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Generative models as parsimonious descriptions of sensorimotor loops

Published online by Cambridge University Press:  28 November 2019

Manuel Baltieri Open the ORCID record for Manuel Baltieri [Opens in a new window]  and
Christopher L. Buckley Open the ORCID record for Christopher L. Buckley [Opens in a new window]
Manuel Baltieri
Affiliation:
EASY Group – Sussex Neuroscience, Department of Informatics, University of Sussex, BrightonBN1 9RH, United Kingdom. [email protected]@sussex.ac.ukhttps://manuelbaltieri.comhttps://christopherlbuckley.com/
Christopher L. Buckley
Affiliation:
EASY Group – Sussex Neuroscience, Department of Informatics, University of Sussex, BrightonBN1 9RH, United Kingdom. [email protected]@sussex.ac.ukhttps://manuelbaltieri.comhttps://christopherlbuckley.com/
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Abstract

The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e218
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Copyright
Copyright © Cambridge University Press 2019

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References

Baltieri, M. & Buckley, C. L. (2017) An active inference implementation of phototaxis. In: CAL 2017: The Fourteenth European Conference on Artificial Life. Artificial Life Conference Proceedings 14:36–43.Google Scholar
Baltieri, M. & Buckley, C. L. (2019a) Nonmodular architectures of cognitive systems based on active inference. arXiv preprint arXiv:1903.09542. DOI: 10.1109/IJCNN.2019.8852048CrossRefGoogle Scholar
Baltieri, M. & Buckley, C. L. (2019b) PID Control as a process of active inference with linear generative models. Entropy 21(3):257.CrossRefGoogle Scholar
Brooks, R. A. (1991b). New approaches to robotics. Science 253(5025):1227–32.CrossRefGoogle Scholar
Bruineberg, J., Kiverstein, J. & Rietveld, E. (2018) The anticipating brain is not a scientist: The free-energy principle from an ecological-enactive perspective. Synthese 195(6):2417–44.CrossRefGoogle Scholar
Clark, A. (2015) Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.Google Scholar
Friston, K. (2011) What is optimal about motor control? Neuron 72(3):488–98.CrossRefGoogle ScholarPubMed
Friston, K., Thornton, C. & Clark, A. (2012) Free-energy minimization and the dark-room problem. Frontiers in Psychology 3:130.CrossRefGoogle Scholar
Hohwy, J. (2013) The predictive mind, Oxford University Press.CrossRefGoogle Scholar
Kolchinsky, A. & Wolpert, D. H. (2018) Semantic information, autonomous agency and non-equilibrium statistical physics. Interface Focus 8(6):20180041.CrossRefGoogle Scholar
Newen, A., De Bruin, L. & Gallagher, S., eds. (2018) The Oxford handbook of 4E cognition. Oxford University Press.CrossRefGoogle Scholar
Todorov, E. (2009) Parallels between sensory and motor information processing. In: The Cognitive Neurosciences (4th edition), ed. Gazzaniga, M. S., pp. 613–24. MIT Press.Google Scholar