This course is an investigation of affective priming and creation of rigorously counterbalanced, fully computerized testing paradigm. Includes background readings, study design, counterbalancing, study execution, data analysis, presentation of poster, and final paper.

Survey of principles underlying the structure and function of the nervous system, integrating molecular, cellular, and systems approaches. Topics: development of the nervous system and its connections, cell biology or neurons, neurotransmitters and synaptic transmission, sensory systems of the brain, the neuroendocrine system, the motor system, higher cortical functions, behavioral and cellular analyses of learning and memory. First half of an intensive two-term survey of brain and behavioral studies for first-year graduate students. Open to graduate students in other departments, with permission of instructor.

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 course explores the cognitive and neural processes that support attention, vision, language, motor control, navigation, and memory. It introduces basic neuroanatomy, functional imaging techniques, and behavioral measures of cognition, and discusses methods by which inferences about the brain bases of cognition are made. We consider evidence from patients with neurological diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, Balint's syndrome, amnesia, and focal lesions from stroke) and from normal human participants.

An introduction to human information processing and learning; 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.

The purpose of this course is to cultivate an understanding of modern computing technology through an in-depth study of the interface between hardware and software. The student will study the history of modern computing technology before learning about modern computer architecture, then the recent switch from sequential processing to parallel processing. Upon completion of this course, students will be able to: identify important advances that have taken place in the history of modern computing and discuss some of the latest trends in computing industry; explain how programs written in high-level programming language, such as C or Java, can be translated into the language of the hardware; describe the interface between hardware and software and explain how software instructs hardware to accomplish desired functions; demonstrate an understanding of the process of carrying out sequential logic design; demonstrate an understanding of computer arithmetic hardware blocks and floating point representation; explain how a hardware programming language is executed on hardware and how hardware and software design affect performance; demonstrate an understanding of the factors that determine the performance of a program; demonstrate an understanding of the techniques that designers use to improve the performance of programs running on hardware; demonstrate an understanding of the importance of memory hierarchy in computer design and explain how memory design impacts overall hardware performance; demonstrate an understanding of storage and I/O devices, their performance measurement, and redundant array of inexpensive disks (more commonly referred to by the acronym RAID) technology; list the reasons for and the consequences of the recent switch from sequential processing to parallel processing in hardware manufacture and explain the basics of parallel programming. (Computer Science 301)

This textbook presents core concepts common to introductory courses. The 15 units cover the traditional areas of intro-to-psychology; ranging from biological aspects of psychology to psychological disorders to social psychology. This book can be modified: feel free to add or remove modules to better suit your specific needs.

This book includes a comprehensive instructor's manual, PowerPoint presentations, a test bank, reading anticipation guides, and adaptive student quizzes.

In this course, the student will learn the theoretical and practical aspects of algorithms and Data Structures. The student will also learn to implement Data Structures and algorithms in C/C++, analyze those algorithms, and consider both their worst-case complexity and practical efficiency. Upon successful completion of this course, students will be able to: Identify elementary Data Structures using C/C++ programming languages; Analyze the importance and use of Abstract Data Types (ADTs); Design and implement elementary Data Structures such as arrays, trees, Stacks, Queues, and Hash Tables; Explain best, average, and worst-cases of an algorithm using Big-O notation; Describe the differences between the use of sequential and binary search algorithms. (Computer Science 201)

Subject has three goals: introduces students to the classic works on ethnic politics, familiarizes students with new research and methodological innovations in the study of ethnic politics, and helps students design and execute original research projects related to ethnic politics. Readings drawn from across disciplines, including political science, anthropology, sociology, and economics. Students read across the four subfields within political science. Graduate students specializing in any subfield are encouraged to take this subject, regardless of their previous empirical or theoretical background. Subject designed as a year-long research workshop, but may also be taken in either semester. This course is designed mainly for political science graduate students conducting or considering conducting research on identity politics. While 17.504 Ethnic Politics I is designed as a primarily theoretical course, Ethnic Politics II switches the focus to methods. It aims to familiarize the student with the current conventional approaches as well as major challenges to them. The course discusses definition and measurement issues as well as briefly addressing survey techniques and modeling.

