Integrated Bayesian models of learning and decision making for saccadic eye movements
Integrated Bayesian models of learning and decision making for saccadic eye movements
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DOI：
10.1016/j.neunet.2008.08.007
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发表时间：
2008-11-01
期刊：
影响因子：
7.8
通讯作者：
Stephan, Klaas E.
中科院分区：
文献类型：
--
作者：
Brodersen, Kay H.;Penny, Will D.;Stephan, Klaas E.
眼球运动的神经生理学已经被广泛研究，并且已经提出了几个计算模型用于决策过程，这些决策过程是在不确定的情况下产生朝向视觉刺激的眼球运动的基础。一类模型，被称为线性阈值上升模型，提供了一个经济的，但广泛适用的，解释观察到的变化之间的潜伏期的周边视觉目标的开始和扫视向它。到目前为止，然而，这些模型没有考虑的动态学习序列的刺激，并且它们不适用于Ssbcycle暴露于具有条件概率的事件的情况。在这篇方法论论文中，我们扩展了线性上升阈值模型的类，以解决这些限制。具体来说，我们重新制定以前的模型的生成，分层模型。通过结合两个独立的子模型来解释试验中目标位置的学习和试验中的决策过程之间的相互作用。我们推导出一个最大似然方案的参数估计以及模型比较的对数似然比的基础上。综合模型的实用性证明了它从三个健康受试者获得的经验眼跳数据。模型比较用于（i）表明眼球运动不仅反映目标位置的边缘概率，而且还反映目标位置的条件概率，以及（ii）揭示试验中特定于受试者的学习概况。这些个体的学习特征是足够不同的，测试样本可以通过朴素贝叶斯分类器成功地映射到正确的主题上。总而言之，我们的方法扩展了扫视决策的线性上升阈值模型，克服了他们以前的一些限制，并使统计推断的目标位置的学习试验和试验内的决策过程。(C)2008爱思唯尔有限公司版权所有。
The neurophysiology of eye movements has been studied extensively, and several computational models have been proposed for decision-making processes that underlie the generation of eye movements towards a Visual stimulus in a Situation of uncertainty. One class of models, known as linear rise-to-threshold models, provides an economical, yet broadly applicable, explanation for the observed variability in the latency between the onset of a peripheral Visual target and the saccade towards it. So far, however, these models do not account for the dynamics of learning across a Sequence of stimuli, and they do not apply to situations in which Ssbjects are exposed to events with conditional probabilities. In this methodological paper, we extend the class of linear rise-to-threshold models to address these limitations. Specifically, we reformulate previous models in terms of a generative, hierarchical model. by combining two separate sub-models that account for the interplay between learning of target locations across trials and the decision-making process within trials. We derive a maximum-likelihood scheme for parameter estimation as well as model comparison on the basis of log likelihood ratios. The utility Of the integrated model is demonstrated by applying it to empirical saccade data acquired from three healthy subjects. Model comparison is used (i) to show that eye movements do not only reflect marginal but also conditional probabilities of target locations, and (ii) to reveal subject-specific learning profiles over trials. These individual learning profiles are Sufficiently distinct that test samples can be Successfully mapped onto the correct subject by a naive Bayes classifier. Altogether, our approach extends the class of linear rise-to-threshold models of saccadic decision making, overcomes some of their previous limitations, and enables statistical inference both about learning of target locations across trials and the decision-making process within trials. (C) 2008 Elsevier Ltd. All rights reserved.