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Predictive perceptual systems

  • S.I. : Predictive Brains
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Abstract

This article attempts to clarify the commitments of a predictive coding approach to perception. After summarizing predictive coding theory, the article addresses two questions. Is a predictive coding perceptual system also a Bayesian system? Is it a Kantian system? The article shows that the answer to these questions is negative.

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Notes

  1. In this first sketch of the theory, I treat expectations and predictions as separate elements. Depending on the specific example, however, there may be no difference between the two (see, predictive coding in lateral and temporal inibition below). In Sect. 3, following the insight of an anonymous referee, I put this distinction into question.

  2. There is also a notion of free-energy that comes from thermodynamics. In thermodynamics, free energy stands for the measure of energy “available to do useful work” (Clark 2016 p. 17; Friston et al. 2006 p. 71). The link between information theoretic, and thermodynamic free-energy is only mathematical. Both notions appeal to the same probabilistic foundations. It is the information theoretic notion that is at play in free-energy minimization theory (Friston and Stephan 2007, p. 420).

  3. There are other ways in which the sketch of PCP just outlined is simplified. For example, I described the visual apparatus as an isolated process while there are certainly influences from other modalities, and from action. I do not think that these simplifications perniciously affect what is argued in this article.

  4. In some Bayesian models of perception, the selection of the percept is made in accord with expected utility maximization which involves calculating the costs and benefits associated with accepting the hypothesis with the highest posterior. In others, like the one I just described, it is made simply as a function of the posterior probability (Maloney et al. 2009, Howe et al. 2006, p. 2; Mamassian et al. 2002, p. 20–21, Mamassian and Landy 1998).

  5. For discussion of this prior, see Stone (2011), p. 179.

  6. A third way in which predictive coding can be given an intellectualist gloss is by intending “error” as the mismatch between how we represent the world to be and how the world is. In this interpretation, reducing this mismatch is tantamount to getting to truth. As far as I can see, this is not a mandatory reading of “error” in predictive coding. “Error”, in this context, is the discrepancy between the information already transmitted, and the information coming in, quite independently of how this information matches what is in the world. The information may concern what is useful for the system to know, rather than what is true. In other words, a system that uses predictive coding may reduce error in the sense of reducing biological disadvantage and enhancing fit. For reasons of space, I do not investigate this aspect of predictive coding any further in the present article.

  7. An anonymous referee rightly points out that work in Bayesian perception often adds a Gaussian noise to the perceptual signal. This addition seems to be un-ecological.

  8. Thanks to an anonymous referee for raising this point.

  9. Thanks to an anonymous referee for raising this point.

  10. Thanks also to Casey O’Callaghan for bringing up this point.

References

  • Barlow, H. B. (1961). Possible principles underlying the transformations of sensory messages. In W. A. Rosenblith (Ed.), Sensory communication. Cambridge: MIT Press.

    Google Scholar 

  • Barlow, H. B. (1981). The ferrier lecture, 1980: Critical limiting factors in the design of the eye and visual cortex. Proceedings of the Royal Society of London B: Biological Sciences, 212(1186), 1–34.

    Article  Google Scholar 

  • Beierholm, U. R., Quartz, S. R., & Shams, L. (2009). Bayesian priors are encoded independently from likelihoods in human multisensory perception. Journal of vision, 9(5), 23.

    Article  Google Scholar 

  • Bowers, J. S., & Davis, C. J. (2012). Bayesian just-so stories in psychology and neuroscience. Psychological Bulletin, 138(3), 389.

    Article  Google Scholar 

  • Brainard, D. H. (2009). Bayesian approaches to color vision. The visual neurosciences (Vol. 4). http://color.psych.upenn.edu/brainard/papers/BayesColorReview.pdf.

  • Clark, A. (2013). Whatever next? predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.

    Article  Google Scholar 

  • Clark, A. (2016). Surfing uncertainty: Prediction, action and the embodied mind. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Clark, A., & Toribio, J. (1994). Doing without representing? Synthese, 101, 401–431.

    Article  Google Scholar 

  • Dan, Y., Atick, J. J., & Reid, R. C. (1996). Efficient coding of natural scenes in the lateral geniculate nucleus: Experimental test of a computational theory. The Journal of Neuroscience, 16(10), 3351–3362.

    Article  Google Scholar 

  • Fodor, J. A. (1983). The modularity of mind: An essay on faculty psychology. Cambridge: MIT press.

    Google Scholar 

  • Friston, K. (2009). The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences, 13(7), 293–301.

    Article  Google Scholar 

  • Friston, K., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. Journal of Physiology-Paris, 100(1), 70–87.

    Article  Google Scholar 

  • Friston, K. J., & Stephan, K. E. (2007). Free-energy and the brain. Synthese, 159(3), 417–458.

    Article  Google Scholar 

  • Geisler, W. (2008). Visual perception and the statistical properties of natural scenes. Annual Review of Psychology, 59, 167–192.

    Article  Google Scholar 

  • Geisler, W., Perry, J., Super, B., Gallogly, D., et al. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision Research, 41(6), 711–724.

    Article  Google Scholar 

  • Gibson, J. (1966). The senses considered as perceptual systems. Boston: Houghton Mifflin.

    Google Scholar 

  • Gładziejewski, P. (2016). Predictive coding and representationalism. Synthese, 193(2), 559–582.

    Article  Google Scholar 

  • Gregory, R. (1966). The intelligent eye. New York: McGrawy Hill.

