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Annual Review of Vision Science

Volume 8, 2022

Statistical Learning in Vision

Abstract

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety.

Keyword(s): hierarchical Bayesian modeling, perceptual learning, probabilistic computation, rule learning, statistical learning, structure learning
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2022-09-15
2024-04-26
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Literature Cited

  1. Acuña DE, Schrater P. 2010. Structure learning in human sequential decision-making. PLOS Comput. Biol. 6:12e1001003
     [Google Scholar]
  2. Adini Y, Wilkonsky A, Haspel R, Tsodyks M, Sagi D. 2004. Perceptual learning in contrast discrimination: the effect of contrast uncertainty. J. Vis. 4:12993–1005
     [Google Scholar]
  3. Ahissar M, Hochstein S. 1997. Task difficulty and the specificity of perceptual learning. Nature 387:6631401–6
     [Google Scholar]
  4. Alhama RG, Zuidema W. 2019. A review of computational models of basic rule learning: the neural-symbolic debate and beyond. Psych. Bull. Rev. 26:41174–94
     [Google Scholar]
  5. Altmann GTM. 2002. Learning and development in neural networks—the importance of prior experience. Cognition 85:2B43–50
     [Google Scholar]
  6. Aslin RN. 2017. Statistical learning: a powerful mechanism that operates by mere exposure. Wiley Interdiscip. Rev. Cogn. Sci. 8:1–2e1373
     [Google Scholar]
  7. Aslin RN, Newport EL. 2012. Statistical learning: from acquiring specific items to forming general rules. Curr. Dir. Psychol. Sci. 21:3170–76
     [Google Scholar]
  8. Austerweil JL, Griffiths TL. 2011. A rational model of the effects of distributional information on feature learning. Cogn. Psychol. 63:4173–209
     [Google Scholar]
  9. Austerweil JL, Sanborn S, Griffiths TL. 2019. Learning how to generalize. Cogn. Sci. 43:8e12777
     [Google Scholar]
  10. Avarguès-Weber A, Finke V, Nagy M, Szabó T, d'Amaro D et al. 2020. Different mechanisms underlie implicit visual statistical learning in honey bees and humans. PNAS 117:4125923–34
     [Google Scholar]
  11. Ball K, Sekuler R. 1987. Direction-specific improvement in motion discrimination. Vis. Res. 27:6953–65
     [Google Scholar]
  12. Barakat BK, Seitz AR, Shams L. 2013. The effect of statistical learning on internal stimulus representations: Predictable items are enhanced even when not predicted. Cognition 129:2205–11
     [Google Scholar]
  13. Baram AB, Muller TH, Nili H, Garvert MM, Behrens TEJ. 2021. Entorhinal and ventromedial prefrontal cortices abstract and generalize the structure of reinforcement learning problems. Neuron 109:4713–23.e7
     [Google Scholar]
  14. Batterink LJ, Paller KA, Reber PJ. 2019. Understanding the neural bases of implicit and statistical learning. Topics Cogn. Sci. 11:3482–503
     [Google Scholar]
  15. Bavelier D, Green CS, Pouget A, Schrater P. 2012. Brain plasticity through the life span: learning to learn and action video games. Annu. Rev. Neurosci. 35:391–416
     [Google Scholar]
  16. Bertels J, Franco A, Destrebecqz A. 2012. How implicit is visual statistical learning?. J. Exp. Psychol. Learn. Mem. Cogn. 38:51425–31
     [Google Scholar]
  17. Bettoni R, Bulf H, Brady S, Johnson SP. 2021. Infants’ learning of non-adjacent regularities from visual sequences. Infancy 26:2319–26
     [Google Scholar]
  18. Bill J, Pailian H, Gershman SJ, Drugowitsch J. 2020. Hierarchical structure is employed by humans during visual motion perception. PNAS 117:3924581–89
     [Google Scholar]
  19. Brady TF, Konkle T, Alvarez GA. 2009. Compression in visual working memory: using statistical regularities to form more efficient memory representations. J. Exp. Psychol. Gen. 138:4487–502
     [Google Scholar]
  20. Brady TF, Konkle T, Alvarez GA, Oliva A. 2008. Visual long-term memory has a massive storage capacity for object details. PNAS 105:3814325–29
     [Google Scholar]
  21. Brainard DH, Freeman WT. 1997. Bayesian color constancy. J. Opt. Soc. Am. A 14:71393–411
     [Google Scholar]
  22. Braun DA, Mehring C, Wolpert DM. 2010. Structure learning in action. Behav. Brain Res. 206:2157–65
     [Google Scholar]
  23. Briggs F, Mangun GR, Usrey WM. 2013. Attention enhances synaptic efficacy and the signal-to-noise ratio in neural circuits. Nature 499:7459476–80
