Department of Education Science, Shanxi Normal University, Taiyuan, China.
Department of Psychology, Center for Child Development, Learning and Cognitive Key Laboratory, Capital Normal University, Beijing, China.
Psychophysiology. 2022 Sep;59(9):e14046. doi: 10.1111/psyp.14046. Epub 2022 May 22.
As relational integration performance can be used to predict reasoning ability, the present study aimed to provide electrophysiological evidence for numerical inductive reasoning. Number series with two levels of relational complexity were utilized, including simple and hierarchical problems (such as "15-16-17" versus "15-16-18"). Two tasks were adopted: a relational integration task that required to determine whether the numerical relations were changed across numbers; a number series task that required to determine whether a hidden rule was acquired (Experiment 1) or to predict the subsequent number (Experiment 2), whose phases were divided as rule searching, rule discovery, and rule following. The event-related potential (ERP) results of both experiments indicated that, in contrast to simple problems, hierarchical problems triggered enhanced N400 and late negative component (LNC), reflecting numerical fact retrieval, and generalizing novel hypotheses about the hidden rules by integrating adjacent numerical relations, respectively; relational integration showed similar N400 and LNC activation patterns to rule discovery (Experiment 1) or rule searching (Experiment 2). Additionally, the N400 and LNC elicited by relational integration showed strong positive correlations and even were able to predict the ones triggered by rule discovery (Experiment 1) or rule searching (Experiment 2). Therefore, the results supported the role of relational integration in numerical inductive reasoning and thereby in intelligence.
由于关系整合绩效可用于预测推理能力，本研究旨在为数值归纳推理提供电生理证据。使用了两种关系复杂性水平的数字序列，包括简单和层次问题（例如“15-16-17”与“15-16-18”）。采用了两项任务：一项是关系整合任务，要求确定数字之间的数值关系是否发生变化；另一项是数字序列任务，要求确定是否获得了隐藏规则（实验 1）或预测后续数字（实验 2），其阶段分为规则搜索、规则发现和规则遵循。两个实验的事件相关电位（ERP）结果表明，与简单问题相比，层次问题引发了增强的 N400 和晚期负成分（LNC），分别反映了数值事实检索以及通过整合相邻数值关系来概括关于隐藏规则的新假设；关系整合表现出与规则发现（实验 1）或规则搜索（实验 2）相似的 N400 和 LNC 激活模式。此外，关系整合引起的 N400 和 LNC 之间存在强烈的正相关关系，甚至可以预测由规则发现（实验 1）或规则搜索（实验 2）引起的 N400 和 LNC。因此，结果支持关系整合在数值归纳推理中的作用，进而支持智力。
