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Abstract: Existing accounts of single-word semantic priming phenomena incorporate multiple mechanisms, such as spreading activation, expectancy-based processes, and postlexical semantic matching. We provide both empirical and computational support for a single-mechanism, distributed network account. Previous studies have found greater semantic priming (i.e., faster RTs following related vs. unrelated primes) for low- compared with high-frequency target words, and inhibition (i.e., slower RTs following unrelated vs. neutral primes) only at long stimulus-onset asynchronies (SOAs). A series of experiments examined the extent to which these effects depended on individual differences among subjects in age or perceptual ability. Third-grade, sixth-grade, and college students performed a lexical decision task on high- and low-frequency target words preceded by related, unrelated, and nonword primes. We found that greater priming for low-frequency targets was exhibited only by subjects with high perceptual ability, and that this restriction held across differences in age and SOA. We also replicated the finding of inhibition at a long but not short SOA for the college students, but found no inhibition for the children even at the long SOA. We provide an account of these results in terms of the properties of distributed network models, and support this account by demonstrating that an implemented simulation closely approximates empirical findings despite the absence of expectancy-based processes and postlexical semantic matching. The results suggest that distributed network models can provide a viable single-mechanism account of lexical processing.
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