Matthias Guggenmos

This paper introduces a modeling framework to infer metacognitive noise and bias parameters from confidence data, showing these parameters correlate with behavioral measures and are independent of performance.

This paper introduces a confidence-based reinforcement learning model that enables self-reinforced perceptual learning without external feedback by using confidence prediction errors to guide network fluctuations.