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Feature Distribution Learning (FDL): A New Method for Studying Visual Ensembles Perception with Priming of Attention Shifts

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Spatial Learning and Attention Guidance

Part of the book series: Neuromethods ((NM,volume 151))

Abstract

We discuss how priming of attention shifts has in recent studies proved to be a useful method for studying internal representations of visual ensembles. Attentional priming is very powerful in particular when role reversals between targets and distractors occur. Such role reversals can be used to assess how expected or unexpected a particular target is. This new method for studying representations of visual ensembles has revealed that observer’s representations are far more detailed than previous studies of ensemble perception have suggested where the emphasis has been on summary statistics, i.e., mean and variance. Observers can represent surprisingly complex distribution shapes such as whether a representation is bimodal or not. We discuss the details of how this feature distribution learning (FDL) method has been used to assess internal representations of visual ensembles. We also speculate that the method can prove to be an important implicit way of assessing how observers represent regularities in their environments.

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References

  1. Maljkovic V, Nakayama K (1994) Priming of pop-out: I. Role of features. Mem Cognit 22:657–672

    CAS  PubMed  Google Scholar 

  2. Bravo MJ, Nakayama K (1992) The role of attention in different visual-search tasks. Percept Psychophys 51:465–472

    CAS  PubMed  Google Scholar 

  3. Pascucci D, Mastropasqua T, Turatto M (2012) Permeability of priming of pop out to expectations. J Vis 12:21

    PubMed  Google Scholar 

  4. Shurygina O, Kristjansson Á, Tudge L et al (2019) Expectations and perceptual priming in a visual search task: evidence from eye movements and behavior. J Exp Psychol Hum Percept Perform 45(4):489–499. https://doi.org/10.1037/xhp0000618

    PubMed  Google Scholar 

  5. Sigurdardottir HM, Kristjánsson Á, Driver J (2008) Repetition streaks increase perceptual sensitivity in visual search of brief displays. Vis Cogn 16(5):643–658. https://doi.org/10.1080/13506280701218364

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kristjánsson Á, Ásgeirsson ÁG (2019) Attentional priming: recent insights and current controversies. Curr Opin Psychol 29:71–75

    PubMed  Google Scholar 

  7. Wang D, Kristjánsson Á, Nakayama K (2005) Efficient visual search without top-down or bottom-up guidance. Percept Psychophys 67:239–253

    PubMed  Google Scholar 

  8. Lamy DF, Antebi C, Aviani N et al (2008) Priming of Pop-out provides reliable measures of target activation and distractor inhibition in selective attention. Vision Res 48:30–41

    PubMed  Google Scholar 

  9. Saevarsson S, Jóelsdóttir S, Hjaltason H et al (2008) Repetition of distractor sets improves visual search performance in hemispatial neglect. Neuropsychologia 46:1161–1169

    PubMed  Google Scholar 

  10. Kristjánsson Á, Driver J (2008) Priming in visual search: separating the effects of target repetition, distractor repetition and role-reversal. Vision Res 48:1217–1232

    PubMed  Google Scholar 

  11. Chetverikov A, Kristjánsson Á (2015) History effects in visual search for monsters: search times, choice biases, and liking. Atten Percept Psychophys 77:402–412

    PubMed  Google Scholar 

  12. Ásgeirsson ÁG, Kristjánsson Á, Bundesen C (2014) Independent priming of location and color in identification of briefly presented letters. Atten Percept Psychophys 76:40–48

    PubMed  Google Scholar 

  13. Hillstrom AP (2000) Repetition effects in visual search. Percept Psychophys 62:800–817

    CAS  PubMed  Google Scholar 

  14. Kristjánsson Á, Wang D, Nakayama K (2002) The role of priming in conjunctive visual search. Cognition 85:37–52

    PubMed  Google Scholar 

  15. Kristjánsson Á, Ingvarsdóttir Á, Teitsdóttir UD (2008) Object- and feature-based priming in visual search. Psychon Bull Rev 15:378–384

    PubMed  Google Scholar 

  16. Huang L, Holcombe AO, Pashler H (2004) Repetition priming in visual search: episodic retrieval, not feature priming. Mem Cognit 32:12–20

    PubMed  Google Scholar 

  17. Kristjánsson Á (2006) Simultaneous priming along multiple feature dimensions in a visual search task. Vision Res 46:2554–2570

    PubMed  Google Scholar 

  18. Belopolsky AV, Schreij D, Theeuwes J (2010) What is top-down about contingent capture? Atten Percept Psychophys 72:326–341

