Finally, and most importantly, Robin wrote an excellent and accessible demo which should allow anyone (you!) to get started with hMFC: github.com/robinvloeber...
25.09.2025 09:13 β π 4 π 0 π¬ 0 π 0@kobedesender.bsky.social
Assistant professor at @KU_Leuven, working on #confidence, #decisionmaking and #cognitivecontrol => DesenderLab.com
Finally, and most importantly, Robin wrote an excellent and accessible demo which should allow anyone (you!) to get started with hMFC: github.com/robinvloeber...
25.09.2025 09:13 β π 4 π 0 π¬ 0 π 0MANY figures in the paper showing that hMFC works, but highlighting this one: with as few as 500 trials per participant hMFC allows excellent recovery of single-trial criterion, look at panel C for a representative example participant - I'm (obviously biased) impressed by this!
25.09.2025 09:13 β π 2 π 0 π¬ 1 π 0We developed hMFC, a Bayesian hierarchical framework which allows estimating single-trial criterion states, by fitting data from different participants while taking into account of the nesting of data within participants.
25.09.2025 09:13 β π 0 π 0 π¬ 1 π 0Ignoring fluctuations in criterion is problematic: simulations show that criterion fluctuations induce apparent history biases (panel C), lead to underestimated psychometric slopes (panel D) and underestimated measures of sensitivity, such as d' (panel D)
25.09.2025 09:13 β π 2 π 1 π¬ 1 π 0Classic models of decision-making, like signal detection theory, assume that choices are made by comparing a decision variable (DV) to a criterion. Often this criterion is (implicitly) assumed to be constant; here we implement a fluctuating criterion following an autoregressive model.
25.09.2025 09:13 β π 3 π 0 π¬ 1 π 0Introducing hMFC: A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion! Now out in @plos.org Comp Bio.
led by Robin Vloeberghs with @anne-urai.bsky.social Scott Linderman
Paper: desenderlab.com/wp-content/u... Thread βββ
#PsychSciSky #Neuroscience #Neuroskyence
Full details, alternative valence-only models, and post-experiment questionnaires targeting awareness, etc. all in the paper!
25.09.2025 08:44 β π 0 π 0 π¬ 0 π 0At the group level, our learning model won over a non-learning alternative, but more participants were actually best fitted by the latter. Closer inspection revealed why: there was a dynamic group (showing a clear confidence learning effect) and a static group (showing, well, nothing)
25.09.2025 08:44 β π 0 π 0 π¬ 1 π 0At the group level, participants adapted their reporting of confidence to subtle changes in feedback (with no effects on accuracy or RTs). Panel E nicely shows how people adapt their confidence to feedback over time, panel D shows that our learning model closely captures this finding!
25.09.2025 08:44 β π 0 π 0 π¬ 1 π 0To experimentally test this, we provided participants with model-generated feedback, reflecting the probability that their choice was correct. Unbeknownst to them, we alternated between between blocks with subtly higher/lower feedback
25.09.2025 08:44 β π 0 π 0 π¬ 1 π 0We know (more or less) how humans compute confidence, but how do we learn to compute confidence? We propose that agents compute prediction errors (confidence-feedback) to update the weights underlying the computation of confidence
25.09.2025 08:44 β π 1 π 0 π¬ 1 π 0"Learning to be confident: How agents learn confidence based on prediction errors"! Now out in @cognitionjournal.bsky.social led by @pierreledenmat.bsky.social
Paper: desenderlab.com/wp-content/u... Thread βββ
#AcademicSky #PsychSciSky #Neuroscience #Neuroskyence
Common neural choice signals reflect accumulated evidence, not confidence! Now out in @cerebralcortex.bsky.social w @helenevanmarcke.bsky.social @pierreledenmat.bsky.social @yfvisser.bsky.social @denizerdil.bsky.social a.o.
Paper: desenderlab.com/wp-content/u... Thread βββ
Read the full paper for fancy time-frequency plots (replicating pre-stimulus occipital alpha & confidence), and multivariate decoding (showing cross-decoding between priors and confidence)!
19.09.2025 10:47 β π 1 π 0 π¬ 1 π 0We instead identified a frontal signal, which tracked confidence and was sensitive to prior beliefs. Although speculative, this might be the signal that integrates priors and evidence into a confidence judgment!
