Evidence map›Paper›PMID 42635862›Full record

ArticleJournal of psycholinguistic research2026

A Methodological Framework for Studying Online Language-Vision Interplay in Human-Robot Interaction.

Amit Singh, Katharina J Rohlfing

Abstract read
In one paragraph

Article in Journal of psycholinguistic research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Amit SinghPsycholinguistics, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany. amit.singh@uni-paderborn.de.ORCID http://orcid.org/0000-0002-7789-1521
Katharina J RohlfingPsycholinguistics, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Investigating the real-time interplay between language and vision has traditionally involved a trade-off between experimental control and interactive setups. To address this challenge, we present a methodological framework that embeds the visual world paradigm (VWP) within a continuous, goal-directed human-robot interaction (HRI) task. By augmenting VWP in an HRI setup to produce standardized yet responsive verbal and non-verbal behaviours, the framework makes it possible to selectively examine comprehension, production, actions and their relation to visual attention in real-time. We demonstrate the utility of the framework through a proof-of-concept manipulating action structure, specifically path and manner, as well as other-initiated repair formats, namely open and restricted requests. The findings show that the setup reliably captures time-locked referential gaze dynamics following comprehension and preceding production processes. They further indicate that differences in action history systematically influence speakers' pre-verbal cognitive processes and their subsequent linguistic choices. These findings suggest that HRI can provide a useful methodological middle ground to investigate language processes online in a tightly controlled setup without disrupting the interaction. More broadly, the framework offers a test bed for examining whether established psycholinguistic findings extend to HRI setups. At the same time, it provides HRI researchers with an empirical basis for developing adaptive agents informed by online language-vision interaction theories.

Indexed as

Eye MovementsLanguagePsycholinguisticsRoboticsVisual PerceptionAdultAttentionComprehensionFemaleHumansMaleYoung AdultEye movementsHuman–robot interaction (HRI)Interactive methodsRepairs

Identifiers

PMID42635862
PMCPMC13503487

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.