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An efficient coding approach to the debate on grounded cognition

  • Abel Wajnerman Paz
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Abstract

The debate between the amodal and the grounded views of cognition seems to be stuck. Their only substantial disagreement is about the vehicle or format of concepts. Amodal theorists reject the grounded claim that concepts are couched in the same modality-specific format as representations in sensory systems. The problem is that there is no clear characterization of (modal or amodal) format or its neural correlate. In order to make the disagreement empirically meaningful and move forward in the discussion we need a neurocognitive criterion for representational format. I argue that efficient coding models in computational neuroscience can be used to characterize modal codes: These are codes which satisfy special informational demands imposed by sensory tasks. Additionally, I examine recent studies on neural coding and argue that although they do not provide conclusive evidence for either the grounded or the amodal views, they can be used to determine what predictions these approaches can make and what experimental and theoretical developments would be required to settle the debate.

Keywords

Concepts Format Grounded cognition Efficient coding 

Notes

Acknowledgements

I would like to thank the members of the Research Group on Cognition, Language and Perception (CLP) from Buenos Aires (Liza Skidelsky, Mariela Destéfano, Sergio Barberis, Sabrina Haimovici, Nicolás Serrano, Fernanda Velázquez Coccia and Cristial Stábile) for many early discussions of this material. I am also grateful to the anonymous reviewers for very helpful suggestions on crucial aspects of the manuscript. Finally, I am indebted to Julieta Picasso Cazón for ongoing support.

Funding

CONICET postdoctoral research grant (2015–2017) (Argentina) and FONDECYT postdoctoral research grant (2018–2020) (Chile).

