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Testing cognitive theories with multivariate pattern analysis of neuroimaging data

Nature Human Behaviour volume 7, pages 1430–1441 (2023)Cite this article

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Abstract

Multivariate pattern analysis (MVPA) has emerged as a powerful method for the analysis of functional magnetic resonance imaging, electroencephalography and magnetoencephalography data. The new approaches to experimental design and hypothesis testing afforded by MVPA have made it possible to address theories that describe cognition at the functional level. Here we review a selection of studies that have used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the ‘how’ of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions.

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Fig. 1: Overview of key steps in MVPA of neuroimaging data, illustrated with the design of previous fMRI and MEG or EEG studies investigating the representation of object category and shape.
Fig. 2: Schematic summary of MVPA evidence investigating the components of human navigation.
Fig. 3: Schematic summary of an MEG study investigating contextual reinstatement during memory retrieval.
Fig. 4: Schematic summary of an fMRI study investigating motor prediction.
Fig. 5: Schematic summary of an MEG study investigating sequential motor behaviour.

References

  1. Churchland, P. S. & Sejnowski, T. J. Perspectives on cognitive neuroscience. Science 242, 741–745 (1988).

    Article  CAS  PubMed  Google Scholar 

  2. Coltheart, M. How can functional neuroimaging inform cognitive theories? Perspect. Psychol. Sci. 8, 98–103 (2013).

    Article  PubMed  Google Scholar 

  3. Downing, P., Liu, J. & Kanwisher, N. Testing cognitive models of visual attention with fMRI and MEG. Neuropsychologia 39, 1329–1342 (2001).

    Article  CAS  PubMed  Google Scholar 

  4. Henson, R. What can functional neuroimaging tell the experimental psychologist? Q. J. Exp. Psychol. A 58, 193–233 (2005).

    Article  PubMed  Google Scholar 

  5. Mather, M., Cacioppo, J. T. & Kanwisher, N. How fMRI can inform cognitive theories. Perspect. Psychol. Sci. 8, 108–113 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Page, M. P. A. What can’t functional neuroimaging tell the cognitive psychologist? Cortex 42, 428–443 (2006).

    Article  PubMed  Google Scholar 

  7. Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424–430 (2006).

    Article  PubMed  Google Scholar 

  8. Haynes, J.-D. A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives. Neuron 87, 257–270 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Poldrack, R. A. & Farah, M. J. Progress and challenges in probing the human brain. Nature 526, 371–379 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Rissman, J. & Wagner, A. D. Distributed representations in memory: insights from functional brain imaging. Annu. Rev. Psychol. 63, 101–128 (2012).

    Article  PubMed  Google Scholar 

  11. Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 37, 435–456 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Hebart, M. N. & Baker, C. I. Deconstructing multivariate decoding for the study of brain function. NeuroImage 180, 4–18 (2018).

    Article  PubMed  Google Scholar 

  13. Grootswagers, T., Wardle, S. G. & Carlson, T. A. Decoding dynamic brain patterns from evoked responses: a tutorial on multivariate pattern analysis applied to time series neuroimaging data. J. Cogn. Neurosci. 29, 677–697 (2017).

    Article  PubMed  Google Scholar 

  14. Carlson, T., Goddard, E., Kaplan, D. M., Klein, C. & Ritchie, J. B. Ghosts in machine learning for cognitive neuroscience: moving from data to theory. NeuroImage 180, 88–100 (2018).

    Article  PubMed  Google Scholar 

  15. de-Wit, L., Alexander, D., Ekroll, V. & Wagemans, J. Is neuroimaging measuring information in the brain? Psychon. Bull. Rev. 23, 1415–1428 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Poeppel, D. The maps problem and the mapping problem: two challenges for a cognitive neuroscience of speech and language. Cogn. Neuropsychol. 29, 34–55 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lebedev, M. A. & Nicolelis, M. A. L. Brain–machine interfaces: from basic science to neuroprostheses and neurorehabilitation. Physiol. Rev. 97, 767–837 (2017).

