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    Formalizing the Function of Anterior lnsula in Rapid Adaptation

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    Author
    Bossaerts, P
    Date
    2018-12-04
    Source Title
    Frontiers in Integrative Neuroscience
    Publisher
    FRONTIERS MEDIA SA
    University of Melbourne Author/s
    Bossaerts, Peter
    Affiliation
    Finance
    Metadata
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    Document Type
    Journal Article
    Citations
    Bossaerts, P. (2018). Formalizing the Function of Anterior lnsula in Rapid Adaptation. FRONTIERS IN INTEGRATIVE NEUROSCIENCE, 12, https://doi.org/10.3389/fnint.2018.00061.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253554
    DOI
    10.3389/fnint.2018.00061
    Abstract
    Anterior insula (aIns) is thought to play a crucial role in rapid adaptation in an ever-changing environment. Mathematically, it is known to track risk and surprise. Modern theories of learning, however, assign a dominant role to signed prediction errors (PEs), not to risk and surprise. Risk and surprise only enter to the extent that they modulate the learning rate, in an attempt to approximate Bayesian learning. Even without such modulation, adaptation is still possible, albeit slow. Here, I propose a new theory of learning, reference-model based learning (RMBL), where risk and surprise are central, and PEs play a secondary, though still crucial, role. The primary goal is to bring outcomes in line with expectations in the reference model (RM). Learning is modulated by how large the PEs are relative to model anticipation, i.e., to surprise as defined by the RM. In a target location prediction task where participants were continuously required to adapt, choices appeared to be closer with to RMBL predictions than to Bayesian learning. aIns reaction to surprise was more acute in the more difficult treatment, consistent with its hypothesized role in metacognition. I discuss links with related theories, such as Active Inference, Actor-Critic Models and Reference-Model Based Adaptive Control.

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