     [Google Scholar]
  24. Buchsbaum D, Griffiths TL, Plunkett D, Gopnik A, Baldwin D. 2015. Inferring action structure and causal relationships in continuous sequences of human action. Cogn. Psychol. 76:30–77
     [Google Scholar]
  25. Bulf H, Johnson SP, Valenza E. 2011. Visual statistical learning in the newborn infant. Cognition 121:1127–32
     [Google Scholar]
  26. Bulf H, Quadrelli E, Brady S, Nguyen B, Cassia VM, Johnson SP. 2021. Rule learning transfer across linguistic and visual modalities in 7-month-old infants. Infancy 26:3442–54
     [Google Scholar]
  27. Buzsáki G, Draguhn A. 2004. Neuronal oscillations in cortical networks. Science 304:56791926–29
     [Google Scholar]
  28. Castañón SH, Cardoso-Leite P, Altarelli I, Green CS, Schrater P, Bavelier D. 2021. A mixture of generative models strategy helps humans generalize across tasks. bioRxiv 2021.02.16.431506. https://doi.org/10.1101/2021.02.16.431506
    [Crossref]
  29. Chaudhuri A. 1990. Modulation of the motion aftereffect by selective attention. Nature 344:626160–62
     [Google Scholar]
  30. Chomsky N. 1956. Three models for the description of language. IEEE Trans. Inf. Theory 2:3113–24
     [Google Scholar]
  31. Cicchini GM, Benedetto A, Burr DC. 2021. Perceptual history propagates down to early levels of sensory analysis. Curr. Biol. 31:61245–50.e2
     [Google Scholar]
  32. Cloherty SL, Hughes NJ, Hietanen MA, Bhagavatula PS, Goodhill GJ, Ibbotson MR 2016. Sensory experience modifies feature map relationships in visual cortex. eLife 5:e13911
     [Google Scholar]
  33. Collins AGE, Frank MJ. 2013. Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychol. Rev. 120:1190–229
     [Google Scholar]
  34. Conway CM, Christiansen MH. 2005. Modality-constrained statistical learning of tactile, visual, and auditory sequences. J. Exp. Psychol. Learn. Mem. Cogn. 31:124–39
     [Google Scholar]
  35. Crist RE, Kapadia MK, Westheimer G, Gilbert CD. 1997. Perceptual learning of spatial localization: specificity for orientation, position, and context. J. Neurophysiol. 78:62889–94
     [Google Scholar]
  36. de Lange FP, de Lange FP, Heilbron M, Kok P. 2018. How do expectations shape perception?. Trends Cogn. Sci. 22:9P764–79
     [Google Scholar]
  37. Dehaene S, Meyniel F, Wacongne C, Wang L, Pallier C 2015. The neural representation of sequences: from transition probabilities to algebraic patterns and linguistic trees. Neuron 88:12–19
     [Google Scholar]
  38. DeValois RL, DeValois KK. 1990. Spatial Vision Oxford, UK: Oxford Univ. Press
  39. DiCarlo JJ, Zoccolan D, Rust NC. 2012. How does the brain solve visual object recognition?. Neuron 73:3415–34
     [Google Scholar]
  40. Dorais A, Sagi D. 1997. Contrast masking effects change with practice. Vis. Res. 37:131725–33
     [Google Scholar]
  41. Dosher B, Lu Z-L. 2017. Visual perceptual learning and models. Annu. Rev. Vis. Sci. 3:343–63
     [Google Scholar]
  42. Echeveste R, Aitchison L, Hennequin G, Lengyel M. 2020. Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nat. Neurosci. 23:91138–49
     [Google Scholar]
  43. Eckstein MK, Collins AGE. 2020. Computational evidence for hierarchically structured reinforcement learning in humans. PNAS 117:4729381–89
     [Google Scholar]
  44. Eckstein MP. 2017. Probabilistic computations for attention, eye movements, and search. Annu. Rev. Vis. Sci. 3:319–42
     [Google Scholar]
  45. Eickenberg M, Gramfort A, Varoquaux G, Thirion B. 2017. Seeing it all: Convolutional network layers map the function of the human visual system. NeuroImage 152:184–94
     [Google Scholar]
  46. Endress AD, Dehaene-Lambertz G, Mehler J. 2007. Perceptual constraints and the learnability of simple grammars. Cognition 105:3577–614
     [Google Scholar]
  47. Endress AD, Johnson SP. 2021. When forgetting fosters learning: a neural network model for statistical learning. Cognition 213:104621
     [Google Scholar]
  48. Erdogan G, Yildirim I, Jacobs RA. 2015. From sensory signals to modality-independent conceptual representations: a probabilistic language of thought approach. PLOS Comput. Biol. 11:11e1004610
     [Google Scholar]
  49. Fahle M, Morgan M. 1996. No transfer of perceptual learning between similar stimuli in the same retinal position. Curr. Biol. 6:3292–97
     [Google Scholar]
  50. Fahle M, Poggio TA. 2002. Perceptual Learning Cambridge, MA: MIT Press
