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Abstract

Patterns of brain activity contain meaningful information about the perceived world. Recent decades have welcomed a new era in neural analyses, with computational techniques from machine learning applied to neural data to decode information represented in the brain. In this article, we review how decoding approaches have advanced our understanding of visual representations and discuss efforts to characterize both the complexity and the behavioral relevance of these representations. We outline the current consensus regarding the spatiotemporal structure of visual representations and review recent findings that suggest that visual representations are at once robust to perturbations, yet sensitive to different mental states. Beyond representations of the physical world, recent decoding work has shone a light on how the brain instantiates internally generated states, for example, during imagery and prediction. Going forward, decoding has remarkable potential to assess the functional relevance of visual representations for human behavior, reveal how representations change across development and during aging, and uncover their presentation in various mental disorders.

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2023-09-15
2024-04-25
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Literature Cited

  1. Albers AM, Kok P, Toni I, Dijkerman HC, de Lange FP 2013. Shared representations for working memory and mental imagery in early visual cortex. Curr. Biol. 23:151427–31
    [Google Scholar]
  2. Ashton K, Zinszer BD, Cichy RM, Nelson CA, Aslin RN, Bayet L. 2022. Time-resolved multivariate pattern analysis of infant EEG data: a practical tutorial. Dev. Cogn. Neurosci. 54:101094
    [Google Scholar]
  3. Baker B, Lansdell B, Kording KP. 2022. Three aspects of representation in neuroscience. Trends Cogn. Sci. 26:11942–58
    [Google Scholar]
  4. Bar M. 2004. Visual objects in context. Nat. Rev. Neurosci. 5:8617–29
    [Google Scholar]
  5. Barron HC, Garvert MM, Behrens TEJ. 2016. Repetition suppression: a means to index neural representations using BOLD?. Philos. Trans. R. Soc. B 371:170520150355
    [Google Scholar]
  6. Bayet L, Zinszer BD, Reilly E, Cataldo JK, Pruitt Z et al. 2020. Temporal dynamics of visual representations in the infant brain. Dev. Cogn. Neurosci. 45:100860
    [Google Scholar]
  7. Blom T, Feuerriegel D, Johnson P, Bode S, Hogendoorn H. 2020. Predictions drive neural representations of visual events ahead of incoming sensory information. PNAS 117:137510–15
    [Google Scholar]
  8. Bode S, Feuerriegel D, Bennett D, Alday PM. 2019. The Decision Decoding ToolBOX (DDTBOX)—a multivariate pattern analysis toolbox for event-related potentials. Neuroinformatics 17:127–42
    [Google Scholar]
  9. 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]
  10. Bracci S, Op de Beeck H. 2022. Understanding human object vision: a picture is worth a thousand representations. Annu. Rev. Psychol. 74:113–35
    [Google Scholar]
  11. 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]
  12. Bracci S, Ritchie JB, Op de Beeck H. 2017. On the partnership between neural representations of object categories and visual features in the ventral visual pathway. Neuropsychologia 105:153–64
    [Google Scholar]
  13. Brandman T, Peelen MV. 2017. Interaction between scene and object processing revealed by human fMRI and MEG decoding. J. Neurosci. 37:327700–10
    [Google Scholar]
  14. Breedlove JL, St-Yves G, Olman CA, Naselaris T. 2020. Generative feedback explains distinct brain activity codes for seen and mental images. Curr. Biol. 30:122211–24.e6
    [Google Scholar]
  15. Brouwer GJ, Heeger DJ. 2009. Decoding and reconstructing color from responses in human visual cortex. J. Neurosci. 29:4413992–93
    [Google Scholar]
  16. Carlson T, Goddard E, Kaplan DM, Klein C, Ritchie JB. 2018. Ghosts in machine learning for cognitive neuroscience: moving from data to theory. NeuroImage 180:Pt A88–100
    [Google Scholar]
  17. Carlson TA, Grootswagers T, Robinson AK 2020. An introduction to time-resolved decoding analysis for M/EEG. The Cognitive Neurosciences D Poeppel, GR Mangun, MS Gazzaniga 679–90. Cambridge, MA: MIT Press. , 6th ed..
