Cognitive Models Of Science


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Cognitive Models of Science


Cognitive Models of Science

Author: Ronald N. Giere

language: en

Publisher: U of Minnesota Press

Release Date: 1992


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This work resulted from a workshop on the implications of the cognitive sciences for the philosophy of science held under the auspices of the Minnesota Center for Philosophy of Science. The workshop's theme was that the cognitive sciences - identified for the purposes of this project with three disciplinary clusters: artificial intelligence, cognitive psychology, and cognitive neuroscience - have reached sufficient maturity that they are now a valuable resource for philosophers of science who are developing general theories of science as a human activity. The emergence of cognitive science has by no means escaped the notice of philosophers or philosophers of science. Within the philosophy of science one can detect an emerging speciality, the philosophy of cognitive science, which would be parallel to such specialities as the philosophy of physics or the philosophy of biology. But the reverse is also happening. That is, the cognitive sciences are beginning to have a considerable impact on the content and methods of philosophy, particularly the philosophy of language and the philosophy of mind, but also on epistemology. The underlying hope is that the cognitive sciences might now come to play the sort of role within the philosophy of science that formal logic played for logical empiricism or that history of science played for the historical school. This development might permit the philosophy of science as a whole finally to move beyond the opposition between "logical" and "historical" approaches that has characterized the field since the 1960s. "Ronald N. Giere is Professor of Philosophy and Director of the Minnesota Center for Philosophy of Science at the University of Minnesota.".

Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models


Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models

Author: Jorge Garza Ulloa

language: en

Publisher: Elsevier

Release Date: 2021-11-30


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Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body. The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body. Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB® and IBM Watson®. - Provides an introduction to Cognitive science, cognitive computing and human cognitive relation to help in the solution of AI Biomedical engineering problems - Explain different Artificial Intelligence (AI) including evolutionary algorithms to emulate natural evolution, reinforced learning, Artificial Neural Network (ANN) type and cognitive learning and to obtain many AI models for Biomedical Engineering problems - Includes coverage of the evolution Artificial Intelligence through Machine Learning (ML), Deep Learning (DL), Cognitive Computing (CC) using MATLAB® as a programming language with many add-on MATLAB® toolboxes, and AI based commercial products cloud services as: IBM (Cognitive Computing, IBM Watson®, IBM Watson Studio®, IBM Watson Studio Visual Recognition®), and others - Provides the necessary tools to accelerate obtaining results for the analysis of injuries, illness, and neurologic diseases that can be detected through the static, kinetics and kinematics, and natural body language data and medical imaging techniques applying AI using ML-DL-CC algorithms with the objective of obtaining appropriate conclusions to create solutions that improve the quality of life of patients

Philosophy and Cognitive Science


Philosophy and Cognitive Science

Author: Lorenzo Magnani

language: en

Publisher: Springer Science & Business Media

Release Date: 2012-07-13


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The book addresses a number of recent topics at the crossroad of philosophy and cognitive science, taking advantage of both the western and the eastern perspectives and conceptions that emerged and were discussed at the PCS2011 Conference recently held in Guangzhou. The ever growing cultural exchange between academics and intellectual belonging to different cultures is reverberated by the juxtaposition of papers, which aim at investigating new facets of crucial problems in philosophy: the role of models in science and the fictional approach; chance seeking dynamics and how affordances work; abductive cognition; visualization in science; the cognitive structure of scientific theories; scientific representation; mathematical representation in science; model-based reasoning; analogical reasoning; moral cognition; cognitive niches and evolution.