Advertisement

An Advanced Version of Cognitive Structural Realism

  • Majid Davoody Beni
Chapter
Part of the Studies in Brain and Mind book series (SIBM, volume 14)

Abstract

In this chapter, I draw on the resources of contemporary computational neuroscience to provide an updated version of CSR. I shall argue that the resources of the Predictive Processing Theory (PPT) can be used to account for both structuralist and realist components of CSR. I argue that PPT provides the necessary inferential links for accounting for CSR’s notion of scientific representation. Since the implemented Bayesian framework that PPT invokes has a natural propensity for being grounded, this version of CSR provides a solution to the problem of representation. But I will conclude the chapter by pointing out that the inferential nature of the invoked inferential links could still harbour the strong version of the problem of representation.

References

  1. Alderson-Day, B., Diederen, K., Fernyhough, C., Ford, J. M., Horga, G., Margulies, D. S., McCarthy-Jones, S., et al. (2016, June). Auditory hallucinations and the Brain’s resting-state networks: Findings and methodological observations. Schizophrenia Bulletin, 42, 1110–1123.  https://doi.org/10.1093/schbul/sbw078.CrossRefGoogle Scholar
  2. Allen, M., & Friston, K. J. (2016, December). From cognitivism to autopoiesis: Towards a computational framework for the embodied mind. Synthese, 195(6), 2459–2482.  https://doi.org/10.1007/s11229-016-1288-5. Dordrecht: Springer.CrossRefGoogle Scholar
  3. Barlow, H. B. (1972). Single units and sensation: A neuron doctrine for perceptual psychology? Perception, 1(4), 371–394 http://www.ncbi.nlm.nih.gov/pubmed/4377168.CrossRefGoogle Scholar
  4. Beni, M. D. (2016). Epistemic informational structural realism. Minds and Machines, 26(4), 323–339.  https://doi.org/10.1007/s11023-016-9403-4. Springer.CrossRefGoogle Scholar
  5. Beni, M. D. (2017a, October). The downward path to epistemic informational structural realism. Acta Analytica, 33, 181–197.  https://doi.org/10.1007/s12136-017-0333-4. SpringerCrossRefGoogle Scholar
  6. Beni, M. D. (2017b). Reconstructing the upward path to structural realism. European Journal for Philosophy of Science, 7(3), 393–409.  https://doi.org/10.1007/s13194-016-0167-8. Springer.CrossRefGoogle Scholar
  7. Beni, M. D. (2018a). Syntactical informational structural realism. Minds and Machines. Springer Netherlands, 1–21. Accessed April 5.  https://doi.org/10.1007/s11023-018-9463-8.CrossRefGoogle Scholar
  8. Beni, M. D. (2018b). Reconstructing probabilistic realism: Re-enacting syntactical structures. Journal for General Philosophy of Science. Springer Netherlands, 1–21. Accessed September 27.  https://doi.org/10.1007/s10838-018-9426-z.
  9. Beni, M. D. (2018c). Commentary: The predictive processing paradigm has Roots in Kant. Frontiers in Systems Neuroscience, 11, 98.  https://doi.org/10.3389/FNSYS.2017.00098.CrossRefGoogle Scholar
  10. Blakemore, S.-J., Wolpert, D. M., & Frith, C. D. (1999). The cerebellum contributes to somatosensory cortical activity during self-produced tactile stimulation. Neuroimage. http://www.sciencedirect.com/science/article/pii/S1053811999904780.
  11. Blakemore, S.-J., Wolpert, D., & Frith, C. (2000). Why can’t you tickle yourself? Neuroreport. http://journals.lww.com/neuroreport/Abstract/2000/08030/Why_can_t_you_tickle_yourself_.2.aspx.
  12. Brenner, N., Bialek, W., & van Steveninck, R. d. R. (2000). Adaptive rescaling maximizes information transmission. Neuron, 26(3), 695–702 http://www.ncbi.nlm.nih.gov/pubmed/10896164.CrossRefGoogle Scholar
  13. Churchland, P. M. (1989). On the nature of theories: A neurocomputational perspective. In C. W. Savage (Ed.), Minnesota studies in the philosophy of science, Volume 14. Scientific theories (pp. 59–101). Minneapolis: University of Minnesota Press.Google Scholar
