{"id":18029,"date":"2025-10-14T21:00:08","date_gmt":"2025-10-14T21:00:08","guid":{"rendered":"https:\/\/dsrc.haifa.ac.il\/?p=18029"},"modified":"2025-10-14T21:17:28","modified_gmt":"2025-10-14T21:17:28","slug":"the-hidden-mechanisms-of-anxiety-and-depression-a-cognitive-data-driven-approach","status":"publish","type":"post","link":"https:\/\/dsrc.haifa.ac.il\/?p=18029","title":{"rendered":"The Hidden Mechanisms of Anxiety and Depression: A Cognitive Data-Driven Approach"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"18029\" class=\"elementor elementor-18029\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-e2c2ef6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e2c2ef6\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d0f2906\" data-id=\"d0f2906\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d56cb5c elementor-invisible elementor-widget elementor-widget-heading\" data-id=\"d56cb5c\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInUp&quot;,&quot;_animation_mobile&quot;:&quot;fadeInUp&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Hidden Mechanisms of Anxiety and Depression: A Cognitive Data-Driven Approach<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-d81fe17 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d81fe17\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2ed58e3\" data-id=\"2ed58e3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3465297 elementor-invisible elementor-widget elementor-widget-heading\" data-id=\"3465297\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInUp&quot;,&quot;_animation_mobile&quot;:&quot;fadeInUp&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Dr Thalia Richter - School of Psychological Sciences<br>Prof. Hagit Hel-Or -  Department of Computer Sciences<br>Prof. Hadas Okon-Singer - School of Psychological Sciences<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-570ebc7 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"570ebc7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-b88aabd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b88aabd\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b5ca55a\" data-id=\"b5ca55a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-inner-section elementor-element elementor-element-6128ba2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6128ba2\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-9f52bbf\" data-id=\"9f52bbf\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d219a85 elementor-widget elementor-widget-text-editor\" data-id=\"d219a85\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Machine Learning<\/strong><strong><br \/><\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-3fd24ae\" data-id=\"3fd24ae\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-378761b elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"378761b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Health<br \/>Privacy<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-6bf714c\" data-id=\"6bf714c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b05963d elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"b05963d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>SEED Grant 2024<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-26b1814 elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"26b1814\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><img fetchpriority=\"high\" decoding=\"async\" class=\"size-medium wp-image-18031 alignleft\" src=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-300x200.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-300x200.png 300w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-1024x683.png 1024w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-768x512.png 768w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-391x260.png 391w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms.png 1536w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p><p>Did you know that anxiety and depression are not just about <em>feeling sad<\/em> or <em>being nervous<\/em>? In addition to emotions, they are shaped by patterns of <strong>biased cognition: ways that our brain perceives, interprets, remembers, and anticipates<\/strong> events. These hidden mechanisms shape the way we see the world and ourselves, often making everyday situations feel more threatening or hopeless than they genuinely are.<\/p><p>Our research focused on examining one of the greatest challenges in mental health: how to accurately diagnose anxiety and depression, which are known to have a vast overlap and heterogeneity of symptoms. While both conditions are usually classified as separate disorders, people with anxiety and depression commonly present shared symptoms, blurring the boundaries between them and forming a spectrum of severity. Our study, aimed to investigate whether underlying cognitive mechanisms, such as attentional biases, memory distortions, and interpretive biases, can offer a more accurate and objective means of assessing symptom severity. By moving beyond the labels of conditions, our goal is to pave the way for mechanism-based diagnosis and, ultimately, more effective interventions.