sense to refer to a number of nonobject semantic fields, including physical actions (e.g., running, drilling, and eating) but also more abstract fields such as states, events, etc. (e.g., glowing, clattering, buying, commanding, etc.).

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lation between features should be between distinctive features and their functions. Note that Garrard et al. (2001) have argued against this account, showing within a set of feature norms that featural intercorrelation is actually greater for distinctive features of living things than either shared or distinctive features for artifacts, and also that representations for artifacts are not strongly dependent upon distinctive form–function correlations. However, some criticisms may be leveled against the feature norms obtained by Garrard et al. (2001). First, they were obtained using a rather restrictive methodology in which participants were asked to generate a certain number of features of a given kind for each word in the set by completing sentence stems (six features for each of the stems ‘‘IS ____ ,’’ ‘‘HAS ____,’’ and ‘‘CAN ____,’’ plus one ‘‘category’’ feature). This methodology may have caused participants to generate an excess of features shared among category exemplars in order to generate a sufficient number of features for each exemplar. Second, as they acknowledge, Garrard et al. used a rather limited set of words, both in terms of number of semantic fields (8), and also in number of exemplars per field (8), all referring to concrete objects. This point is important as sensitivity to artifacts arising from the context (i.e., the other words used) in feature generation can be a crucial weakness of this methodology (see Murphy, 2002). Context effects can be expected to have less of an effect in our featural norms as we presented each individual speaker with broad range of words referring to objects and actions. The speaker-generated featural database we use is described in Vinson and Vigliocco (2002); 280 native speakers of English were presented with 456 words (of which 169 referred to objects, and 277 referred to actions) and were asked to define and describe them using features of meaning. Each speaker was presented with a set of words spanning a broad range of semantic fields; nouns and verbs were disambiguated by presenting them in a minimal syntactic context (e.g., ‘‘the shout’’ vs. ‘‘to shout’’). A full set of words and their semantic fields can be found in Vinson and Vigliocco (2002, Appendix). For each word in the set, 20 participants generated features; featural representations were prepared by combining the features across participants for each word. Critically, we assigned weights to features by counting the number of participants who listed a given feature for a given word, and using that number to represent that featureÕs weight for that word (thus feature weights ranged from 0 to 20). Taking weight into account allows us to consider properties of features in terms of how salient and important they are across exemplars, in contrast to McRae and Cree (2002). The resulting feature norms are used here to investigate differences across domains and fields both in terms of different proportions of feature types across domains as well as differences in terms of feature properties.

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2. Featural analyses and results 2.1. Are different types of features differentially distributed across domains and fields? The first set of analyses aimed at assessing differences in feature types across domains and fields. This allows us to evaluate the basic claim of ‘‘sensory-functional’’ hypotheses of category-specific deficits. In our analyses, we use a fine-grained criteria for classifying the speakergenerated features, described below, which differs from some previous accounts (e.g., Caramazza & Shelton, 1998; Farah & McClelland, 1991; Garrard et al., 2001) in distinguishing between functional and motoric features (providing a more constrained definition of functional features as a result), and also in distinguishing between visual and other perceptual features. Our approach finally differs from the one taken by McRae and Cree (2002) in that we limit our classification to those feature types that can plausibly be represented in the brain following the sensorimotor systems, while these authors, instead, propose a classification that includes higher-order cognitive clusters, such as ‘‘evaluation.’’ 2.1.1. Classifying the speaker-generated features The speaker-generated features were classified in the following five categories by two English speakers; disagreements were discussed and agreed upon. First, perceptual features, defined as ‘‘features that describe information gained through sensory input, including body state and proprioception,’’ were identified; these features were subdivided into Visual features, referring to the sense of vision (22.2% of all features), and Other Perceptual features from other sensory modalities (19.7%); the nonperceptual features were then classified into Functional (those features referring to the purpose of a thing, ‘‘what it is used for,’’ or the purpose or goal of an action; 26.5%), Motoric (‘‘how a thing is used, or how it moves,’’ or any feature describing such motor component of an action; 12.0%),7 and Other (37.6%; the total percentage of scored features exceeds 100%, as some features met criteria for more than one feature type classification). This latter class, which contains the largest proportion of the speaker-generated features, is highly heterogeneous. Some of the features can be considered as reflecting encyclopedic knowledge (e.g., ); while many of the other features reflect relationships among meanings, (e.g., ISA; PART OF) well represented in taxono7 We do not distinguish between features of biological motion and those of nonbiological motion, to a large extent because this distinction could not be made for many of our speaker-generated features (i.e., which can apply to artifactual motion such as for ‘‘the wrench’’ and also for bodily motion such as for ‘‘the shoulder’’).

mies developed by lexicographers (see, e.g., Miller & Fellbaum, 1991). For the purpose of the current work, we do not attempt to further classify these features, since we limit the assumption of modality specific organization to features related to perception and action. The contrast between motoric and functional features was introduced because of the existing evidence (Buxbaum, Veramonti, & Schwartz, 2000) indicating that knowledge of how to use an object and knowledge of what the object is used for can dissociate. Fig. 1 reports average composition of different feature types for exemplars in some different object categories (taking weights into account), and Fig. 2 reports composition of exemplars of some action fields. As can be seen in the Figures, exemplars in different categories differ along the lines of their featural composition. 2.1.2. Visual features Within objects, visual features were most salient for animals (43.9%) and fruits and vegetables (36.8%), moderately salient for vehicles (29.4%) and body parts (30.1%), and less so for other (artifact) fields (26.4% for tools, 21.3% for clothing, and 27.1% for miscellaneous artifacts)—a pattern in line with the distinction between living and nonliving, along the lines of sensory-functional accounts. Living things had significantly higher weighted feature composition than nonliving things; tð142Þ ¼ 4:151; p < :001 (this comparison includes all object fields except body parts). Fine-grained differences were also observed within these fields—among living things, animals were more dependent upon visual features than fruits and vegetables ðtð57Þ ¼ 2:990; p ¼ :002Þ; among artifacts, vehicles were more dependent upon visual features than tools or clothing ðtð57Þ ¼ 2:451; p < :001Þ. Considering actions, visual features were predominantly salient only for the narrow semantic field of light emission (e.g., ‘‘glow,’’ ‘‘shine’’), for which visual features were by far the dominant feature type, amounting to 64.6% of all features. Other action fields had little, if any, dependence upon visual features. This finding lends credence to the possibility that impairments to visual features can result in category-specific impairments targeting objects, and within object fields, living things particularly. 2.1.3. Other perceptual features Considering other perceptual features, within object fields we again observed consistent differences between fields, fruits and vegetables (10.8%) and clothing (10.9%) were most dependent upon nonvisual perceptual features, tools (8.1%) and body parts (7.9%) moderately so, with other fields less dependent (animals ¼ 4.2%, vehicles ¼ 2.8%, and other artifacts ¼ 5.8%). Again finegrained differences were observed within artifact and living domains: e.g., clothing had significantly more other-perceptual features than tools ðp ¼ :005Þ; tools

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Fig. 1. Percentage of feature types in exemplars from various object semantic fields, adjusted by weight.

Fig. 2. Percentage of feature types in exemplars from a subset of action semantic fields, adjusted by weight. Fields were selected to be indicative of the range of featural composition in the complete set of semantic fields.

had more than vehicles ðp < :001Þ; fruit/vegetable had more than animals ðp < :001Þ. Several action fields were more dependent than any object field on nonvisual perceptual modalities, particularly noises (43.6%) and cooking actions (21.4%); communication (10.4%) and light emission (12.7%) also were relatively highly dependent upon other perceptual features. We further performed an analysis combining visual and other perceptual features, and assessing the differential composition of semantic fields as above. Overall, objects were more dependent upon sensory features than actions, with two exceptions: the narrow fields of light emission (77.3% of features are sensory) and noises (46.6%); tð454Þ ¼ 6:215; p < :001. Within object do-

mains, living things were most dependent upon sensory features (48.0% for animals, 47.5% for fruits and vegetables), body parts intermediate (38.0%), and other artifact fields less so (tools 34.5%, clothing 32.2%, vehicles 32.2%, and other artifacts 33.0%). This difference between living and nonliving things (excluding body parts) was significant; tð142Þ ¼ 5:860; p < :001. This is consistent with sensory-functional claims (and highlights the particular salience of visual features). In contrast, most action fields had substantially lower proportions of sensory features (e.g., change of state 14.9%, communication 11.1%), suggesting that, in general, actions should be spared relative to objects when sensory features are impaired.

