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Excited to receive Smarties from
@NeuroLuyckx , and perhaps also hear about his work :-Dpic.twitter.com/zA1GpJUKzv
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We further showed that a centro-parietal evoked potential in the EEG (i.e., the Pe), known to be sensitive to fine-grained levels of decision confidence, predicts subsequent increase of decision bound. 4/5pic.twitter.com/u2RuFkLZi7
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To this end, we fitted data from three studies using the drift-diffusion model. As predicted, estimated decision bounds were increased as a function of confidence on the previous trial (see picture). Importantly, this was found independent of actual accuracy. 3/5pic.twitter.com/unpP8iUe2y
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Very nice thank you slide by
@raquel_e_london :-Dpic.twitter.com/G734i3QrNX
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A multivariate EEG decoder was then trained to predict high vs low confidence, and tested on its ability to classify whether participants wanted to sample more information or not. This was only possible in a time window following the initial speeded response! 4/5pic.twitter.com/YKzdHBFwGY
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In our EEG paradigm, participants made a perceptual averaging choice, and then decided whether they wanted to see an easier version of the stimulus (for a small monetary cost) before providing their final choice and confidence 3/5pic.twitter.com/ZEdazbXhq9
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Couple of years ago I did a p-curve on their 2010 paper science (which was also about incidental haptic sensations) and that p-curve was pretty identical to the one shown by
@mavadillo !pic.twitter.com/ICPJWLxpCM
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