1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Psychology
  4. Volume 74, 2023
  5. Article

Annual Review of Psychology

Volume 74, 2023

Understanding Human Object Vision: A Picture Is Worth a Thousand Representations

Abstract

Objects are the core meaningful elements in our visual environment. Classic theories of object vision focus upon object recognition and are elegant and simple. Some of their proposals still stand, yet the simplicity is gone. Recent evolutions in behavioral paradigms, neuroscientific methods, and computational modeling have allowed vision scientists to uncover the complexity of the multidimensional representational space that underlies object vision. We review these findings and propose that the key to understanding this complexity is to relate object vision to the full repertoire of behavioral goals that underlie human behavior, running far beyond object recognition. There might be no such thing as core object recognition, and if it exists, then its importance is more limited than traditionally thought.

Keyword(s): behavior, DCNNs, deep convolutional neural networks, object recognition, object representations, visual cortex
Loading

Article metrics loading...

/content/journals/10.1146/annurev-psych-032720-041031
2023-01-18
2024-05-08
Loading full text...

Full text loading...

/deliver/fulltext/psych/74/1/annurev-psych-032720-041031.html?itemId=/content/journals/10.1146/annurev-psych-032720-041031&mimeType=html&fmt=ahah

Literature Cited

  1. Arcaro MJ, Livingstone MS 2017. A hierarchical, retinotopic proto-organization of the primate visual system at birth. eLife 6:e26196
     [Google Scholar]
  2. Arcaro MJ, Livingstone MS. 2021. On the relationship between maps and domains in inferotemporal cortex. Nat. Rev. Neurosci. 22:9573–83
     [Google Scholar]
  3. Attneave F. 1957. Physical determinants of the judged complexity of shapes. J. Exp. Psychol. 53:4221–27
     [Google Scholar]
  4. Avberšek LK, Zeman A, Op de Beeck HP. 2021. Training for object recognition with increasing spatial frequency: a comparison of deep learning with human vision. J. Vis. 21:1014
     [Google Scholar]
  5. Baker N, Lu H, Erlikhman G, Kellman PJ. 2020. Local features and global shape information in object classification by deep convolutional neural networks. Vis. Res. 172:46–61
     [Google Scholar]
  6. Baldassi C, Alemi-Neissi A, Pagan M, DiCarlo JJ, Zecchina R, Zoccolan D. 2013. Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons. PLOS Comput. Biol. 9:8e1003167
     [Google Scholar]
  7. Bao P, She L, McGill M, Tsao DY. 2020. A map of object space in primate inferotemporal cortex. Nature 583:7814103–8
     [Google Scholar]
  8. Barton JJ, Press DZ, Keenan JP, O'Connor M. 2002. Lesions of the fusiform face area impair perception of facial configuration in prosopagnosia. Neurology 58:171–78
     [Google Scholar]
  9. Baylis GC, Rolls ET, Leonard CM. 1987. Functional subdivisions of the temporal lobe neocortex. J. Neurosci. 7:2330–42
     [Google Scholar]
  10. Beauchamp MS, Lee KE, Haxby JV, Martin A. 2002. Parallel visual motion processing streams for manipulable objects and human movements. Neuron 34:1149–59
     [Google Scholar]
  11. Bernardi R, Pezzelle S. 2021. Linguistic issues behind visual question answering. Lang. Linguist. Compass 15:6e12417
     [Google Scholar]
  12. Biederman I. 1972. Perceiving real-world scenes. Science 177:404377–80
     [Google Scholar]
  13. Biederman I. 1987. Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94:2115–47
     [Google Scholar]
  14. Blauch NM, Behrmann M, Plaut DC. 2022. A connectivity-constrained computational account of topographic organization in primate high-level visual cortex. PNAS 119:3e2112566119
     [Google Scholar]
  15. Bonner MF, Epstein RA. 2018. Computational mechanisms underlying cortical responses to the affordance properties of visual scenes. PLOS Comput. Biol. 14:4e1006111
