A neural network model of the eriksen task: Reduction, analysis, and data fitting

Authors:
Yuan Sophie Liu;Philip Holmes;Jonathan D. Cohen
Affiliations:
Department of Physics, Princeton University, Princeton, NJ 08544, U.S.A. yuanliu@princeton.edu;Department of Mechanical and Aerospace Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, U.S.A. pholmes@math.princeton.edu;Department of Psychology, and Program in Computational Mathematics, Princeton University, Princeton, NJ 08544, U.S.A. jdc@princeton.edu
Venue:
Neural Computation
Year:
2008

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ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I

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Abstract

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.