In another weird prediction, we show that if the reward in a patch decays linearly when harvested, then the forager should be *more* variable the *earlier* they leave

Also exactly what we see in data: foragers leave earlier in rich environments but are more variable (data, solid; model, dashed)

Perhaps the weirdest prediction is that, under a wide range of conditions, foragers’ stochasticity is independent of when they leave. In other words, their variability is decoupled from their reward information

And that’s exactly what we see in the data (solid lines; model predictions: dashed)

We ask if foragers’ variability can be explained by them making deliberately stochastic leaving choices: basically, whether they flip a biased coin

We show deliberately stochastic choice makes weird predictions for how foragers’ respond to their environment, and test them across tasks and species

🧪Preprint!
How foragers depart from optimal models can tell us a lot about how they compute their decisions.

A strong but underexplored departure is that foragers widely vary when they leave identical patches.

A 🧵
doi.org/10.1101/2025...

With
@emmavscholey.bsky.social @brainapps.bsky.social

Fabulous day at UK Neural Computation 2025!

Thanks to today’s invited speakers Jonathan Cornford, Jenny Bizley, Petr Znamenskiy and Flavia Mancini

Congratulations to ECR speakers Ian Hawes and Andrea Colins Rodriguez, selected from 80+ outstanding abstract submissions

Roll on Day 3!

#UKNC25

Among other things, they got me warbling at them about how to write grants and how they’re assessed…

Fabulous first day of ECR workshops at UK Neural Computation 2025!

Great to see and meet so many of the next generation of terrific scientists

Thanks to @danakarca.bsky.social for leading on this and @neuralreckoning.bsky.social for the pics

#UKNC25