Zoom Link: https://mit.zoom.us/j/92462318525

Speaker: Lakshmi Govindarajan

Bio: Lakshmi is an ICoN Postdoctoral Fellow working with Josh McDermott and Ila Fiete. He obtained his Bachelor’s degree in Computer Science and Biophysics from National University of Singapore, and a Ph.D. in Computational Neuroscience from Brown University where he worked with Thomas Serre. His research seeks to understand the computational and mechanistic principles underlying the feedback loop between sensory representations and higher-order cognition in both biological and artificial systems.

Title: Human Confidence Reflects Calibrated Uncertainty Estimation​​​​​​​

Abstract: Sensory inferences about the state of the world are made from ambiguous observations and are thus inevitably uncertain. Accurate estimation of uncertainty is likely critical to decisions about where and when to act, but little is known about how uncertainty is estimated for real-world perceptual problems. This talk will describe a new class of stimulus-computable models that can be optimized to represent uncertainty in real-world problem domains. Human confidence judgments resemble those of the model, indicating that such judgments are well-calibrated estimates of the actual uncertainty of our perceptual inferences.