References

  1. Allport, D. A. (1985). Distributed memory, modular subsystems and dysphasia. In S. K. Newman & R. Epstein (Eds.), Current perspectives in dysphasia. New York: Churchill Livingstone.Google Scholar
  2. Alon, U. (2007a). An introduction to systems biology: Design principles of biological circuits. Boca Raton, FL: Chapman & Hall.Google Scholar
  3. Alon, U. (2007b). Network motifs: Theory and experimental approaches. Nature Reviews Genetics, 8, 450–461.CrossRefGoogle Scholar
  4. Attneave, F. (1954). Some informational aspects of visual perception. Psychological Review, 61, 183–193.CrossRefGoogle Scholar
  5. Attwell, D., & Laughlin, S. B. (2001). An energy budget for signaling in the grey matter of the brain. Journal of Cerebral Blood Flow and Metabolism, 21, 1133–1145.CrossRefGoogle Scholar
  6. Barlow, H. B. (1959). Symposium on the mechanization of thought processes (Vol. 10, pp. 535–539). London: H. M. Stationary.Google Scholar
  7. Barlow, H. B. (1961). Possible principles underlying the transformation of sensory messages. In W. A. Rosenblith (Ed.), Sensory communication (pp. 217–234). Cambridge, MA: MIT Press.Google Scholar
  8. Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–609.Google Scholar
  9. Barsalou, L. W. (2016). On staying grounded and avoiding Quixotic dead ends. Psychonomic Bulletin and Review, 23, 1122–1142.CrossRefGoogle Scholar
  10. Barsalou, L. W., Simmons, W. K., Barbey, A. K., & Wilson, C. D. (2003). Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences, 7, 84–91.CrossRefGoogle Scholar
  11. Bell, A. J., & Sejnowski, T. J. (1997). The ‘independent components’ of natural scenes are edge filters. Vision Research, 37, 3327–3338.CrossRefGoogle Scholar
  12. Binder, J. R. (2016). In defense of abstract conceptual representations. Psychonomic Bulletin and Review, 23, 1096–1108.CrossRefGoogle Scholar
  13. Binder, J. R., & Desai, R. H. (2011). The neurobiology of semantic memory. Trends in Cognitive Science, 15(11), 527–536.CrossRefGoogle Scholar
  14. Botvinick, M. M., & Plaut, D. C. (2006). Short-term memory for serial order: A recurrent neural network model. Psychological Review, 113, 201–233.CrossRefGoogle Scholar
  15. Bowers, J. S. (2009). On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience. Psychological Review, 116, 220–251.CrossRefGoogle Scholar
  16. Bowers, J. S. (2010). More on grandmother cells and the biological implausibility of PDP models of cognition: A reply to Plaut and McClelland (2010) and Quian Quiroga and Kreiman (2010). Psychological Review, 117, 300–306.CrossRefGoogle Scholar
  17. Bowers, J. S., Vankov, I. I., Damian, M. F., & Davis, C. J. (2014). Neural networks learn highly selective representations in order to overcome the superposition catastrophe. Psychological Review, 121(2), 248–261.CrossRefGoogle Scholar
  18. Bowers, J. S., Vankov, I. I., Damian, M. F., & Davis, C. J. (2016). Why do some neurons in cortex respond to information in a selective manner? Insights from artificial neural networks. Cognition, 148, 47–63.CrossRefGoogle Scholar
  19. Caramazza, A., & Mahon, B. Z. (2006). The organization of conceptual knowledge in the brain: The future’s past and some future directions. Cognitive Neuropsychology, 23, 13–38.CrossRefGoogle Scholar
  20. Carandini, M., & Heeger, D. J. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13(1), 51.CrossRefGoogle Scholar
  21. Chao, L. L., & Martin, A. (2000). Representation of manipulable man-made objects in the dorsal stream. Neuroimage, 12, 478–484.CrossRefGoogle Scholar
  22. Chirimuuta, M. (2014). Minimal models and canonical neural computations: The distinctness of computational explanation in neuroscience. Synthese, 191, 127–153.CrossRefGoogle Scholar
  23. Chirimuuta, M. (2017). Explanation in computational neuroscience: Causal and non-causal. British Journal for the Philosophy of Science.  https://doi.org/10.1093/bjps/axw034.CrossRefGoogle Scholar
  24. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–114.CrossRefGoogle Scholar
  25. Deen, B., & McCarthy, G. (2010). Reading about the actions of others: Biological motion imagery and action congruency influence brain activity. Neuropsychologia, 48, 1607–1615.CrossRefGoogle Scholar
