About

Alyson Zalta is a clinical psychologist and an Associate Professor in the Department of Psychological Science at the University of California, Irvine. Her work focuses on alleviating the mental health burden of trauma by enhancing understanding of risk and resilience factors that contribute to the development of traumatic stress. She explores novel interventions for individuals affected by trauma, aiming to improve prevention and treatment strategies. Dr. Zalta's educational background includes a B.A. from Harvard University, a Ph.D. from the University of Pennsylvania, a clinical internship at the VA Palo Alto Healthcare System, and a postdoctoral fellowship at Rush University Medical Center. She is affiliated with the Interdisciplinary Institute for Salivary Bioscience Research and is a fellow at the Center for the Neurobiology of Learning and Memory. Her research areas include trauma, posttraumatic stress disorder, transdiagnostic factors, resilience, and intervention development. She actively shares her expertise through talks and webinars on topics such as the impact of trauma on the brain and supporting loved ones after traumatic experiences.

Research topics

• Psychiatry
• Clinical psychology
• Medicine
• Psychology
• Political Science
• Computer Security
• Social psychology
• Computer Science
• History
• Gerontology
• Internal medicine

Selected publications

• Psychotherapy treatment manuals for adult populations (1950–2025): A scoping review

  Clinical Psychology Review · 2026-03-02

  articleOpen accessSenior author

  Treatment manuals are essential for the development, evaluation, dissemination, and implementation of psychotherapeutic interventions. However, no comprehensive review has examined the existing landscape of treatment manuals for adult populations. This scoping review identified 1132 book-based treatment manuals published between 1957 and 2025 using traditional and API-based search methods across Google Books, WorldCat, and PsycINFO. A majority of manuals have been published since 2000 ( n = 880; 77.7%), with over half published after 2010 ( n = 592; 52.3%). Across manuals, 98 distinct clinical targets were identified. The most frequently targeted concerns included transdiagnostic substance use (12.0%), posttraumatic stress disorder (11.7%), major depressive disorder (10.5%), and transdiagnostic anxiety symptoms (9.8%). Theoretical approaches were most commonly integrative (33.0%) or second-wave cognitive behavior therapy (CBT; 25.6%), both spanning a range of clinical targets and demonstrating the greatest growth in publication over time. There was also a notable degree of overlap in clinical targets within specific theoretical orientations (e.g., 45 s-wave CBT manuals for major depressive disorder). Adapted treatment manuals were relatively uncommon (10.0%), most frequently addressing gender ( n = 43) and rarely focusing on culture ( n = 2), sexual orientation ( n = 3), or race/ethnicity ( n = 5). Only 13.3% of treatment manuals were recognized in evidence-based treatment guidelines. Overall, treatment manuals continue to increase in number, but their proliferation varies substantially across orientations, clinical targets, and populations. Future work is needed to improve the accessibility and effectiveness of treatment resources to better inform dissemination and implementation of effective treatments. • A scoping review identified 1132 commercially available psychotherapy manuals. • Most manuals were published since 2000, with over 50% published since 2010. • Significant overlap was present across certain treatments and clinical targets. • Adapted manuals (≥50% for a specific population) are limited (10%). • Only 13.3% of manuals were identified in evidence-based treatment guidelines.

