Writing to learn increases long-term memory consolidation: A mental-chronometry and computational-modeling study of “Epistemic writing”
Keywords:drift-diffusion model, Epistemic writing, evidence accumulation models, writing across the curriculum, writing in the disciplines, writing to learn
AbstractIn this paper, we provide a mental-chronometry measurement (reaction time, RT) and a mathematical model to support the hypothesis that writing increases long-term memory (LTM) consolidation. Twenty-five subjects read short passages, wrote or spoke summaries of the texts, and performed a word-recognition episodic memory task. In the recognition task, participants responded faster in the written condition than in the spoken condition. We fit 15 drift-diffusion models to the accuracy and RT data to explore which components of the memory retrieval process reflect the learning effect of writing. Model selection methods showed that the nondecision parameter accounts for this effect, suggesting that initial stages of learning through writing are associated with fast episodic-memory retrieval. We suggest that the current approach could be used as a tool to compare different models of writing to learn. Furthermore, we show how combining mental chronometry, evidence-accumulation models of behavioral data, and dynamic causal models of functional magnetic resonance imaging could further the goal of understanding how writing affects learning. With a broader perspective, this approach provides a feasible experimental link between the field of writing to learn and the cognitive neurosciences.
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Copyright (c) 2019 Angélica M. Silva, Roberto Limongi
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