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Redressing the emperor in causal clothing

Published online by Cambridge University Press:  29 September 2022

Victor J. Btesh
Affiliation:
Experimental Psychology Department, University College London, London WC1H 0AP, UKvictor.btesh.19@ucl.ac.uk d.lagnado@ucl.ac.ukhttps://www.ucl.ac.uk/lagnado-lab/david_lagnado.html
Neil R. Bramley
Affiliation:
Psychology Department, University of Edinburgh, Edinburgh EC8 9JZ, UK neil.bramley@ed.ac.ukhttps://www.bramleylab.ppls.ed.ac.uk/
David A. Lagnado
Affiliation:
Experimental Psychology Department, University College London, London WC1H 0AP, UKvictor.btesh.19@ucl.ac.uk d.lagnado@ucl.ac.ukhttps://www.ucl.ac.uk/lagnado-lab/david_lagnado.html

Abstract

Over-flexibility in the definition of Friston blankets obscures a key distinction between observational and interventional inference. The latter requires cognizers form not just a causal representation of the world but also of their own boundary and relationship with it, in order to diagnose the consequences of their actions. We suggest this locates the blanket in the eye of the beholder.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Barrett, L. F. (2017). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(1), 123. https://doi.org/10.1093/scan/nsw154CrossRefGoogle ScholarPubMed
Blaisdell, A. P., Sawa, K., Leising, K. J., & Waldmann, M. R. (2006). Causal reasoning in rats. Science (New York, N.Y.), 311(5763), 10201022. https://doi.org/10.1126/science.1121872CrossRefGoogle ScholarPubMed
Bramley, N. R., Dayan, P., Griffiths, T. L., & Lagnado, D. A. (2017). Formalizing Neurath's ship: Approximate algorithms for online causal learning. Psychological Review, 124(3), 301338. https://doi.org/10.1037/rev0000061CrossRefGoogle ScholarPubMed
Bramley, N. R., Gerstenberg, T., Mayrhofer, R., & Lagnado, D. A. (2019). Intervening in time. In Kleinberg, S. (Ed.), Time and Causality across the Sciences (pp. 86115). Cambridge University Press. https://doi.org/10.1017/9781108592703.006Google Scholar
Bramley, N. R., Lagnado, D. A., & Speekenbrink, M. (2015). Conservative forgetful scholars: How people learn causal structure through sequences of interventions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(3), 708731.Google ScholarPubMed
Bramley, N. R., Gerstenberg, T., Mayrhofer, R., & Lagnado, D. A. (2018). Time in causal structure learning. Journal of Experimental Psychology: Learning Memory and Cognition, 44(12), 18801910. https://doi.org/10.1037/xlm0000548Google ScholarPubMed
Buckley, C. L., Kim, C. S., McGregor, S., & Seth, A. K. (2017). The free energy principle for action and perception: A mathematical review. Journal of Mathematical Psychology, 81, 5579. https://doi.org/10.1016/j.jmp.2017.09.004CrossRefGoogle Scholar
Clayton, N., & Dickinson, A. (2006). Rational rats. Nature Neuroscience, 9(4), 472474. https://doi.org/10.1038/nn0406-472CrossRefGoogle ScholarPubMed
Friston, K., Daunizeau, J., & Kiebel, S. J. (2009). Reinforcement learning or active inference? PLoS ONE, 4(7), e6421. https://doi.org/10.1371/journal.pone.0006421CrossRefGoogle ScholarPubMed
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O'Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience and Biobehavioral Reviews, 68, 862879. https://doi.org/10.1016/j.neubiorev.2016.06.022CrossRefGoogle Scholar
Friston, K., Mattout, J., & Kilner, J. (2011). Action understanding and active inference. Biological Cybernetics, 104(1–2), 137160. https://doi.org/10.1007/s00422-011-0424-zCrossRefGoogle ScholarPubMed
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015). Active inference and epistemic value. Cognitive Neuroscience, 6(4), 187224. https://doi.org/10.1080/17588928.2015.1020053CrossRefGoogle ScholarPubMed
Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 334384. https://doi.org/10.1016/j.cogpsych.2005.05.004CrossRefGoogle ScholarPubMed
Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116(4), 661716. https://doi.org/10.1037/a0017201CrossRefGoogle ScholarPubMed
