| source Indiana University Bloomington (X) |
level |
department Cognitive Science (X) |
Cognitive Science , Philosophical Foundations of the Cognitive and Information Sciences Q240 , 0001 , C. Zednik --------------------------------------------------------------------- ----------- ________________________________________ Lecture: 11:15a.m. - 12:30, Tu Th Read Hall (RE) 2-120B Discussion: Time (TBA), FR (RE) 2-120B COGS Q240 Philosophical Foundations of Cognitive Science Cognitive Science emerged almost 50 years ago from developments in philosophy, computer science, psychology, and linguistics. Central to this emergence were new ideas about how the notion of mental representation could be understood in computational terms: the computational theory of mind. The belief that intelligence could be understood in terms of physical symbol processing served to unite artificial intelligence and cognitive psychology under a common philosophical framework, and it was believed that computers with human-level capacities would be rapidly achieved. Progress in artificial intelligence, however, has been much slower than anticipated, and developments in neuroscience, in artificial neural networks, and in dynamical and evolutionary approaches to cognition and robotics, have caused some to question whether cognitive science should remain committed to the notion of mental representation. In this course, students will learn about the original promise of computational theory of mind, and how it provided an alternative to earlier philosophical and scientific views about the relationship between mind and body. We will go on to consider the debate about whether evolutionary, embodied, and dynamical systems approaches to cognitive science amount to an overthrow of its traditional representationalist core.
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Cognitive Science , Math & Logic for Cognitiv Science Q250 , 7900 , P. Williams Cognitive Science , Math & Logic for Cognitive Science Q250 , 7900 , P. Williams ________________________________________ Lecture: Time: 9:30AM Â 10:45AM Mo We; Lab (7901): 9:05A-9:55A, F; Room BH 108 Cognitive science aims to provide rigorous explanations of the processes that underlie intelligent behavior. To this end, cognitive scientists employ a wide variety of tools and concepts from logic and various branches of mathematics. In this course, we explore a range of topics in math and logic that are particularly important for cognitive science: propositional and predicate logic, automata theory, and probability theory. For each topic, our goal is twofold: first, to understand the basic mathematical and conceptual underpinnings; second, to understand the relevance for cognitive science. To address the latter, we consider each mathematical topic as it fits within a certain area of cognitive science. Thus, we consider logic and automata theory as they relate to computational models of the mind, and probability theory in the context of Bayesian modeling. Ideally, then, students will come away from this course with two things: (1) a skill set of basic mathematical tools and (2) appreciation and enthusiasm for how these tools can be used to model and understand the mind. The material for the course is self-contained and no prerequisites beyond a sound high school mathematics background are needed.
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Cognitive Science , Experiments & Models in Cognition Q270 , 11246 , R. Goldstone ________________________________________ Q270 , 11246 , Experiments & Models in Cognition TuTh, 2:30-3:45pm, PY 228 This course is designed to provide an intensive introduction to laboratory methods in cognitive science. The formal skills emphasized by this course are: experimental design, statistical analysis, computational modeling of human behavior, and scientific writing. The content areas covered in the course are: perception, pattern recognition, group behavior, social networks, consciousness, concept learning, neural networks, and mathematical psychology. The course is grounded in a "learning by doing philosophy." There will be very few general lectures. The majority of our time will be spent discussing research issues as they relate to particular experiments. You will learn about experimental control, statistical analysis, research writing, and analysis techniques, but you will learn about these topics while investigating real issues in cognitive science. Rather than try to give a broad overview of all of the major areas in cognitive science, I have chosen to select a few specific research areas that are within the mainstream of cognitive science. Although you will not get a general survey of cognitive science, you will acquire a depth of understanding about some areas.
