Whitney R. Ringwald

Cognitive theories are among the leading conceptualizations of posttraumatic stress disorder (PTSD). The core perspective shared by these theories is that distorted thinking (e.g., “no one can be trusted”) is involved in maintaining symptoms. However, the proximal mechanisms linking cognition and PTSD symptoms remains unclear due to two major methodological limitations: 1) lack of research on cognition-symptom dynamics at the daily level, and 2) reliance on self-reported thought distortions, which is a method ill-suited for capturing biases that people often do not have insight into or are outside of their awareness. The present study helps address these limitations in a sample of trauma-exposed patients (N=510) who completed daily video diaries and symptom ratings for 90 days (n=30,958 days). We used two natural language processing techniques to assess cognitive distortions from the diary transcripts: a lexica-based and large language model (LLM) approach. We then established the day-to-day sequencing of cognitive distortion and symptom fluctuations using dynamic structural equation models. Across the lexical and LLM models, distorted thinking did not predict worsening symptoms (bs=-.02–.01). Instead, worsening symptoms predicted more distorted thinking (bs=.03–.07). Also, symptoms and cognitive distortions tended to fluctuate together (bs=.08–.41). The current findings suggest that cognitive distortions do not precede PTSD symptoms as many theories have assumed, but may instead be a consequence or correlate of them. If replicated, our results could help refine cognitive and related models of PTSD.

Dimensional models of psychopathology describe the kinds of mental health problems people can have without explaining what maintains those problems. Explanation requires understanding mechanisms. However, this knowledge has been slow to accumulate because a framework is lacking that can organize, measure, and compare mechanisms. I propose a path forward by recasting mechanisms as dimensional traits—individual differences in how people select, perceive, react to, and shape everyday situations. A trait framework turns psychopathology mechanisms into constructs in their own right that can be taxonomized, measured, compared between people, and used to guide treatment decisions. I outline concrete steps to create a mechanism taxonomy, describe how to assess mechanisms with intensive longitudinal methods, and present current and future applications of this framework to advancing research and mental health treatment.

Closely tracking changes in depression is important for clinical research and practice. However, intensive longitudinal depression assessment relies on self-report surveys that have major limitations. Scoring depression from natural language with large language models (LLMs) could resolve these shortcomings. This study tested the validity of depression rated by LLMs from daily video diaries. We studied 108 participants selected for mental health treatment status (45% in treatment). Participants completed a clinical interview and 14-21 days of depression surveys and video diaries. Daily depression scores were estimated by LLMs from diary transcripts. LLM ratings converged with self-report depression within-person (r=.45;95% CrI[.40, .49]) and when averaged across days for an overall depression score (r=.61;95% CrI[.43,.75]). Averaged LLM ratings correlated with depression symptom severity ascertained by clinical interview (r=.53;95% CrI[.35, .68]). LLM-rated depression and self-report had similar profiles of associations with daily functioning variables (profile r=.98 within-person; r=.94 between-person). Multivariable regression results showed LLM ratings predicted daily functioning variables (partial βs=|.12|-|.28|) and interview-rated depression (partial β=.33;95% CrI[.07, .58]) over and above self-reports, suggesting LLMs capture clinically-relevant information that self-reports miss. Findings establish a strong foundation for a depression assessment approach that addresses shortcomings of self-reports while satisfying the unique demands of intensive longitudinal monitoring.

Temperament and personality traits both refer to relatively stable individual differences in thoughts, feelings, and behaviors, but they are often studied in isolation from each other. Consequently, there are vast bodies of research exploring the nature, development, and consequences of temperament and personality traits, with little integration between areas. The present study examines the overlap vs. distinctness of temperament and personality traits, with the goal of promoting greater integration. First, we review past theory and research on their connections. Then, we apply 10 criteria from Lawson and Robins’ (2021) sibling constructs framework, supplemented by recently developed methods from large language models, to understand associations at the construct and measurement levels — aiming to orient researchers away from black-or-white thinking that has divided the two fields and toward embracing the complex overlap that characterizes the relationship between these domains. Based on our analyses, we argue that temperament and personality traits show substantial overlap at the construct level, differing mainly in how they are measured. We provide recommendations to unify these unnecessarily disparate literatures, paving the way toward a genuine, integrated science of personality development across the lifespan.

