Abstract
In this paper, we combine sophisticated and deep-parametric active inference to create an agent whose affective states change as a consequence of its Bayesian beliefs about how possible future outcomes will affect future beliefs. To achieve this, we augment Markov Decision Processes with a Bayes-adaptive deep-temporal tree search that is guided by a free energy functional which recursively scores counterfactual futures. Our model reproduces the common phenomenon of rumination over a situation until unlikely, yet aversive and arousing situations emerge in one’s imagination. As a proof of concept, we show how certain hyperparameters give rise to neurocognitive dynamics that characterise imagination-induced anxiety.
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References
Barlow, D.H., et al.: Unified Protocol for Transdiagnostic Treatment of Emotional Disorders: Therapist Guide. Oxford University Press, Oxford (2017)
Dayan, P., Huys, Q.J.M.: Serotonin, inhibition, and negative mood. PLoS Comput. Biol. 4(2), e4 (2008). https://doi.org/10.1371/journal.pcbi.0040004
Friston, K., Da Costa, L., Hafner, D., Hesp, C., Parr, T.: Sophisticated Inference (2020). http://arxiv.org/abs/2006.04120
Friston, K.J., Redish, A.D., Gordon, J.A.: Computational nosology and precision psychiatry. Comput. Psychiatr. (Cambridge, Mass.) 1, 2–23 (2017). https://doi.org/10.1162/CPSY_a_00001
Gale, C., Davidson, O.: Generalised anxiety disorder. Br. Med. J. (2007). https://doi.org/10.1136/bmj.39133.559282.BE
Hayes, S.C., Luoma, J.B., Bond, F.W., Masuda, A., Lillis, J.: Acceptance and commitment therapy: model, processes and outcomes. Behav. Res. Ther. 44(1), 1–25 (2006)
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K., Ramstead, M.: Deeply felt affect: the emergence of valence in deep active inference, 3 December 2019. https://doi.org/10.31234/osf.io/62pfd
Huys, Q.J.M., Eshel, N., O’Nions, E., Sheridan, L., Dayan, P., Roiser, J.P.: Bonsai trees in your head: how the pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Comput. Biol. 8(3), e1002410 (2012). https://doi.org/10.1371/journal.pcbi.1002410
Huys, Q.J.M., et al.: Interplay of approximate planning strategies. Proc. Nat. Acad. Sci. U.S.A. 112(10), 3098–3103 (2015). https://doi.org/10.1073/pnas.1414219112
Níally, N., Huys, Q.J.M., Eshel, N., Faulkner, P., Dayan, P., Roiser, J.P.: The neural basis of aversive pavlovian guidance during planning. J. Neurosci. 37(42), 10215–10229 (2017). https://doi.org/10.1523/JNEUROSCI.0085-17.2017
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Hesp, C., Tschantz, A., Millidge, B., Ramstead, M., Friston, K., Smith, R. (2020). Sophisticated Affective Inference: Simulating Anticipatory Affective Dynamics of Imagining Future Events. In: Verbelen, T., Lanillos, P., Buckley, C.L., De Boom, C. (eds) Active Inference. IWAI 2020. Communications in Computer and Information Science, vol 1326. Springer, Cham. https://doi.org/10.1007/978-3-030-64919-7_18
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DOI: https://doi.org/10.1007/978-3-030-64919-7_18
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