Blair Shevlin

@bshev.bsky.social

Faculty @ Icahn School of Medicine. Computational Psychiatry. Neuroeconomics. Decision-Making

@jhaarsma.bsky.socialReposted by @bshev.bsky.social

New vacancy for an assistant professor position in our department at Maastricht. You will establish an independent and internationally visible research programme in predictive brain processes and contribute to the education programmes of the Faculty of Psychology and Neuroscience. Please share!

@nsehgal.bsky.socialReposted by @bshev.bsky.social

One last worrying finding: AI-assisted submissions were approved 99.6% of the time, basically as often as unassisted submissions (99.7%). In 60 cases where we directly observed instructions explicitly banning AI, all 60 were still approved and paid.

@nsehgal.bsky.socialReposted by @bshev.bsky.social

🚨 New paper 🚨

Are your participants using AI to take your study?

We conducted the first study directly observing AI assistance in online research, spanning 700K+ Prolific submissions.

We found very rare, concentrated AI use, but gaps in safeguards.

arxiv.org/abs/2610.09279

@omidvebrahimi.bsky.socialReposted by @bshev.bsky.social

⚡ Out now:

Psychological and health sciences increasingly study how phenomena dynamically influence and reinforce one another over time: for example, to understand the mechanisms maintaining mental disorders.

Can we identify reliable clusters of these dynamics?

A thread 🧵
osf.io/preprints/ps...

@achetverikov.bsky.socialReposted by @bshev.bsky.social

Some months ago, I wrote a skeptical reply to a commentary on bots in online studies in PNAS, arguing that no evidence yet exists for such bots. Together with the authors of the original commentary, we now show that bots can pass (almost?) all online cognitive studies with human-like performance 1/n

@bshev.bsky.social

Our main takeaway: when mood drops, taste may take over the decision process in women with BN, which could help explain the shift from restriction to binge eating. We hope this points toward new targets for intervention.

Two simulated evidence-accumulation plots comparing low and high negative affect. With low negative affect, health information enters early enough to push a high-fat food toward rejection. With high negative affect, health information is delayed, so evidence for the high-fat food keeps building on taste alone and it is accepted before health information has much effect.
@bshev.bsky.social

Participants with BN let taste information into the decision earlier than health information, more so than controls. And under negative mood, this taste bias got stronger!

Plot of when taste versus health information enters the decision (attribute onset) for controls (green) and BN (purple), by mood and food type. Values below zero mean taste enters first. Under neutral mood, taste generally enters before health, with the two groups differing by food type. Under negative mood, taste enters even earlier in both groups, and the shift is largest in the BN group.
@bshev.bsky.social

We fit a cognitive model (a sequential sampling / diffusion decision model) that tracks when different attributes, like taste and health, enter a decision as it's being made.

Four-panel schematic of a sequential sampling model, in which evidence for accepting or rejecting a food accumulates over time until it hits a threshold. Attributes can enter the decision at different times. Panels A and B contrast health information entering first with taste information entering first, and show that a high-fat food can be accepted on taste alone but rejected once health information arrives. Panels C and D show hypothesized effects of negative affect: giving taste more weight, giving health less weight, or delaying health information. Each pushes evidence toward accepting the food.
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