#81 Neuroscience of Perception: Exploring the Brain, with Alan Stocker
Learning Bayesian Statistics
How to Be a Good Bayesian Model
Learn Vision Statistics is a fortnightly podcast on Bayesian inference. This week, Alex and Ora welcome Alan Stoker to learn Baystance. What's a Bayesian? It's someone who doesn't jump to assumptions based on intuitions and prejudice. A Bayesian makes predictions on the best available info, and adjusts the probability. Let me show you how to be a good Bayesian.
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Did you know that the way your brain perceives speed depends on your priors? And it’s not the same at night? And it’s not the same for everybody?
This is another of these episodes I love where we dive into neuroscience, how the brain works, and how it relates to Bayesian stats. It’s actually a follow-up to episode 77, where Pascal Wallisch told us how the famous black and blue dress tells a lot about our priors about how we perceive the world. So I strongly recommend listening to episode 77 first, and then come back here, to have your mind blown away again, this time by Alan Stocker.
Alan was born and raised in Switzerland. After a PhD in physics at ETH Zurich, he somehow found himself doing neuroscience, during a postdoc at NYU. And then he never stopped — still leading the Computational Perception and Cognition Laboratory of the University of Pennsylvania.
But Alan is also a man of music (playing the piano when he can), a man of coffee (he’ll never refuse an olympia cremina or a kafatek) and a man of the outdoors (he loves trashing through deep powder with his snowboard).
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady and Kurt TeKolste.
Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
- Alan’s website: https://www.sas.upenn.edu/~astocker/lab/members-files/alan.php
- Noise characteristics and prior expectations in human visual speed perception: https://www.nature.com/articles/nn1669
- Combining efficient coding with Bayesian inference as a model of human perception:
- Video: https://vimeo.com/138238753
- Paper: https://www.nature.com/articles/nn.4105
- LBS #77 How a Simple Dress Helped Uncover Hidden Prejudices, with Pascal Wallisch: https://learnbayesstats.com/episode/77-how-a-simple-dress-helped-uncover-hidden-prejudices-pascal-wallisch/
- LBS #72 Why the Universe is so Deliciously Crazy, with Daniel Whiteson: https://learnbayesstats.com/episode/72-why-the-universe-is-so-deliciously-crazy-daniel-whiteson/
Abstract
In episode 81 of the podcast, Alan Stocker helps us update our priors of how the brain works. Alan, born in Switzerland, studied mechanical engineering and earned his PhD in physics before being introduced to the field of neuroscience through an internship. He is now Associate Professor at the University of Pennsylvania.
Our conversation covers various topics related to the human brain and whether it what it does can be characterised as a Bayesian inferences.
Low-level visual processing, such as identifying the orientation of moving grids, can be explained with reference to Bayesian priors and updating under uncertainty. We go through several examples of this such as driving a car in foggy conditions.
More abstract cognitive processes, such as reasoning about politics, may be more difficult to explain in Bayesian terms.
We also touch upon the question to what degree priors may be innate and how to educate people to change their priors.
In the end, Alan gives two recommendations for improving your Bayesian inferences in a political context: 1) Go out and get your own feedback and 2) try to give and receive true feedback. Listen to the episode for details.
Transcript
please note that the transcript was generated automatically and may therefore contain errors. Feel free to reach out if you're willing to correct them.
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