What a stimulus predicts, not what it depicts, determines striatal reward signals www.biorxiv.org/content/10.6... This is interesting. nAcc responds most strongly to stimuli that predict reward, but vmPFC responds more to inherently pleasant (erotic 😳) stimuli.
Our new paper asks whether autism is linked to the way people learn from rewards. We’ve previously shown that people not only learn to value the features that predict reward, but also assign credit to features of their actions that they know are irrelevant (in this case, the card's location).
🚨Preprint Alert 🧠
Our new preprint „Menarche onset is an inflection point for mental health and brain development“ is now online!
👉 Preprint available here: www.biorxiv.org/content/10.6...
Recent preprint with Oriel FeldmanHall and Matt Nassar, showing clear neural evidence that adolescent (13-15 yrs) social media and smartphones use are associated with blunted reward signaling in the ventral striatum, the brain's reward processing hub, and worse mental health.
osf.io/preprints/ps...
How do we predict what’s going to happen next?
In a new paper, we @arikahn.bsky.social @licezhang.bsky.social @nathanieldaw.bsky.social @catehartley.bsky.social ask how kids, teens, and adults solve this problem.
onlinelibrary.wiley.com/doi/10.1111/...
Very excited to announce our new preprint, led by @hollysully.bsky.social, that presents, benchmarks, and validates a method to estimate brain iron content from routine fMRI scans!
It comes complete with a BIDS app!
Check out the preprint and thread below!
doi.org/10.64898/202...
Reproducible Human Reward Imaging Phenotypes Exhibit Differential Sensitivity to Dopamine D2 Receptor Antagonism https://www.biorxiv.org/content/10.64898/2026.05.14.724267v1
some big news! hBayesDM has now been fully ported to cmdstan/cmdstanr/cmdstanpy
check out the PR below, or the changelog for an overview:
PR: github.com/CCS-Lab/hBay...
Changelog: ccs-lab.github.io/hBayesDM/new...
stay tuned for some additional features coming soon, including covariate support 🤓
Selecting an effect size for power analysis is hard.
Many researchers fall back on Cohen's thresholds, but they have no empirical basis and vary wildly by field. Our new paper offers a better option: field-specific effect size distributions built from meta-analytic data doi.org/10.3758/s134...