    Article  PubMed  Google Scholar 

  18. Haxby, J. V. Multivariate pattern analysis of fMRI: the early beginnings. NeuroImage 62, 852–855 (2012).

    Article  PubMed  Google Scholar 

  19. Haynes, J.-D. & Rees, G. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7, 523–534 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Tong, F. & Pratte, M. S. Decoding patterns of human brain activity. Annu. Rev. Psychol. 63, 483–509 (2012).

    Article  PubMed  Google Scholar 

  21. Cohen, J. D. et al. Computational approaches to fMRI analysis. Nat. Neurosci. 20, 304–313 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kriegeskorte, N. & Douglas, P. K. Cognitive computational neuroscience. Nat. Neurosci. 21, 1148–1160 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Guest, O. & Love, B. C. What the success of brain imaging implies about the neural code. eLife 6, e21397 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Ritchie, J. B., Kaplan, D. M. & Klein, C. Decoding the brain: neural representation and the limits of multivariate pattern analysis in cognitive neuroscience. Br. J. Phil. Sci. 70, 581–607 (2019).

    Article  Google Scholar 

  26. Kragel, P. A., Koban, L., Barrett, L. F. & Wager, T. D. Representation, pattern information, and brain signatures: from neurons to neuroimaging. Neuron 99, 257–273 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Naselaris, T., Kay, K. N., Nishimoto, S. & Gallant, J. L. Encoding and decoding in fMRI. NeuroImage 56, 400–410 (2011).

    Article  PubMed  Google Scholar 

  28. Sprague, T. C. & Serences, J. T. in An Introduction to Model-Based Cognitive Neuroscience (eds Forstmann, B. & Wagenmakers, E.-J.) 245–274 (Springer, 2015).

  29. Kriegeskorte, N. & Douglas, P. K. Interpreting encoding and decoding models. Curr. Opin. Neurobiol. 55, 167–179 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Op de Beeck, H. P. Against hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses? NeuroImage 49, 1943–1948 (2010).

    Article  PubMed  Google Scholar 

  31. Swisher, J. D. et al. Multiscale pattern analysis of orientation-selective activity in the primary visual cortex. J. Neurosci. 30, 325–330 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Freeman, J., Brouwer, G. J., Heeger, D. J. & Merriam, E. P. Orientation decoding depends on maps, not columns. J. Neurosci. 31, 4792–4804 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Alink, A., Krugliak, A., Walther, A. & Kriegeskorte, N. fMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli. Front. Psychol. 4, 493 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Carlson, T. A. Orientation decoding in human visual cortex: new insights from an unbiased perspective. J. Neurosci. 34, 8373–8383 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Roth, Z. N., Heeger, D. J. & Merriam, E. P. Stimulus vignetting and orientation selectivity in human visual cortex. eLife 7, e37241 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Marr, D. Vision: A Computational Approach (Freeman, 1982).

  37. Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A. & Poeppel, D. Neuroscience needs behavior: correcting a reductionist bias. Neuron 93, 480–490 (2017).

    Article  CAS  PubMed  Google Scholar 

  38. Neisser, U. Cognitive Psychology (Appleton-Century-Crofts, 1967).

  39. Anderson, J. R. Cognitive Psychology and Its Implications (Worth, 2020).

  40. Garner, W. R. The Processing of Information and Structure (Psychology Press, 1974).

  41. Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl Acad. Sci. USA 111, 8619–8624 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Miyake, A. & Friedman, N. P. The nature and organization of individual differences in executive functions: four general conclusions. Curr. Dir. Psychol. Sci. 21, 8–14 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Cohen, J. D., Barch, D. M., Carter, C. & Servan-Schreiber, D. Context-processing deficits in schizophrenia: converging evidence from three theoretically motivated cognitive tasks. J. Abnorm. Psychol. 108, 120–133 (1999).

    Article  CAS  PubMed  Google Scholar 

  44. Markus, H. R. & Kitayama, S. Culture and the self: implications for cognition, emotion, and motivation. Psychol. Rev. 98, 224–253 (1991).