  14. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204.  https://doi.org/10.1017/S0140525X12000477. Cambridge University Press.CrossRefGoogle Scholar
  15. Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8(12), 1704–1711.  https://doi.org/10.1038/nn1560.CrossRefGoogle Scholar
  16. Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz Machine. Neural Computation, 7(5), 889–904. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  17. Floridi, L. (2014). Perception and testimony as data providers. Logique et Analyse, 57(226), 3421–3438. Dordrecht: Springer.Google Scholar
  18. Friston, K. J. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.  https://doi.org/10.1038/nrn2787.CrossRefGoogle Scholar
  19. Friston, K. J. (2012). A free energy principle for biological systems. Entropy (Basel, Switzerland), 14(11), 2100–2121.  https://doi.org/10.3390/e14112100. Europe PMC Funders.CrossRefGoogle Scholar
  20. Friston, K. J., & Stephan, K. E. (2007). Free-energy and the brain. Synthese, 159(3), 417–458.  https://doi.org/10.1007/s11229-007-9237-y.CrossRefGoogle Scholar
  21. Friston, K. J., Daunizeau, J., Kilner, J., & Kiebel, S. J. (2010). Action and behavior: A free-energy formulation. Biological Cybernetics, 102(3), 227–260.  https://doi.org/10.1007/s00422-010-0364-z.CrossRefGoogle Scholar
  22. Grammont, F., & Riehle, A. (2003). Spike synchronization and firing rate in a population of motor cortical neurons in relation to movement direction and reaction time. Biological Cybernetics, 88(5), 360–373.  https://doi.org/10.1007/s00422-002-0385-3.CrossRefGoogle Scholar
  23. Hempel, C. (1965). Aspects of scientific explanation and other essays in the philosophy of science. New York: Free Press.Google Scholar
  24. Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.  https://doi.org/10.1093/acprof:oso/9780199682737.001.0001.CrossRefGoogle Scholar
  25. Hohwy, J. (2014). The self-evidencing brain. Noûs, 50(2), 259–285.  https://doi.org/10.1111/nous.12062.CrossRefGoogle Scholar
  26. Hohwy, J. (2017). How to entrain your evil demon. In T. Metzinger & W. Wiese (Eds.), Philosophy and predictive processing. Frankfurt am Main: MIND Group.  https://doi.org/10.15502/9783958573048.CrossRefGoogle Scholar
  27. Horga, G., Schatz, K. C., Abi-Dargham, A., & Peterson, B. S. (2014). Deficits in predictive coding underlie hallucinations in schizophrenia. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(24), 8072–8082.  https://doi.org/10.1523/JNEUROSCI.0200-14.2014.CrossRefGoogle Scholar
  28. Huang, Y., & Rao, R. P. N. (2011). Predictive coding. Wiley Interdisciplinary Reviews: Cognitive Science, 2(5), 580–593.  https://doi.org/10.1002/wcs.142. Wiley.CrossRefGoogle Scholar
  29. Kilner, J. M., Friston, K. J., & Frith, C. D. (2007). Predictive coding: An account of the mirror neuron system. Cognitive Processing, 8(3), 159–166.  https://doi.org/10.1007/s10339-007-0170-2.CrossRefGoogle Scholar
  30. Kolossa, A., Kopp, B., & Fingscheidt, T. (2015). A computational analysis of the neural bases of Bayesian inference. NeuroImage, 106, 222–237.  https://doi.org/10.1016/j.neuroimage.2014.11.007.CrossRefGoogle Scholar
  31. König, P., Wilming, N., Kaspar, K., Nagel, S. K., & Onat, S. (2013). Predictions in the light of your own action repertoire as a general computational principle. The Behavioral and Brain Sciences, 36(3), 219–220.  https://doi.org/10.1017/S0140525X12002294.CrossRefGoogle Scholar
  32. Lehrer, K., & Cohen, S. (1983). Justification, truth, and coherence. Synthese, 55(2), 191–207.  https://doi.org/10.1007/BF00485068.CrossRefGoogle Scholar
  33. Lipton, P. (2004). Inference to the best explanation (2nd ed.). London: Routledge/Taylor and Francis Group.Google Scholar
