In this book I present a Hypothesis that Evolution is a dual process: Ordinary Darwinian Evolution plus another process which forms part of my Hypothesis. This is based on a proposed new RNA. This book considers various unexplained observed phenomena associated with Darwinian evolution and presents a new Hypothesis through which these problems are analysed and possible solutions provided. My Hypothesis does not reject Darwinian evolution. In fact I stress the fact that Darwinian evolution is necessary in the creation of new species and I explain in more detail as to how this is achieved. My Hypothesis simply complements Darwinian evolution in trying to resolve the difficulties that are encountered in the further development of a species towards perfection and enhancement of its chances for survival.

In the World Library of Psychologists series, international experts themselves present career-long collections of what they judge to be their finest pieces - extracts from books, key articles, salient research findings, and their major theoretical and practical contributions. Jonathan St B T Evans is amongst the foremost cognitive psychologists of his generation, having been influential in spearheading developments in the psychological study of reasoning from its very beginnings in the 1970s up to the present day. This volume of self-selected papers recognises Professor Evan’s major contribution to the psychological study of thinking and reasoning by bringing together his most influential and important works. Early selections in the book focus upon experimental studies of reasoning - matching bias in the Wason selection task, belief bias in syllogistic reasoning, and also seminal work on the understanding of conditional statements. The later selections include Evans’ work on more general forms of dual process and dual system theory, and his recent account of two minds in one brain. The volume also contains chapters which highlight Evans’ contribution to the topic of human rationality, and also his influence on the development of the "new paradigm" in the psychology of reasoning. The key developments in the psychology of reasoning are paralleled by those in Evans’s own intellectual history, and the book will therefore make essential reading for all researchers in the psychology of reasoning, and a wider audience of graduate and upper-level undergraduate students with an interest in reasoning and/or dual process theory.

This book presents a philosophical interpretation to numerical cognition based on dual process theories and heuristics. It shows how investigations in cognitive science can shed light on issues traditionally raised by philosophers of mathematics. The analysis will also help readers to better understand the relationship between current neuroscientific research and the philosophical reflection on mathematics. The author seeks to explain the acquisition of mathematical concepts. To accomplish this, he needs to answer two questions. How can the concepts of approximate numerosity become an object of thought that is so accessible to our consciousness? How are these concepts refined and specified in such a way as to become numbers? Unfortunately, there is currently no model that can truly demonstrate the role of language in the development of numerical skills starting from approximate pre-verbal skills. However, the author details a solution to this problem: dual process theories. It is an approach widely used by theorists focusing on reasoning, decision making, social cognition, and consciousness. Here, he applies this approach to the studies on mathematical knowledge. He details the results brought about by psychological and neuroscientific studies conducted on numerical cognition by key neuroscientists. In the process, he develops the foundations of a new, potential philosophical explanation on mathematical knowledge.

This anthology offers a unique collection of contributions focusing on the discussion about the so-called dual-process theories within the field of moral psychology. In general, dual-process theories state that in cognitive systems, two sorts of processes can be differentiated: an affective, associative process and an analytical, rule-based process. This distinction recently entered the debate on the relationship between intuitive and rational approaches to explaining the phenomenon of moral judgment. The increasing interest in these theories raises questions concerning their general impact on social contexts. The anthology aims at presenting stepping-stones of an analysis of the merits and drawbacks of this development. For that purpose, the authors discuss general questions concerning the relationship between ethics and empirical sciences, methodological questions, reassessments of established terminology and societal implications of dual-process theories in moral psychology.

The field of evolutionary cognitive psychology has stimulated considerable interest and debate among cognitive psychologists and those working in related areas. In this collection, leading experts evaluate the status of this new field, providing a critical analysis of its most controversial hypotheses. These hypotheses have far reaching implications for cognition, including a modular view of the mind, which rejects, in its extreme form, any general learning or reasoning abilities. Some evolutionary psychologists have also proposed content-dependent accounts of conditional reasoning and probability judgements, which in turn have significant, and equally controversial, implications about the nature of human reasoning and decision making. The contributions range from those that are highly critical of the hypotheses to those that support and develop them. The result is a uniquely balanced, cutting-edge evaluation of the field that will be of interest to psychologists, philosophers and those in related subjects who wish to find out what evolutionary considerations can, and cannot, tell us about the human mind.

