AI Models Show Anhedonia-Like Behavior Through Reward Valuation Circuits.

This research explores whether Vision-Language Models (VLMs) exhibit reward valuation mechanisms similar to human cognition, specifically focusing on anhedonia, a symptom of major depressive disorder characterized by a reduced ability to experience pleasure. Drawing inspiration from neuroscience, where anhedonia is linked to dysregulation in the Nucleus Accumbens (NAc) and dopaminergic reward systems, the study sought to find analogous structures in AI. The team functionally identified specific "reward-anticipatory units" within VLMs. When these NAc-selective units were targeted and perturbed, the models displayed behavioral changes consistent with human anhedonia. Specifically, the perturbed models favored low-effort, low-reward options in decision-making tasks, while maintaining baseline performance when reward-based choices were removed. This indicates a specific deficit in reward valuation and anticipation, rather than a general loss of task capability, suggesting that AI models can develop internal circuits for reward processing that parallel human neurological functions.