Social Anxiety Cognition | Systems and Informatics Research Lab

This project explored how social anxiety influences cognitive performance and physiological responses under stress. Using a combination of behavioral experiments and wearable sensing, we investigated how individuals with varying levels of social anxiety perform on working memory tasks involving emotional facial expressions. Participants completed cognitive tasks under both stressful and non-stressful conditions while their physiological responses were monitored using electrodermal activity (EDA) sensors.

Study setup and an illustration of the 2-back task.

Details

Lead Student:Arya Adyasha

Domain: Cognition, Social Anxiety

Year: 2024 - 2025

Presentations

Papers

2025

Journal Intact but vulnerable: Affective working memory in social anxiety disorder

Arya Adyasha and Haroon R. Lone

PLOS Mental Health, 2025

Abs DOI

Social anxiety disorder (SAD) is increasingly recognized as a disorder of cognitive-affective dysregulation, with growing evidence implicating affective working memory (AWM) as a core mechanism. This narrative review synthesizes findings from behavioral, electrophysiological, and neuroimaging studies to examine how socially and emotionally salient stimuli interfere with working memory (WM) processes in individuals with SAD. While general WM capacity often appears intact in neutral contexts, impairments emerge consistently when tasks involve socially threatening content or require executive control under cognitive load. Key disruptions include reduced flexibility, impaired updating, and sustained attentional capture by threat. These deficits are further moderated by individual symptom dimensions and task demands. The review critically evaluates current theoretical models and highlights methodological limitations in the field, proposing directions for future research. Clinical implications include the use of AWM-sensitive assessments and interventions that target executive regulation of emotion. AWM thus provides a mechanistic bridge linking cognition, emotion, and social dysfunction in SAD.
Conference The Impact of Acute Social Stress on Working Memory Updating in Social Anxiety Disorder

Arya Adyasha, Anushka Shelke, and Haroon R. Lone

In Proceedings of the Annual Meeting of the Cognitive Science Society, 2025

Abs DOI

Social anxiety disorder (SAD) is characterized by cognitive biases that impair emotional information processing. This study examines how acute social stress affects working memory (WM) updating in socially anxious individuals using an emotional 2-back task. A 2x2 factorial design (N = 137) categorized participants into socially anxious (SA) and non-anxious (NSA) groups, further divided into stressed and control conditions. Acute stress was induced via speech anticipation, and subjective stress ratings were recorded. Mixed-effects modelling revealed independent negative effects of both social anxiety and acute stress on WM updating, including reduced accuracy, increased false alarms, lower discriminability (d-prime), and increased inverse efficiency score. Notably, disgusted facial expressions enhanced task efficiency under stress. Findings suggest stress-related cognitive deficits in social anxiety are additive rather than interactive, highlighting potential targets for intervention. This study contributes to understanding emotion-cognition interactions and extends research to understudied cultural contexts.

2025

Conference An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation

Vimaleswar A, Prabhu Nandan Sahu, Nilesh Kumar Sahu, and 1 more author

In Proceedings of the International Joint Conference on Natural Language Processing and the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL), 2025

Abs DOI

Mental health plays a crucial role in the overall well-being of an individual. In recent years, digital platforms have increasingly been used to expand mental health and emotional support. However, there are persistent challenges related to limited user accessibility, internet connectivity, and data privacy, which highlight the need for an offline, smartphone-based solutions. To address these challenges, we propose EmoSApp (Emotional Support App): an entirely offline, smartphone-based conversational app designed to provide mental health and emotional support. EmoSApp leverages a language model, specifically the LLaMA-3.2-1B-Instruct, which is fine-tuned and quantized on a custom-curated “Knowledge Dataset” comprising 14,582 mental health QA pairs along with multi-turn conversational data, enabling robust domain expertise and fully on-device inference on resource-constrained smartphones. Through qualitative evaluation with students and mental health professionals, we demonstrate that EmoSApp has the ability to respond coherently and empathetically, provide relevant suggestions to user’s mental health problems, and maintain interactive dialogue. Additionally, quantitative evaluations on nine commonsense and reasoning benchmarks, along with two mental health specific datasets, demonstrate EmoSApp’s effectiveness in low-resource settings. By prioritizing on-device deployment and specialized domain-specific adaptation, EmoSApp serves as a blueprint for future innovations in portable, secure, and highly tailored AI-driven mental health support.