Cited by 141Social Robot Interaction and HRISpeech and dialogue systemsAI in Service Interactions

The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.

Vulnerable robots positively shape human conversational dynamics in a human–robot team

Open access
Traeger, Margaret L., Strohkorb Sebo, Sarah, Jung, Malte, Scassellati, Brian, Christakis, Nicholas A.·2020·Proceedings of the National Academy of Sciences

Abstract

Social robots are becoming increasingly influential in shaping the behavior of humans with whom they interact. Here, we examine how the actions of a social robot can influence human-to-human communication, and not just robot–human communication, using groups of three humans and one robot playing 30 rounds of a collaborative game ( n = 51 groups). We find that people in groups with a robot making vulnerable statements converse substantially more with each other, distribute their conversation somewhat more equally, and perceive their groups more positively compared to control groups with a robot that either makes neutral statements or no statements at the end of each round. Shifts in robot speech have the power not only to affect how people interact with robots, but also how people interact with each other, offering the prospect for modifying social interactions via the introduction of artificial agents into hybrid systems of humans and machines.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.1910402117

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1. Total talking time by condition. Compared to the neutral and silent conditions, human participants in the vulnerable condition spoke more, in total, to the other participants in their group, and increasingly across game rounds. In A, we see that participants in the vulnerable condition (V) spoke significantly more than participants in either the neutral (N) or silent (S) condition (n = 153 participants). In B, the line widths represent the amount of talking by human participants toward their teammates who are connected by the line, in seconds (summed across all groups within a condition (n = 153 participants)). R = robot; P1, P2, and P3 = human participants, in their relative positions around the table. In C and D, the shaded area around each line represents a 95% confidence interval (n = 4,590 rounds); the dots represent the condition average for that round. In C, the vulnerable condition has more talking in every round, and the slope (i.e., the rate of increase in talking per round, across rounds) is higher than the neutral condition (but indistinguishable from the silent condition). In D we see that, compared to the neutral condition, those in the vulnerable condition respond more over time to their fellow human group members (n = 4,590 rounds).
Fig. 1. Total talking time by condition. Compared to the neutral and silent conditions, human participants in the vulnerable condition spoke more, in total, to the other participants in their group, and increasingly across game rounds. In A, we see that participants in the vulnerable condition (V) spoke significantly more than participants in either the neutral (N) or silent (S) condition (n = 153 participants). In B, the line widths represent the amount of talking by human participants toward their teammates who are connected by the line, in seconds (summed across all groups within a condition (n = 153 participants)). R = robot; P1, P2, and P3 = human participants, in their relative positions around the table. In C and D, the shaded area around each line represents a 95% confidence interval (n = 4,590 rounds); the dots represent the condition average for that round. In C, the vulnerable condition has more talking in every round, and the slope (i.e., the rate of increase in talking per round, across rounds) is higher than the neutral condition (but indistinguishable from the silent condition). In D we see that, compared to the neutral condition, those in the vulnerable condition respond more over time to their fellow human group members (n = 4,590 rounds).CC BY · PubMed Central
Fig. 2. Equality of conversation by condition. Although there was (A) no statistical difference between the vulnerable (V) and neutral (N) conditions in the equality in talking time (n = 150 participants; one group did not speak at all and was excluded), the silent (S) condition was less equal than either of the other two conditions. (B) Also, participants in the vulnerable condition directed their utterances more equally to each of their human group members than participants in the silent condition, as measured by the total amount of time spent talking to each participant’s two human partners (n = 144 participants; participants who didn’t speak at all or who did not make directed utterances were excluded).
Fig. 2. Equality of conversation by condition. Although there was (A) no statistical difference between the vulnerable (V) and neutral (N) conditions in the equality in talking time (n = 150 participants; one group did not speak at all and was excluded), the silent (S) condition was less equal than either of the other two conditions. (B) Also, participants in the vulnerable condition directed their utterances more equally to each of their human group members than participants in the silent condition, as measured by the total amount of time spent talking to each participant’s two human partners (n = 144 participants; participants who didn’t speak at all or who did not make directed utterances were excluded).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 49 references

Cited by

Sorted by citations

See all 141

Metadata

Authors
Traeger, M. L., Strohkorb Sebo, S., Jung, M., Scassellati, B., & Christakis, N. A.
Affiliations
  • Yale University (US)
  • Department of Computer Science, Yale University, New Haven, CT 06520;
  • Cornell University (US)
Corresponding author
Christakis, Nicholas A.
Venue
Proceedings of the National Academy of Sciences
Published
2020-03-09
Volume
117
Issue
12
Pages
6370-6375
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W3009999177
Cited by
140
References
49
Citations by year
2026: 16 · 2025: 22 · 2024: 23 · 2023: 13 · 2022: 27 · 2021: 25 · 2020: 13
FWCI
21.34
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • Robert Wood Johnson Foundation (75421)
  • National Science Foundation (1139078, GR104893)
Topics
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
Keywords
social robots, human-robot teams, human-to-human communication, conversational dynamics, collaborative games, group interaction, robot speech, hybrid human-machine systems
MeSH terms
Communication, Cooperative Behavior, Group Processes, Humans, Interpersonal Relations, Robotics, Social Behavior

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review