     [Google Scholar]
  16. Booth AE, Waxman S. 2002. Object names and object functions serve as cues to categories for infants. Dev. Psychol. 38:6948–57
     [Google Scholar]
  17. Bracci S, Caramazza A, Peelen MV. 2015. Representational similarity of body parts in human occipitotemporal cortex. J. Neurosci. 35:3812977–85
     [Google Scholar]
  18. Bracci S, Caramazza A, Peelen MV. 2018. View-invariant representation of hand postures in the human lateral occipitotemporal cortex. Neuroimage 181:446–52
     [Google Scholar]
  19. Bracci S, Cavina-Pratesi C, Ietswaart M, Caramazza A, Peelen MV. 2012. Closely overlapping responses to tools and hands in left lateral occipitotemporal cortex. J. Neurophysiol. 107:51443–56
     [Google Scholar]
  20. Bracci S, Ietswaart M, Peelen MV, Cavina-Pratesi C. 2010. Dissociable neural responses to hands and non-hand body parts in human left extrastriate visual cortex. J. Neurophysiol. 103:63389–97
     [Google Scholar]
  21. Bracci S, Mraz J, Zeman A, Leys G, Op de Beeck HP. 2022. The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities. bioRxiv 456197. https://doi.org/10.1101/2021.08.13.456197
    [Crossref]
  22. Bracci S, Op de Beeck HP. 2016. Dissociations and associations between shape and category representations in the two visual pathways. J. Neurosci. 36:2432–44
     [Google Scholar]
  23. Bracci S, Peelen MV. 2013. Body and object effectors: the organization of object representations in high-level visual cortex reflects body–object interactions. J. Neurosci. 33:4618247–58
     [Google Scholar]
  24. Bracci S, Ritchie JB, Kalfas I, Op de Beeck HP. 2019. The ventral visual pathway represents animal appearance over animacy, unlike human behavior and deep neural networks. J. Neurosci. 39:336513–25
     [Google Scholar]
  25. Bracci S, Ritchie JB, Op de Beeck HP. 2017. On the partnership between neural representations of object categories and visual features in the ventral visual pathway. Neuropsychologia 105:153–64
     [Google Scholar]
  26. Buiatti M, Di Giorgio E, Piazza M, Polloni C, Menna G et al. 2019. Cortical route for facelike pattern processing in human newborns. PNAS 116:104625–30
     [Google Scholar]
  27. Canário N, Jorge L, Silva ML, Soares MA, Castelo-Branco M 2016. Distinct preference for spatial frequency content in ventral stream regions underlying the recognition of scenes, faces, bodies and other objects. Neuropsychologia 87:110–19
     [Google Scholar]
  28. Caramazza A, Shelton JR. 1998. Domain-specific knowledge systems in the brain: the animate-inanimate distinction. J. Cogn. Neurosci. 10:11–34
     [Google Scholar]
  29. Chang L, Tsao DY. 2017. The code for facial identity in the primate brain. Cell 169:61013–28
     [Google Scholar]
  30. Chao LL, Haxby JV, Martin A. 1999. Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects. Nat. Neurosci. 2:10913–19
     [Google Scholar]
  31. Chklovskii DB, Koulakov AA. 2004. Maps in the brain: What can we learn from them?. Annu. Rev. Neurosci. 27:369–92
     [Google Scholar]
  32. Cohen L, Lehéricy S, Chochon F, Lemer C, Rivaud S, Dehaene S. 2002. Language-specific tuning of visual cortex? Functional properties of the Visual Word Form Area. Brain 125:51054–69
     [Google Scholar]
  33. Connolly AC, Guntupalli JS, Gors J, Hanke M, Halchenko YO et al. 2012. The representation of biological classes in the human brain. J. Neurosci. 32:82608–18
     [Google Scholar]
  34. Cox DD, Savoy RL. 2003. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19:2261–70
     [Google Scholar]
  35. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. 2009. ImageNet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition248–55 New York: IEEE
     [Google Scholar]
  36. Desimone R, Albright TD, Gross CG, Bruce C. 1984. Stimulus-selective properties of inferior temporal neurons in the macaque. J. Neurosci. 4:82051–62
     [Google Scholar]
