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9.63 Laboratory in Visual Cognition (MIT) 9.63 Laboratory in Visual Cognition (MIT)

Description

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports. 9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Subjects

cognitive science | cognitive science | human perception | human perception | cognition | cognition | statistical analysis | statistical analysis | signal detection theory | signal detection theory | single factor design | single factor design | factorial design | factorial design | matlab | matlab | correlational studies | correlational studies | ethics in research | ethics in research | visual cognition | visual cognition | thought | thought | psychology and cognitive science | psychology and cognitive science | information processing | information processing | organization of visual cognitive abilities. | organization of visual cognitive abilities.

License

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9.012 The Brain and Cognitive Sciences II (MIT) 9.012 The Brain and Cognitive Sciences II (MIT)

Description

This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives. This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

Subjects

brain | brain | behavioral | behavioral | perception | perception | attention | attention | working memory | working memory | recognition | recognition | recall | recall | language | language | cognitive science | cognitive science | computation | computation | visual perception | visual perception | memory | memory | cognitive architecture | cognitive architecture | learning | learning | reasoning | reasoning | decision-making | decision-making | cognitive development | cognitive development | behavioral perspective | behavioral perspective | computational perspective | computational perspective | neural perspective | neural perspective

License

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9.459 Scene Understanding Symposium (MIT) 9.459 Scene Understanding Symposium (MIT)

Description

What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding. What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding.

Subjects

circuits | mechanisms and representation | circuits | mechanisms and representation | recognition of a visual scene | recognition of a visual scene | Scene Understanding | Scene Understanding | neurophysiology | neurophysiology | cognitive neuroscience | cognitive neuroscience | visual cognition | visual cognition | computational neuroscience | computational neuroscience | computer vision | computer vision | natural image categorization | natural image categorization | contextual effects on object recognition | contextual effects on object recognition | role of attention in scene understanding | role of attention in scene understanding

License

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9.65 Cognitive Processes (MIT) 9.65 Cognitive Processes (MIT)

Description

This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving. This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving.

Subjects

cognitive science | cognitive science | cognitive processes | cognitive processes | cognition | cognition | the mind | the mind | object recognition | object recognition | attention | attention | memory | memory | associative memory | associative memory | learning | learning | implicit memory | implicit memory | conceptual short term memory | conceptual short term memory | working memory | working memory | language | language | concepts | concepts | prototypes | prototypes | psycholinguistics | psycholinguistics | visual knowledge | visual knowledge | mental codes | mental codes | judgement | judgement | reasoning | reasoning | problem-solving | problem-solving | conscious thought | conscious thought | unconscious thought | unconscious thought

License

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9.85 Infant and Early Childhood Cognition (MIT) 9.85 Infant and Early Childhood Cognition (MIT)

Description

This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. Students develop a critical understanding of experimental design and how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds. This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. Students develop a critical understanding of experimental design and how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds.

Subjects

infant cognition | infant cognition | early childhood cognition | early childhood cognition | cognitive development | cognitive development | developmental psychology | developmental psychology | psychology | psychology | developmental research | developmental research | Piaget | Piaget | object knowledge | object knowledge | object individuation | object individuation | object concept | object concept | agents | agents | causal knowledge | causal knowledge | theory of mind | theory of mind | causation | causation | causal transformations | causal transformations

License

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髇 Cognitiva (2009) 髇 Cognitiva (2009)

