{"ID":2830768,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09761","arxiv_id":"2512.09761","title":"Prefrontal scaling of reward prediction error readout gates reinforcement-derived adaptive behavior in primates","abstract":"Reinforcement learning (RL) enables adaptive behavior across species via reward prediction errors (RPEs), but the neural origins of species-specific adaptability remain unknown. Integrating RL modeling, transcriptomics, and neuroimaging during reversal learning, we discovered convergent RPE signatures - shared monoaminergic/synaptic gene upregulation and neuroanatomical representations, yet humans outperformed macaques behaviorally. Single-trial decoding showed RPEs guided choices similarly in both species, but humans disproportionately recruited dorsal anterior cingulate (dACC) and dorsolateral prefrontal cortex (dlPFC). Cross-species alignment uncovered that macaque prefrontal circuits encode human-like optimal RPEs yet fail to translate them into action. Adaptability scaled not with RPE encoding fidelity, but with the areal extent of dACC/dlPFC recruitment governing RPE-to-action transformation. These findings resolve an evolutionary puzzle: behavioral performance gaps arise from executive cortical readout efficiency, not encoding capacity.","short_abstract":"Reinforcement learning (RL) enables adaptive behavior across species via reward prediction errors (RPEs), but the neural origins of species-specific adaptability remain unknown. Integrating RL modeling, transcriptomics, and neuroimaging during reversal learning, we discovered convergent RPE signatures - shared monoamin...","url_abs":"https://arxiv.org/abs/2512.09761","url_pdf":"https://arxiv.org/pdf/2512.09761v2","authors":"[\"Tian Sang\",\"Yichun Huang\",\"Fangwei Zhong\",\"Miao Wang\",\"Shiqi Yu\",\"Jiahui Li\",\"Yuanjing Feng\",\"Yizhou Wang\",\"Kwok Sze Chai\",\"Ravi S. Menon\",\"Meiyun Wang\",\"Fang Fang\",\"Zheng Wang\"]","published":"2025-12-10T15:44:36Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}