Comparing AI and Human Emotional Responses to Color: A Semantic Differential and Word-Color Association Approach

Abstract

This study investigates the ability of artificial intelligence (AI) to simulate human emotional responses to color using two established methods: semantic differential (SD) method and word-color association (WCA) approach. The SD method quantifies emotional reactions to colors through bipolar adjective pairs (e.g., warm–cool, heavy–light), while the WCA method explores associations between specific words and colors. AI responses were compared with data from human participants across various demographics. Results show that AI consistently evaluates basic emotional dimensions, such as warm–cool and heavy–light, with high accuracy, often surpassing human consistency. However, AI struggled with more subjective and culturally influenced dimensions like modern–classical and active-passive. In the WCA experiment, AI replicated many general color associations but faced challenges with complex emotions like joy and anticipation. These findings highlight AI's potential in tasks requiring standardized emotional responses but reveal its limitations in capturing nuanced human emotions, especially in culturally sensitive contexts.