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SUMMARY:Computational Neuroscience Journal Club - Sebastian Schneegans (Ba
 ys Lab)
DTSTART:20170307T160000Z
DTEND:20170307T170000Z
UID:TALK71462@talks.cam.ac.uk
CONTACT:Daniel McNamee
DESCRIPTION:Sebastian Schneegans will cover:\n\n* Fechner’s law in metac
 ognition: A quantitative model of visual working memory confidence\n* van 
 den Berg\, Ronald\; Yoo\, Aspen H.\; Ma\, Wei Ji\n* Psychological Review (
 March 2017)\n* http://paulbays.com/pdf/VandenBerg_Yoo_Ma_2017.pdf\n\nAbstr
 act:\nAlthough visual working memory (VWM) has been studied extensively\, 
 it is unknown how people form confidence judgments about their memories. P
 eirce (1878) speculated that Fechner’s law—which states that sensation
  is proportional to the logarithm of stimulus intensity—might apply to c
 onfidence reports. Based on this idea\, we hypothesize that humans map the
  precision of their VWM contents to a confidence rating through Fechner’
 s law. We incorporate this hypothesis into the best available model of VWM
  encoding and fit it to data from a delayed-estimation experiment. The mod
 el provides an excellent account of human confidence rating distributions 
 as well as the relation between performance and confidence. Moreover\, the
  best-fitting mapping in a model with a highly flexible mapping closely re
 sembles the logarithmic mapping\, suggesting that no alternative mapping e
 xists that accounts better for the data than Fechner’s law. We propose a
  neural implementation of the model and find that this model also fits the
  behavioral data well. Furthermore\, we find that jointly fitting memory e
 rrors and confidence ratings boosts the power to distinguish previously pr
 oposed VWM encoding models by a factor of 5.99 compared to fitting only me
 mory errors. Finally\, we show that Fechner’s law also accounts for meta
 cognitive judgments in a word recognition memory task\, which is a first i
 ndication that it may be a general law in metacognition. Our work presents
  the first model to jointly account for errors and confidence ratings in V
 WM and could lay the groundwork for understanding the computational mechan
 isms of metacognition.
LOCATION:Cambridge University Engineering Department\, CBL\, BE-438 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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