  26. Dehaene, S., Dehaene-Lambertz, G., & Cohen, L. (1998). Abstract representations of numbers in the animal and human brain. Trends in Neurosciences, 21, 355–361.CrossRefGoogle Scholar
  27. DeWeese, M. R., Wehr, M., & Zador, A. M. (2003). Binary spiking in auditory cortex. Journal of Neuroscience, 23, 7940–7949.CrossRefGoogle Scholar
  28. Dove, G. (2009). Beyond perceptual symbols: A call for representational pluralism. Cognition, 110(3), 412–431.CrossRefGoogle Scholar
  29. Duncan, J. (2001). An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2(11), 820–829.CrossRefGoogle Scholar
  30. Eichenbaum, H. (2014). Time cells in the hippocampus: A new dimension for mapping memories. Nature Reviews Neuroscience, 15(11), 732–744.CrossRefGoogle Scholar
  31. Eichenbaum, H. (2016). Still searching for the engram. Learning and Behavior, 44(3), 2–209.CrossRefGoogle Scholar
  32. Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A. Optics and Image Science, 4, 2379–2394.CrossRefGoogle Scholar
  33. Földiák, P. (2002). Sparse coding in the primate cortex. In M. A. Arbib (Ed.), The handbook of brain theory and neural networks (pp. 1064–1068). Second Edition: MIT Press.Google Scholar
  34. Földiák, P. (2013). Sparse and explicit neural coding. In R. Quian Quiroga & S. Panzeri (Eds.), Principles of neural coding (pp. 379–389). Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
  35. Földiák, P., & Endres, D. (2008). Sparse coding. Scholarpedia, 3(1), 2984.CrossRefGoogle Scholar
  36. Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. K. (2001). Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291, 312–316.CrossRefGoogle Scholar
  37. Froudarakis, E., Berens, P., Ecker, A. S., Cotton, R. J., Sinz, F. H., Yatsenko, D., et al. (2014). Population code in mouse V1 facilitates readout of natural scenes through increased sparseness. Nature Neuroscience, 7, 851–857.CrossRefGoogle Scholar
  38. Fusi, S., Miller, E. K., & Rigotti, M. (2016). Why neurons mix: high dimensionality for higher cognition. Current Opinion in Neurobiology, 37, 66–74.CrossRefGoogle Scholar
  39. Fuster, J. M. (2015). The prefrontal cortex (5th ed.). San Diego: Academic Press.Google Scholar
  40. Gage, N., & Hickok, G. (2005). Multiregional cell assemblies, temporal binding and the representation of conceptual knowledge in cortex: A modern theory by a “classical” neurologist, Carl Wernicke. Cortex, 41(6), 823–832.CrossRefGoogle Scholar
  41. Gallese, V., & Lakoff, G. (2005). The brain’s concepts: The role of the sensory-motor system in conceptual knowledge. Cognitive Neuropsychology, 22, 455–479.CrossRefGoogle Scholar
  42. Gill, P. R., Mizumori, S. J., & Smith, D. M. (2011). Hippocampal episode fields develop with learning. Hippocampus, 21, 1240–1249.CrossRefGoogle Scholar
  43. Glenberg, A. M., & Gallese, V. (2012). Action-based language: A theory of language acquisition, comprehension, and production. Cortex, 48, 905–922.CrossRefGoogle Scholar
  44. Hauk, O., Johnsrude, I., & Pulvermüller, F. (2004). Somatotopic Representation of action words in human motor and premotor cortex. Neuron, 41, 301–307.CrossRefGoogle Scholar
  45. Hauk, O., & Tschentscher, N. (2013). The body of evidence: what can neuroscience tell us about embodied semantics? Frontiers in Psychology, 4, 1–14.CrossRefGoogle Scholar
  46. Hillis, A. E., & Caramazza, A. (1995). Cognitive and neural mechanisms underlying visual and semantic processing: Implications from optic aphasia. Journal of Cognitive Neuroscience, 7(4), 457–478.CrossRefGoogle Scholar
  47. Hodges, J. R., Patterson, K., Oxbury, S., & Funnell, E. (1992). Semantic dementia: Progressive fluent aphasia with temporal lobe atrophy. Brain, 115(6), 1783-1806.CrossRefGoogle Scholar
  48. Holtmaat, A., & Caroni, P. (2016). Functional and structural underpinnings of neuronal assembly formation in learning. Nature Neuroscience, 19, 1553–1562.CrossRefGoogle Scholar
  49. Hyvarinen, A., & Hoyer, P. O. (2000). Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces. Neural Computation, 12, 1705–1720.CrossRefGoogle Scholar
