Special Issue "Human-Computer Interaction: New Horizons"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (30 June 2018).

Special Issue Editor

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

Special Issue Information

Dear Colleagues,

I have been asked to be the Guest Editor of a collection of innovative contributions on this increasingly critical, yet amorphous, subject—Human Computer Interaction.

The term Human Computer Interaction was coined soon after personal computers entered the marketplace in the 1970s. Since then, the definitions of both "computer" and "human" have changed drastically. Computers were once meant to be sitting on your desk, laps, or palms. Increasingly, however, the size and physical locations of computers has become irrelevant in defining the term. Likewise, the characterization of "human" in HCI research has also experienced crucial modifications. Humans, in olden days, were information processors that manipulate symbols just in the same way that LISP, a programming language, modifies variables. At the same time, research in cognitive science and behavioral economics revealed systematic fallibilities and limits in human "rational" and "statistical" thinking. Lately a new definition of humans—richly layered nodes and weights combined with new learning algorithms (i.e., deep learning)—emerged as the theme unifying humans and computers.

So, what is Human Computer Interaction now? We would say that it is about interactions between "computing" machines, each of which has its own limits and propensities. "Biological" machines (humans) can interact with "non-biological" machines (computers) via various means—a box sitting on a desk, a dot attached to a home appliance (e.g., Amazon Alexa), simple microprocessors (e.g., Arduino), or conductive yarn that responds to tactile stimulation. Non-biological machines can peek into the working of "biological machines" via electrodes attached to a scalp or fingers (EEG, EMG), or a magnet coil scanning blood flow in the brain (fMRI, fNIR). Though the means are different, both "machines" are embedded in computation.

Contributions are invited on all aspects of interactions between these two types of computing. Exactly what possibilities, problems, and limitations are ahead? What methodological, ethical, and technological innovations should be considered? How should human behavior be understood and modeled to make the two computing machines to communicate seamlessly? Possible themes include, but are not limited to:

  • Affective Computing
  • Human Behavior Understanding (HBU)
  • Artificial Intelligence
  • Deep Learning
  • Wearable Computing
  • Ubiquitous Computing
  • Ambient Computing
  • Brain Computer/Machine Interface
  • Data Mining and Statistical Analysis.
  • Cognitive Modeling
  • Cognitive Science
  • Social Information Processing

Prof. Dr. Takashi Yamauchi
Guest Editor

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. Mathematics is an international peer-reviewed open access monthly 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 1200 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

  • Affective Computing
  • Human Behavior Understanding (HBU)
  • Artificial Intelligence and Data Mining
  • Deep Learning
  • Wearable/Ubiquitous/Ambient Computing
  • Emotion and Cognition
  • Brain Computer/Machine Interface
  • Cognitive Science/Cognitive Modeling
  • Bayesian Modeling
  • Social Information Processing
  • Educational Technology

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Interactive Plants: Multisensory Visual-Tactile Interaction Enhances Emotional Experience
Mathematics 2018, 6(11), 225; https://doi.org/10.3390/math6110225 - 29 Oct 2018
Cited by 1
Abstract
Using a multisensory interface system, we examined how people’s emotional experiences change as their tactile sense (touching a plant) was augmented with visual sense (“seeing” their touch). Our system (the Interactive Plant system) senses the electrical capacitance of the human body and [...] Read more.
Using a multisensory interface system, we examined how people’s emotional experiences change as their tactile sense (touching a plant) was augmented with visual sense (“seeing” their touch). Our system (the Interactive Plant system) senses the electrical capacitance of the human body and visualizes users’ tactile information on a flat screen (when the touch is gentle, the program draws small and thin roots around the pot; when the touch is more harsh or abrupt, big and thick roots are displayed). We contrasted this multimodal combination (touch + vision) with a unimodal interface (touch only or watch only) and measured the impact of the multimodal interaction on participants’ emotion. We found significant emotional gains in the multimodal interaction. Participants’ self-reported positive affect, joviality, attentiveness and self-assurance increased dramatically in multimodal interaction relative to unimodal interaction; participants’ electrodermal activity (EDA) increased in the multimodal condition, suggesting that our plant-based multisensory visual-tactile interaction raised arousal. We suggest that plant-based tactile interfaces are advantageous for emotion generation because haptic perception is by nature embodied and emotional. Full article
(This article belongs to the Special Issue Human-Computer Interaction: New Horizons)
Show Figures

Figure 1

Open AccessArticle
Modeling Mindsets with Kalman Filter
Mathematics 2018, 6(10), 205; https://doi.org/10.3390/math6100205 - 16 Oct 2018
Abstract
Mathematical models have played an essential role in interface design. This study focused on “mindsets”—people’s tacit beliefs about attributes—and investigated the extent to which: (1) mindsets can be extracted in a motion trajectory in target selection, and (2) a dynamic state-space model, such [...] Read more.
Mathematical models have played an essential role in interface design. This study focused on “mindsets”—people’s tacit beliefs about attributes—and investigated the extent to which: (1) mindsets can be extracted in a motion trajectory in target selection, and (2) a dynamic state-space model, such as the Kalman filter, helps quantify mindsets. Participants were experimentally manipulated to hold fixed or growth mindsets in a “mock” memory test, and later performed a concept-learning task in which the movement of the computer cursor was recorded in every trial. By inspecting motion trajectories of the cursor, we observed clear disparities in the impact of mindsets; participants who were induced with a fixed mindset moved the cursor faster as compared to those who were induced with a growth mindset. To examine further the mechanism of this influence, we fitted a Kalman filter model to the trajectory data; we found that system-level error-covariance in the Kalman filter model could effectively separate motion trajectories gleaned from the two mindset conditions. Taken together, results from the experiment suggest that people’s mindsets can be captured in motor trajectories in target selection and the Kalman filter helps quantify mindsets. It is argued that people’s personality, attitude, and mindset are embodied in motor behavior underlying target selection and these psychological variables can be studied mathematically with a feedback control system. Full article
(This article belongs to the Special Issue Human-Computer Interaction: New Horizons)
Show Figures

Figure 1

Open AccessArticle
Causality Effects of Interventions and Stressors on Driving Behaviors under Typical Conditions
Mathematics 2018, 6(8), 139; https://doi.org/10.3390/math6080139 - 14 Aug 2018
Cited by 2
Abstract
In this paper, we demonstrate that interventions and stressors do not necessarily cause the same distractions in all people; therefore, it is impossible to evaluate the impacts of interventions and stressors on traffic accidents. We analyzed publicly available multimodal data that was collected [...] Read more.
In this paper, we demonstrate that interventions and stressors do not necessarily cause the same distractions in all people; therefore, it is impossible to evaluate the impacts of interventions and stressors on traffic accidents. We analyzed publicly available multimodal data that was collected through one of the largest controlled experiments on distracted driving. A crossover design was used to examine the effects of actual and perceived interventions and stressors in driving behaviors and parallel designs on reactivity to a startling event. To analyze this data and make recommendations, we developed and compared a wide variety of mixed effects statistical models and machine learning methods to evaluate the effects of interventions and stressors on driving behaviors. Full article
(This article belongs to the Special Issue Human-Computer Interaction: New Horizons)
Show Figures

Figure 1

Back to TopTop