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SUMMARY:Efficient sensory encoding predicts robust averaging - Ivan Tomic
DTSTART:20230503T140000Z
DTEND:20230503T150000Z
UID:TALK200386@talks.cam.ac.uk
CONTACT:Adam Triabhall
DESCRIPTION:This week we will discuss and debate a very recent paper by Ni
  and Stocker\, published in Cognition (2023).\n\nAbstract: “Not every it
 em in a stimulus ensemble equally contributes to the perceived ensemble av
 erage. Rather\, items with feature values close to the ensemble mean (inly
 ing items) contribute stronger compared to those items whose feature value
 s are further away from the mean (outlying items). This nonuniform weighti
 ng process\, named robust averaging\, has been interpreted as evidence aga
 inst an optimal integration of sensory information. Here\, however\, we sh
 ow that robust averaging naturally emerges from an optimal integration pro
 cess when sensory encoding is efficiently adapted to the ensemble statisti
 cs in the experiment. We demonstrate that such a model can accurately fit 
 several existing datasets showing robust perceptual averaging in discrimin
 ating low-level stimulus features such as orientation. Across various feat
 ure domains\, our model accurately predicts subjects’ decision accuracy 
 and nonuniform weighting profile\, and both their dependency on the specif
 ic stimulus distribution in the experiments. Our results suggest that the 
 human visual system forms efficient sensory representations on short time-
 scales to improve overall decision performance” (Ni & Stocker\, 2023).\n
 \nReference: Ni\, L.\, & Stocker\, A. A. (2023). Efficient sensory encodin
 g predicts robust averaging. Cognition\, 232\, 105334–105334. https://do
 i.org/10.1016/j.cognition.2022.105334
LOCATION:Zoom link: www.bayslab.org/craikjc
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