An economic model of signal detection for the evolution of auditory sensitivity under variable ambient noise conditions

An early signal detection model featured two simplified auditory systems with contrasting sensitivity levels (low vs high) to examine the impact of different background noise conditions on the detection and recognition performance of the system. The model showed that a hypothetical animal listener communicating under variable background conditions must trade off the risk of misrecognizing the sound, incurred only adopting the high-sensitivity auditory system, with the risk of failing to detect a sound received at low amplitude, incurred only adopting the low-sensitivity one. Here, I implement the model to explore the consequences of the costs of hearing the background noise and irrelevant sounds. Results showed that both costs add to the cost of sound misrecognition to decrease the range of noise conditions favoring a high auditory sensitivity. However, their importance is strongly affected by the amount of irrelevant sounds received. When the listener receives many irrelevant sounds, the importance of the costs of hearing these sounds and hearing the background noise in particular, increases at the expense of that of the cost of misrecognition. The model also revealed that a high-sensitivity listener may be favored even in noisy environments if listening to the noise stimuli provides some benefit to the animal.