    [Google Scholar]
  18. Carlson TA, Hogendoorn H, Kanai R, Mesik J, Turret J. 2011. High temporal resolution decoding of object position and category. J. Vis. 11:109
    [Google Scholar]
  19. Carlson TA, Simmons RA, Kriegeskorte N, Slevc LR. 2014. The emergence of semantic meaning in the ventral temporal pathway. J. Cogn. Neurosci. 26:1120–31
    [Google Scholar]
  20. Carlson TA, Tovar DA, Alink A, Kriegeskorte N. 2013. Representational dynamics of object vision: the first 1000 ms. J. Vis. 13:101
    [Google Scholar]
  21. Chang N, Pyles JA, Marcus A, Gupta A, Tarr MJ, Aminoff EM. 2019. BOLD5000, a public fMRI dataset while viewing 5000 visual images. Sci. Data 6:49
    [Google Scholar]
  22. Cichy RM, Pantazis D, Oliva A. 2014. Resolving human object recognition in space and time. Nat. Neurosci. 17:3455–62
    [Google Scholar]
  23. Cichy RM, Pantazis D, Oliva A. 2016. Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition. Cereb. Cortex. 26:83563–79
    [Google Scholar]
  24. Cichy RM, Ramirez FM, Pantazis D. 2015. Can visual information encoded in cortical columns be decoded from magnetoencephalography data in humans?. NeuroImage 121:193–204
    [Google Scholar]
  25. Contini EW, Goddard E, Wardle SG 2021. Reaction times predict dynamic brain representations measured with MEG for only some object categorisation tasks. Neuropsychologia 151:107687
    [Google Scholar]
  26. Contini EW, Wardle SG, Carlson TA. 2017. Decoding the time-course of object recognition in the human brain: from visual features to categorical decisions. Neuropsychologia 105:165–76
    [Google Scholar]
  27. de Haas B, Schwarzkopf DS, Alvarez I, Lawson RP, Henriksson L et al. 2016. Perception and processing of faces in the human brain is tuned to typical feature locations. J. Neurosci. 36:369289–302
    [Google Scholar]
  28. Desimone R, Duncan J. 1995. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18:193–222
    [Google Scholar]
  29. DiCarlo JJ, Cox DD. 2007. Untangling invariant object recognition. Trends Cogn. Sci. 11:8333–41
    [Google Scholar]
  30. Dijkstra N, Ambrogioni L, Vidaurre D, van Gerven M. 2020. Neural dynamics of perceptual inference and its reversal during imagery. eLife 9:e53588
    [Google Scholar]
  31. Dijkstra N, Bosch SE, van Gerven MAJ. 2017a. Vividness of visual imagery depends on the neural overlap with perception in visual areas. J. Neurosci. 37:51367–73
    [Google Scholar]
  32. Dijkstra N, Mostert P, de Lange FP, Bosch S, van Gerven MAJ. 2018. Differential temporal dynamics during visual imagery and perception. eLife 7:e33904
    [Google Scholar]
  33. Dijkstra N, Zeidman P, Ondobaka S, van Gerven MAJ, Friston K. 2017b. Distinct top-down and bottom-up brain connectivity during visual perception and imagery. Sci. Rep. 7:5677
    [Google Scholar]
  34. 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]
  35. Edelman S. 1998. Representation is representation of similarities. Behav. Brain Sci. 21:4449–67; discussion 467–98
    [Google Scholar]
  36. Ekman M, Kok P, De Lange FP. 2017. Time-compressed preplay of anticipated events in human primary visual cortex. Nat. Commun. 8:15276
    [Google Scholar]
  37. Epstein R, Kanwisher N. 1998. A cortical representation of the local visual environment. Nature 392:6676598–601
    [Google Scholar]
  38. Gayet S, Peelen MV. 2022. Preparatory attention incorporates contextual expectations. Curr. Biol. 32:3687–92.e6
    [Google Scholar]
  39. Gifford AT, Dwivedi K, Roig G, Cichy RM. 2022. A large and rich EEG dataset for modeling human visual object recognition. NeuroImage 264:119754
    [Google Scholar]
  40. Goddard E, Carlson TA, Woolgar A. 2022. Spatial and feature-selective attention have distinct, interacting effects on population-level tuning. J. Cogn. Neurosci. 34:2290–312
    [Google Scholar]
  41. Grill-Spector K, Malach R. 2001. fMR-adaptation: a tool for studying the functional properties of human cortical neurons. Acta Psychol. 107:1–3293–321
    [Google Scholar]
  42. 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]
  43. Grootswagers T, Cichy RM, Carlson TA. 2018. Finding decodable information that can be read out in behaviour. NeuroImage 179:252–62