Description

La Estimulaci髇 Cognitiva es una disciplina de intervenci髇 psicol骻ica que integra todo un conjunto de t閏nicas y estrategias sistem醫icas y estandarizadas que tienen por objetivo activar y ejercitar las distintas capacidades y funciones cognitivas del individuo con el fin 鷏timo de mejorar su rendimiento. La Estimulaci髇 Cognitiva puede ser aplicada a cualquier individuo, puesto que cualquiera de nosotros podemos mejorar nuestras capacidades para ser m醩 h醔iles y diestros; pero su objetivo es tambi閚 en muchas ocasiones terap閡tico, puesto que muchas poblaciones que manifiestan alg鷑 tipo de d閒icit o deterioro cognitivo importante y significativo -discapacidad intelectual, trastornos del desarrollo, personas con da駉 cerebral, demencias, etc.- pueden, en mayor o menor med La Estimulaci髇 Cognitiva es una disciplina de intervenci髇 psicol骻ica que integra todo un conjunto de t閏nicas y estrategias sistem醫icas y estandarizadas que tienen por objetivo activar y ejercitar las distintas capacidades y funciones cognitivas del individuo con el fin 鷏timo de mejorar su rendimiento. La Estimulaci髇 Cognitiva puede ser aplicada a cualquier individuo, puesto que cualquiera de nosotros podemos mejorar nuestras capacidades para ser m醩 h醔iles y diestros; pero su objetivo es tambi閚 en muchas ocasiones terap閡tico, puesto que muchas poblaciones que manifiestan alg鷑 tipo de d閒icit o deterioro cognitivo importante y significativo -discapacidad intelectual, trastornos del desarrollo, personas con da駉 cerebral, demencias, etc.- pueden, en mayor o menor med

Subjects

髇 cognitiva | 髇 cognitiva | Entrenamiento cognitivo | Entrenamiento cognitivo | Capacidades cognitivas | Capacidades cognitivas | 閏nicas de Intervenci髇 y Tratamiento Psicol骻ico | 閏nicas de Intervenci髇 y Tratamiento Psicol骻ico | Psicoestimulacion | Psicoestimulacion

License

http://creativecommons.org/licenses/by-nc-sa/3.0/

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9.85 Infant and Early Childhood Cognition (MIT) 9.85 Infant and Early Childhood Cognition (MIT)

Description

This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. It develops a critical understanding of experimental design. The course discusses how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds. It provides instruction and practice in written communication as needed for cognitive science research (including critical reviews of journal papers, a literature review and an original research proposal), as well as instruction and practice in oral communication in the form of a poster presentation of a journal paper. This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. It develops a critical understanding of experimental design. The course discusses how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds. It provides instruction and practice in written communication as needed for cognitive science research (including critical reviews of journal papers, a literature review and an original research proposal), as well as instruction and practice in oral communication in the form of a poster presentation of a journal paper.

Subjects

infant cognition | infant cognition | early childhood cognition | early childhood cognition | cognitive development | cognitive development | developmental psychology | developmental psychology | psychology | psychology | developmental research | developmental research | Piaget | Piaget | object knowledge | object knowledge | object individuation | object individuation | object concept | object concept | agents | agents | causal knowledge | causal knowledge | theory of mind | theory of mind | causation | causation | causal transformations | causal transformations

License

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9.66J Computational Cognitive Science (MIT) 9.66J Computational Cognitive Science (MIT)

Description

This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have? This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?

Subjects

computational theory | computational theory | human cognition | human cognition | artificial intelligence | artificial intelligence | human knowledge representation | human knowledge representation | inductive learning | inductive learning | inductive reasoning | inductive reasoning | innate knowledge | innate knowledge | machine learning | machine learning | cognitive science | cognitive science | computational cognitive science | computational cognitive science | 9.66 | 9.66 | 6.804 | 6.804

License

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9.913 Pattern Recognition for Machine Vision (MIT) 9.913 Pattern Recognition for Machine Vision (MIT)

Description

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering. The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Subjects

comonent analysis | comonent analysis | PCA | PCA | ICA | ICA | fourier analysis | fourier analysis | vision | vision | machine vision | machine vision | pattern matching | pattern matching | pattern analysis | pattern analysis | pattern recognition | pattern recognition | scene analysis | scene analysis | tracking | tracking | feature extraction | feature extraction | color | color | color space | color space | clustering | clustering | bayesian decisions | bayesian decisions | gesture recognition | gesture recognition | action recognition | action recognition | image processing | image processing | image formation | image formation | density estimation | density estimation | classification | classification | morphable models | morphable models | component analysis | component analysis

