The effect of frequency of shared features on judgments of semantic similarity

Abstract

Insight into the structure of conceptual knowledge can be gleaned by examining how statistical regularities in the semantic structure of concepts affect semantic processing. Two similarity judgment experiments revealed that pairs of concepts sharing relatively rare features were judged to be more similar than concepts sharing an equal number of relatively frequent features. Simulations confirmed that these results are consistent with a recurrent connectionist network model of semantic processing in which units corresponding to rare features are activated more quickly and accurately than units corresponding to frequent features. These results support the hypothesis that rare features play a privileged role in the processing of word meanings.The structure of conceptual representations is a critical and controversial issue in theories of language and cognitive processing. One important controversy centers on how feature-concept regularities influence processing. Sensitivity to statistical regularities is an important factor at all levels of language processing, including form (e.g., Saffran, 2003), meaning (e.g., Landauer & Dumais, 1997), and grammar (e.g., McClelland & Patterson, 2002). In general, frequently occurring elements are found to exert a stronger influence on processing and memory than are rarely occurring elements. In the domain of word meaning, one example is the finding that prime-target pairs that share features that frequently co-occur across concepts (such as CAT and BEAVER, which share the features has-fur and has-whiskers) exhibit larger priming effects than those that share features that do not frequently co-occur (such as PIN and COIN, which share the features is-small and is-made-of-metal; McRae, de Sa, & Seidenberg, 1997). McRae et al. (1997) found that this pattern, like other examples of sensitivity to statistical regularities, emerges in simulations of attractor-based connectionist networks. Indeed, coherent covariation -when groups of features are consistently present or absent across concepts-is a central aspect of learning and processing in connectionist models of semantic processing (e.g., McClelland & Rogers, 2003).Interestingly, there is also evidence that rare or idiosyncratic features play a particularly important role in semantic knowledge. The importance of idiosyncratic features has a long history in theories of semantic processing, going at least as far back as the foundational work of Rosch and Mervis (1975), who described the importance of cue validity for conceptual categorization. Cue validity is the conditional probability of concept c, given feature f: p(c | f); by Bayes's rule, this is equal to p(f | c) * p(c)/p(f). It is typical to make the simplifying assumption that the probability of a feature is either 0 or 1 (i.e., a concept either has the feature or does not), meaning that, for a present feature, p(f | c) = 1. Under the further simplifying assumption that concepts are equally likely, p(c) = 1/C, where C is the total number of concepts in the corpus, and p...

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