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Seven trends regarding the categories that tend to be impaired/preserved in category-specific semantic deficits were identified. The authors hypothesized that these trends arise despite the multiple sources of variation in patient testing because numerous factors that structure semantic memory probabilistically converge to make some categories of knowledge more susceptible to damage than others. Analysis of semantic feature norms and corpus data for 541 concepts revealed that differences in the distribution of knowledge types across categories are sufficient to explain 6 of the trends and are necessary to explain loss of knowledge about nonliving things. Feature informativeness, concept confusability, visual complexity, familiarity, and name frequency contributed to this patterning and provide insight into why knowledge about living things is most often impaired.

The authors present data from 2 feature verification experiments designed to determine whether distinctive features have a privileged status in the computation of word meaning. They use an attractor-based connectionist model of semantic memory to derive predictions for the experiments. Contrary to central predictions of the conceptual structure account, but consistent with their own model, the authors present empirical evidence that distinctive features of both living and nonliving things do indeed have a privileged role in the computation of word meaning. The authors explain the mechanism through which these effects are produced in their model by presenting an analysis of the weight structure developed in the network during training.Keywords semantic memory; distinctive features; connectionist attractor network; conceptual structure theory The issue of how word meaning is stored in the mind, and computed rapidly when needed, is central both to theories of language comprehension and production and to understanding knowledge deficits in patients with neural impairments. Although we are far from a complete understanding, significant progress has been made since Tulving (1972) first introduced the term "semantic memory." Much of this progress can be attributed to the implementation and development of semantic memory models and the advancement of tools (e.g., semantic feature production norms and word co-occurrence statistics) that enable the rigorous testing of predictions generated from these models.The first models of semantic memory were hierarchical network theory (Collins & Quillian, 1969) and spreading activation theory (Collins & Loftus, 1975). Although both have been extremely influential and have provided predictions for numerous behavioral studies, both have lost favor owing to a number of well-understood and documented flaws (see Rogers & McClelland, 2004, for a recent review). A number of theories of semantic structure have been proposed as alternatives, including the sensory/functional theory (Warrington & Copyright 2006 by the American Psychological Association Correspondence concerning this article should be addressed to George S. Cree, Department of Life Sciences (Psychology), University of Toronto at Scarborough, Toronto, Ontario M1C 1A4, Canada. gcree@utsc.utoronto.ca. McCarthy, 1987), the conceptual structure account (Tyler & Moss, 2001), and the domainspecific hypothesis (Caramazza & Shelton, 1998). Theories of semantic processing have also been proposed, often framed in terms of connectionist networks, and include the work of Plaut and Shallice (1993), Rumelhart and Todd (1993), Rogers and McClelland (2004), and McRae, de Sa, and Seidenberg (1997). Current research focuses on discriminating among these models, integrating the structural and processing components, and developing them to account for behavioral data. NIH Public AccessOne way in which these models are being tested concerns whether concept-feature and feature-feature statistical relations are embedded in the semantic sys...

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An attractor network was trained to compute from word form to semantic representations that were based on subject-generated features. The model was driven largely by higher-order semantic structure. The network simulated two recent experiments that employed items included in its training set (McRae and Boisvert, 1998). In Simulation 1, short stimulus onset asynchrony priming was demonstrated for semantically similar items. Simulation 2 reproduced subtle effects obtained by varying degree of similarity. Two predictions from the model were then tested on human subjects. In Simulation 3 and Experiment 1, the items from Simulation 1 were reversed, and both the network and subjects showed minimally different priming effects in the two directions. In Experiment 2, consistent with attractor networks but contrary to a key aspect of hierarchical spreading activation accounts priming was determined by featural similarity rather than shared superordinate category. It is concluded that semantic-similarity priming is due to featural overlap that is a natural consequence of distributed representations of word meaning.

The role of feature correlations in semantic memory is a central issue in conceptual representation. In two versions of the feature verification task, subjects were faster to verify that a feature () is part of a concept (grapefruit) if it is strongly rather than weakly intercorrelated with the other features of that concept. Contrasting interactions between feature correlations and SOA were found when the concept versus the feature was presented first. An attractor network model of word meaning that naturally learns and uses feature correlations predicted those interactions. This research provides further evidence that semantic memory includes implicitly-learned statistical knowledge of feature relationships, in contrast to theories such as spreading activation networks, in which feature correlations play no role.

An attractor network was trained to compute from word form to semantic representations that were based on subject-generated features. The model was driven largely by higher-order semantic structure. The network simulated two recent experiments that employed items included in its training set (McRae and Boisvert, 1998). In Simulation 1, short stimulus onset asynchrony priming was demonstrated for semantically similar items. Simulation 2 reproduced subtle effects obtained by varying degree of similarity. Two predictions from the model were then tested on human subjects. In Simulation 3 and Experiment 1, the items from Simulation 1 were reversed, and both the network and subjects showed minimally different priming effects in the two directions. In Experiment 2, consistent with attractor networks but contrary to a key aspect of hierarchical spreading activation accounts priming was determined by featural similarity rather than shared superordinate category. It is concluded that semantic-similarity priming is due to featural overlap that is a natural consequence of distributed representations of word meaning.

The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory.