The brain of the beholder: honouring individual representational idiosyncrasies

Language, Cognition and Neuroscience

Volumn 30, Issue 4, 2015, Pages 367-379

  Charest, Ian ^a   Kriegeskorte, Nikolaus ^a  

^a MEDICAL RESEARCH COUNCIL   (United Kingdom)

Author keywords

fMRI; individual differences; object representations; representational similarity analyses

Indexed keywords

ARTICLE; AUDITORY CORTEX; BEHAVIOR; BRAIN FUNCTION; COGNITION; COMPUTER MODEL; EPISODIC MEMORY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HIPPOCAMPUS; HUMAN; INDIVIDUALIZATION; MULTIVARIATE ANALYSIS; NERVE CELL; PREFRONTAL CORTEX; PSYCHOLOGY; SIGNAL NOISE RATIO; STIMULUS; TEMPORAL CORTEX;

EID: 84938510519     PISSN: 23273798     EISSN: 23273801     Source Type: Journal    
DOI: 10.1080/23273798.2014.1002505     Document Type: Article

References (82)

1. G.K.Aguirre, (2007). Continuous carry-over designs for fMRI. NeuroImage, 35, 1480–1494. doi:10.1016/j.neuroimage.2007.02.005
2. C.F.Cadieu,, H.Hong,, D.L.K.Yamins,, N.Pinto,, D.Ardila,, E.A.Solomon,, J.J.DiCarlo, (2014). Deep neural networks rival the representation of primate IT cortex for core visual object recognition. ArXiv e-prints, 1406.3284.
3. M.J.Chadwick,, H.M.Bonnici,, & E.A.Maguire, (2014). CA3 size predicts the precision of memory recall. Proceedings of the National Academy of Sciences, 111, 10720–10725. doi:10.1073/pnas.1319641111
4. I.Charest,, R.A.Kievit,, T.W.Schmitz,, D.Deca,, & N.Kriegeskorte, (2014). Unique semantic space in the brain of each beholder predicts perceived similarity. Proceedings of the National Academy of Sciences, 111, 14565–14570. doi:10.1073/pnas.1402594111
5. R.M.Cichy,, D.Pantazis,, & A.Oliva, (2014). Resolving human object recognition in space and time. Nature Neuroscience, 17, 455–462. doi:10.1038/nn.3635
6. A.Clarke,, & L.K.Tyler, (2014). Object-specific semantic coding in human perirhinal cortex. Journal of Neuroscience, 34, 4766–4775. doi:10.1523/JNEUROSCI.2828-13.2014
7. M.W.Cole,, T.Yarkoni,, G.Repovs,, A.Anticevic,, & T.S.Braver, (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32, 8988–8999. doi:10.1523/JNEUROSCI.0536-12.2012
8. T.Çukur,, S.Nishimoto,, A.G.Huth,, & J.L.Gallant, (2013). Attention during natural vision warps semantic representation across the human brain. Nature Neuroscience, 16, 763–770. doi:10.1038/nn.3381
9. D.C.Dennett, (1989). The intentional stance. Cambridge, MA: The MIT Press.
10. P.E.Downing, (2001). A cortical area selective for visual processing of the human body. Science, 293, 2470–2473. doi:10.1126/science.1063414
11. S.O.Dumoulin,, & B.A.Wandell, (2008). Population receptive field estimates in human visual cortex. Neuroimage, 39, 647–660. doi:10.1016/j.neuroimage.2007.09.034
12. R.A.Epstein,, & N.Kanwisher, (1998). A cortical representation of the local visual environment. Nature, 392, 598–601.
13. E.Fedorenko,, J.Duncan,, & N.Kanwisher, (2013). Broad domain generality in focal regions of frontal and parietal cortex. Proceedings of the National Academy of Sciences, 110, 16616–16621. doi:10.1073/pnas.1315235110
14. E.Fedorenko,, P.J.Hsieh,, A.Nieto-Castanon,, S.Whitfield-Gabrieli,, & N.Kanwisher, (2010). New method for fMRI investigations of language: Defining ROIs functionally in individual subjects. Journal of Neurophysiology, 104, 1177–1194. doi:10.1152/jn.00032.2010
15. K.J.Friston,, C.D.Frith,, P.F.Liddle,, R.J.Dolan,, A.A.Lammertsma,, & R.S.J.Frackowiak, (1990). The relationship between global and local changes in PET scans. Journal of Cerebral Blood Flow & Metabolism, 10, 458–466. doi:10.1038/jcbfm.1990.88
16. K.J.Friston,, A.P.Holmes,, K.J.Worsley,, J.B.Poline,, C.D.Frith,, & R.S.J.Frackowiak, (1994). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2, 189–210. doi:10.1002/hbm.460020402
17. N.Furl,, L.Garrido,, R.J.Dolan,, J.Driver,, & B.Duchaine, (2011). Fusiform gyrus face selectivity relates to individual differences in facial recognition ability. Journal of Cognitive Neuroscience, 23, 1723–1740. doi:10.1162/jocn.2010.21545
