Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision.

Journal: The Behavioral and brain sciences
Published Date: Dec 6, 2023

Abstract

In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models with the brain and behavior, and they advocate for a fragmented, phenomenon-specific modeling approach. These are unconstructive to scientific progress. We outline how the Brain-Score community is moving forward to add new model-to-human comparisons to its community-transparent suite of benchmarks.

Authors

  • James J DiCarlo
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
  • Daniel L K Yamins
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
  • Michael E Ferguson
    Dept. of Brain and Cognitive Sciences, Quest for Intelligence, and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA dicarlo@mit.edu; https://dicarlolab.mit.edu mferg@mit.edu evelina9@mit.edu; https://evlab.mit.edu/ msch@mit.edu; https://mschrimpf.com/.
  • Evelina Fedorenko
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139; msch@mit.edu ngk@mit.edu evelina9@mit.edu.
  • Matthias Bethge
    University of Tübingen, Tübingen, Germany.
  • Tyler Bonnen
    Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA yamins@stanford.edu bonnen@stanford.edu; http://neuroailab.stanford.edu/research.html.
  • Martin Schrimpf
    Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Center for Brains, Minds and Machines, MIT, Cambridge, MA, USA.

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