About

Erik Nook is an Assistant Professor in the Department of Psychology at Princeton University. He holds a Ph.D. from Harvard University, where he was advised by Steven Mesquiti. His research focuses on understanding how emotions function, particularly examining the interaction between language and emotion. Nook studies how feelings such as joy, fear, sadness, excitement, and pride influence human actions, relationships, and overall life meaning, as well as how they can become overwhelming and contribute to psychological disorders like anxiety and depression. His approach integrates three primary perspectives: a developmental approach to explore how children and adolescents learn to identify their feelings; a neuroscientific approach to investigate how brain systems enable the representation and regulation of emotions; and a translational approach to connect emotions and emotional language with psychopathology and its treatment. As a fully trained clinical psychologist, Nook aims to produce knowledge about human emotion that can enhance psychological health and well-being. His contributions have been recognized with awards such as the Society for Research in Psychopathology's Early Career Award and the APS Rising Star designation.

Research topics

• Psychology
• Clinical psychology
• Psychiatry
• Developmental psychology
• Computer Science
• Cognitive psychology
• Linguistics
• Applied psychology
• Psychotherapist
• Medicine
• Social psychology

Selected publications

• Tracking Shifts in the Latent Meaning of “I” and Its Connection to Mental Health Outcomes in a Large Therapy Dataset

  Open MIND · 2026-01-01

  otherOpen accessSenior author

  This project examines how the semantic meaning of first-person singular pronouns (e.g., “I,” “me,” “my”) changes over the course of psychotherapy and how these shifts relate to mental health outcomes. Using natural language processing and contextual word embeddings applied to a large corpus of psychotherapy transcripts, we track how clients’ self-referential language moves along two theoretically meaningful dimensions—good vs. bad and able vs. unable—across treatment. We test whether changes in these semantic representations predict reductions in depression, anxiety, and internalizing symptoms at both within-person and between-person levels, above and beyond known linguistic confounds (e.g., pronoun frequency, sentiment, word count). Analyses are preregistered on a holdout validation sample from a large digital mental health platform, with the goal of clarifying how evolving self-representation in language reflects psychological change during therapy.

  PublisherDOI

• Low Emotional Awareness as a Transdiagnostic Mechanism Underlying Psychopathology in Adolescence

  UNC Libraries · 2026-04-03

  articleOpen access

  The ability to identify and label one's emotions is associated with effective emotion regulation, rendering emotional awareness important for mental health. We evaluated how emotional awareness was related to psychopathology and whether low emotional awareness was a transdiagnostic mechanism explaining the increase in psychopathology during the transition to adolescence and as a function of childhood trauma-specifically violence exposure. In Study 1, children and adolescents (N=120, aged 7-19 years) reported on emotional awareness and psychopathology. Emotional awareness was negatively associated with psychopathology (p-factor) and worsened across age in females but not males. In Study 2 (N=262, aged 8-16 years), we replicated these findings and demonstrated longitudinally that low emotional awareness mediated increases in p-factor as a function of age in females and violence exposure. These findings indicate that low emotional awareness may be a transdiagnostic mechanism linking adolescent development, sex, and trauma with the emergence of psychopathology.

  PublisherDOI

• Predicting Psychological and Subjective Well-being through Language-based Assessment

  PsyArXiv (OSF Preprints) · 2026-01-08

  preprintOpen accessSenior author

  Well-being is often defined in terms of a person’s comfort, happiness, functioning and flourishing. Scholars distinguish subjective well-being (i.e., perceiving one’s life as pleasant) from psychological well-being (i.e., perceiving one’s life as meaningful). Advances in natural language processing have yielded automated assessments of psychological states and traits from language alone, including subjective well-being. However, the strength of these tools for assessing psychological well-being remains unstudied. Across three studies (one preregistered), we examined the strength of language-based assessments of self-reported subjective and psychological well-being components. Participants gave verbal or written responses to queries regarding their satisfaction with life and autonomy, along with questionnaire measures of subjective and psychological well-being. We then tested the strength of contextual word embeddings generated from AI-based transformers applied to verbal responses in predicting self-reported satisfaction with life and psychological well-being. Predictions generated from word embeddings of open-ended assessments correlated significantly with questionnaire measures of corresponding well-being constructs (rs = .16 < r < .63) and they also generalized across well-being components (rs = .15 < r < .50). However, the strength of these relations was lower than previous studies (rs = .72 < r < .85), and sense of autonomy was consistently less predictable than satisfaction with life. These findings demonstrate that although linguistic measures can significantly correlate with one’s sense of autonomy, it appears to be more challenging to assess than other forms of well-being.

