Insight into how human brains work in their most ubiquitous and biologically meaningful context, social interaction, has remained largely elusive. This course ventures into this “dark matter” of social neuroscience, pursuing the question of what constitutes a “meeting of minds”. Conceptual and methodological challenges of studying human interaction are dealt with in-class discussions, laboratories, and small group research projects on selected topics. Students will be expected to design, run, analyze, and write up an interaction study answering a question of their choosing. Example research projects include but are not limited to studies of human interactive behavior in the real world, the lab, simulated scenarios, or on social media.
Welcome to Interacting Minds, in which we will venture into the “dark matter” of social neuroscience, pursuing the question of what constitutes a “meeting of minds”. The course consists of two modules. The first module is theory-oriented and meant to get you up to speed on the latest knowledge on human interaction. For now, I recommend reading the chapter from the late Nicholas Humphrey, “The society of selves”, on the apparent paradox between our exceptional sociability and our exceptional loneliness. The other article, “Beyond the isolated brain”, discusses ways in which social neuroscience could make headway in understanding both these essential elements of the human condition.
The second module is research-oriented and provides you with an opportunity to extend knowledge on human interaction in the form a principled research project. As do most research projects, part of your project will rely on programming to analyze and visualize data. Today, two similar coding languages are popular in academia, namely MATLAB and Python. Starting week 2, we will use these interchangeably throughout the course. The first, MATLAB, comes with its own interface. For the second, Python, a popular coding environment is Jupyter Notebook, an interactive platform that runs locally in the web browser. Google’s Colab constitutes a Google Docs-like spin-off of this platform. It runs entirely in the cloud, does not require a setup, and can be shared with people and groups. In case you're new to any of these platforms, I recommend you go through the installation and introduction links provided below. Just like how we really learn to handle a car after obtaining our driver's license, the goal here is to get yourself to a level where you “get by” handling and plotting your data.
In this lecture, we will take an excursion into artificial intelligence (AI) to understand why it is so difficult to build a virtual assistant that satisfies human expectations.
In this lab, we will equip an artificial agent with probabilistic reasoning abilities, the latest from cognitive science, and assess what obstacles our agent would face in human interaction.
In this lecture, we will discuss the phenomenon of audience design, a critical yet largely overlooked feature of ordinary language use. When do we deploy it, and how do we acquire this social ability?
In this lecture, we will discuss the foundational thoughts of Wittgenstein, and ask how people in dialogue can come to use the same term when referring to an object.
In this lecture, we will discuss both early and recent approaches to understanding human interaction, asking what key criteria human interaction studies need to satisfy.
In this lab, we will analyze interactive behavior recorded in the Tacit Communication Game, and generate predictions of neural activity supporting effective communication.
In this lecture, we will test the hypothesis that people in dialogue understand one another because they jointly develop and coordinate a shared conceptual space that provides critical context for using and interpreting communicative signals.
In this lecture, we will critically discuss the concept of interpersonal synchrony in the context of human social interaction studies.
In this lab, we will perform and analyze physiological recordings of responses to emotional visual stimuli.
For the second module of the course, we will learn to design and run an interaction study, building on insights gleaned from the first module. We kick this second module off with The Scientific Method, a principled approach for finding explanations of phenomena backed by evidence. Also in this class, each group will propose and pitch an idea for a project in the form of a research hypothesis (~10 mins, no more than 5 slides). The class will collectively provide constructive feedback on the idea and how to potentially improve the hypothesis.
In this lab, we will discuss key considerations in experimental design, followed by analysis of last week's physiological recordings.
In this lab, we will discuss data collection in the lab and online, including scraping of the web and social media.
In this class, each group will propose an experimental design for testing their research hypothesis (~10 mins, no more than 5 slides). The class will collectively provide constructive feedback on the design, including how to potentially improve it.
In this lab, we will cover the basics of data analysis and perform an analysis of an autism communication dataset.
In this lab, we will cover the basics of decoding analysis and attempt to decode emotional content from our physiological recordings.
In this class, each group will present analyses and observations from their experiments (~10 mins, no more than 5 slides). The class will collectively provide constructive feedback, including how to potentially improve the analyses.
In this lecture, we will cover the basics of writing a research report, from its structural elements to communicating your main idea.
In this final presentations class, each group will give a research presentation covering the start to finish of their project (~15 mins, no more than 10 slides). The final product, due next week, is a 12-20 page research report that uses APA formatting (double-spaced, 11pt Ariel/Times font) and consists of an abstract, introduction, methods, results, discussion, and references section. Analysis code goes in the supplemental materials section and does not count towards the page limit. Each individual is expected to write their own introduction and conclusion, but the group can collaborate on the methods and results sections if they want.
For our absolutely final class of the term, we will work toward a synthesis of Interacting Minds and define outstanding conceptual and empirical challenges in the field.