Memory failures are frustrating and often the result of ineffective encoding. One approach to improving memory outcomes is through direct modulation of brain activity with electrical stimulation. Previous efforts, however, have reported inconsistent effects when using open-loop stimulation and often target the hippocampus and medial temporal lobes. Here we use a closed-loop system to monitor and decode neural activity from direct brain recordings in humans. We apply targeted stimulation to lateral temporal cortex and report that this stimulation rescues periods of poor memory encoding. This system also improves later recall, revealing that the lateral temporal cortex is a reliable target for memory enhancement. Taken together, our results suggest that such systems may provide a therapeutic approach for treating memory dysfunction.

Despite the complexity of memory and its diverse manifestations in our daily lives, certain mnemonic effects appear to hold across a wide range of conditions. We identify the effects of recency, contiguity, similarity, primacy, and repetition as potential laws of memory, evaluating their explanatory scope and discussing their theoretical significance. We show that apparent violations of these laws occur when different effects come into conflict, as in the situation of opposing physical forces. We see the search for law-like phenomena as guiding the development and refinement of integrative memory theories.

For more than a half-century, lists of words have served as the memoranda of choice in studies of human memory. To better understand why some words and lists are easier to recall than others, we estimated multivariate models of word and list recall. In each of the 23 sessions, subjects (N = 98) studied and recalled the same set of 576 words, presented in 24 study-test lists. Fitting a statistical model to these data revealed positive effects of animacy, contextual diversity, valence, arousal, concreteness, and semantic structure on recall of individual words. We next asked whether a similar approach would allow us to account for list-level variability in recall performance. Here we hypothesized that semantically coherent lists would be most memorable. Consistent with this prediction, we found that semantic similarity, weighted by temporal distance, was a strong positive predictor of list-level recall. Additionally, we found significant effects of average contextual diversity, valence, animacy, and concreteness on list-level recall. Our findings extend previous models of item-level recall and show that aggregate measures of item recallability also account for variability in list-level performance.

Deep-learning methods can extract high-dimensional feature vectors for objects, concepts, images, and texts from large-scale digital data sets. These vectors are proxies for the mental representations that people use in everyday cognition and behavior. For this reason, they can serve as inputs into computational models of cognition, giving these models the ability to process and respond to naturalistic prompts. Over the past few years, researchers have applied this approach to topics such as similarity judgment, memory search, categorization, decision making, and conceptual knowledge. In this article, we summarize these applications, identify underlying trends, and outline directions for future research on the computational modeling of naturalistic cognition and behavior.

Sloan Foundation, and Wharton Risk Management Center's Russell Ackoff Doctoral Student Fellowship. We thank Drs. Michael Kahana, Eric Bradlow, Christophe Van den Bulte, and members of the Computational Behavioral Sciences Lab for helpful discussion.

Free association among words is a fundamental and ubiquitous memory task. Although distributed semantics (DS) models can predict the association between pairs of words, and semantic network (SN) models can describe transition probabilities in free association data, there have been few attempts to apply established cognitive process models of memory search to free association data. Thus, researchers are currently unable to explain the dynamics of free association using memory mechanisms known to be at play in other retrieval tasks, such as free recall from lists. We address this issue using a popular neural network model of free recall, the context maintenance and retrieval (CMR) model, which we fit using stochastic gradient descent on a large data set of free association norms. Special cases of CMR mimic existing DS and SN models of free association, and we find that CMR outperforms these models on out-of-sample free association data. We also show that training CMR on free association data generates improved predictions for free recall from lists, demonstrating the value of free association for the study of many different types of memory phenomena. Overall, our analysis provides a new account of the dynamics of free association, predicts free association with increased accuracy, integrates theories of free association with established models of memory, and shows how large data sets and neural network training methods can be used to model complex cognitive processes that operate over thousands of representations.

The Penn Electrophysiology of Encoding and Retrieval Study (PEERS) aimed to characterize the behavioral and electrophysiological (EEG) correlates of memory encoding and retrieval in highly practiced individuals. Across five PEERS experiments, 300+ subjects contributed more than 7,000 90 minute memory testing sessions with recorded EEG data. Here we tell the story of PEERS: its genesis, evolution, major findings, and the lessons it taught us about taking a big science approach to the study of memory and the human brain.

We also thank participants of 2020 Society for Judgment and Decision making for their helpful discussion during the conference presentation. No potential conflict of interest was reported by the authors.