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Because networks intersect profusely, sharing commong nodes, a neuronal assembly anywhere in the cortex can be part of many networks, and therefore many items of knowledge. All cognitive functions consist of neural transactions within and between cognitive networks. After reviewing the neurobiology and architecture of cortical networks also named cognits , the author undertakes a systematic study of cortical dynamics in each of the major cognitive functions--perception, memory, attention, language, and intelligence.

In this study, he makes use of a large body of evidence from a variety of methodologies, in the brain of the human as well as the nonhuman primate. The outcome of his interdisciplinary endeavor is the emergence of a structural and dynamic order in the cerebral cortex that, though still sketchy and fragmentary, mirrors with remarkable fidelity the order in the human mind. Pagine selezionate Pagina del titolo. Indice analitico. Indice Introduction. Epilogue on Consciousness. Fuster Anteprima non disponibile - Interdisciplinary work becomes much more interesting when there is theoretical and experimental convergence on conclusions about the nature of mind.

For example, psychology and artificial intelligence can be combined through computational models of how people behave in experiments. The best way to grasp the complexity of human thinking is to use multiple methods, especially psychological and neurological experiments and computational models. Theoretically, the most fertile approach has been to understand the mind in terms of representation and computation. The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures.

While there is much disagreement about the nature of the representations and computations that constitute thinking, the central hypothesis is general enough to encompass the current range of thinking in cognitive science, including connectionist theories which model thinking using artificial neural networks. Most work in cognitive science assumes that the mind has mental representations analogous to computer data structures, and computational procedures similar to computational algorithms. Cognitive theorists have proposed that the mind contains such mental representations as logical propositions, rules, concepts, images, and analogies, and that it uses mental procedures such as deduction, search, matching, rotating, and retrieval.

The dominant mind-computer analogy in cognitive science has taken on a novel twist from the use of another analog, the brain. Connectionists have proposed novel ideas about representation and computation that use neurons and their connections as inspirations for data structures, and neuron firing and spreading activation as inspirations for algorithms. Cognitive science then works with a complex 3-way analogy among the mind, the brain, and computers. Mind, brain, and computation can each be used to suggest new ideas about the others.

There is no single computational model of mind, since different kinds of computers and programming approaches suggest different ways in which the mind might work.

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The computers that most of us work with today are serial processors, performing one instruction at a time, but the brain and some recently developed computers are parallel processors, capable of doing many operations at once. A major trend in current cognitive science is the integration of neuroscience with many areas of psychology, including cognitive, social, developmental, and clinical. This integration is partly experimental, resulting from an explosion of new instruments for studying the brain, such as functional magnetic resonance imaging, transcranial magnetic stimulation, and optogenetics.

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The integration is also theoretical, because of advances in understanding how large populations of neurons can perform tasks usually explained with cognitive theories of rules and concepts. Here is a schematic summary of current theories about the nature of the representations and computations that explain how the mind works. Formal logic provides some powerful tools for looking at the nature of representation and computation.

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Propositional and predicate calculus serve to express many complex kinds of knowledge, and many inferences can be understood in terms of logical deduction with inferences rules such as modus ponens. The explanation schema for the logical approach is:. It is not certain, however, that logic provides the core ideas about representation and computation needed for cognitive science, since more efficient and psychologically natural methods of computation may be needed to explain human thinking.

Much of human knowledge is naturally described in terms of rules of the form IF … THEN …, and many kinds of thinking such as planning can be modeled by rule-based systems. The explanation schema used is:. Computational models based on rules have provided detailed simulations of a wide range of psychological experiments, from cryptarithmetic problem solving to skill acquisition to language use. Rule-based systems have also been of practical importance in suggesting how to improve learning and how to develop intelligent machine systems.

Concepts, which partly correspond to the words in spoken and written language, are an important kind of mental representation. There are computational and psychological reasons for abandoning the classical view that concepts have strict definitions. Instead, concepts can be viewed as sets of typical features. Concept application is then a matter of getting an approximate match between concepts and the world.

Schemas and scripts are more complex than concepts that correspond to words, but they are similar in that they consist of bundles of features that can be matched and applied to new situations. The explanatory schema used in concept-based systems is:. Analogies play an important role in human thinking, in areas as diverse as problem solving, decision making, explanation, and linguistic communication. Computational models simulate how people retrieve and map source analogs in order to apply them to target situations. The explanation schema for analogies is:. The constraints of similarity, structure, and purpose overcome the difficult problem of how previous experiences can be found and used to help with new problems.

