Skip to main navigation menu Skip to main content Skip to site footer

No. 2/19 (2023)

Articles

The theory of predictive processing and the problem of general abstract concepts from the perspective of cognitive linguistics

DOI: https://doi.org/10.25312/j.6248  [Google Scholar]
Published: 2023-12-18

Abstract

The contemporary embodiment paradigm in cognitive linguistics provides a valuable conceptual framework for explaining the grounding of concrete concepts but faces fundamental difficulties in explaining the mechanism of abstract concept formation (the so-called disembodiment problem of concepts). It has been increasingly pointed out that the solution to this difficulty lies in combining the embodiment paradigm with the theory of predictive processing. Although this theory aspires to be a general brain theory in the cognitive sciences, it has some limitations, albeit in explaining the salient features of general abstract concepts. The article analyzes the theory of predictive processing in terms of its ability to explain the composability, productivity, systematicity and generality of conceptual thinking. Despite the limitations pointed out in the article, predictive processing theory, combined with the embodied language paradigm, is a promising proposal within second-generation cognitivism.

References

  1. Ashby W.R. (1963), Wstęp do cybernetyki, PWN, Warszawa. [Google Scholar]
  2. Bermúdez J. (2005), Philosophy of psychology: A contemporary introduction, London. [Google Scholar]
  3. Bruineberg J., Kiverstein J., Rietveld E. (2016), The anticipating brain is not a scientist: The free-energy principle from an ecological-enactive perspective, „Synthese”, vol. 195, s. 1–28, https://doi.org/10.1007/s11229-016-1239-1 DOI: https://doi.org/10.1007/s11229-016-1239-1 [Google Scholar]
  4. Bruineberg J., Rietveld E. (2014), Self-organization, free energy minimization, and optimal grip on a field of affordances, „Frontiers in Human Neuroscience”, vol. 8(599), s. 1–14, https://doi.org/10.3389/fnhum.2014.00599 DOI: https://doi.org/10.3389/fnhum.2014.00599 [Google Scholar]
  5. Chomsky N. (1982), Zagadnienia teorii składni, Wrocław. [Google Scholar]
  6. Clark A. (2000), Mindware, New York. [Google Scholar]
  7. Clark A. (2013), Whatever next? Predictive brains, situated agents, and the future of cognitive science, „Behavioral and Brain Sciences”, vol. 36(3), s. 181–204, https://doi.org/10.1017/S0140525X12000477 DOI: https://doi.org/10.1017/S0140525X12000477 [Google Scholar]
  8. Clark A. (2015), Predicting peace: The end of the representation wars – A reply to Michael Madary, [w:] T. Metzinger, J.M. Windt (red.), Open MIND: 7(R), Frankfurt am Main, https://doi.org/10.15502/9783958570979 [Google Scholar]
  9. Clark A. (2016), Surfing Uncertainty. Prediction, Action, and the Embodied Mind, Oxford. DOI: https://doi.org/10.1093/acprof:oso/9780190217013.001.0001 [Google Scholar]
  10. Dove G. (2011), On the need for embodied and dis-embodied cognition, „Frontiers in Psychology”, vol. 1. DOI: https://doi.org/10.3389/fpsyg.2010.00242 [Google Scholar]
  11. Dove G. (2014), Thinking in words: Language as an embodied medium of thought, „Topics in Cognitive Science”, vol. 6. DOI: https://doi.org/10.1111/tops.12102 [Google Scholar]
  12. Dove G. (2016), Three symbol ungrounding problems: Abstract concepts and the future of embodied cognition, „Psychonomic Bulletin & Review”, vol. 23. DOI: https://doi.org/10.3758/s13423-015-0825-4 [Google Scholar]
  13. Dove G. (2018), Language as a disruptive technology: abstract concepts, embodiment and flexible mind, „Philosophical Transactions of the Royal Society B: Biological Sciences”, vol. 373. DOI: https://doi.org/10.1098/rstb.2017.0135 [Google Scholar]
  14. Fodor J. (1975), The language of thought, Cambridge. [Google Scholar]
