POIROT: Investigating Direct Tangible vs. Digitally Mediated Interaction and Attitude Moderation in Multi-party Murder Mystery Games

Wen Chen; Rongxi Chen; Shankai Chen; Huiyang Gong; Minghui Guo; Yingri Xu; Xintong Wu; Xinyi Fu

cs.ROMar 9, 2026

POIROT: Investigating Direct Tangible vs. Digitally Mediated Interaction and Attitude Moderation in Multi-party Murder Mystery Games

Authors: Wen Chen, Rongxi Chen, Shankai Chen, Huiyang Gong, Minghui Guo, Yingri Xu, Xintong Wu, Xinyi Fu

Organizations: Tsinghua University Beijing, China · University of Pennsylvania Philadelphia, USA · Syracuse University Syracuse, USA · National University of Singapore Singapore, Singapore ·

Abstract

As social robots take on increasingly complex roles like game masters (GMs) in multi-party games, the expectation that physicality universally enhances user experience remains debated. This study challenges the "one-size-fits-all" view of tangible interaction by identifying a critical boundary condition: users' Negative Attitudes towards Robots (NARS). In a between-subjects experiment (N = 67), a custom-built robot GM facilitated a multi-party murder mystery game (MMG) by delivering clues either through direct tangible interaction or a digitally mediated interface. Baseline multivariate analysis (MANOVA) showed no significant main effect of delivery modality, confirming that tangibility alone does not guarantee superior engagement. However, primary analysis using multilevel linear models (MLM) revealed a reliable moderation: participants high in NARS experienced markedly lower narrative immersion under tangible delivery, whereas those with low NARS scores showed no such decrement. Qualitative findings further illuminate this divergence: tangibility provides novelty and engagement for some but imposes excessive proxemic friction for anxious users, for whom the digital interface acts as a protective social buffer. These results advance a conditional model of HRI and emphasize the necessity for adaptive systems that can tailor interaction modalities to user predispositions.

Figures & tables

Figure 1. A gameplay session with POIROT. A picture showing the experimental setup. A group of participants sits around a rectangular dining table in a living-room-style laboratory environment. The custom-built robot, POIROT, stands on the table surface at the head of the table, facing the participants who are engaged in the game.

Narrative Immersion
	b	SE	95% CI	p
Intercept	1.72	1.19	[-0.61, 4.06]	0.147
Condition (App=0, Cards=1)	3.65	1.34	[1.01, 6.28]	0.007
NARS	1.03	0.38	[0.28, 1.78]	0.007
Condition $\times$ NARS	-1.27	0.43	[-2.10, -0.44]	0.003
ICC	0.130

Table 1. Multilevel Models Predicting Narrative Immersion, Game Experience, and Social Presence as a Function of Condition, NARS, and Their Interaction. Random Intercepts were Specified for Game Sessions.

Outcome	NARS level	b	SE	z	$\boldsymbol{p_{unc}}$	$\boldsymbol{p_{corr}}$
Narrative Immersion	Low ( $-1$ SD)	0.66	0.44	1.49	0.138	0.275
	High ( $+1$ SD)	-1.24	0.46	-2.72	0.007	0.014
Game Experience	Low ( $-1$ SD)	0.52	0.45	1.14	0.254	0.508
	High ( $+1$ SD)	-0.80	0.47	-1.72	0.085	0.170

Table 2. Simple Slopes of Condition (Physical-Card vs. In-App) Predicting Outcomes at Low (

-1

SD) and High (

+1

SD) Levels of NARS. P-values are Reported as Uncorrected (

p_{unc}

) and Bonferroni-Corrected (

p_{corr}

Panel of Figure 2 — Figure 2. The interaction effect of experimental condition (Physical-Card vs. In-App) and participants’ Negative Attitudes towards Robots Scale (NARS) on: (a) Narrative Immersion, (b) Game Experience. Shaded areas represent 95% confidence intervals.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Role	Identity	Core Motivation
Fang Qi	Defendant	Maintains innocence
Tang Yali	Victim’s daughter	Justice for her father
Ye Caiyang	Prosecutor	Legal conviction
Yin Hong	Defence atty.	Overturn guilty verdict
Chu Mihe	Nun / Witness	Reveal complete truth
Chen Haotong	Police officer	Support prosecution