  51. Fazeli N, Oller M, Wu J, Wu Z, Tenenbaum JB, Rodriguez A. 2019. See, feel, act: hierarchical learning for complex manipulation skills with multisensory fusion. Sci. Robot. 4:26eaav3123
     [Google Scholar]
  52. Feldman J. 1997. Regularity-based perceptual grouping. Comput. Intell. 13:4582–623
     [Google Scholar]
  53. Ferguson B, Franconeri SL, Waxman SR. 2018. Very young infants learn abstract rules in the visual modality. PLOS ONE 13:1e0190185
     [Google Scholar]
  54. Fiorentini A, Berardi N. 1980. Perceptual learning specific for orientation and spatial frequency. Nature 287:577743–44
     [Google Scholar]
  55. Fiser J. 2009. Perceptual learning and representational learning in humans and animals. Learn. Behav. 37:2141–53
     [Google Scholar]
  56. Fiser J, Aslin RN. 2001. Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychol. Sci. 12:6499–504
     [Google Scholar]
  57. Fiser J, Aslin RN. 2002a. Statistical learning of higher-order temporal structure from visual shape sequences. J. Exp. Psychol. Learn. Mem. Cogn. 28:3458–67
     [Google Scholar]
  58. Fiser J, Aslin RN. 2002b. Statistical learning of new visual feature combinations by infants. PNAS 99:2415822–26
     [Google Scholar]
  59. Fiser J, Aslin RN. 2005. Encoding multielement scenes: statistical learning of visual feature hierarchies. J. Exp. Psychol. Gen. 134:4521–37
     [Google Scholar]
  60. Fiser J, Berkes P, Orbán G, Lengyel M. 2010. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14:3119–30
     [Google Scholar]
  61. Fiser J, Lengyel G. 2019. A common probabilistic framework for perceptual and statistical learning. Curr. Opin. Neurobiol. 58:218–28
     [Google Scholar]
  62. Fitch WT, Friederici AD. 2012. Artificial grammar learning meets formal language theory: an overview. Philos. Trans. R. Soc. Lond. B 367:15981933–55
     [Google Scholar]
  63. Franklin NT, Frank MJ. 2018. Compositional clustering in task structure learning. PLOS Comput. Biol. 14:4e1006116
     [Google Scholar]
  64. Franklin NT, Frank MJ. 2020. Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning. PLOS Comput. Biol. 16:4e1007720
     [Google Scholar]
  65. French RL, DeAngelis GC. 2020. Multisensory neural processing: from cue integration to causal inference. Curr. Opin. Physiol. 16:8–13
     [Google Scholar]
  66. French RM, Addyman C, Mareschal D. 2011. TRACX: a recognition-based connectionist framework for sequence segmentation and chunk extraction. Psychol. Rev. 118:4614–36
     [Google Scholar]
  67. Frisby JP, Stone JV. 2010. Seeing: The Computational Approach to Biological Vision Cambridge, MA: MIT Press
  68. Frost R, Armstrong BC, Christiansen MH. 2019. Statistical learning research: a critical review and possible new directions. Psychol. Bull. 145:121128–53
     [Google Scholar]
  69. Frost R, Armstrong BC, Siegelman N, Christiansen MH. 2015. Domain generality versus modality specificity: the paradox of statistical learning. Trends Cogn. Sci. 19:3117–25
     [Google Scholar]
  70. Froudarakis E, Fahey PG, Reimer J, Smirnakis SM, Tehovnik EJ, Tolias AS. 2019. The visual cortex in context. Annu. Rev. Vis. Sci. 5:317–39
     [Google Scholar]
  71. Gallistel CR. 1990. The Organization of Learning Cambridge, MA: MIT Press
  72. Garber D, Fiser J. 2021a. Pre-training leads to a structural novelty effect in spatial visual statistical learning. Proceedings of the Annual Meeting of the Cognitive Science Society art. 43. N.p. Cogn. Sci. Soc. https://escholarship.org/content/qt9qc0x5n1/qt9qc0x5n1.pdf?t=qwi3u0
    [Google Scholar]
  73. Garber D, Fiser J. 2021b. Recovering spatial structure in spatio-temporal visual statistical learning. J. Vis. 21:92160
     [Google Scholar]
  74. Garner KG, Lynch CR, Dux PE. 2016. Transfer of training benefits requires rules we cannot see (or hear). J. Exp. Psychol. Hum. Percept. Perform. 42:81148–57
     [Google Scholar]
  75. Garvert MM, Dolan RJ, Behrens TE 2017. A map of abstract relational knowledge in the human hippocampal-entorhinal cortex. eLife 6:e17086
     [Google Scholar]
  76. Geirhos R, Medina Temme CR, Rauber J, Schutt HH, Bethge M, Wichmann FA. 2018. Generalisation in humans and deep neural networks. NIPS’18: Proceedings of the 32nd International Conference on Neural Information Processing Systems7549–61 Red Hook, NY: Curran Assoc.