  37. Di Giorgio E, Lunghi M, Simion F, Vallortigara G. 2017. Visual cues of motion that trigger animacy perception at birth: the case of self-propulsion. Dev. Sci. 20:4e12394
     [Google Scholar]
  38. DiCarlo JJ, Maunsell JH. 2003. Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. J. Neurophysiol. 89:63264–78
     [Google Scholar]
  39. DiCarlo JJ, Zoccolan D, Rust NC. 2012. How does the brain solve visual object recognition?. Neuron 73:3415–34
     [Google Scholar]
  40. Dobs K, Martinez J, Kell AJ, Kanwisher N. 2022. Brain-like functional specialization emerges spontaneously in deep neural networks. Sci. Adv. 8:11eabl8913
     [Google Scholar]
  41. Downing PE, Jiang Y, Shuman M, Kanwisher N. 2001. A cortical area selective for visual processing of the human body. Science 293:55392470–73
     [Google Scholar]
  42. Dujmović M, Malhotra G, Bowers JS 2020. What do adversarial images tell us about human vision?. eLife 9:e55978
     [Google Scholar]
  43. Duyck S, Martens F, Chen CY, Op de Beeck HP 2021. How visual expertise changes representational geometry: a behavioral and neural perspective. J. Cogn. Neurosci. 33:122461–76
     [Google Scholar]
  44. Dwivedi K, Bonner MF, Cichy RM, Roig G. 2021. Unveiling functions of the visual cortex using task-specific deep neural networks. PLOS Comput. Biol. 17:8e1009267
     [Google Scholar]
  45. Elmoznino E, Bonner MF. 2022. High-performing neural network models of visual cortex benefit from high latent dimensionality. bioRxiv 499969. https://doi.org/10.1101/2022.07.13.499969
    [Crossref]
  46. Epstein R, Kanwisher N. 1998. A cortical representation of the local visual environment. Nature 392:6676598–601
     [Google Scholar]
  47. Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann FA, Brendel W. 2018. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv:1811.12231 [cs.CV]
  48. Gibson JJ. 1979. The Ecological Approach to Visual Perception New York: Psychol. Press
  49. Gobbini MI, Koralek AC, Bryan RE, Montgomery KJ, Haxby JV. 2007. Two takes on the social brain: a comparison of theory of mind tasks. J. Cogn. Neurosci. 19:111803–14
     [Google Scholar]
  50. Goffaux V, Dakin S. 2010. Horizontal information drives the behavioral signatures of face processing. Front. Psychol. 1:143
     [Google Scholar]
  51. Gomez J, Natu V, Jeska B, Barnett M, Grill-Spector K. 2018. Development differentially sculpts receptive fields across early and high-level human visual cortex. Nat. Commun. 9:788
     [Google Scholar]
  52. Goodale MA, Milner AD. 1992. Separate visual pathways for perception and action. Trends Neurosci. 15:120–25
     [Google Scholar]
  53. Grabner H, Gall J, Van Gool L. 2011. What makes a chair a chair?. CVPR 2011: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition1529–36 New York: IEEE
     [Google Scholar]
  54. Graziano MS, Aflalo TN. 2007. Mapping behavioral repertoire onto the cortex. Neuron 56:2239–51
     [Google Scholar]
  55. Grill-Spector K, Kushnir T, Edelman S, Avidan G, Itzchak Y, Malach R. 1999. Differential processing of objects under various viewing conditions in the human lateral occipital complex. Neuron 24:1187–203
     [Google Scholar]
  56. Grill-Spector K, Kushnir T, Hendler T, Edelman S, Itzchak Y, Malach R. 1998. A sequence of object-processing stages revealed by fMRI in the human occipital lobe. Hum. Brain Mapp. 6:4316–28
     [Google Scholar]
  57. Grill-Spector K, Weiner KS. 2014. The functional architecture of the ventral temporal cortex and its role in categorization. Nat. Rev. Neurosci. 15:8536–48
     [Google Scholar]
  58. Gross CG, Rocha-Miranda CD, Bender DB. 1972. Visual properties of neurons in inferotemporal cortex of the macaque. J. Neurophysiol. 35:196–111
     [Google Scholar]
  59. Grossman ED, Blake R. 2002. Brain areas active during visual perception of biological motion. Neuron 35:61167–75
     [Google Scholar]
