A Computational Model of Auditory Perceptual Learning: Predicting Learning Interference Across Multiple Tasks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work we build a computational model of several auditory perceptual learning experiments. The modeled experiments show a pattern of learning interference which may help shed light on the structure of both short and long term stores of perceptual memory. It is our hypothesis that the observed interference patterns can be explained by the relationship of stimuli across tasks and how these relationships interact with the limits of human memory. We account for the fact that information is shared across tasks in our model through use of methodology from the machine learning community on transfer learning. When we introduce a set of plausible limits on memory, such a model demonstrates the same pattern of learning interference observed in the human experiments.
Original languageEnglish
Title of host publicationProceedings of the 33rd Annual Meeting of the Cognitive Science Society
EditorsLaura Carlson, Christoph Hoelscher, Thomas F. Shipley
PublisherCognitive Science Society
ISBN (Print)9780976831877
StatePublished - 2011
EventProceedings of the 33rd Annual Conference of the Cognitive Science Society - Austin, TX
Duration: Jul 1 2011 → …

Conference

ConferenceProceedings of the 33rd Annual Conference of the Cognitive Science Society
Period7/1/11 → …

Fingerprint

Dive into the research topics of 'A Computational Model of Auditory Perceptual Learning: Predicting Learning Interference Across Multiple Tasks'. Together they form a unique fingerprint.

Cite this