Human-AI interaction
Can an AI teammate guide you without making you feel bossed around?
Open access · cc by · source: Europe PMC
An agent that hinted at the best goal through its own movements helped people pick the better target as often as an agent that told them outright, while leaving them feeling more in control.
Study at a glance
- Design
- Human experiment — Within-subject online experiment: each participant played a pursuit-evasion maze task with a supportive, an explicit-guidance and an implicit-guidance agent in randomised order.
- N
- N=100 · 100 crowdsourced adults recruited; 3 excluded for failing dummy tasks. Each did 15 regular tasks (5 per agent).
- Population
- Adult crowdworkers in Japan (Yahoo! Crowdsourcing)
- Outcome
- Rate of capturing the best target; 7-point ratings of perceived autonomy, ease of collaboration and intention inference
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Agent type strongly affected how often participants captured the best object (F(2, 968) = 79.9); both guidance agents far outperformed the supportive agent, and implicit and explicit guidance did not differ significantly. Perceived autonomy also differed by agent (F(2, 192) = 36.4), with participants feeling more in control with the implicit agent than the explicit one. Explicit guidance was also rated harder to collaborate with, possibly because its interface showed extra information.
Methodology
The authors modelled a human-agent game as a POMDP in which the hidden state is the human's target. They built three agents: a supportive one that simply follows the human's goal, an explicit one that shows the best target, and an implicit one that moves so the human, reasoning via a Bayesian Theory of Mind, can infer the better target. Online participants played maze tasks where the participant and the agent had to trap a fleeing object from both sides, rating each agent afterwards.
Limitations
The mazes were tiny, with only two possible targets and a small discrete action space that made the agent's intentions easy to read, so the advantage may not hold in complex or continuous tasks. The agent assumes every human uses the same fixed, rational cognitive model and trusts the agent. Autonomy was measured by a single general, subjective Likert item, and the crowdsourced online sample was mostly male.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
Subtle guidance can keep performance and preserve autonomy.
How an agent helps shapes perceived autonomy: implicit and explicit guidance agents both improved task success over a supportive agent, but participants felt more in control with implicit guidance.
Evidence for the claim as stated.
Related papers in this topic
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