Fantastic news, congrats Claire 🍾 Lucky UCD!

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?

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 🧩.