Raden Megan J, Jarosz Andrew F
Department of Psychology, Mississippi State University, Starkville, MS 39762, USA.
J Intell. 2023 Apr 21;11(4):77. doi: 10.3390/jintelligence11040077.
The present study investigates how the quality of knowledge representations contributes to rule transfer in a problem-solving context and how working memory capacity (WMC) might contribute to the subsequent failure or success in transferring the relevant information. Participants were trained on individual figural analogy rules and then asked to rate the subjective similarity of the rules to determine how abstract their rule representations were. This rule representation score, along with other measures (WMC and fluid intelligence measures), was used to predict accuracy on a set of novel figural analogy test items, of which half included only the trained rules, and half were comprised of entirely new rules. The results indicated that the training improved performance on the test items and that WMC largely explained the ability to transfer rules. Although the rule representation scores did not predict accuracy on the trained items, rule representation scores did uniquely explain performance on the figural analogies task, even after accounting for WMC and fluid intelligence. These results indicate that WMC plays a large role in knowledge transfer, even when transferring to a more complex problem-solving context, and that rule representations may be important for novel problem solving.
本研究调查了知识表征的质量如何在问题解决情境中促进规则迁移，以及工作记忆容量（WMC）如何可能导致后续相关信息迁移的失败或成功。参与者接受了个体图形类比规则的训练，然后被要求对规则的主观相似性进行评分，以确定他们的规则表征有多抽象。这个规则表征分数，连同其他测量指标（WMC和流体智力测量指标），被用来预测一组新颖图形类比测试项目的准确性，其中一半项目只包含训练过的规则，另一半则完全由全新的规则组成。结果表明，训练提高了测试项目的表现，并且WMC在很大程度上解释了规则迁移的能力。虽然规则表征分数并不能预测训练项目的准确性，但即使在考虑了WMC和流体智力之后，规则表征分数确实独特地解释了图形类比任务的表现。这些结果表明，WMC在知识迁移中起着很大的作用，即使是在迁移到更复杂的问题解决情境时，并且规则表征可能对新颖问题解决很重要。
