Why does peer instruction benefit student learning?

Tullis, J. G., & Goldstone, R. L. (2020).  Why does peer instruction benefit student learning?  Cognitive Research: Principles and Implications, 5:15, 1-12. https://doi.org/10.1186/s41235-020-00218-5

In peer instruction, instructors pose a challenging question to students, students answer the question individually, students work with a partner in the class to discuss their answers, and finally students answer the question again. A large body of evidence shows that peer instruction benefits student learning. To determine the mechanism for these benefits, we collected semester-long data from six classes, involving a total of 208 undergraduate students being asked a total of 86 different questions related to their course content. For each question, students chose their answer individually, reported their confidence, discussed their answers with their partner, and then indicated their possibly revised answer and confidence again. Overall, students were more accurate and confident after discussion than before. Initially correct students were more likely to keep their answers than initially incorrect students, and this tendency was partially but not completely attributable to differences in confidence. We discuss the benefits of peer instruction in terms of differences in the coherence of explanations, social learning, and the contextual factors that influence confidence and accuracy.

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How do people code information in working memory when items share features?

Plancher, G., & Goldstone, R. L. (2020).  How do people code information in working memory when items share features?  Experimental Psychology, 1-9.  https://doi.org/10.1027/1618-3169/a000480.

A large literature suggests that the way we process information is influenced by the categories that we have learned. We examined whether, when we try to uniquely encode items in workingmemory, the information encoded depends on the other stimuli being simultaneously learned. Participants were required to memorize unknown aliens, presented one at the time, for immediate recognition of their features. Some aliens, called twins, were organized into pairs that shared every feature (nondiscriminative feature) except one (discriminative feature), while some other aliens, called hermits, did not share feature. We reasoned that if people develop unsupervised categories by creating a category for a pair of aliens, we should observe better feature identification performance for nondiscriminative features compared to hermit features, but not compared to discriminative features. On the contrary, if distinguishing features draw attention, we should observe better performance when a discriminative rather than nondiscriminative feature was probed. Overall, our results suggest that when items share features, people code items in working memory by focusing on similarities between items, establishing clusters of items in an unsupervised fashion not requiring feedback on cluster membership.

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Simple Threshold Rules Solve Explore/Exploit Trade-offs in a Resource Accumulation Search Task

Sang, K., Todd, P. M., Goldstone, R. L., & Hills, T. T. (2020).  Simple threshold rules solve explore/exploit tradeoffs in a resource accumulation search task. Cognitive Science44, e12817.

How, and how well, do people switch between exploration and exploitation to search for and accumulate resources? We study the decision processes underlying such exploration/exploitation trade-offs using a novel card selection task that captures the common situation of searching among multiple resources (e.g., jobs) that can be exploited without depleting. With experience, participants learn to switch appropriately between exploration and exploitation and approach optimal performance. We model participants’ behavior on this task with random, threshold, and sampling strategies, and find that a linear decreasing threshold rule best fits participants’ results. Further evidence that participants use decreasing threshold-based strategies comes from reaction time differences between exploration and exploitation; however, participants themselves report nondecreasing thresholds. Decreasing threshold strategies that “front-load” exploration and switch quickly to exploitation are particularly effective in resource accumulation tasks, in contrast to optimal stopping problems like the Secretary Problem requiring longer exploration.

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