An Infinite Mixture Model of Context-dependent Learning and Extinction

Abstract

Event Abstract Back to Event An Infinite Mixture Model of Context-dependent Learning and Extinction Dominant theories of human and animal learning posit that in classical conditioning, one of the most basic forms of learning, animals learn to make associations between different stimuli in the environment. These associations are then used to predict future events based on current observations. Although this framework is powerful, we argue that a wealth of behavioral evidence is more consistent with an account of learning in which animals make more complex inferences about the environment. Most notably, responding to a conditioned stimulus appears to depend not only on the reinforcement history of the stimulus but also on its relationship to contextual stimuli (such as the experimental chamber). We thus model learning as inference over latent causes of observed stimuli, rather than of direct causal relationships between them. We focus on two extensively-studied animal conditioning paradigms, latent inhibition and renewal. In latent inhibition, the acquisition of a conditioned response to a cue that is paired with reinforcement is retarded if the cue was previously presented without reinforcement. A change of context between the initial exposure to the unreinforced cue and the subsequent training reduces this retardation. In renewal, the cue is first presented paired with reinforcement (conditioning) in one context and then presented without reinforcement (extinction) in another context, leading to loss of conditioned responding. Returning the animal to the conditioning context renews conditioned responding. Renewal also occurs when the animal is tested in a completely novel context. In both latent inhibition and renewal, lesioning the hippocampus prior to the second phase of training eliminates context-dependence. A similar pattern is observed in infant rats, whose behavior seems to not be sensitive to context. We suggest that these and other context-dependent learning phenomena can be explained by a normative statistical model in which the animal performs inference of latent causes in an infinite mixture model. In contrast to classical associative theories, the infinite mixture model assumes that each observation (trial) is generated by a single latent cause, selected from an unbounded set of latent causes. This identifies the computational problem being solved by the animal as that of how to assign observations to discrete latent causes (ie, clustering) and use these to predict reinforcement. The infinite capacity of the model enables the animal to infer new clusters as it collects observations. Our simulations show that the model predicts the context-dependence of latent inhibition and renewal. We also show how restricting the model's ability to infer new clusters explains developmental changes in learning as well as the effects of hippocampal damage. The key difference between the infinite mixture model and previous models is the explanation of learning as inference over latent causes rather than the formation of associations between observed stimuli, giving rise to novel predictions regarding the effects of training length on learning and context effects, and suggesting manipulations that could reverse behavioral phenomena that were previously regarded as fixed constraints on learning. The model also has implications for understanding context-dependent relaspe in drug addiction and anxiety disorders. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Oral Presentation Topic: Oral Presentations Citation: (2009). An Infinite Mixture Model of Context-dependent Learning and Extinction. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.323 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Feb 2009; Published Online: 04 Feb 2009. 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