     [Google Scholar]
  77. Gerken L. 2006. Decisions, decisions: infant language learning when multiple generalizations are possible. Cognition 98:3B67–74
     [Google Scholar]
  78. Gershman SJ, Tenenbaum JB, Jäkel F. 2016. Discovering hierarchical motion structure. Vis. Res. 126:232–41
     [Google Scholar]
  79. Ghose GM, Yang T, Maunsell JHR 2002. Physiological correlates of perceptual learning in monkey V1 and V2. J. Neurophysiol. 87:41867–88
     [Google Scholar]
  80. Gibson EJ. 1969. Principles of Perceptual Learning and Development, Vol. 6 New York: Appleton-Century-Crofts
  81. Gilbert CD, Li W. 2013. Top-down influences on visual processing. Nat. Rev. Neurosci. 14:350–63
     [Google Scholar]
  82. Gilchrist A, Kossyfidis C, Bonato F, Agostini T, Cataliotti J et al. 1999. An anchoring theory of lightness perception. Psychol. Rev. 106:4795–834
     [Google Scholar]
  83. Glicksohn A, Cohen A. 2011. The role of Gestalt grouping principles in visual statistical learning. Attention Percept. Psychophys. 73:3708–13
     [Google Scholar]
  84. Glicksohn A, Cohen A. 2013. The role of cross-modal associations in statistical learning. Psychon. Bull. Rev. 20:61161–69
     [Google Scholar]
  85. Gómez RL, Gerken L. 1999. Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge. Cognition 70:2109–35
     [Google Scholar]
  86. Gómez RL, Gerken L. 2000. Infant artificial language learning and language acquisition. Trends Cogn. Sci. 4:5P178–86
     [Google Scholar]
  87. Goodman ND, Tenenbaum JB, Feldman J, Griffiths TL. 2008. A rational analysis of rule-based concept learning. Cogn. Sci. 32:1108–54
     [Google Scholar]
  88. Graham N. 1989. Visual Pattern Analyzers Oxford, UK: Oxford Univ. Press
  89. Griffiths TL, Callaway F, Chang MB, Grant E, Krueger PM, Lieder F. 2019. Doing more with less: meta-reasoning and meta-learning in humans and machines. Curr. Opin. Behav. Sci. 29:24–30
     [Google Scholar]
  90. Grosof DH, Shapley RM, Hawken MJ. 1993. Macaque V1 neurons can signal “illusory” contours. Nature 365:6446550–52
     [Google Scholar]
  91. Hafting T, Fyhn M, Molden S, Moser M-B, Moser EI. 2005. Microstructure of a spatial map in the entorhinal cortex. Nature 436:7052801–6
     [Google Scholar]
  92. Harlow HF. 1949. The formation of learning sets. Psychol. Rev. 56:151–65
     [Google Scholar]
  93. Hastie T, Tibshirani R, Friedman J. 2013. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Berlin: Springer
  94. Hayhoe MM. 2017. Vision and action. Annu. Rev. Vis. Sci. 3:389–413
     [Google Scholar]
  95. Heald JB, Lengyel M, Wolpert DM. 2021. Contextual inference underlies the learning of sensorimotor repertoires. Nature 600:489–93
     [Google Scholar]
  96. Hoyer PO, Hyvarinen A. 2003. Interpreting neural response variability as Monte Carlo sampling of the posterior. Advances in Neural Information Processing Systems 15293–300 Cambridge, MA: MIT Press
     [Google Scholar]
  97. Hua T, Bao P, Huang C-B, Wang Z, Xu J et al. 2010. Perceptual learning improves contrast sensitivity of V1 neurons in cats. Curr. Biol. 20:10887–94
     [Google Scholar]
  98. Hupp JM, Sloutsky VM. 2011. Learning to learn: from within-modality to cross-modality transfer during infancy. J. Exp. Child Psychol. 110:3408–21
     [Google Scholar]
  99. Ishikawa T, Mogi K. 2011. Visual one-shot learning as an “anti-camouflage device”: a novel morphing paradigm. Cogn. Neurodyn. 5:3231–39
     [Google Scholar]
  100. Jeter PE, Dosher BA, Liu S-H, Lu Z-L. 2010. Specificity of perceptual learning increases with increased training. Vis. Res. 50:191928–40
     [Google Scholar]
  101. Jing R, Yang C, Huang X, Li W. 2021. Perceptual learning as a result of concerted changes in prefrontal and visual cortex. Curr. Biol. 31:20P4521–33.E3
     [Google Scholar]
  102. Kattner F, Cochrane A, Cox CR, Gorman TE, Green CS. 2017. Perceptual learning generalization from sequential perceptual training as a change in learning rate. Curr. Biol. 27:6840–46
     [Google Scholar]
  103. Kattner F, Cox CR, Green CS. 2016. Transfer in rule-based category learning depends on the training task. PLOS ONE 11:10e0165260
     [Google Scholar]
  104. Kemp C, Goodman ND, Tenenbaum JB. 2010. Learning to learn causal models. Cogn. Sci. 34:71185–243
     [Google Scholar]
  105. Kemp C, Tenenbaum JB. 2008. The discovery of structural form. PNAS 105:3110687–92
     [Google Scholar]
  106. Kemp C, Tenenbaum JB. 2009. Structured statistical models of inductive reasoning. Psychol. Rev. 116:120–58
     [Google Scholar]
  107. Kiefer M, Harpaintner M. 2020. Varieties of abstract concepts and their grounding in perception or action. Open Psychol 2:1119–37
     [Google Scholar]
  108. Kietzmann TC, Spoerer CJ, Sörensen LKA, Cichy RM, Hauk O, Kriegeskorte N. 2019. Recurrence is required to capture the representational dynamics of the human visual system. PNAS 116:4321854–63
