1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Psychology
  4. Volume 69, 2018
  5. Article

Annual Review of Psychology

Volume 69, 2018

Ensemble Perception

Abstract

To understand visual consciousness, we must understand how the brain represents ensembles of objects at many levels of perceptual analysis. Ensemble perception refers to the visual system's ability to extract summary statistical information from groups of similar objects—often in a brief glance. It defines foundational limits on cognition, memory, and behavior. In this review, we provide an operational definition of ensemble perception and demonstrate that ensemble perception spans across multiple levels of visual analysis, incorporating both low-level visual features and high-level social information. Further, we investigate the functional usefulness of ensemble perception and its efficiency, and we consider possible physiological and cognitive mechanisms that underlie an individual's ability to make accurate and rapid assessments of crowds of objects.

Keyword(s): consciousness, crowding, object recognition, scene, summary statistics, texture, vision
Loading

Article metrics loading...

/content/journals/10.1146/annurev-psych-010416-044232
2018-01-04
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/psych/69/1/annurev-psych-010416-044232.html?itemId=/content/journals/10.1146/annurev-psych-010416-044232&mimeType=html&fmt=ahah

Literature Cited

  1. Albrecht AR, Scholl BJ. 2010. Perceptually averaging in a continuous visual world: extracting statistical summary representations over time. Psychol. Sci. 21:4560–67 [Google Scholar]
  2. Albrecht AR, Scholl BJ, Chun MM. 2012. Perceptual averaging by eye and ear: computing summary statistics from multimodal stimuli. Atten. Percept. Psychophys. 74:5810–15 [Google Scholar]
  3. Allik J. 1992. Competing motion paths in sequence of random dot patterns. Vis. Res. 32:1157–65 [Google Scholar]
  4. Allik J, Dzhafarov EN. 1984. Motion direction identification in random cinematograms: a general model. J. Exp. Psychol. Hum. Percept. Perform. 10:3378–93 [Google Scholar]
  5. Allik J, Toom M, Raidvee A, Averin K, Kreegipuu K. 2013. An almost general theory of mean size perception. Vis. Res. 83:25–39 [Google Scholar]
  6. Allik J, Toom M, Raidvee A, Averin K, Kreegipuu K. 2014. Obligatory averaging in mean size perception. Vis. Res. 101:34–40 [Google Scholar]
  7. Alvarez GA. 2011. Representing multiple objects as an ensemble enhances visual cognition. Trends Cogn. Sci. 15:3122–31 [Google Scholar]
  8. Alvarez GA, Oliva A. 2008. The representation of simple ensemble visual features outside the focus of attention. Psychol. Sci. 19:4392–98 [Google Scholar]
  9. Alvarez GA, Oliva A. 2009. Spatial ensemble statistics are efficient codes that can be represented with reduced attention. PNAS 106:187345–50 [Google Scholar]
  10. Anderson NH, Lindner R, Lopes LL. 1973. Integration theory applied to judgments of group attractiveness. J. Personal. Soc. Psychol. 26:3400–8 [Google Scholar]
  11. Anstis S. 1974. A chart demonstrating variations in acuity with retinal position. Vis. Res. 14:589–92 [Google Scholar]
  12. Anstis S, Verstraten FAJ, Mather G. 1998. The motion aftereffect. Trends Cogn. Sci. 2:3111–17 [Google Scholar]
  13. Ariely D. 2001. Seeing sets: representation by statistical properties. Psychol. Sci. 12:2157–62 [Google Scholar]
  14. Attarha M, Moore CM. 2015. The capacity limitations of orientation summary statistics. Atten. Percept. Psychophys. 7:41116–31 [Google Scholar]
  15. Attarha M, Moore CM, Vecera SP. 2014. Summary statistics of size: fixed processing capacity for multiple ensembles but unlimited processing capacity for single ensembles. J. Exp. Psychol. Hum. Percept. Perform. 40:41440–49 [Google Scholar]