  15. Fodor J., Pylyshyn Z. (1988), Connectionism and cognitive architecture: A critical analysis, „Cognition”, vol. 28(1–2), s. 3–71, https://doi.org/10.1016/0010-0277(88)90031-5 DOI: https://doi.org/10.1016/0010-0277(88)90031-5 [Google Scholar]
  16. Fornal M. (2022), Problem ogólnych pojęć abstrakcyjnych w kontekście językoznawstwa kognitywnego, „Językoznawstwo”, nr 1(16), s. 9–27. DOI: https://doi.org/10.25312/2391-5137.16/2022_01mf [Google Scholar]
  17. Friston K.J. (2009), The free-energy principle: A rough guide to the brain?, „Trends in Cognitive Sciences”, vol. 13(7), s. 293–301. DOI: https://doi.org/10.1016/j.tics.2009.04.005 [Google Scholar]
  18. Friston K.J. (2012), A free energy principle for biological systems, „Entropy”, vol. 14(11), s. 2100–2121, https://doi.org/10.3390/e14112100 DOI: https://doi.org/10.3390/e14112100 [Google Scholar]
  19. Friston K.J. (2013a), Active inference and free energy, „Behavioral and Brain Sciences”, vol. 36(3), s. 212–213, https://doi.org/10.1017/S0140525X12002142 DOI: https://doi.org/10.1017/S0140525X12002142 [Google Scholar]
  20. Friston K.J. (2013b), Consciousness and hierarchical inference, „Neuropsychoanalysis”, vol. 15(1), s. 38–42. DOI: https://doi.org/10.1080/15294145.2013.10773716 [Google Scholar]
  21. Friston K.J., Daunizeau J., Kilner J., Kiebel S.J. (2010), Action and behavior: A free-energy formulation, „Biological Cybernetics”, vol. 102, s. 227–260, https://doi.org/10.1007/s00422-010-0364-z DOI: https://doi.org/10.1007/s00422-010-0364-z [Google Scholar]
  22. Friston K.J., Kiebel S.J. (2009), Predictive coding under the free-energy principle, „Philosophical Transactions of the Royal Society B”, vol. 364, s. 1211–1221. DOI: https://doi.org/10.1098/rstb.2008.0300 [Google Scholar]
  23. Friston K.J., Stephan K.E. (2007), Free energy and the brain, „Synthese”, vol. 159, s. 417–458. DOI: https://doi.org/10.1007/s11229-007-9237-y [Google Scholar]
  24. Gładziejewski P. (2016), Predictive coding and representationalism, „Synthese”, vol. 193, s. 559–582, https://doi.org/10.1007/s11229-015-0762-9 DOI: https://doi.org/10.1007/s11229-015-0762-9 [Google Scholar]
  25. Goodman N., Tenenbaum J., Gerstenberg T. (2015), Concepts in a probabilistic language of thought, [w:] E. Margolis, S. Laurence (red.), The conceptual mind: New directions in the study of concepts, Cambridge, s. 623–654. DOI: https://doi.org/10.7551/mitpress/9383.003.0035 [Google Scholar]
  26. Harkness D.L., Keshava A. (2017), Moving from the what to the how and where – Bayesian models and predictive processing, [w:] T. Metzinger, W. Wiese (red.), Philosophy and Predictive Processing, Frankfurt am Main, s. 1–10, https://doi.org/10.15502/9783958573178 [Google Scholar]
  27. Hohwy J. (2013), The Predictive Mind, Oxford. DOI: https://doi.org/10.1093/acprof:oso/9780199682737.001.0001 [Google Scholar]
  28. Hohwy J. (2015a), The neural organ explains the mind, [w:] T. Metzinger, J.M. Windt (red.), Open MIND, 19(T), Frankfurt am Main, s. 1–22, https://doi.org/10.15502/9783958570016 [Google Scholar]
  29. Hohwy J. (2015b), The diversity of Bayesian explanation – a reply to Dominic L. Harkness, [w:] T. Metzinger, J.M. Windt (red.), Open MIND, 19(R), Frankfurt am Main, s. 1–6, https://doi.org/10.15502/9783958570870 [Google Scholar]
  30. Hohwy J. (2016), The self-evidencing brain, „Noûs”, vol. 50(2), s. 259–285, https://doi.org/10.1111/nous.12062 DOI: https://doi.org/10.1111/nous.12062 [Google Scholar]
  31. Hohwy J. (2017a), How to entrain your evil demon, [w:] T. Metzinger, W. Wiese (red.), Philosophy and Predictive Processing, Frankfurt am Main, s. 1–15, https://doi.org/10.15502/9783958573048 [Google Scholar]
  32. Hohwy J. (2017b), Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization, „Consciousness and Cognition”, vol. 47, s. 75–85, https://doi.org/10.1016/j. concog.2016.09.004 DOI: https://doi.org/10.1016/j.concog.2016.09.004 [Google Scholar]
