Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory.

Abstract

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...

An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation

“…Alternative models used attractor networks to simulate phenomena such as semantic priming in normal subject and in schizophrenia (Siekmeier and Hoffman 2002) or to study the type of errors made by dyslexic patients (Cree et al 2006;McLeod et al 2000;McRae 2004;McRae et al 1997) and by patients with dementia (Gonnerman et al 1997). In these networks, semantic memory consists of a number of nodes representing features.…”

Towards Unrestricted, Large-Scale Acquisition of Feature-Based Conceptual Representations from Corpus Data

“…Feature correlation for a pair of features is calculated as the Pearson correlation of their production frequencies across all the concepts. We only calculate correlation strength for pairs of shared features, as some researchers have argued that correlations for distinctive features may be spurious (Cree et al 2006). For each concept, we can then calculate how correlated overall its constituent features are.…”

“…As connectionist models of cognition have grown more sophisticated (and computational power has increased) researchers have implemented models of conceptual representation which use empirically-derived real world conceptual knowledge (McRae et al 1997;Cree et al 2006;Grondin et al 2009;Randall et al 2004). For these models, the feature nodes together with the concept patterns represented on them are intended to reflect the actual conceptual knowledge of people, as derived empirically from property norming studies.…”

“…McRae et al 1997McRae et al , 2005Cree et al 2006;Grondin et al 2009;Tyler and Moss 2001;Moss et al 2007;Taylor et al 2008). Conceptual statistics that can be calculated from property norms include feature distinctiveness (i.e.…”

“…They have been explored and tested with a variety of behavioural tasks, including similarity priming (McRae et al 1997;Masson 1995), object naming (Pexman et al 2002), feature verification (Cree et al 2006;Randall et al 2004), word recognition (Pexman et al 2002), and categorization (Pexman et al 2003).…”