Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

TLDR

Learning can occur without external feedback, but standard reinforcement learning theories struggle to explain this. The study proposes that humans generate internal feedback by monitoring decision variables. Using fMRI and confidence reports in a visual perceptual learning task, the authors applied a novel confidence‑based reinforcement model that revealed mesolimbic regions encoding confidence anticipation and prediction error, mirroring patterns seen with external reward. The model explains choice and confidence data and shows that mesolimbic confidence prediction‑error signals predict individual learning success, offering a neurobiological mechanism for learning without external feedback.

Abstract

It is well established that learning can occur without external feedback, yet normative reinforcement learning theories have difficulties explaining such instances of learning. Here, we propose that human observers are capable of generating their own feedback signals by monitoring internal decision variables. We investigated this hypothesis in a visual perceptual learning task using fMRI and confidence reports as a measure for this monitoring process. Employing a novel computational model in which learning is guided by confidence-based reinforcement signals, we found that mesolimbic brain areas encoded both anticipation and prediction error of confidence—in remarkable similarity to previous findings for external reward-based feedback. We demonstrate that the model accounts for choice and confidence reports and show that the mesolimbic confidence prediction error modulation derived through the model predicts individual learning success. These results provide a mechanistic neurobiological explanation for learning without external feedback by augmenting reinforcement models with confidence-based feedback.