Courses

Prerequisites: PSYC UN1001 or equivalent Traditional psychologists have focused primarily on answering “how?” questions regarding the mechanisms that underlie behavior (i.e. How does the system work?). In contrast, evolutionary psychologists focus primarily on answering “why?” questions (i.e. Why does this system exist, and why does it have the form it does?). This course is designed to apply our knowledge of evolutionary theory to psychology in order to answer such questions.

Clinical psychology is a broad and expanding field. This course will provide students with a broad overview of approaches to treatment and assessment in clinical psychology. These include theoretical orientations and current debates within the field. The course will also provide students with cultural and ethical considerations within the field. Finally, the course will offer many illustrative examples of the application of the provided material.

 

 

Psychedelics are receiving growing attention in the fields of psychology, psychiatry, and neuroscience for their therapeutic potential. Psychedelic compounds like psilocybin, LSD, and DMT produce a wide range of changes to perception, ranging from visual perception to alterations in one’s sense of self. When combined with psychological support or psychotherapy, psychedelics have been shown to lead to rapid and long-lasting therapeutic benefits for a wide range of mental health disorders, including major depression and alcohol use disorder. The acute alterations in perception and long-lasting clinical effects offer exciting insight into the relationship between the mind and the brain. This course will focus on the current state of research on the psychological and neurobiological effects of psychedelics. We will begin with a crash-course into the basics of neuroscience and research methodology. Next, the course will delve into how psychedelics alter brain functioning, both acutely during the drug effects as well as long after they have worn off. Studies covered will span molecular, cellular, and systems level analysis. A core element of the course will include reviewing methodological approaches and neuroscientific evidence for psychedelics interventions in the treatment of clinical/psychiatric disorders. We will also review the clinical data and link neurobiological findings to their practical application to move the field of psychedelic science forward. Throughout the course, there will be a specific focus on critical appraisal of research, identifying strengths and limitations surrounding current research, and important avenues for future research. Students should leave the class with an enhanced ability to evaluate research findings and a broad understanding of the mechanisms of action of psychedelics.

The format of the course will include lectures, class discussions and presentations, and guest speakers.

Prerequisites: PSYC 1001 and any PSYC 2400-level neuroscience course or permission of the instructor.

A seminar for advanced undergraduate students exploring different areas of clinical psychology. The specific focus within clinical psychology may differ each time the course is offered, so it is possible for the course to be retaken for additional credit.

Prerequisites: the instructor's permission required; contact emccaski@barnard.edu. An introductory course in neuroscience like PSYC 1001 or PSYC 2450. Analysis of the assessment of physical and psychiatric diseases impacting the central nervous system, with emphasis on the relationship between neuropathology and cognitive and behavioral deficits.

This seminar will survey historical and modern developments in machine intelligence from fields
such as psychology, neuroscience, and computer science, and from approaches such as
cybernetics, artificial intelligence, machine learning, robotics, connectionism, neural networks,
and deep learning. The emphasis is on the conceptual understanding of topics. The course does
not include, nor require a background in, computer programming and statistics. The overall goal
is for students to become informed consumers of applications of artificial intelligence.

 

Why do we feel the way we do, how do we understand ourselves and others, and how does this relate to our social relationships?  Why does all of this sometimes leave us feeling anxious, depressed, lonely and isolated?  And when that happens, what we can do about it?  In this seminar, we will tackle these questions using an integrative approach that blends social psychology, clinical psychology and neuroscience to uncover how our emotions and social interactions sometimes work well and sometimes falter.  To do this, we will examine human behavior at multiple levels, connecting our social and emotional lives (what we experience and how we act) to cognitive processes (how our minds process information) and underlying neural mechanisms (what's happening in the brain).  As the semester unfolds, we start by building an understanding of where our emotions come from, how we can manage them, how we make sense of other people and how we can form positive connections with them.  This knowledge provides a foundation for taking the next step - understanding what happens when things go awry and we end up with momentary - or long-lasting - bouts of anxiety, depression, loneliness and other clinical conditions.  We end by considering what may be everyone’s life goal - the pursuit of happiness - and how everything we have learned can foster that pursuit through personal actions, social connections and psychotherapeutic support.  The overarching goal is to build a nuanced understanding of how and why we think and feel the way we do – about ourselves and others – that can inform both scientific and personal explorations of what “wellbeing” and “mental health” means.

Prerequisites

Equivalents of an introductory Psychology course and one more advanced lecture, lab or seminar course in Psychology or a related discipline (and/or the instructor's permission).


 

 

 

 

This course is meant to provide an introduction to regression and applied statistics for the social sciences, with a strong emphasis on utilizing the Python software language to perform the key tasks in the data analysis workflow. Topics to be covered include various data structures, basic descriptive statistics, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, data visualization, models for binary outcomes, models for ordered data, first difference analysis, factor analysis, and cluster analysis. Through a variety of lab assignments, students will be able to generate and interpret quantitative data in helpful and provocative ways. Only relatively basic mathematics skills are assumed, but some more advanced math will be introduced as needed. A previous introductory statistics course that includes linear regression is helpful, but not required.