ArticleFrontiers in neurorobotics2026
Designing for gender, age & proficiency: an empirical study of the cognitive ergonomics of dual-task interference in industrial human-robot collaboration.
Article in Frontiers in neurorobotics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: This article reports an empirical study on Human-Robot Collaboration (HRC) under Dual-Task Interference (DTI). To the best of our knowledge, prior research has not investigated the intersection of Dual-Task Interference (DTI) and Human-Robot Collaboration (HRC) in industrial contexts. This study represents one of the first empirical attempts to understand DTI-induced human limitations in collaborative robotics, particularly in industrial HRC settings where stochastic interruptions and competing cognitive demands are common phenomena. Methods: The DTI phenomenon was implemented in a cognitive joint-assembly task involving 172 participants. Each participant collaborated with two robots while simultaneously performing a cognitive task designed to induce DTI during team-work. The performance measures included the number of Human-Robot interactions (measuring user engagement), total robot idle time (measuring team fluency), and the total number of completed cognitive tasks (measuring task performance). Results: Females exhibited significantly lower robot idle times (12%) and higher Human-Robot interactions (14%), indicating greater collaboration with the robot and more fluent team collaboration than males. Adolescents completed the cognitive task significantly faster than adults (21%) and experts (22%), whereas adults and experts demonstrated greater fluency in team collaboration. No significant differences were observed between novices and experts. Novices successfully bridged the skill gap by distributing their attention equivalently across all aspects of the team-work, resulting in performance comparable to that of experts. Discussion: The results reveal that (a) gender and age-group differences significantly influence HRC in DTI-based tasks, (b) performance speed in the primary task negatively affects the team fluency of HRC, and (c) effective team coordination skills are equally as important as task proficiency in HRC. Conclusion: This study contributes to design practice by demonstrating that collaborative robot learning algorithms can classify users into specific population clusters based on their characteristic patterns of collaboration with robots for improved performance and user experience (UX). Furthermore, task proficiency and team coordination emerge as two distinct and mutually independent skills in DTI-based tasks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.