The role of category- and exemplar-specific experience in ensemble processing of objects

People can relatively easily report summary properties for ensembles of objects, suggesting that this information can enrich visual experience and increase the efficiency of perceptual processing. Here, we ask whether the ability to judge diversity within object arrays improves with experience. We surmised that ensemble judgments would be more accurate for commonly experienced objects, and perhaps even more for objects of expertise like faces. We also expected improvements in ensemble processing with practice with a novel category, and perhaps even more with repeated experience with specific exemplars. We compared the effect of experience on diversity judgments for arrays of objects, with participants being tested with either a small number of repeated exemplars or with a large number of exemplars from the same object category. To explore the role of more prolonged experience, we tested participants with completely novel objects (random blobs), with objects familiar at the category level (cars), and with objects with which observers are experts at subordinate-level recognition (faces). For objects that are novel, participants showed evidence of improved ability to distribute attention. In contrast, for object categories with long-term experience, i.e., faces and cars, performance improved during the experiment but not necessarily due to improved ensemble processing. Practice with specific exemplars did not result in better diversity judgments for all object categories. Considered together, these results suggest that ensemble processing improves with experience. However, experience operates rapidly, the role of experience does not rely on exemplar-level knowledge and may not benefit from subordinate-level expertise.

[1]  Timothy F. Brady,et al.  Hierarchical Encoding in Visual Working Memory , 2010, Psychological science.

[2]  Timothy F. Brady,et al.  Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation. , 2015, Journal of experimental psychology. General.

[3]  Brent L. Hughes,et al.  Perceiving Groups: The People Perception of Diversity and Hierarchy , 2018, Journal of personality and social psychology.

[4]  A. Franklin,et al.  Getting the gist of multiple hues: metric and categorical effects on ensemble perception of hue. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  Joshua Correll,et al.  The Chicago face database: A free stimulus set of faces and norming data , 2015, Behavior research methods.

[6]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[7]  Timothy D. Sweeny,et al.  Ensemble Perception of Dynamic Emotional Groups , 2015, Psychological science.

[8]  R. Watt,et al.  The computation of orientation statistics from visual texture , 1997, Vision Research.

[9]  J. D. de Fockert,et al.  Rapid extraction of mean identity from sets of faces. , 2009, Quarterly journal of experimental psychology.

[10]  Cindy M. Bukach,et al.  Limits of generalization between categories and implications for theories of category specificity , 2010, Attention, perception & psychophysics.

[11]  S. Palmer Perceptual Grouping: It's Later Than You Think , 2002 .

[12]  Emma ZeeAbrahamsen,et al.  Correcting “confusability regions” in face morphs , 2018, Behavior Research Methods.

[13]  Timothy D. Sweeny,et al.  Integration and segmentation conflict during ensemble coding of shape. , 2020, Journal of experimental psychology. Human perception and performance.

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

[15]  Randolph Blake,et al.  Composite binocular perception from dichoptic stimulus arrays with similar ensemble information , 2018, Scientific Reports.

[16]  J. Lund,et al.  Compulsory averaging of crowded orientation signals in human vision , 2001, Nature Neuroscience.

[17]  Xiaolan Fu,et al.  Processing of Individual Items during Ensemble Coding of Facial Expressions , 2016, Front. Psychol..

[18]  Jason M Haberman,et al.  Correspondences Rapid extraction of mean emotion and gender from sets of faces , 2007 .

[19]  S. C. Chong,et al.  Accurate but Pathological: Social Anxiety and Ensemble Coding of Emotion , 2013, Cognitive Therapy and Research.

[20]  A. Treisman,et al.  Attentional spread in the statistical processing of visual displays , 2005, Perception & psychophysics.

[21]  Jonathan R. Folstein,et al.  How category learning affects object representations: not all morphspaces stretch alike. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[22]  Timothy D. Sweeny,et al.  Journal of Experimental Psychology : Human Perception and Performance Perceiving Group Behavior : Sensitive Ensemble Coding Mechanisms for Biological Motion of Human Crowds , 2012 .

[23]  G. Alvarez Representing multiple objects as an ensemble enhances visual cognition , 2011, Trends in Cognitive Sciences.

[24]  Isabel Gauthier,et al.  A visual short-term memory advantage for objects of expertise. , 2009, Journal of experimental psychology. Human perception and performance.

[25]  Jason M Haberman,et al.  Efficient summary statistical representation when change localization fails , 2011, Psychonomic bulletin & review.

[26]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[27]  Mackenzie A. Sunday,et al.  Individual Differences in Object Recognition , 2019, Psychological review.

[28]  Greg O. Horne,et al.  Controlling low-level image properties: The SHINE toolbox , 2010, Behavior research methods.

[29]  Jason M Haberman,et al.  Seeing the mean: ensemble coding for sets of faces. , 2009, Journal of experimental psychology. Human perception and performance.

[30]  Reginald B. Adams,et al.  Cross-cultural and hemispheric laterality effects on the ensemble coding of emotion in facial crowds , 2017, Culture and Brain.

[31]  Jason M Haberman,et al.  The visual system discounts emotional deviants when extracting average expression , 2010, Attention, perception & psychophysics.

[32]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[33]  Markus F. Neumann,et al.  Viewers extract mean and individual identity from sets of famous faces , 2013, Cognition.

[34]  A. Treisman,et al.  Representation of statistical properties , 2003, Vision Research.

[35]  D. Whitney,et al.  Gender differences in crowd perception , 2015, Front. Psychol..

[36]  M. Masson,et al.  Bayesian alternatives to null-hypothesis significance testing for repeated-measures designs , 2016 .

[37]  Jennifer E. Corbett,et al.  The Whole Warps the Sum of Its Parts , 2017, Psychological science.