Synthese

pp 1–29 | Cite as

Content and misrepresentation in hierarchical generative models

S.I. : Predictive Brains

Abstract

In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization (PEM) framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski (Synthese 193(2):559–582, 2016) and Gładziejewski and Miłkowski (Biol Philos, 2017), to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts.

Keywords

Problem of content Misrepresentation Functional role semantics Structural resemblance Prediction error minimization Generative model Recognition model Kullbach–Leibler divergence Variational Bayesian inference Unsupervised learning 

References

  1. Adams, R. A., Huys, Q. J. M., & Roiser, J. P. (2015). Computational psychiatry: Towards a mathematically informed understanding of mental illness. Journal of Neurology, Neurosurgery & Psychiatry, 87(1), 53–63.Google Scholar
  2. Allen, M., & Friston, K.J. (2016). From cognitivism to autopoiesis: towards a computational framework for the embodied mind. Synthese. doi:10.1007/s11229-016-1288-5.
  3. Anderson, M., & Chemero, A. (2013). The problem with brain GUTs: Conflation of different senses of ‘prediction’ threatens metaphysical disaster. Behavioral & Brain Sciences, 36, 204–205.CrossRefGoogle Scholar
  4. Apps, M. A. J., & Tsakiris, M. (2014). The free-energy self: A predictive coding account of self-recognition. Neuroscience & Biobehavioral Reviews, 41, 85–97.CrossRefGoogle Scholar
  5. Barrett, L. F. (2016). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12, 1.Google Scholar
  6. Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76(4), 695–711.CrossRefGoogle Scholar
  7. Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 1(2), 1–127.CrossRefGoogle Scholar
  8. Bengio, Y., Courville, A. C., & Vincent, P. (2012). Unsupervised feature learning and deep learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.CrossRefGoogle Scholar
  9. Bishop, C. M. (2007). Pattern recognition and machine learning. Cordrecht: Springer.Google Scholar
  10. Block, N. (1994). Advertisement for a semantics for psychology. In S. P. Stich & T. Warfield (Eds.), Mental representation: A reader. Oxford: Blackwell.Google Scholar
  11. Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and learning. Journal of Mathematical Psychology, 76(Part B), 198–211.CrossRefGoogle Scholar
  12. Brandom, R. (1994). Making it explicit. Cambridge: Harvard University Press.Google Scholar
  13. Bruineberg, J. (2016). The anticipating brain is not a scientist: The free-energy principle from an ecological-enactive perspective. Synthese. doi:10.1007/s11229-016-1239-1.
  14. Burr, C., & Jones, M. (2016). The body as laboratory: Prediction-error minimization, embodiment, and representation. Philosophical Psychology, 29(4), 586–600.CrossRefGoogle Scholar
  15. Carreira-Perpiñán, M. A., & Hinton, G. E. (2005). On contrastive divergence learning. In Proceedings of the tenth international workshop on artificial intelligence and statistics.Google Scholar
  16. Clark, A. (2012). Dreaming the whole cat: Generative models, predictive processing, and the enactivist conception of perceptual experience. Mind, 121(483), 753–771.CrossRefGoogle Scholar
  17. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral & Brain Sciences, 36(3), 181–204.CrossRefGoogle Scholar
  18. Clark, A. (2016). Surfing uncertainty: Prediction, action, and the embodied mind. New York: Oxford University Press.CrossRefGoogle Scholar
  19. Colombo, M., & Seriés, P. (2012). Bayes in the brain–On Bayesian modelling in neuroscience. The British Journal for the Philosophy of Science, 63, 697–723.CrossRefGoogle Scholar
  20. Colombo, M., & Wright, C. (2017). Explanatory pluralism: An unrewarding prediction error for free energy theorists. Brain and Cognition, 112, 3–12.CrossRefGoogle Scholar
  21. Corlett, P. R., & Fletcher, P. C. (2012). The neurobiology of schizotypy: Fronto-striatal prediction error signal correlates with delusion-like beliefs in healthy people. Neuropsychologia, 50(14), 3612–3620.CrossRefGoogle Scholar
  22. Cummins, R. (1994). Interpretational semantics. In S. Stich & T. Warfield (Eds.), Mental representation: A reader. Oxford: Blackwell.Google Scholar
  23. Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience. Cambridge, Mass: MIT Press.Google Scholar
  24. Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz machine. Neural Computation, 7(5), 889–904.CrossRefGoogle Scholar
  25. Dretske, F. (1981). Knowledge and the flow of information. Cambridge, MA: MIT Press.Google Scholar
  26. Eliasmith, C. (2000). How neurons mean: A neurocomputational theory of representational content. Ph.D., Washington University in St.Louis.Google Scholar
  27. Farennikova, A. (2014). Perception of absence and penetration from expectation. Review of Philosophy and Psychology, 6, 1–20.Google Scholar
