Memory enhancements from active control of learning emerge across development

Ruggeri, Azzurra; Markant, Douglas; Gureckis, Todd M.; Bretzke, Maria; Xu, Fei

doi:10.1016/j.cognition.2019.01.010

Cited by 51 publications

(56 citation statements)

References 50 publications

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“…The observed greater effectiveness of GLSs during late-elementary/early-secondary school dovetails with the observation that it is during these grades (i.e., around age 10) that one can observe the emergence of abstract self-reflection in children, which enables an efficient use of knowledge-based learning strategies (e.g., Hasselhorn 1995). Findings are also in line with a recent study that showed that the advantage from giving learners active control over their learning increases across middle childhood (Ruggeri et al 2019). What this line of research cannot explain straightforwardly, however, are the differences in developmental trajectories between strategies.…”

Section: Discussionsupporting

confidence: 90%

Generative Learning: Which Strategies for What Age?

Brod

2020

Educ Psychol Rev

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Generative learning strategies are intended to improve students’ learning by prompting them to actively make sense of the material to be learned. But are they effective for all students? This review provides an overview of six popular generative learning strategies: concept mapping, explaining, predicting, questioning, testing, and drawing. Its main purpose is to review for what ages the effectiveness of these strategies has been demonstrated and whether there are indications of age-related differences in their effectiveness. The description of each strategy covers (1) how it is supposed to work, (2) the evidence on its effectiveness in different age groups, and (3) if there are age-related differences in its effectiveness. It is found that while all six generative learning strategies reviewed have proven effective for university students, evidence is mixed for younger students. Whereas some strategies (practice testing, predicting) seem to be effective already in lower-elementary-school children, others (drawing, questioning) seem to be largely ineffective until secondary school. The review closes with a call for research on the cognitive and metacognitive prerequisites of generative learning that can explain these differences.

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Section: Discussionsupporting

confidence: 90%

Generative Learning: Which Strategies for What Age?

Brod

2020

Educ Psychol Rev

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“…Finally, though few studies have examined causal learning in adolescence, our results demonstrate that causal learning and decision-making continue to change during this period. This finding contributes to the growing body of literature examining how the ability to effectively support one's own learning changes over developmental time (Kachergis, Rhodes, & Gureckis, 2017;Ruggeri, Markant, Gureckis, Bretzke, & Xu, 2019). Future work probing the cognitive mechanisms that drive these changes will inform our understanding of how children and adolescents shape their own learning opportunities as they interact with their environments with increasing independence.…”

Section: Discussionmentioning

confidence: 63%

Causal information-seeking strategies change across childhood and adolescence

Nussenbaum¹,

Cohen²,

Davis³

et al. 2019

Preprint

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Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three- or four-node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip’s hidden wires and had to select a single node to activate. After observing whether their intervention caused other nodes to turn on, participants selected a wire configuration, and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures vs. those that elicited evidence in favor of one of the two causal hypotheses, but did not necessarily discriminate between them. Our model estimates revealed that the use of discriminatory strategies increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that their interventions revealed, as indexed by participants’ confidence in their selections. Our results suggest that improvements in causal information-seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.

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“…The current study aims to advance this debate by investigating the cognitive basis of self-directed learning (T. M. Gureckis & Markant, 2012;Markant et al, 2016). Whereas recent studies have demonstrated that learner control enhances elemental encoding of information directly experienced during study Murty, DuBrow, & Davachi, 2015;A. Ruggeri, Markant, Gureckis, & Xu, 2019;Voss et al, 2011), less is known about how active control impacts integrative encoding, through which experienced information is combined into generalizable knowledge during study.…”

Section: Discussionmentioning

confidence: 99%

Active transitive inference: When learner control facilitates integrative encoding

Markant

2020

Cognition

Self Cite

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A growing body of research indicates that active control of learning improves episodic memory for material experienced during study. It is less clear how active learning impacts the integration of those experiences into flexible, generalizable knowledge. This study presents a novel active transitive inference task to investigate how people learn a relational hierarchy through active selection of premise pairs. Active control improved memory for studied premises as well as transitive inferences involving items that were never experienced together during study. Active learners also exhibited a systematic search preference, generating sequences of overlapping premises that may facilitate relational integration. Critically, however, advantages from active control were not universal: Only participants with higher working memory capacity benefited from the opportunity to select premise pairs during learning. These findings suggest that active control enhances integrative encoding of studied material, but only among individuals with sufficient cognitive resources.

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Memory enhancements from active control of learning emerge across development

Cited by 51 publications

References 50 publications

Generative Learning: Which Strategies for What Age?

Generative Learning: Which Strategies for What Age?

Causal information-seeking strategies change across childhood and adolescence

Active transitive inference: When learner control facilitates integrative encoding

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