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2.1.4. Functional features Within the object domain we observed a consistent difference between artifacts and living things. Clothing (25.0%) and miscellaneous artifacts (27.2%) were most reliant upon functional features, followed by tools (20.8%), body parts (22.0%), and vehicles (19.1%), with animals (6.8%) and fruits and vegetables (7.3%) less dependent upon them. Again fine-grained differences were observed, but this time only between artifact domains, e.g., clothing had significantly more functional features than either tools or vehicles (both pÕs < .001), while fruit/vegetables and animals did not significantly differ ðp ¼ :327Þ. Comparison between living and nonliving things again yielded a significant difference ðtð142Þ ¼ 8:152; p < :001Þ, this time such that nonliving things are more dependent on features of this kind. Considering actions, purposeful acts such as change of state (20.9%), communication (21.0%), cooking (21.2%), and tool actions (20.9%) were most functional, actions were less functional, for example, light emission (7.1%), noises (7.6%), and manner of motion (7.3%).8 Overall, functional features were more characteristic of objects than of actions ðtð454Þ ¼ 2:774; p ¼ :003Þ, again consistent with the notion that impairment to functional features might selectively affect artifacts, typically sparing most other fields. 2.1.5. Motoric features Within objects, vehicles (27.6%) were the most dependent upon motoric features, body parts (17.4%) and miscellaneous artifacts (15.4%) moderately so, followed by fruits and vegetables (13.5%; typically these features related to preparation for consumption, e.g., , ), with other fields less dependent (particularly clothing which had virtually no motoric features: 1.5%). Again, fine-grained similarity effects could be readily observed: vehicles had significantly more motoric features than body parts ðp < :001Þ, which in turn had more than fruits and vegetables ðp ¼ :008Þ, which in turn had more than animals ðp ¼ :021Þ, which in turn had more than clothing ðp < :001Þ. In contrast to objects, actions were far more dependent upon motoric features ðtð454Þ ¼ 15:182; p < :001Þ—especially manner of motion (56.4%), but also change of state (41.1%), body action (36.3%), and tool action (38.0%). Fields of communication (15.0%) and cooking (16.5%) were moderately dependent on such features, with only a few cases (e.g., light emission at 6.6%) in which actions did not involve many motoric features.

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This difference corresponds interestingly to classification of events along lines suggested by Vendler (1957) into activities, accomplishments, achievements, and states. Those events which imply completion (accomplishments and achievements) are more likely to incorporate functional features than those that do not (activities and states).

Overall our findings are consistent with the basic claims of those accounts that assume differences across concepts on the basis of number (and, importantly, weight) of different types of features, but we have gone a step further by investigating feature type at a more finegrained level than previous work, particularly related to distinguishing between visual and other perceptual features. This latter distinction is important with respect to predicting fine-grained patterns of performance. Although selective impairments for living things relative to artifacts are associated both with impairments to visual features and to the combined sensory features, different fine-grained patterns result depending upon which sensory classification is used (e.g., considering visual features alone, animals are more dependent than fruit and vegetables, but they are not different when sensory features are combined). Our findings also provide novel predictions with respect to what type of impairments we should expect, assuming the same underlying organization, in the action domain depending upon the type of features. These results reveal the particular importance of motoric features in the action domain; they also demonstrate that some narrow action fields can be distinguished on the basis of feature types (e.g., the dependence of the light emission field on visual features, noises, and cooking fields on other perceptual features), along similar lines as sensory/functional accounts in the object domain. 2.2. Do concepts in different domains differ in terms of feature properties? In these analyses we first focus on three general dimensions about which claims have been made by previous accounts: (a) overall number of features; (b) shared features; and (c) correlated features. We consider two broad comparisons: (1) whether objects and actions differ along these dimensions; (2) whether living and nonliving things differ. In addition, we assess claims made by Tyler and colleagues regarding the distinctiveness and correlation of features for fields of animals and tools, along the lines of those analyses carried out by Garrard et al. (2001). These analyses were restricted to a subset of the concepts we considered, in order to allow them to be matched for concept familiarity. We selected 12 exemplars from the fields of animals, tools and actions for the following analyses: (Animals: bird, camel, cat, dog, fish, fox, goat, horse, lion, mouse, sheep, and tiger; Tools: fork, tweezers, brush, pencil, pen, pliers, chisel, scissors, razor, gun, file, and hammer; Actions: touch, clang, smell, hold, throw, frown, drill, write, twist, spray, exchange, and inhale) and compared their featural properties, in order to assess the extent to which objects and actions, and then within objects, living and nonliving differ.

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2.2.1. Featural composition: Overall weight For this analysis, in order to consider the salience of individual features, we calculated the summed feature weights for each word in the reduced set, rather than investigating composition in terms of number of features (as done, for example, by McRae et al., 1997). Feature weights were summed for each item and compared by semantic field. Animals had an average summed weight of 121.8, tools, 117.2, and actions, 97.2. No difference was observed between animals and tools; a matchedpairs t test (items matched for familiarity) was not significant: tð11Þ ¼ 0:868; p ¼ :4039, although there was a difference between animals and actions, tð11Þ ¼ 5:111; p < :001. An independent-samples t test was then carried out to contrast objects and actions, tð34Þ ¼ 11:521; p < :001; objects had higher weights than actions. This difference, indicating relative sparseness of featural representations for actions compared to objects, concords well with the difference in concreteness (obtained from the MRC database, Coltheart, 1981) between words referring to objects (mean concreteness among these items ¼ 5.94) and words referring to actions (mean concreteness ¼ 4.52; tð33Þ ¼ 7:23; p < :001), in line with the proposal by Hinton and Shallice (1991). 2.2.2. Shared features Here we assessed the extent to which exemplars tended to share features with other exemplars in a given semantic field. Note that this distinction is straightforward when considering the object domains—shared features can be assessed within category, that is, within animals and within tools. For the action domain, instead, the extent to which features are shared depends upon identifying the specific semantic fields that are involved, which is not always clear-cut. For the purpose of the present analyses we use the level of semantic field description as we have elsewhere in the paper (following Levin, 1993), e.g., manner of motion, light emission, tool action, etc., in order to assess the extent to which actions share features. Because the items in the familiarity-matched subsets are relatively heterogeneous (particularly for actions), we considered the extent to which these items share features not only with others in the subset, but also with all other exemplars from the same field in our complete set of 456 words. Shared feature value was calculated for each exemplar as follows: a feature of a given word was classified as ‘‘shared’’ if it was present (any nonzero value) in 15% or more other exemplars within a given field. The extent of shared features for each word was calculated by summing the weights of shared features for each word. Under this analysis, animals had the highest degree of shared features (average sum ¼ 48.25), followed by tools (41.60), then actions (32.89); paired sample t tests showed these differences were significant (animals vs. tools: tð11Þ ¼ 2:41; p ¼ :0345; tools vs. actions: tð11Þ ¼