Appendix

Table 3. Six-Character Roster for Judgment Quartet

Role	Example Private Clue Excerpts
Ye Caiyang (Prosecutor)	Vehicle CCTV logs, paint-scratch analysis, soil comparison, burial-path reconstruction
Tang Yali (Victim’s Daughter)	Chapel layout constraints, dead-end passages, terrain preventing escape
Fang Qi (Defendant)	Fragmented memories of the parking lot
Chu Mihe (Nun / Witness)	Records of prior disturbances, detailed timeline of the evening
Yin Hong (Defence Lawyer)	Hidden evidence: trunk mat with victim’s urine traces
Chen Haotong (Police)	Cause-of-death analysis, rope-soil forensics, missing earring, suspect bruises

Appendix

Table 4. Sample private clues provided to each role

Panel of Figure 3 — Figure 3. Companion app setup inter face in sequence: (a) room creation interface, (b) room joining interface, (c) role selection interface, and (d) example of a script reading interface (see Section 3.2.3 for details). Note: In the Physical-Card condition, the "Clues" button was also present in the script reading interface but inactive, since all clues were provided via physical delivery. Four sequential screenshots of the mobile app interface. (a) shows a "Create Game" screen with a green button and a Room ID. (b) shows a "Join Game" screen with an input field. (c) displays a list of character names with radio buttons for selection. (d) shows a sample of detailed script page (featuring a photo of a male character in a suit named Ye Caiyang.)

Panel of Figure 4 — Figure 4. Examples of physical clue cards (6.3 cm $\times$ 8.8 cm, laminated): (a) front side showing clue content, and (b) reverse side with neutral labeling. Private clues were dispensed individually to players, while public cards (eight per session) were dispensed at the table center before script reading started. A picture showing the front and back of a sample laminated clue card. The left image (front) contains text labeled "Exclusive Clue for the Police – Clue 3" followed by narrative text about a vehicle. The right image (back) shows the same label on a plain gray background without the narrative text.

Panel of Figure 5 — Figure 5. Digital clues panel: (a) overview of multiple digital clues, and (b) detail view of a single clue, opened by tapping the corresponding item from (a). Two screenshots of the digital clue interfaces on a tablet. The left image shows a scrollable gallery view with two columns of text blocks representing different testimonies. The right image shows a detailed view where a single clue titled "Witness Testimony 4" is displayed in large text.

Figure 6. The POIROT social robot used in the study, shown in its default idle posture. To evoke the style of Agatha Christie’s detective Poirot, a symbolic mustache decal was added beneath its display screen. A front-facing picture of POIROT, which is a white, 3D-printed social robot. It has a smooth, rounded shape with small flipper arms. Its face is a black iPhone screen displaying two pixelated eyes. To evoke the style of Agatha Christie’s detective Poirot, a symbolic mustache decal was added beneath its display screen.

Figure 7. The custom-built clue dispensing module: (left) physical implementation with friction roller above laminated card stack, and (right) electrical diagram showing USB-powered GA12-N20 motor, friction roller, and inline SPST toggle switch. No MCU or feedback mechanism was used. A composite image showing the technical design of the dispenser. The left side shows a 3D physical implementation of the clue cards dispenser containing a stack of cards and a friction roller mechanism. The right side is a schematic diagram illustrating the circuit, connecting a USB power source, a push switch, and a motor to the friction roller.

Component	Specification	Qty
Motor	GA12-N20, 6V, 50rpm DC motor	1
Friction Roller	3D-printed + rubber O-rings	1
Card Housing	PLA, 3D-printed structure	1
Switch	SPST toggle switch	1
Power Supply	USB-A 5V (adapter or power bank)	1
Wiring	2-pin cable	1

Appendix

Table 5. Bill of Materials for Clue Cards Dispenser Module

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When Should Robots Intervene? Balancing Engagement and Intrusiveness in Human-Robot Interaction

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Institut de Robòtica i Informàtica Industrial (CSIC-UPC) · Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain.

Human-Robot Interaction Social Robotics

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Designing effective Human-Robot Interaction in task-oriented settings requires carefully balancing user engagement with socially acceptable levels of robot intrusiveness. In this paper, we examine how different robot intervention strategies shape user experience, interaction dynamics, perceived intrusiveness, and sense of support. We compare two approaches: a continuous engagement-seeking robot strategy, and a context-aware strategy that selectively intervenes based on the user's state and task context. Both approaches rely on multimodal behavioral cues, including body orientation and attentional signals, to guide robot actions. We evaluate these strategies in a user study with 32 participants performing a task in a simulated hospital environment. Our findings show that higher interaction frequency does not necessarily lead to better engagement. Instead, we observe a systematic trade-off between perceived support and intrusiveness, influenced by factors such as physical proximity and user effort. These results provide empirical evidence that effective engagement in HRI depends on adaptive, context-sensitive intervention policies.

Abstract

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