Computational Personality Analysis


Book Description

The emergence of intelligent technologies, sophisticated natural language processing methodologies and huge textual repositories, invites a new approach for the challenge of automatically identifying personality dimensions through the analysis of textual data. This short book aims to (1) introduce the challenge of computational personality analysis, (2) present a unique approach to personality analysis and (3) illustrate this approach through case studies and worked-out examples. This book is of special relevance to psychologists, especially those interested in the new insights offered by new computational and data-intensive tools, and to computational social scientists interested in human personality and language processing.




Character Computing


Book Description

The book gives an introduction into the theory and practice of the transdisciplinary field of Character Computing, introduced by Alia El Bolock. The latest scientific findings indicate that “One size DOES NOT fit all” in terms of how to design interactive systems and predict behavior to tailor the interaction experience. Emotions are one of the essential factors that influence people’s daily experiences; they influence decision making and how different emotions are interpreted by different individuals. For example, some people may perform better under stress and others may break. Building upon Rosalind Picard’s vision, if we want computers to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have and express emotions and how different characters perceive and react to these emotions, hence having richer and truly tailored interaction experiences. Psychological processes or personality traits are embedded in the existing fields of Affective and Personality Computing. However, this book is the first that systematically addresses this including the whole human character; namely our stable personality traits, our variable affective, cognitive and motivational states as well as our morals, beliefs and socio-cultural embedding. The book gives an introduction into the theory and practice of the transdisciplinary field of Character Computing. The emerging field leverages Computer Science and Psychology to extend technology to include the whole character of humans and thus paves the way for researchers to truly place humans at the center of any technological development. Character Computing is presented from three main perspectives: ● Profiling and sensing the character ● Leveraging characters to build ubiquitous character-aware systems ● Investigating how to extend Artificial Intelligence to create artificial characters




The Cambridge Handbook of Computational Psychology


Book Description

A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.




Computational Social Psychology


Book Description

Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology.




Computational Psychiatry


Book Description

Psychiatrists and neuroscientists discuss the potential of computational approaches to address problems in psychiatry including diagnosis, treatment, and integration with neurobiology. Modern psychiatry is at a crossroads, as it attempts to balance neurological analysis with psychological assessment. Computational neuroscience offers a new lens through which to view such thorny issues as diagnosis, treatment, and integration with neurobiology. In this volume, psychiatrists and theoretical and computational neuroscientists consider the potential of computational approaches to psychiatric issues. This unique collaboration yields surprising results, innovative synergies, and novel open questions. The contributors consider mechanisms of psychiatric disorders, the use of computation and imaging to model psychiatric disorders, ways that computation can inform psychiatric nosology, and specific applications of the computational approach. Contributors Susanne E. Ahmari, Huda Akil, Deanna M. Barch, Matthew Botvinick, Michael Breakspear, Cameron S. Carter, Matthew V. Chafee, Sophie Denève, Daniel Durstewitz, Michael B. First, Shelly B. Flagel, Michael J. Frank, Karl J. Friston, Joshua A. Gordon, Katia M. Harlé, Crane Huang, Quentin J. M. Huys, Peter W. Kalivas, John H. Krystal, Zeb Kurth-Nelson, Angus W. MacDonald III, Tiago V. Maia, Robert C. Malenka, Sanjay J. Mathew, Christoph Mathys, P. Read Montague, Rosalyn Moran, Theoden I. Netoff, Yael Niv, John P. O'Doherty, Wolfgang M. Pauli, Martin P. Paulus, Frederike Petzschner, Daniel S. Pine, A. David Redish, Kerry Ressler, Katharina Schmack, Jordan W. Smoller, Klaas Enno Stephan, Anita Thapar, Heike Tost, Nelson Totah, Jennifer L. Zick




Principal Component Analysis


Book Description

Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.




The Oxford Handbook of Psychological Situations


Book Description

Situations matter. They let people express their personalities and values; provoke motivations, emotions, and behaviors; and are the contexts in which people reason and act. The psychological assessment of situations is a new and rapidly developing area of research, particularly within the fields of personality and social psychology. This volume compiles state-of-the-art knowledge on psychological situations in chapters written by experts in their respective research areas. Bringing together historical reviews, theoretical pieces, methodological descriptions, and empirical applications, this volume is the definitive, go-to source for a psychology of situations.




Personality


Book Description

'The best introduction to personality psychology I have ever read' Robert Hogan, founder and president, Hogan Assessment Systems We are each born with a particular genetic makeup and traits that are further shaped by our individual environments and life experiences, creating a personality unique to us. But at no point are we given a manual for how to use that personality in our relationships or at work, for how to make sense of the facets that create our sense of self, or for how to troubleshoot our personality if there are things we are unhappy with. Personality: A User's Guide is that missing manual. An authoritative and comprehensive guide to personality psychology, this book outlines the different building blocks of our personality, presents the many theories and models that have been argued over the years, and helps you identify where you stand on the spectrum of key traits that define us. Using this knowledge about yourself, the book also suggests ways you can better empathise with those around you who may have very different approaches to the world, and how we can all leverage our different strengths. The authors also consider the constantly developing nature of the self, and ways we can turn that to our advantage and make changes for greater success and wellbeing. Finally, the book looks at some specific questions: Can our pets be said to have their own personalities? What happens when the 'dark triad' of negative traits are dominant? And is there a future in store where we can say that an AI has a genuine personality?




Advancement of Artificial Intelligence in Healthcare Engineering


Book Description

"This book explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of challenging healthcare engineering solutions"--




Predictive Clustering


Book Description

This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.