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2:90; p ¼ :0144 (two-tailed)). However, these values are affected not only by the degree to which features are shared within fields, but also by the overall feature weights for the specific words in a field (shown to be different, above, for objects and actions). Therefore we converted our measure into proportions by dividing the weighted, shared features by the sum of all feature weights for each word: averages were 0.39 for animals, 0.36 for tools, and 0.34 for actions. Within-items ANOVA revealed no significant difference between these three groups; F ð1; 11Þ ¼ 1:915; p ¼ :194. This latter finding is in contrast to the results by Garrard et al. (2001) who found more shared features for living things than nonliving; the difference may be a consequence of task differences in the feature collection process as we described above. In particular, in our feature collection participants were not instructed to produce features of any specific types, while Garrard et al. (2001) instructed participants to generate features of designated types (‘‘is’’ attributes, which are descriptive; ‘‘has’’ attributes, referring to parts and components, and ‘‘can’’ attributes, referring to abilities, uses, and purposes). This difference may have led participants to produce different distributions of features, particularly where these types are concerned. 2.2.3. Correlated features We followed the analysis used by Garrard et al. (2001), first calculating the value of the correlation coefficient for all possible pairs of features across exemplars (in the entire set of 456 words). Then we assessed the average feature correlation for feature pairs in the animals, tools, and actions in the limited set above (but considering feature correlation taking all 456 words into account). The average correlation coefficient was 0.146 for animal features, 0.119 for tools, and 0.081 for actions; pairwise comparisons between feature pairs using nonparametric tests (Mann–Whitney U) revealed all three correlations differed significantly, p < :001. This is consistent with other analyses of featural correlations within object domains (e.g., McRae et al., 1997), and extends the finding to the action domain for which correlations are overall lower than for objects. 2.2.4. Distinctiveness and correlation Differences between semantic fields of animals and tools9 were assessed in terms of feature distinctiveness,

9

We excluded actions from this particular analysis because the claims about distinctiveness made in the literature refer specifically to form–function relationships for objects. It is unclear how these arguments might be extended to actions. Also, actions tend to have a much smaller proportion of sensory and functional features than objects, so analysis on the basis of form–function correlations might be misleading.

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along the lines of Garrard et al. (2001). We operationalized a featureÕs distinctiveness as the proportion of words within a semantic field (as described above) that share that feature (weights > 0). Thus a value of 1.0 indicates a feature that is shared among all exemplars among a field, and smaller values indicate greater level of distinctiveness (features with values of zero are excluded here, as they are by definition not representative of any exemplars in a field). Features with values greater than 0.5 on this scale were considered to be ‘‘shared’’ and those 0.5 or less, ‘‘distinctive.’’ To assess whether living things are characterized by shared, correlated sensory features, and artifacts instead characterized by distinctive features (for which form and function are correlated), we performed the following analyses: we first considered only sensory and functional features, and examined the extent to which features tended to be significantly correlated with other features of the same type, vs. those significantly correlated with other features of different types (e.g., form–function correspondences), for those items in our response set. As a dependent measure, we considered the correlation coefficient for a feature pair, factorially combining feature distinctiveness (shared vs. distinctive) with feature type (two features are of the same type, ‘‘intracorrelations’’ as described by Garrard et al. vs. different type, ‘‘intercorrelations’’), carrying out separate analyses for animals and tools. Across all factors, the overall proportion of statistically significant correlations in this reduced feature set was very low (animals ¼ 9.1%, tools ¼ 8.4%); proportion of significant correlations by condition is reported in Fig. 3. Animals had a small but significant tendency to have more correlation involving shared features than

Fig. 3. Average proportion of feature correlations that were statistically significant for animals, tools, and actions as a function of featural distinctiveness and feature-correlation type, considering only sensory and functional features.

distinctive features ðF ¼ 5:21; p ¼ :03Þ, however the effect of correlation-type was not significant, nor did the two factors interact ðF s < 1Þ. For tools instead, we observed main effects of distinctiveness (more distinctive features than shared), as well as a main effect of correlation-type (more correlations within features of the same type than across feature types), but no interaction between the two. This replicates the general pattern found by Garrard, et al.: more correlation among shared than distinctive features for animals, more correlation among distinctive than shared features for tools, but runs counter to the prediction that form–function correlations (intercorrelations) should be more prevalent in artifact domains. In addition to this contrast, we compared animals and tools directly, comparing distinctive features for animals to distinctive features for tools. Distinctive features for animals were more likely to be significantly correlated (8.3% of cases) than those for tools (7.3%), significant at p ¼ :021. To summarize, in the analyses reported above we observed some but not all of the differences that have been claimed to be important in determining concept organization for different fields. Within the object domain, we observed that living things differed from nonliving things with respect to correlated features (more common for living than nonliving) but not with respect to the number of features and the number of shared features, as has been argued by other accounts (e.g. Tyler et al., 2000). Furthermore, despite methodological difference in feature collection methods which yielded different patterns of distinctiveness, we replicated the general finding by Garrard et al. (2001) that distinctive features of living things were more correlated than features (distinctive or not) of artifacts, contrary to the predictions made by Tyler et al. (2000). Hence our work does not support such an account of category specificity. Contrasting the object and action domains, as suggested by Huttenlocher and Lui (1979) we found that objects and actions differ along the crucial dimensions of number of features (richer representations for the objects than for the actions) and correlated features (more represented for the objects than the actions), however, these objects and actions did not differ with respect to number or weight of shared features, underscoring the importance of differences in terms of correlated features across domains.

2.2.5. Interim summary With the analyses above, we extended previous feature-based work into a novel domain of knowledge: action concepts. We further assess some claims made by hypotheses concerning the organization of semantic memory. In particular we have shown that the inter-

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concept organization within the domains of objects and actions differs with respect to: (a) Feature types. Sensory features are more represented in the object than in the action domain, and motoric features are more represented in the action than in the object domain; (b) Feature properties. Correlated features are more represented in the object than in the action domain. Within the domain of object concepts, differences in feature types and feature properties were observed between living and nonliving entities. The finding of different distribution of feature types is in line with accounts that assume a modality-based representation of conceptual information (e.g., Allport, 1985; Farah & McClelland, 1991; Martin & Chao, 2001; Saffran, 2000; Warrington & McCarthy, 1987; Warrington & Shallice, 1984). The finding of differences with respect to feature intercorrelations, instead, is in line with proposals that stress the role of mutual activation of features as a property conferring resistance to damage; different fields can be distinguished also on the basis of correlational properties of their constituent features. Our distributional analyses of feature type also allowed us to develop novel predictions with respect to which semantic fields, within the action domain may be more or less susceptible to damage to specific types of features. Hence, our analyses suggest that both differences in feature types as well as differences in feature properties underscore differences between domains and, within each domain, between semantic fields. If correlation among features renders those features more resistant to damage (Tyler et al., 2000), a greater likelihood of observing deficits for living things than for artifacts might be linked to the fact that shared, but not distinctive, features of living things are correlated, while for artifacts, instead, the distinctive features tend to be correlated. However, featural properties are silent with respect to accounting for other patterns of impairment that, instead, seem to be more straightforwardly accounted for in terms of feature types, as for example the fact that for patient JBR, the field of clothing was impaired along with living (rather than nonliving) entities (Warrington & Shallice, 1984). More generally, accounts of category-specific deficits based only on modality-specific feature type representations and accounts based only on featural properties have been criticized on a number of different grounds. Accounts based on feature type have been criticized because, first, it has been shown that perceptual knowledge and knowledge of living things are not always conjointly impaired in pathology. There are now several cases on record showing patients with category-specific deficits whose knowledge of visual and functional attributes is equally poor (e.g., Caramazza, 1998; Laiacona, Barbarotto, &

