CT-2.1

Mental Imagery: Weak Vision or Compressed Vision?

Tiasha Saha Roy, Jesse Breedlove, Ghislain St-Yves, Kendrick Kay, Thomas Naselaris, University of Minnesota, United States

Session:
Contributed Talks 2 Lecture

Track:
Cognitive science

Location:
South Schools / East Schools

Presentation Time:
Sat, 26 Aug, 16:30 - 16:45 United Kingdom Time

Abstract:
Mental imagery is often described as a reactivation of sensory activity in the brain. However, recent research focusing on vision and imagery suggests that the trial-averaged voxel activity for mental images (i.e. the imagery signal) in the visual cortex varies far less compared to when the images are seen. What might explain this substantial reduction in signal variance during imagery? We consider two of the possible hypotheses: (1) the ``weak vision" hypothesis, in which the imagery signal is simply a scaled-down version of the visual signal and (2) the ``compressed vision" hypothesis, in which imagery signal is a projection of the visual signal onto a lower-dimensional subspace. To compare these two hypotheses, we use voxel-to-voxel predictive models on data from a 7T fMRI experiment in which participants viewed and imagined 12 different stimuli. Voxel-wise prediction of imagery trials seems to particularly benefit from using correlated multi-voxel patterns of activity during vision over voxel-specific responses. Accuracy can be further enhanced by assuming imagery to be a lower-dimensional projection of visual responses. These results offer provisional support for the ``compressed vision" hypothesis.

Manuscript:
License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2023.1693-0
Publication:
2023 Conference on Cognitive Computational Neuroscience
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Session CT-2
CT-2.1: Mental Imagery: Weak Vision or Compressed Vision?
Tiasha Saha Roy, Jesse Breedlove, Ghislain St-Yves, Kendrick Kay, Thomas Naselaris, University of Minnesota, United States
CT-2.2: Leveraging Artificial Neural Networks to Enhance Diagnostic Efficiency in Autism Spectrum Disorder: A Study on Facial Emotion Recognition
Kushin Mukherjee, University of Wisconsin-Madison, United States; Na Yeon Kim, California Institute of Technology, United States; Shirin Taghian Alamooti, York University, Canada; Ralph Adolphs, California Institite of Technology, United States; Kohitij Kar, York University, Canada
CT-2.3: Dropout as a tool for understanding information distribution in human and machine visual systems
Jacob S. Prince, Harvard University, United States; Gabriel Fajardo, Boston College, United States; George A. Alvarez, Talia Konkle, Harvard University, United States
CT-2.4: Humans and 3D neural field models make similar 3D shape judgements
Thomas O'Connell, MIT, United States; Tyler Bonnen, Stanford University, United States; Yoni Friedman, Ayush Tewari, Josh Tenenbaum, Vincent Sitzmann, Nancy Kanwisher, MIT, United States
CT-2.5: Humans and CNNs see differently: Action affordances are represented in scene-selective visual cortex but not CNNs
Clemens G. Bartnik, Iris I.A. Groen, University of Amsterdam, Netherlands
CT-2.6: Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamic Mode Representational Similarity Analysis
Mitchell Ostrow, Adam Eisen, Leo Kozachkov, Ila Fiete, Massachusetts Institute of Technology, United States