  60. Grossman S, Gaziv G, Yeagle EM, Harel M, Mégevand P et al. 2019. Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks. Nat. Commun. 10:4934
     [Google Scholar]
  61. Güçlü U, van Gerven MA. 2015. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35:2710005–14
     [Google Scholar]
  62. Hasson U, Levy I, Behrmann M, Hendler T, Malach R. 2002. Eccentricity bias as an organizing principle for human high-order object areas. Neuron 34:3479–90
     [Google Scholar]
  63. Hebart MN, Zheng CY, Pereira F, Baker CI. 2020. Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nat. Hum. Behav. 4:111173–85
     [Google Scholar]
  64. Hoffman DD, Richards WA. 1984. Parts of recognition. Cognition 18:1–365–96
     [Google Scholar]
  65. Hong H, Yamins DL, Majaj NJ, DiCarlo JJ. 2016. Explicit information for category-orthogonal object properties increases along the ventral stream. Nat. Neurosci. 19:4613–22
     [Google Scholar]
  66. Hu JM, Song XM, Wang Q, Roe AW 2020. Curvature domains in V4 of macaque monkey. eLife 9:e57261
     [Google Scholar]
  67. Hubel DH, Wiesel TN. 1968. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195:1215–43
     [Google Scholar]
  68. Hung CP, Kreiman G, Poggio T, DiCarlo JJ. 2005. Fast readout of object identity from macaque inferior temporal cortex. Science 310:5749863–66
     [Google Scholar]
  69. Jagadeesh AV, Gardner JL. 2022. Texture-like representation of objects in human visual cortex. PNAS 119:17e2115302119
     [Google Scholar]
  70. Jain N, Wang A, Henderson MM, Lin R, Prince JS et al. 2022. Food for thought: selectivity for food in human ventral visual cortex. bioRxiv 492983. https://doi.org/10.1101/2022.05.22.492983
    [Crossref]
  71. James W. 1890. The Principles of Psychology, Vol. 1 London: Macmillan
  72. Jenkins R, Dowsett AJ, Burton AM. 2018. How many faces do people know?. Proc. R. Soc. B 285:188820181319
     [Google Scholar]
  73. Josephs EL, Konkle T. 2020. Large-scale dissociations between views of objects, scenes, and reachable-scale environments in visual cortex. PNAS 117:4729354–62
     [Google Scholar]
  74. Jozwik KM, Najarro E, van den Bosch JJ, Charest I, Kriegeskorte N, Cichy RM. 2021. Disentangling five dimensions of animacy in human brain and behaviour. bioRxiv 459854. https://doi.org/10.1101/2021.09.12.459854
    [Crossref]
  75. Kaiser D, Quek GL, Cichy RM, Peelen MV. 2019. Object vision in a structured world. Trends Cogn. Sci. 23:8672–85
     [Google Scholar]
  76. Kanwisher N, McDermott J, Chun MM. 1997. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17:114302–11
     [Google Scholar]
  77. Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. 2019. Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. Nat. Neurosci. 22:6974–83
     [Google Scholar]
  78. Kayaert G, Biederman I, Op de Beeck HP, Vogels R 2005. Tuning for shape dimensions in macaque inferior temporal cortex. Eur. J. Neurosci. 22:1212–24
     [Google Scholar]
  79. Keller TA, Gao Q, Welling M. 2021. Modeling category-selective cortical regions with topographic variational autoencoders. arXiv:2110.13911 [q-bio.NC]
  80. Khosla M, Murty NAR, Kanwisher NG. 2022. A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition. bioRxiv 496922. https://doi.org/10.1101/2022.06.21.496922
    [Crossref]
  81. Kietzmann TC, Spoerer CJ, Sörensen LK, 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]
  82. Kim E, Rego J, Watkins Y, Kenyon GT. 2020. Modeling biological immunity to adversarial examples. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition4666–75 New York: IEEE
     [Google Scholar]
  83. Konkle T, Alvarez GA. 2022. Beyond category-supervision: computational support for domain-general pressures guiding human visual system representation. Nat. Commun. 13:491
     [Google Scholar]
  84. Konkle T, Oliva A. 2012. A real-world size organization of object responses in occipitotemporal cortex. Neuron 74:61114–24
     [Google Scholar]
