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Active inference and cognitive-emotional interactions in the brain

Published online by Cambridge University Press:  08 June 2015

Giovanni Pezzulo
Affiliation:
Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy. [email protected]@istc.cnr.ithttp://www.istc.cnr.it/people/giovanni-pezzulohttp://www.istc.cnr.it/people/laura-barca
Laura Barca
Affiliation:
Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy. [email protected]@istc.cnr.ithttp://www.istc.cnr.it/people/giovanni-pezzulohttp://www.istc.cnr.it/people/laura-barca
Karl J. Friston
Affiliation:
Wellcome Trust Center for Neuroimaging, University College London, London WC1N 3BG, United Kingdom. [email protected]://www.fil.ion.ucl.ac.uk/Friston/

Abstract

All organisms must integrate cognition, emotion, and motivation to guide action toward valuable (goal) states, as described by active inference. Within this framework, cognition, emotion, and motivation interact through the (Bayesian) fusion of exteroceptive, proprioceptive, and interoceptive signals, the precision-weighting of prediction errors, and the “affective tuning” of neuronal representations. Crucially, misregulation of these processes may have profound psychopathological consequences.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2015 

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References

Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D. & Friston, K. J. (2013) The computational anatomy of psychosis. Frontiers in Psychiatry 4:47. doi: 10.3389/fpsyt.2013.00047.Google Scholar
Barrett, L. F. & Bar, M. (2009) See it with feeling: Affective predictions during object perception. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 364(1521):1325–34. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19528014.CrossRefGoogle ScholarPubMed
Barsalou, L. W. (2008) Grounded cognition. Annual Review of Psychology 59:617–45.Google Scholar
Cisek, P. (1999) Beyond the computer metaphor: Behavior as interaction. Journal of Consciousness Studies 6:125–42.Google Scholar
Cisek, P. & Pastor-Bernier, A. (2014) On the challenges and mechanisms of embodied decisions. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 369(1655):20130479.Google Scholar
Damasio, A. & Carvalho, G. B. (2013) The nature of feelings: Evolutionary and neurobiological origins. Nature Reviews Neuroscience 14:143–52. doi: 10.1038/nrn3403.Google Scholar
Desimone, R. & Duncan, J. (1995) Neural mechanisms of selective visual attention. Annual Review of Neuroscience 18:193222. doi: 10.1146/annurev.ne.18.030195.001205.Google Scholar
Feldman, H. & Friston, K. J. (2010) Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 4:215. doi: 10.3389/fnhum.2010.00215.Google Scholar
Friston, K. (2013) Life as we know it. Journal of the Royal Society Interface 10(86):20130475. doi: 10.1098/rsif.2013.0475.CrossRefGoogle ScholarPubMed
Friston, K., Daunizeau, J. & Kiebel, S. J. (2009) Reinforcement learning or active inference? PLoS ONE 4:e6421. doi: 10.1371/journal.pone.0006421.Google Scholar
Friston, K., Shiner, T., FitzGerald, T., Galea, J. M., Adams, R., Brown, H., Dolan, R. J., Moran, R., Stephan, K. E. & Bestmann, S. (2012) Dopamine, affordance and active inference. PLoS Computational Biology 8:e1002327. doi: 10.1371/journal.pcbi.1002327.Google Scholar
Friston, K. J. (2010) The free-energy principle: A unified brain theory? Nature Reviews. Neuroscience 11:127–38. doi: 10.1038/nrn2787 Google Scholar
Friston, K. J., Stephan, K. E., Montague, R. & Dolan, R. J. (2014) Computational psychiatry: The brain as a phantastic organ. Lancet Psychiatry 1:148–58. doi: 10.1016/S2215-0366(14)70275-5.Google Scholar
Garfinkel, S. N., Minati, L., Gray, M. A., Seth, A. K., Dolan, R. J. & Critchley, H. D. (2014) Fear from the heart: Sensitivity to fear stimuli depends on individual heartbeats. Journal of Neuroscience 34:6573–82. doi: 10.1523/JNEUROSCI.3507-13.2014.Google Scholar