    [Google Scholar]
  44. Grootswagers T, Kennedy BL, Most SB, Carlson TA. 2020. Neural signatures of dynamic emotion constructs in the human brain. Neuropsychologia 145:106535
    [Google Scholar]
  45. Grootswagers T, Robinson AK. 2021. Overfitting the literature to one set of stimuli and data. Front. Hum. Neurosci. 15:682661
    [Google Scholar]
  46. Grootswagers T, Robinson AK, Carlson TA. 2019a. The representational dynamics of visual objects in rapid serial visual processing streams. NeuroImage 188:668–79
    [Google Scholar]
  47. Grootswagers T, Robinson AK, Shatek SM, Carlson TA. 2019b. Untangling featural and conceptual object representations. NeuroImage 202:116083
    [Google Scholar]
  48. Grootswagers T, Robinson AK, Shatek SM, Carlson TA. 2021. The neural dynamics underlying prioritisation of task-relevant information. Neurons Behav. Data Anal. Theory. 5:1 https://doi.org/10.51628/001c.21174
    [Google Scholar]
  49. Grootswagers T, Wardle SG, Carlson TA. 2017. Decoding dynamic brain patterns from evoked responses: a tutorial on multivariate pattern analysis applied to time series neuroimaging data. J. Cogn. Neurosci. 29:4677–97
    [Google Scholar]
  50. Grootswagers T, Zhou I, Robinson AK, Hebart MN, Carlson TA. 2022. Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams. Sci. Data. 9:13
    [Google Scholar]
  51. Haigh S, Robinson A, Grover P, Behrmann M. 2018. Differentiation of types of visual agnosia using EEG. Vision 2:444
    [Google Scholar]
  52. Hanson SJ, Matsuka T, Haxby JV. 2004. Combinatorial codes in ventral temporal lobe for object recognition: Haxby 2001 revisited: Is there a “face” area?. NeuroImage 23:1156–66
    [Google Scholar]
  53. Harel A, Kravitz DJ, Baker CI. 2014. Task context impacts visual object processing differentially across the cortex. PNAS 111:10E962–71
    [Google Scholar]
  54. Harrison SA, Tong F. 2009. Decoding reveals the contents of visual working memory in early visual areas. Nature 458:7238632–35
    [Google Scholar]
  55. Harrison WJ. 2022. Luminance and contrast of images in the THINGS database. Perception 51:4244–62
    [Google Scholar]
  56. Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. 2001. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293:55392425–30
    [Google Scholar]
  57. Haynes J-D, Rees G 2005. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat. Neurosci. 8:5686–91
    [Google Scholar]
  58. He K, Zhang X, Ren S, Sun J. 2015. Deep residual learning for image recognition. arXiv:1512.03385v1 [cs.CV]
  59. Hebart MN, Baker CI. 2018. Deconstructing multivariate decoding for the study of brain function. NeuroImage 180:Pt A4–18
    [Google Scholar]
  60. Hebart MN, Bankson BB, Harel A, Baker CI, Cichy RM. 2018. The representational dynamics of task and object processing in humans. eLife 7:e32816
    [Google Scholar]
  61. Hebart MN, Contier O, Teichmann L, Rockter AH, Zheng CY et al. 2023. THINGS-data: a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior. bioRxiv 2022.07.22.501123. https://doi.org/10.1101/2022.07.22.501123
  62. Hebart MN, Dickter AH, Kidder A, Kwok WY, Corriveau A et al. 2019. THINGS: a database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE 14:10e0223792
    [Google Scholar]
  63. Hebart MN, Görgen K, Haynes J-D. 2015. The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data. Front. Neuroinform. 8:88
    [Google Scholar]
  64. Hillyard SA, Anllo-Vento L. 1998. Event-related brain potentials in the study of visual selectiveattention. PNAS 95:3781–87
    [Google Scholar]
  65. Kaiser D, Azzalini DC, Peelen MV. 2016. Shape-independent object category responses revealed by MEG and fMRI decoding. J. Neurophysiol. 115:42246–50
    [Google Scholar]
  66. Kaiser D, Cichy RM. 2018. Typical visual-field locations enhance processing in object-selective channels of human occipital cortex. J. Neurophysiol. 120:2848–53
    [Google Scholar]
  67. Kaiser D, Peelen MV. 2018. Transformation from independent to integrative coding of multi-object arrangements in human visual cortex. NeuroImage 169:334–41
    [Google Scholar]
  68. Kaiser D, Quek GL, Cichy RM, Peelen MV. 2019. Object vision in a structured world. Trends Cogn. Sci. 23:672–85
