🧪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.socialWe 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
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)
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)
Nov 12, 2025 16:31A nice example of how sequential and simultaneous choice can fundamentally differ: in the latter, the longer a subject waits to decide, the more variable their decision time.
We show foraging decisions can have independence of decision time and variability, or even an inverted relationship!
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