Connectionist Models Of Neurocognition And Emergent Behavior

Introduction / Eddy J. Davelaar -- An ecology-based approach to perceptual modelling / E.L. Byrne, D.P.A Corney and R.B. Lotto -- Early development of visual abilities / Alessio Plebe -- A dynamical neural simulation of feature-based attention and binding in a recurrent model of the ventral stream / D.G. Harrison and M. De Kamps -- Model selection for eye movements : assessing the role of attentional cues in infant learning / Daniel Yurovsky [und weitere] -- The importance of low spatial frequencies for categorization of emotional facial expressions / L. Lopez [und weitere] -- Modeling speech perception with restricted Boltzmann machines / Michael Klein, Louis ten Bosch and Lou Boves -- Early language as multimodal learning / Nadja Althaus and Denis Mareschal -- From Motherese to one-word and two-word child language : a multimodal temporal connectionist model / Abel Nyamapfene -- Learning the visual word code / T. Hannagan and J. Grainger -- What are the functional units in reading? Evidence for statistical variation influencing word processing / Alastair C. Smith and Padraic Monaghan -- Testing computational accounts of response congruency in lexical decision / Sebastian Loth and Colin J. Davis -- Sentence comprehension as mental simulation : an information-theoretic analysis and a connectionist model / Stefan L. Frank -- Modelling free recall - a combined activation-buffer and distributed-context model / Anat Elhalal and Marius Usher -- Inference, ontologies and the pump of thought / Andrzej Wichert -- Modelling correlations in "response inhibition" Richard P. Cooper and Eddy J. Davelaar -- A first approach to an artificial networked cognitive control system based on the shared circuits model of sociocognitive capacities / A. Sanchez Boza and R. Haber Guerra -- Digital typology modelling of cognitive abilities / Agnes Garletti -- Using enriched semantic representations in predictions of human brain activity / Joseph P. Levy and John A. Bullinaria -- Variability in the severity of developmental disorders : a neurocomputational account of developmental regression in autism / Michael SC Thomas, Victoria CP Knowland and Annette Karmiloff-Smith -- How do we use computational models of cognitive processes? / T. Stafford -- Some issues in computational modelling; Occam's razor and Hegel' hair gel / Richard Shillcock [und weitere] -- How is hair gel quantified? / Mark A. Pitt and Jay I. Myung -- What do humanoid robots offer to experimental psychology? / Jochen J. Steil

Computational Models of Cognitive Processes collects refereed versions of papers presented at the 13th Neural Computation and Psychology Workshop (NCPW13) that took place July 2012, in San Sebastian (Spain). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on models of cognitive processes.

This volume presents peer-reviewed versions of papers presented at the 14th Neural Computation and Psychology Workshop (NCPW14), which took place in July 2014 at Lancaster University, UK. The workshop draws international attendees from the cutting edge of interdisciplinary research in psychology, computational modeling, artificial intelligence and psychology, and aims to drive forward our understanding of the mechanisms underlying a range of cognitive processes.

In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main principles of quantum computation — Quantum Fourier transform and Grover search. Then, we indicate how these two principles can be applied to problem solving and finally present a general model of a quantum computer that is based on production systems. Contents:IntroductionComputationProblem SolvingInformationReversible AlgorithmsProbabilityIntroduction to Quantum PhysicsComputation with QubitsPeriodicitySearchQuantum Problem-SolvingQuantum CognitionRelated Approaches Readership: Professionals, academics, researchers and graduate students in artificial intelligence, theoretical computer science, quantum physics and computational physics. Keywords:Quantum Computing;Quantum Theory;Artificial Intelligence;Cognitive Computation;AlgorithmsKey Features:Introduces a new subarea of AI — Quantum Artificial IntelligenceOrients itself on computer science by merging AI and Quantum Computation principles

This edited volume examines the new ways of conceptualizing Psychology as an integrative science to understand human problems at the individual, group, societal, and national levels. It focuses on the need for Psychology to move away from its present reductionist perspective to an integrative psychological science perspective. The volume is organized into three main sections: The first discusses the convergence of qualitative and quantitative methodological approaches in Psychology. The second part highlights the importance of social and personal wellness. The third focuses on studying human behaviour in the context of cultural variations and the impact of cultural context on psychological processes. The book includes contributions from leading scholars in psychology in India whose reference to practical, social and political issues of contemporary interest makes the volume stand out. This book serves as a resource to initiate the dialogue about the need, issues, levels, and integration methods in Psychology, which can be scientifically tested and theoretically explained. The comprehensive and authoritative volume is of interest to researchers and scholars in cognitive psychology, clinical psychology, organizational psychology, social psychology and cross-cultural psychology.

This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making — the core disciplines of artificial intelligence.Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.

Although neural network models have had a dramatic impact on the cognitive and brain sciences, social psychology has remained largely unaffected by this intellectual explosion. The first to apply neural network models to social phenomena, this book includes chapters by nearly all of the individuals currently working in this area. Bringing these various approaches together in one place, it allows readers to appreciate the breadth of these approaches, as well as the theoretical commonality of many of these models. The contributors address a number of central issues in social psychology and show how these kinds of models provide insight into many classic issues. Many chapters hint that this approach provides the seeds of a theoretical integration that the field has lacked. Each chapter discusses an explicit connectionist model of a central problem in social psychology. Since many of the contributors either use a standard architecture or provide a computer program, interested readers, with a little work, should be able to implement their own variations of models. Chapters are devoted to the following topics and models: * the learning and application of social categories and stereotypes; * causal reasoning, social explanation, and person perception; * personality and social behavior; * classic dissonance phenomena; and * belief change and the coherence of large scale belief systems.