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.
Highlights
Change is a defining characteristic of the human environment
Learning signals that have been identified in neural activation within Anterior insula (aIns) appear to be related to risk and surprise
Neural signals correlate with the size of prediction errors (PEs), i.e., the un-signed PE (Fouragnan et al, 2017, 2018). aIns neural signals encode the anticipated size of upcoming PEs, which means that they track risk
Summary
Reviewed by: Alexandre Filipowicz, University of Pennsylvania, United States James Danckert, University of Waterloo, Canada. Anterior insula (aIns) is thought to play a crucial role in rapid adaptation in an everchanging environment. 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. 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. Learning is modulated by how large the PEs are relative to model anticipation, i.e., to surprise as defined by the RM.
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