Background: Accurate psychiatric assessment requires understanding a person’s unique experience within their psychosocial context. Clinical interviews have been the gold standard for assessment as the only methods capable of this complex task, but they are time and resource-intensive. Consequently, psychiatric assessment typically relies on patient report surveys that are decontextualized and narrow in scope. This comprehensiveness-scalability tradeoff is a major bottleneck in studying and treating psychopathology. We propose using large language models (LLMs) to score psychopathology from brief personal narratives as a low-burden, context-sensitive solution. Methods: Participants ( N = 108) completed brief (~1 minute), freeform audio diaries daily for 2 weeks. We used six LLMs to score wide-ranging psychopathology (Internalizing, Detachment, Disinhibition, Antagonism, Anankastia) from the diary transcripts. Leveraging an array of self-report and clinical interview measures, we tested the convergent, discriminant, concurrent, and clinical validity of LLM ratings for between-person differences and within-person fluctuations in psychopathology. Results: Supporting convergent and discriminant validity, LLM ratings correlated most strongly with corresponding self-report domains at the between (average convergent r = .42) and within-person ( r = .28) levels. LLM and self-report ratings had similar patterns of associations with external variables, except for Anankastia and Antagonism. Further, every LLM-rated domain related to psychopathology ascertained by clinical interview. Conclusions: Across multiple forms of validity, we showed that LLMs can assess most major forms of psychopathology from mere minutes of audio. These results support scoring open-ended narratives with LLMs as a scalable, portable method to translate idiographic diagnostic data into standardized psychiatric assessments.

The integration of large language models (LLMs) into clinical psychological science has outpaced our understanding of how psychopathology is represented across the diverse model landscape. We analyzed how 21 text embedding models represented 405 items from the HiTOP-SR, a non-public psychopathology measure absent from training data. Exploratory factor analysis of item-level representations at global and domain-specific levels revealed variable structural consistency across models. Globally, structural congruence frequently fell below meaningful thresholds (CC < .85), although broad two-factor solutions showed strong cross-model convergence. Domain-specific congruence varied considerably, with some domains (e.g., Anankastia) showing strong convergence and others (e.g., Antagonism) showing weak convergence. These findings demonstrate that text embedding models may lack a shared representational framework for psychopathology, yielding structures that are idiosyncratic and sensitive to model-specific features and methodological choices. These inconsistencies underscore the need for careful model selection and rigorous evaluation of LLM-based tools.

