Development and validation of a computational model of higher-order statistical learning on graphs in humans

当人类在他们的环境中导航时，预期、规划和感知都需要一张准确的地图 统计规律支配他们的视觉、语言、听觉和社会经验的统计规律在每一种情况下，胡- 人的经历由一系列事件组成。每个事件根据一组基础的 对可能的事件到事件转换以及每个转换的可能性进行编码的规则。来预测未来的福气- 人类必须通过灵活的行为对环境做出真实的反应，才能推断出这一过渡网络，形成 关于因果关系的认知地图。这样的地图和推论是通过统计学习实现的。 统计学习的研究代表了计算精神病学的一个重大机遇，原因有三。 首先，统计学习在精神疾病、任务领域和时间上显示出不同的准确性 经验的尺度。第二，统计学习具有显著的回译潜力； 统计学习行为及其神经基础在非人类灵长类动物中是保守的，而且更简单 序列学习的形式存在于其他哺乳动物(大鼠和小鼠)以及鸟类中。第三，正如我们在 我们建议的深度-统计学习可以正式地用数学建模。 现在正是开发一种灵活的统计学习计算模型的时候。为了服务于COM的目标- 假想精神病学，这种模型的函数形式应该反映统计学习的一般原则 参数应该对许多特定障碍的行为变异性敏感，在这些障碍中，缺陷 出现。在初步的实验、计算和理论工作中，我们发现了一种新的行为 统计学习的签名；我们还将这一行为转化为正式模型--受原则启发 统计物理学--具有数学上定义良好的参数，从而推导出一种有基础的理论 在我们之前的实验发现中。最后，我们通过实验验证了该模型的正确性。 在一个新的实验中对行为的预测。 在这里，我们汇集了一组互补的联合调查人员，他们成对或三联撰写了31篇论文， 拥有数学建模和统计物理(Bassett)、行为统计模型(Moore)、 密集的纵向实验(Lydon-Staley)、统计学习(Thompson-Schill)和感觉过程- 精神病学(沃尔夫)。我们共同提供了一个完美整合的理论和实验计划来磨练我们的数学- 人类行为的一个方面的数学模型，它没有经过广泛的计算分析，而且在 在精神障碍中，潜在的维度过程会受到影响。我们把我们的目标提炼成可靠性， 我们的模型的相关性和普适性。我们的方法是三管齐下，在实验中进行创新， 以我们团队的不同专业知识为基础的计算和理论。每个分支都将实现所有三个目标，从而 整合我们的努力，构建支持未来发展的统计学习行为计算模型 在计算精神病学方面。我们提出的努力为R01扩展到患者奠定了基础。

As humans navigate their environment, anticipation, planning, and perception all require an accurate map of the statistical regularities governing their visual, linguistic, auditory, and social experiences. In each context, hu- man experience consists of a sequence of events. Each event succeeds another according to a set of underlying rules codifying possible event-to-event transitions, and the likelihood of each. To make predictions about the fu- ture and respond to the environment with flexible behavior, humans must infer this network of transitions, forming a cognitive map of causes and effects. Such maps and inferences are made possible by statistical learning. The study of statistical learning represents a major opportunity for computational psychiatry for three reasons. First, statistical learning shows differential accuracy across psychiatric conditions, task domains, and temporal scales of experience. Second, statistical learning has marked potential for back-translation; multiple features of statistical learning behavior and its neural underpinnings are conserved in non-human primates, and simpler forms of sequence learning exist in other mammals (rats and mice) as well as birds. Third, – as we describe in depth in our proposal – statistical learning can be formally modeled mathematically. It is now timely to develop a flexible computational model of statistical learning. To serve the goals of com- putational psychiatry, the functional form of such a model should reflect general principles of statistical learning and the parameters should be sensitive to variability in behavior across the many specific disorders where deficits appear. In preliminary experimental, computational, and theoretical work, we have uncovered a novel behavioral signature of statistical learning; we have also translated that behavior into a formal model – inspired by principles of statistical physics – with mathematically well-defined parameters, thereby deriving a theory that is grounded in our previous experimental findings. Finally, we have experimentally validated the model by making accurate predictions of behavior in a novel experiment. Here we assemble a complementary set of co-investigators who have co-authored 31 papers in pairs or triplets, with expertise in mathematical modeling and statistical physics (Bassett), statistical models of behavior (Moore), intensive longitudinal experiments (Lydon-Staley), statistical learning (Thompson-Schill), and sensory process- ing in psychiatry (Wolf). Together, we offer a well-integrated theoretical and experimental plan to hone our math- ematical model of an aspect of human behavior that has not been extensively analyzed computationally, and in which the underlying dimensional process is affected in psychiatric disorders. We distill our aims into reliability, relevance, and generalizability of our model. Our approach is three-pronged, with innovations in experiment, computation, and theory building on our team’s diverse expertise. Each prong will address all three aims, thereby integrating our efforts to build a computational model of statistical learning behavior supporting future advances in computational psychiatry. Our proposed efforts provide the foundation for an R01 extending to patients.