Soto Fabian A, Ashby F Gregory
Department of Psychology, Florida International University, 11200 SW 8th St, AHC4 460, Miami, FL, 33199, USA.
Department of Psychological and Brain Sciences, University of California, Santa Barbara, USA.
Psychol Res. 2019 Apr;83(3):544-566. doi: 10.1007/s00426-019-01157-7. Epub 2019 Feb 26.
Humans learn categorization rules that are aligned with separable dimensions through a rule-based learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here, we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button-switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than 1 h) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rule-based system may learn representations "on the fly" that allow rule application. We discuss what kind of neurocomputational model might explain these data best.
人类通过基于规则的学习系统学习与可分离维度一致的分类规则，这使得学习比需要整合来自不同维度信息的分类规则更快且更容易泛化。最近的研究表明，学习沿着全新的维度对物体进行分类会改变其感知表征，使其更易于分离和区分。在这里，我们探讨了这种新学习的维度是否能支持基于规则的类别学习。一组接受了广泛的分类训练，另一组没有接受此类训练。后来，两组都在一个利用类别相关维度的任务中接受训练，然后在类比迁移任务（实验1）和按钮切换干扰任务（实验2）中进行测试。我们预期只有经过广泛预训练（具有良好学习的维度表征）的组在这些任务中会表现出基于规则的行为。令人惊讶的是，两组的表现都符合基于规则学习的预期。第三个实验测试了在分类任务中进行单次训练（少于1小时）是否会促进在需要执行功能的任务中的学习。对具有相关维度简短预训练的组来说有显著的学习优势。我们假设基于规则的类别学习不需要有可分离维度的丰富经验；相反，基于规则的系统可能会“即时”学习表征，从而允许应用规则。我们讨论了哪种神经计算模型可能最能解释这些数据。