  16. Bai Y, Yamanashi Leib A, Puri AM, Whitney D, Peng K. 2015. Gender differences in crowd perception. Front. Psychol. 6:1300 [Google Scholar]
  17. Balas B, Nakano L, Rosenholtz R. 2009. A summary-statistic representation in peripheral vision explains visual crowding. J. Vis. 9:1213 [Google Scholar]
  18. Banno H, Saiki J. 2012. Calculation of the mean circle size does not circumvent the bottleneck of crowding. J. Vis. 12:1113 [Google Scholar]
  19. Bauer B. 2009. Does Stevens's power law for brightness extend to perceptual brightness averaging. Psychol. Res. 59:171–86 [Google Scholar]
  20. Block N. 2011. Perceptual consciousness overflows cognitive access. Trends Cogn. Sci. 15:12567–75 [Google Scholar]
  21. Born RT, Bradley DC. 2005. Structure and function of visual area MT. Annu. Rev. Neurosci. 28:157–89 [Google Scholar]
  22. Brady TF, Alvarez GA. 2011. Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items. Psychol. Sci. 22:3384–92 [Google Scholar]
  23. Brady TF, Tenenbaum JB. 2013. A probabilistic model of visual working memory: incorporating higher order regularities into working memory capacity estimates. Psychol. Rev. 120:185–109 [Google Scholar]
  24. Brand J, Oriet C, Tottenham LS. 2012. Size and emotion averaging: costs of dividing attention after all. Can. J. Exp. Psychol. 66:163–69 [Google Scholar]
  25. Bronfman ZZ, Brezis N, Jacobson H, Usher M. 2014. We see more than we can report: “cost free” color phenomenality outside focal attention. Psychol. Sci. 25:71394–403 [Google Scholar]
  26. Bulakowski PF, Post RB, Whitney D. 2011. Dissociating crowding from ensemble percepts. Atten. Percept. Psychophys. 73:41003–9 [Google Scholar]
  27. Cant JS, Xu Y. 2012. Object ensemble processing in human anterior-medial ventral visual cortex. J. Neurosci. 32227685–700
  28. Cavanagh P, Alvarez GA. 2005. Tracking multiple targets with multifocal attention. Trends Cogn. Sci. 9:7349–54 [Google Scholar]
  29. Chaney W, Fischer J, Whitney D. 2014. The hierarchical sparse selection model of visual crowding. Front. Integr. Neurosci. 8:73 [Google Scholar]
  30. Cheadle S, Wyart V, Tsetsos K, Myers N, de Gardelle V. et al. 2014. Adaptive gain control during human perceptual choice. Neuron 81:61429–41 [Google Scholar]
  31. Chong SC, Joo SJ, Emmanouil TA, Treisman A. 2008. Statistical processing: not so implausible after all. Percept. Psychophys. 70:71327–34 [Google Scholar]
  32. Chong SC, Treisman A. 2003. Representation of statistical properties. Vis. Res. 43:4393–404 [Google Scholar]
  33. Chong SC, Treisman A. 2005a. Attentional spread in the statistical processing of visual displays. Percept. Psychophys. 67:11–13 [Google Scholar]
  34. Chong SC, Treisman A. 2005b. Statistical processing: computing the average size in perceptual groups. Vis. Res. 45:7891–900 [Google Scholar]
  35. Choo H, Franconeri SL. 2010. Objects with reduced visibility still contribute to size averaging. Atten. Percept. Psychophys. 72:186–99 [Google Scholar]
  36. Cohen MA, Dennett DC, Kanwisher N. 2016. What is the bandwidth of perceptual experience. Trends Cogn. Sci. 20:5324–35 [Google Scholar]
  37. Corbett JE, Wurnitsch N, Schwartz A, Whitney D. 2012. An aftereffect of adaptation to mean size. Vis. Cogn. 20:2211–31 [Google Scholar]
  38. Dakin SC. 2001. Information limit on the spatial integration of local orientation signals. J. Opt. Soc. Am. A 18:51016–26 [Google Scholar]
  39. Dakin SC, Bex PJ, Cass JR, Watt RJ. 2009. Dissociable effects of attention and crowding on orientation averaging. J. Vis. 9:1128 [Google Scholar]