18. C.R.Gallistel, (1990). The organization of learning (viii, p. 648).Cambridge, MA: The MIT Press.
19. R.Goldstone, (1994). An efficient method for obtaining similarity data. Behavior Research Methods, Instruments, & Computers, 26, 381–386. doi:10.3758/BF03204653
20. T.Grandin,, & R.Panek, (2014). The autistic brain: Helping different kinds of minds succeed. Boston, MA: Houghton Mifflin Harcourt.
21. K.Grill-Spector,, & K.S.Weiner, (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15, 536–548. doi:10.1038/nrn3747
22. S.Haar,, S.Berman,, M.Behrmann,, & I.Dinstein, (2014). Anatomical abnormalities in autism? Cerebral Cortex. Advance online publication. doi:10.1093/cercor/bhu242
23. A.Harel,, D.J.Kravitz,, & C.I.Baker, (2013). Deconstructing visual scenes in cortex: Gradients of object and spatial layout information. Cerebral Cortex, 23, 947–957. doi:10.1093/cercor/bhs091
24. A.Harel,, D.J.Kravitz,, & C.I.Baker, (2014). Task context impacts visual object processing differentially across the cortex. Proceedings of the National Academy of Sciences, 111, E962–E971. doi:10.1073/pnas.1312567111
25. J.V.Haxby,, M.I.Gobbini,, M.L.Furey,, A.Ishai,, J.L.Schouten,, & P.Pietrini, (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293, 2425–2430. doi:10.1126/science.1063736
26. J.V.Haxby,, S.J.Guntupalli,, A.C.Connolly,, Y.O.Halchenko,, B.R.Conroy,, M.I.Gobbini,, P.J.Ramadge, (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72, 404–416. doi:10.1016/j.neuron.2011.08.026
27. J.D.Haynes,, & G.Rees, (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7, 523–534. doi:10.1038/nrn1931
28. J.H.Holland,, K.J.Holyoak,, R.E.Nisbett,, P.R.Thagard,, & S.W.Smoliar, (1987). Induction: Processes of inference, learning, and discovery. IEEE Expert, 2(3), 92–93.
29. L.T.Hsieh,, M.J.Gruber,, L.J.Jenkins,, & C.Ranganath, (2014). Hippocampal activity patterns carry information about objects in temporal context. Neuron, 81, 1165–1178. doi:10.1016/j.neuron.2014.01.015
30. D.H.Hubel,, & T.N.Wiesel, (1970). The period of susceptibility to the physiological effects of unilateral eye closure in kittens. The Journal of Physiology, 206, 419–436.
31. A.G.Huth,, S.Nishimoto,, A.T.Vu,, & J.L.Gallant, (2012). A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76, 1210–1224. doi:10.1016/j.neuron.2012.10.014
32. Y.Kamitani,, & F.Tong, (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8(5), 679–685. doi:10.1038/nn1444
33. N.Kanwisher,, J.McDermott,, & M.M.Chun, (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311.
34. K.N.Kay,, T.Naselaris,, R.J.Prenger,, & J.L.Gallant, (2008). Identifying natural images from human brain activity. Nature, 452, 352–355. doi:10.1038/nature06713
35. S.M.Khaligh-Razavi,, & N.Kriegeskorte, (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology, 10:e10003915. doi:10.1371/journal.pcbi.1003915
36. N.Kriegeskorte, (2009). Relating population-code representations between man, monkey, and computational models. Frontiers in Neuroscience, 3, 363–373. doi:10.3389/neuro.01.035.2009
37. N.Kriegeskorte, (2011). Pattern-information analysis: From stimulus decoding to computational-model testing. Neuroimage, 56, 411–421. doi:10.1016/j.neuroimage.2011.01.061
38. N.Kriegeskorte,, & P.A.Bandettini, (2007). Analyzing for information, not activation, to exploit high-resolution fMRI. NeuroImage, 38, 649–662. doi:10.1016/j.neuroimage.2007.02.022
39. N.Kriegeskorte,, E.Formisano,, B.Sorger,, & R.Goebel, (2007). Individual faces elicit distinct response patterns in human anterior temporal cortex. PNAS, 104, 20600–20605. doi:10.1073/pnas.0705654104
40. N.Kriegeskorte,, R.Goebel,, & P.A.Bandettini, (2006). Information-based functional brain mapping. PNAS, 103, 3863–3868. doi:10.1073/pnas.0600244103
41. N.Kriegeskorte,, & R.A.Kievit, (2013). Representational geometry: Integrating cognition, computation, and the brain. Trends in Cognitive Sciences, 17, 401–412. doi:10.1016/j.tics.2013.06.007
