Keynote Speakers
We are pleased to announce the following world-class invited speakers.

David Kellen (Syracuse University; Estes Early Career Award winner)
Title: Testing Representations in Recognition Memory: From Model Fits to Critical Tests
Abstract: The topic of this talk concerns a long-standing topic in recognition memory, the comparison between discrete, continuous, and “hybrid” modeling accounts of recognition memory. Specifically, I will discuss how this work has traditionally focused on model fits predicated on strong parametric assumptions, and the importance of a shift towards more general, non-parametric approaches. Here, I will show how some classic results in mathematical psychology, such as Falmagne’s proof on the Block-Marschak inequalities provide a testable foundation for the general notion that memory judgments are based on a latent-strength representation. I will report empirical results supporting the Block-Marschak inequalities, but also show the close relationship between different types of memory judgments. Finally, I will discuss how these general results can be complemented by critical tests that allow us exclude certain types of representations. The focus will be placed on critical tests that can reject a general class of threshold models.

Maithilee Kunda (Vanderbilt University)
Title: Imagery-Based AI
Abstract: Despite evidence for the importance of visual mental imagery in many areas of human intelligence, there are few AI systems that use imagery-like knowledge representations to perform complex tasks. My research on imagery-based AI illustrates how imagery-based representations and reasoning operators can be combined to solve standardized tests of nonverbal cognition, like the Raven’s Progressive Matrices test; how imagery-based reasoning operators, like mental rotation, can be represented in connectionist systems and learned from perceptual experience; and how interactive technologies that scaffold human mental imagery can be used for applications in special education, data visualization, and more.

Jake Hofman (Microsoft Research)
Title: How Predictable is the Spread of Information?
Abstract: How does information spread in online social networks, and how predictable are online information diffusion events? Despite a great deal of existing research on modeling information diffusion and predicting “success” of content in social systems, these questions have remained largely unanswered for a variety of reasons, ranging from the inability to observe most word-of-mouth communication to difficulties in precisely and consistently formalizing different notions of success. This talk will attempt to shed light on these questions through an empirical analysis of billions of diffusion events under one simple but unified framework. We will show that even though information diffusion patterns exhibit stable regularities in the aggregate, it remains surprisingly difficult to predict the success of any particular individual or single piece of content in an online social network, with our best performing models explaining only half of the empirical variance in outcomes. We conclude by exploring this limit theoretically through a series of simulations that suggest that it is the diffusion process itself, rather than our ability to estimate or model it, that is responsible for this unpredictability.