  40. Dakin SC, Mareschal I, Bex PJ. 2005. Local and global limitations on direction integration assessed using equivalent noise analysis. Vis. Res. 45:243027–49 [Google Scholar]
  41. Dakin SC, Watt RJ. 1997. The computation of orientation statistics from visual texture. Vis. Res. 37:223181–92 [Google Scholar]
  42. de Fockert JW, Marchant AP. 2008. Attention modulates set representation by statistical properties. Percept. Psychophys. 70:5789–94 [Google Scholar]
  43. de Fockert JW, Wolfenstein C. 2009. Rapid extraction of mean identity from sets of faces. Q. J. Exp. Psychol. 62:91716–22 [Google Scholar]
  44. Demeyere N, Rzeskiewicz A, Humphreys KA, Humphreys GW. 2008. Automatic statistical processing of visual properties in simultanagnosia. Neuropsychologia 46:112861–64 [Google Scholar]
  45. Dux PE, Marois R. 2009. How humans search for targets through time: a review of data and theory from the attentional blink. Atten. Percept. Psychophys. 71:81683–700 [Google Scholar]
  46. Emmanouil TA, Treisman A. 2008. Dividing attention across feature dimensions in statistical processing of perceptual groups. Percept. Psychophys. 70:6946–54 [Google Scholar]
  47. Faivre N, Berthet V, Kouider S. 2012. Nonconscious influences from emotional faces: a comparison of visual crowding, masking, and continuous flash suppression. Front. Psychol. 3:129 [Google Scholar]
  48. Farzin F, Rivera SM, Whitney D. 2009. Holistic crowding of Mooney faces. J. Vis. 9:618 [Google Scholar]
  49. Fischer J, Whitney D. 2011. Object-level visual information gets through the bottleneck of crowding. J. Neurophysiol. 106:31389–98 [Google Scholar]
  50. Fiser J, Aslin RN. 2001. Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychol. Sci. 12:6499–504 [Google Scholar]
  51. Florey J, Clifford CWG, Dakin S, Mareschal I. 2016. Spatial limitations in averaging social cues. Sci. Rep. 6:32210 [Google Scholar]
  52. Florey J, Dakin SC, Mareschal I. 2017. Comparing averaging limits for social cues over space and time. J. Vis. 17:917 [Google Scholar]
  53. Fouriezos G, Rubenfeld S, Capstick G. 2008. Visual statistical decisions. Percept. Psychophys. 70:3456–64 [Google Scholar]
  54. Freeman J, Simoncelli EP. 2011. Metamers of the ventral stream. Nat. Neurosci. 14:91195–201 [Google Scholar]
  55. Galton F. 1907. Vox populi (the wisdom of crowds). Nature 75:450–51 [Google Scholar]
  56. Gorea A, Belkoura S, Solomon JA. 2014. Summary statistics for size over space and time. J. Vis. 14:922 [Google Scholar]
  57. Haberman J, Brady TF, Alvarez GA. 2015a. Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation. J. Exp. Psychol. Gen. 144:2432–46 [Google Scholar]
  58. Haberman J, Harp T, Whitney D. 2009. Averaging facial expression over time. J. Vis. 9:111 [Google Scholar]
  59. Haberman J, Lee P, Whitney D. 2015b. Mixed emotions: sensitivity to facial variance in a crowd of faces. J. Vis. 15:416 [Google Scholar]
  60. Haberman J, Whitney D. 2007. Rapid extraction of mean emotion and gender from sets of faces. Curr. Biol. 17:7751–53 [Google Scholar]
  61. Haberman J, Whitney D. 2009. Seeing the mean: ensemble coding for sets of faces. J. Exp. Psychol. Hum. Percept. Perform. 35:3718–34 [Google Scholar]
  62. Haberman J, Whitney D. 2010. The visual system discounts emotional deviants when extracting average expression. Atten. Percept. Psychophys. 72:71825–38 [Google Scholar]
  63. Haberman J, Whitney D. 2011. Efficient summary statistical representation when change localization fails. Psychon. Bull. Rev. 18:5855–59 [Google Scholar]
  64. Haberman J, Whitney D. 2012. Ensemble perception: summarizing the scene and broadening the limits of visual processing. From Perception to Consciousness: Searching with Anne Treisman J Wolfe, L Robinson, pp 1–21 Oxford, UK: Oxford Univ. Press [Google Scholar]
