About

Professor Matthew K. Nock leads the Nock Lab, which focuses on research related to understanding suicidal thoughts and behaviors. The lab's work has been recognized in prominent media such as the New York Times and includes innovative studies using daily smartphone assessments to track how suicidal thoughts change over time. Additionally, the lab has conducted research describing the pathways through which individuals progress from suicidal ideation to engaging in suicidal behavior. Professor Nock's research has garnered significant attention, including a MacArthur "Genius" Fellowship, and he actively mentors graduate students, with potential openings for PhD admissions. His work is situated within Harvard's clinical psychology program, which now requires GRE scores for admission consideration.

Research topics

• Psychology
• Computer Science
• Political Science
• Medicine
• Psychiatry
• Applied psychology
• Clinical psychology
• Cognitive psychology
• Public relations
• Medical emergency
• Developmental psychology
• Social psychology
• Epistemology
• Cognitive science
• Psychotherapist
• Risk analysis (engineering)
• Engineering ethics
• Nursing
• Linguistics
• Business
• Engineering
• Emergency medicine

Selected publications

• A Crowdsourced Megastudy of 12 Digital Single-Session Interventions for Depression in American Adults

  2026-01-24

  articleOpen access

  Digital, self-guided, single-session interventions (SSIs) offer a structured psychological intervention within one interaction. We crowdsourced 66 diverse 10-minute SSIs for depression and selected 11 for testing in a pre-registered experiment (ClinicalTrials.gov ID: NCT06856668). American adults (N = 7,505) experiencing elevated depressive symptoms were recruited online and randomly assigned to one of the 11 crowdsourced SSIs, a previously-validated active comparison SSI, or a control without intervention content. Nearly all SSIs boosted agency and hope for improvement immediately after completion (ds ≤ 0.37). However, only two SSIs significantly reduced depression at four-week follow-up (ds = 0.14 and 0.15). Unexpectedly, some SSIs may have decreased readiness to change at four weeks (ds ≤ 0.14). The most successful SSIs provided memorable, engaging, and actionable guidance on a skill that directly addressed users’ struggles. Future work should aim to leverage SSIs’ short-term gains to promote sustained behavior change or service engagement.

  PublisherDOI

• Neural Correlates of the p Factor in Adolescence: Cognitive Control With and Without Enhanced Positive Affective Demands

  UNC Libraries · 2026-03-25

  articleOpen access
  PublisherDOI

• Proinflammatory gene expression is associated with prospective risk for adolescent suicidal thoughts and behaviors over twelve months

  Development and Psychopathology · 2025-01-08 · 2 citations

  articleOpen access

  OBJECTIVE: Recent theories have implicated inflammatory biology in the development of psychopathology and maladaptive behaviors in adolescence, including suicidal thoughts and behaviors (STB). Examining specific biological markers related to inflammation is thus warranted to better understand risk for STB in adolescents, for whom suicide is a leading cause of death. METHOD: = 1.8 years) at increased risk for STB. This study examined the prospective association between basal levels of inflammatory gene expression (average of 15 proinflammatory mRNA transcripts) and subsequent risk for suicidal ideation and suicidal behavior over a 12-month follow-up period. RESULTS: Controlling for past levels of STB, greater proinflammatory gene expression was associated with prospective risk for STB in these youth. Similar effects were observed for CD14 mRNA level, a marker of monocyte abundance within the blood sample. Sensitivity analyses controlling for other relevant covariates, including history of trauma, depressive symptoms, and STB prior to data collection, yielded similar patterns of results. CONCLUSIONS: Upregulated inflammatory signaling in the immune system is prospectively associated with STB among at-risk adolescent females, even after controlling for history of trauma, depressive symptoms, and STB prior to data collection. Additional research is needed to identify the sources of inflammatory up-regulation in adolescents (e.g., stress psychobiology, physiological development, microbial exposures) and strategies for mitigating such effects to reduce STB.

