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MAJOR RESEARCH PROJECTS

Our Ongoing Projects (latest update in Spring 2025)

1. UNDERSTANDING PERCEPTUAL DECISION-MAKING ABOUT EMOTIONAL STIMULI AND THEIR ALTERATIONS IN PSYCHOPATHOLOGY

Our daily life is filled with emotionally charged information (e.g., seeing happy, fearful, and angry faces). Many of these emotional stimuli are easy to recognize, but others can be quite ambiguous to categorize. Most of us identify these visual stimuli easily and have little problem extracting the emotional information from what we see. An automatic task, such as recognizing a fearful face, involves complex computations carried out by the human brain. This project aims at elucidating the psychological, computational, and neural processes giving rise to emotion-related visual perceptual decision-making. Furthermore, we examine how these processes may be altered in internalizing psychopathology, such as depression and anxiety.

Methodology: questionnaires, computer-based behavioral paradigms, eye-tracking, computational modeling, and neuroimaging (fMRI, EEG and intracranial EEG).​

Recent publications:

  1. Hedley, F.E., Ngai, H.H., & Jin, J. (accepted). Looking around in distress: Judgmental and attentional biases revealed in multi-evidence decision making in anxiety. Journal of Psychopathology and Clinical Science

  2. Ngai, H.H., & Jin, J. (2025). Emotion-guided attention impacts deliberate multi-evidence emotion-related perceptual decision making. Psychophysiology. https://doi.org/10.1111/psyp.70059

  3. Mohanty, A., Freeman, J., & Jin, J. (2025). Top-down influences on the perception of emotional stimuli. Nature Reviews Psychology, https://doi.org/10.1038/s44159-025-00446-w

  4. Ngai, H.H., & Jin, J. (2025). Emotion ensemble judgment: cognitive training for a positive perspective. British Journal of Psychology, https://doi.org/10.1111/bjop.12784

  5. Yih, I., Cheung, S.H., Chang, D. H.F., & Jin, J. Elucidating the neural mechanisms of top-down attention-modulated threat-related perceptual decision-making. (accepted for Stage 1, Peer Community In [PCI] -Registered Report [PCI-RR])

  6. Ngai, H. H., Hsiao, J. H., Luhmann, C., Mohanty, A., & Jin, J. (2025). How is emotional evidence from multiple sources used in perceptual decision making? Psychophysiology, 62 (2). https://doi.org/10.1111/psyp.14727

  7. Ozturk, S., Sussman, T. J., Jin, J., Serody, M. R., Imbriano, G. & Mohanty, A. (2024). Perceptual decision-making regarding phylogenetically salient stimuli. Affective Science, 6, 145–158, https://doi.org/10.1007/s42761-024-00271-z 

  8. Ozturk, S., Zhang, X., Glasgow, S., #Karnani, R.R., Imbriano, G., Luhmann, C., Jin, J., & Mohanty, A. (2024). Knowledge of threat biases perceptual decision-making in anxiety: Evidence from signal detection theory and drift diffusion modeling. Biological Psychiatry: Global Open Science, 4(1), 145–154. https://doi.org/10.1016/j.bpsgos.2023.07.005   

  9. Glasgow, S., Imbriano, G., Ozturk, S., Jin, J., & Mohanty, A. (2023). Perceptual thresholds for threat are lowered in anxiety: Evidence from perceptual psychophysics. Clinical Psychological Science, 12(5), 945-961. https://doi.org/10.1177/21677026231211211

2. QUANTITATIVE MODELING OF MENTAL DISORDERS

This project utilizes computer simulation and real human data to gain insights into the hidden computational mechanisms of various mental disorders, in particular anxiety, depression, and psychosis.

Methodology: computer-based behavioral paradigms, questionnaires, computational modeling.