    Article  Google Scholar 

  45. Kriegeskorte, N. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).

    PubMed  PubMed Central  Google Scholar 

  46. Mur, M., Bandettini, P. A. & Kriegeskorte, N. Revealing representational content with pattern-information fMRI—an introductory guide. Soc. Cogn. Affect. Neurosci. 4, 101–109 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cichy, R. M., Pantazis, D. & Oliva, A. Resolving human object recognition in space and time. Nat. Neurosci. 17, 455–462 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Proklova, D., Kaiser, D. & Peelen, M. V. Disentangling representations of object shape and object category in human visual cortex: the animate–inanimate distinction. J. Cogn. Neurosci. 28, 680–692 (2016).

    Article  PubMed  Google Scholar 

  49. Cohen, M. A., Alvarez, G. A., Nakayama, K. & Konkle, T. Visual search for object categories is predicted by the representational architecture of high-level visual cortex. J. Neurophysiol. 117, 388–402 (2017).

    Article  PubMed  Google Scholar 

  50. Groen, I. I. et al. Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior. eLife 7, e32962 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A. & Oliva, A. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci. Rep. 6, 27755 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Smith, P. L. & Little, D. R. Small is beautiful: in defense of the small-N design. Psychon. Bull. Rev. 25, 2083–2101 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Meehl, P. E. Theory-testing in psychology and physics: a methodological paradox. Phil. Sci. 34, 103–115 (1967).

    Article  Google Scholar 

  54. Kanwisher, N. & Wojciulik, E. Visual attention: insights from brain imaging. Nat. Rev. Neurosci. 1, 91–100 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Wagner, A. D. et al. Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science 281, 1188–1191 (1998).

    Article  CAS  PubMed  Google Scholar 

  56. Curtis, C. E. & D’Esposito, M. Persistent activity in the prefrontal cortex during working memory. Trends Cogn. Sci. 7, 415–423 (2003).

    Article  PubMed  Google Scholar 

  57. Kastner, S., Pinsk, M. A., De Weerd, P., Desimone, R. & Ungerleider, L. G. Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron 22, 751–761 (1999).

    Article  CAS  PubMed  Google Scholar 

  58. Jiang, J., Summerfield, C. & Egner, T. Attention sharpens the distinction between expected and unexpected percepts in the visual brain. J. Neurosci. 33, 18438–18447 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Seidl, K. N., Peelen, M. V. & Kastner, S. Neural evidence for distracter suppression during visual search in real-world scenes. J. Neurosci. 32, 11812–11819 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Serences, J. T., Ester, E. F., Vogel, E. K. & Awh, E. Stimulus-specific delay activity in human primary visual cortex. Psychol. Sci. 20, 207–214 (2009).

    Article  PubMed  Google Scholar 

  61. Harrison, S. A. & Tong, F. Decoding reveals the contents of visual working memory in early visual areas. Nature 458, 632–635 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Polyn, S. M., Natu, V. S., Cohen, J. D. & Norman, K. A. Category-specific cortical activity precedes retrieval during memory search. Science 310, 1963–1966 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Kornysheva, K. et al. Neural competitive queuing of ordinal structure underlies skilled sequential action. Neuron 101, 1166–1180.e3 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ariani, G., Pruszynski, J. A. & Diedrichsen, J. Motor planning brings human primary somatosensory cortex into action-specific preparatory states. eLife 11, e69517 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Oosterhof, N. N., Tipper, S. P. & Downing, P. E. Crossmodal and action-specific: neuroimaging the human mirror neuron system. Trends Cogn. Sci. 17, 311–318 (2013).

    Article  PubMed  Google Scholar 

  66. Kaplan, J. T., Man, K. & Greening, S. G. Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations. Front. Hum. Neurosci. 9, 151 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  67. King, J.-R. & Dehaene, S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Haxby, J. V., Guntupalli, J. S., Nastase, S. A. & Feilong, M. Hyperalignment: modeling shared information encoded in idiosyncratic cortical topographies. eLife 9, e56601 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).

