analyticsQ5to verify
Pelikan & Broth 2016 (CHI) — humans adapt their turn designs when playing charades with a Nao humanoid robot
In a multimodal conversation-analytic study of participants playing a charade game with a Nao humanoid robot, humans systematically adjusted their turn designs in response to robot behavior — shortening turns, simplifying vocabulary, and adapting timing. The interactional achievement of 'the robot as an interlocutor' was transient, sustained or lapsing depending on what the robot did and how participants interpreted it.
Human turn-design adaptation patterns (length, vocabulary, timing) during charade gameplay with a Nao robot vs human-only baseline; characterizations of when robot is/isn't treated as an interlocutorConsistent human turn-design adaptation across participants: shorter turns, simpler vocabulary, adjusted prosody and timing when addressing the Nao robot vs human co-participants (exact magnitude/percentages not extracted to verification — primarily a qualitative CA study)
- Sample
- Charade-game sessions with participants and a Nao humanoid robot (specific N participants + N sessions not extracted to verification)
- Methodology
- Multimodal conversation analysis of recorded charade-game sessions; transcription at CA granularity (including pauses, overlap, gaze, gesture); sequential analysis of turn-design adaptation across rounds.
What this means
- Foundational CA-of-HRI demonstration: humans adapt their turn designs to AI/robot interlocutors. This pattern recurs across subsequent CA-of-HAI work and has direct implications for model training — the conversational data the AI sees from users is already adapted.
- The 'interactional achievement of agency as a transient phenomenon' framing is load-bearing for the AHI program: agency in HAI is not a designed-in property but is locally accomplished in interaction, and it can lapse. This is a measurement target the AHI program's multi-session data is well-positioned to capture.
- Implication for AI evaluation: benchmark performance on user-curated test prompts (which are already adapted to AI's expected register) systematically overestimates real-deployment performance, because the deployed system sees user prompts that have been pre-adapted in ways the model's training data shaped.
Source
Why that Nao? How humans adapt to a conventional humanoid robot in taking turns-at-talk
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (ACM) · Hannah R. M. Pelikan & Mathias Broth · 2016 · peer-reviewed
Context
- What came before
- Pre-2016 HRI work focused on robot-side capabilities (perception, recognition, synthesis). Pelikan & Broth shifts the analytical focus to the human side — what humans do to make the interaction work, and how this differs systematically from human-human interaction.
- What comes next
- Verify session counts, participant N, the specific CA-coded adaptation categories. Connect to Albert et al.'s voice-assistant repair work and to the broader CA-of-HAI literature where humans-adapt-to-AI is now a stable finding.
- Where this lands
- Encyclopedia Part II (workforce — implications for measurement: any benchmark using user-collected prompts inherits adapted-input bias), Part V (research frontier — CA-of-HAI as a methodological resource the mainstream HAI evaluation tradition has not absorbed).