HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Liu, Y. S. Articles by Cohen, J. D. PubMed Articles by Liu, Y. S. Articles by Cohen, J. D.

A Neural Network Model of the Eriksen Task: Reduction, Analysis, and Data Fitting

yuanliu{at}princeton.edu Department of Physics, Princeton University, Princeton, NJ 08544, U.S.A.

Philip Holmes

pholmes{at}math.princeton.edu Department of Mechanical and Aerospace Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, U.S.A.

Jonathan D. Cohen

jdc{at}princeton.edu Department of Psychology, Princeton University, Princeton, NJ 08544, U.S.A.

We analyze a neural network model of the Eriksen task: a two-alternative forced-choice task in which subjects must correctly identify a central stimulus and disregard flankers that may or may not be compatible with it. We linearize and decouple the model, deriving a reduced drift-diffusion process with variable drift rate that describes the accumulation of net evidence in favor of either alternative, and we use this to analytically describe how accuracy and response time data depend on model parameters. Such analyses both assist parameter tuning in network models and suggest explanations of changing drift rates in terms of attention. We compare our results with numerical simulations of the full nonlinear model and with empirical data and show good fits to both with fewer parameters.