A relevance model of human sparse communication in cooperation
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a relevance model for human communication, outperforming AI like GPT-4 in a navigation task. The model enhances human-AI collaboration by improving information transfer and task performance.
Area Of Science
- Cognitive Science
- Artificial Intelligence
- Decision Theory
Background
- Human communication is limited by real-time information flow, necessitating condensed data transfer.
- Existing models may not fully capture the nuanced selection of information in spontaneous communication.
Purpose Of The Study
- To introduce and evaluate a novel model of information selection in human communication based on "relevance."
- To compare the model's performance against advanced AI (GPT-4) and a heuristic model in a human-AI collaborative task.
Main Methods
- Developed a "relevance" model integrating decision-making theory and Theory of Mind (ToM).
- Conducted simulated navigation experiments with human participants and an AI agent assisting each other to avoid traps.
- Evaluated model accuracy in predicting human information choices and its impact on collaborative task performance.
Main Results
- The relevance model accurately predicted human choices for communicating trap information.
- The relevance model outperformed GPT-4 in the simulated navigation task.
- AI agents using the relevance model significantly improved human performance and received higher ratings compared to a heuristic model.
Conclusions
- A relevance model grounded in decision theory and ToM effectively explains the sparse and spontaneous nature of human communication.
- This model offers a framework for developing more intuitive and effective AI communication strategies.
- The findings highlight the potential of integrating cognitive principles into AI for enhanced human-AI interaction.
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