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Meta-learning in active inference

Published online by Cambridge University Press:  23 September 2024

O. Penacchio Open the ORCID record for O. Penacchio [Opens in a new window]  and
A. Clemente
O. Penacchio*
Affiliation:
Computer Science Department, Autonomous University of Barcelona, and School of Psychology and Neuroscience, University of St Andrews, Barcelona, Spain [email protected] https://openacchio.github.io/
A. Clemente
Affiliation:
Department of Cognitive Neuropsychology, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany [email protected] https://www.aesthetics.mpg.de/institut/mitarbeiterinnen/ana-clemente.html
*
*Corresponding author.
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Abstract

Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights. We argue that active inference presents similar computational advantages while offering greater mechanistic explanatory power and biological plausibility.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e159
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76(4), 695–711. https://doi.org/10.1016/j.neuron.2012.10.038CrossRefGoogle ScholarPubMed
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477CrossRefGoogle ScholarPubMed
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787CrossRefGoogle ScholarPubMed
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2016). Active inference and learning. Neuroscience & Biobehavioral Reviews, 68, 862–879. https://doi.org/10.1016/j.neubiorev.2016.06.022CrossRefGoogle ScholarPubMed
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017). Active inference: A process theory. Neural Computation, 29(1), 1–49. https://doi.org/10.1162/NECO_a_00912CrossRefGoogle ScholarPubMed
Kaplan, R., & Friston, K. J. (2018). Planning and navigation as active inference. Biological Cybernetics, 112(4), 323–343. https://doi.org/10.1007/s00422-018-0753-2CrossRefGoogle ScholarPubMed
Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature Neuroscience, 21(9), 1148–1160. https://doi.org/10.1038/s41593-018-0210-5CrossRefGoogle ScholarPubMed
Pezzulo, G., Parr, T., & Friston, K. (2024). Active inference as a theory of sentient behavior. Biological Psychology, 186, 108741. https://doi.org/10.1016/j.biopsycho.2023.108741CrossRefGoogle ScholarPubMed
Pezzulo, G., Rigoli, F., & Friston, K. J. (2018). Hierarchical active inference: A theory of motivated control. Trends in Cognitive Sciences, 22(4), 294–306. https://doi.org/10.1016/j.tics.2018.01.009CrossRefGoogle ScholarPubMed
Schwartenbeck, P., FitzGerald, T. H., Mathys, C., Dolan, R., & Friston, K. (2015). The dopaminergic midbrain encodes the expected certainty about desired outcomes. Cerebral Cortex, 25(10), 3434–3445. https://doi.org/10.1093/cercor/bhu159CrossRefGoogle ScholarPubMed
Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H. B., Kronbichler, M., & Friston, K. J. (2019). Computational mechanisms of curiosity and goal-directed exploration. eLife, 8, e41703. https://doi.org/10.7554/eLife.41703CrossRefGoogle ScholarPubMed
Smith, R., Badcock, P., & Friston, K. J. (2021). Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry and Clinical Neurosciences, 75(1), 3–13. https://doi.org/10.1111/pcn.13138CrossRefGoogle ScholarPubMed
Whyte, C. J., & Smith, R. (2021). The predictive global neuronal workspace: A formal active inference model of visual consciousness. Progress in Neurobiology, 199, 101918. https://doi.org/10.1016/j.pneurobio.2020.101918CrossRefGoogle ScholarPubMed
Zador, A., Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., Tsao, D. (2023). Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications, 14(1), 1597. https://doi.org/10.1038/s41467-023-37180-xCrossRefGoogle ScholarPubMed