Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-28T01:23:55.593Z Has data issue: false hasContentIssue false
  • English
  • Français

Representation and agency

Published online by Cambridge University Press:  19 June 2020

Karl Friston Open the ORCID record for Karl Friston [Opens in a new window]
Karl Friston*
Affiliation:
The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, LondonWC1N 3AR, UK. [email protected] https://www.fil.ion.ucl.ac.uk/~karl/
Get access Rights & Permissions [Opens in a new window]

Abstract

Gilead et al. raise some fascinating issues about representational substrates and structures in the predictive brain. This commentary drills down on a core theme in their arguments; namely, the structure of models that generate predictions. In particular, it highlights their factorial nature – both in terms of deep hierarchies over levels of abstraction and, crucially, time – and how this underwrites agency.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e134
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Attias, H. (2003) Planning by probabilistic inference. Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics.Google Scholar
Baker, C. L. & Tenenbaum, J. B. (2014) Modeling human plan recognition using Bayesian theory of mind. In: Plan, activity, and intent recognition, ed. Sukthankar, G., Geib, C., Bui, H. H., Pynadath, D. V. & Goldman, R. P., pp. 177–204. Morgan Kaufmann.CrossRefGoogle Scholar
Clark, A. (2013) Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences 36(3):181–204.CrossRefGoogle ScholarPubMed
Davison, A. J. & Murray, D. W. (2002) Simultaneous localization and map-building using active vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7):865–80. doi: 10.1109/tpami.2002.1017615.CrossRefGoogle Scholar
Doya, K. (2007) Bayesian brain: Probabilistic approaches to neural coding. MIT press.Google Scholar
Ferro, M., Ognibene, D., Pezzulo, G. & Pirrelli, V. (2010) Reading as active sensing: A computational model of gaze planning during word recognition. Frontiers in Neurorobotics 4:1.Google Scholar
Friston, K. (2013) Life as we know it. Journal of the Royal Society, Interface 10(86):20130475.Google Scholar
Friston, K., Mattout, J. & Kilner, J. (2011) Action understanding and active inference. Biological Cybernetics 104:137–60.CrossRefGoogle ScholarPubMed
Friston, K. J., Parr, T. & de Vries, B. (2017c) The graphical brain: Belief propagation and active inference. Network Neuroscience 1(4):381–414. doi: 10.1162/NETN_a_00018.CrossRefGoogle Scholar
Friston, K. J., Rosch, R., Parr, T., Price, C. & Bowman, H. (2017d) Deep temporal models and active inference. Neuroscience & Biobehavioral Reviews 77:388–402. doi: 10.1016/j.neubiorev.2017.04.009.CrossRefGoogle Scholar
Fuster, J. M. (2004) Upper processing stages of the perception–action cycle. Trends in Cognitive Sciences 8(4):143–45.CrossRefGoogle ScholarPubMed
Knill, D. C. & Pouget, A. (2004) The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences 27(12):712–19.CrossRefGoogle ScholarPubMed
MacKay, D. J. C. (1992) Information-based objective functions for active data selection. Neural Computation 4(4):590–604. doi: 10.1162/neco.1992.4.4.590.CrossRefGoogle Scholar
Rao, R. P. N. & 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. Available at: http://doi.org/10.1038/4580.CrossRefGoogle ScholarPubMed
Russek, E. M., Momennejad, I., Botvinick, M. M., Gershman, S. J. & Daw, N. D. (2017) Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLOS Computational Biology 13(9):e1005768. doi: 10.1371/journal.pcbi.1005768.CrossRefGoogle ScholarPubMed
Schmidhuber, J. (2006) Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science 18(2):173–87. doi: 10.1080/09540090600768658.CrossRefGoogle Scholar
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. doi: 10.1080/17588928.2013.877880.CrossRefGoogle ScholarPubMed
Ungerleider, L. G. & Haxby, J. V. (1994) “What” and “where” in the human brain. Current Opinion in Neurobiology 4(2):157–65. doi: 10.1016/0959-4388(94)90066-3.CrossRefGoogle Scholar