Courses

Social & Affective Motivations in Decision-Making

Why do we tip restaurant servers, cab drivers, and coffee baristas? Why does our grocery shopping behavior change when we are hungry? This course will explore the social and affective motivations that influence how we make everyday decisions from the diverse perspectives of psychology, economics, and neurobiology. This course will provide an introduction to how social psychological constructs and feelings can be modeled using tools from decision theory (e.g., value & uncertainty) and how these processes might be instantiated in the brain. Topics to be covered include other- regarding preferences (e.g., trust, reciprocity, fairness, and altruism), affective motivations (e.g., risk, dread, regret, and guilt), and social considerations (e.g., reputation, conformity, and social-comparison). This course incorporates a social impact practicum.

Experimental Study of Social Behavior

As computers become increasingly integrated with our daily lives, there is an unprecedented amount of data recording how humans interact with the world through the internet, mobile sensing, and social media. There is currently overwhelming demand for social scientists who know how to use this data to answer questions about social behavior. This course provides an introduction to data science and explores modern issues pertaining to experimental design, hypothesis testing, and data collection and analysis. Students will be expected to design, run, and analyze, and write up an experiment answering a question of their choosing. Students will learn how to use the open source Python language to process, analyze, and visualize data. All labs can be accessed on github. This class will be useful for students planning to go on to graduate school or work in industry as a data analyst/scientist.

The Power of Beliefs

As computers become increasingly integrated with our daily lives, there is an unprecedented amount of data recording how humans interact with the world through the internet, mobile sensing, and social media. There is currently overwhelming demand for social scientists who know how to use this data to answer questions about social behavior. This course provides an introduction to data science and explores modern issues pertaining to experimental design, hypothesis testing, and data collection and analysis. Students will be expected to design, run, and analyze, and write up an experiment answering a question of their choosing. Students will learn how to use the open source Python language to process, analyze, and visualize data. All labs can be accessed on github. This class will be useful for students planning to go on to graduate school or work in industry as a data analyst/scientist.

Introduction to fMRI Data Analysis

How can we understand how the brain works? This course provides an introduction to in vivo neuroimaging in humans using functional magnetic resonance imaging (fMRI). The goal of the class is to introduce: (1) how the scanner generates data, (2) how psychological states can be probed in the scanner, and (3) how this data can be processed and analyzed. Students will be expected to collect and analyze brain imaging data using the opensource Python programming language. We will be using several packages such as numpy, matplotlib, nibabel, nilearn, fmriprep, and nltools. This course will be useful for students working in neuroimaging labs, completing a neuroimaging thesis, or interested in pursuing graduate training in fields related to cognitive neuroscience.

Summer School

Methods in Neuroscience at Dartmouth Computational Summer School

Computational methods are rapidly transforming psychology and neuroscience research. However, traditional psychology and neuroscience training programs have not been able to keep pace with rapid development of methodological developments. Our vision is to help train the next generation of psychological and brain scientists in the latest mathematical modeling and analysis tools for studying the mind. Training students in computational techniques in graduate programs, workshops, and tutorials is an extraordinarily challenging endeavor due to high levels of variability in (a) mathematical and computer backgrounds, (b) interest in theory vs applications, and (c) computer operating systems and software packages. Each year we select a general theme to help frame the lectures and tutorials included in the course. Importantly, these themes always include ties to psychological and neuroscientific questions at a broad range of scales, ranging from single neurons, to full brains, to interacting groups.