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在投论文
General decision parameters account for shared behavioural and neural variation across interference and response inhibition
Submitted to Cognition
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Hierarchical organization of cognitive processes underlies low cross-task correlations in inhibitory control: Evidence from drift diffusion modeling
Submitted to Journal of Experimental Psychology: General
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Boundaries of Executive Functions in Large Language Models: GPT-4o Selectively Replicates Human Performance
Submitted to Cognitive Science
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发表论文
Zhang, T., Wang, S., Shi, H., Li, J., Yang, X., Jiao, J., Huo, X., & Zhao, X.* (2026). Shared and disorder-specific computational mechanisms of interference and response inhibition in schizophrenia and major depressive disorder. Translational Psychiatry, 16, 590. https://doi.org/10.1038/s41398-026-04345-3 [PDF]
Abstract: Inhibitory control deficits are consistently observed in both schizophrenia (SCZ) and major depressive disorder (MDD), yet whether these impairments reflect shared tansdiagnostic mechanisms or disorder-specific dysfunction remains poorly understood. We recruited N = 259 participants (n = 87 SCZ, n = 86 MDD, n = 86 healthy controls) who completed Stroop and Go/No-Go tasks assessing interference and response inhibition, respectively. Hierarchical drift-diffusion modeling (HDDM) was used to decompose behavioral performance into latent computational parameters. Both patient groups exhibited significant behavioral impairments across most measures, with SCZ showing greater severity. At the computational level, reduced drift rate—reflecting less efficient evidence accumulation—emerged as a core transdiagnostic deficit across both inhibitory subcomponents and both disorders, with more pronounced reductions in SCZ. By contrast, prolonged non-decision time was disorder-differentiating, being consistently elevated across both tasks in SCZ but, in MDD, confined to response inhibition and not robust to processing-speed adjustment. Within-group clinical correlates further diverged, relating to negative-symptom severity and illness duration in SCZ and to current depressive symptom severity in MDD, although these diagnosis-specific associations were not directly compared across disorders. Machine learning classification corroborated these findings, with drift rate serving as the primary shared contributor to patient–control discrimination (area under the receiver operating characteristic curve [AUC] = 0.762–0.846), and non-decision time on the interference task the top-ranked feature in the SCZ–MDD classifier (AUC = 0.718). Together, these findings delineate shared versus disorder-specific computational signatures of inhibitory control impairment in SCZ and MDD, and suggest that computational phenotyping of inhibitory control may serve as a quantitative adjunct in precision psychiatry.
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Zhang, T., Gong, Y., & Zhao, X.* (2026). Two systems, two timelines: Computational evidence for dissociable development in inhibitory control across childhood and adolescence. Child Development, aacag107. https://doi.org/10.1093/chidev/aacag107 [PDF]
Abstract: This study examined inhibitory control development in two samples of Chinese children: a primary sample (n = 1,122; 45.5% female; 91.9% Han, Mage = 12.42 years, range: 6.0–18.7) with 6-month longitudinal follow-up and an independent replication sample (n = 1,026; 45.1% female; 90.8% Han, Mage = 12.44 years, range: 6.1–18.8). Generalized Additive Models applied to Stroop and Go/No-Go tasks revealed four-phase nonlinear developmental trajectories. Response inhibition stabilized by 13.4 years, while interference inhibition developed until 15.8 years. Hierarchical drift diffusion modeling showed that interference inhibition developed through enhanced information accumulation (drift rate), whereas response inhibition developed through enhanced response bias control (starting point). Age-related processing speed improvements suggest shared foundational mechanisms. The findings contribute to a decision-computational framework.
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Zhang, T., Huo, X., Chen, J.*, & Zhao, X.* (2026). Executive function profiles identify stable schizophrenia subtypes with distinct symptoms, biological markers and treatment responses: A cross-time clustering and validation study. BMC Medicine, 24, 403. https://doi.org/10.1186/s12916-026-04955-0 [PDF]
Background: Executive function (EF) is a heterogeneous neuropsychological construct, and impairments in EF dimensions represent a core aspect of psychopathology in schizophrenia that varies across individual patients. Currently, how this inter-individual variability characterizes schizophrenia subgroups, along with their distinctions in clinical characteristics and prognostic outcomes, remains unclear.
