Thomas Alexandre Langlois

Postdoctoral Fellow

UT Austin

Princeton University

University of California, Berkeley


Vision is an active process. Far from being passive recipients of external information, our visual systems are constantly generating meaning by combining sensory information with internal beliefs about the structure of the world around us. From the perspective of Bayesian statistics, these beliefs correspond to perceptual priors. My research interests center around uncovering the hidden structure of these priors and understanding the role that they play in perception and cognition. More broadly, I explore the behavioral, computational, and neural (biological) components of bayesian visual decision making. I also investigate how biological vision systems differ from artificial ones. For a representative example of my work probing visual working memory priors, see our recent publication in PNAS. In this line of work, I use experimental methods based on serial reproduction combined with Bayesian computational modeling to reveal shared perceptual priors and representations in human visual memory. In a second line of work, I compare human vision to contemporary Artificial Neural Networks (ANNs). For a representative example of my current activities in this area, see our oral presentation and paper in NeurIPS 2021. I completed my Ph.D. in Thomas Griffiths’ Computational Cognitive Science Lab at UC Berkeley in August of 2018. Prior to completing my Ph.D., I completed an M.S. in Computer Science (EECS), also at UC Berkeley. I then joined Princeton University as a postdoctoral researcher in the Computational Cognitive Science Lab. During that time, I was also affiliated with the Computational Auditory Perception Research Group in the Max Planck Institute for Empirical Aesthetics, where I was co-advised by Nori Jacoby. I am currently a postdoctoral fellow at UT Austin in the Center for Perceptual Systems (CPS), where I am working in Robbe Goris’ group on uncovering the neural correlates of Bayesian computation during visual decision making. In the fall, I will be starting as a postdoctoral fellow at MIT, where I will be working with Roger Levy, Nidhi Seethapthi and Noga Zaslavsky on applications of the Information Bottleneck (IB) Principle in the Brain and Cognitive Sciences Department (BCS) at MIT, and the Department of Psychology at NYU.


  • Bayesian models of vision
  • Working memory priors
  • Visual psychophysics and serial reproduction
  • Computational models of cognition and perception
  • Interpretable A.I. and computer vision
  • Neural correlates of visual decision making
  • The Information Bottleneck (IB) principle


  • PhD in Psychology (Cognition), 2018

    University of California, Berkeley

  • MS in Computer Science (EECS), 2018

    University of California, Berkeley

  • BA in Psychology, Art History & Studio Art, 2008

    Georgetown University

Recent Publications

(2021). Passive Attention in Artifical Neural Networks Predicts Human Visual Selectivity. In Advances in Neural Information Processing Systems (NeurIPS), 35. Accepted (Oral), ArXiv preprint: 2107.07013.


(2017). Uncovering visual priors in spatial memory using serial reproduction. In Proceedings of the 39th Annual Conference of the Cognitive Science Society.