  85. Kourtzi Z, Kanwisher N. 2001. Representation of perceived object shape by the human lateral occipital complex. Science 293:55341506–9
     [Google Scholar]
  86. Kravitz DJ, Peng CS, Baker CI. 2011. Real-world scene representations in high-level visual cortex: It's the spaces more than the places. J. Neurosci. 31:207322–33
     [Google Scholar]
  87. Kravitz DJ, Saleem KS, Baker CI, Ungerleider LG, Mishkin M. 2013. The ventral visual pathway: an expanded neural framework for the processing of object quality. Trends Cogn. Sci. 17:126–49
     [Google Scholar]
  88. Kriegeskorte N, Mur M, Ruff DA, Kiani R, Bodurka J et al. 2008. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60:61126–41
     [Google Scholar]
  89. Krizhevsky A, Sutskever I, Hinton GE. 2012. ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Proc. Syst. 25:1097–105
     [Google Scholar]
  90. 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]
  91. Ito M, Tamura H, Fujita I, Tanaka K. 1995. Size and position invariance of neuronal responses in monkey inferotemporal cortex. J. Neurophysiol. 73:1218–26
     [Google Scholar]
  92. Laiacona M, Barbarotto R, Capitani E. 1993. Perceptual and associative knowledge in category specific impairment of semantic memory: a study of two cases. Cortex 29:4727–40
     [Google Scholar]
  93. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:7553436–44
     [Google Scholar]
  94. Lee H, Margalit E, Jozwik KM, Cohen MA, Kanwisher N et al. 2020. Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network. bioRxiv 185116. https://doi.org/10.1101/2020.07.09.185116
    [Crossref]
  95. Levy I, Hasson U, Avidan G, Hendler T, Malach R. 2001. Center–periphery organization of human object areas. Nat. Neurosci. 4:5533–39
     [Google Scholar]
  96. Li SPD, Bonner MF. 2021. Tuning in scene-preferring cortex for mid-level visual features gives rise to selectivity across multiple levels of stimulus complexity. bioRxiv 461733. https://doi.org/10.1101/2021.09.24.461733
    [Crossref]
  97. Lindsay GW. 2021. Convolutional neural networks as a model of the visual system: past, present, and future. J. Cogn. Neurosci. 33:102017–31
     [Google Scholar]
  98. Long B, Yu CP, Konkle T. 2018. Mid-level visual features underlie the high-level categorical organization of the ventral stream. PNAS 115:38E9015–24
     [Google Scholar]
  99. Lotter W, Kreiman G, Cox D. 2020. A neural network trained for prediction mimics diverse features of biological neurons and perception. Nat. Mach. Intell. 2:4210–19
     [Google Scholar]
  100. Mahon BZ, Caramazza A. 2011. What drives the organization of object knowledge in the brain?. Trends Cogn. Sci. 15:397–103
     [Google Scholar]
  101. Maimon-Mor RO, Makin TR 2020. Is an artificial limb embodied as a hand? Brain decoding in prosthetic limb users. PLOS Biol. 18:6e3000729
     [Google Scholar]
  102. Malach R, Levy I, Hasson U. 2002. The topography of high-order human object areas. Trends Cogn. Sci. 6:4176–84
     [Google Scholar]
  103. Malach R, Reppas JB, Benson RR, Kwong KK, Jiang H et al. 1995. Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. PNAS 92:188135–39
     [Google Scholar]
  104. Marr D. 1980. Visual information processing: the structure and creation of visual representations. Philos. Trans. R. Soc. B 290:1038199–218
     [Google Scholar]
  105. Marr D, Nishihara HK. 1978. Representation and recognition of the spatial organization of three-dimensional shapes. Proc. R. Soc. B 200:1140269–94
     [Google Scholar]
  106. Martin A, Weisberg J. 2003. Neural foundations for understanding social and mechanical concepts. Cogn. Neuropsychol. 20:3–6575–87
     [Google Scholar]
  107. Mehrer J, Spoerer CJ, Jones EC, Kriegeskorte N, Kietzmann TC. 2021. An ecologically motivated image dataset for deep learning yields better models of human vision. PNAS 118:8e2011417118
     [Google Scholar]
  108. Miller LE, Montroni L, Koun E, Salemme R, Hayward V, Farnè A. 2018. Sensing with tools extends somatosensory processing beyond the body. Nature 561:7722239–42
