The right time to learn: mechanisms and optimization of spaced learning

TL;DR

This paper reviews the cellular and molecular mechanisms behind spaced learning's effectiveness and discusses how computational models can optimize training protocols to improve learning and address impairments.

Contribution

It synthesizes recent data on the mechanisms of spaced learning and proposes model-based approaches to optimize training protocols and therapeutic interventions.

Findings

Spaced training enhances memory formation more than massed training.

Irregular inter-trial intervals can further improve learning outcomes.

Model predictions can inform optimal spaced training protocols.

Abstract

For many types of learning, spaced training that involves repeated long inter-trial intervals (ITIs) leads to more robust memory formation than does massed training that involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently has data begun to delineate the underlying cellular and molecular mechanisms of spaced training. We review these theories and data here. Computational models of the implicated signaling cascades have predicted that spaced training with irregular ITIs can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning.