An attractor model of lexical conceptual processing: simulating semantic priming

Abstract

An attractor network was trained to compute from word form to semantic representations that were based on subject-generated features. The model was driven largely by higher-order semantic structure. The network simulated two recent experiments that employed items included in its training set (McRae and Boisvert, 1998). In Simulation 1, short stimulus onset asynchrony priming was demonstrated for semantically similar items. Simulation 2 reproduced subtle effects obtained by varying degree of similarity. Two predictions from the model were then tested on human subjects. In Simulation 3 and Experiment 1, the items from Simulation 1 were reversed, and both the network and subjects showed minimally different priming effects in the two directions. In Experiment 2, consistent with attractor networks but contrary to a key aspect of hierarchical spreading activation accounts priming was determined by featural similarity rather than shared superordinate category. It is concluded that semantic-similarity priming is due to featural overlap that is a natural consequence of distributed representations of word meaning.

Uncovering the architecture of action semantics.

“…Furthermore, the graded effects of action similarity that we observed in Experiment 2 suggest that similarity within action semantic space is computed in much the same way as other aspects of semantic similarity: similarity is proportional to the overlap between the semantic features of objects (Cree, McRae, & McNorgan, 1999; McRae et al, 1997; Plaut, 1995). While previous studies have shown that action knowledge about tools comes online during semantic tasks, none have speculated about what the elemental features of tool use knowledge might be.…”

Semantic memory: A feature-based analysis and new norms for Italian

“…Many studies have, in fact, used feature representations deriving from feature production norms to account for empirical phenomena, such as semantic priming (Cree, McRae, & McNorgan, 1999;McRae et al, 1997;Vigliocco et al, 2004), feature verification (Ashcraft, 1978, McRae, Cree, Westmacott, & de Sa, 1999Solomon & Barsalou, 2001), categorization (Hampton, 1979;Smith, Shoben, & Rips, 1974), and category learning (Kruschke, 1992), among others (for a more detailed discussion of the aims and limits of feature norms, see McRae, Cree, Seidenberg, & McNorgan, 2005; for a discussion of theories of semantic memory organization that use feature representations to account for category-specific semantic deficits, see Cree & McRae, 2003;Zannino, Perri, Pasqualetti, Caltagirone, & Carlesimo, 2006).…”

Interaction Between Phonological and Semantic Representations: Time Matters

“…To imitate the kind of recurrent connections that would emerge among semantic representations over the course of learning (e.g., Cree, McRae, & McNorgan, 1999; Rogers & McClelland, 2004), semantic units were generally connected by inhibitory weights, but this inhibition was reduced for each concept in which the semantic units (features) co-occurred (for evidence of facilitative effects of feature co-occurrence, see e.g., Cree & McRae, 2003; Rogers & McClelland, 2004). In other words, a semantic feature such as “has wings” was assumed to have inhibitory connections to unrelated features such as “has strings,” excitatory connections to strongly (cor)related features such as “has feathers,” and intermediate weights to weakly related features such as “eats fish.”…”