Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA.
Department of Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Sci Rep. 2024 Aug 27;14(1):19814. doi: 10.1038/s41598-024-70163-6.
Categorical learning is important and often challenging in both specialized domains, such as medical image interpretation, and commonplace ones, such as face recognition. Research has shown that comparing items from different categories can enhance the learning of perceptual classifications, particularly when those categories appear highly similar. Here, we developed and tested novel adaptively triggered comparisons (ATCs), in which errors produced during interactive learning dynamically prompted the presentation of active comparison trials. In a facial identity paradigm, undergraduate participants learned to recognize and name varying views of 22 unknown people. In Experiment 1, single-item classification trials were compared to a condition in which ATC trials were generated whenever a participant repeatedly confused two faces. Comparison trials required discrimination between simultaneously presented exemplars from the confused categories. In Experiment 2, an ATC condition was compared to a non-adaptive comparison condition. Participants learned to accuracy and speed criteria, and completed immediate and delayed posttests. ATCs substantially enhanced learning efficiency in both experiments. These studies, using a novel adaptive procedure guided by each learner's performance, show that adaptively triggered comparisons improve category learning.
类别学习在医学图像解读等专业领域和人脸识别等常见领域都很重要，但通常具有挑战性。研究表明，比较不同类别的项目可以增强感知分类的学习，特别是当这些类别看起来非常相似时。在这里，我们开发并测试了新的自适应触发比较（ATC），在这种比较中，交互学习过程中产生的错误会动态提示呈现主动比较试验。在面部身份范式中，本科参与者学习识别和命名 22 个未知人员的不同视角。在实验 1 中，将单项分类试验与以下条件进行了比较：当参与者反复混淆两张脸时，会生成 ATC 试验。比较试验需要在同时呈现的混淆类别的示例之间进行区分。在实验 2 中，将 ATC 条件与非自适应比较条件进行了比较。参与者根据准确性和速度标准进行学习，并完成即时和延迟后的后测。在这两项实验中，ATC 大大提高了学习效率。这些研究使用了一种新的自适应程序，该程序由每个学习者的表现来指导，表明自适应触发比较可以改善类别学习。
