Covert visual search: prior beliefs are optimally combined with sensory evidence.

Has evolution optimized visual selective attention to make the best possible use of all information available? If so, then Bayesian optimal performance in a localization task is achieved by optimally weighting the visual evidence with one's prior spatial expectations. In 2 psychophysical experiments, participants conducted covert target localization where both visual cues and prior expectations were available. The amount of information conveyed by the visual evidence was held constant, while the degree of belief was manipulated via peripheral cuing (Experiment 1) and spatial probabilities (Experiment 2). A number of findings result: (1) People appear to optimally combine slightly biased prior beliefs with sensory evidence. (2) These biases are directly comparable to those descriptively accounted for by the Prospect Theory. (3) Probabilistic information about a target's upcoming location is integrated identically, irrespective of whether endogenous or exogenous cuing is used. (4) In localization tasks, spatial attention can be understood and quantitatively modeled as a set of prior expectations over space that modulate incoming noisy sensory evidence.

[1]  M. Eckstein The Lower Visual Search Efficiency for Conjunctions Is Due to Noise and not Serial Attentional Processing , 1998 .

[2]  Miguel P Eckstein,et al.  Comparison of two weighted integration models for the cueing task: linear and likelihood. , 2003, Journal of vision.

[3]  H J Müller Qualitative differences in response bias from spatial cueing. , 1994, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[4]  D. Prelec The Probability Weighting Function , 1998 .

[5]  J. Findlay,et al.  Sensitivity and criterion effects in the spatial cuing of visual attention , 1987, Perception & psychophysics.

[6]  H. Akaike A new look at the statistical model identification , 1974 .

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

[8]  P. Verghese Visual Search and Attention A Signal Detection Theory Approach , 2001, Neuron.

[9]  Marilyn L Shaw,et al.  Attending to multiple sources of information: I. The integration of information in decision making , 1982, Cognitive Psychology.

[10]  Britt Anderson,et al.  There is no Such Thing as Attention , 2011, Front. Psychology.

[11]  Songmei Han,et al.  Information-limited Parallel Processing in Difficult Heterogeneous Covert Visual Search Measured the Full Time Course of Visual Search for Different Display Sizes in Demanding Searches and Made Conflicting , 2022 .

[12]  S Fusi,et al.  Forming classes by stimulus frequency: Behavior and theory , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[13]  J. P. Thomas,et al.  A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays , 2000, Perception & psychophysics.

[14]  Joshua A Solomon,et al.  The effect of spatial cues on visual sensitivity , 2004, Vision Research.

[15]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[16]  Iain D Gilchrist,et al.  Target location probability effects in visual search: an effect of sequential dependencies. , 2006, Journal of experimental psychology. Human perception and performance.

[17]  M. Behrmann,et al.  Spatial probability as an attentional cue in visual search , 2005, Perception & psychophysics.

[18]  B. Scholl,et al.  The Automaticity of Visual Statistical Learning Statistical Learning , 2005 .

[19]  A. Pouget,et al.  Near-optimal visual search : behavior and neural basis , 2011 .

[20]  A. Hillstrom Repetition effects in visual search , 2000, Perception & psychophysics.

[21]  Howard S. Bashinski,et al.  Enhancement of perceptual sensitivity as the result of selectively attending to spatial locations , 1980, Perception & psychophysics.

[22]  Don R Lyon,et al.  Time Course of Location-Cuing Effects With a Probability Manipulation. , 1999, The Journal of general psychology.

[23]  Decisions, Uncertainty, and the Brain, Paul W. Glimcher. MIT Press, Cambridge, MA, London, UK (2003), ISBN 0-262-07244-0, 375 + xx pp., index, US$ 37.95. , 2005 .

[24]  Songmei Han,et al.  Parallel processing in visual search asymmetry. , 2004, Journal of experimental psychology. Human perception and performance.

[25]  J. Findlay,et al.  The effect of visual attention on peripheral discrimination thresholds in single and multiple element displays. , 1988, Acta psychologica.