<\/p><p>To explore this, we tested 225 participants with clinical and subclinical levels of symptoms on a comprehensive battery of computerised tasks that measure six types of cognitive biases: selective and spatial attention, expectancy, interpretation, memory, and cognitive control. Their performance was analysed using machine learning models, specifically Random Forest regressors, which allowed us to predict anxiety and depression symptom severity with high accuracy and to identify which cognitive reactions have the most significant impact on severity. Notably, <em>interpretation<\/em> and <em>expectancy<\/em> biases turned out to be the strongest predictors, which shows how much the way we expect the future or interpret daily events can shape the severity of symptoms.<\/p><p>This project is the result of a close collaboration between clinicians, data and computer scientists, and neuroscientists at the University of Haifa, led by<\/p><ul><li><strong>Dr Thalia Richter<\/strong> \u2013 Clinical Psychologist, Postdoctoral Researcher, School of Psychological Sciences, University of Haifa (Currently: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany). Developed and validated the cognitive test battery, led data collection, conceptualised the research questions,\u00a0 provided clinical counselling, guided the methodology, and was the lead author of the manuscript.<\/li><li><strong>Prof. Hagit Hel-Or<\/strong> \u2013 Department of Computer Science, Head of the computational human Behavior Lab and a member of the Artificial Intelligence Research Center (AIRC), University of Haifa. Led the computational aspects of the project, supervised advanced data analysis and machine learning modelling.<\/li><li><strong>Prof. Hadas Okon-Singer<\/strong> \u2013 School of Psychological Sciences, the Integrated Brain and Behavior Research Center, and member of the Artificial Intelligence Research Center (AIRC), University of Haifa. Supervised the study design, writing, and analysis, provided resources, and guided the broader integration of psychological and cognitive neuroscience perspectives.<\/li><\/ul><p>Our findings support a <strong>novel diagnostic framework<\/strong>: instead of relying mainly on self-reports of symptoms, clinicians may be able to incorporate cognitive performance measures to fine-tune the identification of disorder severity and type, by integrating AI tools for analysis. The study also contributes to the ongoing debate between disorder-specific and transdiagnostic approaches for investigating mental health, highlighting the advantages of the transdiagnostic approach and evidence for both unique anxiety-related biases and shared anxiety-depression mechanisms. This dimensional, mechanism-based approach may help increase precision in psychiatric evaluations and lead to more effective interventions.<\/p><p>The key product of this research is our peer-reviewed article, published in the <em>Journal of Affective Disorders (5-year IF: 5.6, Q1 Psychiatry, Q1 Clinical Neurology)<\/em>:<\/p><p>\ud83d\udcc4 Richter, T., Stahi, S., Mirovsky, G., Hel-Or, H., &amp; Okon-Singer, H. (2024). <em>Disorder-specific versus transdiagnostic cognitive mechanisms in anxiety and depression: Machine-learning-based prediction of symptom severity.<\/em> Journal of Affective Disorders, 354, 473\u2013482.<br \/>\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.jad.2024.03.035\">Read the article here<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-77ecfc6 elementor-hidden-desktop elementor-widget elementor-widget-text-editor\" data-id=\"77ecfc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><img fetchpriority=\"high\" decoding=\"async\" class=\"size-medium wp-image-18031 alignleft\" src=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-300x200.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-300x200.png 300w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-1024x683.png 1024w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-768x512.png 768w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms-391x260.png 391w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2025\/10\/Transdiagnostic-mechanisms.png 1536w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p><p>Did you know that anxiety and depression are not just about <em>feeling sad<\/em> or <em>being nervous<\/em>? In addition to emotions, they are shaped by patterns of <strong>biased cognition: ways that our brain perceives, interprets, remembers, and anticipates<\/strong> events. These hidden mechanisms shape the way we see the world and ourselves, often making everyday situations feel more threatening or hopeless than they genuinely are.