ArticleFrontiers in artificial intelligence2020
A Bayesian Account of Generalist and Specialist Formation Under the Active Inference Framework.
Article in Frontiers in artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Evidence for Reduced Sensory Precision and Increased Reliance on Priors in Hallucination-Prone Individuals in a General Population Sample.Schizophrenia bulletin · 2024Article
- Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task.Frontiers in neuroscience · 2023Article
- Everything is connected: Inference and attractors in delusions.Schizophrenia research · 2022Article
- Understanding, Explanation, and Active Inference.Frontiers in systems neuroscience · 2021Article
- A Bayesian Account of Generalist and Specialist Formation Under the Active Inference Framework.Frontiers in artificial intelligence · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
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Abstract
This paper offers a formal account of policy learning, or habitual behavioral optimization, under the framework of Active Inference. In this setting, habit formation becomes an autodidactic, experience-dependent process, based upon what the agent sees itself doing. We focus on the effect of environmental volatility on habit formation by simulating artificial agents operating in a partially observable Markov decision process. Specifically, we used a "two-step" maze paradigm, in which the agent has to decide whether to go left or right to secure a reward. We observe that in volatile environments with numerous reward locations, the agents learn to adopt a generalist strategy, never forming a strong habitual behavior for any preferred maze direction. Conversely, in conservative or static environments, agents adopt a specialist strategy; forming strong preferences for policies that result in approach to a small number of previously-observed reward locations. The pros and cons of the two strategies are tested and discussed. In general, specialization offers greater benefits, but only when contingencies are conserved over time. We consider the implications of this formal (Active Inference) account of policy learning for understanding the relationship between specialization and habit formation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.