    [Google Scholar]
  69. Kamitani Y, Tong F. 2005. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8:5679–85
    [Google Scholar]
  70. Kamitani Y, Tong F. 2006. Decoding seen and attended motion directions from activity in the human visual cortex. Curr. Biol. 16:111096–102
    [Google Scholar]
  71. 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]
  72. Kastner S, De Weerd P, Desimone R, Ungerleider LG. 1998. Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. Science 282:5386108–11
    [Google Scholar]
  73. Kay KN, Naselaris T, Prenger RJ, Gallant JL. 2008. Identifying natural images from human brain activity. Nature 452:7185352–55
    [Google Scholar]
  74. Keller AS, Jagadeesh AV, Bugatus L, Williams LM, Grill-Spector K. 2022. Attention enhances category representations across the brain with strengthened residual correlations to ventral temporal cortex. NeuroImage 249:118900
    [Google Scholar]
  75. Khaligh-Razavi S-M, Cichy RM, Pantazis D, Oliva A. 2018. Tracking the spatiotemporal neural dynamics of real-world object size and animacy in the human brain. J. Cogn. Neurosci. 30:111559–76
    [Google Scholar]
  76. Kiani R, Esteky H, Mirpour K, Tanaka K. 2007. Object category structure in response patterns of neuronal population in monkey inferior temporal cortex. J. Neurophysiol. 97:64296–309
    [Google Scholar]
  77. King J-R, Dehaene S. 2014. Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18:4203–10
    [Google Scholar]
  78. King J-R, Wyart V. 2021. The human brain encodes a chronicle of visual events at each instant of time through the multiplexing of traveling waves. J. Neurosci. 41:347224–33
    [Google Scholar]
  79. Kok P, Failing MF, de Lange FP. 2014. Prior expectations evoke stimulus templates in the primary visual cortex. J. Cogn. Neurosci. 26:71546–54
    [Google Scholar]
  80. Kok P, Mostert P, de Lange FP. 2017. Prior expectations induce prestimulus sensory templates. PNAS 114:3910473–78
    [Google Scholar]
  81. Konkle T, Oliva A. 2012. A real-world size organization of object responses in occipitotemporal cortex. Neuron 74:61114–24
    [Google Scholar]
  82. Kragel PA, Koban L, Barrett LF, Wager TD. 2018. Representation, pattern information, and brain signatures: from neurons to neuroimaging. Neuron 99:2257–73
    [Google Scholar]
  83. Kriegeskorte N, Goebel R, Bandettini P. 2006. Information-based functional brain mapping. PNAS 103:103863–68
    [Google Scholar]
  84. Kriegeskorte N, Kievit RA. 2013. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17:8401–12
    [Google Scholar]
  85. Kriegeskorte N, Mur M, Bandettini PA. 2008a. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2:4
    [Google Scholar]
  86. Kriegeskorte N, Mur M, Ruff DA, Kiani R, Bodurka J et al. 2008b. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60:61126–41
    [Google Scholar]
  87. 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]
  88. Long B, Störmer VS, Alvarez GA. 2017. Mid-level perceptual features contain early cues to animacy. J. Vis. 17:20
    [Google Scholar]
  89. Long B, Yu C-P, Konkle T. 2018. Mid-level visual features underlie the high-level categorical organization of the ventral stream. PNAS 115:E9015–24
    [Google Scholar]
  90. Macevoy SP, Epstein RA. 2009. Decoding the representation of multiple simultaneous objects in human occipitotemporal cortex. Curr. Biol. 19:11943–47
    [Google Scholar]
  91. Mangun GR. 1995. Neural mechanisms of visual selective attention. Psychophysiology 32:14–18
    [Google Scholar]
  92. Mares I, Ewing L, Farran EK, Smith FW, Smith ML. 2020. Developmental changes in the processing of faces as revealed by EEG decoding. NeuroImage 211:116660
    [Google Scholar]
  93. Marti S, Dehaene S. 2017. Discrete and continuous mechanisms of temporal selection in rapid visual streams. Nat. Commun. 8:1955
    [Google Scholar]
  94. McCarthy G, Puce A, Gore JC, Allison T. 1997. Face-specific processing in the human fusiform gyrus. J. Cogn. Neurosci. 9:5605–10
    [Google Scholar]
  95. 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]
