Bees Are Astonishingly Good at Making Decisions—and This Computer Model Explains How That’s Possible

Andrew Barron in Singularity Hub:

A honey bee’s life depends on it successfully harvesting nectar from flowers to make honey. Deciding which flower is most likely to offer nectar is incredibly difficult. Getting it right demands correctly weighing up subtle cues on flower type, age, and history—the best indicators a flower might contain a tiny drop of nectar. Getting it wrong is at best a waste of time, and at worst means exposure to a lethal predator hiding in the flowers. In new research published recently in eLife, my colleagues and I report how bees make these complex decisions.

…To take apart this question, we turned to a computational model, asking what properties a system would need to have to beat the speed-accuracy tradeoff. We built artificial neural networks capable of processing sensory input, learning, and making decisions. We compared the performance of these artificial decision systems to the real bees. From this we could identify what a system had to have if it were to beat the tradeoff. The answer lay in giving “accept” and “reject” responses different time-bound evidence thresholds. Here’s what that means—bees only accepted a flower if, at a glance, they were sure it was rewarding. If they had any uncertainty, they rejected it.

This was a risk-averse strategy and meant bees might have missed some rewarding flowers, but it successfully focused their efforts only on the flowers with the best chance and best evidence of providing them with sugar.

More here.