  Publisher

• Tracking Treatment Outcomes Using Sentiment Analysis

  OSF Preprints (OSF Preprints) · 2026-02-19

  otherOpen accessSenior author

  The present study examines how different measures of client and therapist sentiment (i.e., a measure of a text’s emotional tone) relate to treatment outcomes using a large dataset of naturalistic psychotherapeutic exchanges.

  Publisher

• Measuring Experiential Avoidance

  OSF Preprints (OSF Preprints) · 2026-05-08

  otherOpen access

  This project contains code, coding manual, and aggregated data outputs for Steinbrenner et al. (2026), 'Avoidance Is Reflected in Language: A Theory-Guided Framework for Measuring Experiential Avoidance Using Large Language Models'

  Publisher

• Large Language Models in Mental Health Research and Treatment

  Annual Review of Biomedical Data Science · 2026-05-18

  articleSenior author

  Mental health challenges add immensely to the global burden of disease, yet traditional approaches to psychological assessment and care remain resource intensive and often inaccessible. There is widespread interest in testing whether advances in artificial intelligence (AI), particularly large language models (LLMs), could address these constraints. This review focuses on LLMs, given the field's explosive interest in testing whether their ability to generate context-sensitive language representations can aid large-scale assessment and intervention. We synthesize recent applications of LLMs, including language-based assessment of psychopathology, digital phenotyping, electronic health record analysis, and early integrations into psychotherapy. However, we highlight deep challenges of AI that loom large in the highly sensitive space of mental health treatment, including clear risks of bias, hallucinations, inappropriate (or even dangerous) therapeutic recommendations, and limited regulatory oversight. We conclude with future directions that are critical for the safe and equitable use of LLMs in mental health.

  PublisherDOI

• Empathy Regulation in Clinical Science: Regulating the Therapeutic Emotional Circuit

  Clinical Psychological Science · 2026-04-11

  article1st authorCorresponding

  Is a more empathic therapist more effective? Classic models in clinical science rightfully describe empathy as an important therapeutic tool, but emerging evidence indicates that it can interfere with therapeutic goals in some settings. Here, we provide a contemporary framework that addresses this tension. We propose a model in which empathy and emotion regulation combine to create a “therapeutic emotional circuit” in which emotions flow from therapist to client and back to the therapist again via empathy. Critically, therapists can use empathy regulation to modulate this emotional flow to achieve specific goals for both their own and their clients’ emotional experiences. We then illustrate how optimal empathy regulation diverges across two empirically supported interventions: To best support clients, exposure therapy requires down-regulating affect sharing, whereas motivational interviewing requires up-regulating this empathic process. This model challenges classic intuition, revealing new directions for clinical research, training, and practice.

  PublisherDOI

• Expansive Thought Dynamics Support Emotional Recovery

  PsyArXiv (OSF Preprints) · 2026-01-22

  preprintOpen accessSenior author

  People spend up to half of their waking life spontaneously wandering alone through a vast landscape of thought, recalling past experiences and recombining them to peer into an uncertain future. These spontaneous thoughts are not merely breaks from our otherwise goal-oriented lives. Instead, they may serve important functions. Here, we focus on the function thoughts may play in people’s emotional lives. We test whether expansive thought dynamics, in which thoughts move broadly across conceptual space, support emotional change. Across two studies, we measured if stable or state-dependent expansive dynamics predict how well an individual will recover from a negative mood. Participants in Study 1 (N = 759) underwent a negative or positive mood induction via memory-recall and then verbalized their spontaneous thoughts for 10 minutes. Participants in Study 2 (N = 233) verbalized their spontaneous thoughts for 5 minutes on 21 consecutive days, with an average of 4 days preceded by a negative mood induction. To quantify the expansiveness of thought streams, we computed four complementary measures that characterize how thoughts relate to one another and unfold over time: structural expansiveness, novelty, divergence in subsequent thought similarity, and momentum away from the recalled memory. Results revealed that more expansive thought dynamics predicted greater improvements in emotion, specifically when people were in negative emotional states. Expansive thought dynamics were associated with more frequent transitions into, and greater persistence within, emotionally neutral states. Together, these findings suggest that expansiveness operates as a homeostatic mechanism, helping people return toward their emotional equilibrium.

