Kobe Desender

⏳ Just over 2 weeks left to apply! ⏳

We are looking for talented Cognitive Neuroscientists to join our team at Trinity College Dublin for postdoc positions funded by a European Research Council (ERC) Consolidator grant.
www.jobs.ac.uk/job/DPV296/r...

www.ktsetsoslab.net/_files/ugd/0...

the heading of the paper "Intuitive insight: Fast associative processes drive sound creative thinking" at Cognition

1/13

New paper with @wimdeneys.bsky.social accepted at @cognitionjournal.bsky.social 🥳

Is creativity intuitive? 👩‍🎨

A 🧵👇

Modeling Speed–Accuracy Trade-Offs in the Stopping Rule for Confidence Judgments! Now out in #PsychologicalReview (aka we can finally say we do comp models)! Led by @stefherregods.bsky.social @lucvermeylen.bsky.social @pierreledenmat.bsky.social

Paper: desenderlab.com/wp-content/u... Thread ↓↓↓

Re different models: under certain parameter combinations CCB boils down to FCB, and in those cases only a negative rtconf, conf association can be predicted

Thanks! Good question. Re sampling error: in an EEG follow-up we collected way more data (>2000 tr/sub) but I don't remember the win per participant, @johnpgrogan1.bsky.social do you remember?

Finally, please get in touch if you want to apply the FCB model to your data (required: carefully measured RTs and binary choices, and carefully measured confidence RTs and confidence). See e.g. this preprint for a good example of how the FCB model can provide unique insight osf.io/preprints/ps...

GitHub - StefHerregods/ConfidenceBounds: Research project investigating the addition of confidence bounds to the drift diffusion model (DDM) in order to explain confidence ratings and their associated... Research project investigating the addition of confidence bounds to the drift diffusion model (DDM) in order to explain confidence ratings and their associated reaction times. - GitHub - StefHerre...

MUCH more information about the models, behavioral results, etc. can all be found in the manuscript! Also, all code, pre-registration and raw data can be found in the github repo! github.com/StefHerregod...

Inspection of the FCB parameters reveals that (as expected) SATO instructions about choice and confidence mostly target the choice boundaries and the confidence boundaries, respectively, leaving the other parameters more-or-less unaffected.

Qual. inspection showed that the FCB is favoured mostly because it can account for different patterns in confidence (eg. panel A shows an inverted-U between confidence and confidenceRT which the other models have much trouble with), such as acc~confRT, mean(conf)~confRT and sd(conf)~confRT

The Flexible Confidence Boundary model (FCB) provided the best quantitative fit to the data and was the best fitting model for most participants. Hurray :)

To manipulate the stopping rule of confidence, we ran two experiments manipulating speed-accuracy tradeoff for choices (try to be fast vs accurate) and confidence confidence (try to make fast vs careful confidence judgments)

Model confusion: when simulating data under these different models, our model fitting procedure was able to reliably identify the generative model. In other words, we can now collect data and compare these different models!

Optional stopping (E) assumes that post-decision accumulation stops after reaching certain confidence boundaries with a certain probability, and finally (F) implements a race model architecture following Van Zandt

We introduce the Flexible Confidence Boundary (FCB, internally better known as "the herregods model") which assumes two opposing confidence boundaries that slowly collapse, potentially at independent rates (i.e. an evidence-based stopping criterion)

The collapsing confidence boundary (CCB) model assumes a slowly collapsing confidence boundary, in which confidence decreases as time passes.

Here, we compare 5 competing models in their ability to explain the termination of post-decision accumulation. First, the most influential approach was proposed by the 2DSD model, which assumes a fixed post-decision duration period (i.e. a time-based stopping criterion)

Computational models of confidence often assume that some form of post-decision accumulation underlies confidence computation. Yet, it remains underspecified how/when people decide to stop this post-decision accumulation process

Modeling Speed–Accuracy Trade-Offs in the Stopping Rule for Confidence Judgments! Now out in #PsychologicalReview (aka we can finally say we do comp models)! Led by @stefherregods.bsky.social @lucvermeylen.bsky.social @pierreledenmat.bsky.social

Paper: desenderlab.com/wp-content/u... Thread ↓↓↓

Representational Momentum Transcends Motion Dillon Plunkett & Jorge Morales (2025) Psychological Science

When we see something that's moving, our memories about it end up projected forward in time: We remember it further along than it was. In a new paper in 𝘗𝘴𝘺𝘤𝘩𝘰𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘚𝘤𝘪𝘦𝘯𝘤𝘦, out today and led by @dillonplunkett.bsky.social, we demonstrate that this happens even when there is 𝙣𝙤 𝙢𝙤𝙩𝙞𝙤𝙣 𝙬𝙝𝙖𝙩𝙨𝙤𝙚𝙫𝙚𝙧.🧵

Conscious awareness, sensory integration, and evidence accumulation in bodily self-perception | PNAS Conscious awareness refers to the subjective experience of perceiving, thinking, and feeling and the ability to report these experiences. These per...

Happy to share our new and groundbreaking study on the relationship between conscious awareness and the sense of bodily self! With @brainself.bsky.social at @ki.se and out today in PNAS: www.pnas.org/doi/10.1073/...

Well this is exciting!

The Department of Psychological & Brain Sciences at Johns Hopkins University (@jhu.edu) invites applications for a full-time tenured or tenure-track faculty member in Cognitive Psychology, in any area and at any rank!

Application + more info: apply.interfolio.com/178146

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 ↓↓↓

In sum, our results reveal that the sense is sensitive to the amount of time spent accumulating evidence, which is under the control of the decision boundary (set in response to expectations about difficulty), and to variations in single-trial P3 (reflecting task difficulty)

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)

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

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 ↓↓↓

Too quick? Read the full paper :)

In sum, contrary to the often-studied positive evidence bias (here referred to as response-congruent evidence effect), we found that variation in response-incongruent evidence contributes more to confidence - and we explain why! If you actually want to understand all of this, read the paper :)