Methods: Three EF dimensions (inhibitory control, working memory, cognitive flexibility) were assessed in the main sample (N = 329), its follow-up subset, and an independently “recurring local validation” patient sample (N = 114). Fuzzy clustering was applied to baseline EF assessments to discover and validate the core subtypes after excluding cluster-ambiguous cases in the main and independent samples, respectively. Subtype-based classification trained on the main sample was then tested in the independent sample. Importantly, the stability of these subtypes and their remission statuses, along with associated longitudinal changes in clinical and biological factors, were evaluated, and baseline subtype statuses were also used to predict outcomes.
Results: Two longitudinally stable, independently validated core EF subtypes were identified, with Subtype I exhibiting selective deficits limited to response inhibition and cognitive flexibility (“EF-Focal”) and Subtype II showing broad EF impairments across all dimensions (“EF-Global”). Compared to the “EF-Global” subtype, patients within the “EF-Focal” subtype presented significantly milder positive, affective, and cognitive symptoms, worse working memory updating, lower inflammatory markers, and better metabolic profiles at baseline. Models trained on baseline EF assessments from the main sample accurately classified patient subtypes in the independent sample (area under the curve [AUC] = 0.79); these baseline subtypes moreover classified patients’ remission statuses independently of overall severity at intake (AUC = 0.78). Remitted patients were observed to have significantly greater reductions in negative and cognitive symptoms, improved working memory maintenance, lower peripheral inflammatory levels, and superior metabolic functions over time.
Conclusions: EF subtyping was associated with symptomatic, biochemical, and prognostic variations in individuals with schizophrenia, which could help to stratify patients within this disorder for targeted treatments.
Methods: Three EF dimensions (inhibitory control, working memory, cognitive flexibility) were assessed in the main sample (N = 329), its follow-up subset, and an independently “recurring local validation” patient sample (N = 114). Fuzzy clustering was applied to baseline EF assessments to discover and validate the core subtypes after excluding cluster-ambiguous cases in the main and independent samples, respectively. Subtype-based classification trained on the main sample was then tested in the independent sample. Importantly, the stability of these subtypes and their remission statuses, along with associated longitudinal changes in clinical and biological factors, were evaluated, and baseline subtype statuses were also used to predict outcomes.
Results: Two longitudinally stable, independently validated core EF subtypes were identified, with Subtype I exhibiting selective deficits limited to response inhibition and cognitive flexibility (“EF-Focal”) and Subtype II showing broad EF impairments across all dimensions (“EF-Global”). Compared to the “EF-Global” subtype, patients within the “EF-Focal” subtype presented significantly milder positive, affective, and cognitive symptoms, worse working memory updating, lower inflammatory markers, and better metabolic profiles at baseline. Models trained on baseline EF assessments from the main sample accurately classified patient subtypes in the independent sample (area under the curve [AUC] = 0.79); these baseline subtypes moreover classified patients’ remission statuses independently of overall severity at intake (AUC = 0.78). Remitted patients were observed to have significantly greater reductions in negative and cognitive symptoms, improved working memory maintenance, lower peripheral inflammatory levels, and superior metabolic functions over time.
Conclusions: EF subtyping was associated with symptomatic, biochemical, and prognostic variations in individuals with schizophrenia, which could help to stratify patients within this disorder for targeted treatments.
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Zhang, T.†, Zhao, X.†,*, Yeo, B. T. T., Huo, X., Eickhoff, S. B., & Chen, J.* (2026). Leveraging Stacked Classifiers for Multi-task Executive Function in Schizophrenia Yields Diagnostic and Prognostic Insights. Schizophrenia Bulletin, 52(2), sbaf218. https://doi.org/10.1093/schbul/sbaf218 [PDF]
Background: Executive functioning (EF) impairments are often seen in mental disorders, particularly schizophrenia, where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of schizophrenia remains opaque.