License

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9.67 Object and Face Recognition (MIT) 9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems. Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | object recognition | neural | neural | computation | computation | representation | representation | 3-D objects | 3-D objects | 2-D images | 2-D images | face recognition | face recognition | human face recognition | human face recognition | artificial computational systems | artificial computational systems

License

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9.67 Object and Face Recognition (MIT) 9.67 Object and Face Recognition (MIT)

Description

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems. Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | object recognition | neural | neural | computation | computation | representation | representation | 3-D objects | 3-D objects | 2-D images | 2-D images | face recognition | face recognition | human face recognition | human face recognition | artificial computational systems | artificial computational systems

License

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9.63 Laboratory in Visual Cognition (MIT)

Description

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Subjects

cognitive science | human perception | cognition | statistical analysis | signal detection theory | single factor design | factorial design | matlab | correlational studies | ethics in research | visual cognition | thought | psychology and cognitive science | information processing | organization of visual cognitive abilities.

License

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9.012 The Brain and Cognitive Sciences II (MIT)

Description

This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

Subjects

brain | behavioral | perception | attention | working memory | recognition | recall | language | cognitive science | computation | visual perception | memory | cognitive architecture | learning | reasoning | decision-making | cognitive development | behavioral perspective | computational perspective | neural perspective

License

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7.342 Cancer Biology: From Basic Research to the Clinic (MIT) 7.342 Cancer Biology: From Basic Research to the Clinic (MIT)

Description

This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. In 1971, President Nixon declared the "War on Cancer," but after three decades the war is still raging. How much progress have we made toward winning the war and what are we doing to improve the fight? Understanding the molecular and cellular events involved in tumor formation, progression, and metastasis is crucial to the development of innovative therapy for cancer patients. Insights into these processes have been gleaned through basic research using biochemical, molecular, and genetic ana This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. In 1971, President Nixon declared the "War on Cancer," but after three decades the war is still raging. How much progress have we made toward winning the war and what are we doing to improve the fight? Understanding the molecular and cellular events involved in tumor formation, progression, and metastasis is crucial to the development of innovative therapy for cancer patients. Insights into these processes have been gleaned through basic research using biochemical, molecular, and genetic ana

Subjects

cancer | cancer | tumor | tumor | metastasis | metastasis | genetic analysis | genetic analysis | cancer biology | cancer biology | model organisms | model organisms | genetic pathways | genetic pathways | uncontrolled growth | uncontrolled growth | tumor suppressor genes | tumor suppressor genes | oncogenes | oncogenes | tumor initiation | tumor initiation | cell cycle | cell cycle | chromosomal aberration | chromosomal aberration | apoptosis | apoptosis | cell death | cell death | signal transduction pathways | signal transduction pathways | proto-oncogene | proto-oncogene | mutation | mutation | DNA mismatch repair | DNA mismatch repair | telomeres | telomeres | mouse models | mouse models | tissue specificity | tissue specificity | malignancy | malignancy | stem cells | stem cells | therapeutic resistance | therapeutic resistance | differentiation | differentiation | caner research | caner research | cancer therapeutics | cancer therapeutics | chemotherapy | chemotherapy

License

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9.459 Scene Understanding Symposium (MIT)

Description

What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding.

Subjects

circuits | mechanisms and representation | recognition of a visual scene | Scene Understanding | neurophysiology | cognitive neuroscience | visual cognition | computational neuroscience | computer vision | natural image categorization | contextual effects on object recognition | role of attention in scene understanding

License

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Analysis (MIT) Analysis (MIT)

Description

Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research. Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.