  65. Harp TD, Bressler DW, Whitney D. 2007. Position shifts following crowded second-order motion adaptation reveal processing of local and global motion without awareness. J. Vis. 7:215 [Google Scholar]
  66. Hochstein S, Pavlovskaya M, Bonneh YS, Soroker N. 2015. Global statistics are not neglected. J. Vis. 15:47 [Google Scholar]
  67. Huang L. 2015. Statistical properties demand as much attention as object features. PLOS ONE 10:8e0131191 [Google Scholar]
  68. Hubert-Wallander B, Boynton GM. 2015. Not all summary statistics are made equal: evidence from extracting summaries across time. J. Vis. 15:45 [Google Scholar]
  69. Ikeda H, Watanabe K, Cavanagh P. 2013. Crowding of biological motion stimuli. J. Vis. 13:420 [Google Scholar]
  70. Im HY, Chong SC. 2014. Mean size as a unit of visual working memory. Perception 43:7663–76 [Google Scholar]
  71. Im HY, Halberda J. 2013. The effects of sampling and internal noise on the representation of ensemble average size. Atten. Percept. Psychophys. 75:2278–86 [Google Scholar]
  72. Intraub H. 1981. Rapid conceptual identification of sequentially presented pictures. J. Exp. Psychol. Hum. Percept. Perform. 7:3604–10 [Google Scholar]
  73. Jacoby O, Kamke MR, Mattingley JB. 2012. Is the whole really more than the sum of its parts? Estimates of average size and orientation are susceptible to object substitution masking. J. Exp. Psychol. Hum. Percept. Perform. 39:1233–44 [Google Scholar]
  74. Ji L, Chen W, Fu X. 2014. Different roles of foveal and extrafoveal vision in ensemble representation for facial expressions. Engineering Psychology and Cognitive Ergonomics D Harris 164–73 Berlin: Springer [Google Scholar]
  75. Joo SJ, Shin K, Chong SC, Blake R. 2009. On the nature of the stimulus information necessary for estimating mean size of visual arrays. J. Vis. 9:97 [Google Scholar]
  76. Karaminis T, Neil L, Manning C, Turi M, Fiorentini C. et al. 2017. Investigating ensemble perception of emotions in autistic and typical children and adolescents. Dev. Cogn. Neurosci. 24:51–62 [Google Scholar]
  77. Kimchi R, Pirkner Y. 2015. Multiple level crowding: crowding at the object parts level and at the object configural level. Perception 44:111275–92 [Google Scholar]
  78. Koenderink JJ, Van Doorn AJ Pont SC. 2004. Light direction from shad(ow)ed random Gaussian surfaces. Perception 33:121405–20 [Google Scholar]
  79. Kouider S, Berthet V, Faivre N. 2011. Preference is biased by crowded facial expressions. Psychol. Sci. 22:2184–89 [Google Scholar]
  80. Kowler E. 2011. Eye movements: the past 25 years. Vis. Res. 51:131457–83 [Google Scholar]
  81. Landy MS. 2014. Texture analysis and perception. The New Visual Neurosciences JS Werner, LM Chalupa 639–52 Cambridge, MA: MIT Press [Google Scholar]
  82. Leon M, Oden GC, Anderson NH. 1973. Functional measurement of social values. J. Personal. Soc. Psychol. 27:3301–10 [Google Scholar]
  83. Lew TF, Vul E. 2015. Ensemble clustering in visual working memory biases location memories and reduces the Weber noise of relative positions. J. Vis. 15:410 [Google Scholar]
  84. Li H, Ji L, Tong K, Ren N, Chen W. et al. 2016. Processing of individual items during ensemble coding of facial expressions. Front. Psychol. 7:1332 [Google Scholar]
  85. Louie EG, Bressler DW, Whitney D. 2007. Holistic crowding: selective interference between configural representations of faces in crowded scenes. J. Vis. 7:224 [Google Scholar]
  86. Luck SJ, Vogel EK. 2013. Visual working memory capacity: from psychophysics and neurobiology to individual differences. Trends Cogn. Sci. 17:8391–400 [Google Scholar]