Study Design: We used classification models with a stacking approach on systematically measured EFs using 6 tasks to discriminate 195 patients with schizophrenia from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with four symptom dimensions.
Study Results: The models highlighted the importance of inhibitory control (interference and response inhibitions) or working memory in accurately identifying individuals with schizophrenia (area under the curve [AUC] = 0.87) or those in remission (AUC = 0.81). Patients who are correctly classified, in association with the contribution of interference inhibition function to our diagnostic classifier, present more severe baseline negative symptoms compared to those who are more likely to be misclassified. Also, linked to the function of working memory updating, patients who are successfully classified as remitted display milder cognitive symptoms at follow-up. Remitted patients do not differ significantly from non-remitted cases in baseline EF assessments or overall symptom severity.
Study Design: We used classification models with a stacking approach on systematically measured EFs using 6 tasks to discriminate 195 patients with schizophrenia from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with four symptom dimensions.
Study Results: The models highlighted the importance of inhibitory control (interference and response inhibitions) or working memory in accurately identifying individuals with schizophrenia (area under the curve [AUC] = 0.87) or those in remission (AUC = 0.81). Patients who are correctly classified, in association with the contribution of interference inhibition function to our diagnostic classifier, present more severe baseline negative symptoms compared to those who are more likely to be misclassified. Also, linked to the function of working memory updating, patients who are successfully classified as remitted display milder cognitive symptoms at follow-up. Remitted patients do not differ significantly from non-remitted cases in baseline EF assessments or overall symptom severity.
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Zhang, T.†, Su, M.†, Huo, X., & Zhao, X.* (2025). Rethinking the effects of working memory training on executive functions in schizophrenia: A machine learning approach. International Journal of Clinical and Health Psychology, 25(4), 100628. https://doi.org/10.1016/j.ijchp.2025.100628 [PDF]
Background: Executive dysfunction in schizophrenia profoundly impairs functional outcomes and remains insufficiently addressed by standard pharmacological treatments. While computerized cognitive training offers promise, traditional evaluation methods often fail to capture nuanced improvements along the psychosis-health continuum. This study aims to quantify executive function (EF) profile changes following working memory training and identify robust baseline predictors of treatment response.
Methods: Ninety-four schizophrenia patients were randomized to adaptive N-back training (n = 32), non-adaptive 1-back control (n = 33), or treatment-as-usual (n = 29). EF was assessed across working memory, cognitive flexibility, and inhibitory control domains. A support vector machine classifier, trained on an independent sample (195 patients, 169 controls) and calibrated via Platt scaling, quantified EF profile changes. An exploratory framework based on Granger causality principles identified baseline treatment predictors.
Results: Adaptive training produced significant near-transfer effects on untrained working memory tasks and reduced general psychopathology (pfdr < 0.05), but no far-transfer effects to other EF domains. The probability of neurotypical EF classification increased substantially in the adaptive group (13.21% to 38.79%, p < 0.001), correlating with symptom reduction. Working memory maintenance and response inhibition emerged as the most robust baseline predictors of treatment response.
Conclusions: Working memory training induces meaningful shifts in EF profiles in schizophrenia, promoting movement along the psychosis-health continuum toward neurotypical functioning. The classifier-based approach provides a more refined assessment compared to traditional binary measures, while the exploratory framework identifies specific EF domains predicting treatment response with potential causal relevance. These findings warrant validation through larger, multi-center trials with extended follow-up periods.
Methods: Ninety-four schizophrenia patients were randomized to adaptive N-back training (n = 32), non-adaptive 1-back control (n = 33), or treatment-as-usual (n = 29). EF was assessed across working memory, cognitive flexibility, and inhibitory control domains. A support vector machine classifier, trained on an independent sample (195 patients, 169 controls) and calibrated via Platt scaling, quantified EF profile changes. An exploratory framework based on Granger causality principles identified baseline treatment predictors.
