GIDS Seminar Series Presents Amy Perfors
January 11, 2017
Abstract: How do we know what we know, and how does this knowledge drive further learning? I combine empirical work with computational and mathematical modelling to investigate how data in the world, along with people's assumptions about it, shape and are shaped by cognition. I argue that because humans are social creatures, understanding cognition requires understanding the social assumptions we make about the origin of the data we see; this is evident in areas from concept learning to decision making to information search. As I show, this insight has especially strong implications for understanding language, because linguistic systems are not just learned by people; they are created by them as well.
Bio: I'm interested many different questions in higher-order cognition, from language to concept learning to decision making. My approach combines experiments with people (usually, but not always, adults) with computational models (usually, but not always, Bayesian). My general research questions all revolve around how the structure of data in the world, and people's assumptions about it, shape and are shaped by cognition. Much of my work takes place within a theoretical framework which suggests that human inference and reasoning can all be explained as a byproduct of reasoning about where the data came from and how it was generated. My research program is constantly evolving. But here is an overview of some of the work I've done in each of my three main areas.
Host: Greg DeAgelis, firstname.lastname@example.org