Evidence map›Paper›PMID 37965633›Full record

ArticleFrontiers in robotics and AI2023

Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue.

André Groß, Amit Singh, Ngoc Chi Banh, Birte Richter, Ingrid Scharlau, Katharina J Rohlfing, Britta Wrede

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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.

André GroßMedical Assistance Systems, Medical School OWL, Bielefeld University, Bielefeld, Germany.
Amit SinghPsycholinguistics, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany.
Ngoc Chi BanhCognitive Psychology, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany.
Birte RichterMedical Assistance Systems, Medical School OWL, Bielefeld University, Bielefeld, Germany.
Ingrid ScharlauCognitive Psychology, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany.
Katharina J RohlfingPsycholinguistics, Faculty of Arts and Humanities, Paderborn University, Paderborn, Germany.
Britta WredeMedical Assistance Systems, Medical School OWL, Bielefeld University, Bielefeld, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts-measured as breaks in the finger movement data before the correct gesture was carried out-when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.

Indexed as

explaininggestureHRInegationtouch interactionunderstandingXAI

Identifiers

PMID37965633
PMCPMC10642948

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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.