     [Google Scholar]
  109. Kim R, Seitz A, Feenstra H, Shams L. 2009. Testing assumptions of statistical learning: Is it long-term and implicit?. Neurosci. Lett. 461:2145–49
     [Google Scholar]
  110. Kirkham NZ, Slemmer JA, Johnson SP. 2002. Visual statistical learning in infancy: evidence for a domain general learning mechanism. Cognition 83:2B35–42
     [Google Scholar]
  111. Knill DC, Pouget A. 2004. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27:12712–19
     [Google Scholar]
  112. Koblinger Á, Fiser J, Lengyel M. 2021. Representations of uncertainty: Where art thou?. Curr. Opin. Behav. Sci. 38:150–62
     [Google Scholar]
  113. Kok P, de Lange FP. 2014. Shape perception simultaneously up- and downregulates neural activity in the primary visual cortex. Curr. Biol. 24:131531–35
     [Google Scholar]
  114. Kok P, Jehee JFM, de Lange FP. 2012. Less is more: Expectation sharpens representations in the primary visual cortex. Neuron 75:2265–70
     [Google Scholar]
  115. Kourtzi Z, Welchman AE. 2019. Learning predictive structure without a teacher: decision strategies and brain routes. Curr. Opin. Neurobiol. 58:130–34
     [Google Scholar]
  116. Kriegeskorte N. 2015. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 1:417–46
     [Google Scholar]
  117. Kuai S-G, Zhang J-Y, Klein SA, Levi DM, Yu C. 2005. The essential role of stimulus temporal patterning in enabling perceptual learning. Nat. Neurosci. 8:111497–99
     [Google Scholar]
  118. Kubilius J, Bracci S, Op de Beeck HP. 2016. Deep neural networks as a computational model for human shape sensitivity. PLOS Comput. Biol. 12:4e1004896
     [Google Scholar]
  119. Lake BM, Baroni M. 2018. Generalization without systematicity: on the compositional skills of sequence-to-sequence recurrent networks. Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden2873–82 N.p.: PMLR
     [Google Scholar]
  120. Lake BM, Salakhutdinov R, Tenenbaum JB. 2015. Human-level concept learning through probabilistic program induction. Science 350:62661332–38
     [Google Scholar]
  121. Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ. 2017. Building machines that learn and think like people. Behav. Brain Sci. 40:e253
     [Google Scholar]
  122. Law C-T, Gold JI. 2008. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11:505–13
     [Google Scholar]
  123. Lee ALF, Liu Z, Lu H. 2021. Parts beget parts: bootstrapping hierarchical object representations through visual statistical learning. Cognition 209:104515
     [Google Scholar]
  124. Lee TS, Mumford D. 2003. Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A 20:71434–48
     [Google Scholar]
  125. LeMessurier AM, Feldman DE. 2018. Plasticity of population coding in primary sensory cortex. Curr. Opin. Neurobiol. 53:50–56
     [Google Scholar]
  126. Lengyel G, Fiser J. 2019. The relationship between initial threshold, learning, and generalization in perceptual learning. J. Vis. 19:428
     [Google Scholar]
  127. Lengyel G, Nagy M, Fiser J. 2021. Statistically defined visual chunks engage object-based attention. Nat. Commun. 12:272
     [Google Scholar]
  128. Lengyel G, Žalalytė G, Pantelides A, Ingram JN, Fiser J et al. 2019. Unimodal statistical learning produces multimodal object-like representations. eLife 8:e43942
     [Google Scholar]
  129. Li W. 2016. Perceptual learning: use-dependent cortical plasticity. Annu. Rev. Vis. Sci. 2:109–30
     [Google Scholar]
  130. Luo Y, Zhao J. 2018. Statistical learning creates novel object associations via transitive relations. Psychol. Sci. 29:81207–20
     [Google Scholar]
  131. Ma WJ, Beck JM, Latham PE, Pouget A. 2006. Bayesian inference with probabilistic population codes. Nat. Neurosci. 9:111432–38
     [Google Scholar]
  132. MacKenzie K, Fiser J. 2010. Sensitivity of implicit visual rule-learning to the saliency of the stimuli. J. Vis. 8:474
     [Google Scholar]
  133. Maniglia M, Seitz AR. 2018. Towards a whole brain model of perceptual learning. Curr. Opin. Behav. Sci. 20:47–55
     [Google Scholar]
  134. Marcus GF, Johnson S, Fernandes K, Slemmer J. 2004. Rules, statistics and domain-specificity: evidence from prelinguistic infants Paper presented at the 29th Annual Meeting of the Boston University Conference on Language Development Nov. 5–7. https://www.bu.edu/bucld/files/2011/06/handbook-292004.pdf
  135. Marcus GF, Vijayan S, Bandi Rao S, Vishton PM 1999. Rule learning by seven-month-old infants. Science 283:539877–80
     [Google Scholar]
  136. Mareschal D, French RM. 2017. TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning. Philos. Trans. R. Soc. Lond. B 372:171120160057
     [Google Scholar]
  137. Mark S, Moran R, Parr T, Kennerley SW, Behrens TEJ. 2020. Transferring structural knowledge across cognitive maps in humans and models. Nat. Commun. 11:4783