  87. Manassi M, Sayim B, Herzog MH. 2012. Grouping, pooling, and when bigger is better in visual crowding. J. Vis. 12:1013 [Google Scholar]
  88. Marchant AP, Simons DJ, de Fockert JW. 2013. Ensemble representations: effects of set size and item heterogeneity on average size perception. Acta Psychol 142:2245–50 [Google Scholar]
  89. Mareschal I, Otsuka Y, Clifford MD. 2016. “Are you looking at me?” How children's gaze judgments improve with age. Dev. Psychol. 52:695–703 [Google Scholar]
  90. Maule J, Franklin A. 2016. Accurate rapid averaging of multihue ensembles is due to a limited capacity subsampling mechanism. J. Opt. Soc. Am. A. 33:A22–29 [Google Scholar]
  91. McClelland T, Bayne T. 2016. Ensemble coding and two conceptions of perceptual sparsity. Trends Cogn. Sci. 20:9641–42 [Google Scholar]
  92. McDermott J, Schemitsch M, Simoncelli EP. 2013. Summary statistics in auditory perception. Nat. Neurosci. 16:4493–98 [Google Scholar]
  93. McNair NA, Goodbourn PT, Shone LT, Harris IM. 2016. Summary statistics in the attentional blink. Atten. Percept. Psychophys. 79:100–16 [Google Scholar]
  94. Melcher D, Kowler E. 1999. Shapes, surfaces and saccades. Vis. Res. 39:172929–46 [Google Scholar]
  95. Michael E, de Gardelle V, Summerfield C. 2014. Priming by the variability of visual information. PNAS 111:217873–78 [Google Scholar]
  96. Miller AL, Sheldon R. 1969. Magnitude estimation of average length and average inclination. J. Exp. Psychol. 81:116–21 [Google Scholar]
  97. Morgan MJ, Chubb C, Solomon JA. 2008. A “dipper” function for texture discrimination based on orientation variance. J. Vis. 8:119 [Google Scholar]
  98. Myczek K, Simons DJ. 2008. Better than average: alternatives to statistical summary representations for rapid judgments of average size. Percept. Psychophys. 70:5772–88 [Google Scholar]
  99. Navon D. 1977. Forest before trees: the precedence of global features in visual perception. Cogn. Psychol. 9:353–83 [Google Scholar]
  100. Neumann MF, Schweinberger SR, Burton AM. 2013. Viewers extract mean and individual identity from sets of famous faces. Cognition 128:156–63 [Google Scholar]
  101. Norman LJ, Heywood CA, Kentridge RW. 2015. Direct encoding of orientation variance in the visual system. J. Vis. 15:3 [Google Scholar]
  102. Nothdurft HC. 1993. Faces and facial expressions do not pop out. Perception 22:111287–98 [Google Scholar]
  103. Okazawa G, Tajima S, Komatsu H. 2015. Image statistics underlying natural texture selectivity of neurons in macaque V4. PNAS 112:4E351–60 [Google Scholar]
  104. Oliva A, Torralba A. 2006. Building the gist of a scene: the role of global image features in recognition. Prog. Brain Res. 155:23–36 [Google Scholar]
  105. Oriet C, Brand J. 2013. Size averaging of irrelevant stimuli cannot be prevented. Vis. Res. 79:8–16 [Google Scholar]
  106. Oriet C, Corbett JE. 2008. Evidence for rapid extraction of average size in RSVP displays of circles. J. Vis. 8:613 [Google Scholar]
  107. Oriet C, Hozempa K. 2016. Incidental statistical summary representation over time. J. Vis. 16:33 [Google Scholar]
  108. Parkes L, Lund J, Angelucci A. 2001. Compulsory averaging of crowded orientation signals in human vision. Nat. Neurosci. 4:7739–44 [Google Scholar]
  109. Pelli DG. 2008. Crowding: a cortical constraint on object recognition. Curr. Opin. Neurobiol. 18:4445–51 [Google Scholar]
  110. Piazza EA, Sweeny TD, Wessel D, Silver MA, Whitney D. 2013. Humans use summary statistics to perceive auditory sequences. Psychol. Sci. 24:81389–97 [Google Scholar]
  111. Portilla J, Simoncelli EP. 2000. A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40:149–71 [Google Scholar]
