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Predicting Semantic Priming at the Item Level

The current study explores a set of variables that have the potential to predict semantic priming effects for 300 prime–target associates at the item level. Young and older adults performed either lexical decision (LDT) or naming tasks. A multiple regression procedure was used to predict priming based upon prime characteristics, target characteristics, and prime–target semantic similarity. Results indicate that semantic priming (a) can be reliably predicted at an item level; (b) is equivalent in magnitude across standardized measures of priming in LDTs and naming tasks; (c) is greater following quickly recognized primes; (d) is greater in LDTs for targets that produce slow lexical decision latencies; (e) is greater for pairs high in forward associative strength across tasks and across stimulus onset asynchronies (SOAs); (f) is greater for pairs high in backward associative strength in both tasks, but only at a long SOA; and (g) does not vary as a function of estimates from latent semantic analysis (LSA). Based upon these results, it is suggested that researchers take extreme caution in comparing priming effects across different item sets. Moreover, the current findings lend support to spreading activation and feature overlap theories of priming, but do not support priming based upon contextual similarity as captured by LSA.