Subjects

machine and human learning | machine and human learning | unsupervised learning and clustering | unsupervised learning and clustering | non-parametric methods | non-parametric methods | Bayesian estimation | Bayesian estimation | maximum likelihood | maximum likelihood | statistical classification | statistical classification | decision theory | decision theory | physiological analysis | physiological analysis | computer vision | computer vision | peech recognition and understanding | peech recognition and understanding | recognition | recognition | numerical data | numerical data | 1.126 | 1.126

License

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STS.010 Neuroscience and Society (MIT) STS.010 Neuroscience and Society (MIT)

Description

This course explores the social relevance of neuroscience, considering how emerging areas of brain research at once reflect and reshape social attitudes and agendas. Topics include brain imaging and popular media; neuroscience of empathy, trust, and moral reasoning; new fields of neuroeconomics and neuromarketing; ethical implications of neurotechnologies such as cognitive enhancement pharmaceuticals; neuroscience in the courtroom; and neuroscientific recasting of social problems such as addiction and violence. Guest lectures by neuroscientists, class discussion, and weekly readings in neuroscience, popular media, and science studies. This course explores the social relevance of neuroscience, considering how emerging areas of brain research at once reflect and reshape social attitudes and agendas. Topics include brain imaging and popular media; neuroscience of empathy, trust, and moral reasoning; new fields of neuroeconomics and neuromarketing; ethical implications of neurotechnologies such as cognitive enhancement pharmaceuticals; neuroscience in the courtroom; and neuroscientific recasting of social problems such as addiction and violence. Guest lectures by neuroscientists, class discussion, and weekly readings in neuroscience, popular media, and science studies.

Subjects

cognitive science | cognitive science | evolutionary psychology | evolutionary psychology | neurobiology | neurobiology | brain imaging | brain imaging | MRI | MRI | CT scan | CT scan | fMRI | fMRI | brain | brain | mind | mind | morality | morality | moral reasoning | moral reasoning | decision making | decision making | intelligence | intelligence | empathy | empathy | trust | trust | religion | religion | love | love | emotion | emotion | gender differences | gender differences | sexuality | sexuality | stress | stress | prejudice | prejudice | attention | attention | psychopharmaceuticals | psychopharmaceuticals | antidepressant | antidepressant | neuroeconomics | neuroeconomics | neuromarketing | neuromarketing | neurotheology | neurotheology | cognitive enhancement | cognitive enhancement | witness | witness | courtroom testimony | courtroom testimony | addiction | addiction | violence | violence | learning | learning | behavior | behavior

License

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24.231 Ethics (MIT) 24.231 Ethics (MIT)

Description

This will be a seminar on classic and contemporary work on central topics in ethics. The first third of the course will focus on metaethics: we will examine the meaning of moral claims and ask whether there is any sense in which moral principles are objectively valid. The second third of the course will focus on normative ethics: what makes our lives worth living, what makes our actions right or wrong, and what do we owe to others? The final third of the course will focus on moral character: what is virtue, and how important is it? Can we be held responsible for what we do? When and why? This will be a seminar on classic and contemporary work on central topics in ethics. The first third of the course will focus on metaethics: we will examine the meaning of moral claims and ask whether there is any sense in which moral principles are objectively valid. The second third of the course will focus on normative ethics: what makes our lives worth living, what makes our actions right or wrong, and what do we owe to others? The final third of the course will focus on moral character: what is virtue, and how important is it? Can we be held responsible for what we do? When and why?

Subjects

ethics | ethics | euthyphro | euthyphro | Plato | Plato | goodness | goodness | non-naturalism | non-naturalism | G. E. Moore | G. E. Moore | non-cognitivism | non-cognitivism | Alfred Jules Ayer | Alfred Jules Ayer | David Brink | David Brink | cognitivism | cognitivism | Gilbert Harman | Gilbert Harman | Nicholas Sturgeon | Nicholas Sturgeon | observation | observation | morality | morality | moral relativism | moral relativism | Philippa Foot | Philippa Foot | David Lyons | David Lyons | incoherence | incoherence | ethical relativism | ethical relativism | John Stuart Mill | John Stuart Mill | utilitarianism | utilitarianism | Robert Nozick | Robert Nozick | Derek Parfit | Derek Parfit | Alastair Norcross | Alastair Norcross | philosophy | philosophy | Bernard Williams | Bernard Williams | James Lenman | James Lenman | consequentialism | consequentialism | cluelessness | cluelessness | Peter Singer | Peter Singer | act-utilitarianism | act-utilitarianism | John Rawls | John Rawls | rules | rules | Thomas Nagel | Thomas Nagel | famine | famine | affluence | affluence | Nomy Arpaly | Nomy Arpaly | moral worth | moral worth | Susan Wolf | Susan Wolf | moral saints | moral saints | Peter van Inwagen | Peter van Inwagen | free will | free will | determinism | determinism | Harry Frankfurt | Harry Frankfurt | moral responsibility | moral responsibility | moral luck | moral luck

