Individual and developmental differences in semantic priming: Empirical and computational support for a single-mechanism account of lexical processing.

Abstract

Existing accounts of single-word semantic priming phenomena incorporate multiple mechanisms, such as spreading activation, expectancy-based processes, and postlexical semantic matching. The authors provide empirical and computational support for a single-mechanism distributed network account. Previous studies have found greater semantic priming for low- than for high-frequency target words as well as inhibition following unrelated primes only at long stimulus-onset asynchronies (SOAs). A series of experiments examined the modulation of these effects by individual differences in age or perceptual ability. Third-grade, 6th-grade, and college students performed a lexical-decision task on high- and low-frequency target words preceded by related, unrelated, and nonword primes. Greater priming for low-frequency targets was exhibited only by participants with high perceptual ability. Moreover, unlike the college students, the children showed no inhibition even at the long SOA. The authors provide an account of these results in terms of the properties of distributed network models and support this account with an explicit computational simulation.

Putting the bits together: an information theoretical perspective on morphological processing

“…These properties emerge naturally in distributed connectionist models of lexical processing (e.g. Gaskell & Marslen-Wilson, 1997;Plaut & Booth, 2000;Plaut & Gonnerman, 2000;Seidenberg & Gonnerman, 2000), which view morphological relations as the simultaneous effect of similarity between distributed representations of form and meaning. In this respect, McKay (2003) indicates that information theory is indeed the apropriate tool for analyzing the behavior of a neuronal network.…”

Developmental changes in brain regions involved in phonological and orthographic processing during spoken language processing

“…Therefore, phonological and orthographic representations become closely linked during reading acquisition. Behavioral and computational modeling research has shown that phonological and orthographic processes are more interactive in skilled versus less skilled readers (Booth, et al 1999;Plaut and Booth 2000). However, the neural mechanisms that underlie interactions between phonological and orthographic processes are not clearly understood.…”

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

“…However, some considerations can be drawn. In past years, autoassociative networks with attractor dynamics have frequently been used to explain priming (Kawamoto 1993;McRae and Boisvert 1998;Plaut and Booth 2000). A typical idea in these models is that a first object (the prime) which has some semantic similarity with a second object (the target) would place the network in a position of the state space close to the attractor of the target.…”