  PublisherOA PDFDOI

• Predicting suicide attempts among US Army soldiers using information available at the time of periodic health assessments

  Nature Mental Health · 2025-01-06 · 5 citations

  articleOpen access
  PublisherOA PDFDOI

• Using large language models to create lexicons for interpretable text models with high content validity: the Suicide Risk Lexicon

  2025-09-09

  preprintOpen access

  Researchers often want to measure a variety of constructs such as anxiety, discrimination, or loneliness in text data from surveys, interviews, social media, and electronic health records. Using large language models (LLMs) --while optimal for text classification-- remain infeasible for some researchers due to concerns around computational expertise, cost, privacy, and compute requirements. Therefore, some researchers prefer to use lightweight models for large datasets or interpretable models to avoid mistakes in high-stakes scenarios such as suicide risk detection. Lexicons offer simple baselines to LLMs by searching for relevant phrases --and can be used together with LLMs to guarantee capturing specific keywords in a deterministic way. However, building new lexicons is resource intensive. In this study, we found that GPT-4 turbo was able to automatically create a lexicon for 49 known risk factors for suicidal thoughts and behaviors, which we release as the Suicide Risk Lexicon. Generating a lexicon with LLMs quickly measures most constructs relevant for suicide risk detection, resulting in high content validity. This lexicon was able to accurately predict risk in crisis counseling conversations. After validating the lexicon with clinician ratings, the lexicon modestly outperformed the LIWC lexicon --which has low content validity for mental illness-- and performed similarly to some black-box deep learning models. Due to using an interpretable approach with high content validity, we discovered that active suicidal ideation and direct self-injury were stronger indicators of imminent risk than passive suicidal ideation and depressed mood in this ecological setting (i.e., naturalistic counseling conversations as the crisis is happening, as opposed to assessments or interviews about retrospective symptoms). To simplify creating new lexicons for other research domains, we introduce a Python package, construct-tracker, that works with a variety of LLMs. In sum, while we recommend using LLMs for text classification, they remain out of reach for many researchers. Our work demonstrates that LLMs --despite being black-boxes-- can counterintuitively create interpretable models by generating lexicons, when this is preferred. However, more studies are needed to test how these methods generalize to other datasets and psychological domains. Furthermore, we highlight the broader application of lexicons beyond measurement, including their use in benchmarking LLM performance.

  PublisherDOI

• Identifying therapeutic characteristics of digital social media narratives about suicide: a mixed methods investigation

  npj Mental Health Research · 2025-09-03 · 3 citations

  articleOpen accessSenior author

  Recently, we found adults who received digital bibliotherapy featuring lived-experience narratives related to suicidal thoughts and behaviors reported lower suicidal thoughts, mediated by increased social connectedness and optimism. This study aimed to identify characteristics of narratives associated with decreased suicidal thinking and increased social connectedness and optimism. 1532 users of a social media platform responded to surveys before and after reading narratives. Mixed-methods content analysis and clustered multidimensional scaling tested whether clusters that shared narrative characteristics were related to suicidal thoughts, social connectedness and optimism. We identified three narrative clusters: Cluster 1: "Encouraging Readers to Live," Cluster 2: "Sharing Personal Stories," and Cluster 3: "Detailed Accounts." Clusters 2 and 3 were associated with greatest reduction in suicidal thoughts, Cluster 2 with the greatest increase in social connectedness, and Cluster 3 with the greatest increase in optimism. Results suggest the optimal narratives for reducing suicidal thoughts are personal, detailed accounts.

  PublisherOA PDFDOI

• Emotion Regulation in Daily Life among Adults with Suicidal Thoughts

  2025-05-28

  preprintOpen accessSenior author

  Emotion regulation difficulties are implicated as a risk factor for suicidal thoughts, yet little is known about how adults with suicidal thoughts regulate emotions in daily life or which deficits are specific to suicidality versus shared across psychopathology. In two Ecological Momentary Assessment studies (N1=396; N2=195, recruited online), we compared adults with current suicidal thoughts to those with past or no suicidal history (Study 1) and to psychiatric and healthy controls (Study 2). Participants with current (vs. past) suicidal thoughts reported greater substance-use and self-injury to regulate emotions (Study 1). Compared to psychiatric controls, participants with suicidal thoughts reported higher regulatory effort and substance-use, while compared to healthy controls, they additionally reported greater distraction, rumination, and lower regulatory success (Study 2). Self-injury and substance-use uniquely predicted momentary suicidal thinking (Study 2). Findings highlight substance-use, self-injury, and heightened regulatory effort as potentially distinct emotion regulation processes associated with suicidal thoughts.