  28. Field, H. (1977). Logic, meaning and conceptual role. Journal of Philosophy, 74(69), 379–409.Google Scholar
  29. Fletcher, P. C., & Frith, C. D. (2009). Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews Neuroscience, 10(1), 48–58.CrossRefGoogle Scholar
  30. Fodor, J. A. (1975). The language of thought. Cambridge: Harvard University Press.Google Scholar
  31. Fodor, J. A. (1990). A theory of content and other essays. Cambridge, Mass: MIT Press.Google Scholar
  32. Friston, K. J. (2005). A theory of cortical responses. Philosophical Transactions: Biological Sciences, 369(1456), 815–836.CrossRefGoogle Scholar
  33. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.CrossRefGoogle Scholar
  34. Friston, K. (2013). Life as we know it. Journal of The Royal Society Interface, 10(86), 1–12.CrossRefGoogle Scholar
  35. Friston, K., Levin, M., Sengupta, B., & Pezzulo, G. (2015). Knowing one’s place: A free-energy approach to pattern regulation. Journal of The Royal Society Interface, 12(105), 20141383.CrossRefGoogle Scholar
  36. Gallagher, B. (2006). Matching structure and semantics: A survey on graph-based pattern matching. In AAAI fall symposium on capturing and using patterns for evidence detection (pp. 45–53). American Association for Artificial Intelligence.Google Scholar
  37. Gallagher, S., & Allen, M. (2016). Active inference, enactivism and the hermeneutics of social cognition. Synthese. doi:10.1007/s11229-016-1269-8.
  38. Gładziejewski, P. (2016). Predictive coding and representationalism. Synthese, 193(2), 559–582.CrossRefGoogle Scholar
  39. Gładziejewski, P., & Miłkowski, M. (2017). Structural representations: Causally relevant and different from detectors. Biology and Philosophy. doi:10.1007/s10539-017-9562-6.
  40. Godfrey-Smith, P. (1996). Complexity and the function of mind in nature. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  41. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press.Google Scholar
  42. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence & K. Q. Weinberger (Eds.), Advances in neural information processing systems (Vol. 27, pp. 2672–2680).Google Scholar
  43. Grush, R. (2004). The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences, 27, 377–442.Google Scholar
  44. Harman, G. (1973). Thought. Princeton: Princeton University Press.Google Scholar
  45. Harman, G. (1999). Reasoning, meaning and mind. Oxford: Oxford University Press.CrossRefGoogle Scholar
  46. Harrison, L. M., Stephan, K. E., Rees, G., & Friston, K. J. (2007). Extra-classical receptive field effects measured in striate cortex with fMRI. NeuroImage, 34(3), 1199–1208.CrossRefGoogle Scholar
  47. Hinton, G. E. (2005). What kind of graphical model is the brain? In International joint conference on artificial intelligence 2005, Edinburgh.Google Scholar
  48. Hinton, G. E. (2007). Learning multiple layers of representation. Trends in Cognitive Sciences, 11(10), 428–434.CrossRefGoogle Scholar
  49. Hinton, G. E., & Sejnowski, T. J. (1983). Optimal perceptual inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  50. Hinton, G. E., & Sejnowski, T. J. (1999). Unsupervised learning: Foundations of neural computation. In G. E. Hinton & T. J. Sejnowski (Eds.), Unsupervised learning: Foundations of neural computation. Cambridge, MA: MIT Press.Google Scholar
  51. Hohwy, J. (2004). Top–down and bottom–up in delusion formation. Philosophy, Psychiatry and Psychology, 11(1), 65–70.CrossRefGoogle Scholar
  52. Hohwy, J. (2011). Phenomenal variability and introspective reliability. Mind & Language, 26(3), 261–286.CrossRefGoogle Scholar
  53. Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.CrossRefGoogle Scholar
  54. Hohwy, J. (2015a). The neural organ explains the mind. In T. Metzinger & J. M. Windt (eds.) Open MIND (pp. 1–23). Frankfurt am Main: MIND Group.Google Scholar
  55. Hohwy, J. (2015b). Prediction error minimization, mental and developmental disorder, and statistical theories of consciousness. In R. Gennaro (Ed.), Disturbed consciousness: New essays on psychopathology and theories of consciousness (pp. 293–324). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  56. Hohwy, J. (2016a). Prediction, agency, and body ownership. In A. Engel, K. Friston, & D. Kragic (Eds.), Where is the action? The pragmatic turn in cognitive science. Cambridge, MA: MIT Press.Google Scholar
  57. Hohwy, J. (2016b). The self-evidencing brain. Noûs, 50(2), 259–285.CrossRefGoogle Scholar
  58. Hohwy, J. (2017). Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization. Consciousness and Cognition, 47, 75–85.CrossRefGoogle Scholar
  59. Hohwy, J., & Michael, J. (2017). Why would any body have a self. In F. Vignemont & A. Alsmith (Eds.), The body and the self. Cambridge, MA: MIT Press.Google Scholar