42. N.Kriegeskorte,, & G.Kreiman, (2012). Visual population codes: Toward a common multivariate framework for cell recording and functional imaging. Cambridge, MA: The MIT Press.
43. N.Kriegeskorte,, & M.Mur, (2012). Inverse MDS: Inferring dissimilarity structure from multiple item arrangements. Frontiers in Psychology, 3. doi:10.3389/fpsyg.2012.00245
44. N.Kriegeskorte,, M.Mur,, & P.A.Bandettini, (2008). Representational similarity analysis – Connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience. doi:10.3389/neuro.06.004.2008
45. N.Kriegeskorte,, M.Mur,, D.A.Ruff,, R.Kiani,, J.Bodurka,, H.Esteky,, P.A.Bandettini, (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60, 1126–1141. doi:10.1016/j.neuron.2008.10.043
46. A.Krizhevsky,, I.Sutskever,, & G.E.Hinton, (2012, December). Imagenet classification with deep convolutional neural networks (pp. 1097–1105). Paper presented at the Advances in neural information processing systems, Lake Tahoe, NV.
47. Q.V.Le,, M.A.Ranzato,, R.Monga,, M.Devin,, K.Chen,, G.Corrado,, A.Ng, (2012, June-July). Building high-level features using large scale unsupervised learning. Presented at the Proceedings of the 29th International Conference on Machine Learning (ICML-12), (pp. 81–88), Edinburgh: IEEE.
48. D.D.Leeds,, D.A.Seibert,, J.A.Pyles,, & M.J.Tarr, (2013). Comparing visual representations across human fMRI and computational vision. Journal of Vision, 13, 25–25. doi:10.1167/13.13.25
49. D.Marr, (1982). Vision: A computational approach. New-York, NJ: Freeman.
50. D.Marr, (2010). Vision: A computational investigation into the human representation and processing of visual information. Cambridge, MA: MIT Press.
51. S.Martens,, M.Kandula,, & J.Duncan, (2010). Restricted attentional capacity within but not between sensory modalities: An individual differences approach. PLoS One, 5, e15280. doi:10.1371/journal.pone.0015280
52. G.A.Miller, (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41. doi:10.1145/219717.219748
53. M.B.Miller,, C.L.Donovan,, C.M.Bennett,, E.M.Aminoff,, & R.E.Mayer, (2012). Individual differences in cognitive style and strategy predict similarities in the patterns of brain activity between individuals. NeuroImage, 59(1), 83–93. doi:10.1016/j.neuroimage.2011.05.060
54. T.M.Mitchell,, S.V.Shinkareva,, A.Carlson,, K.M.Chang,, V.L.Malave,, R.A.Mason,, & M.A.Just, (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320, 1191–1195. doi:10.1126/science.1152876
55. M.Mur,, M.Meys,, J.Bodurka,, R.Goebel,, P.A.Bandettini,, & N.Kriegeskorte, (2013). Human object-similarity judgments reflect and transcend the primate-it object representation. Frontiers in Psychology, 4, 128. doi:10.3389/fpsyg.2013.00128
56. M.Mur,, D.A.Ruff,, J.Bodurka,, P.De Weerd,, P.A.Bandettini,, & N.Kriegeskorte, (2012). Categorical, yet graded - Single-image activation profiles of human category-selective cortical regions. Journal of Neuroscience, 32, 8649–8662. doi:10.1523/JNEUROSCI.2334-11.2012
57. K.A.Norman,, S.M.Polyn,, G.J.Detre,, & J.V.Haxby, (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10, 424–430. doi:10.1016/j.tics.2006.07.005
58. K.N.Ochsner,, S.A.Bunge,, J.J.Gross,, & J.D.E.Gabrieli, (2002). Rethinking feelings: An fMRI Study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14, 1215–1229. doi:10.1162/089892902760807212
59. A.J.O’Toole,, F.Jiang,, H.Abdi,, & J.V.Haxby, (2005). Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17, 580–590. doi:10.1162/0898929053467550
60. R.Parasuraman,, & Y.Jiang, (2012). Individual differences in cognition, affect, and performance: Behavioral, neuroimaging, and molecular genetic approaches. NeuroImage, 59(1), 70–82. doi:10.1016/j.neuroimage.2011.04.040
61. A.Pascual-Leone,, A.Amedi,, F.Fregni,, & L.B.Merabet, (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401. doi:10.1146/annurev.neuro.27.070203.144216
62. A.Pascual-Leone,, C.Freitas,, L.Oberman,, J.C.Horvath,, M.Halko,, M.Eldaief,, A.Rotenberg, (2011). Characterizing brain cortical plasticity and network dynamics across the age-span in health and disease with TMS-EEG and TMS-fMRI. Brain Topography, 24, 302–315. doi:10.1007/s10548-011-0196-8