  96. Moerel D, Grootswagers T, Robinson A, Engeler P, Holcombe AO, Carlson TA. 2022a. Rotation-tolerant representations elucidate the time-course of high-level object processing. PsyArXiv. https://doi.org/10.31234/osf.io/wp73u
  97. Moerel D, Grootswagers T, Robinson AK, Shatek SM, Woolgar A et al. 2022b. The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes. Sci. Rep. 12:6968
    [Google Scholar]
  98. Mohsenzadeh Y, Qin S, Cichy RM, Pantazis D. 2018. Ultra-rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway. eLife 7:e36329
    [Google Scholar]
  99. Moshel ML, Robinson AK, Carlson TA, Grootswagers T. 2022. Are you for real? Decoding realistic AI-generated faces from neural activity. Vis. Res. 199:108079
    [Google Scholar]
  100. Mur M, Meys M, Bodurka J, Goebel R, Bandettini P, Kriegeskorte N. 2013. Human object-similarity judgments reflect and transcend the primate-IT object representation. Front. Psychol. 4:128
    [Google Scholar]
  101. Naselaris T, Kay KN, Nishimoto S, Gallant JL. 2011. Encoding and decoding in fMRI. NeuroImage 56:2400–10
    [Google Scholar]
  102. Naselaris T, Olman CA, Stansbury DE, Ugurbil K, Gallant JL. 2015. A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes. NeuroImage 105:215–28
    [Google Scholar]
  103. Oosterhof NN, Connolly AC, Haxby JV. 2016. CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab/GNU Octave. Front. Neuroinform. 10:27
    [Google Scholar]
  104. O'Toole AJ, Jiang F, Abdi H, Haxby JV. 2005. Partially distributed representations of objects and faces in ventral temporal cortex. J. Cogn. Neurosci. 17:4580–90
    [Google Scholar]
  105. Pereira F, Mitchell T, Botvinick M. 2009. Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45:1 SupplS199–209
    [Google Scholar]
  106. Philiastides MG, Sajda P. 2006. Temporal characterization of the neural correlates of perceptual decision making in the human brain. Cereb. Cortex 16:4509–18
    [Google Scholar]
  107. Popham SF, Huth AG, Bilenko NY, Deniz F, Gao JS et al. 2021. Visual and linguistic semantic representations are aligned at the border of human visual cortex. Nat. Neurosci. 24:111628–36
    [Google Scholar]
  108. Posner MI, Snyder CRR, Davidson BJ. 1980. Attention and the detection of signals. J. Exp. Psychol. Gen. 109:2160–74
    [Google Scholar]
  109. Puce A, Allison T, Asgari M, Gore JC, McCarthy G. 1996. Differential sensitivity of human visual cortex to faces, letterstrings, and textures: a functional magnetic resonance imaging study. J. Neurosci. 16:165205–15
    [Google Scholar]
  110. Quax SC, Dijkstra N, van Staveren MJ, Bosch SE, van Gerven MAJ. 2019. Eye movements explain decodability during perception and cued attention in MEG. NeuroImage 195:444–53
    [Google Scholar]
  111. Quek GL, Peelen MV. 2020. Contextual and spatial associations between objects interactively modulate visual processing. Cereb. Cortex 30:126391–404
    [Google Scholar]
  112. Ragni F, Tucciarelli R, Andersson P, Lingnau A. 2020. Decoding stimulus identity in occipital, parietal and inferotemporal cortices during visual mental imagery. Cortex 127:371–87
    [Google Scholar]
  113. Rajaei K, Mohsenzadeh Y, Ebrahimpour R, Khaligh-Razavi S-M. 2019. Beyond core object recognition: recurrent processes account for object recognition under occlusion. PLOS Comput. Biol. 15:5e1007001
    [Google Scholar]
  114. Ramkumar P, Jas M, Pannasch S, Hari R, Parkkonen L 2013. Feature-specific information processing precedes concerted activation in human visual cortex. J. Neurosci. 33:187691–99
    [Google Scholar]
  115. Ritchie JB, Carlson TA. 2016. Neural decoding and “inner” psychophysics: a distance-to-bound approach for linking mind, brain, and behavior. Front. Neurosci. 10:190
    [Google Scholar]
  116. Ritchie JB, Tovar DA, Carlson TA. 2015. Emerging object representations in the visual system predict reaction times for categorization. PLOS Comput. Biol. 11:6e1004316
    [Google Scholar]
  117. 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]
  118. Rivolta D, Woolgar A, Palermo R, Butko M, Schmalzl L, Williams MA. 2014. Multi-voxel pattern analysis (MVPA) reveals abnormal fMRI activity in both the “core” and “extended” face network in congenital prosopagnosia. Front. Hum. Neurosci. 8:925