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Capitani, 1993).10 The imaging results also cannot be fully interpreted in terms of activations linked to sensorimotor processing: some of the activations observed in the frontal cortex, as well as in parts of the temporal neocortex, appear to be related to processing aspects, such as selection (Thompson-Schill, DÕEsposito, & Kan, 1999) or retrieval (Fletcher & Henson, 2001) of semantic knowledge. Second, it is difficult for this view to accommodate the finer-grained category-specific deficits that have been described in the literature, particularly dissociations within the living domain (such as impairments limited only to animals, or to fruit and vegetables alone, or differential graded impairment to these two fields; e.g., Hart et al., 1985; Hillis & Caramazza, 1991; Samson, Pillon, & De Wilde, 1998; Siri et al., 2003). Finally, these hypotheses per se cannot account for patients whose category-specific deficit is limited to naming tasks, since they place the locus of impairment at a level at which any meaning-related task should be affected. Accounts based on feature properties (both accounts such as OUCH and the proposal by Tyler et al., 2000), on the other hand, have been criticized because they have difficulties in accounting for those patients for whom there seems to be category specificity associated with impairment to features of a particular modality, and also for the imaging data that also suggest a modality-specific organization. Each account is also subject to more specific criticism. As pointed out by Caramazza and Shelton (1998), OUCH as stated is too unconstrained—further constraints would be necessary to render it testable or explanatory. The proposal by Tyler et al., instead, has problems explaining how deficits limited to artifacts in the face of normal performance with living entities may arise. This criticism also applies to other proposals in the literature (Devlin, Gonnerman, Andersen, & Seidenberg, 1998) that attempt to capture the progressive deterioration of semantic knowledge 10 Also, some patients demonstrate an inability to appreciate features of a particular type, but nonetheless demonstrate no categoryspecificity. Such patients appear problematic for the present account, also for sensory-functional view. For example, Lambon Ralph, Howard, Nightingale, and Ellis (1998) report the case of patient IW who has less appreciation for visual features but no associated category-specific impairment for living things; see also patient AC (Coltheart et al., 1998) who suffers from visual feature impairment yet exhibits no difference between living and nonliving things. Both cases are subject to criticism: Borgo and Shallice (2001) point out that the results from patient IW do not take word frequency into account, and also that IW produces very few items overall in a feature generation task, rendering her results questionable. Patient AC, instead, exhibited no category-specificity partly because he was completely unable to name pictures, along with a number of other cognitive impairments. Indeed, ACÕs performance, in which he performs feature verification tasks at chance level for visual features but performs well for feature verification involving other modalities, is in line with the assumption that conceptual knowledge is organized along lines of feature modality.

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solely on the basis of differences of correlated features across living and nonliving things.11 Finally, difficulties arise from the findings reported by Garrard et al. (2001) and replicated here: features of form and function do not appear to be more correlated for artifacts than for living things, contrary to the predictions of this account. It is also difficult within this account to explain how narrower category deficits may arise (e.g., patient EW, Caramazza & Shelton, 1998) or patients who present with selective impairments of nonliving things (e.g., Cappa et al., 1998a; Tippett, Glosser, & Farah, 1996; Warrington & McCarthy, 1987). In summary, it appears that variability in patientsÕ performance poses different challenges to different hypotheses. This variability has sometimes been disregarded as due to methodological issues, or because only patients showing a specific type of specificity may be considered to be relevant to the organization of semantic memory, however, this variability may also be a consequence of the structure of semantic memory (see also Rosazza et al., 2002), inviting hypotheses in which semantic memory is organized according to more than one principle. Although feature type and featural properties would seem to be two likely candidates, other possibilities exist. For example, Caramazza and Shelton (1998) propose a framework in which the semantic system is organized both according to real categories (for evolutionarily relevant domains, such as animals and plants, i.e., living things as distinct from artifacts) as well as on the basis of featural properties for those domains that arguably did not arise as a consequence of evolutionary pressure. Because this framework incorporates different principles of organization, it can account for a larger degree of variability in patientsÕ performance (e.g., both broader semantic deficits and narrower deficits). This hypothesis, however, has also been criticized on a number of different grounds. First, like the other proposals that do not posit a role of feature type, this view cannot account for cases in which the categoryspecific deficit is associated with featural deficits, or for cases in which artifact categories are also impaired along with living things (e.g., Borgo & Shallice, 2001). Also, as discussed in Rogers and Plaut (2002), as the repertoire of case studies showing narrow category-specific deficits increases, a risk implicit to the theory is the possible necessity to extend the number of evolutionary motivated modules solely on logical grounds, e.g., the module already proposed for selective sparing of body parts (Shelton, Fouch, & Caramazza, 1998). We follow Caramazza and Shelton (1998) in assuming more than one organizational principle of semantic memory, however, rather than invoking evolutionarily motivated 11

These two accounts also differ in that they make opposite predictions with respect to whether living or nonliving things would be most susceptible to impairment at the initial stages of AD.

mechanisms, we present a hybrid account in which both differences in feature type and differences in feature properties are considered to be important organizational principles.

3. Modeling the semantic space: Integrating differences in feature type and feature properties into a hybrid account In previous work we have developed a hybrid hypothesis that we have labeled the Featural and Unitary Semantic Space (FUSS) hypothesis (Vigliocco et al., 2003; Vinson & Vigliocco, 2002) according to which the semantic system is organized at two different levels. Damage, therefore, may occur at either of these levels and in both cases may result in category-specific deficits. The first level of representation, which we label the ‘‘conceptual featural space,’’ is organized by feature type, in a manner closely following the organization of sensorimotor systems. Impairment of specific feature types at this level can give rise to category-related impairments, given that, as we discussed above (see Figs. 1 and 2), features of different types are differentially distributed across semantic fields. The second level of representation, which we label the ‘‘lexico-semantic space,’’ would bind the distributed features represented in the conceptual feature space. The organization of this level of representation would be dictated by featural properties, such as feature weight, shared, and correlated features. Lesions at this level may result in narrow category-related impairments. From the point of view of the possible neurological underpinnings, this distinction between these two levels of representation is compatible with neurological models which postulate a distinction for object concepts between a level of distributed conceptual representations, involving the associative cortical areas of both hemispheres, and a left-lateralized semantic system, possibly localized in the temporal neocortex. Regarding the object domain, in a large group study, Damasio et al. (1996) have shown that lexical retrieval disorders which disproportionately affect specific semantic categories are associated with different lesion sites in the left temporal lobe. On the other hand, conceptual disorders for the same categories are associated with damage to posterior associative cortices in both hemispheres (Tranel et al., 1997). These observations led Damasio et al. to the suggestion that distributed conceptual features are bound into mediational structures, presumably localized in left infero-temporal (for objects) and possibly premotor/prefrontal areas (for actions) (Tranel et al., 2001). A comparable distinction between object naming disorders, associated with prominent left-sided atrophy, and semantic impairment for objects, due to bilateral involvement, was observed in semantic dementia patients by Lambon Ralph et al. (2001).

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Assuming distributed featural representations, features must be bound in order to represent specific concepts. This binding must be flexible, allowing for the representation of lexicalized concepts but also novel and nonlexicalized concepts and could be achieved in two different manners. One manner is in the form of distributed networks of co-activated features (e.g., Martin & Chao, 2001); another is a mapping between distributed featural representations and semantic representations which develop on the basis of regularities implicit in the features (such as feature weights, shared, and correlated features). These two different manners of binding distributed features to represent concepts do not need to be incompatible, and may co-exist in the adult system. McClelland, McNaughton, and OÕReilly (1995) proposed a similar view of the organization of episodic memory: consolidation of episodic memories, in the form of binding features from different modalities, is at first mediated only by a distinct level of representation (in the hippocampus) and later, through repetition, features co-activate other features, at which point their retrieval would no longer need to be mediated by the hippocampal structures. As discussed in Vigliocco et al. (2003) for semantic memory, an independent motivation for assuming a semantic level of representation separate from featural representations is that the semantic level is particularly important for language use. Using language requires associating featural representations with other linguistic information (such as syntax, phonology, and orthography) which can be only arbitrarily linked to semantics. Hence, assuming the existence of this type of intermediate level solves the linear separability problem (as discussed in Zorzi & Vigliocco, 1999) which would arise in the mapping between different types of distinct information (semantic and phonological) that are only arbitrarily related (see also Vigliocco et al., 2003; for additional arguments for the existence of this level). Thus, the organization of each level would follow different principles. At the conceptual featural level, the organizational principle would be related to the modality by which we experience things and actions in the world (e.g., along the lines of featural types, as discussed above). The organization of the lexico-semantic level would be, instead, dictated by featural properties. In contrast to other accounts (e.g., Devlin et al., 1998; Tyler et al., 2000), however, we do not assume that only certain specific featural properties are crucial in the representation of concepts in specific domains; instead, we assume that all featural properties will contribute, albeit to different extents, to the organization of the lexico-semantic space. This assumption is motivated by the observation that when we move from the object to the action domain, it is very difficult to make specific claims related to patterns of breakdown, given that only a few studies have addressed the semantic organization

