Jade Duffy

Hi Charlotte, very excited for you as you embark on this next chapter—wishing you all the best!🌟

Was there a prize for best fan?

Interactions Between Musicality, Neurodivergent Traits, and Wellbeing: An Anonymous Survey Study Take this survey powered by surveymonkey.com. Create your own surveys for free.

New research study from my music and health psychology lab -

Masters student Erin Smith is looking for adults to answer her 15 minute survey about musicality and neurodivergent traits.

#music #autism #adhd #skills

eu.surveymonkey.com/r/XL38HFY

Disentangling sources of variability in decision-making - Nature Reviews Neuroscience Identifying the psychological and neurobiological processes underpinning intra-individual variations in choice behaviour presents a formidable challenge. In this Review, Duffy et al. discuss how algor...

Disentangling sources of variability in decision-making — a Review by Jade S. Duffy, Mark A. Bellgrove, Peter R. Murphy & Redmond G. O’Connell
www.nature.com/articles/s41...

@jduffy9.bsky.social @neuromurphy.bsky.social @redmondoconnell.bsky.social
#neuroscience

I'm looking for a public dataset of young+older people doing a simple perceptual decision-making task, with sufficient trial + subject numbers. Ideally, multiple stimulus difficulty levels but no manipulations (conditions, task switching etc).

This must exist - tips for where to look? 🧒👨‍🦳🧠

These approaches will enable SSMs to handle greater complexity without adding free parameters 🔧. By combining them, we open exciting avenues to deepen our understanding of choice variability in both health and disease 🧠💡.

The final stream leverages electrophysiology ⚡ to identify neural variability 🧠 and integrate it into models 📊. Measuring neural signals 🧬 helps constrain variability parameters 🔧, bridging abstraction levels and deepening our understanding of variability 🔄.

Another promising avenue is developing advanced task paradigms 🎮 that carefully manipulate stimuli and timings 🎯, enabling estimation of parameters 🔍 often challenging to identify in conventional models 📊. This approach uncovers deeper insights into decision-making variability 🧠.

Recent advances bring us closer to models capturing distinct variability sources 🔄. A key area is how behavioral 👆 and neural 🧠 states evolve during a task ⏳. Accounting for these changes may improve parameter estimates 📊 and clarify variability 🔄.

Identifying the psychological 🧠 and neurobiological 🧬 causes of variability is a key goal of computational research 💻. Sequential sampling models (SSMs) capture variability 🔄, accuracy ✅, and RT ⏱️ across tasks 🎯, but limitations 🔧 prevent them from capturing all variability sources🔄 and timescales⏳.

Variability, in itself, is a stable, reliable trait—increased across many brain disorders 🧠⚠️—and can explain more variance ⚖️ than even the most robust experimental manipulations 🔬. Disentangling its many sources operating across distinct timescales ⏳⏰is no easy feat 🧩.

Disentangling sources of variability in decision-making - Nature Reviews Neuroscience Identifying the psychological and neurobiological processes underpinning intra-individual variations in choice behaviour presents a formidable challenge. In this Review, Duffy et al. discuss how algor...

In life, 3 things are certain: death, taxes, and decision-making variability🧠. Our review tinyurl.com/4dcwafc4 explores how computational models 💻 explain variability, their limits, and recent advances. A thread🧵👇

@redmondoconnell.bsky.social @neuromurphy.bsky.social Mark Bellgrove (not on Bluesky)