  50. Jamrozik, A., McQuire, M., Cardillo, E. R., & Chatterjee, A. (2016). Metaphor: Bridging embodiment to abstraction. Psychonomic Bulletin and Review, 23, 1080–1089.CrossRefGoogle Scholar
  51. Kiefer, M., & Pulvermüller, F. (2011). Conceptual representations in mind and brain: Theoretical developments, current evidence and future directions. Cortex, 48, 805–825.CrossRefGoogle Scholar
  52. Kosslyn, S. M. (1980). Image and mind. Cambridge, MA: Harvard University Press.Google Scholar
  53. Kosslyn, S. M., Thompson, W. L., & Ganis, G. (2006). The case for mental imagery. New York, NY: Oxford University Press.CrossRefGoogle Scholar
  54. Lambon Ralph, M. (2014). Neurocognitive insights on conceptual knowledge and its breakdown. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 369(1634), 20120392.CrossRefGoogle Scholar
  55. Lambon Ralph, M., Sage, K., Jones, R., & Mayberry, E. (2010). Coherent concepts are computed in the anterior temporal lobes. Proceedings of the National Academy of Sciences of the United States of America, 107, 2717–2722.CrossRefGoogle Scholar
  56. Lehky, S. R., Sejnowski, T. J., & Desimone, R. (2005). Selectivity and sparseness in the responses of striate complex cells. Vision Research, 45, 57–73.CrossRefGoogle Scholar
  57. Leshinskaya, A., & Caramazza, A. (2016). For a cognitive neuroscience of concepts: Moving beyond the grounding issue. Psychonomic Bulletin and Review, 23, 991–1001.CrossRefGoogle Scholar
  58. Levy, W. B., & Baxter, R. A. (1996). Energy-efficient neural codes. Neural Computation, 8, 531–543.CrossRefGoogle Scholar
  59. Machery, E. (2007). Concept empiricism: A methodological critique. Cognition, 104(1), 19–46.CrossRefGoogle Scholar
  60. Machery, E. (2009). Doing without concepts. Oxford: Oxford University Press.CrossRefGoogle Scholar
  61. Machery, E. (2016). The amodal brain and the offloading hypothesis. Psychonomic Bulletin and Review, 23(4), 1090–1095.CrossRefGoogle Scholar
  62. Mahon, B. Z. (2015). What is embodied about cognition? Language, Cognition and Neuroscience, 30, 420–429.CrossRefGoogle Scholar
  63. Manns, J. R., Howard, M., & Eichenbaum, H. (2007). Gradual changes in hippocampal activity support remembering the order of events. Neuron, 56, 530–540.CrossRefGoogle Scholar
  64. Marr, D. (1971). Simple memory: A theory for archicortex. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences, 262(841), 23–81.CrossRefGoogle Scholar
  65. Martin, A. (2007). The representation of object concepts in the brain. Annual Review of Psychology, 58, 25–45.CrossRefGoogle Scholar
  66. Martin, A. (2009). Circuits in mind: The neural foundations for object concepts. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (4th ed., pp. 1031–1045). Cambridge, MA: MIT Press.Google Scholar
  67. Martin, A. (2016). GRAPES—Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain. Psychonomic Bulletin and Review, 23, 979–990.CrossRefGoogle Scholar
  68. Martin, A., Haxby, J. V., Lalonde, F. M., Wiggs, C. L., & Ungerleider, L. G. (1995). Discrete cortical regions associated with knowledge of color and knowledge of action. Science, 270, 102–105.CrossRefGoogle Scholar
  69. McCaffrey, J., & Machery, E. (2012). Philosophical issues about concepts. Wiley Interdisciplinary Reviews: Cognitive Science, 3(2), 265–279.CrossRefGoogle Scholar
  70. McDonald, C. J., Lepage, K. Q., Eden, U. T., & Eichenbaum, H. (2011). Hippocampal “time cells” bridge the gap in memory for discontiguous events. Neuron, 71, 737–749.CrossRefGoogle Scholar
  71. Mély, D. A., & Serre, T. (2017). Towards a theory of computation in the visual cortex. In Q. Zhao (Ed.), Computational and cognitive neuroscience of vision. Cognitive science and technology (pp. 59–84). Singapore: Springer.CrossRefGoogle Scholar
  72. Meteyard, L., Rodriguez-Cuadrado, S., Bahrami, B., & Vigliocco, G. (2012). Coming of age: A review of embodiment and the neuroscience of semantics. Cortex, 48, 788–804.CrossRefGoogle Scholar
  73. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824–827.CrossRefGoogle Scholar
  74. Muzzio, I. A., Levita, L., Kulkarni, J., Monaco, J., Kentros, C., Stead, M., et al. (2009). Attention enhances the retrieval and stability of visuospatial and olfactory representations in the dorsal hippocampus. PLoS Biology, 7(6), e1000140.  https://doi.org/10.1371/journal.pbio.1000140.CrossRefGoogle Scholar
