# GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective

arXiv: https://arxiv.org/abs/2408.13718 · HTML: https://mlsanity.com/paper/2408.13718/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 11 Aug 2024.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2307.09042/) (2023)
- [Is ChatGPT Equipped with Emotional Dialogue Capabilities?](https://mlsanity.com/paper/2304.09582/) (2023)
- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [Fine-grained Affective Processing Capabilities Emerging from Large Language Models](https://mlsanity.com/paper/2309.01664/) (2023)
- [Investigating Large Language Models' Perception of Emotion Using Appraisal Theory](https://mlsanity.com/paper/2310.04450/) (2023)
- [Is GPT a Computational Model of Emotion? Detailed Analysis](https://mlsanity.com/paper/2307.13779/) (2023)
- [GPT-ology, Computational Models, Silicon Sampling: How should we think about LLMs in Cognitive Science?](https://mlsanity.com/paper/2406.09464/) (2024)
- [The Inner Sentiments of a Thought](https://mlsanity.com/paper/2307.01784/) (2023)
- [What's Next in Affective Modeling? Large Language Models](https://mlsanity.com/paper/2310.18322/) (2023)

## Built on by (most cited first, 16 of 16)

- [Mechanistic Interpretability of Emotion Inference in Large Language Models](https://mlsanity.com/paper/2502.05489/) (2025)
- [Human-like Affective Cognition in Foundation Models](https://mlsanity.com/paper/2409.11733/) (2024)
- [Decoding Emotion in the Deep: A Systematic Study of How LLMs Represent, Retain, and Express Emotion](https://mlsanity.com/paper/2510.04064/) (2025)
- [Emergence of Hierarchical Emotion Organization in Large Language Models](https://mlsanity.com/paper/2507.10599/) (2025)
- [Rethinking Emotion Annotations in the Era of Large Language Models](https://mlsanity.com/paper/2412.07906/) (2024)
- [Large Language Models Think Too Fast To Explore Effectively](https://mlsanity.com/paper/2501.18009/) (2025)
- [PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health](https://mlsanity.com/paper/2412.16882/) (2024)
- [Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors](https://mlsanity.com/paper/2410.13776/) (2024)
- [Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents](https://mlsanity.com/paper/2602.01063/) (2026)
- [A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment](https://mlsanity.com/paper/2502.00136/) (2025)
- [From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics](https://mlsanity.com/paper/2509.05617/) (2025)
- [CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning](https://mlsanity.com/paper/2605.17176/) (2026)
- [Leveraging Large Language Models for Cost-Effective, Multilingual Depression Detection and Severity Assessment](https://mlsanity.com/paper/2504.04891/) (2025)
- [Do We Talk to Robots Like Therapists, and Do They Respond Accordingly? Language Alignment in AI Emotional Support](https://mlsanity.com/paper/2506.16473/) (2025)
- [Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition](https://mlsanity.com/paper/2505.18040/) (2025)
- [From Process Loss to Assembly Bonus: Human-Grounded Diagnosis of Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2609.13261/) (2026)

## Abstract (arXiv)

This paper extends recent investigations on the emotional reasoning abilities of Large Language Models (LLMs). Current research on LLMs has not directly evaluated the distinction between how LLMs predict the self-attribution of emotions and the perception of others' emotions. We first look at carefully crafted emotion-evoking stimuli, originally designed to find patterns of brain neural activity representing fine-grained inferred emotional attributions of others. We show that GPT-4 is especially accurate in reasoning about such stimuli. This suggests LLMs agree with humans' attributions of others' emotions in stereotypical scenarios remarkably more than self-attributions of emotions in idiosyncratic situations. To further explore this, our second study utilizes a dataset containing annotations from both the author and a third-person perspective. We find that GPT-4's interpretations align more closely with human judgments about the emotions of others than with self-assessments. Notably, conventional computational models of emotion primarily rely on self-reported ground truth as the gold standard. However, an average observer's standpoint, which LLMs appear to have adopted, might be more relevant for many downstream applications, at least in the absence of individual information and adequate safety considerations.