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of the action domain (e.g., Kemmerer, Tranel, & Barrash, 2001; Marshall et al., 1996). Furthermore, as also shown in the analyses by McRae and Cree (2002), feature properties (e.g., correlated features) show substantial variability within domains (e.g., ‘‘clothing’’ and ‘‘buildings’’ have more correlated features than ‘‘vegetables’’ in McRae and CreeÕs analysis). This fact further suggests that an approach in which the different featural properties are allowed to affect semantic organization in a combined manner may bear greater promise. In our work, having operationalized the conceptual features in terms of speaker-generated features, we have developed the lexico-semantic space, organized on the basis of featural properties using self-organizing maps (SOMs) (Kohonen, 1997). SOMs are a computational tool that allows us to reduce the dimensionality of the featural representations on the basis of their structural properties (e.g., weight of the different features, correlation among features, and shared features among words) without additional architectural assumptions. Each word in the set was represented by a numeric feature vector, each entry in the vector corresponding to the weight of a given feature. Feature vectors were used as input to train a SOM, creating a two-dimensional (40
25 unit) output map which develops topographic representations based upon similarity of wordsÕ featural properties (e.g., affected by shared, correlated, and distinctive features). On the trained output map, the unit that responded maximally to any one vector was considered to correspond to the lexico-semantic representation for that word, and therefore to be further linked to linguistic information such as syntax, phonology, and orthography. Because the number of units on the map exceeds the number of words to be represented, a number of units do not correspond to lexico-semantic representations for any word. However, a consequence of the SOM training is that these unlabeled units are differentially sensitive to input vectors; such units can be considered to represent concepts that are not lexicalized in the language (or not yet lexicalized by a particular speaker) but that nonetheless correspond to particular featural properties. We choose to describe this level of organization as ‘‘lexico-semantic’’ because our model is based on feature norms obtained from words. A composite distance space was created by taking an ensemble average of distances from 100 different randomly initialized maps, in order to avoid artifacts that may arise based upon the initial random states (see also Vigliocco et al., 2002, 2003; for evidence that using tools of this kind allow us to derive novel predictions of behavioral performance, both in the object and action domains). Descriptions of the clustering properties of the resulting semantic space are reported in Vigliocco et al. (2003); perhaps the most relevant here is the difference in featural properties between objects and action concepts. While the semantic space for objects was

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lumpy with clearly defined boundaries between categories, it was, instead, smooth in the action domain, corresponding to the intuition that while it is easy to classify objects into categories, it is much more difficult to classify actions into semantic fields. 3.1. Accounting for category-specific deficits As introduced above, in FUSS, category-specific deficits may emerge first of all as a consequence of lesions to modality specific features. Concepts in different categories/semantic fields would be more or less vulnerable to sensory damage, for example, depending upon the weight of sensory features, along the lines as illustrated in Figs. 1 and 2. Patterns of distribution of feature types raise intriguing empirical questions; for example, do patients who seem to have impairments for sensory features (based upon difficulties in the object domain) also show impairment for ‘‘light emission’’ concepts (such as ‘‘shine,’’ ‘‘sparkle,’’ etc., for which visual features dominate) or ‘‘noises’’ (such as ‘‘clang,’’ ‘‘screech,’’ etc., for which acoustic features dominate)? Category-specific deficits can, however, also arise as a consequence of lesions to the lexico-semantic space. In order to obtain a better characterization of the consequences of such lesions, we performed simulated lesions on the resulting composite map. We created these lesions in a manner parallel to that reported in Vinson and Vigliocco (2002): simulating 100 mild lesions (affecting fewer than 30% of words in the set). We selected this type of lesion on the basis of the number of object categories and the number of exemplars within categories in the present set (larger lesions are more suited to investigating object/action dissociations, as detailed in Vinson & Vigliocco, 2002). Using the SOM-based model of the lexico-semantic space, we randomly selected lesion locations; the radius of impact of each was expanded until it encompassed a specified proportion of word nodes, which were considered to be ‘‘impaired’’ while all others were ‘‘spared.’’ We focus first upon lesions which primarily affect regions in which objects are represented (63 such cases). These simulated impairments (an average of 36 words impaired per lesion), were likely to produce ‘‘categoryspecific’’ impairments. For each lesion, we identified a category of primary impairment: that category whose members were most impaired by a given lesion (affecting more than 40% of words in that category); we then identified secondary impaired categories as those which had 10% or more exemplars affected by a lesion. This classification of lesions by primary category is possible because of the ‘‘lumpy’’ organization of object space— most lesions of smaller sizes (i.e., the ‘‘mild’’ ones used here) yield less than 20% performance in a single, identifiable category (at least for objects). Note that the relative frequency of observing impairments to a given

category may be due in part to the number of exemplars in the different categories in our set (categories with more exemplars tend to occupy more space overall on the composite SOM, and as such are more likely to be affected by a lesion to a random location), therefore we will not discuss these lesions in terms of their relative occurrence by categories, but instead in terms of withinand between-category effects. Of the 63 lesions, 10 primarily affected the category of fruits and vegetables (secondary impairments in these cases were always to the animal field; other fields were fully spared). Thirteen lesions primarily affected the field of animals (fruits and vegetables or body part fields were also impaired to a lesser extent, again, other fields were spared). Tools were primarily affected by 14 of the lesions, which also tended to affect body-parts, some miscellaneous artifacts, and (to a lesser extent) tool actions. Nine of the lesions primarily affected the field of clothing, usually with secondary damage to body parts. Body parts themselves were focally impaired in 12 cases, sometimes with secondary damage to animals or clothing or tools, but also sometimes involving a number of body actions. Interestingly, it was possible in some of these cases that different subfields of body parts were differentially impaired; these broke down along the line of head/other body parts (head spared 4 times, body spared 3 times, both impaired together 5 times). The remaining five lesions in the object fields primarily affected vehicles or miscellaneous artifacts (partially a consequence of the low number of vehicles in the set, and a more widely distributed organization of the miscellaneous artifacts relative to the other object categories used). These impairments did allow the observation of category-specific impairments in many cases—e.g., the impairment to fruits, vegetables, and partially animals, which parallels reported impairments to living things with artifacts being spared. Impairments also were found that closely converged with some patients in the literature with particularly narrow impairments. For example, the naming performance of patient MD (63% fruits and vegetables, 97% on others; Hart et al., 1985) was well matched by one of simulated impairments (58% fruits and vegetables, 100% on others). Although the close match between cases like MD and our model is promising, it is a rather trivial observation given an organization along category lines—such organization should lead to the possibility of narrow category-specific impairments in general, leaving other fields spared. This type of pattern should emerge in any system in which categories are represented in a separable manner, whether strictly separated as in the domain-specific view (Caramazza & Shelton, 1998) or merely differentiated along some dimension (such as feature modality or featural properties). More interestingly, however, is the possibility of investigating graded, inter-category effects of impairments of a given type.

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361

Table 1 Confusion matrix indicating co-impairment of object categories with mild impairments at the lexical-semantic level Main field affected by lesion Animal Secondary field Animal — Body Y Clothing N Fruit/Veg Y Tools N Vehicles N Misc. N

Bodya

Clothing

Fruit/Veg

Toolsb

Vehicles

Misc.c artifacts

Y —

N Y

Y N Y N N

—

Y N N

N N N N

N Y N N

N N N

N N N N N —

n/a n/a n/a n/a n/a n/a

Y

—

—

—

N Y

‘‘Y(es)’’ indicates that the categories tended to be impaired by the same simulated lesions (more than 10% of exemplars were affected by the lesion); ‘‘N(o)’’ indicates that they did not. a Impairments to body parts also sometimes impaired the action domain of body actions in addition to object fields. b Impairments to tools also sometimes impaired tool actions as well. c The field of miscellaneous artifacts was not strongly cohesive, as it contained exemplars from a variety of different semantic fields including furniture, other household objects, and parts of buildings. Therefore it was rare to observe impairments that primarily affected this field; impairments were usually secondary to other fields more compactly represented.

