Does precision decrease with set size?

The brain encodes visual information with limited precision. Contradictory evidence exists as to whether the precision with which an item is encoded depends on the number of stimuli in a display (set size). Some studies have found evidence that precision decreases with set size, but others have reported constant precision. These groups of studies differed in two ways. The studies that reported a decrease used displays with heterogeneous stimuli and tasks with a short-term memory component, while the ones that reported constancy used homogeneous stimuli and tasks that did not require short-term memory. To disentangle the effects of heterogeneity and short-memory involvement, we conducted two main experiments. In Experiment 1, stimuli were heterogeneous, and we compared a condition in which target identity was revealed before the stimulus display with one in which it was revealed afterward. In Experiment 2, target identity was fixed, and we compared heterogeneous and homogeneous distractor conditions. In both experiments, we compared an optimal-observer model in which precision is constant with set size with one in which it depends on set size. We found that precision decreases with set size when the distractors are heterogeneous, regardless of whether short-term memory is involved, but not when it is homogeneous. This suggests that heterogeneity, not short-term memory, is the critical factor. In addition, we found that precision exhibits variability across items and trials, which may partly be caused by attentional fluctuations.

[1]  David C Burr,et al.  Feature-based integration of orientation signals in visual search , 2000, Vision Research.

[2]  J. Palmer Attentional limits on the perception and memory of visual information. , 1990, Journal of experimental psychology. Human perception and performance.

[3]  Eero P. Simoncelli,et al.  Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics , 2011, Nature Neuroscience.

[4]  S. Hochstein,et al.  View from the Top Hierarchies and Reverse Hierarchies in the Visual System , 2002, Neuron.

[5]  W. W. Peterson,et al.  The theory of signal detectability , 1954, Trans. IRE Prof. Group Inf. Theory.

[6]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[7]  R. K. Simpson Nature Neuroscience , 2022 .

[8]  J. Palmer Set-size effects in visual search: The effect of attention is independent of the stimulus for simple tasks , 1994, Vision Research.

[9]  W. Schneider,et al.  Attentional and sensory effects of lowered levels of intrinsic alertness , 2009, Neuropsychologia.

[10]  J. Palmer,et al.  Measuring the effect of attention on simple visual search. , 1993, Journal of experimental psychology. Human perception and performance.

[11]  John Palmer,et al.  Set-size effects for identification versus localization depend on the visual search task. , 2008, Journal of experimental psychology. Human perception and performance.

[12]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[13]  M. Paradiso,et al.  A theory for the use of visual orientation information which exploits the columnar structure of striate cortex , 2004, Biological Cybernetics.

[14]  Timothy F. Brady,et al.  Encoding higher-order structure in visual working memory: A probabilistic model , 2010 .

[15]  R. Catrambone,et al.  Proceedings of the 32nd Annual Conference of the Cognitive Science Society , 2010 .

[16]  Preeti Verghese,et al.  Comparing integration rules in visual search. , 2002, Journal of vision.

[17]  S. Luck,et al.  Discrete fixed-resolution representations in visual working memory , 2008, Nature.

[18]  R. Rosenholtz Visual search for orientation among heterogeneous distractors: experimental results and implications for signal-detection theory models of search. , 2001, Journal of experimental psychology. Human perception and performance.

[19]  Preeti Verghese,et al.  The psychophysics of visual search , 2000, Vision Research.

[20]  Wasserman,et al.  Bayesian Model Selection and Model Averaging. , 2000, Journal of mathematical psychology.

[21]  Wei Ji Ma,et al.  Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.

[22]  H Sompolinsky,et al.  Simple models for reading neuronal population codes. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Wei Ji Ma,et al.  Variability in encoding precision accounts for visual short-term memory limitations , 2012, Proceedings of the National Academy of Sciences.

[24]  M. Carrasco,et al.  Attention enhances contrast sensitivity at cued and impairs it at uncued locations , 2005, Vision Research.

[25]  M. Posner,et al.  Orienting of Attention* , 1980, The Quarterly journal of experimental psychology.

[26]  Wei Ji Ma,et al.  No capacity limit in attentional tracking: evidence for probabilistic inference under a resource constraint. , 2009, Journal of vision.

[27]  A. Pouget,et al.  Behavior and neural basis of near-optimal visual search , 2011, Nature Neuroscience.

[28]  N. Cowan The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.

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

[30]  D. Burr,et al.  Visual Clutter Causes High-Magnitude Errors , 2006, PLoS biology.

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

[32]  W. Ma,et al.  A detection theory account of change detection. , 2004, Journal of vision.

[33]  Paul M Bays,et al.  The precision of visual working memory is set by allocation of a shared resource. , 2009, Journal of vision.