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线性可分性、无关变异性和分类难度。

Linear separability, irrelevant variability, and categorization difficulty.

机构信息

Dynamical Neuroscience.

出版信息

J Exp Psychol Learn Mem Cogn. 2022 Feb;48(2):159-172. doi: 10.1037/xlm0001000. Epub 2021 Apr 19.

DOI:10.1037/xlm0001000
PMID:33871263
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8523591/
Abstract

In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. This study investigates the effects of two different category properties on learning difficulty in category learning tasks-namely, linear separability and variability on stimulus dimensions that are irrelevant to the categorization decision. Previous research had reported that linearly separable II categories are easier to learn than nonlinearly separable categories, but Experiment 1, which compared performance on linearly and nonlinearly separable categories that were equated as closely as possible on all other factors that might affect difficulty, found that linear separability had no effect on learning. Experiments 1 and 2 together also established a novel dissociation between RB and II category learning: increasing variability on irrelevant stimulus dimensions impaired II learning but not RB learning. These results are all predicted by the best available measures of difficulty in RB and II tasks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

摘要

在基于规则(RB)的类别学习任务中,最优策略是一个简单的明确规则,而在信息整合(II)任务中,最优策略是无法用语言描述的。本研究考察了两种不同的类别属性对类别学习任务中学习难度的影响,即刺激维度上的线性可分离性和与分类决策无关的可变性。先前的研究报告称,线性可分离的 II 类比非线性可分离的类别更容易学习,但实验 1 比较了在其他可能影响难度的因素尽可能接近的情况下,线性可分离和非线性可分离类别之间的表现,发现线性可分离性对学习没有影响。实验 1 和实验 2 一起还建立了 RB 和 II 类别学习之间的新的分离:在不相关的刺激维度上增加可变性会损害 II 类学习,但不会损害 RB 学习。这些结果都可以用 RB 和 II 任务中最可用的难度衡量标准来预测。(PsycInfo 数据库记录(c)2022 APA,保留所有权利)。

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本文引用的文献

1
Dissociations between rule-based and information-integration categorization are not caused by differences in task difficulty.基于规则的分类和信息整合分类之间的分离并非由任务难度差异所致。
Mem Cognit. 2020 May;48(4):541-552. doi: 10.3758/s13421-019-00988-4.
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Revisiting the linear separability constraint: New implications for theories of human category learning.重新审视线性可分性约束:对人类范畴学习理论的新启示。
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A difficulty predictor for perceptual category learning.一种用于感知类别学习的难度预测器。
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