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Preface– Readers/Users Guide

This book is more personal than is typical for a textbook, and therefore I begin with an atypical preface.

Fundamentals is personal in being genuinely written by a single author. Many textbooks appear to be written by one person, but the one name often conceals a team. A scientist might have grad students draw up lists of topics, research ideas, and write drafts. A publisher, or a famous author with an advance, might hire professional researchers, writers, and editors. I taught cognitive science for 35 years, am now retired, and I wrote every word (except a few changed by an eagle-eyed copy editor) and found each reference.

Fundamentals became especially personal because in the Fall of 2019 I experienced a catastrophic health crisis that put me in two hospitals near death and in physical rehab for 3 months, and into assisted living just in time for COVID-19, which, fortunately, I did not get. So, I have eschewed the authorial Voice of God I use for handbook and encyclopedia articles, where authority and authorial go together, for a more relaxed blog or newsletter style. My aim is not only to inform, but to give you a perspective, a point of view, on cognitive science. Above all, I want to place it in an intellectual setting.

At the center is a distinction made by the philosopher Wilfred Sellars between the manifest image of human beings and the scientific image. The manifest image is our understanding of ourselves as personal and social beings. We all use a theoretical psychology called Theory of Mind that serves us well in daily life and that underlies constitutions, law, politics and other formal institutions. However, the manifest image comes to us from a past littered with wrong ideas about the universe that have been replaced by science. And science—cognitive science—is building a rival scientific image of human beings. The foremost aim of Fundamentals is to sketch this image; to sketch because the book is short and the field is large, and because the image is neither complete at the edges nor filled in with detail. As I draw the sketch, I will explore the ways in which the emerging scientific image agrees with and disagrees with the manifest image and investigate the consequences for us.

I attempt to achieve both aims by writing a different sort of textbook, organized around themes that provide a frame for the sketch. I once worked on a task force designed to measure what our VCU students had learned in their major at graduation. The team I led aimed to assess knowledge in the natural sciences. Our most revealing finding was that majors had learned many facts about their science, but that each fact stood alone. I call this the ping-pong ball mind—knowledge of science was a bag of facts from which right answers for tests could be grabbed, but each student had a different bunch of balls in the bag, not organized knowledge. In Fundamentals of cognitive science, I strive to do better by you.

My first unifying theme is historical/philosophical. I think a good way to organize the ping pong balls is on a grid of history and viewpoints. Most of the manifest image was historically constructed by thinkers, mostly philosophers, who proposed systematically different ways of thinking about the mind. When scientific psychology began in the 19^th century, these evolved viewpoints and the questions they posed shaped psychology’s experimental investigation of consciousness and mental processes. To use an important technical term from cognitive science, psychologists inherited a set of schemata that guided the questions they asked, their interpretations of the results they found, and the theories they devised. I use these schemata to present a picture of cognitive science as a largely—though not entirely—coherent enterprise.

Related is my emphasis on architectures of cognition. The implicit schemata of cognitive science are not entirely unconscious but are manifested as different quasi-philosophical viewpoints—ideologies—about what minds are, what they are for, and how they work, and these viewpoints guide and justify the research and theory adherents pursue. The phrase arose from the artificial intelligence branch of cognitive science. If one sets out to build a mind from scratch, one needs a plan of construction—an architecture to fill in. The architectures sometimes ally and sometimes do battle, and as the 8 contentious Coalitions against Napoleon shaped world history, so have the critiques and mutual support among advocates of the various architectures shaped cognitive science.

The third theme takes us to the conflict of the manifest image vs. the scientific image. Research and theory in cognitive science, from the simplest studies of sensation and perception to research on thinking and decision-making, and in cognitive neuroscience converge on a big conclusion: We do not know ourselves and our minds and the causes of our actions as well as we think[*]. I stress these findings throughout the book and try to make sense of them—and cognitive science—along the way.

The final theme is personal, practical, and fun. My first route into cognitive science was through magic. As a kid, I watched a weekly magic show on TV (Mark Wilson) and began to read magic books and spend my allowance on magic tricks at Al’s Magic in Washington, DC. Magic is the original applied cognitive science, and it goes back at least to the ancient Egyptians. My first research was using magic tricks to assess development of Piagetian cognitive development. I never thought to become a magician, because I’ve not got the right personality. I was once offered a summer gig at a hotel lounge but quailed at the thought of dealing with obstreperous drunks. On the plus side, performing magic is good practice for teaching. If you can survive looking a fool by muffing a trick, you can beat stage fright.

I should say something about my professional background. I got into psychology and history by reading Isaac Asimov’s science fiction Foundation trilogy as a teen. It introduced the idea of psychohistory, which does exist in the profession of history, but it’s not what Asimov meant, using psychological science to predict the long-term future. I discovered the brand-new cognitive psychology of Chomsky and Piaget in college and pursued it in grad school at the University of Illinois at Urbana-Champaign under William F. Brewer, once called in a book review “Mr. Autobiographical Memory” for reviving its study in the 1970s. I did my thesis on reasoning and my dissertation on cognitive development. However, my real talents lay in history of psychology and philosophy of psychology, and my work there made my reputation.