A growing body of evidence from the sciences suggests that our moral beliefs have an evolutionary basis. To explain how human morality evolved, some philosophers have called for the study of morality to be naturalized, i.e., to explain it in terms of natural causes by looking at its historical and biological origins. The present literature has focused on the link between evolution and moral realism: if our moral beliefs enhance fitness, does this mean they track moral truths? In spite of the growing empirical evidence, these discussions tend to remain high-level: the mere fact that morality has evolved is often deemed enough to decide questions in normative and meta-ethics. This volume starts from the assumption that the details about the evolution of morality do make a difference, and asks how. It presents original essays by authors from various disciplines, including philosophy, anthropology, developmental psychology, and primatology, who write in conversation with neuroscience, sociology, and cognitive psychology.

Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. In Evolutionary Deep Learning you will learn how to: Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization Use unsupervised learning with a deep learning autoencoder to regenerate sample data Understand the basics of reinforcement learning and the Q-Learning equation Apply Q-Learning to deep learning to produce deep reinforcement learning Optimize the loss function and network architecture of unsupervised autoencoders Make an evolutionary agent that can play an OpenAI Gym game Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture. About the technology Deep learning meets evolutionary biology in this incredible book. Explore how biology-inspired algorithms and intuitions amplify the power of neural networks to solve tricky search, optimization, and control problems. Relevant, practical, and extremely interesting examples demonstrate how ancient lessons from the natural world are shaping the cutting edge of data science. About the book Evolutionary Deep Learning introduces evolutionary computation (EC) and gives you a toolbox of techniques you can apply throughout the deep learning pipeline. Discover genetic algorithms and EC approaches to network topology, generative modeling, reinforcement learning, and more! Interactive Colab notebooks give you an opportunity to experiment as you explore. What's inside Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters Apply Q-Learning to deep learning to produce deep reinforcement learning Optimize the loss function and network architecture of unsupervised autoencoders Make an evolutionary agent that can play an OpenAI Gym game About the reader For data scientists who know Python. About the author Micheal Lanham is a proven software and tech innovator with over 20 years of experience. Table of Contents PART 1 - GETTING STARTED 1 Introducing evolutionary deep learning 2 Introducing evolutionary computation 3 Introducing genetic algorithms with DEAP 4 More evolutionary computation with DEAP PART 2 - OPTIMIZING DEEP LEARNING 5 Automating hyperparameter optimization 6 Neuroevolution optimization 7 Evolutionary convolutional neural networks PART 3 - ADVANCED APPLICATIONS 8 Evolving autoencoders 9 Generative deep learning and evolution 10 NEAT: NeuroEvolution of Augmenting Topologies 11 Evolutionary learning with NEAT 12 Evolutionary machine learning and beyond

The biological and neurological capacity to symbolize, and the products of behavioral, cognitive, sociocultural, linguistic, and technological uses of symbols (symbolism), are fundamental to every aspect of human life. The Oxford Handbook of Human Symbolic Evolution explores the origins of our characteristically human abilities - our ability to speak, create images, play music, and read and write. The book investigates how symbolization evolved in human evolution and how symbolism is expressed across the various areas of human life. The field is intrinsically interdisciplinary - considering findings from fossil studies, scientific research from primatology, developmental psychology, and of course linguistics. Written by world leading experts, thirty-eight topical chapters are grouped into six thematic parts that respectively focus on epistemological, psychological, anthropological, ethological, linguistic, and social-technological aspects of human symbolic evolution. The handbook presents an in-depth but comprehensive and interdisciplinary overview of the of the state of the art in the science of human symbolic evolution. This work will be of interest to academics and students active in all fields contributing to the study of human evolution.

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"This volume provides an authoritative synthesis of a dynamic, influential area of psychological research. Leading investigators address all aspects of dual-process theories: their core assumptions, conceptual foundations, and applications to a wide range of social phenomena. In 38 chapters, the volume addresses the pivotal role of automatic and controlled processes in attitudes and evaluation; social perception; thinking and reasoning; self-regulation; and the interplay of affect, cognition, and motivation. Current empirical and methodological developments are described. Critiques of the duality approach are explored and important questions for future research identified"--