Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Nada Attar

Second Advisor

Sayma Akther

Third Advisor

Aditya Puranik

Keywords

Cultural Bias, Emotion Recognition, Contextual Perception, Eye-Tracking Analysis, Large Language Models (LLMs), Human-AI Comparison, Implicit Bias, Visual Attention, Perceptual Decision-Making, Human-Computer Interaction (HCI)

Abstract

In this study, we investigate the intersection of cultural context, visual perception, and implicit bias in interpreting neutral human expressions. Specifically, we explore how emotionally charged backgrounds can shape viewers interpretations of neutral facial expressions. Using an experimental setup, participants are shown neutral human portraits paired with varying background types ranging from emotionally neutral or pleasant to evocative scenes. Participants are tasked with selecting which background best matches the emotional state of the person depicted in the portraits. Eye-tracking data is collected to analyze visual attention patterns and cognitive processing. In parallel, we also evaluate how large language models (LLMs) respond to the same stimuli, identifying whether similar biases appears in AI driven interpretations. Our preliminary results reveal that both human participants and LLMs tend to project emotional meaning onto neutral faces based on background context, indicating some susceptibility to contextual and cultural bias. These findings contribute to a deeper understanding of human machine perception and also pave way for developing fairer, culturally sensitive AI systems.

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Available for download on Monday, May 25, 2026

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