     [Google Scholar]
  138. Marr D. 1982. Vision: A Computational Investigation Into the Human Representation and Processing of Visual Information New York: Freeman
  139. Maunsell JHR. 2015. Neuronal mechanisms of visual attention. Annu. Rev. Vis. Sci. 1:373–91
     [Google Scholar]
  140. Minda JP, Smith JD. 2001. Prototypes in category learning: the effects of category size, category structure, and stimulus complexity. J. Exp. Psychol. Learn. Mem. Cogn. 27:3775–99
     [Google Scholar]
  141. Murphy RA, Mondragon E, Murphy VA. 2008. Rule learning by rats. Science 319:58711849–51
     [Google Scholar]
  142. Murray RF. 2021. Lightness perception in complex scenes. Annu. Rev. Vis. Sci. 7:417–36
     [Google Scholar]
  143. Murray SO, Kersten D, Olshausen BA, Schrater P, Woods DL. 2002. Shape perception reduces activity in human primary visual cortex. PNAS 99:2315164–69
     [Google Scholar]
  144. Musz E, Weber MJ, Thompson-Schill SL. 2015. Visual statistical learning is not reliably modulated by selective attention to isolated events. Atten. Percept. Psychophys. 77:178–96
     [Google Scholar]
  145. Nadasdy Z, Nguyen TP, Török Á, Shen JY, Briggs DE et al. 2017. Context-dependent spatially periodic activity in the human entorhinal cortex. PNAS 114:17E3516–25
     [Google Scholar]
  146. Nemeth D, Janacsek K, Londe Z, Ullman MT, Howard DV, Howard JH. 2010. Sleep has no critical role in implicit motor sequence learning in young and old adults. Exp. Brain Res. 201:351–58
     [Google Scholar]
  147. Niv Y. 2019. Learning task-state representations. Nat. Neurosci. 22:101544–53
     [Google Scholar]
  148. O'Keefe J, Dostrovsky J. 1971. The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34:171–75
     [Google Scholar]
  149. O'Keefe J, Nadel L. 1978. The Hippocampus as a Cognitive Map Oxford, UK: Oxford Univ. Press
  150. Ongchoco J, Uddenberg S, Chun M. 2016. Statistical learning of movement. J. Vis. 16:1079
     [Google Scholar]
  151. Orbán G, Berkes P, Fiser J, Lengyel M. 2016. Neural variability and sampling-based probabilistic representations in the visual cortex. Neuron 92:2530–43
     [Google Scholar]
  152. Orbán G, Fiser J, Aslin RN, Lengyel M. 2008. Bayesian learning of visual chunks by human observers. PNAS 105:72745–50
     [Google Scholar]
  153. Otsuka S, Saiki J. 2016. Gift from statistical learning: Visual statistical learning enhances memory for sequence elements and impairs memory for items that disrupt regularities. Cognition 147:113–26
     [Google Scholar]
  154. Overlan MC, Jacobs RA, Piantadosi ST. 2017. Learning abstract visual concepts via probabilistic program induction in a language of thought. Cognition 168:320–34
     [Google Scholar]
  155. Palmer S, Rock I. 1994. Rethinking perceptual organization: the role of uniform connectedness. Psychon. Bull. Rev. 1:29–55
     [Google Scholar]
  156. Peña M, Bonatti LL, Nespor M, Mehler J. 2002. Signal-driven computations in speech processing. Science 298:5593604–7
     [Google Scholar]
  157. Perruchet P. 2019. What mechanisms underlie implicit statistical learning? Transitional probabilities versus chunks in language learning. Topics Cogn. Sci. 11:3520–35
     [Google Scholar]
  158. Perruchet P, Pacton S. 2006. Implicit learning and statistical learning: one phenomenon, two approaches. Trends Cogn. Sci. 10:5233–38
     [Google Scholar]
  159. Perruchet P, Vinter A. 1998. PARSER: a model for word segmentation. J. Mem. Lang. 39:2246–63
     [Google Scholar]
  160. Pinker S, Jackendoff R. 2005. The faculty of language: What's special about it?. Cognition 95:2201–36
     [Google Scholar]
  161. Plaut DC, Vande Velde AK 2017. Statistical learning of parts and wholes: a neural network approach. J. Exp. Psychol. Gen. 146:3318–36
     [Google Scholar]
  162. Pomerantz JR, Sager LC, Stoever RJ. 1977. Perception of wholes and of their component parts: some configural superiority effects. J. Exp. Psychol. Hum. Percept. Perform. 3:3422–35
     [Google Scholar]
  163. Pouget A, Beck JM, Ma WJ, Latham PE. 2013. Probabilistic brains: knowns and unknowns. Nat. Neurosci. 16:91170–78
     [Google Scholar]
  164. Pouncy T, Tsividis P, Gershman SJ. 2021. What is the model in model-based planning?. Cogn. Sci. 45:e12928
     [Google Scholar]
  165. Rabagliati H, Ferguson B, Lew-Williams C. 2019. The profile of abstract rule learning in infancy: meta-analytic and experimental evidence. Dev. Sci. 22:e12704
     [Google Scholar]
  166. Rabi R, Minda JP 2014. Rule-based category learning in children: the role of age and executive functioning. PLOS ONE 9:e85316
     [Google Scholar]
  167. Radulescu A, Shin YS, Niv Y. 2021. Human representation learning. Annu. Rev. Neurosci. 44:253–73
     [Google Scholar]
  168. Retailleau A, Morris G. 2018. Spatial rule learning and corresponding CA1 place cell reorientation depend on local dopamine release. Curr. Biol. 28:6836–46.e4