Three particular patients are considered here, all of whom critically appear to demonstrate graded impairments in naming across different categories of objects. Patient PS (Hillis & Caramazza, 1991) performed at 25% for fruits and vegetables, 39% for animals, and 89% for words of other categories; patient GP (Cappa et al., 1998a) performed at 25% for tools, 53% for furniture, and 82% for others; and patient Jennifer (Samson et al., 1998) performed at 22% for animals, 50% for fruits and vegetables, and 78% for others. We found very similar cases to these patients within our set of simulated lesions. PSÕs results were paralleled closely by one lesion (22% fruits and vegetables, 47% animals, and 95% others), GPÕs by another (27% tools, 50% furniture, and 96% others), and Jennifer by a third (21% animal, 56% fruits and vegetables, and 87% others).12 The contrast between PS and Jennifer is particularly important with regards to sensory-functional theories of category-specificity—here we have a case of a double dissociation between levels of performance on the narrow categories of fruits and vegetables on one hand, and animals on the other. Assuming that the same type of impairment (to sensory features) causes both patientsÕ specific impairment for living things, it is difficult to explain these finergrained differences between the two patients. The correspondence between our present model and these patients suggests that our account may be able to predict additional patterns of graded performance across categories of objects in cases of focal lesions. Some such predictions are that impairments to tools will be associated with (lesser) impairments to body parts and/or actions involving tools; impairments to animals with lesser impairments to body parts; impairments to 12

Importantly, these lesions arose from the unconstrained set of 100 random-location lesions, rather than being specifically sought to match these patientsÕ performance.

clothing and also body parts.13 Note the central involvement of the field of body parts, which are closely related to a number of different fields (both objects and actions). Crucially, however, certain types of associations between fields are never observed. Table 1 summarizes the co-occurrence of cross-field deficits in our simulated lesions. We now turn briefly to those 37 mild lesions that affected primarily the action space. Because concepts are more uniformly represented in this portion of the semantic space (and also as a consequence of the smaller number of words per semantic field and the difficulty of labeling words with a single ‘‘category’’ label) we observed very few ‘‘category-specific’’ impairments in the sense reported for objects, i.e., impairment to one category with the sparing of the next. As a result, we will report the outcome of these lesions in general terms, describing the fields that tend to be impaired together, and those that instead dissociate. A majority of the actions involved the body; such actions are very closely related in the semantic space. This resulted in some common impairments that tended to affect together fields of contact, tool action, construction, and other motions involving the body. Such impairments tended to spare actions like light emission, cooking, communication, and sounds. Actions of communication instead tended to be impaired along with sounds/noises, as well as (sometimes) exchange. Fields like light emission and cooking, instead, tended to co-occur in impairments, and usually only co-occurred with sensory (primarily visual) actions. Although it is clear that the possibility of observing graded effects across semantic fields emerges from the 13

Graded semantic effects between the categories of clothing and body parts have also been observed in a naming task (Vigliocco et al., 2002).

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action domain in parallel to that of the objects, such discussion may be premature: patients have not been reported to demonstrate graded effects of performance in action domains (perhaps a consequence of the materials on which they are tested), but also the inherent difficulty of categorizing verbs into semantic fields, as discussed above. Up to this point we have discussed how our hybrid model can account for variability in the patterns of spared and impaired semantic knowledge, including some cases problematic for other accounts of category specificity. One further dimension of variability we have not yet directly addressed concerns whether a patientÕs category-specific deficit can be characterized as conceptual or lexical. A number of reports on record suggest that category-related deficits may be manifested to a greater extent on linguistic tasks only, particularly naming (Cappa et al., 1998a; Damasio et al., 1996; Farah & Wallace, 1992; Tranel et al., 2001). With one exception (which we will discuss below), in fact it appears that a general trend is for narrower category-related deficits (e.g., fruits and vegetables only) to manifest primarily in linguistic tasks. In the hybrid model presented here, we have characterized the lexico-semantic level of representation as being necessary for binding the featural information to other linguistic information (syntax, orthography, and phonology), hence as a necessary intermediate level in mapping between conceptual (nonlinguistic) information and linguistic information. Thus this level of representation provides an interface between the conceptual, nonlinguistic domain and the linguistic domain, and, because it serves as such a binding site, is closely related to the conceptual featural level (as suggested by findings of largely overlapping patterns of activation in imaging studies for pictures and words (Perani et al., 1995; Vandenberghe, Price, Wise, Josephs, & Frackowiak, 1996)). Thus, assuming that feature-binding at this level is necessary for language use, lesions at this level would primarily affect linguistic tasks. Preserved (or at least less impaired) conceptual knowledge is accounted for by allowing binding of conceptual features at the lexico-semantic level, as well as co-activation of distributed features. There is however an exception: Patient EW, reported by Caramazza and Shelton (1998), whose category-specific deficits for animals were observed in a variety of tasks, involving lexical retrieval and also nonlinguistic tasks. Within the assumptions of FUSS, EWÕs results can only be explained by assuming that binding of information at the lexico-semantic level is necessary not only for tasks requiring linguistic knowledge but is also essential for those tasks requiring nonlinguistic conceptual knowledge. However, this assumption is based only on the performance of a single patient whose lesion (left posterior frontal and parietal lobes) does not easily fit into any neuroanatomical

model of semantic representation, and contrasts with broader empirical evidence suggesting that (narrower) category-related impairments can be limited to the linguistic domain (e.g., Cappa et al., 1998a; Damasio et al., 1996).

4. Conclusions Category-related deficits in the object domain have provided important constraints to modeling the semantic system. Starting from constraints arising from patientsÕ studies, current models of semantic organization assume that differential susceptibility of object categories to selective breakdown results either from differences in feature type across object categories, or from differences in feature-properties. We empirically assessed differences in feature type and feature-properties for a large number of words, crucially moving beyond the object domain to the action domain, a domain of knowledge that has received less attention. These analyses provide us with converging evidence concerning the object domain; further, they allow us to derive a number of novel predictions concerning the action domain, predictions that await empirical scrutiny. On the basis of the results from these analyses (and from the existing literature), we have concluded that theories embedding a single principle of semantic organization, while parsimonious, are insufficient to account for the variability observed in patientsÕ performance, thus inviting the hypothesis that the semantic system is organized according to more than one principle. In this light, we introduced FUSS, a hybrid account according to which semantic knowledge is organized at different levels, according to both feature type and featureproperties. Can our account explain such variability, particularly cases problematic for other accounts? Lesions to feature types can account for: (a) patients showing an association between lack of featural knowledge and categoryspecific deficits; (b) patients with category-specific deficits crossing the living/nonliving divide. Note that such impairments are difficult to accommodate within views that do not assume organization at a featural level. Lesions to the lexico-semantic space, instead can give rise to deficits specific to narrow categories (e.g., animals, fruits and vegetables, body parts, and medical terms); and deficits that may encompass the living or nonliving domain without associated differential featural impairments. Crucially, we also observed simulated lesions that differentially most affected fruits and vegetables or animals within overall cases of impairment to living things. These deficits are problematic for views in which feature type impairments are the only source of category-specificity.

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Acknowledgments The work reported here was supported by a Human Frontier Science Program Grant (RG148/2000) to Gabriella Vigliocco; Stefano Cappa, Merrill Garrett, Peter Indefrey, Monserrat Sanz and Patrizia Tabossi, and a James McDonnell Foundation Pilot Award (21002048) to Gabriella Vigliocco. This paper is dedicated to the memory of Eleanor Saffran, whose research in this area has inspired us to pursue this line of investigation.

