Socio-cognitive and affective computing / / edited by Antonio Fernández-Caballero [and three others].

• TeilnehmendeR: Fernández-Caballero, Antonio,
• Place / Publishing House: Basel, Switzerland : : MDPI - Multidisciplinary Digital Publishing Institute,, 2018.
• Year of Publication: 2018
• Language: English
• Physical Description: 1 online resource (254 pages)

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in our social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Thus, Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Moreover, Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This Special Issue on Socio-Cognitive and Affective Computing aimed at integrating these various albeit complementary fields. Proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems were welcome. Designing this kind of system requires combining knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing. Papers with a special focus on multidisciplinary approaches and multimodality were especially welcome.

Table of Contents

About the Special Issue Editors -- Antonio Fernández-Caballero, Pascual González, María T. López and Elena Navarro -- Special Issue on Socio-Cognitive and Affective Computing Reprinted from: Appl. Sci. 2018, 8, 1371, doi: 10.3390/app8081371 -- Agnieszka Landowska Towards New Mappings between Emotion Representation Models Reprinted from: Appl. Sci. 2018, 8, 274, doi: 10.3390/app8020274 -- Ricardo Rosales, Manuel Castañón-Puga, Felipe Lara-Rosano, Richard Evans, Nora Osuna-Millan and Maria Virginia Flores-Ortiz Modelling the Interruption on HCI Using BDI Agents with the Fuzzy Perceptions Approach: An Interactive Museum Case Study in Mexico Reprinted from: Appl. Sci. 2017, 7, 832, doi: 10.3390/app7080832 -- Euijung Yang and Michael C. Dorneich Evaluating Human-Automation Etiquette Strategies to Mitigate User Frustration and Learning in Affect-Aware Tutoring Improve Reprinted from: Appl. Sci. 2018, 8, 895, doi: 10.3390/app8060895 -- Mohamed Z. Ramadan, Mohammed H. Alhaag and Mustufa Haider Abidi Effects of Viewing Displays from Different Distances on Human Visual System Reprinted from: Appl. Sci. 2017, 7, 1153, doi: 10.3390/app7111153 -- Roberto Zangróniz, Arturo Martínez-Rodrigo, José Manuel Pastor, María T. López and Antonio Fernández-Caballero Estimation of Mental Distress from Photoplethysmography Reprinted from: Appl. Sci. 2018, 8, 69, doi: 10.3390/app8010069 -- Michal R. Wrobel Applicability of Emotion Recognition and Induction Methods to Study the Behavior of Programmers Reprinted from: Appl. Sci. 2018, 8, 323, doi: 10.3390/app8030323 -- Nuno Rodrigues and António Pereira A User-Centred Well-Being Home for the Elderly Reprinted from: Appl. Sci. 2018, 8, 850, doi: 10.3390/app8060850 -- Ranjan Kumar Behera, Santanu Kumar Rath, Sanjay Misra, Robertas Damaševicius and Rytis Maskeliunas Large Scale Community Detection Using a Small World Model Reprinted from: Appl. Sci. 2017, 7, 1173, doi: 10.3390/app7111173 -- Fengcai Qiao, Xin Zhang, Pei Li, Zhaoyun Ding, Shanshan Jia and Hui Wang A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark Reprinted from: Appl. Sci. 2018, 8, 230, doi: 10.3390/app8020230 -- Aitor Almeida and Gorka Azkune Predicting Human Behaviour with Recurrent Neural Networks Reprinted from: Appl. Sci. 2018, 8, 305, doi: 10.3390/app8020305 -- Pengzhan Chen, Xiaoyan Zhang, Xiaoyue Chen and Mengchao Liu Path Planning Strategy for Vehicle Navigation Based on User Habits Reprinted from: Appl. Sci. 2018, 8, 407, doi: 10.3390/app8030407 -- Muhammad Fahim Uddin, Jeongkyu Lee, Syed Rizvi and Samir Hamada Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes Reprinted from: Appl. Sci. 2018, 8, 646, doi: 10.3390/app8040646 -- Wenjun Bai, Changqin Quan and Zhi-Wei Luo Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis Reprinted from: Appl. Sci. 2018, 8, 300, doi: 10.3390/app8020300.