  75. Neininger, B., & Pulvermüller, F. (2003). Word-category specific deficits after lesions in the right hemisphere. Neuropsychologia, 41, 53–70.CrossRefGoogle Scholar
  76. Nieder, A. (2016). The neuronal code for number. Nature Reviews Neuroscience, 17, 366–382.CrossRefGoogle Scholar
  77. O’Reilly, R. C., & Busby, R. S. (2001). Generalizable relational binding from coarse-coded distributed representations. Advances in Neural Information Processing Systems, 14, 75–82.Google Scholar
  78. O’Keefe, J., & Dostrovsky, J. (1971). The hippocampus as a spatial map: Preliminary evidence from unit activity in the freely-moving rat. Brain Research, 34, 171–175.CrossRefGoogle Scholar
  79. Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607–609.CrossRefGoogle Scholar
  80. Olshausen, B. A., & Field, D. J. (2004). Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14(4), 481–487.CrossRefGoogle Scholar
  81. Page, M. P. A. (2000). Connectionist modeling in psychology; A localist manifesto. Behavioral and Brain Sciences, 23, 443–467.CrossRefGoogle Scholar
  82. Palm, G., Knoblauch, A., Hauser, F., & Schüz, A. (2014). Cell assemblies in the cerebral cortex. Biological Cybernetics, 108, 559–572.CrossRefGoogle Scholar
  83. Pastalkova, E., Itskov, V., Amarasingham, A., & Buzsáki, G. (2008). Internally generated cell assembly sequences in the rat hippocampus. Science, 321, 1322–1327.CrossRefGoogle Scholar
  84. Perez-Orive, J., Mazor, O., Turner, G. C., Cassenaer, S., Wilson, R. I., & Laurent, G. (2002). Oscillations and sparsening of odor representations in the mushroom body. Science, 297, 359–365.CrossRefGoogle Scholar
  85. Piazza, M., Mechelli, A., Price, C. J., & Butterworth, B. (2006). Exact and approximate judgements of visual and auditory numerosity: An fMRI study. Brain Research, 1106, 177–188.CrossRefGoogle Scholar
  86. Plaut, D. C., & McClelland, J. L. (2010). Locating object knowledge in the brain: Comment on Bowers’s (2009) attempt to revive the grandmother cell hypothesis. Psychological Review, 117, 284–288.CrossRefGoogle Scholar
  87. Prinz, J. (2002). Furnishing the mind: Concepts and their perceptual basis. Cambridge, MA: MIT Press.Google Scholar
  88. Pulvermüller, F. (1999). Words in the brain’s language. Behavioral and Brain Sciences, 22, 253–336.CrossRefGoogle Scholar
  89. Pulvermüller, F. (2001). Brain reflections of words and their meaning. Trends in Cognitive Sciences, 5, 517–524.CrossRefGoogle Scholar
  90. Pulvermüller, F. (2005). Brain mechanisms linking language and action. Nature Reviews Neuroscience, 6, 576–582.CrossRefGoogle Scholar
  91. Pulvermüller, F. (2013). Semantic embodiment, disembodiment or misembodiment? In search of meaning in modules and neuron circuits. Brain and Languag, 127, 86–103.CrossRefGoogle Scholar
  92. Pylyshyn, Z. (2003). Return of the mental image: Are there really pictures in the brain? Trends in Cognitive Sciences, 7, 113–118.CrossRefGoogle Scholar
  93. Quian Quiroga, R., & Panzeri, S. (2009). Extracting information from neural populations: Information theory and decoding approaches. Nature Reviews Neuroscience, 10, 173–185.CrossRefGoogle Scholar
  94. Quian Quiroga, R., & Panzeri, S. (2013). Principles of neural coding. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
  95. Quiroga, R., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 435, 1102–1107.CrossRefGoogle Scholar
  96. Reilly, J., Peelle, J., Garcia, A., & Crutch, S. (2016). Linking somatic and symbolic representation in semantic memory: The dynamic multilevel reactivation framework. Psychonomic Bulletin and Review, 23, 1002–1014.CrossRefGoogle Scholar
  97. Reynolds, J. (2009). Canonical neural computation: A summary and a roadmap. A Workshop at Villa La Pietra, Florence, 17–19 April 2009.Google Scholar
  98. Rigotti, M., Barak, O., Warden, M. R., Wang, X., Daw, N. D., Miller, E. K., et al. (2013). The importance of mixed selectivity in complex cognitive tasks. Nature, 497, 585–590.CrossRefGoogle Scholar
