Evidence map›Paper›PMID 40329976›Full record

ArticleFrontiers in robotics and AI2025

Effects of gentle and rude vitality forms in social robots on humans during cognitive multitasking.

Motonobu Aoki, Francesco Rea, Giuseppe Di Cesare, Giulio Sandini, Takura Yanagi, Atsushi Takamatsu, Tomohiro Yamamura

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Motonobu AokiMobility and AI Laboratory, Nissan Motor Co., Ltd., Atsugi, Kanagawa, Japan.
Francesco ReaDepartment of Robotics Brain and Cognitive Sciences, Italian Institute of Technology, Genoa, Liguria, Italy.
Giuseppe Di CesareDepartment of Medicine and Surgery, The University of Parma, Parma, Emilia-Romagna, Italy.
Giulio SandiniDepartment of Robotics Brain and Cognitive Sciences, Italian Institute of Technology, Genoa, Liguria, Italy.
Takura YanagiMobility and AI Laboratory, Nissan Motor Co., Ltd., Atsugi, Kanagawa, Japan.
Atsushi TakamatsuMobility and AI Laboratory, Nissan Motor Co., Ltd., Atsugi, Kanagawa, Japan.
Tomohiro YamamuraResearch Planning Department, Nissan Motor Co., Ltd., Atsugi, Kanagawa, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Designing a social humanoid robot to enhance human cognitive multitasking in a mixed human-robot team is anything but straightforward. In fact, the robot's presence and behavior can either improve or impair human performance. In this study, we examined how different vitality forms-expressed through the robot's actions, speech and facial expressions-affect cognitive multitasking. Analysis of human facial expressions and skin conductance data revealed that a robot exhibiting a gentle vitality form fostered a more positive and relaxed state than one displaying a rude vitality form. Moreover, the gentle vitality form improved human performance on tasks involving short-term memory and continuous target tracking. To our knowledge, this is the first study to explore the long-term impact of vitality forms. Overall, our findings suggest that properly designing a social humanoid robot's vitality forms can significantly enhance cognitive multitasking performance in human-robot teams.

Indexed as

cognitive multitaskingfacial expressionshuman-robot interactionmental workloadsocial robotsvitality forms

Identifiers

PMID40329976
PMCPMC12053143

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.