[26]  M. Posner Chronometric explorations of mind , 1978 .

[27]  M. Carrasco,et al.  Signal detection theory applied to three visual search tasks--identification, yes/no detection and localization. , 2004, Spatial vision.

[28]  Miguel P. Eckstein,et al.  The footprints of visual attention during search with 100% valid and 100% invalid cues , 2004, Vision Research.

[29]  H. J. Muller,et al.  Reflexive and voluntary orienting of visual attention: time course of activation and resistance to interruption. , 1989, Journal of experimental psychology. Human perception and performance.

[30]  J. Kruschke Doing Bayesian Data Analysis: A Tutorial with R and BUGS , 2010 .

[31]  K. Nakayama,et al.  Sustained and transient components of focal visual attention , 1989, Vision Research.

[32]  Richard Gonzalez,et al.  On the Shape of the Probability Weighting Function , 1999, Cognitive Psychology.

[33]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[34]  Jan Theeuwes,et al.  Endogenous and exogenous attention shifts are mediated by the same large-scale neural network , 2004, NeuroImage.

[35]  R. Aslin,et al.  Statistical learning of higher-order temporal structure from visual shape sequences. , 2002, Journal of experimental psychology. Learning, memory, and cognition.

[36]  M. Eckstein,et al.  Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner. , 2004, Journal of vision.

[37]  Matthew F. Peterson,et al.  Statistical decision theory to relate neurons to behavior in the study of covert visual attention , 2009, Vision Research.

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

[39]  Timothy F. Brady,et al.  PSYCHOLOGICAL SCIENCE Research Article Statistical Learning Using Real-World Scenes Extracting Categorical Regularities Without Conscious Intent , 2022 .

[40]  Bradley J Wolfgang,et al.  Spatial uncertainty explains exogenous and endogenous attentional cuing effects in visual signal detection. , 2007, Journal of vision.

[41]  J. C. Johnston,et al.  Involuntary attentional capture by abrupt onsets , 1992, Perception & psychophysics.

[42]  Tom Troscianko,et al.  Optimal feature integration in visual search. , 2009, Journal of vision.

[43]  A. Martins Probability biases as Bayesian inference , 2006, Judgment and Decision Making.

[44]  Rajesh P. N. Rao,et al.  Bayesian brain : probabilistic approaches to neural coding , 2006 .

[45]  H J Müller,et al.  Movement versus focusing of visual attention , 1989, Perception & psychophysics.

[46]  A. Martins Adaptive Probability Theory: Human Biases as an Adaptation , 2005 .

[47]  J. Jonides Voluntary versus automatic control over the mind's eye's movement , 1981 .

[48]  M. Cheal,et al.  Central and Peripheral Precuing of Forced-Choice Discrimination , 1991, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[49]  R. Aslin,et al.  PSYCHOLOGICAL SCIENCE Research Article UNSUPERVISED STATISTICAL LEARNING OF HIGHER-ORDER SPATIAL STRUCTURES FROM VISUAL SCENES , 2022 .

[50]  M. Posner,et al.  Attention and the detection of signals. , 1980, Journal of experimental psychology.

[51]  William C. Ogden,et al.  Attended and unattended processing modes: The role of set for spatial location , 2014 .

[52]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[53]  Benjamin T. Vincent,et al.  Search asymmetries: Parallel processing of uncertain sensory information , 2011, Vision Research.

[54]  T. Bayes An essay towards solving a problem in the doctrine of chances , 2003 .

[55]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[56]  Miguel P Eckstein,et al.  Learning cue validity through performance feedback. , 2009, Journal of Vision.

[57]  P. L. Smith,et al.  Attention and luminance detection: effects of cues, masks, and pedestals. , 2000, Journal of experimental psychology. Human perception and performance.

[58]  Miguel P Eckstein,et al.  The footprints of visual attention in the Posner cueing paradigm revealed by classification images. , 2002, Journal of vision.

[59]  Britt Anderson,et al.  Spatial Probability Aids Visual Stimulus Discrimination , 2010, Front. Hum. Neurosci..