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联合规则基范畴学习中的认知变化:ERP 研究方法。

Cognitive changes in conjunctive rule-based category learning: An ERP approach.

机构信息

The Rotman Research Institute of Baycrest Centre, Toronto, Canada.

Department of Psychology & Brain and Mind Institute, The University of Western Ontario, London, ON, N6A 5C2, Canada.

出版信息

Cogn Affect Behav Neurosci. 2018 Oct;18(5):1034-1048. doi: 10.3758/s13415-018-0620-6.

Abstract

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule ("easy" stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule ("difficult" stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning.

摘要

在学习基于规则的类别时,需要足够的认知资源来测试假设,在工作记忆中维持当前活跃的规则,在反馈后更新规则,并在必要时选择新规则。先前的研究表明,合取规则比单维规则更复杂,对工作记忆等执行功能的要求更高。在我们的研究中,参与者在进行具有逐试反馈的合取规则基础类别学习任务时记录了事件相关电位 (ERP)。与先前的研究一致,正确的分类响应导致刺激锁定的晚期正复合波比错误响应更大,可能索引了记忆中规则信息的更新。错误试验引起了明显的反馈锁定 P300,表明感知与基于规则的策略之间存在脱节。我们还研究了能够通过次优单维规则(“简单”刺激)正确分类的刺激与只能通过最优的合取规则(“困难”刺激)正确分类的刺激的差异处理。在强学习者中,与简单刺激相比,困难刺激出现更大、更晚的正慢波,表明即使强学习者在合取类别集中表现良好,类别项目的处理也存在差异。总的来说,这些发现表明,ERP 结合计算模型可以用于更好地理解基于规则的类别学习所涉及的认知过程。

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