An empirical test of signal detection theory as it applies to Batesian mimicry

Abstract

Signal detection theory (SDT) has been repeatedly invoked to understand how palatable prey might gain an advantage by resembling unpalatable prey. Here we developed an experimental test of the theory in which we sequentially presented computer-generated Mimics (profitable to attack) and Models (unprofitable to attack) to human volunteers, and asked them to forage in a way that maximized their personal scores. Both the Mimics and Models exhibited normally distributed variation in a single stimulus dimension. When we varied the mean similarity of Mimics to Models, and the proportion of all prey items that were Mimics, our human predators made foraging decisions that were close to those predicted by SDT, including the adoption of a threshold in appearance beyond which prey items were unlikely to be attacked. The fit of predictions to observations was marginally closer when including the time taken to handle the two types of prey. When Mimics and Models were allowed to evolve in appearance subject to selection, the evolutionary trajectory fitted the predictions of SDT closely. While our system was not appropriate to test all predictions of recent SDT theory, it provides strong support for the SDT framework as it applies to Batesian mimicry.