Palmeri Current Courses Taught
NSC3270 / NSC5270 Computational Neuroscience (Spring 2025)
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We will introduce theoretical, mathematical, and simulation models that range from detailed biophysical models of how neurons produce action potentials, to more simplified integrate and fire models, to rate coded and activation models of neurons and neural networks. We will discuss connections between low-level biological models of individual neurons and high-level cognitive and economic models of complex adaptive behavior. We will discuss mathematical, simulation, and computational techniques for understanding how neurons, neural networks, or brain systems operate. We will also discuss computational approaches to analyzing and understanding neurophysiological, electrophysiological, or brain imaging data. We will rely heavily on demonstrations and hands on experience developing, testing, and evaluating models.
PSY4218 / PSY6218 Computational Cognitive Modeling (Spring 2020)
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This course provides an overview of the how-tos and the whys of computational modeling. This course is not intended to be a general survey of computational models of human cognition and perception. Instead, we will talk about what models are, why we use models, how to recognize good modeling versus bad modeling, how to implement a model, how to fit a model to data, how to evaluate the fit of a model, how to compare and contrast competing models, how to evaluate special cases of a model, and how to develop and test new models. We will talk about a number of real-world practical issues involved in implementing models. We will primarily talk about models that account for response probabilities, response times, and neural data in a few selected domains. We will talk about why we develop and test models, when it is appropriate and inappropriate to test models, what kinds of choices are made when developing a model, what are the best ways to use modeling most effectively, and what we can learn from models. By taking this course, you will be able to implement models, simulate models, make model predictions, fit models to data, and contrast competing models. This course will also give you the tools and background to take a more critical eye to modeling work you might read in the literature. We’ll cover a variety of practical issues like random number generators, Monte Carlo simulations, using the high-performance computing facility at ACCRE, speeding up simulations, using bootstrapping techniques. Knowledge of Matlab or Python is required.
PSY4219 / PSY6219 Scientific Computing for Psychological and Brain Sciences (Fall 2020)
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This course is an introduction to scientific computing for psychological and brain sciences. The target audience is first or second year graduate students in Psychological Sciences, the Neuroscience Graduate Program, and related disciplines. The course is also open to undergraduates doing honors or independent study with permission of the instructor. The goal of the course is for students to develop some proficiency in designing, writing, and debugging computer programs to control experiments, perform data analyses, and simulate neural and psychological mechanisms. We will discuss computer programming methods, algorithms and data structures, computational and numerical methods, and high performance computing techniques as applied to common problems in psychological and brain sciences.
Palmeri Past Courses Taught
PSY300 / PHD396 Research Seminar
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This is the required seminar for all first-year graduate students in Psychological Sciences. Core competencies of a successful scientist include scientific knowledge, research skills, communication skills, teaching, professionalism, leadership and management skills, and responsible conduct of research. This course will provide part of your introduction to each of these core competencies that will further develop as you progress through your graduate and postdoctoral training.
PSY208 Principles of Experimental Design
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Knowledge acquired through scientific research is bounded by the conditions under which the research is carried out. Consequently, informed consumers of information must understand how scientific research is carried out in order to decide what is true and what is not. This course provides an introduction to research methods in psychological science, experimental design, and data interpretation. Students will develop an appreciation for the methods involved in carrying out research on issues in psychology and, hopefully, will become critical, but not cynical, consumers of scientific results, learning to distinguish sound conclusions from those based on faulty reasoning or flawed experiments. Students in this course will gain real experience by working in a small group to design and conduct an experiment of their own, present their results as a group, and write up the results individually in an APA-style research paper.
PSY225 Cognitive Psychology
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This course is an introduction to cognitive psychology. The fundamental goal of this area of study is to understand the nature of human thought processes and how they work. Some of the issues we will examine include: How do we perceive objects and events in the world? What are the processes involved in learning and remembering? How is knowledge organized in memory and how do we access that knowledge? What are the processes involved in problem solving, decision making, and reasoning? What is the nature of expertise? What is creativity? What is intelligence? How do we understand and produce spoken and written language? Are there unconscious thought processes? What areas of the brain are involved in various cognitive processes? How does brain damage affect cognition?
PSY276 Knowledge, Brain, and Culture
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Human conceptual knowledge arises from an interaction of mind, brain, and culture. To understand the nature of conceptual knowledge, we will be examining a variety of findings from psychology (mind), neuroscience (brain), and anthropology (culture). We will examine how conceptual knowledge is organized in the human mind, whether some kinds of knowledge might be innate and some kinds of knowledge might learned, how conceptual knowledge might be acquired from the world around us, how knowledge is acquired by children, how conceptual knowledge is similar and how it varies across cultures, and how knowledge is processed and represented by the human brain. This course will be structured as a seminar, with some of the time devoted to class discussion of original research papers and some of the time devoted to lectures on related materials.
PSY351 Models of Categorization
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This course will investigate how we recognize objects, how we place objects into learned categories, and how these abilities change as we gain expertise in recognizing and classifying objects. Until quite recently, research on object recognition, categorization, and perceptual expertise have remained largely independent lines of investigation. The goal of this graduate seminar is to attempt to bridge between these various domains both empirically and theoretically. Indeed, the motivation for this course is not simply to convey the contemporary body of knowledge in each of these areas, but also to understand why some of these issues have been studied in isolation from one another and to achieve an understanding of what may have been overlooked because of that isolation and what may be gained by considering these domains together. We hope that the group effort in this seminar will generate ideas for new empirical investigation and new theoretical integration. We will be reading a wide variety of original research articles investigating behavioral studies of normal individuals, behavioral studies of brain-damaged individuals, brain imaging studies, single-unit recordings of awake behaving primates, and formal computational models. Course requirements will focus on readings, discussion, and very short reaction papers.
PSY351 Object Recognition, Categorization, and Perceptual Expertise
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This course will investigate how we recognize objects, how we place objects into learned categories, and how these abilities change as we gain expertise in recognizing and classifying objects. Until quite recently, research on object recognition, categorization, and perceptual expertise have remained largely independent lines of investigation. The goal of this graduate seminar is to attempt to bridge between these various domains both empirically and theoretically. Indeed, the motivation for this course is not simply to convey the contemporary body of knowledge in each of these areas, but also to understand why some of these issues have been studied in isolation from one another and to achieve an understanding of what may have been overlooked because of that isolation and what may be gained by considering these domains together. We hope that the group effort in this seminar will generate ideas for new empirical investigation and new theoretical integration. We will be reading a wide variety of original research articles investigating behavioral studies of normal individuals, behavioral studies of brain-damaged individuals, brain imaging studies, single-unit recordings of awake behaving primates, and formal computational models. Course requirements will focus on readings, discussion, and very short reaction papers.
SC3250 / SC5250 Scientific Computing Toolbox
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An astronomer studying the formation of massive black holes, an economist studying complex financial markets, a neuroscientist studying brain networks for human memory, a chemist studying the structure of large proteins, and an engineer designing new nanostructured materials could not appear be more different. Their research involves vastly different forces that govern physical, biological, or social interactions, for structures with spatial scales ranging from subatomic to extragalactic and timescales ranging from picoseconds to gigayears. Yet, from a computational standpoint, the astronomer, economist, neuroscientist, chemist, and engineer face similar challenges in working to understand the behavior of complex systems. — This course introduces scientific computing tools used by scientists and engineers to understand complex physical, biological, and social systems. Students may be introduced to numerical and computational methods for simulating models of complex systems, techniques for optimizing and evaluating models, scientific visualization and data mining techniques for detecting structure in large multidimensional data sets, and high performance computing techniques for simulating models and analyzing data.