Ling-Qi Zhang
@lingqiz.bsky.social
280 followers 340 following 18 posts
🧠 Comp Neuro, Behavior, Theory and Machine Learning | Theory Fellow @HHMI Janelia | 💻 https://lingqiz.github.io/
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In this sense, the normative theory / optimization framework is less of a theory itself, but a epistemological tool to organize our knowledege about why certain "solution" exists in the brain.
And then he wrote: "Hence, the study of the
behavior of an adaptive system like the human mind is
not *a logical study of optimization* but an *empirical study
of the side conditions* that place limits on the approach to the optimum".
The debate of whether the brain is optimal is the wrong one. Quoting one of my favorite writings from Herbert Simon (1992): The predictions of an optimizing theory depend as much on the postulated side conditions as on the optimization assumption. (By side conditions here, he meant constraints).
I mean, efficient coding and optimal control work *far* better than they have a right to, and they generate predictions. They are not falsifiable hypotheses except in the narrowest sense, but they are programs that can identify parsimonious principles for explaining real observations.
Reposted by Ling-Qi Zhang
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Bayesians in shambles
Saturday morning coffee on my deck🌞

I’m sending this message out to all the 🍊🤡 cult members who are trying to destroy science in America…it has survived for almost 250 years & your little temper tantrum isn’t going to stop it now
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Reposted by Ling-Qi Zhang
Reposted by Ling-Qi Zhang
What is the probability of an image? What do the highest and lowest probability images look like? Do natural images lie on a low-dimensional manifold?
In a new preprint with Zahra Kadkhodaie and @eerosim.bsky.social, we develop a novel energy-based model in order to answer these questions: 🧵
Thanks!! There are no major changes in the main text re results. We did refine the vignetting analysis, but it didn't make a huge difference in the end results. We also added a polar ROI analysis in the supplement, complementary to the eccentricity ROI in Fig. 5.
Reposted by Ling-Qi Zhang
In addition, the change in encoding is sufficient to predict the behavioral characteristics of the tilt illusion using a Bayesian observer model. (4/6)
With an oriented surround, encoding precision is locally increased for stimuli similar to the surround orientation, specifically in higher areas of the visual cortex. This encoding change also reflects the surround-conditioned orientation statistics in natural scene. (3/6)
We measured sensory encoding precision (in terms of Fisher information) from both behavioral and neural data in an orientation estimation task. At baseline, encoding reflects the natural scene statistics of orientation. (We observe the cardinal bias in different parts of the visual cortex!). (2/6)
many many tiny contextual boundaries!
And 2) If we can create a surround that REALLY modulates the representation at the boundary, we might be able to break the segmentation cue, and see an illusion as you suggested. This will require an image synthesis method for surround modulation, but people are working on this type of idea!
This does lead to some "really cool if true" predictions: 1) If we gradually increase the grey center aperture, the illusion strength should first increase (as we are removing orientation information not modulated by the surround), and then decrease (once we are in the "center-surround" boundary).
Great question! My explanation would be that there are strong enough segmentation cues in the stimulus for the visual system to integrate the orientation information across the center, to form a holistic orientation estimate.