License

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9.63 Laboratory in Cognitive Science (MIT) 9.63 Laboratory in Cognitive Science (MIT)

Description

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports. 9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Subjects

cognitive science | cognitive science | human perception | human perception | cognition | cognition | statistical analysis | statistical analysis | signal detection theory | signal detection theory | single factor design | single factor design | factorial design | factorial design | matlab | matlab | correlational studies | correlational studies | ethics in research | ethics in research

License

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24.941J The Lexicon and Its Features (MIT) 24.941J The Lexicon and Its Features (MIT)

Description

This course provides an overview of the distinctive features which distinguish sound categories of languages of the world. Theories which relate these categories to their acoustic and articulatory correlates, both universally and in particular languages, are covered. Models of word recognition by listeners, features, and phonological structure are also discussed. In addition, the course offers a variety of perspectives on these issues, drawn from Electrical Engineering, Linguistics and Cognitive Science. This course provides an overview of the distinctive features which distinguish sound categories of languages of the world. Theories which relate these categories to their acoustic and articulatory correlates, both universally and in particular languages, are covered. Models of word recognition by listeners, features, and phonological structure are also discussed. In addition, the course offers a variety of perspectives on these issues, drawn from Electrical Engineering, Linguistics and Cognitive Science.

Subjects

24.941 | 24.941 | 6.543 | 6.543 | 9.587 | 9.587 | HST.727 | HST.727 | lexicon | lexicon | features | features | sound categories | sound categories | acoustic and articulatory correlates | acoustic and articulatory correlates | languages | languages | models of word recognition | models of word recognition | linguistics | linguistics | cognitive science | cognitive science

License

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14.13 Economics and Psychology (MIT) 14.13 Economics and Psychology (MIT)

Description

This course integrates psychological insights into economic models of behavior. It discusses the limitations of standard economic models and surveys the ways in which psychological experiments have been used to learn about preferences, cognition, and behavior. Topics include: trust, vengeance, fairness, impatience, impulsivity, bounded rationality, learning, reinforcement, classical conditioning, loss-aversion, over-confidence, self-serving biases, cognitive dissonance, altruism, subjective well-being, and hedonic adaptation. Economic concepts such as equilibrium, rational choice, utility maximization, Bayesian beliefs, game theory, and behavior under uncertainty are discussed in light of these phenomena. This course integrates psychological insights into economic models of behavior. It discusses the limitations of standard economic models and surveys the ways in which psychological experiments have been used to learn about preferences, cognition, and behavior. Topics include: trust, vengeance, fairness, impatience, impulsivity, bounded rationality, learning, reinforcement, classical conditioning, loss-aversion, over-confidence, self-serving biases, cognitive dissonance, altruism, subjective well-being, and hedonic adaptation. Economic concepts such as equilibrium, rational choice, utility maximization, Bayesian beliefs, game theory, and behavior under uncertainty are discussed in light of these phenomena.

Subjects

behavioral economics | behavioral economics | finance | finance | psychology | psychology | prospect | prospect | prospect theory | prospect theory | bias | bias | probabilistic judgment | probabilistic judgment | self-control | self-control | mental accounting | mental accounting | fairness | fairness | altruism | altruism | public goods | public goods | market anomalies | market anomalies | market theories | market theories | economics | economics | behavior | behavior | preferences | preferences | cognition | cognition | trust | trust | vengence | vengence | impatience | impatience | impulsivity | impulsivity | bounded rationality | bounded rationality | learning | learning | reinforcement | reinforcement | classical conditioning | classical conditioning | loss-aversion | loss-aversion | over-confidence | over-confidence | self-serving biases | self-serving biases | cognitive dissonance | cognitive dissonance | subjective well-being | subjective well-being | hedonic adaptation | hedonic adaptation | equilibrium | equilibrium | rational choice | rational choice | utility maximization | utility maximization | Bayesian beliefs | Bayesian beliefs | game theory | game theory | neuroeconomics | neuroeconomics

