Investigations of exemplar and decision bound models in large, ill-defined category structures.
Investigations of exemplar and decision bound models in large, ill-defined category structures.
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对大型、定义不明确的类别结构中的范例和决策约束模型进行研究。
DOI：
10.1037//0096-1523.21.1.128
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
Nosofsky,RM
中科院分区：
文献类型：
--
作者：
McKinley,SC;Nosofsky,RM
进行了涉及大尺寸、不明确类别的实验，以区分示例模型的预测与线性和二次决策界模型的预测。在最佳分类边界的形式比二次模型更复杂的情况下，范例模型比决策界限模型对研究参与者的数据提供了明显更好的解释，即使在线性界限会产生接近最佳性能的情况下也是如此。结果表明，参与者不倾向于或受限于使用线性或二次决策界限来对多维感知刺激进行分类，并且示例模型可以为实验参与者使用的复杂类型的决策界限提供简约的过程级说明。与示例模型的预测相比，结果还表明可学习的决策边界的复杂性存在一些限制。
Experiments involving large-size, ill-defined categories were conducted to distinguish between the predictions of an exemplar model and linear and quadratic decision bound models. In conditions in which the optimal classification boundary was of a more complex form than the quadratic model, the exemplar model provided significantly better accounts of study participants' data than did the decision bound models, even in situations in which a linear bound would have yielded nearly optimal performance. The results suggest that participants are not predisposed or constrained to use linear or quadratic decision bounds for classifying multidimensional perceptual stimuli and that exemplar models may provide a parsimonious process-level account of the complex types of decision bounds used by experiment participants. The results also suggest some limitations on the complexity of the decision bounds that can be learned, in contrast to the predictions of the exemplar model.
行为模型：心理学中的随机过程
DOI：
--
发表时间：
1982
期刊：
影响因子：
--
作者：
T. Wickens
通讯作者：
T. Wickens
人类的学习和记忆。
影响因子：
24.8
作者：
Johnson,MK;Hasher,L
通讯作者：
Hasher,L