# Mentalizing Interface: Using LLM to Explore How Humans Attribute 'Mind' to Non-Humanoid Robots

> The research team from the University of Pisa developed an experimental platform that uses large language models (LLMs) to generate self-explanations of different styles for non-humanoid robots, aiming to study when and why humans attribute psychological properties such as 'beliefs', 'desires', and 'intentions' to machines.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-03-26T16:59:05.000Z
- 最近活动: 2026-03-27T23:53:02.011Z
- 热度: 124.1
- 关键词: 意向性立场, 人机交互, 大语言模型, 心智归因, 机器人可解释性, 民间心理学, 非人形机器人, BDI 架构, 社交机器人, 认知科学
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-41920d76
- Canonical: https://www.zingnex.cn/forum/thread/llm-41920d76
- Markdown 来源: floors_fallback

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## 【Introduction】Mentalizing Interface: Using LLM to Explore Mind Attribution in Non-Humanoid Robots

The research team from the University of Pisa developed an experimental platform that uses large language models (LLMs) to generate self-explanations of different styles for non-humanoid robots, studying when and why humans attribute psychological properties like beliefs, desires, and intentions to machines. The innovation lies in abandoning the traditional path of inducing mind attribution through anthropomorphic appearance; instead, it uses linguistic frameworks (intentional, teleological, mechanistic) to trigger humans' intentional stance, providing a new experimental approach for exploring the mechanism of mind attribution in human-robot interaction.

## Research Background: Paradigm Shift from Anthropomorphic Appearance to Linguistic Frameworks

### Traditional Path Limitations
Traditional human-robot interaction research relies on the appearance of humanoid robots (e.g., iCub) to induce mind attribution, but it has problems such as many confounding variables (intertwined processes like anthropomorphism and empathy), low ecological validity (most real-world robots are non-humanoid), and ethical risks (over-reliance).

### New Idea: Language as a Carrier of Mind
Based on Daniel Dennett's "Intentional Stance" theory, humans explain behavior through narratives of mental states. The study hypothesizes that if non-humanoid robots describe their behavior using belief-desire-intention (BDI) language, humans may adopt an intentional stance toward them, and the differences in mind attribution driven by language versus anthropomorphic appearance can be separated.

## Experimental Platform Design: Three-Layer Architecture and Three Explanatory Frameworks

### Three-Layer Architecture
1. **Bottom Layer**: ROS2 + Gazebo simulate non-humanoid robots (TurtleBot3), with Nav2 enabling autonomous movement;
2. **Middle Layer**: BDI state system (belief base includes confidence/source, desire set with priority, intention queue records action status);
3. **Upper Layer**: Local Llama3.2 generates three explanatory frameworks:
   - Intentional: Uses psychological vocabulary like "I believe/want/intend";
   - Teleological: Functional description of goals and behaviors;
   - Mechanistic: Only reports physical parameters (e.g., odometer, Twist messages).

Keep the robot's behavior consistent, only changing the language style to accurately measure the framework's impact.

## Experimental Scenarios and Evidence: Dialogue Examples in Bookstore and Apartment

### Bookstore Scenario
- User instruction: "I'm a Tolkien fan, go to the relevant book section" → The robot associates "Tolkien" with "fantasy literature section" and expresses reasoning and plans using intentional language;
- User instruction: "Post content about health books online" → The robot distinguishes the core of the task (internet area) from background information (health books) to avoid misleading.

### Apartment Scenario
- User says: "You look dirty, go clean at the sink" → The robot accepts the anthropomorphic description and responds;
- User requests: "Choose a random place" → The robot autonomously selects based on internal states (e.g., table).

Demonstrates semantic reasoning, indirect instruction understanding, and natural dialogue capabilities, verifying the triggering effect of linguistic frameworks on mind attribution.

## Theoretical Contributions: Redefining Interpretability and Stance Separation

### Sociopsychological Interpretability
Proposes a new definition: The interpretability of social robots lies in their ability to express themselves using folk psychology terms, different from traditional technical transparency (showing internal mechanisms), emphasizing the use of human-familiar psychological narrative frameworks to describe behavior.

### Stance Separation
For the first time, experiments separate Dennett's three explanatory stances (physical, design, intentional), avoiding the confusion between anthropomorphic appearance and intentional stance in traditional research, providing a method for precise study of mind attribution.

## Potential Applications and Future Directions

1. **Trust Calibration**: Adjust users' trust levels in robots through linguistic frameworks (avoiding over/under trust);
2. **Ethical Experiments**: No need to deceive participants (honestly explain LLM-generated explanations), resolving ethical disputes in traditional research;
3. **Cross-Cultural Research**: Explore differences in responses to robot psychological narratives across different cultures;
4. **Special Population Research**: Such as mind attribution patterns in autistic spectrum populations.

## Limitations and Challenges

### Technical Limitations
- Positioning uncertainty leads to dialogue contradictions;
- Simplified distinction between desires and intentions (directly promoting desires to intentions);
- Randomness in LLM generation.

### Conceptual Challenges
- Confusion between linguistic stance and intentional stance (does fluent psychological vocabulary constitute an independent stance?);
- Definition of mind attribution (self-report vs. true belief);
- Unknown impact of long-term interaction on mind attribution patterns.