Title: A neural population model for visual pattern detection
Authors: Goris, Robbe L. T. ×
Putzeys, Tom
Wagemans, Johan
Wichmann, Felix A. #
Issue Date: 2013
Publisher: American Psychological Association
Series Title: Psychological Review vol:120 issue:3 pages:472-496
Abstract: Pattern detection is the bedrock of modern vision science. Nearly half a century ago, psychophysicists advocated a quantitative theoretical framework that connected visual pattern detection with its neurophysiological underpinnings. In this theory, neurons in primary visual cortex constitute linear and independent visual channels whose output is linked to choice behavior in detection tasks via simple read-out mechanisms. This model has proven remarkably successful in accounting for threshold vision. It is fundamentally at odds, however, with current knowledge about the neurophysiological underpinnings of pattern vision. In addition, the principles put forward in the model fail to generalize to suprathreshold vision or perceptual tasks other than detection. We propose an alternative theory of detection in which perceptual decisions develop from maximum-likelihood decoding of a neurophysiologically inspired model of population activity in primary visual cortex. We demonstrate that this theory explains a broad range of classic detection results. With a single set of parameters, our model can account for several summation, adaptation, and uncertainty effects, thereby offering a new theoretical interpretation for the vast psychophysical literature on pattern detection.
ISSN: 0033-295X
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Experimental Psychology
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
Goris et al 2013.pdf Published 699KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science