Observed attention allocation processes in category learning
Observed attention allocation processes in category learning
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DOI：
10.1080/17470210701438194
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发表时间：
2008-07-01
影响因子：
1.7
通讯作者：
Corter, James E.
中科院分区：
文献类型：
--
作者：
Matsuka, Toshihiko;Corter, James E.
在两项关于类别学习过程中注意力分配的实证研究中，我们探讨了类别学习者在刺激维度上学习最佳分配注意力的想法。我们认为，“最佳”模式的注意力分配模型或过程的具体，人类学习者并不总是优化注意力，他们这样做的一个原因是，在某些条件下，信息检索或使用的成本可能会影响学习者采取的注意力策略。我们实证调查这些问题，使用计算机接口，将“信息板”显示，收集详细的信息，参与者的注意力分配和信息搜索在学习试验的模式。实验一考察了在不同刺激维度上和在同一刺激维度上完美分布诊断特征对注意分配的影响。观看时间的整体模式支持最佳注意力分配假设，但更详细的分析产生了实例或类别特定的注意力分配的证据，这一现象没有预测的类别学习的突出计算模型。实验二考察了类别学习者在遇到冗余完全预测线索时所采取的策略。在这里，大多数参与者学会了在成本效益的意义上最佳地分配注意力，将注意力主要分配给两个完全预测的维度中的一个。这些结果表明，学习者可能会考虑到情境成本和收益，他们提出的挑战计算模型的学习，分配注意力的加权刺激尺寸。
In two empirical studies of attention allocation during category learning, we investigate the idea that category learners learn to allocate attention optimally across stimulus dimensions. We argue that "optimal" patterns of attention allocation are model or process specific, that human learners do not always optimize attention, and that one reason they fall to do so is that under certain conditions the cost of information retrieval or use may affect the attentional strategy adopted by learners. We empirically investigate these issues using a computer interface incorporating an "information-board" display that collects detailed information on participants' patterns of attention allocation and information search during learning trials. Experiment 1 investigated the effects on attention allocation of distributing perfectly diagnostic features across stimulus dimensions versus within one dimension. The overall pattern of viewing times supported the optimal attention allocation hypothesis, but a more detailed analysis produced evidence of instance- or category-specific attention allocation, a phenomenon not predicted by prominent computational models of category learning. Experiment 2 investigated the strategies adopted by category learners encountering redundant perfectly predictive cues. Here, the majority of participants learned to distribute attention optimally in a cost-benefit sense, allocating attention primarily to only one of the two perfectly predictive dimensions. These results suggest that learners may take situational costs and benefits into account, and they present challenges for computational models of learning that allocatc attention by weighting stimulus dimensions.