Despite vast efforts to better understand human learning, some principles have been overlooked; specifically, that less familiar stimuli are more difficult to combine to create new knowledge and that this is because less familiar stimuli consume more working memory resources. Participants previously unfamiliar with Chinese characters were trained to discriminate visually similar characters during a visual search task over the course of a month, during which half of the characters appeared much more frequently. Ability to form associations involving these characters was tested via cued recall for novel associations consisting of two Chinese characters and an English word. Each week performance improved on the cued-recall task. Crucially, however, even though all Chinese character pairs were novel each week, those pairs consisting of more familiar characters were more easily learned. Performance on a working-memory task was better for more familiar stimuli, consistent with the claim that familiar stimuli consume fewer working memory resources. These findings have implications for optimal instruction, including second language learning.Keywords Episodic memory . Human memory and learning . Working memory . Encoding effectsIn the quest to ever improve humans' ability to learn, researchers have explored many factors that impact learning

People learn better when re-study opportunities are replaced with tests. While researchers have begun to speculate on why testing is superior to study, few studies have directly examined the neural underpinnings of this effect. In this fMRI study, participants engaged in a study phase to learn arbitrary word pairs, followed by a cued recall test (recall second half of pair when cued with first word of pair), re-study of each pair, and finally another cycle of cued recall tests. Brain activation patterns during the first test (recall) of the studied pairs predicts performance on the second test. Importantly, while subsequent memory analyses of encoding trials also predict later accuracy, the brain regions involved in predicting later memory success are more extensive for activity during retrieval (testing) than during encoding (study). Those additional regions that predict subsequent memory based on their activation at test but not at encoding may be key to understanding the basis of the testing effect.

The hippocampus plays a critical role in the rapid learning of new episodic memories. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i.e., “cells that fire together, wire together”). However, Hebbian learning is computationally suboptimal as it does not learn in a way that is driven toward, and limited by, the objective of achieving effective retrieval. Thus, Hebbian learning results in more interference and a lower overall capacity. Our previous computational models have utilized a powerful, biologically plausible form of error-driven learning in hippocampal CA1 and entorhinal cortex (EC) (functioning as a sparse autoencoder) by contrasting local activity states at different phases in the theta cycle. Based on specific neural data and a recent abstract computational model, we propose a new model called Theremin (Total Hippocampal ERror MINimization) that extends error-driven learning to area CA3—the mnemonic heart of the hippocampal system. In the model, CA3 responds to the EC monosynaptic input prior to the EC disynaptic input through dentate gyrus (DG), giving rise to a temporal difference between these two activation states, which drives error-driven learning in the EC→CA3 and CA3↔CA3 projections. In effect, DG serves as a teacher to CA3, correcting its patterns into more pattern-separated ones, thereby reducing interference. Results showed that Theremin, compared with our original Hebbian-based model, has significantly increased capacity and learning speed. The model makes several novel predictions that can be tested in future studies.