Poster F53, Tuesday, March 27, 8:00-10:00 am, Exhibit Hall C
Semantic Priming of Reading by Visual Processing Stream: Exploring Encoding Through Stimulus Quality.
Josh Neudorf1, Chelsea Ekstrand1, Shaylyn Kress1, Alexandra Neufeldt1, Ron Borowsky1; 1University of Saskatchewan
Converging evidence from neuroimaging, computational modeling, and neuropathology cases supports the Distributed-Plus-Hub view of semantic processing, in which there are distributed connected, modality specific, sub-systems for processing shape, colour, and action, connected to an amodal semantic hub supporting integration of semantic representations (Patterson et al., 2007). Furthermore, neuroimaging and neuropathology evidence suggests that the visual sub-systems for colour and shape are processed mainly along the ventral visual processing stream while the action sub-system is processed mainly along the dorsal visual processing stream (e.g., Whitwell et al., 2014). Priming was used to examine the sharing of the visual semantic sub-system with the ventral-lexical reading stream, and the action semantic sub-system with the dorsal-sublexical reading stream. Participants named a word or pseudohomophone (PH) after reading a prime that required imagining either visualizing an object word, priming the ventral stream, or performing an action word, priming the dorsal stream. Target items were degraded in a second experiment to explore processing at the encoding level. In a Linear Mixed Model analysis of reaction time (RT), shared-stream overall facilitation was observed for words but not PHs, whereby visual primes produced faster naming of the targets. Exploring encoding effects, when the target stimuli were degraded priming effects were not larger than those seen with intact targets, suggesting there was no semantic feedback to the encoding level. Reading words benefited more from the visual primes, reflecting the degree of shared-stream activation. Semantic priming was similar regardless of prime type, reflecting the degree of amodal semantic hub integration.
Topic Area: LANGUAGE: Semantic