  33. Hohwy J. (2018), The predictive processing hypothesis, [w:] A. Newen, L.S. de Bruin, S. Gallagher (red.), The Oxford handbook of 4e cognition, Oxford, s. 129–145. DOI: https://doi.org/10.1093/oxfordhb/9780198735410.013.7 [Google Scholar]
  34. Jackendoff R. (2002), Foundations of language, New York. DOI: https://doi.org/10.1093/acprof:oso/9780198270126.001.0001 [Google Scholar]
  35. Kawalec P. (2003), Zagadnienia metodologiczne w bayesowskiej teorii konfirmacji, „Roczniki Filozoficzne”, t. LI, s. 113–142. [Google Scholar]
  36. Kawalec P. (2012), Bayesianizm w polskiej tradycji probabilizmu – studium stanowiska Kazimierza Ajdukiewicza, „Ruch Filozoficzny”, nr 1(69), s. 111–122. [Google Scholar]
  37. Kiefer A., Hohwy J. (2017), Content and misrepresentation in hierarchical generative models, „Synthese”, vol. 195, s. 2387–2415, https://doi.org/10.1007/s11229-017-1435-7 DOI: https://doi.org/10.1007/s11229-017-1435-7 [Google Scholar]
  38. Kirchhoff M., Parr T., Palacios E., Friston K., Kiverstein J. (2018), The Markov blankets of life: Autonomy, active inference and the free energy principle, „Journal of the Royal Society Interface”, vol. 15, s. 1–11, https://doi.org/10.1098/rsif.2017.0792 DOI: https://doi.org/10.1098/rsif.2017.0792 [Google Scholar]
  39. Kwisthout J., van Rooij I. (2019), Computational resource demands of a predictive Bayesian brain, „Computational Brain & Behavior”, vol. 3(3), s. 1–15, https://doi.org/10.1007/s42113-019- 00032-3 DOI: https://doi.org/10.1007/s42113-019-00032-3 [Google Scholar]
  40. Pearl J. (1988), Probabilistic reasoning in intelligent systems, San Francisco. [Google Scholar]
  41. Pylyshyn Z., Fodor J. (2015), Minds without meanings: An essay on the content of concepts, Cambridge. [Google Scholar]
  42. Ramstead M.J.D., Kirchhoff M.D., Friston K.J. (2019), A tale of two densities: Active inference is enactive inference, „Adaptive Behavior”, vol. 28(4), s. 1–15, https://doi.org/10.1177/1059712319862774 DOI: https://doi.org/10.1177/1059712319862774 [Google Scholar]
  43. Russell S., Norvig P. (2010), Artificial intelligence: A modern approach, London. [Google Scholar]
  44. Schwartenbeck P., FitzGerald T., Dolan R.J., Friston K. (2013), Exploration, novelty, surprise, and free energy minimization, „Frontiers in Psychology”, vol. 4(710), s. 1–5, https://doi.org/10.3389/fpsyg.2013.00710 DOI: https://doi.org/10.3389/fpsyg.2013.00710 [Google Scholar]
  45. Sims A. (2016), A problem of scope for the free energy principle as a theory of cognition, „Philosophical Psychology”, vol. 29, s. 967–980, https://doi.org/10.1080/09515089.2016.1200024 DOI: https://doi.org/10.1080/09515089.2016.1200024 [Google Scholar]
  46. Tenenbaum J., Kemp C., Griffiths T., Goodman N. (2011), How to grow a mind: Statistics, structure, and abstraction, „Science”, vol. 331(6022), s. 1279–1285, https://doi.org/10.1126/science.1192788 DOI: https://doi.org/10.1126/science.1192788 [Google Scholar]
  47. Varela F., Thompson E., Rosch E. (1991), The embodied mind: Cognitive science and human experience, Cambridge–London. DOI: https://doi.org/10.7551/mitpress/6730.001.0001 [Google Scholar]
  48. Venter E. (2021), Toward an Embodied, Embedded Predictive Processing Account, „Frontiers in Psychology”, vol. 12, https://doi.org/10.3389/fpsyg.2021.543076 DOI: https://doi.org/10.3389/fpsyg.2021.543076 [Google Scholar]
  49. Ward D., Silverman D., Villalobos M. (2017), Introduction: The varieties of enactivism, „Topoi”, vol. 36, s. 365–375, https://doi.org/10.1007/s11245-017-9484-6 DOI: https://doi.org/10.1007/s11245-017-9484-6 [Google Scholar]
  50. Wiese W., Metzinger T. (2017), Vanilla PP for Philosophers: A Primer on Predictive Processing, [w:] T. Metzinger, W. Wiese (red.), Philosophy and Predictive Processing, Frankfurt am Main. DOI: https://doi.org/10.7551/mitpress/9780262036993.003.0007 [Google Scholar]

Downloads

Download data is not yet available.