Rapid withdrawal from a threatening animal is movement-specific and mediated by reflex-like neural processing.

Responses to potentially dangerous stimuli are among the most basic animal behaviors. While research has shown that threats automatically capture the attention of human participants, research has failed to demonstrate automatic behavioral responses to threats in humans. Using a novel naturalistic paradigm, we show that two species of animals humans often report fearing trigger rapid withdrawal responses: participants withdrew their arm from photos of snakes and spiders faster, and with higher acceleration when compared to bird and butterfly stimuli. The behavior was specific to withdrawal as approach movements or button-press/release tasks failed to detect a similar difference. Moreover, between-participant differences in how aversive they found the stimuli predicted the participant's withdrawal speed, indicating that the paradigm was also sensitive to trait-level differences between individuals. Using electroencephalography (EEG), we show that the fast withdrawal was mediated by two attentional processes. First, fast withdrawal responses were associated with early amplification of sensory signals (40-110 ms after stimulus). Second, a later correlate of feature-based attention (early posterior negativity, EPN, 200-240 ms after stimulus) revealed the opposite pattern: Stronger EPN was associated with slower behavioral responses, suggesting that the deployment of attention towards the threatening stimulus features, or failure to "disengage" attention from the stimulus, was detrimental for withdrawal speed. Altogether, the results suggest that rapid behavioral withdrawal from a threatening animal is mediated by reflex-like attentional processing, and later, conscious attention to stimulus features may hinder escaping the treat.

Adolescents' perceptions of active school transport in northern Sweden.

Active school transport (AST) refers to using active means of transport such as walking, cycling, or riding a non-motorised scooter to school. It can help improve adolescents' physical activity levels and create a more sustainable environment. The study involved 70 adolescents (45 boys and 25 girls) aged 13 to 14 from one school in Skellefteå, in Northern Sweden. In an online questionnaire, they were asked about their perceptions of cycling, walking, and riding a non-motorised scooter to school. This study used descriptive statistics, multiple regression analysis, and hypothesis testing with ANOVA to analyse the collected data and compare the perceptions of different types of transport on safety, environmental, and personal factors among adolescents in Northern Sweden. According to the results, more adolescents walked to school than cycled, and significantly few rode a non-motorised scooter to school. Most adolescents believe walking or cycling to school is a great way to exercise. Furthermore, the study also revealed that many adolescents avoid using AST due to the time it takes. Although the study showed that adolescents felt sufficient support for using AST from schools and parents, the number of adolescents using motorised transport is higher during winter than in summer. Additionally, most of them were more confident about cycling and walking to school than riding a non-motorised scooter and thought using AST was nice. Finally, most adolescents perceived having complete control over their transport options when going to school. The research indicates that it is crucial to implement interventions that inspire children to be interested and excited about using AST. These strategies should include fostering an AST culture that is fun and positive, as well as creating environments that are safe and supportive. The research results will guide the creation of a persuasive game that can motivate adolescents to use AST and measure its effectiveness.

A Security Framework for Increasing Data and Device Integrity in Internet of Things Systems.

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices' collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework's usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions' outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.

Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment.

Virtual Reality (VR) has been adopted as a leading technology for the metaverse, yet most previous VR systems provide one-size-fits-all experiences to users. Context-awareness in VR enables personalized experiences in the metaverse, such as improved embodiment and deeper integration of the real world and virtual worlds. Personalization requires context data from diverse sources. We proposed a reusable and extensible context data collection framework, ManySense VR, which unifies data collection from diverse sources for VR applications. ManySense VR was implemented in Unity based on extensible context data managers collecting data from data sources such as an eye tracker, electroencephalogram, pulse, respiration, galvanic skin response, facial tracker, and Open Weather Map. We used ManySense VR to build a context-aware embodiment VR scene where the user's avatar is synchronized with their bodily actions. The performance evaluation of ManySense VR showed good performance in processor usage, frame rate, and memory footprint. Additionally, we conducted a qualitative formative evaluation by interviewing five developers (two males and three females; mean age: 22) after they used and extended ManySense VR. The participants expressed advantages (e.g., ease-of-use, learnability, familiarity, quickness, and extensibility), disadvantages (e.g., inconvenient/error-prone data query method and lack of diversity in callback methods), future application ideas, and improvement suggestions that indicate potential and can guide future development. In conclusion, ManySense VR is an efficient tool for researchers and developers to easily integrate context data into their Unity-based VR applications for the metaverse.

A Reusable Multiplayer Game for Promoting Active School Transport: Development Study.

BACKGROUND: Most children and adolescents in Sweden do not meet the recommended daily physical activity levels of the World Health Organization. Active school transport (AST) and gamification are potential methods for increasing children's daily physical activity. We previously developed a game named Tic-Tac-Training for promoting active transport at workplaces; however, the game has not been applied to AST. OBJECTIVE: The objectives of this study are to investigate how Tic-Tac-Training functions to promote AST among schoolchildren in northern Sweden, improve the game to be more suitable for schoolchildren, and construct a road map for future development based on children's ideas. METHODS: First, we developed Tic-Tac-Training using the Scrum agile software development method. Second, we conducted a questionnaire-based formative evaluation of the game with schoolchildren (n=16; 9/16, 56% male; 6/16, 38% female; and 1/16, 6% other aged 11-12 years) in Luleå, Sweden. Third, we conducted focus group interviews with 33 children (13/33, 39% male and 20/33, 61% female aged 12-13 years) to gather ideas for gamifying AST. We mapped the interview results to the Octalysis gamification framework and established a road map for future development. RESULTS: The formative evaluation revealed several issues, including a lack of interesting game features, lack of support for continuous engagement, disliked competitive features, and lack of incentives for discourse and participation. New features such as rewards, collectibles, and levels were implemented based on the results. The focus group interviews revealed additional ideas for gamifying AST, such as using avatars, in-game currency and trading, and context-sensitive tasks. CONCLUSIONS: The results have several potential impacts on how reusable, gamified AST interventions can be developed and what kind of gamification elements schoolchildren in northern Sweden wish to see. These results can interest game researchers and teachers who wish to apply gamification in school contexts. Finally, we aim to continue developing the game based on the road map.

