Пространственный компонент в структуре глаголов физического действия

Цель данной статьи – определить роль пространственного компонента в семантической структуре глаголов физического действия

Theory-based Habit Modeling for Enhancing Behavior Prediction

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors (e.g., toothbrushing) is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users' habit strengths can be very useful for behavior change support systems (BCSS), for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, built on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies, where we trained participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. Through hierarchical cross-validation, we found that for the task of predicting future brushing behavior, computed habit strength clearly outperformed self-reported habit strength (in both studies) and was also superior to models based on past behavior frequency (in the larger second study). Our findings provide initial support for our theory-based approach of modeling user habits and encourages the use of habit computation to deliver personalized and adaptive interventions

Intelligent Mobile Support for Therapy Adherence and Behavior Change

Mobile applications have proven to be a promising tool to support people in adhering to their health goals. Although coaching and reminder apps abound, few of them are based on established theories of behavior change. In the present work, a behavior change support system is presented that uses a computational model based on multiple psychological theories of behavior change. The system determines the user’s reason for non-adherence using a mobile phone app and an online lifestyle diary. The user automatically receives generated messages with persuasive, tailored content. The system was designed to support chronic patients with type 2 diabetes, HIV, and cardiovascular disease, but can be applied to many health and lifestyle domains. The main focus of this work is the development of the model and the underlying reasoning method. Furthermore, the implementation of the system and some preliminary results of its functioning will be discussed

Conceptual and Computational Analysis of the Role of Emotions and Social Influence in Learning

In this paper it is analysed how emotions and the social environment affect active and reflective learning processes. First a conceptual analysis is made using recent insights from Cognitive, Affective and Social Neuroscience on the roles of emotions and social interactions on learning. Next, a computational analysis is made using a computational model of learning processes following these insights. In this analysis neural mechanisms for the impact of both a person’s own emotions and the emotions of others are taken into account. In particular, it is considered how these impacts work for different learning types, such as active or reflective learners. The analysis shows how the impacts of emotions and social interaction strengthen the learning process. It is discussed how from these insights indicators can be obtained that can be used to design technology-enhanced learning environments able to exploit these impacts. © 2012 Published by Elsevier Ltd

Analysis and Support of Lifestyle via Emotions Using Social Media

Abstract. Using recent insights from Cognitive, Affective and Social Neuroscience this paper addresses how affective states in social interactions can be used through social media to analyze and support behaviour for a certain lifestyle. A computational model is provided integrating mechanisms for the impact of one’s emotions on behaviour, and for the impact of emotions of others on one’s own emotion. The model is used to reason about and assess the state of a user with regard to a lifestyle goal (such as exercising frequently), based on extracted information about emotions exchanged in social interaction. Support is provided by proposing ways to affecting these social interactions, thereby indirectly influencing the impact of the emotions of others. An ambient intelligent system incorporating this has been implemented for the social medium Twitter

Developing a virtual coach for chronic patients: A user study on the impact of similarity, familiarity and realism

Healthcare costs are increasing dramatically due to disproportional consumption of healthcare resources by chronic patients. Automated forms of health coaching can contribute to improved patient self-management while reducing costs due to increased scalability and availability of the use of human health coaches. Embodied Conversational Agents (ECAs) seem to be good candidates to function as automated coaches, as they introduce a social component to human-computer interactions which makes them particularly suitable to influence a user’s attitude or behavior. To date, there is limited knowledge on the impact of appearance-related characteristics of an ECA as a virtual coach among a chronically ill elderly population. The primary aim of this study is to investigate the impact of three appearance cues on user acceptance: (i) similarity; (ii) familiarity; and (iii) realism. Findings demonstrate that patients (a) preferred the realistic-looking ECA over the more stylized one; (b) showed no preference for the familiar over the unfamiliar ECAs (but did evaluate the unfamiliar ECAs as more positive than the familiar one); and (c) evaluated an ECA as virtual coach for self-management support as useful