    PubMed  Google Scholar 

  19. Theeuwes J, van der BE (2011) On the limits of top-down control of visual selection. Atten Percept Psychophys 73:2092–2103

    PubMed  PubMed Central  Google Scholar 

  20. Folk CL, Remington RW, Johnston JC (1992) Involuntary covert orienting is contingent on attentional control settings. J Exp Psychol Hum Percept Perform 18:1030–1044

    CAS  PubMed  Google Scholar 

  21. Carlisle NB, Kristjánsson Á (2017) How visual working memory contents influence priming of visual attention. Psychol Res 82:833–839

    PubMed  Google Scholar 

  22. Kristjánsson Á, Saevarsson S, Driver J (2013) The boundary conditions of priming of visual search: from passive viewing through task-relevant working memory load. Psychon Bull Rev 20:514–521

    PubMed  Google Scholar 

  23. Muller HJ, Reimann B, Krummenacher J (2003) Visual search for singleton feature targets across dimensions: stimulus- and expectancy-driven effects in dimensional weighting. J Exp Psychol Hum Percept Perform 29:1021–1035

    PubMed  Google Scholar 

  24. Found A, Müller HJ (1996) Searching for unknown feature targets on more than one dimension: investigating a “dimension-weighting” account. Percept Psychophys 58:88–101

    CAS  PubMed  Google Scholar 

  25. Becker SI (2010) The role of target-distractor relationships in guiding attention and the eyes in visual search. J Exp Psychol Gen 139:247–265

    PubMed  Google Scholar 

  26. Kristjánsson Á, Campana G (2010) Where perception meets memory: a review of repetition priming in visual search tasks. Atten Percept Psychophys 72:5–18

    PubMed  Google Scholar 

  27. Martini P (2010) System identification in Priming of Pop-Out. Vision Res 50:2110–2115

    PubMed  Google Scholar 

  28. Brascamp JW, Pels E, Kristjánsson Á (2011) Priming of pop-out on multiple time scales during visual search. Vision Res 51:1972–1978

    PubMed  Google Scholar 

  29. Kruijne W, Brascamp JW, Kristjánsson Á et al (2015) Can a single short-term mechanism account for priming of pop-out? Vision Res 115:17–22

    PubMed  Google Scholar 

  30. Kruijne W, Meeter M (2015) The long and the short of priming in visual search. Atten Percept Psychophys 77:1558–1573

    PubMed  PubMed Central  Google Scholar 

  31. McPeek RM, Maljkovic V, Nakayama K (1999) Saccades require focal attention and are facilitated by a short-term memory system. Vision Res 39:1555–1566

    CAS  PubMed  Google Scholar 

  32. Maljkovic V, Martini P (2005) Implicit short-term memory and event frequency effects in visual search. Vis Res 45(21):2831–2846. https://doi.org/10.1016/j.visres.2005.05.019

    Article  PubMed  Google Scholar 

  33. Theeuwes J, Reimann B, Mortier K (2006) Visual search for featural singletons: no top-down modulation, only bottom-up priming. Vis Cogn 14:466–489

    Google Scholar 

  34. Folk CL, Remington RW (2008) Bottom-up priming of top-down attentional control settings. Vis Cogn 16:215–231

    Google Scholar 

  35. Wolfe JM, Butcher SJ, Lee C et al (2003) Changing your mind: on the contributions of top-down and bottom-up guidance in visual search for feature singletons. J Exp Psychol Hum Percept Perform 29:483–502

    PubMed  Google Scholar 

  36. Chetverikov A, Campana G, Kristjánsson Á (2016) Building ensemble representations: How the shape of preceding distractor distributions affects visual search. Cognition 153:196–210

    PubMed  Google Scholar 

  37. Girshick AR, Landy MS, Simoncelli EP (2011) Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat Neurosci 14:926–932

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Rao RP, Olshausen BA, Lewicki MS (2002) Probabilistic models of the brain: perception and neural function. MIT Press, Cambridge, MA

    Google Scholar 

  39. Pouget A, Beck JM, Ma WJ et al (2013) Probabilistic brains: knowns and unknowns. Nat Neurosci 16:1170–1178

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Ma WJ (2012) Organizing probabilistic models of perception. Trends Cogn Sci 16:511–518