Results: Adaptive training produced significant near-transfer effects on untrained working memory tasks and reduced general psychopathology (pfdr < 0.05), but no far-transfer effects to other EF domains. The probability of neurotypical EF classification increased substantially in the adaptive group (13.21% to 38.79%, p < 0.001), correlating with symptom reduction. Working memory maintenance and response inhibition emerged as the most robust baseline predictors of treatment response.
Conclusions: Working memory training induces meaningful shifts in EF profiles in schizophrenia, promoting movement along the psychosis-health continuum toward neurotypical functioning. The classifier-based approach provides a more refined assessment compared to traditional binary measures, while the exploratory framework identifies specific EF domains predicting treatment response with potential causal relevance. These findings warrant validation through larger, multi-center trials with extended follow-up periods.
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Zhang, T., Yang, X., Mu, P., Huo, X., & Zhao, X.* (2025). Stage-specific computational mechanisms of working memory deficits in first-episode and chronic schizophrenia. Schizophrenia Research, 282, 203-213. https://doi.org/10.1016/j.schres.2025.06.012 [PDF]
Background: Cognitive dysfunction, particularly working memory (WM) impairment, constitutes a core feature of schizophrenia and is largely unresponsive to available antipsychotic treatments. The computational mechanisms underlying WM deficits at different illness stages and their associations with clinical symptom dimensions remain poorly understood.
Methods: We applied hierarchical drift diffusion modeling (HDDM) to dissect latent cognitive processes underlying WM performance in a two-back task among patients with first-episode schizophrenia (FES, N = 103, illness duration ≤2 years), chronic schizophrenia (ChSz, N = 108, illness duration ≥5 years), and healthy controls (HCs, N = 85). Multiple regression and mediation analyses were conducted to examine associations between HDDM parameters, clinical symptoms, and conventional metrics.
Results: Both patient groups exhibited significant WM deficits compared to HCs, with ChSz patients demonstrating more pronounced impairments than FES patients. HDDM analysis revealed that patients showed significantly reduced drift rate and prolonged non-decision time compared to HCs. Notably, while non-decision time remained comparable between FES and ChSz groups, drift rate was significantly lower in ChSz patients, mediated the relationship between illness stage and WM performance, and negatively correlated with negative symptoms and general psychopathology.
Conclusions: This study reveals distinct computational profiles of WM deficits across different stages of schizophrenia. While non-decision time impairments emerge early and persist, reduced drift rate progressively deteriorates with illness duration and is closely linked to specific clinical symptoms. These findings enhance our understanding of WM dysfunction across illness stages and support the development of targeted cognitive interventions tailored to illness stage and symptom severity.
Methods: We applied hierarchical drift diffusion modeling (HDDM) to dissect latent cognitive processes underlying WM performance in a two-back task among patients with first-episode schizophrenia (FES, N = 103, illness duration ≤2 years), chronic schizophrenia (ChSz, N = 108, illness duration ≥5 years), and healthy controls (HCs, N = 85). Multiple regression and mediation analyses were conducted to examine associations between HDDM parameters, clinical symptoms, and conventional metrics.
Results: Both patient groups exhibited significant WM deficits compared to HCs, with ChSz patients demonstrating more pronounced impairments than FES patients. HDDM analysis revealed that patients showed significantly reduced drift rate and prolonged non-decision time compared to HCs. Notably, while non-decision time remained comparable between FES and ChSz groups, drift rate was significantly lower in ChSz patients, mediated the relationship between illness stage and WM performance, and negatively correlated with negative symptoms and general psychopathology.
Conclusions: This study reveals distinct computational profiles of WM deficits across different stages of schizophrenia. While non-decision time impairments emerge early and persist, reduced drift rate progressively deteriorates with illness duration and is closely linked to specific clinical symptoms. These findings enhance our understanding of WM dysfunction across illness stages and support the development of targeted cognitive interventions tailored to illness stage and symptom severity.