     [Google Scholar]
  109. Mishkin M, Ungerleider LG. 1982. Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys. Behav. Brain Res. 6:157–77
     [Google Scholar]
  110. Morgenstern Y, Hartmann F, Schmidt F, Tiedemann H, Prokott E et al. 2021. An image-computable model of human visual shape similarity. PLOS Comput. Biol. 17:6e1008981
     [Google Scholar]
  111. Mormann F, Kornblith S, Cerf M, Ison MJ, Kraskov A et al. 2017. Scene-selective coding by single neurons in the human parahippocampal cortex. PNAS 114:51153–58
     [Google Scholar]
  112. Nasr S, Echavarria CE, Tootell RB. 2014. Thinking outside the box: Rectilinear shapes selectively activate scene-selective cortex. J. Neurosci. 34:206721–35
     [Google Scholar]
  113. Nguyen A, Yosinski J, Clune J. 2015. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. 2015 IEEE Conference on Computer Vision and Pattern Recognition427–36 New York: IEEE
     [Google Scholar]
  114. Oakes LM, Madole KL. 2008. Function revisited: how infants construe functional features in their representation of objects. Adv. Child Dev. Behav. 36:135–85
     [Google Scholar]
  115. Op de Beeck HP, Deutsch JA, Vanduffel W, Kanwisher NG, DiCarlo JJ. 2008a. A stable topography of selectivity for unfamiliar shape classes in monkey inferior temporal cortex. Cereb. Cortex 18:71676–94
     [Google Scholar]
  116. Op de Beeck HP, Haushofer J, Kanwisher NG. 2008b. Interpreting fMRI data: maps, modules and dimensions. Nat. Rev. Neurosci. 9:2123–35
     [Google Scholar]
  117. Op de Beeck HP, Pillet I, Ritchie JB 2019. Factors determining where category-selective areas emerge in visual cortex. Trends Cogn. Sci. 23:9784–97
     [Google Scholar]
  118. Op de Beeck HP, Torfs K, Wagemans J. 2008c. Perceived shape similarity among unfamiliar objects and the organization of the human object vision pathway. J. Neurosci. 28:4010111–23
     [Google Scholar]
  119. Op De Beeck HP, Vogels R 2000. Spatial sensitivity of macaque inferior temporal neurons. J. Comp. Neurol. 426:4505–18
     [Google Scholar]
  120. Op De Beeck HP, Wagemans J, Vogels R. 2001. Inferotemporal neurons represent low-dimensional configurations of parameterized shapes. Nat. Neurosci. 4:121244–52
     [Google Scholar]
  121. Papeo L, Stein T, Soto-Faraco S. 2017. The two-body inversion effect. Psychol. Sci. 28:3369–79
     [Google Scholar]
  122. Peelen MV, Downing PE. 2017. Category selectivity in human visual cortex: beyond visual object recognition. Neuropsychologia 105:177–83
     [Google Scholar]
  123. Pitcher D, Charles L, Devlin JT, Walsh V, Duchaine B. 2009. Triple dissociation of faces, bodies, and objects in extrastriate cortex. Curr. Biol. 19:4319–24
     [Google Scholar]
  124. Pitcher D, Ungerleider LG. 2021. Evidence for a third visual pathway specialized for social perception. Trends Cogn. Sci. 25:2100–10
     [Google Scholar]
  125. Powell LJ, Kosakowski HL, Saxe R 2018. Social origins of cortical face areas. Trends Cogn. Sci. 22:9752–63
     [Google Scholar]
  126. Proklova D, Goodale MA. 2022. The role of animal faces in the animate-inanimate distinction in the ventral temporal cortex. Neuropsychologia 169:108192
     [Google Scholar]
  127. Proklova D, Kaiser D, Peelen MV. 2016. Disentangling representations of object shape and object category in human visual cortex: the animate–inanimate distinction. J. Cogn. Neurosci. 28:5680–92
     [Google Scholar]
  128. Rajimehr R, Devaney KJ, Bilenko NY, Young JC, Tootell RB. 2011. The “parahippocampal place area” responds preferentially to high spatial frequencies in humans and monkeys. PLOS Biol. 9:4e1000608
     [Google Scholar]
  129. Ratan Murty NA, Bashivan P, Abate A, DiCarlo JJ, Kanwisher N 2021. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nat. Commun. 12:5540
     [Google Scholar]