    PubMed  Google Scholar 

  41. Fiser J, Berkes P, Orbán G et al (2010) Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn Sci 14(3):119–130

    PubMed  PubMed Central  Google Scholar 

  42. Feldman J (2014) Probabilistic models of perceptual features. In: Wagemans J (ed) Oxford handbook of perceptual organization. Oxford University Press, Oxford, pp 933–947

    Google Scholar 

  43. Vincent BT (2015) A tutorial on Bayesian models of perception. J Math Psychol 66:103–114

    Google Scholar 

  44. Whitney D, Yamanashi-Leib A (2018) Ensemble perception. Annu Rev Psychol 69:105–129

    PubMed  Google Scholar 

  45. Kuriki I (2004) Testing the possibility of average-color perception from multi-colored patterns. Opt Rev 11(4):249–257. https://doi.org/10.1007/s10043-004-0249-2

    Article  Google Scholar 

  46. Ma WJ, Navalpakkam V, Beck JM et al (2011) Behavior and neural basis of near-optimal visual search. Nat Neurosci 14:783–790

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Ma WJ, Shen S, Dziugaite G et al (2015) Requiem for the max rule. Vision Res 116:179–193

    PubMed  PubMed Central  Google Scholar 

  48. Chetverikov A, Campana G, Kristjánsson Á (2017) Rapid learning of visual ensembles. J Vis 17:1–15

    Google Scholar 

  49. Chetverikov A, Campana G, Kristjánsson Á (2017) Set size manipulations reveal the boundary conditions of distractor distribution learning. Vision Res 140:144–156

    PubMed  Google Scholar 

  50. Utochkin IS, Tiurina NA (2014) Parallel averaging of size is possible but range-limited: a reply to Marchant, Simons, and De Fockert. Acta Psychol (Amst) 146:7–18

    Google Scholar 

  51. Chetverikov A, Campana G, Kristjánsson Á (2017) Representing Color Ensembles. Psychol Sci 28:1–8

    Google Scholar 

  52. Chetverikov A, Campana G, Kristjánsson Á (2018) Probabilistic rejection templates in visual working memory. Submitted for Review. doi: https://doi.org/10.31234/osf.io/vrbgh. Preprint available at https://psyarxiv.com/vrbgh/

  53. Hansmann-Roth S, Chetverikov A, Kristjánsson Á (2019) Representing color and orientation ensembles: can observers learn multiple feature distributions? Submitted for Review

    Google Scholar 

  54. Duncan J, Humphreys GW (1989) Visual search and stimulus similarity. Psychol Rev 96:433–458

    CAS  PubMed  Google Scholar 

  55. Palmer EM, Horowitz TS, Torralba A et al (2011) What are the shapes of response time distributions in visual search? J Exp Psychol Hum Percept Perform 37:58–71

    PubMed  PubMed Central  Google Scholar 

  56. Kristjánsson Á, Jóhannesson ÓI (2014) How priming in visual search affects response time distributions: analyses with ex-Gaussian fits. Atten Percept Psychophys 76:2199–2211

    PubMed  Google Scholar 

  57. Luce RD (1986) Response times: their role in inferring elementary mental organization. Oxford University Press, New York, NY

    Google Scholar 

  58. Muggeo VMR (2003) Estimating regression models with unknown break-points. Stat Med 22:3055–3071

    PubMed  Google Scholar 

  59. Muggeo VMR (2008) Segmented: an R package to fit regression models with broken-line relationships. R News 8:20–25

    Google Scholar 

  60. Davies RB (1987) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74:33–43

    Google Scholar 

  61. Chetverikov A, Campana G, Kristjánsson Á (2018) Probabilistic perceptual landscapes. J Vis 18:529

    Google Scholar 

  62. Atchley P, Andersen GJ (1995) Discrimination of speed distributions: sensitivity to statistical properties. Vision Res 35:3131–3144

    CAS  PubMed  Google Scholar 

  63. Morgan MJ, Chubb C, Solomon JA (2008) A “dipper” function for texture discrimination based on orientation variance. J Vis 8:9–9

    PubMed  Google Scholar 

  64. Im HY, Chong SC (2014) Mean size as a unit of visual working memory. Perception 43:663–676

    PubMed  Google Scholar 

  65. Chong SC, Treisman A (2003) Representation of statistical properties. Vision Res 43:393–404

    PubMed  Google Scholar 

  66. Webster J, Kay P, Webster MA (2014) Perceiving the average hue of color arrays. J Opt Soc Am A Opt Image Sci Vis 31:A283–A292

    PubMed  PubMed Central  Google Scholar 

  67. Attarha M, Moore CM (2015) The capacity limitations of orientation summary statistics. Atten Percept Psychophys 77:1116–1131