63. I.D.Popivanov,, J.Jastorff,, W.Vanduffel,, & R.Vogels, (2013). Heterogeneous single-unit selectivity in an fMRI-defined body-selective patch. Journal of Neuroscience, 34(1), 95–111. doi:10.1523/JNEUROSCI.2748-13.2014
64. R.D.S.Raizada,, & A.C.Connolly, (2012). What makes different people's representations alike: Neural similarity space solves the problem of across-subject fMRI decoding. Journal of Cognitive Neuroscience, 24, 868–877. doi:10.1162/jocn_a_00189
65. R.D.S.Raizada,, F.M.Tsao,, H.M.Liu,, I.D.Holloway,, D.Ansari,, & P.K.Kuhl, (2010). Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math. NeuroImage, 51, 462–471. doi:10.1016/j.neuroimage.2010.01.080
66. R.D.S.Raizada,, F.M.Tsao,, H.M.Liu,, & P.K.Kuhl, (2010). Quantifying the adequacy of neural representations for a cross-language phonetic discrimination task: Prediction of individual differences. Cerebral Cortex, 20(1), 1–12. doi:10.1093/cercor/bhp076
67. M.Riesenhuber,, & T.Poggio, (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019–1025. doi:10.1038/14819
68. T.T.Rogers,, J.Hocking,, U.Noppeney,, A.Mechelli,, M.L.Gorno-Tempini,, K.Patterson,, & C.J.Price, (2006). Anterior temporal cortex and semantic memory: Reconciling findings from neuropsychology and functional imaging. Cognitive, Affective & Behavioral Neuroscience, 6, 201–213. doi:10.3758/CABN.6.3.201
69. Z.M.Saygin,, D.E.Osher,, K.Koldewyn,, G.Reynolds,, J.D.E.Gabrieli,, & R.R.Saxe, (2011). Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nature Neuroscience, 15, 321–327. doi:10.1038/nn.3001
70. D.S.Schwarzkopf,, & G.Rees, (2013). Subjective size perception depends on central visual cortical magnification in human V1. PLoS One, 8, e60550. doi:10.1371/journal.pone.0060550
71. D.S.Schwarzkopf,, C.Song,, & G.Rees, (2010). The surface area of human V1 predicts the subjective experience of object size. Nature Neuroscience, 14(1), 28–30. doi:10.1038/nn.2706
72. T.Serre,, A.Oliva,, & T.Poggio, (2007). A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences of the United States of America, 104, 6424–6429. doi:10.1073/pnas.0700622104
73. S.V.Shinkareva,, V.L.Malave,, M.A.Just,, & T.M.Mitchell, (2012). Exploring commonalities across participants in the neural representation of objects. Human Brain Mapping, 33, 1375–1383. doi:10.1002/hbm.21296
74. D.R.Simmons,, A.E.Robertson,, L.S.McKay,, E.Toal,, P.McAleer,, & F.E.Pollick, (2009). Vision in autism spectrum disorders. Vision Research, 49, 2705–2739. doi:10.1016/j.visres.2009.08.005
75. M.Spiridon,, & N.Kanwisher, (2002). How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35, 1157–1165. doi:10.1016/S0896-6273(02)00877-2
76. V.Troiani,, A.Stigliani,, M.E.Smith,, & R.A.Epstein, (2012). Multiple object properties drive scene-selective regions. Cerebral Cortex. doi:10.1093/cercor/bhs364
77. L.K.Tyler,, E.A.Stamatakis,, P.Bright,, K.Acres,, S.Abdallah,, J.M.Rodd,, & H.E.Moss, (2004). Processing objects at different levels of specificity. Journal of Cognitive Neuroscience, 16, 351–362.
78. L.G.Ungerleider,, & J.V.Haxby, (1994). “What” and “Where” in the human brain. Current Opinion in Neurobiology, 4, 157–165. doi:10.1016/0959-4388(94)90066-3
79. K.S.Weiner,, & K.Grill-Spector, (2010). Sparsely-distributed organization of face and limb activations in human ventral temporal cortex. NeuroImage, 52, 1559–1573. doi:10.1016/j.neuroimage.2010.04.262
80. K.S.Weiner,, & K.Grill-Spector, (2011). Neural representations of faces and limbs neighbor in human high-level visual cortex: Evidence for a new organization principle. Psychological Research, 77(1), 74–97. doi:10.1007/s00426-011-0392-x
81. K.J.Worsley,, A.C.Evans,, S.Marrett,, & P.Neelin, (1992). A three-dimensional statistical analysis for CBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 12, 900–918. doi:10.1038/jcbfm.1992.127
82. M.D.Zeiler,, & R.Fergus, (2013). Visualizing and understanding convolutional neural networks. CoRR, abs/1311.2901.