Journal of Experimental Psychology: General Copyright 1997 by the American Psychological Association, Inc. 1997, Vol. 126, No, 2, 99-130 0096-3445/97/$3.00 On the Nature and Scope of Featural Representations of Word Meaning Ken McRae University of Western Ontario Mark S. Seidenberg University of Southern Cahfornia Virginia R. de Sa University of Rochester Behavioral experiments and a connectionist model were used to explore the use of featural representations in the computation of word meaning. The research focused on the role of correlations among features, and differences between speeded and untimed tasks with respect to the use of featural information. The results indicate that featural representations are used in the initial computation of word meaning (as in an attractor network), patterns of feature correlations differ between artifacts and living things, and the degree to which features are intercorrelated plays an important role in the organization of semantic memory. The studies also suggest that it may be possible to predict semantic priming effects from independently motivated featural theories of semantic relatedness. Implications for related behavioral phenomena such as the semantic impairments associated with Alzheimer's disease (AD) are discussed. Many theories have assumed that word meaning is rep- resented, at least in part, in terms of featural primitives (see, e.g., Collins & Quillian, 1969; Minsky, 1975; Norman & Rumelhart, 1975; Shallice, 1988; and Smith & Medin, 1981, for overviews). Several properties of such representations have been explored in detail and have proven to have explanatory value. For example, a number of studies have shown that different features are activated depending on the context in which the word occurs (Barsalou, 1982), suggest- ing that word meanings are not like fixed dictionary entries. The purpose of the present research was to examine the role of featural representations in the processing of word mean- ing. Three general issues were addressed: the relevance of featural representations to different types of semantic tasks; the nature of featural representations, focusing on the way in Ken McRae, Department of Psychology, University of Western Ontario, London, Ontario, Canada; Virginia R. de Sa, Department of Computer Science, University of Rochester; Mark S. Seiden- berg, Neuroscience Program, University of Southern California. Virginia R. de Sa is now at the Department of Physiology, Uni- versity of San Francisco. This work was supported by Natural Sciences and Engineering Research Council (NSERC) Grant OGP0155704, an NSERC post- doctoral fellowship, National Institute of Mental Health Grant MH47566, and Research Scientist Development Award MH01188. Part of this research formed Ken McRae's McGill University doctoral dissertation. We would like to thank the McGill-International Business Machines (IBM) cooperative project in Science, Medicine, and Engineering for donating IBM microcomputers used in these ex- periments. We are indebted to Mike Tanenhaus, Michael Spivey- Knowlton, Kyunghee Koh, and Rob Goldstone for comments and helpful suggestions on drafts of this article. Correspondence concerning this article should be addressed to Ken McRae, Department of Psychology, Social Science Centre, University of Western Ontario, London, Ontario, Canada N6A 5C2. Electronic mail may be sent via Internet to kenm@ sunrae.sscl.uwo.ca. which feature correlations might be learned and what their subsequent role in word recognition might be; and the organization of semantic memory, with particular emphasis on defining semantic relatedness and specifying the source of automatic semantic priming. THE SCOPE OF FEATURAL REPRESENTATIONS The validity of a featural approach to word meaning has been widely questioned in recent years. Much contemporary research on concepts has focused on higher level knowl- edge, such as people's naive theories of biology (for dis- cussion, see Jones & Smith, 1993, and associated commen- taries). Knowledge-based theories (Medin, 1989; Murphy & Medin, 1985) attempt to account for phenomena such as the development of conceptual structures (Keil, 1989) and peo- ple's capacity to reason about category membership (Rips, 1989). In this approach, higher level knowledge is assumed to be central to concepts and is positioned as an alternative to feature-based accounts. However, the two views seem to address different phenomena: Theories seem irrelevant to recognizing and reacting appropriately to everyday objects, whereas features cannot account for people's performance in tasks such as Rips' that involve explicit reasoning based on conceptual representations. The present research exam- ined the hypothesis that whereas featural representations are central to the initial computation of word meaning, they are less relevant to the kinds of tasks that typically lend support to knowledge-based theories. Jones and Smith (1993) have noted that the tasks used in studies of conceptual representation may yield data about numerous aspects of human knowledge and processing be- cause they vary greatly in terms of the information required to perform them. The tasks that have been used to assess people's concepts can be viewed as spanning a continuum that extends from high-level, untimed reasoning tasks to 99