Shah Vedant Biren, Schlegelmilch René, von Helversen Bettina
Allgemeine Psychologie, University of Bremen, Bremen, Germany.
Psychol Res. 2025 Sep 18;89(5):144. doi: 10.1007/s00426-025-02180-7.
Classification is a common cognitive task, which requires assigning objects or events to categories based on shared features or rules (e.g., red objects are fruit, brown objects are mushrooms). In everyday scenarios, however, objects usually belong to more than one category (e.g., red objects can also be classified as edible, and brown objects could be poisonous). This study investigates whether humans can learn corresponding regularities between outcomes of such multiple categorizations when performed in a series of decisions for each stimulus. We therefore translated classical category learning designs, known as Type I (one-dimensional rule) and Type II (disjunctive rule), into a temporal context. We compared these cases to conditions in which no correlations existed between the series of categorization outcomes, and only the visual stimulus predicted each category outcome. Besides the structural complexity, we also tested in Type I scenarios whether learning and generalization were moderated by the temporal proximity of the successive decisions (adjacent vs. non-adjacent categorizations). The results show that participants can abstract away from the visual stimulus with a temporal Type I regularity, but there was no evidence for a corresponding effect with a temporal Type II regularity. The role of adjacency was not clear-cut, but there was no strong evidence favoring stronger performance with adjacent relative to non-adjacent categorizations. We discuss these findings before the background of category- and artificial grammar-learning research, and expand on potential moderating factors such as the cognitive effort of keeping the necessary amount of information in working memory and the modality of category predictors when determining whether people will extract rules or rely on memory-based learning.
分类是一项常见的认知任务，它要求根据共享特征或规则将对象或事件归类（例如，红色物体是水果，棕色物体是蘑菇）。然而，在日常场景中，物体通常属于不止一个类别（例如，红色物体也可以归类为可食用的，棕色物体可能是有毒的）。本研究调查了人类在对每个刺激进行一系列决策时执行此类多重分类的结果之间是否能够学习相应的规律。因此，我们将经典的类别学习设计，即I型（一维规则）和II型（析取规则），转化到了时间背景中。我们将这些情况与分类结果系列之间不存在相关性，只有视觉刺激预测每个类别结果的情况进行了比较。除了结构复杂性，我们还在I型场景中测试了学习和泛化是否受到连续决策的时间接近性（相邻与非相邻分类）的调节。结果表明，参与者能够从具有时间I型规律的视觉刺激中抽象出来，但没有证据表明时间II型规律会产生相应的效果。相邻性的作用并不明确，但没有强有力的证据表明相邻分类相对于非相邻分类表现更强。我们在类别和人工语法学习研究的背景下讨论这些发现，并扩展了潜在的调节因素，如在工作记忆中保持必要信息量的认知努力以及在确定人们是否会提取规则或依赖基于记忆的学习时类别预测器的模态。