I try to give a brief but broad treatment of cognitive science, swooping down to give detailed pictures of interest and importance to me, and I hope, to you too.

A word on editions. Fundamentals is available as an ebook and two dead-tree books, paperback and hard cover. Given the rise of etextbooks, I have written with the ebook version in mind. I have put in lots of links to videos and other materials (but with the full address given). I have tried to give the doi addresses for the references, so in the e-version they should be clickable.

Part of cognitive science is artificial intelligence, and people today worry about losing jobs to AIs, and fear (or welcome) the AI apocalypse (or Singularity). The AI Deep Mind plays chess and Go better than anyone, and GPT-3 can write simple news stories. But ponder this: one of the oldest human jobs defeats expensive AIs: There is no robot bricklayer.

Now, think about the social status of bricklayers.

Finally, I have set up this website, fundamentalsofcognitivescience.com.

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[*] The message has reached the New York Times’ David Brooks: https://www.nytimes.com/2021/09/16/opinion/psychology-consciousness-behavior.html

Chapter Abstracts

Chapter 01. What is a Mind?

Historical Background for Cognitive Science. The concept of mind in commonsense and philosophy is discussed. The problem of knowledge—the relationship between the world and the mind—is presented as fundamental to cognitive science. Classical positions on epistemology, naïve realism, Plato’s Idealism, Aristotle’s empiricism and Stoic computationalism are sketched. The impact of the Scientific Revolution, especially its new conception of matter and its creation of the idea of consciousness, culminating in the Cartesian Paradigm and the invention of psychology as a discipline and is limned. The reformulated versions of 3 Classic epistemologies, modern empiricism, realism, and idealism are laid out.

Chapter 02. Mind Design.

Building, Investigating and Explaining Minds. Traces development of artificial intelligence before, during, and after WW II. Describes philosophical foundations of computing in interpreted formal systems. Impact of the war on creation of cybernetics, the science of purposive behavior via feedback; Turing’s code-breaking, his mathematical invention of the Turing machine, and construction of the first computer at Bletchley Park, and in the USA by von Neumann. Presents Turing’s paper on artificial intelligence and his conversational test to detect it. Philosophy of science is applied to cognitive science. Mind is used equivocally to refer to causes of behavior and to consciousness. The issue of reducing psychological theory to neuroscience, or its replacement by neuroscience is considered. Proximate (psychological, neurological, and genetic) causes are distinguished from ultimate (evolutionary) causes. Cognitive science is presented as reverse engineering in which mental processes are inferred from behavior and computational models of are constructed. Goals are set, one for cognitive psychology—understanding natural, i.e., evolutionary, mind design, and one for artificial intelligence, constructing artificial minds. Marr’s levels of cognitive analysis are established as a framework for theory construction. Two mental systems, System 1 (unconscious, fast) and System 2 (conscious, slow) are introduced as a pedagogical framework.

Chapter 03. Learning

Behaviorism. Begins by describing why psychologists abandoned the introspective study of consciousness for the study of behavior developed in comparative cognition. The first behaviorisms are described: eliminative behaviorism—proposing to explain behavior and consciousness physiologically—which was premature, and methodological behaviorism—proposing to ignore consciousness and develop non-mentalistic theories of learning—which dominated cognitive science until c. 1970, and which was, in effect, a computational architecture of cognition. The discovery of Pavlovian (classical) and instrumental conditioning is described, emphasizing the former. Hull’s and Tolman’s theories of conditioning are briefly compared and evaluated, with focus on lessons for later cognitive science. Contemporary learning theory is described, especially the experimental analysis of behavior and its extension to humans, and the neo-associationist, predictive, theory of Pavlovian conditioning. Closes with discussion of poor evidence of learning without awareness in humans and its implications.

Chapter 04. Architectures of Cognition.

The meta-theoretical frameworks in which cognitive science is done. They are divided into two classes, computational and non-computational. Computational architectures explain behavior by positing mental rules that process information: (1) The founding symbol-system approach pioneered by Turing and developed by Simon and Newell, positing rules that process symbolic representations of the world; and (2) connectionism and its sibling Bayesian predictive processing, which posit quantitative mathematical rules without symbolic representations. Non-computational architectures eschew internal states and rules. They include: (3) Radical behaviorism, founded by Skinner; (4) Embodied cognition, an interdisciplinary movement that locates intelligence not in the brain alone but in the body; and (5) Rational Choice Theory, from economics and sociology. These 3 architectures dispense with, downplay, or deny the existence of, inner mental processes. Mainstream cognitive science is dominated by computational architectures, and are challenged by the others, who sometimes make common cause. The nature, strengths, and weaknesses of each architecture are discussed, and important concepts such as modularity, computational tractability (the real-time and frame problems), artificial neural networks, and Bayes’ theorem are introduced.

