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SUMMARY:Scaling models of visual working memory to natural images - Stella
  Wernicke
DTSTART:20240124T150000Z
DTEND:20240124T160000Z
UID:TALK211447@talks.cam.ac.uk
CONTACT:Adam Triabhall
DESCRIPTION:This week we will discuss and debate a recent paper by Bates a
 nd colleagues\, published in Communications Psychology (2024).\n\nAbstract
 : “Over the last few decades\, psychologists have developed precise quan
 titative models of human recall performance in visual working memory (VWM)
  tasks. However\, these models are tailored to a particular class of artif
 icial stimulus displays and simple feature reports from participants (e.g.
 \, the color or orientation of a simple object). Our work has two aims. Th
 e first is to build models that explain people’s memory errors in contin
 uous report tasks with natural images. Here\, we use image generation algo
 rithms to generate continuously varying response alternatives that differ 
 from the stimulus image in natural and complex ways\, in order to capture 
 the richness of people’s stored representations. The second aim is to de
 termine whether models that do a good job of explaining memory errors with
  natural images also explain errors in the more heavily studied domain of 
 artificial displays with simple items. We find that: (i) features taken fr
 om state-of-the-art deep encoders predict trial-level difficulty in natura
 l images better than several reasonable baselines\; and (ii) the same visu
 al encoders can reproduce set-size effects and response bias curves in the
  artificial stimulus domains of orientation and color. Moving forward\, ou
 r approach offers a scalable way to build a more generalized understanding
  of VWM representations by combining recent advances in both AI and cognit
 ive modeling” (Bates et al.\, 2024).\n\nReference: Bates\, C. J.\, Alvar
 ez\, G. A.\, & Gershman\, S. J. (2024). Scaling models of visual working m
 emory to natural images. Communications Psychology\, 2(3)\, 1. https://doi
 .org/10.1038/s44271-023-00048-3
LOCATION:https://cam-ac-uk.zoom.us/j/92612577704?pwd=MUtqMjVQdXNmUTVIYjRkM
 G1NUW9GZz09
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