Meeter Martijn, Myers Catherine E, Shohamy Daphna, Hopkins Ramona O, Gluck Mark A
Department of Cognitive Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands.
Learn Mem. 2006 Mar-Apr;13(2):230-9. doi: 10.1101/lm.43006. Epub 2006 Mar 17.
The "Weather Prediction" task is a widely used task for investigating probabilistic category learning, in which various cues are probabilistically (but not perfectly) predictive of class membership. This means that a given combination of cues sometimes belongs to one class and sometimes to another. Prior studies showed that subjects can improve their performance with training, and that there is considerable individual variation in the strategies subjects use to approach this task. Here, we discuss a recently introduced analysis of probabilistic categorization, which attempts to identify the strategy followed by a participant. Monte Carlo simulations show that the analysis can, indeed, reliably identify such a strategy if it is used, and can identify switches from one strategy to another. Analysis of data from normal young adults shows that the fitted strategy can predict subsequent responses. Moreover, learning is shown to be highly nonlinear in probabilistic categorization. Analysis of performance of patients with dense memory impairments due to hippocampal damage shows that although these patients can change strategies, they are as likely to fall back to an inferior strategy as to move to more optimal ones.
“天气预报”任务是用于研究概率类别学习的一项广泛使用的任务，在该任务中，各种线索对类别归属具有概率性（但并非完全准确）的预测作用。这意味着给定的线索组合有时属于一个类别，有时属于另一个类别。先前的研究表明，受试者可以通过训练提高其表现，并且受试者用于处理此任务的策略存在相当大的个体差异。在此，我们讨论一种最近引入的概率分类分析方法，该方法试图识别参与者所采用的策略。蒙特卡洛模拟表明，如果使用该分析方法，确实能够可靠地识别这样一种策略，并且能够识别从一种策略到另一种策略的转变。对正常年轻成年人的数据进行分析表明，拟合出的策略可以预测后续反应。此外，在概率分类中学习表现出高度的非线性。对因海马体损伤而患有严重记忆障碍的患者的表现进行分析表明，尽管这些患者可以改变策略，但他们回到较差策略的可能性与转向更优策略的可能性一样大。
