Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.
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Active Inference: The Free Energy Principle in Mind, Brain, and Behavior
By Thomas Parr, Giovanni Pezzulo and Karl J. Friston
https://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.org/
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https://www.youtube.com/watch?v=QdyHzs1Slec
All Active Inference Institute livestreams and videos:
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Active Inference: The Free Energy Principle in Mind, Brain, and Behavior
By Thomas Parr, Giovanni Pezzulo and Karl J. Friston
https://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.org/
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https://www.youtube.com/watch?v=BKgKVl_soZA
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https://www.youtube.com/playlist?list=PLNm0u2n1Iwdq74rPEYLx4VwYCI_QiXbEd https://www.activeinference.org/
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https://www.youtube.com/watch?v=7Ti-r8f15So
Qualia structure.
Tsuchiya, N., Saigo, H., & Phillips, S. (2022, Dec 14, accepted by Frontiers in Psychology). An adjunction hypothesis between qualia and reports. Retrieved from psyarxiv.com/q8ndj Volume 13 - 2022 | doi: 10.3389/fpsyg.2022.1053977
Tsuchiya, Naotsugu, Steven Phillips, and Hayato Saigo. “Enriched Category as a Model of Qualia Structure Based on Similarity Judgements.” Consciousness and Cognition 101 (May 1, 2022): 103319. https://doi.org/10.1016/j.concog.2022.103319. OSF preprints https://osf.io/ucjmz/ (free PDF till 2022 July 4 https://www.sciencedirect.com/science/article/pii/S1053810022000514?dgcid=author ) https://doi.org/10.1016/j.concog.2022.103319
Naotsugu Tsuchiya & Hayato Saigo “A relational approach to consciousness: categories of level and contents of consciousness“ (2021) Neuroscience of Consciousness Volume 2021, Issue 2, 2021, niab034, https://doi.org/10.1093/nc/niab034 link Accepted version [(2020, Apr 27) Applying Yoneda's lemma to consciousness research: categories of level and contents of consciousness OSF preprint PDF doi:10.31219/osf.io/68nhy]
Webpage for the 5-year grant called "Qualia Structure"
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https://arxiv.org/abs/2203.10592
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
Alessandro Barp, Lancelot Da Costa, Guilherme França, Karl Friston, Mark Girolami, Michael I. Jordan, Grigorios A. Pavliotis
Background & Context on the paper.
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Second participatory group discussion on the paper
Active Data Selection and Information Seeking
https://www.mdpi.com/1999-4893/17/3/118
Parr, Thomas, Karl Friston, and Peter Zeidman. 2024. "Active Data Selection and Information Seeking" Algorithms 17, no. 3: 118. https://doi.org/10.3390/a17030118
Abstract
Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the selection of data—either through sampling subsets of data from a large dataset or through optimising experimental design—based upon the models we have of how those data are generated. Optimising data-selection ensures we can achieve good inference with fewer data, saving on computational and experimental costs. This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. Finally, we consider how this approach to data selection could be applied to the design of (Bayes-adaptive) clinical trials.
Keywords: experimental design; active sampling; information gain; Bayesian inference
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Constructing cultural landscapes: Active Inference for the Social Sciences
Avel Guénin-Carlut, Ben White, Mahault Albarracin, Lorena Sganzerla, Daniel Friedman
A participatory online course in 2023 co-organized by Kairos Research and the Active Inference Institute
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https://www.activeinference.org/education/active-inference-for-the-social-sciences
https://coda.io/@active-inference-institute/active-inference-social-science-aii-2023
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https://www.youtube.com/watch?v=MrAiB9X7Ock