About

Pearl Chiu, Ph.D., is a professor at the Fralin Biomedical Research Institute at VTC, Virginia Tech, where she also holds the Patricia Caldwell Faculty Fellow position. Her research focuses on the neuroscience underlying human motivation and social decision-making, particularly how these processes are affected by mental health disorders such as depression, addiction, post-traumatic stress disorder, and autism. Dr. Chiu's laboratory employs a multidisciplinary approach, combining behavioral assessments, clinical interviews, self-report measures, computational modeling, and functional neuroimaging to study decision-making and its alterations in mental illness. Her work aims to develop biologically-informed interventions to help individuals overcome decision-making impairments caused by disease. She has contributed to the emerging field of Computational Psychiatry, exploring how tools from computer science and mathematics can enhance understanding of how the brain learns and processes social signals. Dr. Chiu's research has significant implications for understanding and treating mental health conditions, and she is recognized for her leadership in integrating clinical psychology and neuroscience.

Research topics

• Psychology
• Computer Security
• Clinical psychology
• Chemistry
• Social psychology
• Medicine
• Psychiatry
• Internal medicine
• Developmental psychology
• Neuroscience

Selected publications

• Dopamine dynamics in human anterior cingulate cortex during Pavlovian-instrumental conflict

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-06

  articleOpen access

  Dopamine is believed to modulate not only instrumental learning about the link between states, actions, and outcomes but also reflexive behaviours, such as a Pavlovian bias to approach in rewarding states and freeze in aversive ones. We studied these dual roles in the human brain, by combining intracranial dopamine recordings from the anterior cingulate cortex (ACC) - a region implicated in behavioural and cognitive control - with a motivational Go/NoGo task involving conflict between instrumental and Pavlovian action selection. We found evidence that dopamine in the ACC is involved in evaluating whether Pavlovian responding should guide behaviour. This computational motif was observed across multiple task events, including in response to rewards and punishments, and in analyses based on a reinforcement learning model. Our results indicate that dopamine supports learning at the more abstract level of behavioural policies in addition to the more concrete levels of states and actions.

  PublisherOA PDFDOI

• 451. Neural Substrates of Trust and Social Responsiveness in Veterans With Posttraumatic Stress Symptoms

  Biological Psychiatry · 2025-04-09

  articleSenior author
  PublisherDOI

• Emotional words evoke region- and valence-specific patterns of concurrent neuromodulator release in human thalamus and cortex

  Cell Reports · 2025-01-01 · 18 citations

  articleOpen access

  Words represent a uniquely human information channel-humans use words to express thoughts and feelings and to assign emotional valence to experience. Work from model organisms suggests that valence assignments are carried out in part by the neuromodulators dopamine, serotonin, and norepinephrine. Here, we ask whether valence signaling by these neuromodulators extends to word semantics in humans by measuring sub-second neuromodulator dynamics in the thalamus (N = 13) and anterior cingulate cortex (N = 6) of individuals evaluating positive, negative, and neutrally valenced words. Our combined results suggest that valenced words modulate neuromodulator release in both the thalamus and cortex, but with region- and valence-specific response patterns, as well as hemispheric dependence for dopamine release in the anterior cingulate. Overall, these experiments provide evidence that neuromodulator-dependent valence signaling extends to word semantics in humans, but not in a simple one-valence-per-transmitter fashion.

  PublisherDOI

• Can Interpersonal Trust Predict and Account for Symptom Change During Group Cognitive Processing Therapy for PTSD? An Investigation of the Iterated Trust Game

  Journal of Clinical Psychology · 2025-07-22

  articleOpen accessSenior author

  OBJECTIVE: Group cognitive processing therapy (GCPT) is frequently utilized to treat PTSD within the VA healthcare system, but its mechanisms are not well understood. Interpersonal trust could be an important change process in GCPT given its relevance to group-based therapy and its role in CPT, but self-report measures are inadequate for capturing the dynamic interplay that defines interpersonal trust. Here, we examined the degree to which interpersonal could predict and account for PTSD symptom change in GCPT using the iterated trust game (ITG)-a behavioral task used to approximate real-world trust behavior. METHODS: Participants were Veterans with PTSD who participated in an effectiveness trial comparing a 12-week course of GCPT (n = 37) to a treatment-as-usual (TAU) waitlist condition (n = 23) of equivalent length. Both groups completed the ITG and measures of PTSD before and after treatment as well as a pencil-paper measure of interpersonal trust before treatment. Participants in GCPT completed measures of PTSD severity, group relationship quality, and therapist relationship quality at each treatment session. RESULTS: Pre-post changes in ITG-measured trust behavior did not differ between GCPT and TAU (p = 0.075). However, improvements in ITG scores partially accounted for decreased PTSD symptoms in GCPT, as demonstrated by a more modest change in PTSD symptoms when ITG was in, b = -5.95, p = 0.032, versus not in the model, b = -9.05, p = 0.001. Additionally, higher ITG scores, but not self-reported trust, predicted steeper reductions in PTSD symptoms, b = -0.50, p = 0.042, and improvements in group relationship quality, b = 0.28, p = 0.037, across GCPT sessions. CONCLUSIONS: Interpersonal trust improvement may predict and account for symptom change in GCPT. Targeting interpersonal trust during GCPT could render the treatment more effective.

