Human group coordination in a sensorimotor task with neuron-like decision-making

Abstract The formation of cooperative groups of agents with limited information-processing capabilities to solve complex problems together is a fundamental building principle that cuts through multiple scales in biology from groups of cells to groups of humans. Here, we study an experimental paradig...

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Autores principales:	Gerrit Schmid, Daniel A. Braun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c656d7e73f6a467ba09e1d66a3b87de7
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spelling	oai:doaj.org-article:c656d7e73f6a467ba09e1d66a3b87de72021-12-02T14:58:53ZHuman group coordination in a sensorimotor task with neuron-like decision-making10.1038/s41598-020-64091-42045-2322https://doaj.org/article/c656d7e73f6a467ba09e1d66a3b87de72020-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-64091-4https://doaj.org/toc/2045-2322Abstract The formation of cooperative groups of agents with limited information-processing capabilities to solve complex problems together is a fundamental building principle that cuts through multiple scales in biology from groups of cells to groups of humans. Here, we study an experimental paradigm where a group of humans is joined together to solve a common sensorimotor task that cannot be achieved by a single agent but relies on the cooperation of the group. In particular, each human acts as a neuron-like binary decision-maker that determines in each moment of time whether to be active or not. Inspired by the population vector method for movement decoding, each neuron-like decision-maker is assigned a preferred movement direction that the decision-maker is ignorant about. From the population vector reflecting the group activity, the movement of a cursor is determined, and the task for the group is to steer the cursor into a predefined target. As the preferred movement directions are unknown and players are not allowed to communicate, the group has to learn a control strategy on the fly from the shared visual feedback. Performance is analyzed by learning speed and accuracy, action synchronization, and group coherence. We study four different computational models of the observed behavior, including a perceptron model, a reinforcement learning model, a Bayesian inference model and a Thompson sampling model that efficiently approximates Bayes optimal behavior. The Bayes and especially the Thompson model excel in predicting the human group behavior compared to the other models, suggesting that internal models are crucial for adaptive coordination. We discuss benefits and limitations of our paradigm regarding a better understanding of distributed information processing.Gerrit SchmidDaniel A. BraunNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-18 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gerrit Schmid Daniel A. Braun Human group coordination in a sensorimotor task with neuron-like decision-making
description	Abstract The formation of cooperative groups of agents with limited information-processing capabilities to solve complex problems together is a fundamental building principle that cuts through multiple scales in biology from groups of cells to groups of humans. Here, we study an experimental paradigm where a group of humans is joined together to solve a common sensorimotor task that cannot be achieved by a single agent but relies on the cooperation of the group. In particular, each human acts as a neuron-like binary decision-maker that determines in each moment of time whether to be active or not. Inspired by the population vector method for movement decoding, each neuron-like decision-maker is assigned a preferred movement direction that the decision-maker is ignorant about. From the population vector reflecting the group activity, the movement of a cursor is determined, and the task for the group is to steer the cursor into a predefined target. As the preferred movement directions are unknown and players are not allowed to communicate, the group has to learn a control strategy on the fly from the shared visual feedback. Performance is analyzed by learning speed and accuracy, action synchronization, and group coherence. We study four different computational models of the observed behavior, including a perceptron model, a reinforcement learning model, a Bayesian inference model and a Thompson sampling model that efficiently approximates Bayes optimal behavior. The Bayes and especially the Thompson model excel in predicting the human group behavior compared to the other models, suggesting that internal models are crucial for adaptive coordination. We discuss benefits and limitations of our paradigm regarding a better understanding of distributed information processing.
format	article
author	Gerrit Schmid Daniel A. Braun
author_facet	Gerrit Schmid Daniel A. Braun
author_sort	Gerrit Schmid
title	Human group coordination in a sensorimotor task with neuron-like decision-making
title_short	Human group coordination in a sensorimotor task with neuron-like decision-making
title_full	Human group coordination in a sensorimotor task with neuron-like decision-making
title_fullStr	Human group coordination in a sensorimotor task with neuron-like decision-making
title_full_unstemmed	Human group coordination in a sensorimotor task with neuron-like decision-making
title_sort	human group coordination in a sensorimotor task with neuron-like decision-making
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/c656d7e73f6a467ba09e1d66a3b87de7
work_keys_str_mv	AT gerritschmid humangroupcoordinationinasensorimotortaskwithneuronlikedecisionmaking AT danielabraun humangroupcoordinationinasensorimotortaskwithneuronlikedecisionmaking
_version_	1718389228437504000

Human group coordination in a sensorimotor task with neuron-like decision-making

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