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kenzakdr.bsky.social

@kenzakdr.bsky.social
33 followers 29 following 6 posts
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Taiki Oka @toka-neuro.bsky.social · 03/09/2026
1/7 Excited to share our new @elife.bsky.social Reviewed Preprint! 🧠 How do higher-order cognitive processes like abstraction & metacognition map onto transdiagnostic mental health dimensions? We combined computational modeling of a RL task with symptom profiling 🔗 doi.org/10.7554/eLif...
doi.org
Mapping abstraction and metacognition onto distinct transdiagnostic symptom profiles
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kenzakdr.bsky.social @kenzakdr.bsky.social · 18/08/2026
1/6 New preprint! 🧵 How do we decide to decide? With @aleyamarzuki.bsky.social we showed that the interplay between urgency, evidence accumulation, and prediction errors converges in the dmPFC, into one signal: keep gathering information, or stop and commit to a choice. doi.org/10.64898/202...
doi.org
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Leya @aleyamarzuki.bsky.social · 14/08/2026
📢 Our paper is now out in @natcomms.nature.com! www.nature.com/articles/s41... We know that symptoms of OCD fluctuate substantially. But what are the exact conditions that precede symptom escalations? We found evidence for metacognitive processes being a key antecedent. 1/
nature.com
Drops in confidence precede symptoms of obsessive-compulsive disorder - Nature Communications
To devise effective interventions for obsessive-compulsive disorder, it is crucial to understand antecedents to symptom emergence. Here, the authors show evidence for self- and decision making underco...
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Lénárd Döme @lenarddome.bsky.social · 07/08/2026
🧠New preprint: OSCAR, an auto-associative model that generates its own error signal by reconstructing its input, and so fits the inverse base-rate effect across supervised, observational, and unsupervised paradigms with one single architecture. Two new experiments included. arxiv.org/abs/2608.06149
arxiv.org
Errorless Irrationality: A unified computational account of the inverse base-rate effect across predictive, observational, and unsupervised procedures
The inverse base-rate effect is a robust bias in how people resolve ambiguity between competing categories, and the most prominent theories explain it through prediction error. Across two experiments ...
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Lénárd Döme @lenarddome.bsky.social · 14/07/2026
📢 Out now in PLOS Comp Biol: **cpm** — a Python library for theory-driven modelling in computational psychiatry. No scikit-learn for mechanistic cognitive models — so we built one: RL, prospect-theory & metacognition, hierarchical Bayes, and as many more as you can code 🔗 doi.org/10.1371/jour...
doi.org
cpm: A python library for theory-driven modelling in computational psychiatry
Author summary Computational psychiatry is a field that makes extensive use of computational methods to understand mental health. In this field and related disciplines, there is a strong need to desig...
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Ben Jonathan Wagner @benjwagner.bsky.social · 30/05/2026
We @saschafrolich.bsky.social @stefankiebel.bsky.social investigated how habitual- and goal-directed control interact when actions must be made under time pressure. www.nature.com/articles/s44...
nature.com
Context-dependent interaction between goal-directed and habitual control under time pressure - Communications Psychology
This study investigates the dynamic interplay between habitual and goal-directed control. Using RL and drift-diffusion modelling, we show that these systems interact in a context-dependent manner: hab...
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Reposted by @kenzakdr.bsky.social
Tricia Seow @seowxft.bsky.social · 12/11/2025
Very pleased to share our new review in @natrevpsychol.nature.com on integrating cognitive neuroscience and clinical perspectives of metacognition in mental health! With @tobiasuhauser.bsky.social, Lena Jelinek, and Steffen Moritz. (1/3) Web: www.nature.com/articles/s44... PDF: rdcu.be/ePxUG
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Integrating cognitive neuroscience and clinical perspectives on metacognitive mechanisms in psychopathology - Nature Reviews Psychology
Cognitive neuroscience and clinical psychology have made substantial advances in knowledge about metacognitive processes, but these fields have progressed in parallel. In this Review, Seow et al. inte...
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Tobias Hauser @tobiasuhauser.bsky.social · 09/10/2025
Come and say hi to @seowxft.bsky.social @floorburghoorn.bsky.social @lenarddome.bsky.social & @kenzakdr.bsky.social who will be demonstrating our brain explorer app and research at the @medresfdn.bsky.social stand at New Scientist Live event in London @uclqsion.bsky.social @unituebingen.bsky.social
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Lénárd Döme @lenarddome.bsky.social · 16/09/2025
A new preprint 📝 with @tobiasuhauser.bsky.social @kenzakdr.bsky.social @benjwagner.bsky.social and Andrew Webb accompanying our cpm-toolbox.net python modelling library - including details about our motivations, toolbox features, framework and workflows! 👉 osf.io/preprints/ps...
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Reposted by @kenzakdr.bsky.social
Leya @aleyamarzuki.bsky.social · 11/09/2025
🚨New pre-print out! 🍂 Using daily gamified smartphone 📱 tracking, we found that reductions in both self- and decision-making confidence precede when people are likely to report experiencing symptoms of OCD. www.researchsquare.com/article/rs-7...
researchsquare.com
Metacognitive antecedents to states of mental ill-health: Drops in confidence precede symptoms of OCD
Mental health symptoms, like those in obsessive-compulsive disorder (OCD), show pronounced fluctuations over time. However, little is known about the underlying factors driving these fluctuations. Whi...
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Lénárd Döme @lenarddome.bsky.social · 18/07/2025
A quote from Rumelhart/McClelland: "...real biological systems cannot be Turing machines because they have finite hardware." A sentiment somewhat related to our g-distance framework. What portion of unobserved but possible results your model rejects?
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Lénárd Döme @lenarddome.bsky.social · 14/07/2025
Come check out my poster on an accessible new python library cpm (cpm-toolbox.net) designed to ease you into modelling in computational psychiatry #CPConf2025 I’m poster 1.2 👀
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Lénárd Döme @lenarddome.bsky.social · 16/04/2025
This one is finally out! 🎉 We introduce g-distance, a measure that assesses model adequacy by comparing the range of behaviours exhibited by models to that of humans. #computationalmodeling #cognitivescience psycnet.apa.org/doi/10.1037/...
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