     [Google Scholar]
  169. Riesenhuber M, Poggio T. 1999. Hierarchical models of object recognition in cortex. Nat. Neurosci. 2:1019–25
     [Google Scholar]
  170. Roelfsema PR, de Lange FP. 2016. Early visual cortex as a multiscale cognitive blackboard. Annu. Rev. Vis. Sci. 2:131–51
     [Google Scholar]
  171. Rosa-Salva O, Fiser J, Versace E, Dolci C, Chehaimi S et al. 2018. Spontaneous learning of visual structures in domestic chicks. Animals 8:8135
     [Google Scholar]
  172. Rosch E. 1973. Natural categories. Cogn. Psychol. 4:3328–50
     [Google Scholar]
  173. Rosch E. 1975. Cognitive representations of semantic categories. J. Exp. Psychol. Gen. 104:3192–233
     [Google Scholar]
  174. Saffran JR, Aslin RN, Newport EL. 1996. Statistical learning by 8-month-old infants. Science 274:52941926–28
     [Google Scholar]
  175. Saffran JR, Hauser M, Seibel R, Kapfhamer J, Tsao F, Cushman F. 2008. Grammatical pattern learning by human infants and cotton-top tamarin monkeys. Cognition 107:2479–500
     [Google Scholar]
  176. Saffran JR, Kirkham NZ. 2018. Infant statistical learning. Annu. Rev. Psychol. 69:181–203
     [Google Scholar]
  177. Saffran JR, Pollak SD, Seibel RL, Shkolnik A. 2007. Dog is a dog is a dog: Infant rule learning is not specific to language. Cognition 105:3669–80
     [Google Scholar]
  178. Sagi D. 2011. Perceptual learning in vision research. Vis. Res. 51:131552–66
     [Google Scholar]
  179. Sagi D, Tanne D. 1994. Perceptual learning: learning to see. Curr. Opin. Neurobiol. 4:2195–99
     [Google Scholar]
  180. Santolin C, Rosa-Salva O, Vallortigara G, Regolin L. 2016. Unsupervised statistical learning in newly hatched chicks. Curr. Biol. 26:23R1218–20
     [Google Scholar]
  181. Schapiro AC, Turk-Browne NB, Botvinick MM, Norman KA. 2017. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos. Trans. R. Soc. Lond. B 372:171120160049
     [Google Scholar]
  182. Schonberg C, Marcus GF, Johnson SP. 2018. The roles of item repetition and position in infants’ abstract rule learning. Infant Behav. Dev. 53:64–80
     [Google Scholar]
  183. Schoups A, Vogels R, Orban GA. 1995. Human perceptual learning in identifying the oblique orientation: retinotopy, orientation specificity and monocularity. J. Physiol. 483:Pt. 3797–810
     [Google Scholar]
  184. Schoups A, Vogels R, Qian N, Orban G. 2001. Practising orientation identification improves orientation coding in V1 neurons. Nature 412:6846549–53
     [Google Scholar]
  185. Schulz E, Franklin NT, Gershman SJ. 2020. Finding structure in multi-armed bandits. Cogn. Psychol. 119:101261
     [Google Scholar]
  186. Semedo JD, Zandvakili A, Machens CK, Yu BM, Kohn A. 2019. Cortical areas interact through a communication subspace. Neuron 102:1249–59.e4
     [Google Scholar]
  187. Sherman BE, Turk-Browne NB. 2020. Statistical prediction of the future impairs episodic encoding of the present. PNAS 117:3722760–70
     [Google Scholar]
  188. Siegelman N, Bogaerts L, Armstrong BC, Frost R. 2019. What exactly is learned in visual statistical learning? Insights from Bayesian modeling. Cognition 192:104002
     [Google Scholar]
  189. Siegelman N, Bogaerts L, Elazar A, Arciuli J, Frost R. 2018. Linguistic entrenchment: Prior knowledge impacts statistical learning performance. Cognition 177:198–213
     [Google Scholar]
  190. Smith FW, Muckli L. 2010. Nonstimulated early visual areas carry information about surrounding context. PNAS 107:4620099–103
     [Google Scholar]
  191. Solway A, Diuk C, Córdova N, Yee D, Barto AG et al. 2014. Optimal behavioral hierarchy. PLoS Comput. Biol. 10:8e1003779
     [Google Scholar]
  192. Sotiropoulos G, Seitz AR, Seriès P. 2011. Changing expectations about speed alters perceived motion direction. Curr. Biol. 21:21R883–84
     [Google Scholar]
  193. Srivastava S, Ben-Yosef G, Boix X. 2019. Minimal images in deep neural networks: fragile object recognition in natural images. arXiv:1902.03227 [cs.CV]
  194. Tan Q, Wang Z, Sasaki Y, Watanabe T. 2019. Category-induced transfer of visual perceptual learning. Curr. Biol. 29:81374–78.e3
     [Google Scholar]
  195. Tartaglia EM, Bamert L, Mast FW, Herzog MH. 2009. Human perceptual learning by mental imagery. Curr. Biol. 19:24P2081–85
     [Google Scholar]
  196. Tenenbaum JB, Kemp C, Griffiths TL, Goodman ND. 2011. How to grow a mind: statistics, structure, and abstraction. Science 331:60221279–85
     [Google Scholar]
  197. Tervo D, Gowanlock R, Tenenbaum JB, Gershman SJ. 2016. Toward the neural implementation of structure learning. Curr. Opin. Neurobiol. 37:99–105
     [Google Scholar]
  198. Tolman EC. 1948. Cognitive maps in rats and men. Psychol. Rev. 55:4189–208
     [Google Scholar]
  199. Tomov MS, Schulz E, Gershman SJ. 2021. Multi-task reinforcement learning in humans. Nat. Hum. Behav. 5:6764–73