References Allport, P. (1985). Distributed memory, modular subsystems and dysphasia. Current perspectives in dysphasia. In S. K. Newman, & R. Epstein (Eds.), Current perspectives in dysphasia (pp. 32–60). Edimburgh: Churchill Livingstone. Barbarotto, R., Capitani, E., Spinnler, H., & Trivelli, C. (1995). Slowly progressive semantic impairment with category specificity. Neurocase, 1, 107–119. Barsalou, L. (1993). Flexibility, structure, and linguistic vagary in concepts: Manifestations of a compositional system of perceptual symbols. In A. F. Collins, S. E. Gathercole, M. A. Conway, & P. E. Morris (Eds.), Theories of memory (pp. 29–101). Sussex: Erlbaum. Basso, A., & Capitani, E. (1988). Progressive language impairment without dementia: A case with isolated category specific semantic impairment. Journal of Neurology Neurosurgery and Psychiatry, 51, 1201–1207. Bird, H., Lambon Ralph, M. A., Patterson, K., & Hodges, J. R. (2000). The rise and fall of frequency and imageability: Noun and verb production in semantic dementia. Brain and Language, 73, 17–49. Borgo, F., & Shallice, T. (2001). When living things and other sensory quality categories go together: A novel category-specific effect. Neurocase, 7, 201–220. Breedin, S. D., Saffran, E. M., & Coslett, H. B. (1994). Reversal of the concreteness effect in a patient with semantic dementia. Cognitive Neuropsychology, 11, 617–660. Buxbaum, L. J., Veramonti, T., & Schwartz, M. F. (2000). Function and manipulation tool knowledge in apraxia: Knowing Ôwhat forÕ but not ÔhowÕ. Neurocase, 6, 83–97. Cappa, S. F., Frugoni, M., Pasquali, P., Perani, D., & Zorat, F. (1998a). Category-specific naming impairment for artefacts: A new case. Neurocase, 4, 391–397. Cappa, S. F., Binetti, G., Pezzini, A., Padovani, A., Rozzini, L., & Trabucchi, M. (1998b). Object and action naming in AlzheimerÕs disease and frontotemporal dementia. Neurology, 50, 351–355. Caramazza, A. (1998). The interpretation of semantic category-specific deficits: What do they reveal about the organization of conceptual knowledge in the brain? Neurocase, 4, 265–272. Caramazza, A., Hillis, A. E., Rapp, B., & Romani, C. (1990). The multiple semantics hypothesis: Multiple confusions. Cognitive Neuropsychology, 7, 161–189. Caramazza, A., & Shelton, J. R. (1998). Domain-specific knowledge systems in the brain: The animate–inanimate distinction. Journal of Cognitive Neuroscience, 10, 1–34. Chao, L. L., Haxby, J. V., & Martin, A. (1999). Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects. Nature Neuroscience, 2, 913–919. Coltheart, M. (1981). The MRC psycholinguistic database. Quarterly Journal of Experimental Psychology, 33A, 497–505, online database at http://www.psy.uwa.edu.au/MRCDataBase/uwa_mrc.htm.

363

Coltheart, M., Inglis, L., Cupples, L., Michie, P., Bates, A., & Budd, B. (1998). A semantic subsystem of visual attributes. Neurocase, 4, 353–370. Crosson, B., Moberg, P. J., Boone, J. R., Gonzalez Rothi, L. J., & Raymer, A. (1997). Category-specific naming deficit for medical terms after dominant thalamic/capsular hemorrhage. Brain and Language, 60, 407–442. Damasio, H., Grabowski, T., Tranel, D., Hichwa, R. D., & Damasio, A. R. (1996). A neural basis for lexical retrieval. Nature, 380, 499–505. Damasio, H., Grabowski, T., Tranel, D., Ponto, L. L. B., Hichwa, R. D., & Damasio, A. R. (2001). Neural correlates of naming actions and of naming spatial relations. Neuroimage, 13, 1053–1064. De Renzi, E., & Lucchelli, F. (1994). Are semantic systems separately represented in the brain—the case of living category impairment. Cortex, 30, 3–25. Devlin, J. T., Gonnerman, L. M., Andersen, E. S., & Seidenberg, M. S. (1998). Category-specific semantic deficits in focal and widespread brain damage: A computational account. Journal of Cognitive Neuroscience, 10, 77–94. Farah, M. J., & McClelland, J. L. (1991). A computational model of semantic memory impairment: Modality specificity and emergent category specificity. Journal of Experimental Psychology: General, 120, 339–357. Farah, M. J., McMullen, P. A., & Meyer, M. M. (1991). Can recognition of living things be selectively impaired? Neuropsychologia, 29, 185–193. Farah, M. J., & Wallace, M. A. (1992). Semantically-bounded anomia: Implications for the neural implementation of naming. Neuropsychologia, 30, 609–621. Fletcher, P. C., & Henson, R. N. A. (2001). Frontal lobes and human memory: Insights from functional imaging. Brain, 124, 849–881. Forde, E. M., Francis, E. D., Riddoch, M. J., Rumiati, R. I., & Humphreys, G. W. (1997). On the links between visual knowledge and naming: A single case study of a patient with a categoryspecific impairment for living things. Cognitive Neuropsychology, 14, 403–458. Gainotti, G. (2000). What the locus of brain lesion tells us about the nature of the cognitive defect underlying category-specific disorders: A review. Cortex, 36, 539–559. Gainotti, G., & Silveri, M. C. (1996). Cognitive and anatomical locus of lesion in a patient with a category-specific semantic impairment for living beings. Cognitive Neuropsychology, 13, 357. Gainotti, G., Silveri, M. C., Daniele, A., & Giustolisi, L. (1995). Neuroanatomical correlates of category-specific semantic disorders—a critical survey. Memory, 3, 247. Garrard, P., Lambon Ralph, M. A., Hodges, J. R., & Patterson, K. (2001). Prototypicality, distinctiveness, and intercorrelation: Analyses of the semantic attributes of living and nonliving concepts. Cognitive Neuropsychology, 18, 125–174. Grabowski, T. J., Damasio, H., & Damasio, A. R. (1998). Premotor and prefrontal correlates of category-related lexical retrieval. Neuroimage, 7, 232–243. Graesser, A. C., Hopkinson, P. L., & Schmid, C. (1987). Differences in interconcept organization between nouns and verbs. Journal of Memory and Language, 26, 242–253. Hart, J., Berndt, R. S., & Caramazza, A. (1985). Category-specific naming deficit following cerebral infarction. Nature, 316, 439–440. Hart, J., & Gordon, B. (1992). Neural subsystems for object knowledge. Nature, 359, 60–64. Hillis, A. E., & Caramazza, A. (1991). Category-specific naming and comprehension impairment—a double dissociation. Brain, 114, 2081–2094. Hinton, G. E., & Shallice, T. (1991). Lesioning an attractor network: Investigations of acquired dyslexia. Psychological Review, 98, 74–95. Huttenlocher, J., & Lui, F. (1979). The semantic organization of some simple nouns and verbs. Journal of Verbal Learning and Verbal Behavior, 18, 141–179.