License

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9.675 The Development of Object and Face Recognition (MIT) 9.675 The Development of Object and Face Recognition (MIT)

Description

This course takes a 'back to the beginning' view that aims to better understand the end result. What might be the developmental processes that lead to the organization of 'booming, buzzing confusions' into coherent visual objects? This course examines key experimental results and computational proposals pertinent to the discovery of objects in complex visual inputs. The structure of the course is designed to get students to learn and to focus on the genre of study as a whole; to get a feel for how science is done in this field. This course takes a 'back to the beginning' view that aims to better understand the end result. What might be the developmental processes that lead to the organization of 'booming, buzzing confusions' into coherent visual objects? This course examines key experimental results and computational proposals pertinent to the discovery of objects in complex visual inputs. The structure of the course is designed to get students to learn and to focus on the genre of study as a whole; to get a feel for how science is done in this field.

Subjects

computational theories of human cognition | computational theories of human cognition | principles of inductive learning and inference | principles of inductive learning and inference | representation of knowledge | representation of knowledge | computational frameworks | computational frameworks | Bayesian models | Bayesian models | hierarchical Bayesian models | hierarchical Bayesian models | probabilistic graphical models | probabilistic graphical models | nonparametric statistical models | nonparametric statistical models | Bayesian Occam's razor | Bayesian Occam's razor | sampling algorithms for approximate learning and inference | sampling algorithms for approximate learning and inference | probabilistic models defined over structured representations such as first-order logic | probabilistic models defined over structured representations such as first-order logic | grammars | grammars | relational schemas | relational schemas | core aspects of cognition | core aspects of cognition | concept learning | concept learning | concept categorization | concept categorization | causal reasoning | causal reasoning | theory formation | theory formation | language acquisition | language acquisition | social inference | social inference

License

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9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans (MIT) 9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans (MIT)

Description

Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning. Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning.

Subjects

vision | vision | object recognition | object recognition | monkey versus human | monkey versus human | object representations | object representations | fMRI | fMRI | temporal lobe | temporal lobe | visual cortex | visual cortex | neuronal representations | neuronal representations | neurophysiology | neurophysiology | retinal image | retinal image | pattern recognition | pattern recognition | perceptual awareness | perceptual awareness

License

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6.345 Automatic Speech Recognition (MIT) 6.345 Automatic Speech Recognition (MIT)

Description

Includes audio/video content: AV special element audio. 6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts. Part I deals with background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation. Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. Part III compares and contrasts the various approaches to speech recognition, and describes advanced techniques used for acoustic-phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing. Includes audio/video content: AV special element audio. 6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts. Part I deals with background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation. Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. Part III compares and contrasts the various approaches to speech recognition, and describes advanced techniques used for acoustic-phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing.

Subjects

speech recognition | speech recognition | automatic speech recognition | automatic speech recognition | acoustic theory | acoustic theory | speech production | speech production | acoustic-phonetics | acoustic-phonetics | signal representation | signal representation | pattern classification | pattern classification | search algorithms | search algorithms | stochastic modelling | stochastic modelling | language modelling | language modelling | speaker adaptation | speaker adaptation | paralinguistic information | paralinguistic information | speech understanding | speech understanding | multimodal processing | multimodal processing

License

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6.803 The Human Intelligence Enterprise (MIT) 6.803 The Human Intelligence Enterprise (MIT)

Description

6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course. 6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | Human Intelligence Enterprise | artificial intelligence | artificial intelligence | computational models | computational models | perception | perception | cognition | cognition | neuroscience | neuroscience | human behavior | human behavior | communication | communication | heuristics | heuristics | object tracking | object tracking | object recognition | object recognition | change representation | change representation | language evolution | language evolution | Turing | Turing | Minsky | Minsky

License

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