Evidence map›Paper›PMID 36899729›Full record

ArticleAnimals : an open access journal from MDPI2023

Social Reward Behaviour in Two Groups of European Grey Wolves (

Hana Tebelmann, Udo Gansloßer

Full text readCase Reports
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Hana TebelmannInstitute of Zoology and Evolutionary Research with Phyletic Museum, Friedrich Schiller University Jena, 07743 Jena, Germany.
Udo GansloßerInstitute of Zoology and Evolutionary Research with Phyletic Museum, Friedrich Schiller University Jena, 07743 Jena, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prosociality occurs in many species and is likely to be a crucial factor for the survival of group-living animals. Social feedback is an important mechanism for the coordination of group decisions. Since group-living animals with specific personality axes, i.e., boldness, are known to provide certain benefits for their group, bold actions might receive more prosocial feedback than other actions. Our case study aims to determine whether bold behaviour, i.e., novel object interaction (Nobj), might be answered more frequently with prosocial behaviours. We investigated the differences in the frequency of occurrence in prosocial behaviours after three different individual actions in two groups of grey wolves. We aim to outline the development of a social reward behavioural category as part of social feedback mechanisms. We used Markov chain models for probability analyses, and a non-parametric ANOVA to test for differences between the influences of individual behaviours on the probability of a prosocial behaviour chain. We additionally tested for the potential influences of age, sex and personality on the frequency of Nobj. Our results suggest that bold interactions are more often responded to with prosocial behaviour. Bold behaviour might be more often socially rewarded because of its benefits for group-living animals. More research is needed to investigate whether bold behaviour is more frequently responded to prosocially, and to investigate the social reward phenomenon.

Indexed as

behavioural chainsCanis lupus lupusmammalsnovel objectprosocialitysocial behavioursocial rewardwolves

Identifiers

PMID36899729
PMCPMC10000159

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read5
table measurements read4
reference markers read1
Read underepoch 390

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.