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Dr. Sarah Chen
Stanford · NLP & interpretability
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Fairness + NLP projects overlap with her recent work.
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Ask how her lab is extending interpretability methods into fairness audits for real-world AI systems.
Ada Aka
· Assistant Professor of Marketing at the Graduate School of BusinessVerified
Journal of Experimental Psychology Learning Memory and Cognition · 2024-07-18 · 9 citations
articleOpen access
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).
Oxford University Press eBooks · 2024-07-18 · 5 citations
book-chapterSenior author
Abstract The search for laws—invariances across conditions and subjects—is essential to the project of understanding memory. Despite the complexity of memory and its diverse manifestations in people’s daily lives, certain mnemonic effects appear to hold across a wide range of conditions. This chapter discusses the effects of recency, contiguity, similarity, primacy, and repetition as potential laws of memory, evaluating their explanatory scope and discussing their theoretical significance. The chapter shows that apparent violations of these laws occur when different effects come into conflict, as in the situation of opposing physical forces. The chapter notes that the search for law-like phenomena is guiding the development and refinement of integrative memory theories.
Journal of Experimental Psychology Learning Memory and Cognition · 2024-04-04 · 2 citations
articleOpen access
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) 2025 APA, all rights reserved).
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) 2025 APA, all rights reserved).
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: it's 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.
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.
Current Directions in Psychological Science · 2022 · 32 citations
Senior authorCorresponding
Artificial Intelligence
Computer Science
Psychology
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.
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 considerably 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.
