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Kobe Desender @kobedesender.bsky.social · 13d

Finally, and most importantly, Robin wrote an excellent and accessible demo which should allow anyone (you!) to get started with hMFC: github.com/robinvloeber...

GitHub - robinvloeberghs/hMFC: Repository for the Hierarchical Model for Fluctuations in Criterion (hMFC)

Repository for the Hierarchical Model for Fluctuations in Criterion (hMFC) - robinvloeberghs/hMFC

github.com

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Kobe Desender @kobedesender.bsky.social · 13d

MANY 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!

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Kobe Desender @kobedesender.bsky.social · 13d

We 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.

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Kobe Desender @kobedesender.bsky.social · 13d

Ignoring 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)

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Kobe Desender @kobedesender.bsky.social · 13d

Classic 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.

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Kobe Desender @kobedesender.bsky.social · 13d

Introducing 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 ↓↓↓

#PsychSciSky #Neuroscience #Neuroskyence

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Kobe Desender @kobedesender.bsky.social · 13d

Full details, alternative valence-only models, and post-experiment questionnaires targeting awareness, etc. all in the paper!

Kobe Desender @kobedesender.bsky.social · 13d

At 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)

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Kobe Desender @kobedesender.bsky.social · 13d

At 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!

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Kobe Desender @kobedesender.bsky.social · 13d

To 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

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Kobe Desender @kobedesender.bsky.social · 13d

We 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

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Kobe Desender @kobedesender.bsky.social · 13d

"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 ↓↓↓

#AcademicSky #PsychSciSky #Neuroscience #Neuroskyence

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Reposted by Kobe Desender

Kobe Desender @kobedesender.bsky.social · 19d

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 ↓↓↓

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Kobe Desender @kobedesender.bsky.social · 19d

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)!

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Kobe Desender @kobedesender.bsky.social · 19d

We 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!

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Kobe Desender @kobedesender.bsky.social · 19d

Critically, 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)!

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Kobe Desender @kobedesender.bsky.social · 19d

Replicating previous work, our manipulations had clear and consistent effect on confidence: confidence integrates prior beliefs about performance with accumulated evidence.

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Kobe Desender @kobedesender.bsky.social · 19d

This 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)

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Kobe Desender @kobedesender.bsky.social · 19d

In 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.

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Kobe Desender @kobedesender.bsky.social · 19d

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 ↓↓↓

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Kobe Desender @kobedesender.bsky.social · 28d

Can we use confidence-driven information-seeking as a tool to combat fake news!? Really cool study by really cool @helenevanmarcke.bsky.social ↓↓↓

Hélène Van Marcke @helenevanmarcke.bsky.social · 29d

Warning: this is *not* fake news! New preprint out w/ S. Kunkle & @kobedesender.bsky.social on how confidence-driven information seeking is suboptimal in the context of fake news 📰🔍🧵 (1/6):
osf.io/preprints/ps...

OSF

osf.io

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Kobe Desender @kobedesender.bsky.social · Aug 11

Full-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

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Kobe Desender @kobedesender.bsky.social · Aug 7

Or 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)

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Kobe Desender @kobedesender.bsky.social · Aug 6

The 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 :)

Manipulating Prior Beliefs Causally Induces Under- and Overconfidence - PubMed

Humans differ vastly in the confidence they assign to decisions. Although such under- and overconfidence relate to fundamental life outcomes, a computational account specifying the underlying mechanis...

pubmed.ncbi.nlm.nih.gov

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Kobe Desender @kobedesender.bsky.social · Jul 5

Impressive behavioral + modelling + eeg work, led by @lucvermeylen.bsky.social , relating time-on-task changes in comp parameters to changes in metacognitive reports!

Communications Psychology @commspsychol.nature.com · Jul 4

The study suggests a few metacognitive states correlate with rational behavioural adjustments; this might help steering behaviour towards optimality.
@lucvermeylen.bsky.social @sebraem.bsky.social @kobedesender.bsky.social @gonzalezgarcia.bsky.social @mruz.bsky.social
www.nature.com/articles/s44...

The temporal dynamics of metacognitive experiences track rational adaptations in task performance - Communications Psychology

Using computational modeling and EEG, the study suggests several metacognitive states correlate with rational adjustments in behavior; this might benefit steering behavior towards optimality

www.nature.com

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