    Article  CAS  PubMed  Google Scholar 

  70. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat. Neurosci. 25, 1257–1272 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Vass, L. K. & Epstein, R. A. Abstract representations of location and facing direction in the human brain. J. Neurosci. 33, 6133–6142 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Nielson, D. M., Smith, T. A., Sreekumar, V., Dennis, S. & Sederberg, P. B. Human hippocampus represents space and time during retrieval of real-world memories. Proc. Natl Acad. Sci. USA 112, 11078–11083 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Biederman, I., Mezzanotte, R. J. & Rabinowitz, J. C. Scene perception: detecting and judging objects undergoing relational violations. Cogn. Psychol. 14, 143–177 (1982).

    Article  CAS  PubMed  Google Scholar 

  74. Davenport, J. L. & Potter, M. C. Scene consistency in object and background perception. Psychol. Sci. 15, 559–564 (2004).

    Article  PubMed  Google Scholar 

  75. Bar, M. Visual objects in context. Nat. Rev. Neurosci. 5, 617–629 (2004).

    Article  CAS  PubMed  Google Scholar 

  76. Henderson, J. M. & Hollingworth, A. High-level scene perception. Annu. Rev. Psychol. 50, 243–271 (1999).

    Article  CAS  PubMed  Google Scholar 

  77. Brandman, T. & Peelen, M. V. Interaction between scene and object processing revealed by human fMRI and MEG decoding. J. Neurosci. 37, 7700–7710 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Chun, M. M., Golomb, J. D. & Turk-Browne, N. B. A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101 (2011).

    Article  PubMed  Google Scholar 

  79. Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995).

    Article  CAS  PubMed  Google Scholar 

  80. Duncan, J. & Humphreys, G. W. Visual search and stimulus similarity. Psychol. Rev. 96, 433–458 (1989).

    Article  CAS  PubMed  Google Scholar 

  81. Chelazzi, L., Miller, E. K., Duncan, J. & Desimone, R. A neural basis for visual search in inferior temporal cortex. Nature 363, 345–347 (1993).

    Article  CAS  PubMed  Google Scholar 

  82. Battistoni, E., Stein, T. & Peelen, M. V. Preparatory attention in visual cortex: preparatory attention in visual cortex. Ann. N. Y. Acad. Sci. 1396, 92–107 (2017).

    Article  PubMed  Google Scholar 

  83. Stokes, M., Thompson, R., Nobre, A. C. & Duncan, J. Shape-specific preparatory activity mediates attention to targets in human visual cortex. Proc. Natl Acad. Sci. USA 106, 19569–19574 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Peelen, M. V. & Kastner, S. A neural basis for real-world visual search in human occipitotemporal cortex. Proc. Natl Acad. Sci. USA 108, 12125–12130 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Gayet, S. & Peelen, M. V. Preparatory attention incorporates contextual expectations. Curr. Biol. 32, 687–692.e6 (2022).

    Article  CAS  PubMed  Google Scholar 

  86. Tulving, E. & Thomson, D. M. Encoding specificity and retrieval processes in episodic memory. Psychol. Rev. 80, 352–373 (1973).

    Article  Google Scholar 

  87. Jafarpour, A., Fuentemilla, L., Horner, A. J., Penny, W. & Duzel, E. Replay of very early encoding representations during recollection. J. Neurosci. 34, 242–248 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Mack, M. L., Preston, A. R. & Love, B. C. Decoding the brain’s algorithm for categorization from its neural implementation. Curr. Biol. 23, 2023–2027 (2013).

    Article  CAS  PubMed  Google Scholar 

  89. Posner, M. I. & Keele, S. W. On the genesis of abstract ideas. J. Exp. Psychol. 77, 353–363 (1968).

    Article  CAS  PubMed  Google Scholar 

  90. Medin, D. L. & Schaffer, M. M. Context theory of classification learning. Psychol. Rev. 85, 207–238 (1978).

    Article  Google Scholar 

  91. Barsalou, L. W. Perceptual symbol systems. Behav. Brain Sci. 22, 577–660 (1999).

    Article  CAS  PubMed  Google Scholar 

  92. Glenberg, A. M. Few believe the world is flat: how embodiment is changing the scientific understanding of cognition. Can. J. Exp. Psychol. 69, 165–171 (2015).

