Phonetic priming , neighborhood activation , and PARSYN PAUL

Virtually all current models of spoken word recognition share the assumption that the perception of spoken words involves two fundamental processes: activation and competition (see P. In such activation–competition models, the hallmark of the discrimination process is competition among multiple representations of words activated in memory. As a result, the role of competition has been a primary focus of research and theory on spoken word recognition in the last few years (e. One example of an activation– competition model is the neighborhood activation model (NAM; P. A. Luce & Pisoni, 1998). According to NAM, stimulus input activates a set (or neighborhood) of acoustic–phonetic patterns in memory. Patterns are activated to the degree to which they match the stimulus input These acoustic–phonetic patterns then activate a system of word decision units that are tuned to the patterns. Throughout the recognition process, the word decision units monitor three sources of information: (1) the activation levels of the acoustic–phonetic patterns to which the units are tuned, (2) higher level lexical information (specifically, lexical frequency), and (3) the overall level of activity in the entire system of decision units. It is assumed that each of the decision units continuously computes decision values on the basis of these three sources of information. Decision values are computed using a frequency-biased, activation-based version of R. D. Luce's (1959) choice rule (see P. A. Luce, 1986, for a complete discussion of the choice rule; see also McClelland & Elman, 1986). In particular , the activation level of a pattern is continuously compared with the overall activity level within the system. When the activity level is high (i.e., when many patterns , or neighbors, are consistent with the input), the decision values computed by the word units will tend to be low. Furthermore, lexical frequency biases the decisions of the word units by differentially amplifying activation levels of patterns corresponding to high-and low-frequency words. All other things being equal, decision values for high-frequency words will exceed those for low-frequency 615 We thank Dennis Norris and an anonymous reviewer for their advice and comments. We also thank Jim Sawusch for many helpful discussions and Michael S. Cluff for his assistance in running subjects. Correspondence concerning this article should be addressed to P. Perceptual identification of spoken words in noise is less accurate when the target words are preceded by spoken phonetically related primes (Goldinger, Luce, & Pisoni, 1989). The present …