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Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments
Achieving successful human–agent collaboration in the context of smart environments requires the modeling of human behavior for predicting people’s decisions. The goal of the current study was to utilize the TBR and the Alpha band as electrophysiological features that will discriminate between diffe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460739/ https://www.ncbi.nlm.nih.gov/pubmed/36080985 http://dx.doi.org/10.3390/s22176526 |
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author | Mizrahi, Dor Zuckerman, Inon Laufer, Ilan |
author_facet | Mizrahi, Dor Zuckerman, Inon Laufer, Ilan |
author_sort | Mizrahi, Dor |
collection | PubMed |
description | Achieving successful human–agent collaboration in the context of smart environments requires the modeling of human behavior for predicting people’s decisions. The goal of the current study was to utilize the TBR and the Alpha band as electrophysiological features that will discriminate between different tasks, each associated with a different depth of reasoning. To that end, we monitored the modulations of the TBR and Alpha, while participants were engaged in performing two cognitive tasks: picking and coordination. In the picking condition (low depth of processing), participants were requested to freely choose a single word out of a string of four words. In the coordination condition (high depth of processing), participants were asked to try and select the same word as an unknown partner that was assigned to them. We performed two types of analyses, one that considers the time factor (i.e., observing dynamic changes across trials) and the other that does not. When the temporal factor was not considered, only Beta was sensitive to the difference between picking and coordination. However, when the temporal factor was included, a transition occurred between cognitive effort and fatigue in the middle stage of the experiment. These results highlight the importance of monitoring the electrophysiological indices, as different factors such as fatigue might affect the instantaneous relative weight of intuitive and deliberate modes of reasoning. Thus, monitoring the response of the human–agent across time in human–agent interactions might turn out to be crucial for smooth coordination in the context of human–computer interaction. |
format | Online Article Text |
id | pubmed-9460739 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94607392022-09-10 Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments Mizrahi, Dor Zuckerman, Inon Laufer, Ilan Sensors (Basel) Article Achieving successful human–agent collaboration in the context of smart environments requires the modeling of human behavior for predicting people’s decisions. The goal of the current study was to utilize the TBR and the Alpha band as electrophysiological features that will discriminate between different tasks, each associated with a different depth of reasoning. To that end, we monitored the modulations of the TBR and Alpha, while participants were engaged in performing two cognitive tasks: picking and coordination. In the picking condition (low depth of processing), participants were requested to freely choose a single word out of a string of four words. In the coordination condition (high depth of processing), participants were asked to try and select the same word as an unknown partner that was assigned to them. We performed two types of analyses, one that considers the time factor (i.e., observing dynamic changes across trials) and the other that does not. When the temporal factor was not considered, only Beta was sensitive to the difference between picking and coordination. However, when the temporal factor was included, a transition occurred between cognitive effort and fatigue in the middle stage of the experiment. These results highlight the importance of monitoring the electrophysiological indices, as different factors such as fatigue might affect the instantaneous relative weight of intuitive and deliberate modes of reasoning. Thus, monitoring the response of the human–agent across time in human–agent interactions might turn out to be crucial for smooth coordination in the context of human–computer interaction. MDPI 2022-08-30 /pmc/articles/PMC9460739/ /pubmed/36080985 http://dx.doi.org/10.3390/s22176526 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mizrahi, Dor Zuckerman, Inon Laufer, Ilan Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments |
title | Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments |
title_full | Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments |
title_fullStr | Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments |
title_full_unstemmed | Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments |
title_short | Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments |
title_sort | electrophysiological features to aid in the construction of predictive models of human–agent collaboration in smart environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460739/ https://www.ncbi.nlm.nih.gov/pubmed/36080985 http://dx.doi.org/10.3390/s22176526 |
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