Testing computational models of learning from social, real, and fictive feedback in human and nonhuman primates

该联盟将开发学习和决策的计算模型，并在人类和猕猴两种生物系统中进行测试。这些模型应考虑到对行动结果的不同信息来源的使用，以指导未来的行为：对实际结果的反馈，对选择不同行动可能获得的结果的有效反馈，以及对社会情境中其他行动者所看到的行动结果的观察反馈。我们期望相同的计算原理适用于所有信息源，但学习参数在反馈类型和物种之间存在定量差异。模型拟合行为和基于模型的神经数据分析将揭示计算变量的大脑相关性。我们期望在监测不同的反馈信息来源时发现解剖和功能上的分离，然后在实现对未来行为的改变的单一机制上收敛。在两种灵长类动物中使用互补方法将有助于更好地推广模型，更好地理解潜在的神经机制，以及相互了解记录地点和分析的选择。

The consortium will develop computational models of learning and decision making and test them in two biological systems, humans and macaques. The models shall account for the use of different sources of information on action outcomes to guide future behavior: feedback on real outcomes, fictive feedback on outcomes that would have been obtained had a different action been chosen, and observational feedback on action outcomes seen in other actors in social situations. We expect that the same computational principles apply for all information sources but that the learning parameters differ quantitatively between feedback type and species. Model fits to behavior and model-based analyses of neural data will reveal brain correlates of the computational variables. We expect to find anatomical and functional dissociations during monitoring of the different sources of feedback information and later convergence on a single mechanism implementing changes to future behavior. Using complementary methods in two primate species will lead to better generalizability of the models, better understanding of underlying neural mechanisms, and mutually informed choice of recording sites and analysis.