364

D.P. Vinson et al. / Brain and Language 86 (2003) 347–365

Ishai, A., Ungerleider, L. G., Martin, A., Schouten, J. L., & Haxby, J. K. (1999). Distributed representation of objects in the human ventral visual pathway. Proceedings of the National Academy of Sciences of the United States of America, 96, 9379–9384. Kemmerer, D., Tranel, D., & Barrash, J. (2001). Patterns of dissociation in the processing of verb meanings in brain-damaged subjects. Language and Cognitive Processes, 16, 1–34. Kohonen, T. (1997). Self-organizing maps. New York: Springer. Laiacona, M., Barbarotto, R., & Capitani, E. (1993). Perceptual and associative knowledge in category-specific impairment of semantic memory—a study of 2 cases. Cortex, 29, 727–740. Lambon Ralph, M. A., Howard, D., Nightingale, G., & Ellis, A. W. (1998). Are living and non-living category-specific deficits causally linked to impaired perceptual or associative knowledge? Evidence from a category-specific double dissociation. Neurocase, 4, 311–338. Lambon Ralph, M. A., Powell, J., Howard, D., Whitworth, A. B., Garrard, P., & Hodges, J. R. (2001). Semantic memory is impaired in both dementia with Lewy Bodies (DLB) and dementia of AlzheimerÕs type (DAT): A comparative neuropsychological study and literature review. Journal of Neurology Neurosurgery and Psychiatry, 70, 149–156. Levin, B. (1993). English verb classes and alternations. A preliminary investigation. Chicago: University of Chicago Press. Luckhurst, L., & Lloyd-Jones, T. J. (2001). A selective deficit for living things after temporal lobectomy for relief of epileptic seizures. Brain and Language, 79, 266–296. Marshall, J., Chiat, S., Robson, J., & Pring, T. (1996). Calling a salad a federation: An investigation of semantic jargon: 2. Verbs. Journal of Neurolinguistics, 9, 251–260. Martin, A., & Chao, L. L. (2001). Semantic memory and the brain: Structure and processes. Current Opinion in Neurobiology, 11, 194–201. Martin, A. C., Wiggs, C. L., Ungerleider, L. G., & Haxby, J. V. (1996). Neural correlates of category-specific knowledge. Nature, 379, 649–652. Masson, M. E. J. (1995). A distributed memory model of semantic priming. Journal of Experimental Psychology: Learning, Memory and Cognition, 21, 3–23. McClelland, J. L., McNaughton, B. L., & OÕReilly, R. C. (1995). Why there are complementary learning systems in hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419–457. McKenna, P., & Warrington, E. K. (1978). Category-specific naming preservation: A single case study. Journal of Neurology, Neurosurgery, and Psychiatry, 41, 571–574. McRae, K., & Cree (2002). Factors underlying category-specific semantic deficits. In E. Forde, & G. Humphreys (Eds.), Category specificity in brain and mind. Psychology Press. McRae, K., de Sa, V., & Seidenberg, M. (1997). On the nature and scope of featural representations of word meaning. Journal of Experimental Psychology: General, 126, 99–130. Medin, D. L. (1989). Concepts and conceptual structure. American Psychologist, 44, 1469–1481. Miller, G. A., & Fellbaum, C. (1991). Semantic networks of English. Cognition, 41, 197–229. Moss, H. E., Tyler, L. K., & Jennings, G. (1997). When leopards lose their spots: Knowledge of visual properties in categoryspecific deficits for living things. Cognitive Neuropsychology, 14, 901–950. Murphy, G. L. (2002). The big book of concepts. Cambridge, MA: MIT Press. Perani, D., Cappa, S. F., Bettinardi, V., Bressi, S., Gornotempini, M., Matarrese, M., & Fazio, F. (1995). Different neural systems for the recognition of animals and man-made tools. Neuroreport, 6, 1637– 1641.

Perani, D., Schnur, T., Tettamanti, C., Gorno-Tempini, M., Cappa, S. F., & Fazio, F. (1999). Word and picture matching: A PET study of semantic category effects. Neuropsychologia, 37, 293–306. Plaut, D. C. (1995). Semantic and associative priming in a distributed attractor network. Proceedings of the Seventeen Annual Conference of the Cognitive Science Society, 17, 37–42. Rapp, B. C., Hillis, A. E., & Caramazza, A. (1993). The role of representations in cognitive theory: More on multiple semantics and the agnosias. Cognitive Neuropsychology, 10, 235–249. Rogers, T. T., & Plaut, D. C. (2002). Connectionist perspectives on category-specific deficits. In E. Forde, & G. Humphreys, (Eds.), Category specificity in brain and mind. Psychology Press. Rosazza, C., Imbornone, E., Zorzi, M., Farina, E., Chiavari, L., & Cappa, S. F. (2002). The heterogeneity of category-specific semantic disorders: Evidence from a new case (manuscript submitted for publication). Rosch, E., & Mervis, C. B. (1975). Family resemblance: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605. Saffran, E. M. (2000). The organization of semantic memory: In support of a distributed model. Brain and Language, 71, 204–212. Samson, D., Pillon, A., & De Wilde, V. (1998). Impaired knowledge of visual and non-visual attributes in a patient with a semantic impairment for living entities: A case of a true category-specific deficit. Neurocase, 4, 273–290. Sartori, G., & Job, R. (1988). The oyster with 4 legs: A neuropsychological study of the interaction of visual and semantic information. Cognitive Neuropsychology, 5, 105–132. Shelton, J. R., Fouch, E., & Caramazza, A. (1998). The selective sparing of body part knowledge: A case study. Neurocase, 4, 339–351. Silveri, M. C., & Di Betta, A. M. (1997). Noun–verb dissociations in brain-damaged patients: Further evidence. Neurocase, 3, 477–488. Silveri, M. C., & Gainotti, C. (1988). Interaction between vision and language in category-specific semantic impairment. Cognitive Neuropsychology, 5, 677–709. Siri, S., Kensinger, E. A., Cappa, S. F., Hood, K. L., & Corkin, S. (2003). Questioning the living/nonliving dichotomy: Evidence from a patient with an unusual semantic dissociation. Neuropsychology, in press. Smith, E. E., & Medin, D. L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press. Smith, E. E., Osherson, D. N., Rips, L. J., & Keane, M. (1988). Combining prototypes: A selective modification model. Cognitive Science, 12, 485–527. Strauss, E., Semenza, C., Hunter, M., Hermann, B., Barr, W., Chelune, G., Lavdovsky, S., Loring, D., Perrine, K., Trenerry, M., & Westerveld, M. (2000). Left anterior lobectomy and category-specific naming. Brain and Cognition, 43, 403–406. Thompson-Schill, S. L., DÕEsposito, M., & Kan, I. P. (1999). A neural basis for category and modality specificity of semantic knowledge. Neuropsychology, 37, 671–676. Tippett, L. J., Glosser, G., & Farah, M. J. (1996). A category-specific naming impairment after temporal lobectomy. Neuropsychologia, 34, 139–146. Tranel, D., Adolph, R., Damasio, H., & Damasio, A. R. (2001). A neural basis for the retrieval of words for actions. Cognitive Neuropsychology, 18, 655–670. Tranel, D., Damasio, H., & Damasio, A. R. (1997). A neural basis for the retrieval of conceptual knowledge. Neuropsychologia, 35, 1319– 1327. Tyler, L. K., Moss, H. E., Durrant-Peatfield, M. R., & Levy, J. P. (2000). Conceptual structure and the structure of concepts: A distributed account of category-specific deficits. Brain and Language, 75, 195–231. Vandenberghe, R., Price, C., Wise, R., Josephs, O., & Frackowiak, R. S. J. (1996). Functional anatomy of a common semantic system for words and pictures. Nature, 383, 254–256.

D.P. Vinson et al. / Brain and Language 86 (2003) 347–365 Vendler, Z. (1957). Verbs and times. Philosophical Review, 66, 143–160. Vigliocco, G., Vinson, D. P., Damian, M. F., & Levelt, W. J. M. (2002). Semantic distance effects on object and action naming. Cognition, 85, B61–B69. Vigliocco, G., Vinson, D. P., Lewis, W., & Garrett, M. F. (2003). The meaning of object and action words (manuscript submitted for publication). Vinson, D. P., & Vigliocco, G. (2002). A semantic analysis of noun– verb dissociation in aphasia. Journal of Neurolinguistics, 15, 317–351.

365

Warrington, E. K. (1975). The selective impairment of semantic memory. Quarterly Journal of Experimental Psychology, 27, 635–657. Warrington, E. K., & McCarthy, R. A. (1987). Categories of knowledge: Further fractionations and an attempted integration. Brain, 110, 1273–1296. Warrington, E. K., & Shallice, T. (1984). Category specific semantic impairments. Brain, 107, 829–854. Zorzi, M., & Vigliocco, G. (1999). Compositional semantics and the lemma dilemma. Behavioral and Brain Sciences, 22, 60.