  112. Post RB, Haberman J, Iwaki L, Whitney D. 2012. The frozen face effect: why static photographs may not do you justice. Front. Psychol. 3:22 [Google Scholar]
  113. Potter M. 1975. Meaning in visual search. Science 187:4180965–66 [Google Scholar]
  114. Price PC, Kimura NM, Smith AR, Marshall LD. 2014. Sample size bias in judgments of perceptual averages. J. Exp. Psychol. Learn. 40:51321–31 [Google Scholar]
  115. Rhodes G, Neumann MF, Ewing L, Palermo R. 2015. Reduced set averaging of face identity in children and adolescents with autism. Q. J. Exp. Psychol. 68:71391–403 [Google Scholar]
  116. Richards BA, Xia F, Santoro A, Husse J, Woodin MA. et al. 2014. Patterns across multiple memories are identified over time. Nat. Neurosci. 17:7981–86 [Google Scholar]
  117. Robitaille N, Harris IM. 2011. When more is less: extraction of summary statistics benefits from larger sets. J. Vis. 11:1218 [Google Scholar]
  118. Rosenholtz R, Huang J, Ehinger KA. 2012. Rethinking the role of top-down attention in vision: effects attributable to a lossy representation in peripheral vision. Front. Psychol. 3:13 [Google Scholar]
  119. Sanders K, Haberman J, Whitney D. 2010. Mean representation beyond a shadow of a doubt: summary statistical representation of shadows and lighting direction. J. Vis. 8:6334 [Google Scholar]
  120. Schwarzkopf DS, Song C, Rees G. 2011. The surface area of human V1 predicts the subjective experience of object size. Nat. Neurosci. 14:128–30 [Google Scholar]
  121. Shea N, Bayne T. 2010. The vegetative state and the science of consciousness. Br. J. Philos. Sci. 61:3459–84 [Google Scholar]
  122. Simons DJ, Levin DT. 1997. Change blindness. Trends Cogn. Sci. 1:7261–67 [Google Scholar]
  123. Solomon JA. 2010. Visual discrimination of orientation statistics in crowded and uncrowded arrays. J. Vis. 10:1419 [Google Scholar]
  124. Solomon JA, Morgan MJ, Chubb C. 2011. Efficiencies for the statistics of size discrimination. J. Vis. 11:1213 [Google Scholar]
  125. Solso RL, McCarthy JE. 1981. Prototype formation of faces: a case of pseudo-memory. Br. J. Psychol. 72:4499–503 [Google Scholar]
  126. Strasburger H, Rentschler I, Jüttner M. 2011. Peripheral vision and pattern recognition: a review. J. Vis. 11:513 [Google Scholar]
  127. Surowiecki J. 2004. The Wisdom of Crowds New York: Random House
  128. Sweeny TD, Haroz S, Whitney D. 2013. Perceiving group behavior: sensitive ensemble coding mechanisms for biological motion of human crowds. J. Exp. Psychol. Hum. Percept. Perform. 39:2329–37 [Google Scholar]
  129. Sweeny TD, Whitney D. 2014. Perceiving crowd attention: ensemble perception of a crowd's gaze. Psychol. Sci. 25:101903–13 [Google Scholar]
  130. Sweeny TD, Wurnitsch N, Gopnik A, Whitney D. 2015. Ensemble perception of size in 4–5-year-old children. Dev. Sci. 18:4556–68 [Google Scholar]
  131. Thorpe S, Fize D, Marlot C. 1996. Speed of processing in the human visual system. Nature 381:6582520–22 [Google Scholar]
  132. Tong F, Nakayama K. 1999. Robust representation for faces: evidence from visual search. J. Exp. Psychol. Hum. Percept. Perform. 25:41016–35 [Google Scholar]
  133. van Osch Y, Blanken I, Meijs MHJ, van Wolferen J. 2015. A group's physical attractiveness is greater than the average attractiveness of its members: the group attractiveness effect. Personal. Soc. Psychol. Bull. 41:4559–74 [Google Scholar]
  134. Verstraten FAJ, Cavanagh P, Labianca A. 2000. Limits of attentive tracking reveal temporal properties of attention. Vis. Res. 40:263651–64 [Google Scholar]
  135. Virsu V, Rovamo J. 1979. Visual resolution, contrast sensitivity, and the cortical magnification factor. Exp. Brain Res. 37:3475–94 [Google Scholar]
