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Conference Paper
Does the Emotional Understanding of LVLMs Vary Under High-Stress Environments and Across Different Demographic Attributes?
- Authors
- Jaewook Lee, Yeajin Jang, Oh-Woog Kwon, Harksoo Kim
- Issue Date
- 2025-07
- Citation
- Annual Meeting of the Association for Computational Linguistics (ACL) 2025, pp.23196-23210
- Language
- English
- Type
- Conference Paper
- Abstract
- According to psychological and neuroscientific research, a high-stress environment can restrict attentional resources and intensify negative affect, thereby impairing the ability to understand emotions. Furthermore, demographic attributes such as race, gender, and age group have been repeatedly reported to cause significant differences in emotional expression and recognition. This study is the first to systematically verify whether these psychological findings observed in humans also apply to the latest Large Vision Language Models (LVLMs). We constructed low-stress versus high-stress environments and generated an image dataset (a total of 540 images) that combines race, gender, and age group. Based on this, we applied the Pretend prompt technique to induce LVLMs to interpret others' emotions from the standpoint of the assigned environment and persona. An analysis of the models' emotional understanding ability, using EQ-Bench-based metrics, revealed that (1) under high-stress environments, the accuracy of emotion understanding significantly declined in most LVLMs, and (2) performance disparities were confirmed across race, gender, and age group. These findings suggest that the effects of high-stress and demographic attributes identified in human research may also be reflected in LVLMs.
- KSP Keywords
- Age group, Demographic attributes, Emotion understanding, Emotional expression, Negative Affect, Stress environment, attentional resources, image datasets, language models, low-stress
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
