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SUMMARY:A unifying theory explains seemingly contradictory biases in perce
 ptual estimation - Xiaolu Wang
DTSTART:20240417T130000Z
DTEND:20240417T140000Z
UID:TALK215947@talks.cam.ac.uk
CONTACT:Adam Triabhall
DESCRIPTION:This week we will discuss and debate a very recent paper by Ha
 hn and Wei\, published in Nature Neuroscience (2024).\n\nAbstract: “Perc
 eptual biases are widely regarded as offering a window into the neural com
 putations underlying perception. To understand these biases\, previous wor
 k has proposed a number of conceptually different\, and even seemingly con
 tradictory\, explanations\, including attraction to a Bayesian prior\, rep
 ulsion from the prior due to efficient coding and central tendency effects
  on a bounded range. We present a unifying Bayesian theory of biases in pe
 rceptual estimation derived from first principles. We demonstrate theoreti
 cally an additive decomposition of perceptual biases into attraction to a 
 prior\, repulsion away from regions with high encoding precision and regre
 ssion away from the boundary. The results reveal a simple and universal ru
 le for predicting the direction of perceptual biases. Our theory accounts 
 for\, and yields\, new insights regarding biases in the perception of a va
 riety of stimulus attributes\, including orientation\, color and magnitude
 . These results provide important constraints on the neural implementation
 s of Bayesian computations” (Hahn & Wei\, 2024).\n\nReference: Hahn\, M.
 \, & Wei\, X.X. (2024). A unifying theory explains seemingly contradictory
  biases in perceptual estimation. Nature Neuroscience\, 27(4)\, 793–804.
  https://doi.org/10.1038/s41593-024-01574-x\n
LOCATION:https://cam-ac-uk.zoom.us/j/92612577704?pwd=MUtqMjVQdXNmUTVIYjRkM
 G1NUW9GZz09
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