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合作论文
Wang, L., Chu, R., Zhang, T., Li, H., Shi, H., & Wang, Z.* (2026). Domain-dependent mechanisms of L2 proficiency in interference suppression: A drift-diffusion analysis of verbal and spatial Stroop in Chinese–English bilinguals. Bilingualism: Language and Cognition, 1–16. https://doi.org/10.1017/S1366728926101965 [PDF]
Whether and how second-language (L2) proficiency shapes cognitive control remain debated. We examined its influence on interference suppression across verbal and spatial domains in 154 Chinese–English bilinguals who completed parallel verbal and spatial Stroop tasks. L2 proficiency was modeled in three groups and continuously, and latent processes were decomposed via hierarchical Bayesian drift-diffusion modeling. Higher proficiency was associated with reduced interference in both domains, with a greater reduction in the verbal domain. Group-based model comparison favored verbal drift-rate variation and spatial decision-threshold variation. Joint regressions included proficiency, daily L2 use, L2 exposure, age of acquisition and years of learning. Proficiency was associated with higher drift rates and thresholds and shorter nondecision times in both tasks. Daily L2 use and L2 exposure were associated with verbal and spatial parameters, respectively. These findings provide computational evidence for a task-dependent account of bilingual cognitive control involving proficiency and other experience dimensions.
Shi, H.†, Zhang, T.†, Wang, Z., Yang, X., Shao, Z., & Ma, X.* (2026). Latent profiles of generative artificial intelligence use among Chinese college students: Associations with depression and anxiety. Journal of Affective Disorders, 415, 122428. https://doi.org/10.1016/j.jad.2026.122428 [PDF]
Background: While generative artificial intelligence (GenAI) has been rapidly adopted by college students, its relationship with mental health remains unclear. Most studies treat AI users as homogeneous, overlooking heterogeneity in GenAI use. This study identified latent profiles of GenAI use among Chinese college students and examined their associations with depression and anxiety.
Methods: A cross-sectional survey of 5748 Chinese college students assessed AI usage, motivations, AI literacy, dependency, and symptoms of depression and anxiety using the Beck Depression Inventory–II and Beck Anxiety Inventory. Latent profile analysis identified usage patterns. An exploratory random-forest classifier with SHapley Additive exPlanations (SHAP) evaluated whether these profiles could be predicted from psychological and demographic correlates not used for profile construction and identified key distinguishing factors.
Results: Four profiles were identified: Rational-Tool, Moderate-Recreational, Problem-Dependent, and Light-Exploratory. The Problem-Dependent profile (15%), characterized by high escapism motivation, low AI literacy, and high dependency, showed the highest depression and anxiety levels, whereas the Rational-Tool profile, characterized by high AI literacy and instrumental motivation, showed the most favorable outcomes. The profiles were recovered with good discrimination (accuracy = 0.81; macro-average AUC = 0.89), with the Problem-Dependent profile being the most distinguishable (AUC = 0.94). SHAP analysis identified depression, smartphone addiction, and executive-function difficulties as key correlates of the Problem-Dependent profile, whereas conscientiousness was central to the Rational-Tool profile.
Conclusions: GenAI use profiles were associated with different levels of depression and anxiety among college students. Person-centered prevention strategies should focus on usage motivation, AI literacy, and dependency rather than frequency alone.
Methods: A cross-sectional survey of 5748 Chinese college students assessed AI usage, motivations, AI literacy, dependency, and symptoms of depression and anxiety using the Beck Depression Inventory–II and Beck Anxiety Inventory. Latent profile analysis identified usage patterns. An exploratory random-forest classifier with SHapley Additive exPlanations (SHAP) evaluated whether these profiles could be predicted from psychological and demographic correlates not used for profile construction and identified key distinguishing factors.