Principles of supervisory control and telerobotics. Different levels of automation are discussed, as well as the allocation of roles and authority between humans and machines. Human-vehicle interface design in highly automated systems. Decision aiding. Tradeoffs between human control and human monitoring. Automated alerting systems and human intervention in automatic operation. Enhanced human interface technologies such as virtual presence. Performance, optimization, and social implications of the human-automation system. Examples from aerospace, ground, and undersea vehicles, robotics, and industrial systems. Human Supervisory Control of Automated Systems discusses elements of the interactions between humans and machines. These elements include: assignment of roles and authority; tradeoffs between human control and human monitoring; and human intervention in automatic processes. Further topics comprise: performance, optimization and social implications of the system; enhanced human interfaces; decision aiding; and automated alterting systems. Topics refer to applications in aerospace, industrial and transportation systems.

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development. Alternate years.

" This course is an introduction to the mammalian nervous system, with emphasis on the structure and function of the human brain. Topics include the function of nerve cells, sensory systems, control of movement, learning and memory, and diseases of the brain."

When you teach Introduction to Psychology, do you find it difficult — much harder than teaching classes in statistics or research methods? Do you easily give a lecture on the sympathetic nervous system, a lecture on Piaget, and a lecture on social cognition, but struggle with linking these topics together for the student? Do you feel like you are presenting a laundry list of research findings rather than an integrated set of principles and knowledge? Have you wondered how to ensure your course is relevant to your students? Introduction to Psychology utilizes the dual theme of behavior and empiricism to make psychology relevant to intro students. The author wrote this book to help students organize their thinking about psychology at a conceptual level. Five or ten years from now, he does not expect his students to remember the details of most of what he teaches them. However, he does hope that they will remember that psychology matters because it helps us understand behavior and that our knowledge of psychology is based on empirical study.

This is a derivative of INTRODUCTION TO PSYCHOLOGY by a publisher who has requested that they and the original author not receive attribution, which was originally released and is used under CC BY-NC-SA. This work, unless otherwise expressly stated, is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This course is a survey of the scientific study of human nature, including how the mind works, and how the brain supports the mind. Topics include the mental and neural bases of perception, emotion, learning, memory, cognition, child development, personality, psychopathology, and social interaction. Students will consider how such knowledge relates to debates about nature and nurture, free will, consciousness, human differences, self, and society.

A discussion of memory and Atkinson & Shiffrin's Stage Theory Model.https://cunysps.voicethread.com/share/19838630/ Memory - Atkinson & Shiffrin's Stage Theory Model 

This course highlights the interplay between cellular and molecular storage mechanisms and the cognitive neuroscience of memory, with an emphasis on human and animal models of hippocampal mechanisms and function. Class sessions include lectures and discussion of papers.

Roles of neural plasticity in learning and memory and in development of invertebrates and mammals. An in-depth critical analysis of current literature of molecular, cellular, genetic, electrophysiological, and behavioral studies. Discussion of original papers supplemented by introductory lectures.

The course presents an overview of the history and structure of modern operating systems, analyzing in detail each of the major components of an operating system, and exploring more advanced topics in the field, such as security concerns. Upon successful completion of this course, the student will be able to: explain what an operating system does and how it is used; identify the various components of a computer system and how they interact with an operating system; describe the differences between a 32-bit and 64-bit operating system; explain the different types of operating systems and the major ones in use today; discuss the importance and use of threads and processes in an operating system; describe concurrency; explain the difference between a thread and a process; discuss context switching and how it is used in an operating system; describe synchronization; explain a race condition; discuss interprocess communication; describe how semaphores can be used in an operating system; discuss three of the classic synchronization problems; explain the alternatives to semaphores; discuss CPU scheduling and its relevance to operating systems; explain the general goals of CPU scheduling; describe the differences between pre-emptive and non-preemptive scheduling; discuss four CPU scheduling algorithms; explain what deadlock is in relation to operating systems; discuss deadlock prevention, avoidance, and their differences; describe deadlock detection and recovery; explain the memory hierarchy; discuss how the operating system interacts with memory; describe how virtual memory works; discuss three algorithms for dynamic memory allocation; explain methods of memory access; describe paging and page replacement algorithms; describe a file system and its purpose; discuss various file allocation methods; explain disk allocation and associated algorithms; discuss types of security threats; describe the various types of malware; explain basic security techniques; explain basic networking principles; discuss protocols and how they are used; explain reference models, particularly TCP/IP and OSI. (Computer Science 401)

Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan