Introduction to Cognitive Science

INTRO TO COGNITIVE SCIENC

The goal of cognitive science — and of this course — is to understand how the mind works. Trying to understand our own minds is perhaps the most ambitious and exciting (and difficult) project in all of science, and this project requires tools drawn from fields including experimental psychology, computer science and artificial intelligence, linguistics, vision science, philosophy, anthropology, behavioral economics, and several varieties of neuroscience (among others). This course will introduce you to the major tools and theories from these areas, as they relate to the study of the mind. We will employ these perspectives while exploring the nature of mental processes such as perception, reasoning, memory, attention, imagery, language, intelligence, decision-making, morality— and even attraction and love. In sum, this course will expose you to cognitive science, the assumptions on which it rests, and many of the most important and fascinating results obtained so far.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Brendan I Fleig-Goldstein
  Info
• 3 points

INTRODUCTION TO LINGUISTICS

An introduction to the study of language from a scientific perspective. The course is divided into three units: language as a system (sounds, morphology, syntax, and semantics), language in context (in space, time, and community), and language of the individual (psycholinguistics, errors, aphasia, neurology of language, and acquisition). Workload: lecture, weekly homework, and final examination.

• TR 5:40 p.m.-6:55 p.m.
• 3 points

Senior Project Seminar

SENIOR PROJECT SEMINAR

Discussion of senior research projects during the fall and spring terms that culminate in written and oral senior theses. Each project must be supervised by a cognitive scientist working at Barnard or Columbia.

• M 4:10 p.m.-6 p.m.
• Instructor: John Morrison
  CULPA: 12 h-index: 7 Info
• 1 points

Senior Project

Senior Project in Cognitive Science.

• Instructor: John Morrison
  CULPA: 12 h-index: 7 Info
• Instructor: Ann Senghas
  CULPA: 17 Info
• Instructor: Bj Casey
  Info
• Instructor: Luca Iemi
  Info
• Instructor: Karen Lewis
  CULPA: 12 h-index: 7 Info
• 3 points

Computational Models of Decision-Making

Comp Models of Decision-M

We make decisions countless times a day. Computational models have been developed that improve our understanding of how these decisions are made. This course is organized in three parts: perceptual decision-making, value-based decision-making, and computational psychiatry. In part one, perceptual decision-making, we will focus on computational models that can capture and explain decisions in perception, such as categorizing an orientation, or discriminating the direction of moving dots, or estimating the magnitude of a stimulus (e.g., time). We will start by laying the foundations of signal detection theory and Bayesian inference under uncertainty and build to models that incorporate confidence ratings and reaction times. In part two, value-based decision-making, we will move on to decisions that incorporate our values (e.g., ‘Should I go out or stay in and study?’, ‘Should I eat a burger or a salad?’). We will learn the basics of a computational modeling framework that captures how we learn values from rewards and punishments, reinforcement learning, as well as about model-free and model-based learning. Lastly, we will learn how impairments in decision-making that occur in psychopathology (e.g., addiction, anorexia nervosa, anxiety) have been conceptualized and quantified in the relatively new field of computational psychiatry.

• R 12:10 p.m.-2 p.m.
• 4 points

Philosophy of Computing

Advances in artificial intelligence carry potential for both social good and ethical danger. The purpose of this course is to explore both foundational and applied debates in the philosophy of computing, with a focus on machine learning technologies. Drawing from works in philosophy, computer science, literature, and policy, this course will comprehensively examine the conceptual and normative challenges artificial intelligence presents. The course analyzes present-day challenges through the prism of specific technologies and tools, namely predictive analytics, computer vision, and large language models, and also investigates moral and social questions on the horizon, with an eye to how advancements in computing will impact responsibility, moral status, and relationships.

• T 6:10 p.m.-8 p.m.
• 4 points