     [Google Scholar]
  200. Toro JM, Trobalón JB. 2005. Statistical computations over a speech stream in a rodent. Percept. Psychophys. 67:867–75
     [Google Scholar]
  201. Turk-Browne NB, Isola PJ, Scholl BJ, Treat TA. 2008. Multidimensional visual statistical learning. J. Exp. Psychol. Learn. Mem. Cogn. 34:2399–407
     [Google Scholar]
  202. Turk-Browne NB, Jungé J, Scholl BJ. 2005. The automaticity of visual statistical learning. J. Exp. Psychol. Gen. 134:4552–64
     [Google Scholar]
  203. Ullman S, Assif L, Fetaya E, Harari D. 2016. Atoms of recognition in human and computer vision. PNAS 113:102744–49
     [Google Scholar]
  204. van Bergen RS, Ma WJ, Pratte MS, Jehee JFM. 2015. Sensory uncertainty decoded from visual cortex predicts behavior. Nat. Neurosci. 18:121728–30
     [Google Scholar]
  205. Vapnik VN. 1999. An overview of statistical learning theory. IEEE Trans. Neural Netw. 10:5988–99
     [Google Scholar]
  206. von der Heydt R, Peterhans E, Baumgartner G. 1984. Illusory contours and cortical neuron responses. Science 224:46541260–62
     [Google Scholar]
  207. von Luxburg U, Schölkopf B 2011. Statistical learning theory: models, concepts, and results. Handbook of the History of Logic DM Gabbay, S Hartmann, J Woods 651–706 Amsterdam: Elsevier
     [Google Scholar]
  208. 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]
  209. Wang JX. 2021. Meta-learning in natural and artificial intelligence. Curr. Opin. Behav. Sci. 38:90–95
     [Google Scholar]
  210. Wang R, Wang J, Zhang J-Y, Xie X-Y, Yang Y-X et al. 2016. Perceptual learning at a conceptual level. J. Neurosci. 36:72238–46
     [Google Scholar]
  211. Wang R, Zhang J-Y, Klein SA, Levi DM, Yu C. 2014. Vernier perceptual learning transfers to completely untrained retinal locations after double training: a “piggybacking” effect. J. Vis. 14:1312
     [Google Scholar]
  212. Watanabe T, Sasaki Y. 2015. Perceptual learning: toward a comprehensive theory. Annu. Rev. Psychol. 66:197–221
     [Google Scholar]
  213. Wenliang LK, Seitz AR. 2018. Deep neural networks for modeling visual perceptual learning. J. Neurosci. 38:276028–44
     [Google Scholar]
  214. Werchan DM, Amso D. 2020. Top-down knowledge rapidly acquired through abstract rule learning biases subsequent visual attention in 9-month-old infants. Dev. Cogn. Neurosci. 42:100761
     [Google Scholar]
  215. Werchan DM, Collins AGE, Frank MJ, Amso D. 2015. 8-Month-old infants spontaneously learn and generalize hierarchical rules. Psychol. Sci. 26:6805–15
     [Google Scholar]
  216. Woods KJP, McDermott JH. 2018. Schema learning for the cocktail party problem. PNAS 115:14E3313–22
     [Google Scholar]
  217. Wu CM, Schulz E, Garvert MM, Meder B, Schuck NW. 2020. Correction: similarities and differences in spatial and non-spatial cognitive maps. PLOS Comput. Biol. 16:10e1008384
     [Google Scholar]
  218. Wu CM, Schulz E, Speekenbrink M, Nelson JD, Meder B. 2018. Generalization guides human exploration in vast decision spaces. Nat. Hum. Behav. 2:12915–24
     [Google Scholar]
  219. Xiao L-Q, Zhang J-Y, Wang R, Klein SA, Levi DM, Yu C. 2008. Complete transfer of perceptual learning across retinal locations enabled by double training. Curr. Biol. 18:241922–26
     [Google Scholar]
  220. Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ. 2014. Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS 111:238619–24
     [Google Scholar]
  221. Yang S, Bill J, Drugowitsch J, Gershman SJ. 2021. Human visual motion perception shows hallmarks of Bayesian structural inference. Sci. Rep. 11:3714
     [Google Scholar]
  222. Yildirim I, Jacobs RA. 2013. Transfer of object category knowledge across visual and haptic modalities: experimental and computational studies. Cognition 126:2135–48
     [Google Scholar]
  223. Yu C, Klein SA, Levi DM. 2004. Perceptual learning in contrast discrimination and the (minimal) role of context. J. Vis. 4:3169–82
     [Google Scholar]
  224. Yuille A, Kersten D. 2006. Vision as Bayesian inference: analysis by synthesis?. Trends Cogn. Sci. 10:7301–8
     [Google Scholar]
  225. Zhang J-Y, Kuai S-G, Xiao L-Q, Klein SA, Levi DM, Yu C 2008. Stimulus coding rules for perceptual learning. PLOS Biol. 6:8e197
     [Google Scholar]
  226. Zhao J, Ngo N, McKendrick R, Turk-Browne NB. 2011. Mutual interference between statistical summary perception and statistical learning. Psychol. Sci. 22:91212–19
     [Google Scholar]
  227. Zhou H, Friedman HS, von der Heydt R. 2000. Coding of border ownership in monkey visual cortex. J. Neurosci. 20:176594–611
     [Google Scholar]
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Statistical Learning in Vision
Annual Review of Vision Science 8, 265 (2022); https://doi.org/10.1146/annurev-vision-100720-103343
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