  130. Riesenhuber M, Poggio T. 1999. Hierarchical models of object recognition in cortex. Nat. Neurosci. 2:111019–25
     [Google Scholar]
  131. Ritchie JB, Zeman AA, Bosmans J, Sun S, Verhaegen K, Op de Beeck HP. 2021. Untangling the animacy organization of occipitotemporal cortex. J. Neurosci. 41:337103–19
     [Google Scholar]
  132. Rosch E, Mervis CB, Gray WD, Johnson DM, Boyes-Braem P. 1976. Basic objects in natural categories. Cogn. Psychol. 8:3382–439
     [Google Scholar]
  133. Rosenke M, van Hoof R, van den Hurk J, Grill-Spector K, Goebel R. 2021. A probabilistic functional atlas of human occipito-temporal visual cortex. Cereb. Cortex 31:1603–19
     [Google Scholar]
  134. Saxe R, Wexler A. 2005. Making sense of another mind: the role of the right temporo-parietal junction. Neuropsychologia 43:101391–99
     [Google Scholar]
  135. Saygin ZM, Osher DE, Koldewyn K, Reynolds G, Gabrieli JD, Saxe RR. 2012. Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nat. Neurosci. 15:2321–27
     [Google Scholar]
  136. Schrimpf M, Kubilius J, Hong H, Majaj NJ, Rajalingham R et al. 2020. Brain-score: Which artificial neural network for object recognition is most brain-like?. bioRxiv 407007. https://doi.org/10.1101/407007
    [Crossref]
  137. Seijdel N, Loke J, Van de Klundert R, Van der Meer M, Quispel E et al. 2021. On the necessity of recurrent processing during object recognition: It depends on the need for scene segmentation. J. Neurosci. 41:296281–89
     [Google Scholar]
  138. Sha L, Haxby JV, Abdi H, Guntupalli JS, Oosterhof NN et al. 2015. The animacy continuum in the human ventral vision pathway. J. Cogn. Neurosci. 27:4665–78
     [Google Scholar]
  139. Shepard RN, Chipman S. 1970. Second-order isomorphism of internal representations: shapes of states. Cogn. Psychol. 1:11–17
     [Google Scholar]
  140. Taubert J, Ritchie JB, Ungerleider LG, Baker CI. 2022. One object, two networks? Assessing the relationship between the face and body-selective regions in the primate visual system. Brain Struct. Funct. 227:41423–38
     [Google Scholar]
  141. Thorat S, Proklova D, Peelen MV 2019. The nature of the animacy organization in human ventral temporal cortex. eLife 8:e47142
     [Google Scholar]
  142. Van den Heiligenberg FM, Orlov T, Macdonald SN, Duff EP, Henderson Slater D et al. 2018. Artificial limb representation in amputees. Brain 141:51422–33
     [Google Scholar]
  143. Vogels R. 1999. Categorization of complex visual images by rhesus monkeys. Part 2: single-cell study. Eur. J. Neurosci. 11:41239–55
     [Google Scholar]
  144. Voynov A, Babenko A. 2020. Unsupervised discovery of interpretable directions in the GAN latent space. PMLR 119:9786–96
     [Google Scholar]
  145. Yovel G, Kanwisher N. 2005. The neural basis of the behavioral face-inversion effect. Curr. Biol. 15:242256–62
     [Google Scholar]
  146. Wardle SG, Taubert J, Teichmann L, Baker CI. 2020. Rapid and dynamic processing of face pareidolia in the human brain. Nat. Commun. 11:4518
     [Google Scholar]
  147. Wurm MF, Caramazza A. 2022. Two “what” pathways for action and object recognition. Trends Cogn. Sci. 26:2103–16
     [Google Scholar]
  148. Zeman AA, Ritchie JB, Bracci S, de Beeck HO. 2020. Orthogonal representations of object shape and category in deep convolutional neural networks and human visual cortex. Sci. Rep. 10:12453
     [Google Scholar]
  149. Zhang M, Tseng C, Kreiman G. 2020. Putting visual object recognition in context. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition12985–94 New York: IEEE
     [Google Scholar]
/content/journals/10.1146/annurev-psych-032720-041031
Loading
Understanding Human Object Vision: A Picture Is Worth a Thousand Representations
Annual Review of Psychology 74, 113 (2023); https://doi.org/10.1146/annurev-psych-032720-041031
/content/journals/10.1146/annurev-psych-032720-041031
/content/journals/10.1146/annurev-psych-032720-041031
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