Intolerance of uncertainty (IU) is central to cognitive models of posttraumatic stress disorder (PTSD), reflecting a maladaptive process that maintains hypervigilant threat appraisals and symptom persistence. Traditional IU self-report assessments, however, are constrained to explicit, trait-like evaluations and may not capture IU’s implicit and context-dependent nature. Ecological momentary assessment (EMA) combined with large language models (LLMs) offers a scalable approach for detecting subtle linguistic markers of IU in daily life. Here, we introduce a novel language-based method for monitoring IU through naturalistic daily diaries and examine its role in perpetuating PTSD symptoms. In a 90-day EMA study of 432 trauma-exposed individuals with elevated PTSD symptoms (29,623 diary entries with 32,941 daily PTSD symptom ratings), we developed an LLM-based IU assessment, yielding the LLM-based similarity between daily transcribed speech and established IU scale language (r = .60 between LLM measure and human ratings). Using dynamic structural equation modeling (DSEM), we found that higher daily IU predicted greater next-day PTSD symptom severity (β = 0.041, 95% CI [0.024, 0.056]), even after controlling for prior-day symptoms. Conversely, higher daily PTSD symptom severity predicted greater next-day IU (β = 0.035, 95% CI [0.018, 0.054]). The strength of these bidirectional effects varied across individuals (IU→PTSD variance = 0.014, 95% CI [0.007, 0.022]; PTSD→IU variance = 0.037, 95% CI [0.030, 0.047]), reflecting substantial heterogeneity in daily IU–PTSD dynamics. Finally, individuals with stronger IU persistence (IUₜ₋₁ → IUₜ) tended to exhibit enhanced same-day IU–PTSD associations (IUₜ ↔ PCLₜ; r = 0.23, 95% CI = [0.016, 0.410]). In contrast, greater PTSD persistence (PCLₜ₋₁ → PCLₜ) covaried negatively with both lagged (IUₜ₋₁ → PCLₜ; r = -0.38, 95% CI = [-0.505, -0.246]) and contemporaneous (IUₜ ↔ PCLₜ; r = -0.18, 95% CI = [-0.327, -0.024]) IU–PTSD effects, suggesting divergent dynamic patterns relative to IU. Complementary multilevel analyses demonstrated that language-derived IU provides incremental validity, explaining variance in PTSD symptom severity beyond sentiment (NRCSent) and positive and negative affect (LIWC). These findings extend cognitive models of PTSD with IU as a dynamic mechanism influencing symptom trajectories in daily life and demonstrate an ecologically valid framework for tracking implicit cognitive processes via natural language.

Language-based assessments of health have demonstrated validity in terms of convergence with self-report questionnaire scores, but recent work has shown they lack an important psychometric property: discriminant validity, the ability of the measure to distinguish the target construct from a related one. For example, a language-based general mental health assessment may accurately assess the degree of mental health, but it may also produce scores that correlate with general physical health well beyond theoretical expectations. Here, we propose and evaluate two methods for altering the standard loss function to directly penalize off-target correlations while retaining convergent validity. Evaluated across two clinical language datasets spanning physical and mental health, the augmentations substantially reduced off-target correlations with minimal loss in convergent validity (on-target correlations). Limiting loss in convergent validity to 0.005 Pearson correlation points, the loss function using Squared Cosine Similarity Discrimination significantly improved discriminant validity of language-based mental and physical health assessments (r = 0.454 → r = 0.322; p < 0.05); and fundamental psychopathology dimensions (r = 0.442 → 0.430; p < 0.05). These findings demonstrate that enforcing discriminant validity as a training objective is effective, moving language-based assessments closer to the specificity required for differential clinical use.

Background: Accurate psychiatric assessment requires understanding a person’s unique experience within their psychosocial context. Clinical interviews have been the gold standard for assessment as the only methods capable of this complex task, but they are time and resource intensive. Consequently, psychiatric assessment typically relies on patient report surveys that are decontextualized and narrow in scope. This comprehensiveness-scalability tradeoff is a major bottleneck in studying and treating psychopathology. We propose using large language models (LLMs) to score psychopathology from brief personal narratives as a low-burden, context-sensitive solution. Methods: Participants (N=108) completed brief (~1 minute), freeform audio diaries daily for two weeks. We used six LLMs to score wide-ranging psychopathology (Internalizing, Detachment, Disinhibition, Antagonism, and Anankastia) from the diary transcripts. Leveraging an array of self-report and clinical interview measures, we tested the convergent, discriminant, concurrent, and clinical validity of LLM ratings for both between-person differences and within-person fluctuations in psychopathology.Results: Supporting convergent and discriminant validity, LLM ratings correlated most strongly with corresponding self-report domains at both the between (average convergent r = .42) and within-person (r = .28) levels. LLM and self-report ratings had similar patterns of associations with external variables, except for Anankastia and Antagonism. Further, every LLM-rated domain related to psychopathology ascertained by clinical interview.Conclusions: Across multiple forms of validity, we showed that LLMs can assess most major forms of psychopathology from mere minutes of audio. These results support scoring open-ended narratives with LLMs as a scalable, portable method to translate idiographic diagnostic data into standardized psychiatric assessments.