  PublisherDOI

• Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data

  JMIR Formative Research · 2026-05-19

  articleOpen accessSenior author

  Background: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on self-report measures, yet discrepancies exist between clinical diagnoses and probable PTSD derived from self-reports. Objective: This study explored whether wrist actigraphy and sleep logs could be used to accurately predict clinician-rated PTSD diagnosis and probable diagnosis of PTSD based on established self-report cutoffs (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5] ≥31 and ≥38) among trauma-exposed service members and veterans. We also explored which features were most strongly predictive of each outcome and whether models were able to predict PTSD diagnosis even when accounting for other mental health disorders. Methods: Wrist actigraphy data and daily sleep logs were collected over 1 week from trauma-exposed male service members and veterans (N=36; mean age 41, SD 5.3 y). Candidate features were identified using univariate feature selection. Extreme gradient boosting models were trained using leave-one-subject-out cross-validation to predict the diagnosis of PTSD and probable diagnosis of PTSD based on 2 self-report cutoffs (PCL-5≥31 and ≥38). Performance metrics were then calculated at the person level. Linear regression was used to assess the discriminant validity of model-predicted scores and each PTSD outcome specifically, relative to other mental health diagnoses. Results: Machine learning models predicting PTSD diagnosis and probable PTSD based on the PCL-5≥31 threshold demonstrated satisfactory performance in this sample. The diagnosis model achieved an area under the curve (AUC) of 0.83 (95% CI 0.61-1.00), with high accuracy (88%) and specificity (96%) and moderate sensitivity (63%). The PCL-5≥31 model yielded comparable performance (AUC=0.84, 95% CI 0.71-0.98) with balanced sensitivity (73%) and specificity (82%). For both models, a combination of subjective and objective features was the most impactful. These models were able to predict PTSD even when accounting for non-PTSD mental health diagnoses, as model-predicted scores were significantly associated with 2 outcomes: clinician-rated PTSD (B=0.19; P=.002) and probable PTSD based on a PCL-5≥31 cutoff (B=0.24; P=.003). In contrast, the model predicting probable PTSD based on the PCL-5≥38 threshold performed poorly (AUC=0.47, 95% CI 0.24-0.69), with a nonsignificant relationship between model-predicted scores and the outcome (B<0.01; P=.89). Conclusions: Both subjective and objective rest-activity features may improve the prediction of PTSD. Further research is needed to validate these findings and explore the use of integrating wearable sensor data and subjective information to support PTSD assessment.

  PublisherDOI

• Understanding facilitators and barriers to conducting individual participant data meta-analyses

  Open MIND · 2026-01-01

  articleOpen access1st authorCorresponding

  The goal of the current project was to understand the experiences of psychological scientists when conducting independent participant data (IPD) meta-analyses.

  PublisherDOI

• Associations Between Childhood Abuse Severity and Fear Learning Processes in Adulthood: Assessing the Role of Vagal Signaling

  Biopsychosocial Science and Medicine · 2025-07-22 · 2 citations

  articleSenior author

  OBJECTIVE: Childhood abuse is known to be a vulnerability factor for psychopathology in adulthood, which is posited to occur, at least in part, through influencing fear learning processes. This study tested a model by which the severity of childhood abuse had indirect effects on fear learning processes via vagal signaling as indexed by heart rate variability (HRV). METHODS: A sample of N =123 individuals assigned female at birth ( Mage =22.99; 35.8% Hispanic; 45.5% Asian, 27.6% White, 7.3% Other) completed a single study visit during which they completed a fear learning task where physiological measurements were collected. Exposure to childhood abuse was assessed using the abuse subscales of the Childhood Trauma Questionnaire. RESULTS: Results found that higher severity of childhood abuse was significantly associated with poorer fear/safety discrimination (β = -0.23 , p =.01), and lower resting HRV was significantly associated with higher initial startle during fear conditioning (β=-0.17 , p =.047). Notably, a probe of the relationship between the abuse subscales (physical, sexual, and emotional) and fear/safety discrimination found only sexual abuse was significantly associated with poorer fear/safety discrimination (β = -0.21 , p =.022). CONCLUSIONS: Findings suggest that fear/safety discrimination impairments may be an important physiological marker for survivors of childhood abuse, and that sexual abuse may be more strongly associated with impairments in fear/safety discrimination than other early abuse exposures. A greater understanding of the underlying factors contributing to psychopathology risk in this population is needed.

