Thoughtful Friends: Executive Function Relates to Social Problem Solving and Friendship Quality in Middle Childhood

Abstract

The present study examined links between best friendship quality, social problem solving in response to a transgression and conscious control of behavior (i.e., executive function or EF). Eighty-one 7- to 10-year-olds answered questions about their best friendship quality and responses to friendship transgressions (i.e., interpretations, goals, and strategies they would endorse). They also completed a battery of EF tasks measuring working memory, inhibition, and shifting. Results revealed few relations between social problem solving and best friendship quality. Social problem solving related to EF abilities, with inhibition relating to fewer revenge goals and cognitive flexibility relating to more neutral interpretations. Better working memory related to worse best friendship quality. Finally, verbal IQ was a strong predictor of several positive social problem-solving interpretations, goals, and strategies. Results suggest cognitive abilities in EF and language may be important to consider during middle childhood in this period of advancing social problem solving and friendships.     

两人问题解决中惯例的测量及其微观发生过程

惯例是一种群体的程序化行为，类似于思维定势，其实验测量最早源于 Cohen 与 Bacdayan (1994)发明的TTT扑克牌游戏。研究选取35对两人组被试，在问题解决的框架下采用微观发生法的思路重新设计实验任务，探讨了两人问题解决中惯例的表现，并在此基础上探讨了惯例从无到有的产生过程。结果表明：(1)修改后的40局TTT扑克牌游戏中存在明显的惯例现象，这可以从可靠性、速度、重复的行动序列、基于特定策略的行为模式和偶尔的次优性5个指标进行验证和描述。(2)惯例的产生是在行为掌握基础上，不断进行表征重述的结果。这一过程体现了惯例的产生是程序性知识转向陈述性知识、无意识转向有意识、稳定性与可变性的统一。     

Memory for goals: an activation‐based model

Goal‐directed cognition is often discussed in terms of specialized memory structures like the “goal stack.” The goal‐activation model presented here analyzes goal‐directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means‐ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.     

Gesture offers insight into problem‐solving in adults and children

When asked to explain their solutions to a problem, both adults and children gesture as they talk. These gestures at times convey information that is not conveyed in speech and thus reveal thoughts that are distinct from those revealed in speech. In this study, we use the classic Tower of Hanoi puzzle to validate the claim that gesture and speech taken together can reflect the activation of two cognitive strategies within a single response. The Tower of Hanoi is a well‐studied puzzle, known to be most efficiently solved by activating subroutines at theoretically defined choice points. When asked to explain how they solved the Tower of Hanoi puzzle, both adults and children produced significantly more gesture‐speech mismatches—explanations in which speech conveyed one path and gesture another—at these theoretically defined choice points than they produced at non‐choice points. Even when the participants did not solve the problem efficiently, gesture could be used to indicate where the participants were deciding between alternative paths. Gesture can, thus, serve as a useful adjunct to speech when attempting to discover cognitive processes in problem‐solving.     

An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.