Recent publications:

  1. Zhang, Y., Hedley, F.E., Zhang, R.Y., & Jin, J. (2025). Toward quantitative cognitive-behavioral models: An active inference account of social anxiety disorder. Journal of Psychopathology and Clinical Science, 134(4), 363–388. https://doi.org/10.1037/abn0000972

  2. Pan, Y., Wen, Y., Jin, J., & Chen, J. (2023). The interpersonal computational psychiatry of social coordination in schizophrenia. The Lancet Psychiatry, 10(10), 801–808. https://doi.org/10.1016/s2215-0366(23)00146-3

  3. Jin, J., Zeidman, P., Friston, K.J., & Kotov, R. (2023). Inferring trajectories of psychotic disorders using dynamic causal modeling. Computational Psychiatry, 7(1), 60–75. https://doi.org/10.5334/cpsy.94

  4. Jin, J., Jonas, K., & Mohanty, A. (2023). Linking the past to the future by predictive processing: Implications for psychopathology. Journal of Psychopathology and Clinical Science, 132(3), 249-262. https://doi.org/10.1037/abn0000730

3. IDENTIFY COMPUTATIONAL AND NEURAL CORRELATES AND RISK FACTORS OF DEPRESSION AND ANXIETY

Collaborating with research teams globally, our lab has been involved in investigating cognitive behavioral, and neural risk factors related to adult and pediatric depression and anxiety disorders.

Methodology: clinical diagnostic interview, questionnaires, and neuroimaging (e.g., fMRI, EEG).

Recent publications:

  1. Pitliya, J., Burani, K., Nelson, B,D., Hajcak, G., & Jin, J. (2025). Reward-related brain activity mediates the relationship between decision-making deficits and pediatric depression symptom severity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 10 (2), 138-147. https://doi.org/10.1016/j.bpsc.2024.06.007

  2. Jiang, Z., Cui, Y., Xu, H., Abbey, C., Xu, W., Guo, W., Zhang, D., Liu, J., Jin, J., Li, Y. (2024). Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach. Annals of General Psychiatry, 23, 48. https://doi.org/10.1186/s12991-024-00534-w

  3. Zhong, Y., Perlman, G., Klein, D., Jin, J., Kotov, R. (2024). The prospective predictive power of parent-reported personality traits and facets in first-onset depression in adolescent girls. Research on Child and Adolescent Psychopathology, 52, 1221–1231. https://doi.org/10.1007/s10802-024-01186-w

  4. Pitliya, R.J., Nelson, B.D., Hajcak, G., & Jin, J. (2022). Drift-diffusion model reveals impaired reward-based perceptual decision-making processes associated with depression in late childhood and early adolescent girls. Research on Child and Adolescent Psychopathology, 50(11), 1515–1528. https://doi.org/10.1007/s10802-022-00936-y

  5. Jin, J., Delaparte, L., Chen, H.W., DeLorenzo, C., Perlman, G., Klein, D.N., Mohanty, A., & Kotov, R. (2022). Structural connectivity between rostral anterior cingulate cortex and amygdala predicts first onset of depressive disorders in adolescence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7(3), 249–255. https://doi.org/10.1016/j.bpsc.2021.01.012

  6. Jin, J., Van Snellenberg, J.X., Perlman, G., DeLorenzo, C., Klein, D.N., Kotov, R., & Mohanty, A. (2020). Intrinsic neural circuitry of depression in adolescent females. Journal of Child Psychology and Psychiatry, 61(4), 480–491. https://doi.org/10.1111/jcpp.13123

4. UNDERSTANDING UNCERTAINTY PROCESSING AMONG GENERAL AND CLINICAL POPULATIONS

How we process uncertainty, including forming and updating our beliefs about uncertain events that are emotionally charged, plays a crucial role in our daily decisions. Uncertainty processing has also been a longstanding pursuit in clinical psychology research. We are interested in deepening the conceptualization of uncertainty and developing novel measurements and paradigms to investigate uncertainty processing. This line of research will help gain insights into general emotion and motivation, as well as clinical phenomena, especially fear and anxiety.

Methodology: clinical diagnostic interview, questionnaires, behavioral paradigms, computational modeling, and psychophysiology.

Recent publications:

  1. Hedley, F,E., Larsen, E., Mohanty, A., Liu, J.Z., & Jin, J. (2024). Understanding anxiety through uncertainty quantification. British Journal of Psychology, https://doi.org/10.1111/bjop.12693

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