    Article  PubMed  Google Scholar 

  93. Fernandino, L., Tong, J.-Q., Conant, L. L., Humphries, C. J. & Binder, J. R. Decoding the information structure underlying the neural representation of concepts. Proc. Natl Acad. Sci. USA 119, e2108091119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Levenson, R. W. Basic emotion questions. Emot. Rev. 3, 379–386 (2011).

    Article  Google Scholar 

  95. Gendron, M. & Feldman Barrett, L. Reconstructing the past: a century of ideas about emotion in psychology. Emot. Rev. 1, 316–339 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Ekman, P. Universal and cultural differences in facial expressions of emotions. In Nebraska Symposium on Motivation, 1971 (ed. Cole, J.) 207–283 (University of Nebraska Press, 1972).

  97. Saarimäki, H. et al. Discrete neural signatures of basic emotions. Cereb. Cortex 26, 2563–2573 (2016).

    Article  PubMed  Google Scholar 

  98. Skerry, A. E. & Saxe, R. Neural representations of emotion are organized around abstract event features. Curr. Biol. 25, 1945–1954 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Peelen, M. V., Atkinson, A. P. & Vuilleumier, P. Supramodal representations of perceived emotions in the human brain. J. Neurosci. 30, 10127–10134 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Ellsworth, P. C. Appraisal theory: old and new questions. Emot. Rev. 5, 125–131 (2013).

    Article  Google Scholar 

  101. Scherer, K. R. The nature and dynamics of relevance and valence appraisals: theoretical advances and recent evidence. Emot. Rev. 5, 150–162 (2013).

    Article  Google Scholar 

  102. Gallese, V. & Goldman, A. Mirror neurons and the simulation theory of mind-reading. Trends Cogn. Sci. 2, 493–501 (1998).

    Article  CAS  PubMed  Google Scholar 

  103. Saxe, R. Against simulation: the argument from error. Trends Cogn. Sci. 9, 174–179 (2005).

    Article  PubMed  Google Scholar 

  104. Corradi-Dell’Acqua, C., Tusche, A., Vuilleumier, P. & Singer, T. Cross-modal representations of first-hand and vicarious pain, disgust and fairness in insular and cingulate cortex. Nat. Commun. 7, 10904 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Koster-Hale, J., Bedny, M. & Saxe, R. Thinking about seeing: perceptual sources of knowledge are encoded in the theory of mind brain regions of sighted and blind adults. Cognition 133, 65–78 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  106. von Holst, E. & Mittelstaedt, H. The reafference principle: interaction between the central nervous system and the periphery. Naturwissenschaften 37, 464–476 (1950).

    Google Scholar 

  107. Wolpert, D. M. & Flanagan, J. R. Motor prediction. Curr. Biol. 11, R729–R732 (2001).

    Article  CAS  PubMed  Google Scholar 

  108. Umeda, T., Isa, T. & Nishimura, Y. The somatosensory cortex receives information about motor output. Sci. Adv. 5, eaaw5388 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Gale, D. J., Flanagan, J. R. & Gallivan, J. P. Human somatosensory cortex is modulated during motor planning. J. Neurosci. 41, 5909–5922 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Terrace, H. S. The simultaneous chain: a new approach to serial learning. Trends Cogn. Sci. 9, 202–210 (2005).

    Article  PubMed  Google Scholar 

  111. Houghton, G. & Hartley, T. Parallel models of serial behaviour: Lashley revisited. Psyche 2, 25 (1995).

    Google Scholar 

  112. Williams, M. A., Dang, S. & Kanwisher, N. G. Only some spatial patterns of fMRI response are read out in task performance. Nat. Neurosci. 10, 685–686 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Ritchie, J. B. & Carlson, T. A. Neural decoding and ‘inner’ psychophysics: a distance-to-bound approach for linking mind, brain, and behavior. Front. Neurosci. 10, 190 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Wischnewski, M. & Peelen, M. V. Causal neural mechanisms of context-based object recognition. eLife 10, e69736 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Thielen, J., Bosch, S. E., van Leeuwen, T. M., van Gerven, M. A. J. & van Lier, R. Evidence for confounding eye movements under attempted fixation and active viewing in cognitive neuroscience. Sci. Rep. 9, 17456 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Proklova, D., Kaiser, D. & Peelen, M. V. MEG sensor patterns reflect perceptual but not categorical similarity of animate and inanimate objects. NeuroImage 193, 167–177 (2019).