  PublisherDOI

• Examining photoperiod, chronotype, and adherence as predictors of morning light treatment effects on depression symptoms: A study of five clinical trials

  Journal of Affective Disorders · 2025-09-11 · 4 citations

  article1st authorCorresponding
  PublisherDOI

• A Daily Diary Study of How Affective States Are Associated with and Predict Suicidal Ideation in Adults Seeking Intensive Outpatient Treatment

  Archives of Suicide Research · 2025-10-10

  articleSenior author

  OBJECTIVE: Negative affective states are known risk factors for suicidal ideation (SI). However, most research to date has used cross-sectional or longitudinal designs with long follow-up periods to understand these relationships. Thus, the current study aimed to understand how specific negative affective states may act as acute risk factors for same day SI and predict next day SI. METHODS: = 29.5; 67% female; 61% White) seeking treatment at an 8-week dialectical behavioral therapy (DBT) intensive outpatient program were analyzed. Sadness, anger, anxiety, guilt, and shame were independently evaluated to understand their association with same day SI intensity and examine how they predicted next day SI intensity. RESULTS: Multilevel regression revealed at the within person level that increases in all five affective states was associated with same day SI. However, only increased sadness and guilt, and decreased anxiety, predicted next day SI, covarying for same day SI. CONCLUSION: Sadness and guilt may be salient acute risk factors for next day SI. Clinicians who implement treatments that use daily diary cards, such as DBT, may want to attend to these specific affective states when monitoring client diary cards and during suicide risk assessment.

  PublisherDOI

• Guilt, Not Shame, Mediates the Longitudinal Relationship Between Moral Distress and Suicidal Ideation Among Frontline Nurses During COVID‐19

  Journal of Clinical Psychology · 2025-10-21

  articleOpen accessSenior authorCorresponding

  OBJECTIVE: Nurses frequently experienced moral distress during the COVID-19 pandemic. Moral distress can lead to negative mental health outcomes, such as depression and suicidal ideation, but the mechanisms underlying these relationships are not well understood. The current study examined whether guilt and shame resulting from morally distressing events mediate the relationships between moral distress and depression symptoms/suicidal ideation during the COVID-19 pandemic. METHODS: Registered nurses (N = 96) who directly cared for COVID-19 patients completed self-report assessments at two time points over 3 months between May and November 2021. RESULTS: Time 1 guilt (OR = 1.39, p = 0.03) mediated the relationship between Time 1 moral distress and Time 2 suicidal ideation, controlling for Time 1 suicidal ideation. By contrast, Time 1 shame was not a significant mediator (OR = 0.84, p = 0.40). Neither guilt (B = 0.03, p = 0.75) nor shame (B = -0.05, p = 0.59) at Time 1 mediated the relationship between Time 1 moral distress and Time 2 depression symptoms, controlling for Time 1 depression symptoms. However, there was a significant direct effect of Time 1 moral distress on Time 2 depression symptoms (B = 0.20, p = 0.03) in this model. CONCLUSIONS: Our findings suggest the importance of educating the nursing workforce on the psychological consequences of workplace moral distress. Intervening on feelings of guilt may reduce suicidal ideation in nurses endorsing moral distress.

  PublisherOA PDFDOI

• Investigating Associations Between Neighborhood Characteristics and Fear Learning Processes in Female Survivors of Childhood Abuse

  Psychophysiology · 2025-12-01

  articleSenior author

  Survivors of childhood abuse are at greater risk for a wide range of health disorders in adulthood, which is posited to occur in part through alterations to threat-related processes such as fear learning. Neighborhood characteristics such as area disadvantage and exposure to neighborhood crime are associated with threat processing in trauma-exposed individuals; however, their relationship with fear learning has not been studied to date. This study assessed relationships between three measures of neighborhood safety (neighborhood disadvantage, crime exposure, and self-reported neighborhood safety in childhood) with three established markers of fear learning (fear/safety discrimination, startle habituation, and fear extinction). A sample of N = 92 individuals assigned female at birth with varying levels of exposure to childhood abuse completed an established fear-potentiated startle task and reported their lifetime trauma history, as well as a brief measure of perceived neighborhood safety. The Trauma History Questionnaire (THQ) was used to identify the year and zip code of residence of their earliest abuse exposure; this was then used to identify census-derived indices for neighborhood disadvantage and neighborhood crime. Results showed that higher levels of neighborhood disadvantage were significantly associated with poorer fear/safety discrimination in adulthood (ß = -0.39, p = 0.03), and that this relationship remained significant after adjusting for the severity of childhood abuse (ß = -0.28, p = 0.01). Additionally, greater neighborhood disadvantage was associated with slightly blunted initial startle values during habituation (ß = -0.01, p = 0.03). No significant relationships were found between other neighborhood variables and markers of fear learning. These findings demonstrate the need for greater research into how neighborhood characteristics may influence recovery from traumatic experiences, particularly in terms of their influence on fear learning and memory processes.

