Rosedahl Luke A, Ashby F Gregory
Dynamical Neuroscience, University of California, Santa Barbara, CA, USA.
J Vis. 2019 Jun 3;19(6):20. doi: 10.1167/19.6.20.
Predicting human performance in perceptual categorization tasks in which category membership is determined by similarity has been historically difficult. This article proposes a novel biologically motivated difficulty measure that can be generalized across stimulus types and category structures. The new measure is compared to 12 previously proposed measures on four extensive data sets that each included multiple conditions that varied in difficulty. The studies were highly diverse and included experiments with both continuous- and binary-valued stimulus dimensions, a variety of different stimulus types, and both linearly and nonlinearly separable categories. Across these four applications, the new measure was the most successful at predicting the observed rank ordering of conditions by difficulty, and it was also the most accurate at predicting the numerical values of the mean error rates in each condition.
在类别归属由相似性决定的感知分类任务中，预测人类的表现一直以来都颇具难度。本文提出了一种全新的、受生物学启发的难度度量方法，该方法可推广至不同的刺激类型和类别结构。在四个广泛的数据集上，将这一新度量方法与之前提出的12种度量方法进行了比较，每个数据集都包含多个难度各异的条件。这些研究具有高度的多样性，包括连续值和二元值刺激维度的实验、多种不同的刺激类型，以及线性和非线性可分的类别。在这四个应用场景中，新度量方法在按难度预测观察到的条件排序方面最为成功，并且在预测每个条件下平均错误率的数值时也最为准确。