    Article  PubMed  Google Scholar 

  117. Mole, C. & Klein, C. in Foundational Issues of Human Brain Mapping (eds Hanson, S. H. & Bunzl, M.) 99–112 (MIT Press, 2010).

  118. Charest, I., Kievit, R. A., Schmitz, T. W., Deca, D. & Kriegeskorte, N. Unique semantic space in the brain of each beholder predicts perceived similarity. Proc. Natl Acad. Sci. USA 111, 14565–14570 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Anderson, A. J. et al. Decoding individual identity from brain activity elicited in imagining common experiences. Nat. Commun. 11, 5916 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Feilong, M., Guntupalli, J. S. & Haxby, J. V. The neural basis of intelligence in fine-grained cortical topographies. eLife 10, e64058 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Braunlich, K. & Love, B. C. Occipitotemporal representations reflect individual differences in conceptual knowledge. J. Exp. Psychol. Gen. 148, 1192–1203 (2019).

    Article  PubMed  Google Scholar 

  122. Anzellotti, S. & Coutanche, M. N. Beyond functional connectivity: investigating networks of multivariate representations. Trends Cogn. Sci. 22, 258–269 (2018).

    Article  PubMed  Google Scholar 

  123. Ju, H. & Bassett, D. S. Dynamic representations in networked neural systems. Nat. Neurosci. 23, 908–917 (2020).

    Article  CAS  PubMed  Google Scholar 

  124. van Gerven, M. A. J., Seeliger, K., Güçlü, U. & Güçlütürk, Y. in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (eds Samek, W. et al.) 379–394 (Springer International, 2019).

  125. Lawrence, S. J. D., Formisano, E., Muckli, L. & de Lange, F. P. Laminar fMRI: applications for cognitive neuroscience. NeuroImage 197, 785–791 (2019).

    Article  PubMed  Google Scholar 

  126. De Vos, M. & Debener, S. Mobile EEG: towards brain activity monitoring during natural action and cognition. Int. J. Psychophysiol. 91, 1–2 (2014).

    Article  PubMed  Google Scholar 

  127. Snow, J. C. & Culham, J. C. The treachery of images: how realism influences brain and behavior. Trends Cogn. Sci. 25, 506–519 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Willems, R. M. & Peelen, M. V. How context changes the neural basis of perception and language. iScience 24, 102392 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  129. van Rooij, I. & Baggio, G. Theory before the test: how to build high-verisimilitude explanatory theories in psychological science. Perspect. Psychol. Sci. 16, 682–697 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Guest, O. & Martin, A. E. How computational modeling can force theory building in psychological science. Perspect. Psychol. Sci. 16, 789–802 (2021).

    Article  PubMed  Google Scholar 

  131. Muthukrishna, M. & Henrich, J. A problem in theory. Nat. Hum. Behav. 3, 221–229 (2019).

    Article  PubMed  Google Scholar 

  132. Walther, A. et al. Reliability of dissimilarity measures for multi-voxel pattern analysis. NeuroImage 137, 188–200 (2016).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 725970). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank F. de Lange, R. Willems and P. Medendorp for helpful comments on an earlier version of the manuscript.

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  1. These authors contributed equally: Marius V. Peelen, Paul E. Downing.

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  1. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands

    Marius V. Peelen

  2. Cognitive Neuroscience Institute, Department of Psychology, Bangor University, Bangor, UK

    Paul E. Downing

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  1. Marius V. Peelen
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  2. Paul E. Downing
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Correspondence to Marius V. Peelen or Paul E. Downing.

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Peelen, M.V., Downing, P.E. Testing cognitive theories with multivariate pattern analysis of neuroimaging data. Nat Hum Behav 7, 1430–1441 (2023). https://doi.org/10.1038/s41562-023-01680-z

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