May 21, 2021 – Chris Honey

Timescales in Natural and Artificial Intelligence I study how people integrate information over time, as they seek to understand their environment and learn from it. Temporal integration is ubiquitous, because our world unfolds over time: hearing a fragment of sound, we perceive it as part of a mockingbird’s melody; reading one word, we understand it […]

April 30, 2021 – Jesse Mu

Towards more human-like language in multi-agent communication Recent research has trained artificial agents to communicate with each other via task-oriented language, for the dual aims of (1) improving agent collaboration and (2) studying language evolution in artificial settings.  This talk will describe two studies of the emergent linguistic phenomena in these multi-agent systems. First, pragmatics: I’ll […]

May 14, 2021 – Andrew Nam

What underlies rapid learning and out-of-distribution transfer in humans? Humans can sometimes learn a procedure from one or a small number of examples and then apply what they have learned to a much larger range of examples. Here, we explore this ability as it arises in learning a strategy from Sudoku. Participants naive to Sudoku […]

March 12, 2021 – Faculty Lightning Talks

For the 2021 Admissions Weekend, FriSem will be hosting a panel of faculty speakers to present their ongoing research. Mike Frank: Towards predictive models of early language learningTobi Gerstenberg: Understanding “why”: The role of causality in cognitionNoah Goodman: Research in the Goodman LabKalanit Grill-Spector: Neural investigations of high-level vision in the Vision and Perception Neuroscience […]

January 22, 2021 – Nilam Ram

Changing How We Model Change: The Need for and Challenge of Flexibility  Growth models are often used to reconcile theoretical propositions about development with longitudinal data. We use them extensively to describe and test hypotheses about  individuals’ behavioral trajectories (e.g., learning curves). In this talk I share how a deep-dive into the biologically-inspired von Bertalanffy […]

October 23, 2020 – Zach Davis

Learning and controlling dynamic systems We live and act in a messy world. Scientists’ best models of real-world causal processes typically involve not just stochasticity, but real-valued variables, complex functional forms, delays, dose-dependence, and feedback leading to rich and often non-linear emergent dynamics. How do we learn the causal structure of the world, given these difficulties? In […]