  136. Vishwanath D, Kowler E. 2003. Localization of shapes: eye movements and perception compared. Vis. Res. 43:151637–53 [Google Scholar]
  137. Wagemans J, Elder JH, Kubovy M, Palmer SE, Peterson MA. et al. 2012. A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure-ground organization. Psychol. Bull. 138:61172–217 [Google Scholar]
  138. Walker D, Vul E. 2013. Hierarchical encoding makes individuals in a group seem more attractive. Psychol. Sci. 25:1230–35 [Google Scholar]
  139. Wallis TSA, Bethge M, Wichmann FA. 2016. Testing models of peripheral encoding using metamerism in an oddity paradigm. J. Vis. 16:24 [Google Scholar]
  140. Ward EJ, Bear A, Scholl BJ. 2016. Can you perceive ensembles without perceiving individuals? The role of statistical perception in determining whether awareness overflows access. Cognition 152:78–86 [Google Scholar]
  141. Wardle SG, Bex PJ, Alais D. 2017. Stereoacuity in the periphery is limited by internal noise. J. Vis. 12:612 [Google Scholar]
  142. Watamaniuk SN, Duchon A. 1992. The human visual system averages speed information. Vis. Res. 32:5931–41 [Google Scholar]
  143. Watamaniuk SN, McKee SP. 1998. Simultaneous encoding of direction at a local and global scale. Percept. Psychophys. 60:2191–200 [Google Scholar]
  144. Watamaniuk SN, Sekuler R, Williams DW. 1989. Direction perception in complex dynamic displays: the integration of direction information. Vis. Res. 29:147–59 [Google Scholar]
  145. Webster J, Kay P, Webster MA. 2014. Perceiving the average hue of color arrays. J. Opt. Soc. Am. A 31:4A283–92 [Google Scholar]
  146. Whiting BF, Oriet C. 2011. Rapid averaging? Not so fast! Psychon. Bull. Rev. 18:3484–89 [Google Scholar]
  147. Whitney D, Haberman J, Sweeny T. 2014. From textures to crowds: multiple levels of summary statistical perception. The New Visual Neuroscience JS Werner, LM Chalupa 695–710 Cambridge, MA: MIT Press [Google Scholar]
  148. Whitney D, Levi DM. 2011. Visual crowding: a fundamental limit on conscious perception and object recognition. Trends Cogn. Sci. 15:4160–68 [Google Scholar]
  149. Wolfe BA, Kosovicheva AA, Yamanashi Leib A, Wood K, Whitney D. 2015. Foveal input is not required for perception of crowd facial expression. J. Vis. 15:411 [Google Scholar]
  150. Wolfe JM. 1994. Guided Search 2.0: a revised model of visual search. Psychon. Bull. Rev. 1:2202–38 [Google Scholar]
  151. Yamanashi Leib A, Fischer J, Liu Y, Qiu S, Robertson L. et al. 2014. Ensemble crowd perception: a viewpoint-invariant mechanism to represent average crowd identity. J. Vis. 14:826 [Google Scholar]
  152. Yamanashi Leib A, Kosovicheva A, Whitney D. 2016. Fast ensemble representations for abstract visual impressions. Nat. Commun. 7:13186 [Google Scholar]
  153. Yamanashi Leib A, Landau AN, Baek Y, Chong SC, Robertson L. 2012a. Extracting the mean size across the visual field in patients with mild, chronic unilateral neglect. Front. Hum. Neurosci. 6:267 [Google Scholar]
  154. Yamanashi Leib A, Puri AM, Fischer J, Bentin S, Whitney D, Robertson L. 2012b. Crowd perception in prosopagnosia. Neuropsychologia 50:71698–707 [Google Scholar]
  155. Ying H, Xu H. 2017. Adaptation reveals that facial expression averaging occurs during rapid serial presentation. J. Vis. 17:115 [Google Scholar]
/content/journals/10.1146/annurev-psych-010416-044232
Loading
Ensemble Perception
Annual Review of Psychology 69, 105 (2018); https://doi.org/10.1146/annurev-psych-010416-044232
/content/journals/10.1146/annurev-psych-010416-044232
/content/journals/10.1146/annurev-psych-010416-044232
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error