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Warwick Psychology

@warwickpsych.bsky.social did:plc:3puciobsli4rnt2x3meurc2l

@uni-of-warwick.bsky.social Home of psychological scientists researching behavioural & computational sciences, language & learning, and health & wellbeing across the lifespan. Sharing latest research updates. Website: https://warwick.ac.uk/fac/sci/psych

Sur Bluesky depuis le

Abonnés
296
Abonnements
315
Posts
309
Jours dans les classements
0

Résumé

Warwick Psychology (@warwickpsych.bsky.social) est sur Bluesky depuis le 29 janvier 2025. Le compte a 296 abonnés, suit 315 comptes et a publié 309 posts.

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Les meilleurs jours où les posts de ce compte sont entrés dans le top quotidien de Debya.

Ce compte n’est encore apparu dans aucun classement quotidien.

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  • Matthew Mak@matthewmakpsy.bsky.social

    Every time I get peer review, I’m amazed by how thoughtful & constructive most reviewers are (whoever you are — thank you). They often point out things I hadn’t considered and make me a better researcher. Peer review as a system may be in trouble, but I’ve been a major beneficiary of the process.

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  • Nicole Tang@nkytang.bsky.social

    🧪Thrilled to see our paper highlighted by PAIN. Thank you! For many patients, chronic pain is not only about intensity, but also about the feeling that pain is taking over and eroding their sense of self. We hope this work helps inform more person-centred interventions for #chronicpain management.

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  • Warwick Lifecourse and Neonatal Group@warwicklng.bsky.social

    New in JAMA Pediatrics: @dieterwolke.bsky.social and team examine childhood factors linked to positive functioning in adulthood among those born very preterm or with very low birth weight. Read here 👇 doi.org/10.1001/jama... #PretermBirth #ChildDevelopment @warwickpsych.bsky.social

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  • Adam Sanborn@asanborn.bsky.social

    There were also many great ideas from the audience for how this approach could be applied to their own research areas.

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  • Adam Sanborn@asanborn.bsky.social

    Following the introduction, @lcastillo.bsky.social gave a tutorial on producing human-like biases from MCMC models in R, as well as fitting human behaviour with simulations and likelihood-free methods.

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