Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Artificial Intelligence, Technische Universität Berlin, Berlin, Germany.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
J Neurosci Methods. 2018 Sep 1;307:175-187. doi: 10.1016/j.jneumeth.2018.04.006. Epub 2018 Apr 19.
The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease.
We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning.
We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels.
COMPARISON WITH EXISTING METHOD(S): In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences.
Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups.
对受试者群体的学习进行研究，可以深入了解大脑在衰老、药物干预和精神疾病过程中的变化。
我们引入了一种可分离的二维（2D）随机场（RF）模型，用于分析在多个学习日中进行的物体-奖励关联学习过程中获得的二元反应数据。该方法可以量化每天和每天之间的性能变化，并可以捕捉学习过程中的突然变化。
我们将该方法应用于执行反转学习任务的年轻和老年猕猴的数据中。该方法为每个年龄组提供了每天的性能估计值，以及每只猴子的多天学习率。我们发现，作为一个整体，老年猴子比年轻猴子需要更多的试验才能学习物体辨别，而年轻猴子的认知灵活性更高。我们还使用模型估计的性能作为特征对猴子进行聚类，分为两组。聚类结果大部分与年龄组形成的分组一致。模拟研究表明，聚类可以捕捉到个体之间的性能水平差异。
与广义线性模型相比，该方法更能捕捉数据的固有二维性质，并发现组间差异。
将该模型应用于进行多日行为实验的个体群体的二元反应数据，可以区分组间差异并识别亚组。