  PublisherDOI

• Mental Health and Clinical Psychological Science in the Time of COVID-19: Challenges, Opportunities, and a Call to Action

  UNC Libraries · 2025-05-13

  articleOpen access1st authorCorresponding

  COVID-19 presents significant social, economic, and medical challenges. Because COVID-19 has already begun to precipitate huge increases in mental health problems, clinical psychological science must assert a leadership role in guiding a national response to this secondary crisis. In this article, COVID-19 is conceptualized as a unique, compounding, multidimensional stressor that will create a vast need for intervention and necessitate new paradigms for mental health service delivery and training. Urgent challenge areas across developmental periods are discussed, followed by a review of psychological symptoms that likely will increase in prevalence and require innovative solutions in both science and practice. Implications for new research directions, clinical approaches, and policy issues are discussed to highlight the opportunities for clinical psychological science to emerge as an updated, contemporary field capable of addressing the burden of mental illness and distress in the wake of COVID-19 and beyond.

  PublisherDOI

• Text Psychometrics: Assessing Psychological Constructs in Text Using Natural Language Processing

  2025-07-29

  preprintOpen access

  Natural language processing (NLP) has advanced tremendously thanks to innovations in large language models (LLMs) and generative AI. However, when these technologies are used to assess psychological constructs in text, they are generally not evaluated for the types of validity, reliability, and standardization typically expected from questionnaires that use rating scales and diagnostic instruments. This study seeks to bridge this gap by demonstrating how to evaluate psychometric properties of text-based models, which we call Text Psychometrics.Here we provide an overview of different NLP methods, compare how they can address key constraints in psychological research (e.g., explainability and data privacy), and provide a framework for evaluating these models on a broader range of psychometric properties than is typically evaluated. We then carry out multiple studies to validate NLP models for psychological assessment from text. In study 1, we evaluate concurrent criterion validity by classifying thousands of text-based crisis counseling conversations and Reddit posts into different types of mental health issues. We use a wide array of methods, from traditional lexicons to open-source LLMs. We introduce a novel way to evaluate NLP models for content validity, the degree to which a test captures most expressions of a construct it should capture. Importantly, we find certain high-performing models fail to identify many obvious expressions of a construct; for example, one model that achieved a mean ROC AUC of 0.82 failed to detect 49% of obvious expressions, on average. It is likely other NLP models used in the field for psychological assessment could suffer from the same lack of content validity, but it is rarely evaluated. In study 2, we evaluate prospective criterion validity and estimate how 49 known suicide risk factors are associated with imminent risk in crisis counseling conversations. We find that mentioning lethal means for suicide, the police, depressed mood, and anxiety in text as well as the texter writing more than the counselor characterize the conversations that end up needing an emergency service intervention for imminent suicide risk. This work extends findings from retrospective surveys by analyzing text data in an ecological, high-risk setting as it occurs in real time.In sum, we provide a blueprint for assessing and validating constructs in text through a variety of traditional and state-of-the-art methods that can be chosen depending on the particular constraints researchers may have. While most NLP studies in psychology rely on a single type of metric to validate their models, here we demonstrate why we should validate models more broadly when using NLP and LLMs in psychological research.

  PublisherOA PDFDOI

• A Crowdsourced Megastudy of 12 Digital Single-Session Interventions for Depression in American Adults

  Research Square · 2025-08-25

  preprintOpen access
  PublisherOA PDFDOI

Recent grants

• NIH Grant R01MH077883

  NIH · $1.4M · 2011

• Multilevel Biomarkers for Suicidal Behavior: From Interpersonal Stress to Gene Expression in a Longitudinal Study of Adolescent Girls

  NIH · $4.6M · 2015–2021

• Intensive longitudinal study of suicidal behaviors and related health outcomes

  NIH · $4.3M · 2022–2024

• NIH Grant F31MH012923

  NIH · $58k · 2002

• NIH Grant R03MH076047

  NIH · $82k · 2007

Frequent coauthors

• Ronald C. Kessler

  308 shared

• Murray B. Stein

  VA San Diego Healthcare System

  219 shared

• Alexander J. Millner

  Harvard University

  214 shared

• Robert J. Ursano

  Uniformed Services University of the Health Sciences

  210 shared

• Nancy A. Sampson

  Harvard University

  178 shared

• Evan M. Kleiman

  Rutgers, The State University of New Jersey

  142 shared

• James A. Naifeh

  Uniformed Services University of the Health Sciences

  125 shared

• Alan M. Zaslavsky

  Harvard University

  117 shared

Education

• Ph.D., Psychology

  Harvard University

  2002

• M.A., Psychology

  Harvard University

  1999

• B.A., Psychology

  Harvard University

  1996

Awards & honors

• Early career awards from the American Psychological Associat…
• Early career awards from the Association for Behavioral and…
• Early career awards from the American Association of Suicido…
• MacArthur “Genius” Award (2011)
• Roslyn Abramson Teaching Award