  Publisher

• Expansive Thought Dynamics Support Emotional Recovery

  2026-01-23

  articleOpen access

  People spend up to half of their waking life spontaneously wandering alone through a vast landscape of thought, recalling past experiences and recombining them to peer into an uncertain future. These spontaneous thoughts are not merely breaks from our otherwise goal-oriented lives. Instead, they may serve important functions. Here, we focus on the function thoughts may play in people’s emotional lives. We test whether expansive thought dynamics, in which thoughts move broadly across conceptual space, support emotional change. Across two studies, we measured if stable or state-dependent expansive dynamics predict how well an individual will recover from a negative mood. Participants in Study 1 (N = 759) underwent a negative or positive mood induction via memory-recall and then verbalized their spontaneous thoughts for 10 minutes. Participants in Study 2 (N = 233) verbalized their spontaneous thoughts for 5 minutes on 21 consecutive days, with an average of 4 days preceded by a negative mood induction. To quantify the expansiveness of thought streams, we computed four complementary measures that characterize how thoughts relate to one another and unfold over time: structural expansiveness, novelty, divergence in subsequent thought similarity, and momentum away from the recalled memory. Results revealed that more expansive thought dynamics predicted greater improvements in emotion, specifically when people were in negative emotional states. Expansive thought dynamics were associated with more frequent transitions into, and greater persistence within, emotionally neutral states. Together, these findings suggest that expansiveness operates as a homeostatic mechanism, helping people return toward their emotional equilibrium.

  PublisherDOI

• Affective Abstraction Predicts Variation in Alexithymia, Depression, and Autism Spectrum Quotient

  UNC Libraries · 2026-05-02

  articleOpen access

  Affective abstraction refers to how people conceptualize affective states in terms of category-level representations that generalize across specific situations (e.g., "fear" as evoked by heights, predators, and haunted houses). Here, we develop a novel task for assessing affective abstraction and test its relations with trait alexithymia, depression, and autism spectrum quotient. In a preregistered online study, participants completed a set of tasks in which they matched a cue image with one of two probe images based on similarity of affective experience. In a discrete emotion version of the task, the cue and target probe matched on a discrete emotion category while controlling for valence. In a valence version of the task, the cue and target probe matched on valence (i.e., pleasantness or unpleasantness). We further varied the degree of abstraction such that some judgments crossed semantic categories (e.g., a house cue with animal probes). Accuracy, as indexed by the proportion of choices that accorded with norms, predicted trait measures of alexithymia, depression, and autism quotient with medium effect sizes. We conducted an integrative data analysis by including data from three other (nonpreregistered) samples (<em>N</em> = 435) and found substantial moderation by sampling population (Amazon Mechanical Turk, college students) and partial moderation by gender identity. Additional constraints on generalization include that our sample included predominantly White American adults between the ages of 23 and 64. These results provide preliminary support for the notion that affective abstraction may reflect a transdiagnostic psychological process of broad relevance to individual differences in affective processing.&nbsp;

  PublisherDOI

Frequent coauthors

• Leah H. Somerville

  Harvard University

  18 shared

• Adam C. Jaroszewski

  Boston University

  16 shared

• Katie A. McLaughlin

  Harvard University Press

  16 shared

• Ellen F. Finch

  Harvard University Press

  16 shared

• Lois W. Choi‐Kain

  McLean Hospital

  16 shared

• Stephanie F. Sasse

  Harvard University

  10 shared

• Juliet Y. Davidow

  Monash University

  9 shared

• Kevin N. Ochsner

  Columbia University

  8 shared

Labs

• Logic of Emotion LabPI

Education

• Ph.D., Psychology

  Harvard University

  2021

• B.A., Psychology

  Columbia University

  2012

Awards & honors

• Society for Research in Psychopathology's Early Career Award
• APS Rising Star