Sarah Schwettmann, Neural representation of intuitive physical dimensions
Engaging with the world requires a model of its physical structure and dynamics – how objects rest on and support each other, how much force would be required to move them, and how they behave when they fall, roll, or collide. Humans demonstrate remarkable ability to infer the physical properties of objects and predict physical events in dynamic scenes, yet little is known about the brain regions recruited to make intuitive physical judgments. Recent behavioral and computational studies of human physical scene understanding suggest that people’s judgments can be modeled as probabilistic simulations of a mental physics engine akin to 3D physics engines used in computer simulations and video games. Physics engines share a common structure: enduring properties of objects such as mass and friction serve as inputs to models of world dynamics. We question whether such a physics engine exists in the brain, and begin by searching for neural representations of fundamental physical variables that define objects. In this talk I will present evidence for a situation invariant representation of object mass decoded from neural activity in candidate physics regions. This finding suggests that multivariate analyses are a powerful way forward in uncovering neural representations of physical variables. I will discuss the extension of this approach to other physical variables such as friction, and consider more broadly the selection of an appropriate search space for intuitive physical dimensions.
Sean Houlihan Abstract:
Humans readily and flexibly incorporate information about others' emotions when reasoning about people and the world. These inferences can be modeled as an intuitive, generative, causal theory that links observable features (expressions, actions, events) with latent variables (emotions, beliefs, goals). I present initial work characterizing how people's intuitive theories of emotion interact with naturalistic perceptual information to support specific interpretations, which are accurate in some cases and markedly incorrect in others. The accuracy of these judgments shows structured individual differences. Using an intuitive theory of mind as a framework to formalize observers’ inferences about a target's emotions offers a powerful tool to capture, and even recreate, people's knowledge of how others feel, both when it is accurate and when it is wrong.
Note: In order to fit multiple talks within the hour-long slot, the first talk will start promptly at 12:05, and lunch has been ordered to arrive at 11:50. Please try to come early, collect your lunch, and be seated by 12:05.
UPCOMING COG LUNCH TALKS:
10/17/17 - Meilin Zhan (Levy Lab), Morteza Sarafyazd (Jazayeri Lab)
10/24/17 - Maddie Pelz (Schulz Lab), Matthias Hofer (Levy Lab), and Andres Campero (Tenenbaum Lab)
10/31/17 - Mika Braginksy (Levy Lab), Jenelle Feather (McDermott Lab), and Andrew Francl (McDermott Lab)
11/07/17 - Richard McWalter, Ph.D. (McDermott Lab)
11/14/17 - Kevin Ellis (Tenenbaum Lab)
11/21/17 - Dian Yu (Rosenholtz Lab)
11/28/17 - Yang Wu (Schulz Lab)
12/05/17 - Melissa Kline, Ph.D.
12/12/17 - Rachel Magid (Schulz Lab)