Kobe Desender
@kobedesender.bsky.social
Assistant professor at @KU_Leuven, working on #confidence, #decisionmaking and #cognitivecontrol => DesenderLab.com
Finally, analysis of the neural data showed that subjective effort ratings tracked neural signals associated with task difficulty (P3) but not neural signals associated with task preparation (CNV).
November 4, 2025 at 12:37 PM
Finally, analysis of the neural data showed that subjective effort ratings tracked neural signals associated with task difficulty (P3) but not neural signals associated with task preparation (CNV).
hDDM fits revealed that when participants expected a hard trial, they selectively increased the decision boundary. Crucially, results of a regression model showed that effort ratings were sensitive to such variation in decision boundary (higher boundary -> longer sampling -> higher effort)
November 4, 2025 at 12:37 PM
hDDM fits revealed that when participants expected a hard trial, they selectively increased the decision boundary. Crucially, results of a regression model showed that effort ratings were sensitive to such variation in decision boundary (higher boundary -> longer sampling -> higher effort)
Critically, we inserted medium difficulty trials, to asses the pure influence of expectation/preparation. On those medium trials, ppts were a bit slower and experienced more effort when they expected a difficult trial. So, what is happening here?
November 4, 2025 at 12:37 PM
Critically, we inserted medium difficulty trials, to asses the pure influence of expectation/preparation. On those medium trials, ppts were a bit slower and experienced more effort when they expected a difficult trial. So, what is happening here?
When we say something 'feels effortful" what sort of computations underlie those feelings? Theoretically, subjective effort = preparation (CNV) + task difficulty (P3). To test this, participants decided whether to solve an easy/hard equation, and then actually solved an easy/medium/hard equation
November 4, 2025 at 12:37 PM
When we say something 'feels effortful" what sort of computations underlie those feelings? Theoretically, subjective effort = preparation (CNV) + task difficulty (P3). To test this, participants decided whether to solve an easy/hard equation, and then actually solved an easy/medium/hard equation
Brace yourself: neural and computational insights into
the experience of mental effort! Now out in @cerebralcortex.bsky.social Led by Gaia Corlazzoli.
Paper: desenderlab.com/wp-content/u.... Thread ↓↓↓
the experience of mental effort! Now out in @cerebralcortex.bsky.social Led by Gaia Corlazzoli.
Paper: desenderlab.com/wp-content/u.... Thread ↓↓↓
November 4, 2025 at 12:37 PM
Brace yourself: neural and computational insights into
the experience of mental effort! Now out in @cerebralcortex.bsky.social Led by Gaia Corlazzoli.
Paper: desenderlab.com/wp-content/u.... Thread ↓↓↓
the experience of mental effort! Now out in @cerebralcortex.bsky.social Led by Gaia Corlazzoli.
Paper: desenderlab.com/wp-content/u.... Thread ↓↓↓
Finally, we confirm a novel prediction from this model: when the same "purple" stimulus is presented in the context of blue vs red stimuli, the contribution of elements to confidence (i.e. the RIE effect) should flip. This is exactly what we found (panel B)
October 29, 2025 at 4:22 PM
Finally, we confirm a novel prediction from this model: when the same "purple" stimulus is presented in the context of blue vs red stimuli, the contribution of elements to confidence (i.e. the RIE effect) should flip. This is exactly what we found (panel B)
Robust averaging assumes that elements closer to the decision boundary have higher SNR (and thus contribute more). Indeed, dropping the robust averaging principle from the model (cf. Model 3) predicts equal regression slopes (a.k.a. a complete misfit)!
October 29, 2025 at 4:22 PM
Robust averaging assumes that elements closer to the decision boundary have higher SNR (and thus contribute more). Indeed, dropping the robust averaging principle from the model (cf. Model 3) predicts equal regression slopes (a.k.a. a complete misfit)!
Across 9 datasets, we observe a clear RIE: variation in response-incongruent evidence contributes MORE to confidence than variation in response-congruent evidence. This pattern is captured by a model (shades) implementing robust averaging ↓(Crucially, the model is fit on means, not on coefficients)
October 29, 2025 at 4:22 PM
Across 9 datasets, we observe a clear RIE: variation in response-incongruent evidence contributes MORE to confidence than variation in response-congruent evidence. This pattern is captured by a model (shades) implementing robust averaging ↓(Crucially, the model is fit on means, not on coefficients)
When judging the average color of 8 elements, how do you weigh the individual elements when computing confidence. Classically, researchers found a response-congruent evidence bias (RCE; a.k.a. "positive evidence bias"). However, robust averaging predicts the opposite effect (RIE)
October 29, 2025 at 4:22 PM
When judging the average color of 8 elements, how do you weigh the individual elements when computing confidence. Classically, researchers found a response-congruent evidence bias (RCE; a.k.a. "positive evidence bias"). However, robust averaging predicts the opposite effect (RIE)
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!
September 25, 2025 at 9:13 AM
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!
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.
September 25, 2025 at 9:13 AM
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.
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)
September 25, 2025 at 9:13 AM
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)
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.
September 25, 2025 at 9:13 AM
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.
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)
September 25, 2025 at 8:44 AM
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)
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!
September 25, 2025 at 8:44 AM
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!
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
September 25, 2025 at 8:44 AM
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
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
September 25, 2025 at 8:44 AM
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
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!
September 19, 2025 at 10:47 AM
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!
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)!
September 19, 2025 at 10:47 AM
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)!
Replicating previous work, our manipulations had clear and consistent effect on confidence: confidence integrates prior beliefs about performance with accumulated evidence.
September 19, 2025 at 10:47 AM
Replicating previous work, our manipulations had clear and consistent effect on confidence: confidence integrates prior beliefs about performance with accumulated evidence.
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)
September 19, 2025 at 10:47 AM
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)
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.
September 19, 2025 at 10:47 AM
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.
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
August 11, 2025 at 1:58 PM
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
Become our new colleague at the #kuleuven!↓↓↓
The Faculty of Psychology and Educational Sciences invites applications for a full-time Research Professorship in Experimental Cognitive Psychology within the B&C research unit.
#PsychSciSky #Neuroscience #Neuroskyence
The Faculty of Psychology and Educational Sciences invites applications for a full-time Research Professorship in Experimental Cognitive Psychology within the B&C research unit.
#PsychSciSky #Neuroscience #Neuroskyence
May 24, 2025 at 8:01 AM
Become our new colleague at the #kuleuven!↓↓↓
The Faculty of Psychology and Educational Sciences invites applications for a full-time Research Professorship in Experimental Cognitive Psychology within the B&C research unit.
#PsychSciSky #Neuroscience #Neuroskyence
The Faculty of Psychology and Educational Sciences invites applications for a full-time Research Professorship in Experimental Cognitive Psychology within the B&C research unit.
#PsychSciSky #Neuroscience #Neuroskyence
As expected, group > individual performance, and this was more the case with better metacognitive performance. We also replicated confidence matching
@danbang.bsky.social. Critically, participants adapted their average level of confidence depending on the metacognitive ability of the collaborator
@danbang.bsky.social. Critically, participants adapted their average level of confidence depending on the metacognitive ability of the collaborator
April 30, 2025 at 12:30 PM
As expected, group > individual performance, and this was more the case with better metacognitive performance. We also replicated confidence matching
@danbang.bsky.social. Critically, participants adapted their average level of confidence depending on the metacognitive ability of the collaborator
@danbang.bsky.social. Critically, participants adapted their average level of confidence depending on the metacognitive ability of the collaborator