  PublisherDOI

• Consequences of encountering potentially morally injurious events among child protective workers.

  Traumatology An International Journal · 2025-04-24

  articleSenior author
  PublisherDOI

• Comparing Generative Artificial Intelligence and Mental Health Professionals for Clinical Decision-Making With Trauma-Exposed Populations: Vignette-Based Experimental Study

  JMIR Mental Health · 2025-09-10 · 4 citations

  articleOpen accessSenior author

  Background: Trauma exposure is highly prevalent and associated with various health issues. However, health care professionals can exhibit trauma-related diagnostic overshadowing bias, leading to misdiagnosis and inadequate treatment of trauma-exposed populations. Generative artificial intelligence (GAI) models are increasingly used in health care contexts. No research has examined whether GAI demonstrates this bias in decision-making and how rates of this bias may compare to mental health professionals (MHPs). Objective: This study aimed to assess trauma-related diagnostic overshadowing among frontier GAI models and compare evidence of trauma-related diagnostic overshadowing between frontier GAI models and MHPs. Methods: MHPs (N=232; mean [SD] age 43.7 [15.95] years) completed an experimental paradigm consisting of 2 vignettes describing adults presenting with obsessive-compulsive symptoms or substance abuse symptoms. One vignette included a trauma exposure history (ie, sexual trauma or physical trauma), and one vignette did not include a trauma exposure history. Participants answered questions about their preferences for diagnosis and treatment options for clients within the vignettes. GAI models (eg, Gemini 1.5 Flash, ChatGPT-4o mini, Claude Sonnet, and Meta Llama 3) completed the same experimental paradigm, with each block being reviewed by each GAI model 20 times. Mann-Whitney U tests and chi-square analyses were used to assess diagnostic and treatment decision-making across vignette factors and respondents. Results: GAI models, similar to MHPs, demonstrated some evidence of trauma-related diagnostic overshadowing bias, particularly in Likert-based ratings of posttraumatic stress disorder diagnosis and treatment when sexual trauma was present (P<.001). However, GAI models generally exhibited significantly less bias than MHPs across both Likert and forced-choice clinical decision tasks. Compared to MHPs, GAI models assigned higher ratings for the target diagnosis and treatment in obsessive-compulsive disorder vignettes (rb=0.43-0.63; P<.001) and for the target treatment in substance use disorder vignettes (rb=0.57; P<.001) when trauma was present. In forced-choice tasks, GAI models were significantly more accurate than MHPs in selecting the correct diagnosis and treatment for obsessive-compulsive disorder vignettes (χ²1=48.84-61.07; P<.001) and for substance use disorder vignettes involving sexual trauma (χ²1=15.17-101.61; P<.001). Conclusions: GAI models demonstrate some evidence of trauma-related diagnostic overshadowing bias, yet the degree of bias varied by task and model. Moreover, GAI models generally demonstrated less bias than MHPs in this experimental paradigm. These findings highlight the importance of understanding GAI biases in mental health care. More research into bias reduction strategies and responsible implementation of GAI models in mental health care is needed.

  PublisherDOI

Recent grants

• Using CBT to Probe Psychobiobehavioral Resilience to Post-trauma Psychopathology

  NIH · $769k · 2014–2021

Frequent coauthors

• Mark H. Pollack

  173 shared

• Helen J. Burgess

  University of Michigan–Ann Arbor

  115 shared

• Zerbrina Valdespino‐Hayden

  VA North Texas Health Care System

  111 shared

• Sheila M. Dowd

  Rush University Medical Center

  70 shared

• Elizabeth E Adkins

  University of California, Irvine

  64 shared

• Naomi M. Simon

  51 shared

• Israel Liberzon

  Mitchell Institute

  41 shared

• Charles F. Reynolds

  40 shared

Education

• B.A.

  Harvard University

• Ph.D.

  University of Pennsylvania