Choice of saccade endpoint under risk.

Eye movements function to bring detailed information onto the high-resolution region of the retina. Previous research has shown that human observers select fixation points that maximize information acquisition and minimize target location uncertainty. In this study, we ask whether human observers choose the saccade endpoint that maximizes gain when there are explicit rewards associated with correctly detecting the target. Observers performed an 8-alternative forced-choice detection task for a contrast-defined target in noise. After a single saccade, observers indicated the target location. Each potential target location had an associated reward that was known to the observer. In some conditions, the reward at one location was higher than at the other locations. We compared human saccade endpoints to those of an ideal observer that maximizes expected gain given the respective human observer's visibility map, i.e., d' for target detection as a function of retinal location. Varying the location of the highest reward had a significant effect on human observers' distribution of saccade endpoints. Both human and ideal observers show a high density of saccades made toward the highest rewarded and actual target locations. But humans' overall spatial distributions of saccade endpoints differed significantly from the ideal observer as they made a greater number of saccade to locations far from the highest rewarded and actual target locations. Suboptimal choice of saccade endpoint, possibly in combination with suboptimal integration of information across saccades, had a significant effect on human observers' ability to correctly detect the target and maximize gain.

[1]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[2]  M. Carrasco,et al.  Characterizing visual performance fields: effects of transient covert attention, spatial frequency, eccentricity, task and set size. , 2001, Spatial vision.

[3]  T. Wickens Elementary Signal Detection Theory , 2001 .

[4]  Laurence T. Maloney,et al.  Distributional Assumptions and Observed Conservatism in the Theory of Signal Detectability , 1991 .

[5]  L. Stone,et al.  Effects of Prior Information and Reward on Oculomotor and Perceptual Choices , 2008, The Journal of Neuroscience.

[6]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[7]  W. Geisler,et al.  Optimal Eye Movement Strategies in Visual Search ( Supplement ) , 2005 .

[8]  B. Treutwein Adaptive psychophysical procedures , 1995, Vision Research.

[9]  D. Darling The Kolmogorov-Smirnov, Cramer-von Mises Tests , 1957 .

[10]  R. Poldrack,et al.  Prospect Theory and the Brain , 2009 .

[11]  Pietro Perona,et al.  Optimal reward harvesting in complex perceptual environments , 2010, Proceedings of the National Academy of Sciences.

[12]  J. Findlay Saccade Target Selection During Visual Search , 1997, Vision Research.

[13]  M. Goldberg,et al.  Attention, intention, and priority in the parietal lobe. , 2010, Annual review of neuroscience.

[14]  W. Newsome,et al.  Matching Behavior and the Representation of Value in the Parietal Cortex , 2004, Science.

[15]  Lutz Hein,et al.  α2-Adrenoceptor Blockade Accelerates the Neurogenic, Neurotrophic, and Behavioral Effects of Chronic Antidepressant Treatment , 2010, The Journal of Neuroscience.

[16]  L. Chelazzi,et al.  Behavioral/systems/cognitive Reward Changes Salience in Human Vision via the Anterior Cingulate , 2022 .

[17]  Martin Rolfs,et al.  Adaptive deployment of spatial and feature-based attention before saccades , 2013, Vision Research.

[18]  R. J. van Beers,et al.  The Sources of Variability in Saccadic Eye Movements , 2007, The Journal of Neuroscience.

[19]  F. Previc Functional specialization in the lower and upper visual fields in humans: Its ecological origins and neurophysiological implications , 1990, Behavioral and Brain Sciences.

[20]  Marisa Carrasco,et al.  Neural correlates of the visual vertical meridian asymmetry. , 2006, Journal of vision.

[21]  Jeffrey S. Perry,et al.  Visual search: the role of peripheral information measured using gaze-contingent displays. , 2006, Journal of vision.

[22]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[23]  Colin Camerer,et al.  Neuroeconomics: decision making and the brain , 2008 .

[24]  Jiri Najemnik,et al.  Eye movement statistics in humans are consistent with an optimal search strategy. , 2008, Journal of vision.

[25]  S. Syrjala,et al.  A statistical test for a difference between the spatial distributions of two populations , 1996 .

[26]  J. Theeuwes,et al.  Reward grabs the eye: Oculomotor capture by rewarding stimuli , 2012, Vision Research.

[27]  Melchi M. Michel,et al.  Intrinsic position uncertainty explains detection and localization performance in peripheral vision. , 2011, Journal of vision.

[28]  Frans W Cornelissen,et al.  The Eyelink Toolbox: Eye tracking with MATLAB and the Psychophysics Toolbox , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[29]  John Roberts,et al.  Development and Testing of a Model of Consideration Set Composition , 1991 .

[30]  Preeti Verghese,et al.  Where to look next? Eye movements reduce local uncertainty. , 2007, Journal of vision.

[31]  M. Eckstein,et al.  Quantifying the Performance Limits of Human Saccadic Targeting during Visual Search , 2001, Perception.

[32]  C. H. Coombs,et al.  Mathematical psychology : an elementary introduction , 1970 .

[33]  Wilson S. Geisler,et al.  Optimal eye movement strategies in visual search , 2005, Nature.

[34]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[35]  R. Herrnstein On the law of effect. , 1970, Journal of the experimental analysis of behavior.

[36]  M. Carrasco,et al.  Rapid Simultaneous Enhancement of Visual Sensitivity and Perceived Contrast during Saccade Preparation , 2012, The Journal of Neuroscience.