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Really radical?

Published online by Cambridge University Press:  08 May 2023

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, London WC1N 3AR, UK. k.friston@ucl.ac.uk https://www.fil.ion.ucl.ac.uk/~karl/
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Abstract

I enjoyed reading this compelling account of Conviction Narrative Theory (CNT). As a theoretical neurobiologist, I recognised – and applauded – the tenets of CNT. My commentary asks whether its claims could be installed into a Bayesian mechanics of decision-making, in a way that would enable theoreticians to model, reproduce and predict decision-making.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e93
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Albarracin, M., Demekas, D., Ramstead, M. J. D., & Heins, C. (2022). Epistemic communities under active inference. Entropy, 24(4).CrossRefGoogle ScholarPubMed
Attias, H. (2003). Planning by probabilistic inference, Proc. of the 9th Int. Workshop on Artificial Intelligence and Statistics.Google Scholar
Berger, J. O. (2011). Statistical decision theory and Bayesian analysis. Springer.Google Scholar
Botvinick, M., & Toussaint, M. (2012). Planning as inference. Trends in Cognitive Sciences, 16, 485–488.CrossRefGoogle ScholarPubMed
Brown, L. D. (1981). A complete class theorem for statistical problems with finite-sample spaces. Annals of Statistics, 9, 1289–1300.CrossRefGoogle Scholar
Chatzilena, A., van Leeuwen, E., Ratmann, O., Baguelin, M., & Demiris, N. (2019). Contemporary statistical inference for infectious disease models using Stan. Epidemics, 29, 100367.CrossRefGoogle ScholarPubMed
Cornish, N. J., & Littenberg, T. B. (2007). Tests of Bayesian model selection techniques for gravitational wave astronomy. Physical Review D, 76(8).CrossRefGoogle Scholar
Feynman, R. P. (1972). Statistical mechanics. Benjamin.Google Scholar
FitzGerald, T., Dolan, R., & Friston, K. (2014). Model averaging, optimal inference, and habit formation. Frontiers in Human Neuroscience, 8, 457. doi: 10.3389/fnhum.2014.00457 [KF]CrossRefGoogle ScholarPubMed
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O'Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience and Biobehavioral Reviews, 68, 862–879.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Friston, K., Parr, T., & de Vries, B. (2017a). The graphical brain: Belief propagation and active inference. Network Neuroscience, 1, 381–414.CrossRefGoogle ScholarPubMed
Friston, K., Parr, T., & Zeidman, P. (2018). Bayesian model reduction. arXiv preprint arXiv:1805.07092.Google Scholar
Friston, K. J., Daunizeau, J., Kilner, J., & Kiebel, S. J. (2010). Action and behavior: A free-energy formulation. Biological Cybernetics, 102, 227–260.CrossRefGoogle ScholarPubMed
Friston, K. J., Rosch, R., Parr, T., Price, C., & Bowman, H. (2017b). Deep temporal models and active inference. Neuroscience and Biobehavioral Reviews, 77, 388–402.CrossRefGoogle ScholarPubMed
Gershman, S. J., & Blei, D. M. (2012). A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology, 56, 1–12.CrossRefGoogle Scholar
Girolami, M., & Calderhead, B. (2011). Riemann manifold Langevin and Hamiltonian Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2), 123–214.CrossRefGoogle Scholar
Hohwy, J. (2016). The self-evidencing brain. Noûs, 50, 259–285.CrossRefGoogle Scholar
Howard, R. (1966). Information value theory. IEEE Transactions on Systems, Science and Cybernetics SSC, 2, 22–26.CrossRefGoogle Scholar
Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical Review Series II, 106, 620–630.Google Scholar
Kamar, E., & Horvitz, E. (2013). Light at the end of the tunnel: a Monte Carlo approach to computing value of information. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, pp. 571–578.Google Scholar
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.CrossRefGoogle Scholar
Lindley, D. V. (1956). On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27, 986–1005.CrossRefGoogle Scholar
Ma, Y.-A., Chen, T., & Fox, E. B. (2015). A complete recipe for stochastic gradient MCMC, p. arXiv:1506.04696.Google Scholar
Mansell, W. (2011). Control of perception should be operationalized as a fundamental property of the nervous system. Topics in Cognitive Science, 3, 257–261.CrossRefGoogle ScholarPubMed
Marino, J. (2021). Predictive coding, variational autoencoders, and biological connections. Neural Computation, 34, 1–44.CrossRefGoogle ScholarPubMed
Matsumoto, T., & Tani, J. (2020). Goal-directed planning for habituated agents by active inference using a variational recurrent neural network. Entropy, 22, 564.CrossRefGoogle ScholarPubMed
Moulin, C., & Souchay, C. (2015). An active inference and epistemic value view of metacognition. Cognitive Neuroscience, 6, 221–222.CrossRefGoogle ScholarPubMed
Parr, T., Rees, G., & Friston, K. J. (2018). Computational neuropsychology and Bayesian inference. Frontiers in Human Neuroscience, 12, 61.CrossRefGoogle ScholarPubMed
Ramstead, M. J. D., Sakthivadivel, D. A. R., Heins, C., Koudahl, M., Millidge, B., Da Costa, L., … Friston, K. J. (2022). On Bayesian mechanics: A physics of and by beliefs, p. arXiv:2205.11543.Google Scholar
Sakthivadivel, D. A. R. (2022). A constraint geometry for inference and integration, p. arXiv:2203.08119.Google Scholar
Salakhutdinov, R., Tenenbaum, J. B., & Torralba, A. (2013). Learning with hierarchical-deep models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1958–1971.CrossRefGoogle ScholarPubMed
Schwartenbeck, P., & Friston, K. (2016). Computational phenotyping in psychiatry: A worked example. eNeuro, 3, 0049-16.2016.CrossRefGoogle ScholarPubMed
Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H., Kronbichler, M., & Friston, K. J. (2019). Computational mechanisms of curiosity and goal-directed exploration. eLife, 8, e41703.CrossRefGoogle ScholarPubMed
Silver, D., & Veness, J. (2010). Monte-Carlo planning in large POMDPs. Proceedings of the Conference on Neural Information Processing Systems.Google Scholar
Smith, R., Khalsa, S. S., & Paulus, M. P. (2021). An active inference approach to dissecting reasons for nonadherence to antidepressants. Biological Psychiatry-Cognitive Neuroscience and Neuroimaging, 6, 919–934.CrossRefGoogle ScholarPubMed
Smith, R., Schwartenbeck, P., Parr, T., & Friston, K. J. (2020). An active inference approach to modeling structure learning: Concept learning as an example case. Frontiers in Computational Neuroscience, 14, 41.CrossRefGoogle ScholarPubMed
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285.CrossRefGoogle Scholar
Tervo, D. G., Tenenbaum, J. B., & Gershman, S. J. (2016). Toward the neural implementation of structure learning. Current Opinion in Neurobiology, 37, 99–105.CrossRefGoogle ScholarPubMed
Veissiere, S. P. L., Constant, A., Ramstead, M. J. D., Friston, K. J., & Kirmayer, L. J. (2019). Thinking through other minds: A variational approach to cognition and culture. The Behavioral and Brain Sciences, 43, e90.CrossRefGoogle ScholarPubMed
Wald, A. (1947). An essentially complete class of admissible decision functions. The Annals of Mathematical Statistics, 18, 549–555.CrossRefGoogle Scholar
Winn, J., & Bishop, C. M. (2005). Variational message passing. Journal of Machine Learning Research, 6, 661–694.Google Scholar