# LLM Decision Reasoning Recognition: Decoding the Causes of Human Decisions from Verbal Reports

> Studies show that large language models (LLMs) can accurately identify decision-making reasons in verbal reports, providing a new research path for understanding human decision-making processes and developing explainable AI.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-03-31T15:45:38.000Z
- 最近活动: 2026-03-31T15:58:13.550Z
- 热度: 148.8
- 关键词: 大型语言模型, 决策科学, 口语报告分析, 可解释AI, 自然语言处理, 认知心理学, 行为研究
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-df037203
- Canonical: https://www.zingnex.cn/forum/thread/llm-df037203
- Markdown 来源: floors_fallback

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## [Introduction] LLM Decision Reasoning Recognition: A New Path to Decoding Human Decision-Making Reasons

This article explores how to use large language models (LLMs) to analyze human verbal reports and automatically identify decision-making reasons. Traditional decision-making research methods (such as post-hoc questionnaires and laboratory tasks) struggle to capture the complexity of decision-making processes, while LLMs, with their strong language understanding capabilities, provide an innovative direction for understanding human decision-making and developing explainable AI. The core finding of the study is that the accuracy of LLMs in identifying decision-making reasons is comparable to that of human experts, and even superior in terms of consistency, efficiency, and other aspects.

## Research Background: Methodological Challenges in Decision Science

Understanding decision-making processes is crucial for psychology, economics, neuroscience, and AI fields, but traditional methods have limitations:
- **Choice tasks**: Only observe results, cannot understand the process;
- **Verbal reports**: Rich data but time-consuming and subjective to analyze;
- **Eye-tracking/neuroimaging**: Expensive, invasive, and the mapping between signals and psychological processes is unclear.
In addition, decision-making processes are mostly implicit, and people often struggle to accurately describe the reasons for their decisions.

## Research Innovation: LLM-Driven Design for Decision Reason Identification

The core innovation of the study is the use of LLMs to automatically analyze decision-making verbal reports. The design steps are as follows:
1. Participants complete a choice task and give a verbal report;
2. The report is transcribed into text;
3. The LLM analyzes the text to identify decision-making reasons;
4. Results are compared with annotations from human experts.
At the same time, a classification system for decision-making reasons was established, including categories based on attributes, comparisons, emotions, rules, external factors, etc.

## Methods for LLM Analysis of Decision-Making Reasons

The study used multiple LLM analysis methods:
- **Zero-shot classification**: Directly provide text and categories, relying on general knowledge and prompt engineering;
- **Few-shot learning**: Help the LLM understand the task through examples;
- **Chain-of-thought prompting**: Require the LLM to analyze step-by-step and explain the identification reasons;
- **Fine-tuning**: Optimize the model with annotated data, requiring more resources but achieving better performance.

## Research Findings: Accuracy and Advantages of LLM Identification

Key findings: The performance of LLMs in identifying decision-making reasons is comparable to, or even exceeds, that of human experts.
- **Accuracy metrics**: Include precision (fewer false positives), recall (fewer false negatives), and F1 score (comprehensive evaluation);
- **Comparison with humans**: LLMs have high annotation consistency, fast efficiency, strong objectivity, and can capture subtle language differences (such as hints and euphemisms).

## Research Significance: Cross-Domain Impacts and Implications

The research has broad significance:
- **Decision science**: Enable large-scale data collection, real-time process tracking, and cross-cultural comparisons;
- **AI development**: Facilitate the construction of explainable AI, optimization of human-machine collaboration, and preference learning;
- **NLP applications**: Promote progress in fields such as fine-grained sentiment analysis and dialogue understanding.

## Limitations and Future Research Directions

The study has limitations: reliance on the quality of verbal reports, difficulty in inferring causal mechanisms, need to verify cross-cultural applicability, and involvement of privacy ethics.
Future directions:
- Multimodal analysis (combining data such as voice and facial expressions);
- Development of causal inference methods;
- Personalized model training;
- Construction of real-time decision intervention systems.