  99. Rogers, T. T., & McClelland, J. L. (2014). Parallel distributed processing at 25: Further explorations in the microstructure of cognition. Cognitive Science, 38, 1024–1077.CrossRefGoogle Scholar
  100. Saygin, A. P., McCullough, S., Alac, M., & Emmorey, K. (2010). Modulation of BOLD response in motion-sensitive lateral temporal cortex by real and fictive motion sentences. Journal of Cognitive Neuroscience, 22, 2480–2490.CrossRefGoogle Scholar
  101. Shallice, T., & Cooper, R. (2013). Is there a semantic system for abstract words? Frontiers in Human Neuroscience, 7, 1–10.CrossRefGoogle Scholar
  102. Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. (2002). Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31, 64–68.CrossRefGoogle Scholar
  103. Smith, F. W., & Goodale, M. A. (2015). Decoding visual object categories in early somatosensory cortex. Cerebral Cortex, 25, 1020–1031.CrossRefGoogle Scholar
  104. Sokoloff, L. (1989). Circulation and energy metabolism of the brain. In G. J. Siegel, W. Agranoff, R. W. Albers, & P. B. Molinoff (Eds.), Basic neurochemistry: Molecular, cellular, and medical aspects (4th ed., pp. 565–590). New York: Raven Press.Google Scholar
  105. Solomon, K. O., & Barsalou, L. W. (2001). Representing properties locally. Cognitive Psychology, 43, 129–169.CrossRefGoogle Scholar
  106. Stokes, M. G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., & Duncan, J. (2013). Dynamic coding for cognitive control in prefrontal cortex. Neuron, 78, 364–375.CrossRefGoogle Scholar
  107. Stopfer, M. (2007). Olfactory processing: Massive convergence onto sparse codes. Current Biology, 17, R363–R364.CrossRefGoogle Scholar
  108. Swindale, N. V. (2008). Visual map. Scholarpedia, 3(6), 4607.CrossRefGoogle Scholar
  109. Thorpe, S. (1995). Localized versus distributed representations. In M. A. Arbib (Ed.), The handbook of brain theory and neural networks. London: MIT Press.Google Scholar
  110. Ustione, A., & Piston, D. W. (2011). A simple introduction to multiphoton microscopy. Journal of Microscopy, 243, 221–226.CrossRefGoogle Scholar
  111. van Hateren, J. H., & Ruderman, D. L. (1998). Independent component analysis of natural image sequences yields spatiotemporal filters similar to simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological Sciences, 265, 2315–2320.CrossRefGoogle Scholar
  112. van Hateren, J. H., & van der Schaaf, A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological Sciences, 265, 359–366.CrossRefGoogle Scholar
  113. Vergara, J., Rivera, N., Rossi-Pool, R., & Romo, R. (2016). A neural parametric code for storing information of more than one sensory modality in working memory. Neuron, 89, 54–62.CrossRefGoogle Scholar
  114. Von der Malsburg, C. (1986). Am I thinking assemblies? In G. Palm & A. Aertsen (Eds.), Brain theory. Berlin: Springer.Google Scholar
  115. Warrington, E. K. (1975). The selective impairment of semantic memory. The Quarterly Journal of Experimental Psychology, 27, 635–657.CrossRefGoogle Scholar
  116. Weiskopf, D. (2009). The plurality of concepts. Synthese, 169, 145–173.CrossRefGoogle Scholar
  117. Willmore, B. D., Mazer, J. A., & Gallant, J. L. (2011). Sparse coding in striate and extrastriate visual cortex. Journal of Neurophysiology, 105, 2907–2919.CrossRefGoogle Scholar
  118. Willmore, B., & Tolhurst, D. J. (2001). Characterizing the sparseness of neural codes. Network, 12, 255–270.CrossRefGoogle Scholar
  119. Wittgenstein, L. (1919). Tractatus Logico-philosophicus (D. Pears & B. McGuinness, Trans.). London: Routledge.Google Scholar
  120. Woolgar, A., Hampshire, A., Thompson, R., & Duncan, J. (2011). Adaptive coding of task-relevant information in human frontoparietal cortex. Journal of Neuroscience, 31, 14592–14599.CrossRefGoogle Scholar
  121. Wu, L. L. (1995). Perceptual representation in conceptual combination. Doctoral dissertation. University of Chicago.Google Scholar
  122. Zwaan, R. (2014). Embodiment and language comprehension: Reframing the discussion. Trends in Cognitive Sciences, 18, 229–234.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Universidad de Buenos Aires, Facultad de Filosofía y Letras, Instituto de FilosofíaBuenos AiresArgentina

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