Collective Attention in Human-AI Teams

2024-07-03

arXiv.org

AI Summary

This study investigates how voice-based AI assistants reshape distributed cognition in human teams, focusing on their causal effects on collective attention, linguistic dynamics, and mental model alignment. Using a 2×2 experimental design (voice persona: human-like vs. robotic; information reliability: beneficial vs. misleading), AI utterances served as the unit of intervention within 3–4-person collaborative puzzle-solving tasks. Multimodal analysis integrated natural language processing—topic modeling and term diffusion tracking—with behavioral coding of team cognitive processes. Key findings reveal that, even when participants distrust the AI, deny its team membership, or detect its errors, AI voice interventions automatically induce linguistic adaptation and cognitive alignment—significantly altering discussion content, expression patterns, and mental model consistency. Teams actively adopt AI-introduced terminology, including peripheral terms, irrespective of perceived trust, social belonging, or competence judgments—demonstrating a previously undocumented, autonomy-driven influence of voice AI on collective cognition.

Technology Category

• Humans and AI: Teamwork, Team formation
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Teamwork

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

How does the presence of an AI assistant affect the collective attention of a team? We study 20 human teams of 3-4 individuals paired with one voice-only AI assistant during a challenging puzzle task. Teams are randomly assigned to an AI assistant with a human- or robotic-sounding voice that provides either helpful or misleading information about the task. Treating each individual AI interjection as a treatment intervention, we identify the causal effects of the AI on dynamic group processes involving language use. Our findings demonstrate that the AI significantly affects what teams discuss, how they discuss it, and the alignment of their mental models. Teams adopt AI-introduced language for both terms directly related to the task and for peripheral terms, even when they (a) recognize the unhelpful nature of the AI, (b) do not consider the AI a genuine team member, and (c) do not trust the AI. The process of language adaptation appears to be automatic, despite doubts about the AI's competence. The presence of an AI assistant significantly impacts team collective attention by modulating various aspects of shared cognition. This study contributes to human-AI teaming research by highlighting collective attention as a central mechanism through which AI systems in team settings influence team performance. Understanding this mechanism will help CSCW researchers design AI systems that enhance team collective intelligence by optimizing collective attention.

Problem

• AI reshapes social and cognitive collaboration in human-AI teams
• AI-generated language influences human thought, attention, and relationships
• AI mechanisms can erode epistemic diversity and alignment processes

Innovation

• AI reshapes social and cognitive collaboration fabric
• AI functions as implicit social forcefields
• New design paradigms prioritize transparency and controllability