Results: Four profiles were identified: Rational-Tool, Moderate-Recreational, Problem-Dependent, and Light-Exploratory. The Problem-Dependent profile (15%), characterized by high escapism motivation, low AI literacy, and high dependency, showed the highest depression and anxiety levels, whereas the Rational-Tool profile, characterized by high AI literacy and instrumental motivation, showed the most favorable outcomes. The profiles were recovered with good discrimination (accuracy = 0.81; macro-average AUC = 0.89), with the Problem-Dependent profile being the most distinguishable (AUC = 0.94). SHAP analysis identified depression, smartphone addiction, and executive-function difficulties as key correlates of the Problem-Dependent profile, whereas conscientiousness was central to the Rational-Tool profile.
Conclusions: GenAI use profiles were associated with different levels of depression and anxiety among college students. Person-centered prevention strategies should focus on usage motivation, AI literacy, and dependency rather than frequency alone.
Yang, X.†, Zhang, T.†, Ren, X., Wang, Y., Lv, H., Su, M., Huo, X.*, & Zhao, X.* (2026). Differential profiles of executive function dimensions in first-episode and chronic schizophrenia: Identifying markers of illness stage. The European Journal of Psychiatry, 40(2), 100351. https://doi.org/10.1016/j.ejpsy.2026.100351 [PDF]
Background and objectives: Executive function (EF) impairment represents a core feature of schizophrenia, yet how specific EF dimensions differ across illness stages remains poorly understood. This study aimed to characterize the differential profiles of three EF dimensions between first-episode schizophrenia (FES) and chronic schizophrenia (ChSz) patients, and to identify which dimensions most strongly differentiate between these illness stages.
Methods: A total of 182 schizophrenia patients (85 FES, illness duration ≤2 years; 97 ChSz, illness duration ≥5 years) completed five behavioral paradigms assessing inhibitory control, working memory, and cognitive flexibility. Mixed-model ANCOVA examined differential EF impairment patterns between groups. Random forest classifiers with nested cross-validation and Shapley Additive Explanations (SHAP) were employed to determine the relative multivariate importance of EF dimensions in distinguishing between illness stages.
Results: Mixed-model ANCOVA revealed a significant group-by-dimension interaction. FES patients exhibited milder deficits in inhibitory control and working memory updating compared to ChSz patients (all pfdr < 0.05), with the largest effect size for interference inhibition (η² = 0.140). Correlation analyses linked EF impairments to illness duration, symptom severity, and antipsychotic dosage. SHAP analysis identified interference inhibition and working memory updating as the dimensions most strongly associated with illness stage.
Conclusions: This study revealed stage-specific EF impairment profiles in schizophrenia, with inhibitory control and working memory updating showing the greatest differences between FES and ChSz patients. These dimensions may serve as sensitive markers for differentiating illness stages and represent potential targets for stage-specific cognitive interventions.
Methods: A total of 182 schizophrenia patients (85 FES, illness duration ≤2 years; 97 ChSz, illness duration ≥5 years) completed five behavioral paradigms assessing inhibitory control, working memory, and cognitive flexibility. Mixed-model ANCOVA examined differential EF impairment patterns between groups. Random forest classifiers with nested cross-validation and Shapley Additive Explanations (SHAP) were employed to determine the relative multivariate importance of EF dimensions in distinguishing between illness stages.
Results: Mixed-model ANCOVA revealed a significant group-by-dimension interaction. FES patients exhibited milder deficits in inhibitory control and working memory updating compared to ChSz patients (all pfdr < 0.05), with the largest effect size for interference inhibition (η² = 0.140). Correlation analyses linked EF impairments to illness duration, symptom severity, and antipsychotic dosage. SHAP analysis identified interference inhibition and working memory updating as the dimensions most strongly associated with illness stage.
Conclusions: This study revealed stage-specific EF impairment profiles in schizophrenia, with inhibitory control and working memory updating showing the greatest differences between FES and ChSz patients. These dimensions may serve as sensitive markers for differentiating illness stages and represent potential targets for stage-specific cognitive interventions.