Eckstein M P, Thomas J P, Palmer J, Shimozaki S S
Cedars Sinai Medical Center, Los Angeles, California, USA.
Percept Psychophys. 2000 Apr;62(3):425-51. doi: 10.3758/bf03212096.
Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.
最近，基于信号检测理论的定量模型已成功应用于预测人类在视觉搜索中寻找与干扰项在单一属性上不同的目标（特征搜索）时的准确率。本文将这些视觉搜索准确率模型扩展到多维搜索显示中，其中目标在多个特征维度上与干扰项不同（合取、析取和三联合显示）。该模型假设显示中的每个元素都会为每个相关特征维度引发一个有噪声的表征。观察者将跨特征维度的表征进行组合以获得单个决策变量，具有最大值的刺激决定反应。该模型准确地预测了关于对比度和方向的合取与析取的视觉搜索准确率的人类实验数据。该模型解释了性能下降的情况，而无需借助一种有限容量的空间定位和时间序列机制来跨特征维度绑定信息。
