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Special Issue "Sensors for Behavioral Science—Social, Affective, and Cognitive Science Perspectives"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Takashi Yamauchi
Website
Guest Editor
Dept. of Psychological and Brain Sciences, Texas A&M University" to be "Department of Psychological and Brain Sciences, Texas A&M University, USA
Interests: cognitive science; cognitive neuroscience; psychopathology; affective computing; neuroeconomics; cognitive computational neuroscience; computational psychiatry
Special Issues and Collections in MDPI journals
Prof. Dr. Theodora Chaspari
Website
Guest Editor
Department of Computer Science & Engineering, Texas A&M University
Interests: machine learning; signal processing; affective computing; ambulatory monitoring; behavioral signal processing; speech; physiology

Special Issue Information

Dear Colleagues,

Sensor technologies have changed the landscape of behavioral science. Traditional measures of behavior, response time, and accuracy have been supplemented with genetic, physiological, and activity-based indices taken from high-end imaging equipment and off-the-shelf wearable devices. This Special Issue focuses on research, development, and applications of sensors as analytical tools for social, affective, and cognitive science, and cognitive neuroscience. The use of sensors in behavioral science is diverse, ranging from medical-level brain sensors (e.g., fMRI, PET, EEG, fNIR, EDA) to off-the-shelf consumer-grade wearables (Kinect, MUSE, wearables, smartphones, tablets, smart speakers, VR, AR, pupillometry, and eye trackers); it includes creative applications of IoT devices (e.g., Arduino, Raspberry Pi) in the arena of ubiquitous and ambient computing. This Special Issue aims to organize these far-reaching applications of sensors in behavioral science across a potential range of coherent themes, including (1) experimental design and data acquisition; (2) hypothesis-driven research melded with sensor devices; (3) data cleaning and processing; and (4) sensor data analysis.

The topical areas include psychopathology, neuroeconomics, decision making, biofeedback, cognitive control, emotion regulation, interpersonal communication, personality, temperament, cognitive computational neuroscience, computational psychiatry, mental health intervention, teaching, and learning. Innovative and creative use of sensors, theory-driven data analysis, data fusion techniques, Bayesian cognitive modeling, machine learning, probabilistic programming, neural network, and reinforcement learning are also welcome. The main motive is to apply sensor technologies to advance our understanding of human behavior as manifested in emotion, social interaction, cognition, and mental health.

We would like to invite you to participate by submitting original research papers, review articles, short commentaries, theoretical inquiries, and/or tutorials about the use of sensors in human behavior understanding.

Prof. Dr. Takashi Yamauchi
Prof. Dr. Theodora Chaspari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cognitive science
  • cognitive neuroscience
  • wearables
  • IoT
  • EEG
  • neuroimaging
  • EDA
  • peripheral physiology
  • ambulatory monitoring
  • data quality concerns and mitigation

Published Papers (2 papers)

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Research

Open AccessArticle
Speech Discrimination in Real-World Group Communication Using Audio-Motion Multimodal Sensing
Sensors 2020, 20(10), 2948; https://doi.org/10.3390/s20102948 - 22 May 2020
Abstract
Speech discrimination that determines whether a participant is speaking at a given moment is essential in investigating human verbal communication. Specifically, in dynamic real-world situations where multiple people participate in, and form, groups in the same space, simultaneous speakers render speech discrimination that [...] Read more.
Speech discrimination that determines whether a participant is speaking at a given moment is essential in investigating human verbal communication. Specifically, in dynamic real-world situations where multiple people participate in, and form, groups in the same space, simultaneous speakers render speech discrimination that is solely based on audio sensing difficult. In this study, we focused on physical activity during speech, and hypothesized that combining audio and physical motion data acquired by wearable sensors can improve speech discrimination. Thus, utterance and physical activity data of students in a university participatory class were recorded, using smartphones worn around their neck. First, we tested the temporal relationship between manually identified utterances and physical motions and confirmed that physical activities in wide-frequency ranges co-occurred with utterances. Second, we trained and tested classifiers for each participant and found a higher performance with the audio-motion classifier (average accuracy 92.2%) than both the audio-only (80.4%) and motion-only (87.8%) classifiers. Finally, we tested inter-individual classification and obtained a higher performance with the audio-motion combined classifier (83.2%) than the audio-only (67.7%) and motion-only (71.9%) classifiers. These results show that audio-motion multimodal sensing using widely available smartphones can provide effective utterance discrimination in dynamic group communications. Full article
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Open AccessFeature PaperArticle
Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment
Sensors 2020, 20(10), 2750; https://doi.org/10.3390/s20102750 - 12 May 2020
Abstract
Simulations and games bring the possibility to research complex processes of managerial decision-making. However, this modern field requires adequate methodological procedures. Many authors recommend the use of a combination of concurrent think-aloud (CTA) or retrospective think-aloud (RTA) with eye-tracking to investigate cognitive processes [...] Read more.
Simulations and games bring the possibility to research complex processes of managerial decision-making. However, this modern field requires adequate methodological procedures. Many authors recommend the use of a combination of concurrent think-aloud (CTA) or retrospective think-aloud (RTA) with eye-tracking to investigate cognitive processes such as decision-making. Nevertheless, previous studies have little or no consideration of the possible differential impact of both think-aloud methods on data provided by eye-tracking. Therefore, the main aim of this study is to compare and assess if and how these methods differ in terms of their impact on eye-tracking. The experiment was conducted for this purpose. Participants were 14 managers who played a specific simulation game with CTA use and 17 managers who played the same game with RTA use. The results empirically prove that CTA significantly distorts data provided by eye-tracking, whereas data gathered when RTA is used, provide independent pieces of evidence about the participants’ behavior. These findings suggest that RTA is more suitable for combined use with eye-tracking for the purpose of the research of decision-making in the game environment. Full article
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