Chapter 05. Early Information Processing.

From Sensation to Short-Term Memory. The information-processing mind-as-computer metaphor is expanded, and a pedagogical heuristic model, the modal model of human information processing is introduced. Serial processing, in which computational steps are taken one step at a time is distinguished from parallel processing, in which multiple steps are taken together; the modal model is serial, but reality is parallel. Automatic (system 1) and controlled (System 2) processing are distinguished, as are bottom-up (data-driven) and top-down (conceptually-driven) processes. The movement of input through the modal model is traced, beginning with sensory memory, the brief initial representation of a stimulus. Then occurs pattern recognition, when input stimuli are identified and classified. Theories are introduced and critiqued, including associationism and Gestalt theory, ending with deep learning in artificial neural nets. Then comes attention, in which some input streams are processed to consciousness and others are not. Cause theories and effect theories are proposed and critiqued, and deep challenges to theory, especially cognitive blindness, are discussed. The cognitive penetrability of information processing is considered in the Bayesian predictive processing framework of establishing priors. Finally, the characteristics of short-term memory are demonstrated, and the replacement construct of working memory is introduced.

Chapter 06. Memory.

Importance of memory to personal identity and self-knowledge is discussed. Myths about memory are surveyed: Most people believe memory is a faithful, stable, repository of past experience, but in fact it is a partial interpretation of experience whose storage is unstable; nor is there a single place in the brain where memories are stored—they are assembled from parts as needed. Permanent memory can be divided into different types: Autobiographical (episodic) memory vs. knowledge (semantic memory); declarative (what we can describe) vs. procedural (what we’ve learned to do); and explicit (memory report on request) vs. implicit (memory affects behavior unconsciously). Long-term memory is explored in detail, with emphasis on autobiographical memory and the concept of schemata. Implications of research for flashbulb memories, implantation of false memories and for eyewitness testimony are considered.

Chapter 07. The Higher Mental Processes

Thinking, Reasoning, and Decision-Making. Being rational is an important normative concept, said to distinguish humans from other animals. Herbert Simon distinguishes two kinds of intelligence, problem-solving and intuition, addressing the first through problem representation and heuristic search, the latter through knowledge engineering and production systems. Neural networks are also used to model both kinds of intelligence. These approaches are described and evaluated. Three projects in studying rationality are explained: (1) the normative project in which standards for correct reasoning and decision-making are established; (2) the descriptive project, in which human use of the norms is investigated; and (3) the remediation project, in which human reasoning is taught. However, human rationality has become contentious. Research is reviewed with primary foci on logical reasoning and economic decision making indicating that people typically do not reason or decide by applying normative rules but employ cognitive shortcuts—heuristics—and are subject to reason-distorting biases. A great rationality debate occurs between those who see heuristics and biases as cognitive sins, and those seeing them as effective ways of thinking and deciding under conditions of bounded rationality, which yields satisficing rather than optimizing decisions. Possible ways to improve human rationality are reviewed.

Chapter 08. Cognitive Neuroscience.

Mind and Brain. The history of neuroscience is sketched. The relation of psychological theory to neuroscientific theory is discussed, emphasizing reduction vs replacement and the future of psychology. Top-down (localization) and bottom-up (connectome) strategies for investigating the brain are contrasted. Methods in cognitive neuroscience are discussed, with special emphasis on clinical methods (split brain, blindsight, Capgras syndrome), experimentation (animal models of learning) and imaging (PET, MRI, fMRI). Then cognitive neuroscience of memory is explored as a case study using all the methods. The standard medial-temporal-lobe theory of memory is presented and critiqued with special attention to social aspects of memory, and an evolutionary alternative sketched. The issue of reduction or replacement of psychology to neuroscience is revisited in light of empirical findings and theoretical critiques.

Chapter 09. Evolution of Cognition.

What is cognition for? Proximate and ultimate causation are fully distinguished. Traces the history of evolution up to Darwin’s Origin (1859). Basic concepts in evolutionary theory are defined: Natural selection, sexual selection, social selection; types, levels, and units of selection; concept of adaptation and its types; bottleneck effects; and relationship of similar traits by homology and analogy. Relevant ideas from genetics are discussed, focusing on behavioral genetics and its three laws, genome-wide studies, and the use of genetics in studying evolution. Evolutionary psychology is defined, and its schools presented: (1) The Santa Barbara School; (2) Human Behavioral Ecology; and (3) Gene-Culture Co-evolution, with the latter two merged into an alliance. Research methods are considered, and Niko Tinbergen’s Four Questions (What is a trait’s function? How does it develop? How did it evolve? How does it work?) proposed as a framework for investigation. Evolution of aggression, altruism, and cooperation are treated before taking up evolution of intelligence, which is discussed in detail with focus on emergence of Homo sapiens and then the impact of agriculture and the appearance of modernity’s WEIRD people. Mate choice is presented as a computational case study in evolution of practical human intelligence. (196)