  34. Maxwell, G. (1970). Theories, perception and structural realism. In R. Colodny (Ed.), The nature and function of scientific theories (pp. 3–34). Pittsburgh: University of Pittsburgh.Google Scholar
  35. Northoff, G. (2014a). Unlocking the brain: Volume 1: Coding. New York: Oxford University Press.Google Scholar
  36. Northoff, G. (2014b). Unlocking the brain: Volume 2: Consciousness. New York: Oxford University Press.  https://doi.org/10.1093/acprof:oso/9780199826995.001.0001.CrossRefGoogle Scholar
  37. Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37(23), 3311–3325 http://www.ncbi.nlm.nih.gov/pubmed/9425546.CrossRefGoogle Scholar
  38. Olshausen, B. A., & Field, D. J. (2004). Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14, 481–487.  https://doi.org/10.1016/j.conb.2004.07.007.CrossRefGoogle Scholar
  39. Pezzulo, G. (2012, November). An active inference view of cognitive control. Frontiers in Psychology, 3, 478.  https://doi.org/10.3389/fpsyg.2012.00478.CrossRefGoogle Scholar
  40. Poo, C., & Isaacson, J. S. (2009). Odor representations in olfactory cortex: “Sparse” coding, global inhibition, and oscillations. Neuron, 62(6), 850–861.  https://doi.org/10.1016/j.neuron.2009.05.022.CrossRefGoogle Scholar
  41. Psillos, S. (1999). Scientific realism: How science tracks truth. New York: Routledge.Google Scholar
  42. Psillos, S. (2001). Is structural realism possible? Philosophy of Science, 68(S3), S13–S24.  https://doi.org/10.1086/392894.CrossRefGoogle Scholar
  43. Psillos, S. (2007). The fine structure of inference to the best explanation. Philosophy and Phenomenological Research, 74(2), 441–448.  https://doi.org/10.1111/j.1933-1592.2007.00030.x. Blackwell Publishing Ltd.CrossRefGoogle Scholar
  44. Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2017). Answering Schrödinger’s question: A free-energy formulation. Physics of Life Reviews.  https://doi.org/10.1016/J.PLREV.2017.09.001 .CrossRefGoogle Scholar
  45. 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.  https://doi.org/10.1038/4580.CrossRefGoogle Scholar
  46. Russell, B. (1927). The analysis of matter. London: Kegan Paul.Google Scholar
  47. 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.  https://doi.org/10.1080/17588928.2013.877880.CrossRefGoogle Scholar
  48. Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience, 24(1), 1193–1216.  https://doi.org/10.1146/annurev.neuro.24.1.1193.CrossRefGoogle Scholar
  49. Swanson, L. R. (2016). The predictive processing paradigm has roots in Kant. Frontiers in Systems Neuroscience, 10, 79.  https://doi.org/10.3389/fnsys.2016.00079. Frontiers Media SA.CrossRefGoogle Scholar
  50. Trappenberg, T., & Hollensen, P. (2013). Sparse coding and challenges for Bayesian models of the brain. Behavioral and Brain Sciences, 36(3), 232–233.  https://doi.org/10.1017/S0140525X12002300.CrossRefGoogle Scholar
  51. van Fraassen, B. C. (1989). Laws and symmetry. Oxford: Oxford University Press.  https://doi.org/10.1093/0198248601.001.0001.CrossRefGoogle Scholar
  52. Vinje, W. E., & Gallant, J. L. (2000). Sparse coding and decorrelation in primary visual cortex during natural vision. Science (New York), 287(5456), 1273–1276 http://www.ncbi.nlm.nih.gov/pubmed/10678835.CrossRefGoogle Scholar
  53. Zylberberg, J., Murphy, J. T., & DeWeese, M. R. (2011). A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields. Edited by O. Sporns. PLoS Computational Biology, 7(10), e1002250.  https://doi.org/10.1371/journal.pcbi.1002250. Public Library of ScienceCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Majid Davoody Beni
    • 1
  1. 1.Department of Management, Science, and TechnologyAmirkabir University of TechnologyTehranIran

Personalised recommendations