PDF(Pubmed)
Abstract：
Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. Here we examine the reliability of reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N = 50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Reward and punishment learning rates from the reinforcement learning model showed good reliability and reward and punishment sensitivity from the same model had fair reliability; while risk aversion and loss aversion parameters from a prospect theory model exhibited good and excellent reliability, respectively. Both models were further able to predict future behaviour above chance within individuals. This prediction was better when based on participants\' own model parameters than other participants\' parameter estimates. These results suggest that reinforcement learning, and particularly prospect theory parameters, as derived from a restless four-armed bandit and a calibrated gambling task, can be measured reliably to assess learning and decision-making mechanisms. Overall, these findings indicate the translational potential of clinically-relevant computational parameters for precision psychiatry.
摘要：
计算模型可以提供对认知的机械洞察力，因此有可能改变我们对精神疾病及其治疗的理解。为了使翻译工作取得成功，计算措施必须可靠地捕获个体特征。在这里，我们研究了从两个常用任务中得出的强化学习和经济模型的可靠性。健康个体(N=50)两次完成了一个不安分的四臂强盗和一个校准的赌博任务,相隔两周.强化学习模型的奖励和惩罚学习率表现出良好的可靠性，同一模型的奖励和惩罚敏感性具有相当的可靠性；而前景理论模型的风险厌恶和损失厌恶参数表现出良好的可靠性。分别。这两个模型都能够进一步预测未来的行为，而不是个体内部的机会。当基于参与者自己的模型参数比其他参与者的参数估计时，这种预测更好。这些结果表明，强化学习，特别是前景理论参数，源于一个不安分的四臂强盗和一个校准的赌博任务，可以可靠地衡量，以评估学习和决策机制。总的来说,这些发现表明临床相关计算参数对精确精神病学的翻译潜力.
