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Predicting response time and error rates in visual search

Chen, Bo and Navalpakkam, Vidhya and Perona, Pietro (2012) Predicting response time and error rates in visual search. In: Advances in Neural Information Processing Systems 24 (NIPS 2011). Advances in Neural Information Processing Systems. No.24. Neural Information Processing Systems , La Jolla, CA. ISBN 9781618395993.

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A model of human visual search is proposed. It predicts both response time (RT) and error rates (RT) as a function of image parameters such as target contrast and clutter. The model is an ideal observer, in that it optimizes the Bayes ratio of target present vs target absent. The ratio is computed on the firing pattern of V1/V2 neurons, modeled by Poisson distributions. The optimal mechanism for integrating information over time is shown to be a ‘soft max’ of diffusions, computed over the visual field by ‘hypercolumns’ of neurons that share the same receptive field and have different response properties to image features. An approximation of the optimal Bayesian observer, based on integrating local decisions, rather than diffusions, is also derived; it is shown experimentally to produce very similar predictions to the optimal observer in common psychophysics conditions. A psychophyisics experiment is proposed that may discriminate between which mechanism is used in the human brain.

Item Type:Book Section
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URLURL TypeDescription
Chen, Bo0000-0001-5566-7361
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2012 Neural Information Processing Systems. We thank the three anonymous referees for many insightful comments and suggestions; thanks to M. Shadlen for a tutorial discussion on the history of discrimination models. This research was supported by the California Institute of Technology.
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Series Name:Advances in Neural Information Processing Systems
Issue or Number:24
Record Number:CaltechAUTHORS:20160401-163510393
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65861
Deposited By: Kristin Buxton
Deposited On:03 Apr 2016 13:31
Last Modified:03 Oct 2019 09:51

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