Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package [0.03%]
利用hBayesDM包揭示强化学习和决策制定的神经计算机制
Woo-Young Ahn,Nathaniel Haines,Lei Zhang
Woo-Young Ahn
Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel a...
Implications of Information Theory for Computational Modeling of Schizophrenia [0.03%]
信息论对精神分裂症计算模型的启示
Steven M Silverstein,Michael Wibral,William A Phillips
Steven M Silverstein
Information theory provides a formal framework within which information processing and its disorders can be described. However, information theory has rarely been applied to modeling aspects of the cognitive neuroscience of schizophrenia. T...
Peter Dayan,Read Montague
Peter Dayan
Ali Yousefi,Darin D Dougherty,Emad N Eskandar et al.
Ali Yousefi et al.
Censored data occur commonly in trial-structured behavioral experiments and many other forms of longitudinal data. They can lead to severe bias and reduction of statistical power in subsequent analyses. Principled approaches for dealing wit...
Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia [0.03%]
情绪障碍中的学习与选择——寻找欣快症的计算参数
Oliver J Robinson,Henry W Chase
Oliver J Robinson
Computational approaches are increasingly being used to model behavioral and neural processes in mood and anxiety disorders. Here we explore the extent to which the parameters of popular learning and decision-making models are implicated in...
Karl J Friston,A David Redish,Joshua A Gordon
Karl J Friston
This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs a...