Large language models show fragile cognitive reasoning about human emotions

TL;DR

This paper investigates whether large language models can reason about human emotions through cognitive dimensions, revealing they capture some systematic relations but also show misalignment and instability.

Contribution

Introduces CoRE, a benchmark to evaluate LLMs' reasoning about emotions via cognitive appraisals, highlighting their strengths and limitations.

Findings

LLMs capture systematic relations between appraisals and emotions

LLMs show misalignment with human judgments

LLMs exhibit instability across different contexts

Abstract

Affective computing seeks to support the holistic development of artificial intelligence by enabling machines to engage with human emotion. Recent foundation models, particularly large language models (LLMs), have been trained and evaluated on emotion-related tasks, typically using supervised learning with discrete emotion labels. Such evaluations largely focus on surface phenomena, such as recognizing expressed or evoked emotions, leaving open whether these systems reason about emotion in cognitively meaningful ways. Here we ask whether LLMs can reason about emotions through underlying cognitive dimensions rather than labels alone. Drawing on cognitive appraisal theory, we introduce CoRE, a large-scale benchmark designed to probe the implicit cognitive structures LLMs use when interpreting emotionally charged situations. We assess alignment with human appraisal patterns, internal consistency, cross-model generalization, and robustness to contextual variation. We find that LLMs capture systematic relations between cognitive appraisals and emotions but show misalignment with human judgments and instability across contexts.