Understanding the design of warning signals: a predator’s view

Abstract Animal warning signals show remarkable diversity, yet subjectively appear to share visual features that make defended prey stand out and look different from more cryptic palatable species. Here we develop and apply a computational model that emulates avian visual processing of pattern and colour to Lepidopteran wing patterns to show that warning signals have specific neural signatures that set them apart not only from the patterns of undefended species but also from natural scenes. For the first time, we offer an objective and quantitative neural-level definition of warning signals based on how the pattern generates neural activity in the brain of the receiver. This opens new perspectives for understanding and testing how warning signals function and evolve, and, more generally, how sensory systems constrain general principles for signal design.