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| Autore principale: | |
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| Natura: | Recurso digital |
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Zenodo
2026
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| Accesso online: | https://doi.org/10.5281/zenodo.19535884 |
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Sommario:
- Rewardpredict is defined here as neural prediction error mechanism encoding differences between expected and actual rewards, driving reinforcement learning through dopaminergic signaling in corticostriatal circuits. This paper develops Rewardpredict as a computational framework for neuroeconomics by linking the concept to reward prediction error dynamics shaping adaptive economic behavior, formalizing differences between expected and realized rewards updating value estimates and future choices, and operationalizing Rewardpredict with a transparent index built from channels such as prediction error magnitude, reward volatility, learning rate, and dopamine signal proxy, while also showing how Rewardpredict can support comparative statics, empirical measurement, and policy simulation in reproducible economic research.