Pierre Boucher presented his work on how urgency alters pre-stimulus state and defended his master’s degree with aplomb! Congratulations Pierre!
Author: Chandramouli Chandrasekaran
Our paper on multi-area RNNs as a model for understanding perceptual decision-making is now accepted as a full paper at the NeurIPS conference.
Check out our new tool, openEyeTrack, a low-cost open-source high-speed eye tracker for tracking eye position in head-fixed applications. This was a summer project for a talented undergraduate in the lab, Jorge Paolo Casas. We were able to architect a multi-threaded eye tracker using OpenCV, Teledyne DALSA Camera, and C++. We are currently running behavioral […]
If you are interested in arbitrating between DDMs, urgency gating or collapsing boundary models to describe the behavior of observers in perceptual decision-making tasks, then check out our paper (ChaRTr paper) and accompanying toolbox (https://github.com/mailchand/CHaRTr).
We have had a reasonably productive and successful Year in 2019. Karen Marmon joined the lab. She comes to us from Salzmann lab and will be a research tech with us and lab manager for Jerry Chen. Kenji Lee joined us as a graduate student. He comes from the Allen Institute and brings a lot […]
Our first paper from the new lab – Audiovisual detection at various intensities and delays has been accepted into Journal of Mathematical Psychology Chandrasekaran C, Gondan MG, Audiovisual detection at different intensities and delays, under revision for Journal of Mathematical Psychology, see biorxiv preprint (link). In this paper, we modeled the accuracy (i.e., hit rate) […]
If you are like me and obsessed about understanding the reaction times and choice behavior of monkeys performing cognitive tasks, then check out our new toolbox, CHaRTr, which has the ability to use a wide range of decision-making models to describe behavior. The biorxiv paper is here and the toolbox is available here :)
Matt golub, a collaborator of mine from Stanford will be presenting our work titled “Joint neural-behavioral models of perceptual decision making”. Matt has worked on a new framework to train RNNs to model both the neural data and behavior simultaneously. He will be presenting this work at COSYNE.