    PubMed  PubMed Central  Google Scholar 

  68. Norman LJ, Heywood CA, Kentridge RW (2015) Direct encoding of orientation variance in the visual system. J Vis 15:1–14

    Google Scholar 

  69. Michael E, de Gardelle V, Summerfield C (2014) Priming by the variability of visual information. Proc Natl Acad Sci 111:7873–7878

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Corbett JE, Melcher D (2014) Stable statistical representations facilitate visual search. J Exp Psychol Hum Percept Perform 40:1915–1925

    PubMed  Google Scholar 

  71. Meyniel F, Sigman M, Mainen ZF (2015) Confidence as Bayesian probability: from neural origins to behavior. Neuron 88:78–92

    CAS  PubMed  Google Scholar 

  72. Solomon JA (2010) Visual discrimination of orientation statistics in crowded and uncrowded arrays. J Vis 10:19

    PubMed  Google Scholar 

  73. Lau JS, Brady TF (2018) Ensemble statistics accessed through proxies: range heuristic and dependence on low-level properties in variability discrimination. J Vis 18:3

    PubMed  PubMed Central  Google Scholar 

  74. Chetverikov A, Campana G, Kristjánsson Á (2017) Learning features in a complex and changing environment: a distribution-based framework for visual attention and vision in general. Prog Brain Res 236:97–120. https://doi.org/10.1016/bs.pbr.2017.07.001

    Google Scholar 

  75. Hansmann-Roth S, Kristjansson Á, Whitney D et al (2018) Explicit and implicit judgments of distribution characteristics: Do they lead to different results? Oral presentation at European Conference on Visual Perception 2018, Trieste, Italy. Abstract available at https://guidebook.com/guide/123359/poi/10443998/

  76. Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12:97–136

    CAS  PubMed  Google Scholar 

  77. Won BY, Geng JJ (2018) Learned suppression for multiple distractors in visual search. J Exp Psychol Hum Percept Perform 44:1128–1141

    PubMed  Google Scholar 

  78. Geng JJ, Witkowski P (2019) Template-to-distractor distinctiveness regulates visual search efficiency. Curr Opin Psychol 29:119–125

    PubMed  PubMed Central  Google Scholar 

  79. Hout MC, Goldinger SD (2014) Target templates: the precision of mental representations affects attentional guidance and decision-making in visual search. Atten Percept Psychophys 77:128–149

    Google Scholar 

  80. Ma WJ, Husain M, Bays PM (2014) Changing concepts of working memory. Nat Neurosci 17:347–356

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Bays PM (2015) Spikes not slots: noise in neural populations limits working memory. Trends Cogn Sci 19:431–438

    PubMed  Google Scholar 

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Acknowledgments

SHR, ODT, and AK were supported by grant IRF #173947-052 from the Icelandic Research Fund and by a grant from the Research Fund of the University of Iceland. AC was supported by a Radboud Excellence Fellowship.

Author information

Authors and Affiliations

  1. Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, The Netherlands

    Andrey Chetverikov

  2. Cognitive Research Lab, Russian Academy of National Economy and Public Administration, Moscow, Russia

    Andrey Chetverikov

  3. School of Health Sciences, University of Iceland, Reykjavík, Iceland

    Andrey Chetverikov, Sabrina Hansmann-Roth, Ömer Dağlar Tanrıkulu & Árni Kristjánsson

  4. School of Psychology, National Research University Higher School of Economics, Moscow, Russia

    Árni Kristjánsson

Authors
  1. Andrey Chetverikov
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  2. Sabrina Hansmann-Roth
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  3. Ömer Dağlar Tanrıkulu
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  4. Árni Kristjánsson
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Editors and Affiliations

  1. Institute of Psychology, Otto-von-Guericke-University-Magdeburg, Magdeburg, Germany

    Stefan Pollmann

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Chetverikov, A., Hansmann-Roth, S., Tanrıkulu, Ö.D., Kristjánsson, Á. (2019). Feature Distribution Learning (FDL): A New Method for Studying Visual Ensembles Perception with Priming of Attention Shifts. In: Pollmann, S. (eds) Spatial Learning and Attention Guidance. Neuromethods, vol 151. Humana, New York, NY. https://doi.org/10.1007/7657_2019_20

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  • DOI: https://doi.org/10.1007/7657_2019_20

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9947-7

  • Online ISBN: 978-1-4939-9948-4

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