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Speed versus accuracy in visual search: Optimal performance and neural architecture

Chen, Bo and Perona, Pietro (2015) Speed versus accuracy in visual search: Optimal performance and neural architecture. Journal of Vision, 15 (16). Art. No. 9. ISSN 1534-7362.

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Searching for objects among clutter is a key ability of the visual system. Speed and accuracy are the crucial performance criteria. How can the brain trade off these competing quantities for optimal performance in different tasks? Can a network of spiking neurons carry out such computations, and what is its architecture? We propose a new model that takes input from V1-type orientation-selective spiking neurons and detects a target in the shortest time that is compatible with a given acceptable error rate. Subject to the assumption that the output of the primary visual cortex comprises Poisson neurons with known properties, our model is an ideal observer. The model has only five free parameters: the signal-to-noise ratio in a hypercolumn, the costs of false-alarm and false-reject errors versus the cost of time, and two parameters accounting for nonperceptual delays. Our model postulates two gain-control mechanisms—one local to hypercolumns and one global to the visual field—to handle variable scene complexity. Error rate and response time predictions match psychophysics data as we vary stimulus discriminability, scene complexity, and the uncertainty associated with each of these quantities. A five-layer spiking network closely approximates the optimal model, suggesting that known cortical mechanisms are sufficient for implementing visual search efficiently.

Item Type:Article
Related URLs:
URLURL TypeDescription ItemBook Chapter
Chen, Bo0000-0001-5566-7361
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2015 The Association for Research in Vision and Ophthalmology, Inc. Received January 30, 2015; published December 16, 2015. The authors acknowledge comments from and discussions with Jeremy M. Wolfe, Jeffrey D. Schall, and Ueli Rutishauser. This work was funded by ONR N00014-10-1-0933 and Gordon and Betty Moore Foundation.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-10-1-0933
Gordon and Betty Moore FoundationUNSPECIFIED
Subject Keywords:visual search, ideal observer, speed–accuracy tradeoff, spiking neural networks, gain control
Issue or Number:16
Record Number:CaltechAUTHORS:20160204-130043407
Persistent URL:
Official Citation:Bo Chen, Pietro Perona; Speed versus accuracy in visual search: Optimal performance and neural architecture. Journal of Vision 2015;15(16):9. doi: 10.1167/15.16.9
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64230
Deposited By: Tony Diaz
Deposited On:04 Feb 2016 21:10
Last Modified:03 Oct 2019 09:35

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