Category verification: Towards combining connectionist attractor network and random walk accounts of human data.
Published: 2006
Language: eng
Number of pages: 70
ISBN-13: 9780494213872
Description: This thesis presents the results from two category verification experiments and proposes a computational model to account for these results. This task has been widely used in the semantic memory literature, but the current experiments are novel in that they provided the most detailed control over confounding variables to date. Experiment 1 showed a non-significant typicality effect, which was likely due to the sample's high English proficiency variability. Confirming this possibility, Experiment 2 showed the typicality effect in a sample of proficient English speakers. I propose a combination of a connectionist attractor network and a diffusion model that can run simulations of these data. This is beneficial in that the exact same items will be used in the behavioral studies and simulations. This will be the first attempt to apply this model to this task, and is a promising extension of the attractor network approach to semantic cognition.
Notes: Source: Masters Abstracts International, Volume: 45-03, page: 1676.

Thesis (M.A.)--University of Toronto, 2006.

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