Jeannerod, M. (2006) Motor cognition. Oxford University Press.Google Scholar
Kahneman, D. (2003a) A perspective on judgment and choice: Mapping bounded rationality. American Psychologist 58:697720.Google Scholar
Keizer, A., Smeets, M. A., Dijkerman, H. C., Uzunbajakau, S. A., van Elburg, A. & Postma, A. (2013) Too fat to fit through the door: First evidence for disturbed body-scaled action in anorexia nervosa during locomotion. PLoS ONE 8:e64602.CrossRefGoogle ScholarPubMed
Lepora, N. F. & Pezzulo, G. (in press) oEmbodied choice: How action influences perceptual decision making. PLOS Computational Biology. Google Scholar
Machens, C. K., Gollisch, T., Kolesnikova, O. & Herz, A. V. M. (2005) Testing the efficiency of sensory coding with optimal stimulus ensembles. Neuron 47:447–56. doi: 10.1016/j.neuron.2005.06.015.Google Scholar
Montague, P. R. & King-Casas, B. (2007) Efficient statistics, common currencies and the problem of reward-harvesting. Trends in Cognitive Sciences 11:514–19. doi: 10.1016/j.tics.2007.10.002.Google Scholar
Mysore, S. P. & Knudsen, E. I. (2011) The role of a midbrain network in competitive stimulus selection. Current Opinions in Neurobiology 21:653–60. doi: 10.1016/j.conb.2011.05.024.Google Scholar
Panksepp, J. (2011) The basic emotional circuits of mammalian brains: Do animals have affective lives? Neuroscience and Biobehavioral Reviews 35(9):1791–804.Google Scholar
Pavlov, I. (1927) Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex. Lecture One.Google Scholar
Pessoa, L. (2013) The cognitive-emotional brain. From interactions to integration. MIT Press.Google Scholar
Pezzulo, G. (2012) An Active Inference view of cognitive control. Frontiers in Theoretical and Philosophical Psychology 3:478. doi: 10.3389/fpsyg.2012.00478.Google Scholar
Pezzulo, G., (2013) Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference. Cognitive, Affective, and Behavioral Neuroscience 14(3):902–11.Google Scholar
Pezzulo, G. (2014) Goals reconfigure cognition by modulating predictive processes in the brain. Behavioral and Brain Sciences 37:154–55. doi: 10.1017/S0140525X13002148.Google Scholar
Pezzulo, G., Barsalou, L. W., Cangelosi, A., Fischer, M. H., McRae, K. & Spivey, M. (2011) The mechanics of embodiment: A dialogue on embodiment and computational modeling. Frontiers in Cognitive Sciences 2:121.Google Scholar
Pezzulo, G., Barsalou, L. W., Cangelosi, A., Fischer, M. H., McRae, K. & Spivey, M. J. (2013) Computational grounded cognition: A new alliance between grounded cognition and computational modeling. Frontiers in Psychology 3:612. doi: 10.3389/fpsyg.2012.00612.Google Scholar
Pezzulo, G. & Castelfranchi, C. (2009) Thinking as the control of imagination: A conceptual framework for goal-directed systems. Psychological Research 73:559–77.Google Scholar
Pezzulo, G., van der Meer, M. A., Lansink, C. S. & Pennartz, C. (2014) Internally generated sequences in learning and executing goal-directed behavior. Trends in Cognitive Sciences 18(12):647–57.CrossRefGoogle ScholarPubMed
Rolls, E. T. (2005) What are emotions, why do we have emotions, and what is their computational basis in the brain? In: Who needs emotions: The brain meets the robot, ed. Fellous, J.-M. & Arbib, M., pp. 117–46. Oxford University Press.Google Scholar
Seth, A. K. (2013) Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences 17:565–73. doi: 10.1016/j.tics.2013.09.007.Google Scholar
Seth, A. K., Suzuki, K. & Critchley, H. D. (2012) An interoceptive predictive coding model of conscious presence. Frontiers in Psychology 2:395. doi: 10.3389/fpsyg.2011.00395.Google Scholar
Verschure, P., Pennartz, C. M. A. & Pezzulo, G. (2014) The why, what, where, when and how of goal-directed choice: Neuronal and computational principles. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 369:20130483.Google Scholar
Vickery, T. J., Chun, M. M. & Lee, D. (2011) Ubiquity and specificity of reinforcement signals throughout the human brain. Neuron 72:166–77. doi: 10.1016/j.neuron.2011.08.011.Google Scholar