Ell Shawn W, Ing A David, Maddox W Todd
Psychology Department, University of Maine, 5742 Little Hall, Room 301, Orono, ME 04469-5742, USA.
Atten Percept Psychophys. 2009 Aug;71(6):1263-75. doi: 10.3758/APP.71.6.1263.
Variability in the representation of the decision criterion is assumed in many category-learning models, yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks, where learning depends on the maintenance and manipulation of decision criteria. In three experiments, we tested this hypothesis and examined the impact of working memory on slowing the drift rate. In Experiment 1, we examined the effect of drift by inserting a 5-sec delay between the categorization response and the delivery of corrective feedback, and working memory demand was manipulated by varying the number of decision criteria to be learned. Delayed feedback adversely affected performance, but only when working memory demand was high. In Experiment 2, we built on a classic finding in the absolute identification literature and demonstrated that distributing the criteria across multiple dimensions decreases the impact of drift during the delay. In Experiment 3, we confirmed that the effect of drift during the delay is moderated by working memory. These results provide important insights into the interplay between criterial noise and working memory, as well as providing important constraints for models of rule-based category learning.
许多类别学习模型都假定决策标准的表征存在变异性，但很少有研究直接考察其影响。在每次试验中，标准噪声会导致标准漂移，并对分类准确性产生负面影响，尤其是在基于规则的分类任务中，学习依赖于决策标准的维持和操作。在三个实验中，我们检验了这一假设，并考察了工作记忆对减缓漂移率的影响。在实验1中，我们通过在分类反应和提供纠正反馈之间插入5秒延迟来检验漂移的影响，并通过改变要学习的决策标准数量来操纵工作记忆需求。延迟反馈对表现有不利影响，但仅在工作记忆需求较高时如此。在实验2中，我们基于绝对识别文献中的一个经典发现，并证明将标准分布在多个维度上可减少延迟期间漂移的影响。在实验3中，我们证实延迟期间漂移的影响受工作记忆调节。这些结果为标准噪声与工作记忆之间的相互作用提供了重要见解，同时也为基于规则的类别学习模型提供了重要限制。
