Third-party punishment by preverbal infants

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Abstract

Third-party punishment of antisocial others is unique to humans and seems to be universal across cultures. However, its emergence in ontogeny remains unknown. We developed a participatory cognitive paradigm using gaze-contingency techniques, in which infants can use their gaze to affect agents displayed on a monitor. In this paradigm, fixation on an agent triggers the event of a stone crushing the agent. Throughout five experiments (total N = 120), we show that eight-month-old infants punished antisocial others. Specifically, infants increased their selective looks at the aggressor after watching aggressive interactions. Additionally, three control experiments excluded alternative interpretations of their selective gaze, suggesting that punishment-related decision-making influenced looking behaviour. These findings indicate that a disposition for third-party punishment of antisocial others emerges in early infancy and emphasize the importance of third-party punishment for human cooperation. This behavioural tendency may be a human trait acquired over the course of evolution.

Abstract

Using a participatory cognitive paradigm based on gaze contingency, Kanakogi et al. show that eight-month-olds increased their selective gaze to antisocial others to punish them, suggesting that preverbal infants engage in third-party punishment.

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brms: An R Package for Bayesian Multilevel Models Using Stan

Paul-Christian Bürkner (2017)

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Random effects structure for confirmatory hypothesis testing: Keep it maximal.

Dale Barr, Roger Levy, Christoph Scheepers … (2013)

Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.

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Stan: A Probabilistic Programming Language

Daniel Z Lee, Michael Betancourt, Marcus A. Brubaker … (2017)

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

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Author and article information

Contributors

Yasuhiro Kanakogi:

ORCID: http://orcid.org/0000-0003-0636-1619

y-kanakogi@hus.osaka-u.ac.jp

Journal

Journal ID (nlm-ta): Nat Hum Behav

Journal ID (iso-abbrev): Nat Hum Behav

Title: Nature Human Behaviour

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2397-3374

Publication date (Electronic): 9 June 2022

Publication date PMC-release: 9 June 2022

Publication date (Print): 2022

Volume: 6

Issue: 9

Pages: 1234-1242

Affiliations

[1 ]GRID grid.136593.b, ISNI 0000 0004 0373 3971, Graduate School of Human Sciences, , Osaka University, ; Suita, Japan

[2 ]GRID grid.412426.7, ISNI 0000 0001 0683 0599, Faculty of Social Information Studies, , Otsuma Women’s University, ; Chiyoda-ku, Japan

[3 ]GRID grid.136593.b, ISNI 0000 0004 0373 3971, Graduate School of Engineering Science, , Osaka University, ; Toyonaka, Japan

[4 ]GRID grid.258799.8, ISNI 0000 0004 0372 2033, Graduate School of Letters, , Kyoto University, ; Kyoto, Japan

[5 ]GRID grid.419819.c, ISNI 0000 0001 2184 8682, NTT Communication Science Laboratories, ; Seika, Japan

[6 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Graduate School of Arts and Sciences, , The University of Tokyo, ; Meguro-ku, Japan

Author information

Yasuhiro Kanakogi http://orcid.org/0000-0003-0636-1619

Michiko Miyazaki http://orcid.org/0000-0003-2952-2745

Hideyuki Takahashi http://orcid.org/0000-0001-9313-0346

Hiroki Yamamoto http://orcid.org/0000-0003-1051-6599

Tessei Kobayashi http://orcid.org/0000-0002-6671-460X

Article

Publisher ID: 1354

DOI: 10.1038/s41562-022-01354-2

PMC ID: 9489529

PubMed ID: 35680993

SO-VID: 9ec27885-85b0-46bb-a95f-160da7f2137f

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 16 January 2021

Date accepted : 1 April 2022

Funding

Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);

Award ID: 20H04495

Award ID: 16K21341

Award ID: 20H05555

Award Recipient : Yasuhiro Kanakogi Michiko Miyazaki Kazuo Hiraki

Funded by: FundRef https://doi.org/10.13039/501100002241, MEXT | Japan Science and Technology Agency (JST);

Award ID: JPMJCR18A4

Award Recipient : Kazuo Hiraki

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Keywords: human behaviour,development studies

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Keywords: human behaviour, development studies

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Third-party punishment by preverbal infants

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Most cited references 46

brms: An R Package for Bayesian Multilevel Models Using Stan

Random effects structure for confirmatory hypothesis testing: Keep it maximal.

Stan: A Probabilistic Programming Language

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