19.09.2025 10:47 β π 0 π 0 π¬ 1 π 0Critically, EEG measurements confirmed the key prediction: although the stimulus-locked CPP and response-locked Pe were sensitive to high vs low confidence (which happens because confidence is correlated with evidence), they were _not_ modulated by prior beliefs condition (panels B and C)!
19.09.2025 10:47 β π 0 π 0 π¬ 1 π 0Replicating previous work, our manipulations had clear and consistent effect on confidence: confidence integrates prior beliefs about performance with accumulated evidence.
19.09.2025 10:47 β π 0 π 0 π¬ 1 π 0This work makes a key prediction: evidence accumulation signals (such as CPP and Pe) reflect accumulated evidence which feeds into confidence, but do not directly reflect confidence. To test this, we trained people on easy/hard tasks and provided pos/neg feedback (i.e. to manipulate priors)
19.09.2025 10:47 β π 0 π 0 π¬ 1 π 0In 2024, @helenevanmarcke.bsky.social @pierreledenmat.bsky.social showed that confidence is computed _conditional_ on prior beliefs about task performance (journals.sagepub.com/doi/abs/10.1...), represented by the heat map in the figure.
19.09.2025 10:47 β π 1 π 0 π¬ 1 π 0Common neural choice signals reflect accumulated evidence, not confidence! Now out in @cerebralcortex.bsky.social w @helenevanmarcke.bsky.social @pierreledenmat.bsky.social @yfvisser.bsky.social @denizerdil.bsky.social a.o.
Paper: desenderlab.com/wp-content/u... Thread βββ
Can we use confidence-driven information-seeking as a tool to combat fake news!? Really cool study by really cool @helenevanmarcke.bsky.social βββ
10.09.2025 12:26 β π 12 π 2 π¬ 0 π 0Full-Force at #ccn2025 in Amsterdam. Come along for a chat if you're interested in metacognition, confidence, computational modelling, reasoning, etc. @yfvisser.bsky.social @jeremiebeucler.bsky.social @helenevanmarcke.bsky.social @alexandre-lietard.bsky.social @zoepurcell.bsky.social
11.08.2025 13:58 β π 10 π 2 π¬ 0 π 0Or if you want to make your life easier, simplify to SDT and see whether biased criterion alone suffices, or whether you also need biased confidence criteria (mapping onto v and v_s)
07.08.2025 12:40 β π 1 π 0 π¬ 0 π 0The model used here: pubmed.ncbi.nlm.nih.gov/38427319/ where you can test whether the prior affects confidence directly (via z or drift bias) or indirectly (via v_s), or both. These parameters are then updated based on the prediction error cue-stim weighted by a learning rate. Good luck :)
06.08.2025 18:40 β π 6 π 0 π¬ 1 π 0Impressive behavioral + modelling + eeg work, led by @lucvermeylen.bsky.social , relating time-on-task changes in comp parameters to changes in metacognitive reports!
05.07.2025 10:41 β π 12 π 3 π¬ 0 π 0Performance feedback counterintuitively _increases_ overconfidence in young children. Great Collab @dsotob.bsky.social
26.06.2025 07:35 β π 8 π 3 π¬ 0 π 0Performance feedback triggers liberal detection andξperceptual confidence biases inξearly childhood: Implications forξmetacognitive training @kobedesender.bsky.social
pubmed.ncbi.nlm.nih.gov/40555904/
Very cool new work led by the incredible @helenevanmarcke.bsky.social in @cognitionjournal.bsky.social Go and Read if you're into information seeking, metacognitieve confidence and (!) _beautiful_ figures :)
16.06.2025 16:00 β π 7 π 0 π¬ 0 π 0Impressive study led by @johnpgrogan1.bsky.social and @lucvermeylen.bsky.social combining evidence accumulation modeling and EEG to find a computatioanl account that explains the computations of confidence. So many answers in this manuscript :)
13.06.2025 08:28 β π 11 π 0 π¬ 0 π 0If you're in Dublin, go and talk to @yfvisser.bsky.social she's doing some really cool stuff on insight into our own biases!!
11.06.2025 15:44 β π 4 π 0 π¬ 0 π 0