Not all thinking is analogical, and using inappropriate analogies can hinder thinking, but analogies can be very effective in applications such as education and design. Visual and other kinds of images play an important role in human thinking. Pictorial representations capture visual and spatial information in a much more usable form than lengthy verbal descriptions. Computational procedures well suited to visual representations include inspecting, finding, zooming, rotating, and transforming.

Such operations can be very useful for generating plans and explanations in domains to which pictorial representations apply. The explanatory schema for visual representation is:. Imagery can aid learning, and some metaphorical aspects of language may have their roots in imagery.

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Psychological experiments suggest that visual procedures such as scanning and rotating employ imagery, and neurophysiological results confirm a close physical link between reasoning with mental imagery and perception. Imagery is not just visual, but can also operate with other sensory experiences such as hearing, touch, smell, taste, pain, balance, nausea, fullness, and emotion. Connectionist networks consisting of simple nodes and links are very useful for understanding psychological processes that involve parallel constraint satisfaction.

Such processes include aspects of vision, decision making, explanation selection, and meaning making in language comprehension. Connectionist models can simulate learning by methods that include Hebbian learning and backpropagation. The explanatory schema for the connectionist approach is:.

Simulations of various psychological experiments have shown the psychological relevance of the connectionist models, which are, however, only very rough approximations to actual neural networks. Theoretical neuroscience is the attempt to develop mathematical and computational theories and models of the structures and processes of the brains of humans and other animals.

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It differs from connectionism in trying to be more biologically accurate by modeling the behavior of large numbers of realistic neurons organized into functionally significant brain areas. Computational models of the brain have become biologically richer, both with respect to employing more realistic neurons such as ones that spike and have chemical pathways, and with respect to simulating the interactions among different areas of the brain such as the hippocampus and the cortex.

These models are not strictly an alternative to computational accounts in terms of logic, rules, concepts, analogies, images, and connections, but should mesh with them and show how mental functioning can be performed at the neural level. The explanatory schema for theoretical neuroscience is:.

From the perspective of theoretical neuroscience, mental representations are patterns of neural activity, and inference is transformation of such patterns. Bayesian models are prominent in cognitive science, with applications to such psychological phenomena as learning, vision, motor control, language, and social cognition. They have also had effective applications in robotics. The explanatory schema for Bayesian cognition is:. Although Bayesian methods have had impressive applications to a wide range of phenomena, their psychological plausibility is debatable because of assumptions about optimality and computations based on probability theory.

Artificial intelligence has been a central part of cognitive since the s, and the most dramatic recent advances in AI have come from the approach of deep learning, which has produced major breakthroughs in fields that include game playing, object recognition, and translation. Deep learning builds on ideas from connectionism and theoretical neuroscience, but uses neural networks with more layers and improved algorithms, benefitting from faster computers and large data bases of examples.

Ideas from deep learning are spreading back into neuroscience and also beginning to influence research in cognitive psychology. The explanatory schema for deep learning is:. Although deep learning has produced dramatic improvements in some AI systems, it is not clear how it can be applied to aspects of human thought that include imagery, emotion, and analogy.

Some philosophy, in particular naturalistic philosophy of mind, is part of cognitive science. But the interdisciplinary field of cognitive science is relevant to philosophy in several ways. First, the psychological, computational, and other results of cognitive science investigations have important potential applications to traditional philosophical problems in epistemology, metaphysics, and ethics.

Second, cognitive science can serve as an object of philosophical critique, particularly concerning the central assumption that thinking is representational and computational. Third and more constructively, cognitive science can be taken as an object of investigation in the philosophy of science, generating reflections on the methodology and presuppositions of the enterprise.

Much philosophical research today is naturalistic, treating philosophical investigations as continuous with empirical work in fields such as psychology. From a naturalistic perspective, philosophy of mind is closely allied with theoretical and experimental work in cognitive science. Metaphysical conclusions about the nature of mind are to be reached, not by a priori speculation, but by informed reflection on scientific developments in fields such as psychology, neuroscience, and computer science. Similarly, epistemology is not a stand-alone conceptual exercise, but depends on and benefits from scientific findings concerning mental structures and learning procedures.

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Ethics can benefit by using greater understanding of the psychology of moral thinking to bear on ethical questions such as the nature of deliberations concerning right and wrong. Here are some philosophical problems to which ongoing developments in cognitive science are highly relevant. Links are provided to other relevant articles in this Encyclopedia. Additional philosophical problems arise from examining the presuppositions of current approaches to cognitive science.

The claim that human minds work by representation and computation is an empirical conjecture and might be wrong. Although the computational-representational approach to cognitive science has been successful in explaining many aspects of human problem solving, learning, and language use, some philosophical critics have claimed that this approach is fundamentally mistaken. Critics of cognitive science have offered such challenges as:.