This course will introduce you to cognitive psychology. Memory, along with attention, perception, language, and decision making, are among the most prominent topics within this broad and diverse field. Upon successful completion of this course, students will be able to: Identify underlying theoretical considerations in the field of cognitive psychology; Describe the historical context in which cognitive psychology emerged as a field; Define cognitive psychology as is was historically defined and is now defined; Identify the main academic fields and other subdisciplines of psychology to which cognitive psychology is tied; Describe the main findings in the primary areas of scientific research within cognitive psychology; Compare and contrast the theories associated within the primary areas of scientific research in cognitive psychology (e.g., models of memory, attention, etc.). (Psychology 206)
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Repeated motion is present everywhere in nature. Learn how to 'make waves' with your own movements using a motion detector to plot your position as a function of time, and try to duplicate wave patterns presented in the activity. Investigate the concept of distance versus time graphs and see how your own movement can be represented on a graph.
In the Mapping Earthquakes to Save the World activity, students leverage real-time data to plot earthquakes on a world map. The fate of the world is in their hands – the President of the United States has asked for their help to save humankind. Students identify patterns in their data and connect earthquakes with tectonic plates, making recommendations back to the President about where people are safe and where people are most at risk. This activity was heavily inspired by a project from the Stevens Institute for Technology Center for Innovation in Engineering and Science Education.
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.
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.
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.
Students apply several methods developed to identify and interpret patterns to the identification of fingerprints. They look at their classmates' fingerprints, snowflakes, and "spectral fingerprints" of elements. They learn to identify each image as unique, yet part of a group containing recognizable similarities.