On the nature and scope of featural representations of word meaning.

McRae, Ken, de Sa, Virginia R., Seidenberg, Mark S.·1997·Journal of Experimental Psychology: General

Abstract

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.

Cites (10)

  1. Judgment under Uncertainty: Heuristics and Biases
    Tversky, Amos, Kahneman, Daniel · 1975 · Utility, Probability, and Human Decision Making
  2. The Organization of Behavior
    Hebb, D.O. · 2005
  3. Neurons with graded response have collective computational properties like those of two-state neurons.
    Hopfield, J J · 1984 · Proceedings of the National Academy of Sciences
  4. Basic objects in natural categories
    Rosch, Eleanor, Mervis, Carolyn B, Gray, Wayne D, Johnson, David M, Boyes-Braem, Penny · 1976 · Cognitive Psychology
  5. From Neuropsychology to Mental Structure
    Shallice, Tim · 1988
  6. Context theory of classification learning
    Medin, Douglas L., Schaffer, Marguerite M. · 1978 · Psychological Review
  7. Models of reading aloud: Dual-route and parallel-distributed-processing approaches.
    Coltheart, Max, Curtis, Brent, Atkins, Paul, Haller, Micheal · 1993 · Psychological Review
  8. A retrieval theory of priming in memory.
    Ratcliff, Roger, McKoon, Gail · 1988 · Psychological Review
  9. Lesioning an attractor network: Investigations of acquired dyslexia.
    Hinton, Geoffrey E., Shallice, Tim · 1991 · Psychological Review
  10. Splitting the Differences: A Structural Alignment View of Similarity
    Markman, A.B., Gentner, D. · 1993 · Journal of Memory and Language

Show all 10 references

Cited by (12)

  1. Words in the brain's language
    Pulvermüller, Friedemann · 1999 · Behavioral and Brain Sciences
  2. Structure and Deterioration of Semantic Memory: A Neuropsychological and Computational Investigation.
    Rogers, Timothy T., Lambon Ralph, Matthew A., Garrard, Peter, Bozeat, Sasha, McClelland, James L., Hodges, John R., Patterson, Karalyn · 2004 · Psychological Review
  3. Hierarchies, similarity, and interactivity in object recognition: “Category-specific” neuropsychological deficits
    Humphreys, Glyn W., Forde, Emer M. E. · 2001 · Behavioral and Brain Sciences
  4. The real-time mediation of visual attention by language and world knowledge: Linking anticipatory (and other) eye movements to linguistic processing
    Altmann, Gerry T.M., Kamide, Yuki · 2007 · Journal of Memory and Language
  5. Toward a brain-based componential semantic representation
    Binder, Jeffrey R., Conant, Lisa L., Humphries, Colin J., Fernandino, Leonardo, Simons, Stephen B., Aguilar, Mario, Desai, Rutvik H. · 2016 · Cognitive Neuropsychology

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CoChat. (2026). Citation check: On the nature and scope of featural representations of word meaning. https://app.cochat.ai/papers/10.1037//0096-3445.126.2.99
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McRae, K., de Sa, V. R., & Seidenberg, M. S. (1997). On the nature and scope of featural representations of word meaning. Journal of Experimental Psychology: General. https://doi.org/10.1037//0096-3445.126.2.99

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