  PublisherOA PDFDOI

• 202. Guided Reward Learning Decreases Depression Symptoms Over Repeated Sessions

  Biological Psychiatry · 2025-04-09

  articleSenior author
  PublisherDOI

• Reinforcement learning processes as forecasters of depression remission

  Journal of Affective Disorders · 2024-09-11

  articleOpen accessSenior authorCorresponding

  BACKGROUND: Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness. METHODS: We applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit. RESULTS: Both nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status. LIMITATIONS: Our sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling). CONCLUSIONS: Results support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.

  PublisherDOI

• Emotional Words Evoke Region and Valence-Specific Patterns of Concurrent Neuromodulator Release in Human Thalamus and Cortex

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Reinforcement-Learning-Informed Queries Guide Behavioral Change

  Clinical Psychological Science · 2024-01-24 · 2 citations

  articleOpen accessSenior author

  Algorithmically defined aspects of reinforcement learning correlate with psychopathology symptoms and change with symptom improvement following cognitive-behavioral therapy (CBT). Separate work in nonclinical samples has shown that varying the structure and statistics of task environments can change learning. Here, we combine these literatures, drawing on CBT-based guided restructuring of thought processes and computationally defined mechanistic targets identified by reinforcement-learning models in depression, to test whether and how verbal queries affect learning processes. Using a parallel-arm design, we tested 1,299 online participants completing a probabilistic reward-learning task while receiving repeated queries about the task environment (11 learning-query arms and one active control arm). Querying participants about reinforcement-learning-related task components altered computational-model-defined learning parameters in directions specific to the target of the query. These effects on learning parameters were consistent across depression-symptom severity, suggesting new learning-based strategies and therapeutic targets for evoking symptom change in mood psychopathology.

  PublisherOA PDFDOI

• Evidence for preference consistency across risky, ambiguous, and vague gambles

  OSF Preprints (OSF Preprints) · 2024-03-08

  otherOpen access

  Humans have variable preferences over the uncertainty of potential outcomes during decision-making. To date, most uncertainty research has focused on how individuals form subjective values of risky and ambiguous choices where uncertainty results from known or unknown outcome variability, rather than exploring situations where outcome values are uncertain. Here we examine ‘vagueness,’ defined as a specific form of uncertainty in which a player is informed only that the outcome of a decision lies somewhere within a known range of values. We use a model-based approach and an experimental gambling task to examine whether individuals evaluate uncertainty in outcome values differently from uncertainty in the probability of the outcomes. Our results show that subjective valuation of vagueness, risk, and ambiguity depend upon individuals’ distinct preferences for the first and second moments of gamble outcomes, indicating that individuals have preferences for uncertainty in mean outcome values and outcome variability, respectively, that guide decision-making across types of uncertain situations.

  Publisher

• Social conformity is a heuristic when individual risky decision-making is disrupted

  PLoS Computational Biology · 2024-12-02 · 6 citations

  articleOpen accessSenior authorCorresponding

  When making risky choices in social contexts, humans typically combine social information with individual preferences about the options at stake. It remains unknown how such decisions are made when these preferences are inaccessible or disrupted, as might be the case for individuals confronting novel options or experiencing cognitive impairment. Thus, we examined participants with lesions in insular or dorsal anterior cingulate cortex, key regions implicated in risky decision-making, as they played a gambling task where choices were made both alone and after observing others' choices. Participants in both lesion groups showed disrupted use of standard utility-based computations about risky options. For socially situated decisions, these participants showed increased conformity with the choices of others, independent from social utility-based computations. These findings suggest that in social contexts, following others' choices may be a heuristic for decision-making when utility-based risk processing is disrupted.

  PublisherDOI

Recent grants

• NIH Grant R01MH087692

  NIH · $1.6M · 2016

• NIH Grant R01MH091872

  NIH · $1.8M · 2016

• Neural Substrates of Reinforcement Learning and its Training in Major Depression

  NIH · $3.7M · 2016–2021

• Neural mechanisms of social influence on risky decisions in cocaine dependence

  NIH · $443k · 2017–2020

Frequent coauthors

• Brooks King‐Casas

  Virginia Tech

  189 shared

• Katherine McCurry

  Michigan Medicine

  66 shared

• Brooks Casas

  Biomedical Research Institute

  42 shared

• Dongil Chung

  40 shared

• Lusha Zhu

  Peking University

  40 shared

• Vanessa M. Brown

  Emory University

  37 shared

• Wright Williams

  Baylor College of Medicine

  33 shared

• David P. Graham

  Baylor College of Medicine

  29 shared

Education

• PhD

  Harvard University

  2006

Awards & honors

• Source of the Week, National Public Radio (2015)
• Scholar of the Week, Virginia Tech (2012)
• Biobehavioral Research Award for Innovative New Scientists (…
• American Psychological Association Diversity in Neuroscience…
• Dissertation Completion Merit Fellowship, Harvard University…