    Google Scholar 

  • Grush, R. (1997). The architecture of representation. Philosophical Psychology, 10(1), 5–23.

    Article  Google Scholar 

  • Harrison, C. W. (1952). Experiments with linear prediction in television. Bell System Technical Journal., 31(4), 764–783.

    Article  Google Scholar 

  • Helmholtz von, H. (1867/1925). Treatise on physiological optics (Vol. 3). New York: Courier Dover Publications.

  • Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Hohwy, J., Roepstorff, A., & Friston, K. (2008). Predictive coding explains binocular rivalry: An epistemological review. Cognition, 108(3), 687–701.

    Article  Google Scholar 

  • Hosoya, T., Baccus, S. A., & Meister, M. (2005). Dynamic predictive coding by the retina. Nature, 436(7047), 71–77.

    Article  Google Scholar 

  • Howe, C. Q., Beau Lotto, R., & Purves, D. (2006). Comparison of bayesian and empirical ranking approaches to visual perception. Journal of Theoretical Biology, 241(4), 866–875.

    Article  Google Scholar 

  • Jacobs, R. A., & Kruschke, J. K. (2011). Bayesian learning theory applied to human cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 2(1), 8–21.

    Google Scholar 

  • Köhler, W. (1920). Physical gestalten at rest and in steady state: A natural-philosophical investigation. In A Source Book of Gestalt Psychology. London: Routledge (Reimpresión en Die Physischen Gestalten in Ruhe und im stationciren Zustand Eine nature-philosophische Untersuchung por W Köhler 1920, Braunsschweig Germany: Friedr, Vieweg und Sohn).

  • Kruschke, J. K. (2008). Bayesian approaches to associative learning: From passive to active learning. Learning and Behavior, 36(3), 210–226.

    Article  Google Scholar 

  • MacKay, D. M. (1956). The epistemological problem for automata. In C. E. Shannon & J. McCarthy (Eds.), Automata studies (pp. 235–251). Princeton: Princeton University Press.

    Google Scholar 

  • Maloney, L. T., Mamassian, P., et al. (2009). Bayesian decision theory as a model of human visual perception: Testing bayesian transfer. Visual Neuroscience, 26(01), 147–155.

    Article  Google Scholar 

  • Mamassian, P., & Landy, M. S. (1998). Observer biases in the 3d interpretation of line drawings. Vision Research, 38(18), 2817–2832.

    Article  Google Scholar 

  • Mamassian, P., Landy, M., Maloney, L. T. (2002). Bayesian modelling of visual perception. In R. P. N. Rao, B. A. Olshausen, M. S. Lewicki (Eds.), Probabilistic models of the brain: Perception and neural function (pp. 13–36). MIT press.

  • Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. New York: Henry Holt and Co., Inc.

    Google Scholar 

  • Neisser, U. (1967). Cognitive Psychology. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Oliver, B. (1952). Efficient coding. Bell System Technical Journal, 31(4), 724–750.

    Article  Google Scholar 

  • Orlandi, N. (2014). The innocent eye: Why vision is not a cognitive process. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Palmer, S. E. (1999). Vision science: Photons to phenomenology (Vol. 1). Cambridge: MIT Press.

    Google Scholar 

  • Ramachandran, V. S. (1988). Perceiving shape from shading. Scientific American, 259(2), 76–83.

    Article  Google Scholar 

  • Ramsey, W. M. (2007). Representation reconsidered. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87.

    Article  Google Scholar 

  • Rescorla, M. (2013). Bayesian perceptual psychology. In M. Matthen (Ed.), The oxford handbook of the philosophy of perception. Oxford: Oxford University Press.

    Google Scholar 

  • Rescorla, M. (2015). Review of nico orlandi’s the innocent eye. Notre Dame Philosophical Reviews. http://ndpr.nd.edu/news/the-innocent-eye-why-vision-is-not-a-cognitive-process/.

  • Rock, I. (1983). The logic of perception. Cambridge: MIT press.

    Google Scholar 

  • Shi, Y. Q., & Sun, H. (1999). Image and video compression for multimedia engineering: Fundamentals, algorithms, and standards. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982). Predictive coding: A fresh view of inhibition in the retina. Proceedings of the Royal Society of London B: Biological Sciences, 216(1205), 427–459.

    Article  Google Scholar 

  • Stocker, A. A., & Simoncelli, E. P. (2006). Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience, 9(4), 578–585.

    Article  Google Scholar 

  • Stone, J. V. (2011). Footprints sticking out of the sand (part ii): Children’s bayesian priors for shape and lighting direction. Perception, 40(2), 175–190.

    Article  Google Scholar 

  • Yang, Z., & Purves, D. (2004). The statistical structure of natural light patterns determines perceived light intensity. Proceedings of the National Academy of Sciences of the United States of America, 101(23), 8745–8750.

    Article  Google Scholar 

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Authors and Affiliations

  1. UC Santa Cruz, Santa Cruz, CA, USA

    Nico Orlandi

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  1. Nico Orlandi
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Correspondence to Nico Orlandi.

Additional information

This article develops themes from Orlandi 2014. I thank Jona Vance and Martin Thomson-Jones for assistance in understanding Bayesian decision theory. Needless to say, any mistake present in the paper is my own. I also thank two anonymous referees selected by Synthese, for constructive criticism on an earlier version of this article.

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Orlandi, N. Predictive perceptual systems. Synthese 195, 2367–2386 (2018). https://doi.org/10.1007/s11229-017-1373-4

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  • DOI: https://doi.org/10.1007/s11229-017-1373-4

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