Tracing Eye Movement Protocols with Cognitive Process Models
Tracing Eye Movement Protocols with Cognitive Process Models
复制标题
使用认知过程模型追踪眼动协议
DOI：
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复制
发表时间：
1998
期刊：
影响因子：
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通讯作者：
John R. Anderson
中科院分区：
文献类型：
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作者：
Dario D. Salvucci;John R. Anderson
在使用眼动来开发认知模型时，研究人员通常使用聚合测量和关于这些测量的测试模型来分析眼动协议。由于聚合分析有时会隐藏信息的低层次行为，协议分析比较模型的预测，个人的审判协议往往是可取的，然而，眼动数据的协议分析往往是繁琐和耗时的。我们描述了如何使用隐马尔可夫模型自动化的眼球运动的协议分析。从一个方程求解任务的数据工作，我们展示了两种方法跟踪眼动数据，即映射眼动的认知过程模型的顺序预测。我们评估了这些跟踪方法在实验中，参与者被指示执行给定的方程求解策略。当根据给定的策略对实验协议进行编码时，自动跟踪方法在一小部分时间内与人类专家编码器一样好。
In using eye movements to develop cognitive models, researchers typically analyze eye movement protocols with aggregate measures and test models with respect to these measures. Because aggregate analyses sometimes conceal informative low-level behavior, protocol analyses comparing model predictions to individual trial protocols are frequently desirable; however, protocol analysis for eye movement data is often tedious and time-consuming. We describe how to automate the protocol analysis of eye movements using hidden Markov models. Working with data from an equation-solving task, we demonstrate two methods of tracing eye movement data—that is, mapping eye movements to the sequential predictions of a cognitive process model. We evaluated these tracing methods in an experiment where participants were instructed to execute given equation-solving strategies. When coding the experimental protocols in terms of the given strategies, the automated tracing methods performed as well as human expert coders in a fraction of the time.
影响因子：
6.3
作者：
M. Just;P. Carpenter
通讯作者：
M. Just;P. Carpenter