<\/p><p>Our research focused on examining one of the greatest challenges in mental health: how to accurately diagnose anxiety and depression, which are known to have a vast overlap and heterogeneity of symptoms. While both conditions are usually classified as separate disorders, people with anxiety and depression commonly present shared symptoms, blurring the boundaries between them and forming a spectrum of severity. Our study, aimed to investigate whether underlying cognitive mechanisms, such as attentional biases, memory distortions, and interpretive biases, can offer a more accurate and objective means of assessing symptom severity. By moving beyond the labels of conditions, our goal is to pave the way for mechanism-based diagnosis and, ultimately, more effective interventions.<\/p><p>To explore this, we tested 225 participants with clinical and subclinical levels of symptoms on a comprehensive battery of computerised tasks that measure six types of cognitive biases: selective and spatial attention, expectancy, interpretation, memory, and cognitive control. Their performance was analysed using machine learning models, specifically Random Forest regressors, which allowed us to predict anxiety and depression symptom severity with high accuracy and to identify which cognitive reactions have the most significant impact on severity. Notably, <em>interpretation<\/em> and <em>expectancy<\/em> biases turned out to be the strongest predictors, which shows how much the way we expect the future or interpret daily events can shape the severity of symptoms.<\/p><p>This project is the result of a close collaboration between clinicians, data and computer scientists, and neuroscientists at the University of Haifa, led by<\/p><ul><li><strong>Dr Thalia Richter<\/strong> \u2013 Clinical Psychologist, Postdoctoral Researcher, School of Psychological Sciences, University of Haifa (Currently: Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany). Developed and validated the cognitive test battery, led data collection, conceptualised the research questions,\u00a0 provided clinical counselling, guided the methodology, and was the lead author of the manuscript.<\/li><li><strong>Prof. Hagit Hel-Or<\/strong> \u2013 Department of Computer Science, Head of the computational human Behavior Lab and a member of the Artificial Intelligence Research Center (AIRC), University of Haifa. Led the computational aspects of the project, supervised advanced data analysis and machine learning modelling.<\/li><li><strong>Prof. Hadas Okon-Singer<\/strong> \u2013 School of Psychological Sciences, the Integrated Brain and Behavior Research Center, and member of the Artificial Intelligence Research Center (AIRC), University of Haifa. Supervised the study design, writing, and analysis, provided resources, and guided the broader integration of psychological and cognitive neuroscience perspectives.<\/li><\/ul><p>Our findings support a <strong>novel diagnostic framework<\/strong>: instead of relying mainly on self-reports of symptoms, clinicians may be able to incorporate cognitive performance measures to fine-tune the identification of disorder severity and type, by integrating AI tools for analysis. The study also contributes to the ongoing debate between disorder-specific and transdiagnostic approaches for investigating mental health, highlighting the advantages of the transdiagnostic approach and evidence for both unique anxiety-related biases and shared anxiety-depression mechanisms. This dimensional, mechanism-based approach may help increase precision in psychiatric evaluations and lead to more effective interventions.<\/p><p>The key product of this research is our peer-reviewed article, published in the <em>Journal of Affective Disorders (5-year IF: 5.6, Q1 Psychiatry, Q1 Clinical Neurology)<\/em>:<\/p><p>\ud83d\udcc4 Richter, T., Stahi, S., Mirovsky, G., Hel-Or, H., &amp; Okon-Singer, H. (2024). <em>Disorder-specific versus transdiagnostic cognitive mechanisms in anxiety and depression: Machine-learning-based prediction of symptom severity.<\/em> Journal of Affective Disorders, 354, 473\u2013482.<br \/>\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.jad.2024.03.035\">Read the article here<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-3839ea6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3839ea6\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4efc12b\" data-id=\"4efc12b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-152d16e elementor-widget elementor-widget-spacer\" data-id=\"152d16e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Dr. Thalia Richter<br \/>\nProf. Hagit Hel-Or<br \/>\nProf. Hadas Okon-Singer <\/p>\n","protected":false},"author":93,"featured_media":18039,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","footnotes":""},"categories":[130,93,71,64,73,112,23,81],"tags":[34],"class_list":["post-18029","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-130","category-computer-science","category-health","category-machine-learning","category-privacy","category-psychology-department","category-research-blog","category-seed","tag-machine-learning","entry","has-media"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - 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