Co-design of mini games for learning computational thinking in an online environment.

Understanding the principles of computational thinking (CT), e.g., problem abstraction, decomposition, and recursion, is vital for computer science (CS) students. Unfortunately, these concepts can be difficult for novice students to understand. One way students can develop CT skills is to involve them in the design of an application to teach CT. This study focuses on co-designing mini games to support teaching and learning CT principles and concepts in an online environment. Online co-design (OCD) of mini games enhances students' understanding of problem-solving through a rigorous process of designing contextual educational games to aid their own learning. Given the current COVID-19 pandemic, where face-to-face co-designing between researchers and stakeholders could be difficult, OCD is a suitable option. CS students in a Nigerian higher education institution were recruited to co-design mini games with researchers. Mixed research methods comprising qualitative and quantitative strategies were employed in this study. Findings show that the participants gained relevant knowledge, for example, how to (i) create game scenarios and game elements related to CT, (ii) connect contextual storyline to mini games, (iii) collaborate in a group to create contextual low-fidelity mini game prototypes, and (iv) peer review each other's mini game concepts. In addition, students were motivated toward designing educational mini games in their future studies. This study also demonstrates how to conduct OCD with students, presents lesson learned, and provides recommendations based on the authors' experience.

Learning History with Location-Based Applications: An Architecture for Points of Interest in Multiple Layers.

Location-based applications (LBAs) capture the user's physical location via satellite navigation sensors and integrate it as part of the digital application. Because of this connection, the real-world environment needs to be accounted for in LBA design. In this work, we focused on creating a database of geographically distributed points of interest (PoIs) that is optimal for learning local history. First, we conducted a requirements elicitation study at three outdoor archaeological sites and identified issues in existing solutions. Second, we designed a multi-layered prototype solution. Third, we evaluated the solution with nine experts who had prior experience with LBAs or similar systems. We incorporated their feedback to our design to iteratively improve it. As a whole, our work contributes to the LBA design literature by proposing a solution that is optimized for the learning of local history.

EEG-Based Emotion Classification for Alzheimer's Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models.

As the number of patients with Alzheimer's disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential pathway for developing healthcare services for AD patients. For instance, if a virtual reality (VR) system can provide emotion-adaptive content, the time that AD patients spend interacting with VR content is expected to be extended, allowing caregivers to focus on other tasks. As the first step towards this goal, in this study, we develop a classification model that detects AD patients' emotions (e.g., happy, peaceful, or bored). We first collected electroencephalography (EEG) data from 30 Korean female AD patients who watched emotion-evoking videos at a medical rehabilitation center. We applied conventional machine learning algorithms, such as a multilayer perceptron (MLP) and support vector machine, along with deep learning models of recurrent neural network (RNN) architectures. The best performance was obtained from MLP, which achieved an average accuracy of 70.97%; the RNN model's accuracy reached only 48.18%. Our study results open a new stream of research in the field of EEG-based emotion detection for patients with neurological disorders.

Did location-based games motivate players to socialize during COVID-19?

Location-based games (LBGs) are typically played outdoors, as moving in the game is done by moving in the real world. However, during the COVID-19 pandemic, people were advised and even forced by governments to stay home and avoid social contact to slow down the spreading of the virus. The major LBG developers reacted by making in-game adjustments that allow playing from home, while still maintaining some incentives for players to go outdoors and socialise. For investigating factors influencing intention to play LBGs socially during the on-going pandemic, we collected cross-sectional survey data (N = 855) from Finnish players of the most popular LBG, Pokémon GO. The results showed that perceived severity of the pandemic and a positive attitude towards both governmental measures and in-game changes for combatting COVID-19 predicted intention to reduce social playing. Fear of missing out and deficient self-regulation increased playing intensity, which in turn negatively correlated with the intention to reduce social playing. Our findings demonstrate the influence that LBGs can have on human behaviour even during global crises such as COVID-19. As such, LBGs can be considered a resource in designing interventions for influencing movement at a population level.

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data.

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user's emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants' questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.

Unlike in clinical blindsight patients, unconscious processing of chromatic information depends on early visual cortex in healthy humans.

BACKGROUND: Large amount of data concerning the neural mechanisms that mediate unconscious vision come from cortically blind patients who can process stimuli presented in their blind visual field. How well the findings generalize to neurologically healthy humans remains open. OBJECTIVE: Using transcranial magnetic stimulation (TMS), we studied whether chromatic processing depends on the early visual cortex in healthy participants. METHOD: We employed a phenomenon called the redundant target effect (RTE): simple reaction times to two stimuli are faster than to one stimulus, even when one of the stimuli is presented outside consciousness. RESULTS: The RTE produced by chromatically defined stimuli disappeared when the contralateral stimulus of two bilateral stimuli was suppressed from consciousness. CONCLUSIONS: In contrast to studies on blindsight patients, the results imply that the early visual cortex is necessary for the processing of chromatic information in neurologically healthy humans.