Rethinking reinforcement learning: the interoceptive origins of reward
We all love a good ice-cream. But what exactly is rewarding about consuming it? In conventional reinforcement learning models, the environment emits scalar ‘ground-truth’ reward signals that the agent can use to learn what to do. But in biological agents, ‘reward’ is subjective, dynamic and state-dependent – generated within the organism, and inferred from noisy interoceptive signals. In the Cognitive Modeling group, we study the subjectivity and flexibility of reward functions in biological agents using both experiments and conceptual work extending conventional RL models. We are also interested in how this perspective can inform our understanding of disturbances in reward learning across mental health.
Relevant publications:
Bi et al. (2026). Environmental uncertainty shapes human effort learning. PLoS Biology
Weber et al. (2025). The interoceptive origin of reinforcement learning. Trends in Cognitive Sciences
Petzschner et al. (2018). Focus of attention modulates the heartbeat evoked potential. NeuroImage
Petzschner et al. (2017). Computational Psychosomatics and Computational Psychiatry: Toward a Joint Framework for Differential Diagnosis. Biological Psychiatry
Stephan et al. (2016). Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression. Frontiers in Human Neuroscience