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Cognitive Science , Intro. To Art Intel/Comp Simul Q351 , 8152 , J. Bonner Fall '09 semester MoWe 5:45p.m. - 7:15 p.m.; RM Psychology (PY) 109 __________________________________________________________________ Q351 (cross reference with B351) Introduction to Artificial Intro. to Artificial Intelligence and Computer Simulation (3 cr.) N & M P: C211 or Q320. A survey of techniques for machine intelligence and their relation to human intelligence. Typical topics include problem solving, knowledge and reasoning, planning and acting, machine learning, and perception. Credit given for only one of B351 and COGS Q351.
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Cognitive Science , Philosophical Foundations of Cognitive & Info. Science Q540 , 2124 , Colin Allen ________________________________________ Cognitive Science, Philosophical Foundations of Cognitive & Info. Science Q540, 2124, C. Allen MW, 2:30-3:45pm, room: Woodburn Hall (WH) 002 Cognitive Science, Philosophical Foundations of Cognitive & Info. This course examines the motivations and challenges shaping the cognitive sciences' multi-disciplinary attack on questions about the nature of mental information processing. The major goal of the course is to understand how philosophical arguments and empirical investigation have shaped each other and lead to new conceptions of mind. We will begin by examining the emergence of computational and functionalist approaches to the mind-brain relationship, leading to a comparison of classical symbol-processing and connectionist accounts. We will look at how theoretical challenges to computationalism combined with recent work in robotics, dynamical systems theory, cognitive neuroscience, and artificial life contribute to an emerging conception of mind that emphasizes embodiment and action. We will explore experimental and philosophical treatments of concepts such as symbol, representation, meaning, and concept (itself). Along the way we will encounter and discuss some big but hard to answer questions about the nature of conscious experience, the evolution of human and animal minds, and the boundaries of self and intentional agency in the world.
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Cognitive Science , The Brain And Cognition Q551 , 2125, Sharlene Newman ________________________________________ TuTh, 9:30-10:45am, Rm. PY 228 Perceptual and cognitive states depend on the activity of the brain. What can the study of the nervous system tell us about how humans perceive, remember and think? The course will cover: basic neurosciences from a cognitive perspective; a review of the experimental methods used to observe brain activity (focusing primarily on fMRI); organization of the cerebral cortex; visual perception and object recognition; attention; language and the brain; problem-solving; and the concept of intelligence. Emphasis will be put on using the knowledge gained in this course to formulate, address and solve problems in the area of cognitive neuroscience.
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Cognitive Science , Behavior - Based Robotics Q570 , 26662 , M. Scheutz --------------------------------------------------------------------- ----------- Cognitive Science , Behavior-Based Robots Q570 , 11250 , M. Scheutz ________________________________________ 4:00PM-6:30PM, WED, RM. EG 821 COGS 570 ÂBehavior-Based Robotics (3 cr) This course is designed to investigate and study methods and models in embodied cognitive science, with particular focus on behavior- based techniques on robots. All models and architectures will be theoretically scrutinized and evaluated with respect to their conceptual clarity, support by empirical data, plausibility, etc. without neglecting issues of practicality such as feasibility of implementation, real-time/real-world issues, computational resources, etc. These practical considerations will turn out to be particularly important for model implementations on robots.
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BRAIN-BODY-ENVIRONMENT SYSTEMS Q700 , 11837, TUTH 1:00-2:15PM Eigenmann Hall (EG) 833 Notions of embodiment, situatedness and dynamics are becoming increasingly important in cognitive science, converging on a view in which an agent's behavior and cognition are seen not merely as products of its brain alone, but rather as arising from the interaction between the agent's nervous system, body and environment. In order to evaluate the significance of these ideas, this course will examine the construction and analysis of models of complete brain-body-environment systems, with a particular emphasis on the use of tools from complex systems (including evolutionary algorithms, dynamical systems theory and information theory) to study how such coupled systems work. Behaviors to be studied range from basic motor behavior and sensorimotor learning to categorical perception, selective attention, agency detection and communication. We will also examine models and theories of agency, as well as some of the philosophical implications of the brain-body-environment perspective.
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