    [Google Scholar]
  119. Robinson AK, Grootswagers T, Carlson TA. 2019. The influence of image masking on object representations during rapid serial visual presentation. NeuroImage 197:224–31
    [Google Scholar]
  120. Robinson AK, Grootswagers T, Shatek SM, Gerboni J, Holcombe A, Carlson TA. 2021. Overlapping neural representations for the position of visible and imagined objects. Neurons Behav. Data Anal. Theory. 4:11–28
    [Google Scholar]
  121. Robinson AK, Rich AN, Woolgar A. 2022. Linking the brain with behavior: the neural dynamics of success and failure in goal-directed behavior. J. Cogn. Neurosci. 34:4639–54
    [Google Scholar]
  122. Robinson AK, Venkatesh P, Boring MJ, Tarr MJ, Grover P, Behrmann M. 2017. Very high density EEG elucidates spatiotemporal aspects of early visual processing. Sci. Rep. 7:16248
    [Google Scholar]
  123. Rosenthal IA, Singh SR, Hermann KL, Pantazis D, Conway BR. 2021. Color space geometry uncovered with magnetoencephalography. Curr. Biol. 31:3515–26.e5
    [Google Scholar]
  124. Seymour K, Clifford CWG, Logothetis NK, Bartels A. 2009. The coding of color, motion, and their conjunction in the human visual cortex. Curr. Biol. 19:3177–83
    [Google Scholar]
  125. 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]
  126. Shatek SM, Grootswagers T, Robinson AK, Carlson TA. 2019. Decoding images in the mind's eye: the temporal dynamics of visual imagery. Vision 3:453
    [Google Scholar]
  127. Shatek SM, Robinson AK, Grootswagers T, Carlson TA. 2022. Capacity for movement is an organisational principle in object representations. NeuroImage 261:119517
    [Google Scholar]
  128. Shepard RN, Chipman S. 1970. Second-order isomorphism of internal representations: shapes of states. Cogn. Psychol. 1:11–17
    [Google Scholar]
  129. Stokes MG, Kusunoki M, Sigala N, Nili H, Gaffan D, Duncan J. 2013. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78:2364–75
    [Google Scholar]
  130. Sulfaro AA, Robinson AK, Carlson TA. 2022. Perception as a hierarchical competition: a model that differentiates imagined, veridical, and hallucinated percepts. bioRxiv 2022.09.02.506121. https://doi.org/10.1101/2022.09.02.506121
  131. Summerfield C, de Lange FP. 2014. Expectation in perceptual decision making: neural and computational mechanisms. Nat. Rev. Neurosci. 15:11745–56
    [Google Scholar]
  132. Taubert J, Wardle SG, Ungerleider LG. 2020. What does a “face cell” want?. Prog. Neurobiol. 195:101880
    [Google Scholar]
  133. Teichmann L, Moerel D, Baker C, Grootswagers T. 2021. An empirically-driven guide on using Bayes Factors for M/EEG decoding. Aperture Neuro. https://doi.org/10.52294/ApertureNeuro.2022.2.MAOC6465
    [Google Scholar]
  134. Teichmann L, Moerel D, Rich AN, Baker CI. 2022. The nature of neural object representations during dynamic occlusion. Cortex 153:66–86
    [Google Scholar]
  135. Teichmann L, Quek GL, Robinson AK, Grootswagers T, Carlson TA, Rich AN. 2020. The influence of object-colour knowledge on emerging object representations in the brain. J. Neurosci. 40:356779–89
    [Google Scholar]
  136. Thorat S, Proklova D, Peelen MV. 2019. The nature of the animacy organization in human ventral temporal cortex. eLife 8:e47142
    [Google Scholar]
  137. Wang R, Janini D, Konkle T. 2022. Mid-level feature differences support early animacy and object size distinctions: evidence from electroencephalography decoding. J. Cogn. Neurosci. 34:91670–80
    [Google Scholar]
  138. Wardle SG, Kriegeskorte N, Grootswagers T, Khaligh-Razavi SM, Carlson TA 2016. Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG. NeuroImage 132:59–70
    [Google Scholar]
  139. 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]
  140. Webster MA. 2011. Adaptation and visual coding. J. Vis. 11:53
    [Google Scholar]
  141. Williams MA, Dang S, Kanwisher NG. 2007. Only some spatial patterns of fMRI response are read out in task performance. Nat. Neurosci. 10:6685–86
    [Google Scholar]
  142. Wischnewski M, Peelen MV. 2021. Causal neural mechanisms of context-based object recognition. eLife 10:e69736
    [Google Scholar]
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