Bayesian analysis of interleaved learning and response bias in behavioral experiments.
Bayesian analysis of interleaved learning and response bias in behavioral experiments.
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DOI：
10.1152/jn.00946.2006
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发表时间：
2007-03
影响因子：
2.5
通讯作者：
Anne C. Smith;S. Wirth;W. Suzuki;E. Brown
中科院分区：
文献类型：
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作者：
Anne C. Smith;S. Wirth;W. Suzuki;E. Brown
学习实验期间行为的准确表征对于理解学习的神经基础至关重要。尽管学习实验经常给受试者同时学习多项任务，但大多数实验都分别分析受试者在每项任务上的表现。这种分析策略忽略了任务的真实交错呈现顺序，并且无法区分学习行为和可能代表受试者的偏见或策略的反应偏好。我们提出了状态空间模型的贝叶斯分析，用于表征多个任务的同时学习，并评估具有交错任务呈现的学习实验中的行为偏差。在贝叶斯分析下，使用蒙特卡罗马尔可夫链方法计算模型参数和学习状态的后验概率密度。学习度量，包括学习曲线、理想观察者曲线和学习试验，直接从我们之前基于可能性的状态空间模型分析转化而来。我们在模拟条件 T 迷宫任务和实际对象位置关联任务的分析中比较了贝叶斯方法和当前基于可能性的方法。对实验的交错学习特征以及动物的反应序列进行建模，使我们能够消除实际学习与反应偏差的歧义。使用 WinBUGS 软件实施贝叶斯分析提供了一种测试不同模型的有效方法，而无需为每个模型开发新算法。新的状态空间模型和贝叶斯估计程序提出了一种改进的、计算高效的方法，用于准确表征行为实验中的学习。
Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood-based approaches in the analysis of a simulated conditioned T-maze task and of an actual object-place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.