Memory modeling of counterfactual generation.

We use a computational model of memory search to study how people generate counterfactual outcomes in response to an established target outcome. Hierarchical Bayesian model fitting to data from six experiments reveals that counterfactual outcomes that are perceived as more desirable and more likely to occur are also more likely to come to mind and are generated earlier than other outcomes. Additionally, core memory mechanisms such as semantic clustering and word frequency biases have a strong influence on retrieval dynamics in counterfactual thinking. Finally, we find that the set of counterfactuals that come to mind can be manipulated by modifying the total number of counterfactuals that participants are prompted to generate, and our model can predict these effects. Overall, our findings demonstrate how computational memory search models can be integrated with current theories of counterfactual thinking to provide novel insights into the process of generating counterfactual thoughts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Penn Electrophysiology of Encoding and Retrieval Study.

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 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 scientific approach in studying memory and the human brain. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Free association in a neural network.

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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Semantic determinants of memorability.

We examine why some words are more memorable than others by using predictive machine learning models applied to word recognition and recall datasets. Our approach provides more accurate out-of-sample predictions for recognition and recall than previous psychological models, and outperforms human participants in new studies of memorability prediction. Our approach's predictive power stems from its ability to capture the semantic determinants of memorability in a data-driven manner. We identify which semantic categories are important for memorability and show that, unlike features such as word frequency that influence recognition and recall differently, the memorability of semantic categories is consistent across recognition and recall. Our paper sheds light on the complex psychological drivers of memorability, and in doing so illustrates the power of machine learning methods for psychological theory development.

What I like is what I remember: Memory modulation and preferential choice.

Memory is a crucial component of everyday decision making, yet little is known about how memory and choice processes interact and whether or not established memory regularities persist during memory-based decision making. In this paper, we introduce a novel experimental paradigm to study the differences between memory processes at play in standard list recall versus in preferential choice. Using computational memory models, fit to data from 2 preregistered experiments, we find that some established memory regularities (primacy, recency, semantic clustering) emerge in preferential choice, whereas others (temporal clustering) are significantly weakened relative to standard list recall. Notably, decision-relevant features, such as item desirability, play a stronger role in guiding retrieval in choice. Our results suggest memory processes differ across preferential choice and standard memory tasks, and that choice modulates memory by differentially activating decision-relevant features such as what we like. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Predicting recall of words and lists.

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. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Closed-loop stimulation of temporal cortex rescues functional networks and improves memory.

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.