  60. Hosoya, T., Baccus, S. A., & Meister, M. (2005). Dynamic predictive coding by the retina. Nature, 436(7047), 71.CrossRefGoogle Scholar
  61. Hutto, D. (2017). Getting into the great guessing game: Bootstrap heaven or hell? Synthese. doi:10.1007/s11229-017-1385-0.
  62. Hutto, D., & Myin, E. (2013). Radicalizing enactivism: Basic minds without content. Cambridge, MA: MIT Press.Google Scholar
  63. Kirchhoff, M. (2016). Autopoiesis, free energy, and the life–mind continuity thesis. Synthese. doi:10.1007/s11229-016-1100-6.
  64. Klein, C. (2016). What do predictive coders want? Synthese. doi:10.1007/s11229-016-1250-6.
  65. Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., et al. (2012). Building high-level features using large scale unsupervised learning. In Proceedings of the 29th international conference on machine learning, Edinburgh.Google Scholar
  66. Limanowski, J., & Blankenburg, F. (2013). Minimal self-models and the free energy principle. Frontiers in Human Neuroscience, 7, 1–12.CrossRefGoogle Scholar
  67. Loughlin, V. (2016). Jakob hohwy: The predictive mind. Phenomenology and the Cognitive Sciences. doi:10.1007/s11097-016-9479-6.
  68. Lupyan, G. (2015). Cognitive penetrability of perception in the age of prediction: Predictive systems are penetrable systems. Review of Philosophy and Psychology, 6(4), 547–569.CrossRefGoogle Scholar
  69. Macpherson, F. (2017). The relationship between cognitive penetration and predictive coding. Consciousness and Cognition, 47, 6–16.CrossRefGoogle Scholar
  70. Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., et al. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in Human Neuroscience, 8, 825.CrossRefGoogle Scholar
  71. Metzinger, T., & Wiese, W. (Eds.). (2017). Philosophy and predictive processing. MIND Group: Frankfurt am Main.Google Scholar
  72. Millikan, R. (1984). Language, thought, and other biological categories. Cambridge, MA: MIT Press.Google Scholar
  73. Millikan, R. (1989). Biosemantics. The Journal of Philosophy, 86(6), 281–291.CrossRefGoogle Scholar
  74. Neal, R. M., & Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. NATO ASI Series D Behavioural and Social Sciences, 89, 355–370.Google Scholar
  75. O’Brien, G., & Opie, J. (2004). Notes toward a structuralist theory of mental representation. In H. Clapin, P. Staines, & P. Slezak (Eds.), Represenation in mind: New approaches to mental representation. Oxford: Clarendon Press.Google Scholar
  76. Orlandi, N. (2014). The innocent eye: Why vision is not a cognitive process. Oxford: Oxford University Press.CrossRefGoogle Scholar
  77. Orlandi, N. (2016). Bayesian perception as ecological perception. Philosophical Topics, 44(2), 327–351.CrossRefGoogle Scholar
  78. Palmer, C. J., Lawson, R. P., & Hohwy, J. (2017). Bayesian approaches to autism: Towards volatility, action, and behavior. Psychological Bulletin, 143(5), 521–542.CrossRefGoogle Scholar
  79. Papineau, D. (1984). Representation and explanation. Philosophy of Science, 51(4), 550–572.CrossRefGoogle Scholar
  80. Rao, R., & Ballard, D. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2, 79–87.CrossRefGoogle Scholar
  81. Sellars, W. (1953). Inference and meaning. Mind, 62(247), 313–338.CrossRefGoogle Scholar
  82. Sellars, W. (2007). In the space of reasons. Cambridge: Harvard University Press.Google Scholar
  83. Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565–573.CrossRefGoogle Scholar
  84. Seth, A. K. (2014). A predictive processing theory of sensorimotor contingencies: Explaining the puzzle of perceptual presence and its absence in synesthesia. Cognitive Neuroscience, 5(2), 97–118.CrossRefGoogle Scholar
  85. Seth, A. K., Suzuki, K., & Critchley, H. D. (2012). An interoceptive predictive coding model of conscious presence. Frontiers in Psychology, 2, 1–16.CrossRefGoogle Scholar
  86. Shea, N. (2014). Exploitable isomorphism and structural representation. Proceedings of the Aristotelian Society Supplementary, 114(2), 123–144.CrossRefGoogle Scholar
  87. Sims, A. (2016). A problem of scope for the free energy principle as a theory of cognition. Philosophical Psychology, 29(7), 967–980.CrossRefGoogle Scholar
  88. Trappenberg, T. (2010). Fundamentals of computational neuroscience. Oxford: Oxford University Press.Google Scholar
  89. Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., Lee de-Wit, L., et al. (2014). Precise minds in uncertain worlds: Predictive coding in autism. Psychological Review, 121(4), 649–675.CrossRefGoogle Scholar
  90. Vetter, P., & Newen, A. (2014). Varieties of cognitive penetration in visual perception. Consciousness and Cognition, 27, 62–75.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.City University of New York Graduate CenterNew YorkUSA
  2. 2.Cognition & Philosophy LabMonash UniversityMelbourneAustralia

Personalised recommendations