Models for the mechanisms of perceptual learning: linking predictions for brain and behavior

Abstract

Event Abstract Back to Event Models for the mechanisms of perceptual learning: linking predictions for brain and behavior Michael Wenger1* and Von Der H. Rebecca2 1 The Pennsylvania State University , Department of Psychology, United States 2 The Pennsylvania State University , United States Standard indicators of the acquisition of visual perceptual expertise include systematic reductions in detection and identification thresholds, along with decreases in mean response times (RTs). Two additional patterns have emerged in recent studies of perceptual learning for gray-scale contrast: systematic increases in false alarm rates in detection (but not identification), and systematic increases in the ability to adapt to variations in perceptual workload (capacity, as measured at the level of the hazard function of the RT distribution). The present effort is an initial step in developing a modeling approach capable of accounting for these behavioral results—with a specific focus on changes in capacity in the present effort—while simultaneously predicting patterns of scalp-level EEG. The approach is intended to allow for the representation of multiple competing hypotheses for the neural mechanisms responsible for these observable variables (i.e., placing the alternative hypotheses on a "level playing field"), and for the ability to systematically relate these hypotheses to formal models for perceptual behavior. The neural modeling approach uses populations of discrete-time integrate-and-fire neurons, connected as networks. The architecture is based on the known circuitry of early visual areas as well as known connectivity into and out of early visual areas. The architecture is shown to be capable of instantiating a set of prominent competing hypotheses for neural mechanisms (Gilbert, Sigman, & Crist, 2001): changes in cortical recruitment, sharpening of feature-specific tuning curves, changes in synaptic weightings, changes in within-region synchrony, and changes in across-region coherence, in both feed-forward and feed-back relations. In addition, it is shown that under reasonable simplifying assumptions, the models are also capable of making predictions for both observable response behavior and scalp-level EEG. Analysis of the computational models for the set of contrasting hypotheses reveals that (a) although all of the hypotheses are capable of accounting for some of the standard empirical regularities, at least one (the cortical recruitment hypothesis) is shown to be unable to account for all those empirical regularities, ruling it out as a general explanation for the neural mechanisms; and (b) many of the standard measures of EEG (e.g., peak values of early negative and positive components) are unable to distinguish among the competing hypotheses; and (c) measures of synchrony across and within regions do offer potential for testing among competing hypotheses. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session I Citation: Wenger M and Rebecca VH (2010). Models for the mechanisms of perceptual learning: linking predictions for brain and behavior. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00157 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Mar 2010; Published Online: 02 Mar 2010. * Correspondence: Michael Wenger, The Pennsylvania State University, Department of Psychology, University Park, PA, United States, mjw19@psu.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Michael Wenger Von Der H Rebecca Google Michael Wenger Von Der H Rebecca Google Scholar Michael Wenger Von Der H Rebecca PubMed Michael Wenger Von Der H Rebecca Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.