Hagmayer, Y., Sloman, S. A., Lagnado, D. A., & Waldmann, M. R. (2007). Causal reasoning through intervention. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 86100). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195176803.003.0007CrossRefGoogle Scholar
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, 15(138), 20170792. https://doi.org/10.1098/rsif.2017.0792CrossRefGoogle ScholarPubMed
Lagnado, D. A., & Sloman, S. A. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning Memory and Cognition, 30(4), 856876. https://doi.org/10.1037/0278-7393.30.4.856Google ScholarPubMed
Lagnado, D. A., & Sloman, S. A. (2006). Time as a guide to cause. Journal of Experimental Psychology: Learning Memory and Cognition, 32(3), 451460. https://doi.org/10.1037/0278-7393.32.3.451Google ScholarPubMed
Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond covariation: Cues to causal structure. In Gopnik, A. & Schulz, L. (Eds.), Causal learning: Psychology, philosophy, and computation (Vol. 44, pp. 154172). https://doi.org/10.1093/acprof:oso/9780195176803.003.0011CrossRefGoogle Scholar
Palacios, E., Isomura, T., Parr, T., & Friston, K. (2019). The emergence of synchrony in networks of mutually inferring neurons. Scientific Reports, 9(1), 114. https://doi.org/10.1038/s41598-019-42821-7CrossRefGoogle ScholarPubMed
Parr, T. (2020). Choosing a Markov blanket. Behavioral and Brain Sciences, 43, E112. http://dx.doi.org/10.1017/S0140525X19002632CrossRefGoogle ScholarPubMed
Parr, T., & Friston, K. J. (2018). Active inference and the anatomy of oculomotion. Neuropsychologia, 111(October 2017), 334343. https://doi.org/10.1016/j.neuropsychologia.2018.01.041CrossRefGoogle ScholarPubMed
Pearl, J. (2009). Models, reasoning, and inference (2nd ed.). Cambridge University Press.CrossRefGoogle Scholar
Pezzulo, G., Rigoli, F., & Friston, K. (2015). Active inference, homeostatic regulation and adaptive behavioural control. Progress in Neurobiology, 134, 1735. https://doi.org/10.1016/j.pneurobio.2015.09.001CrossRefGoogle ScholarPubMed
Ramstead, M. J. D., Kirchhoff, M. D., Constant, A., & Friston, K. J. (2021). Multiscale integration: Beyond internalism and externalism. Synthese, 198, 4170. doi: https://doi.org/10.1007/s11229-019-02115-xCrossRefGoogle ScholarPubMed
Ramstead, M. J. D., Kirchhoff, M., & Friston, K. (2020). A tale of two densities: Active inference is enactive inference. Adaptive Behavior, 28(4), 225239. https://doi.org/10.1177/1059712319862774CrossRefGoogle ScholarPubMed
Rothe, A., Deverett, B., Mayrhofer, R., & Kemp, C. (2018). Successful structure learning from observational data. Cognition, 179(March 2017), 266297. https://doi.org/10.1016/j.cognition.2018.06.003CrossRefGoogle ScholarPubMed
Seth, A. K., & Friston, K. J. (2016). Active interoceptive inference and the emotional brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1708), 20160007. https://doi.org/10.1098/rstb.2016.0007CrossRefGoogle ScholarPubMed
Sloman, A. (2013). What else can brains do? Behavioral and Brain Sciences, 36(3), 230231. https://doi.org/10.1017/S0140525X12002439CrossRefGoogle Scholar
Sloman, S. A. & Lagnado, D. A. (2005). Do we “do”? Cognitive Science, 29, 539.CrossRefGoogle Scholar
Sloman, S. A., & Lagnado, D. A. (2015). Causality in thought. Annual Review of Psychology, 66(1), 223247. https://doi.org/10.1146/annurev-psych-010814-015135CrossRefGoogle ScholarPubMed
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27(3), 453489. doi: https://doi.org/10.1016/S0364-0213(03)00010-7CrossRefGoogle Scholar
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10(7), 309318. https://doi.org/10.1016/j.tics.2006.05.009CrossRefGoogle ScholarPubMed
Veissière, S. P. L., Constant, A., Ramstead, M. J. D., Friston, K., & Kirmayer, L. J. (2019). Thinking through other minds: A variational approach to cognition and culture. Behavioral and Brain Sciences, 43, e90: 175. doi: https://doi.org/10.1017/S0140525X19001213Google ScholarPubMed