Here we extend ideas from ideal observer theory to investigate the environmental constraints under which human behaviour emerges, using contemporary machine learning to optimize models for behaviourally relevant tasks in simulated environments. Rigorously evaluating adaptedness has thus remained out of reach for many domains. Despite recent advances, such models remain intractable for many real-world behaviours. Ideal observers are typically derived analytically, but as a result are often limited to simple psychophysical tasks 11, 12, 13, 14, 15, 16. The consequences of such optimization for perceptual systems have traditionally been revealed by ideal observer models-systems that perform a task optimally under environmental constraints 9, 10 and whose behavioural characteristics can be compared to actual behaviour. Well-known phenomena attributed to implementation constraints include aftereffects 1, 2, masking 3, 4, poor visual motion and form perception for equiluminant colour stimuli 5 and limits on the information that can be extracted from high-frequency sound 6, 7, 8.Įvolution and development can be viewed as an optimization process that produces a system that functions well in its environment. Many phenomena are not obviously a consequence of adaptation to the environment, and perceptual traits are often proposed to reflect implementation constraints rather than the consequences of performing a task well. Yet adaptedness is often difficult to test. Why do we see or hear the way we do? Perception is believed to be adapted to the world, shaped over evolution and development to help us survive in our ecological niche. The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can reveal the real-world constraints that shape perception. But when trained in unnatural environments without reverberation, noise or natural sounds, these performance characteristics deviated from those of humans. In simulated experiments, the model exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, biases for sound onsets and limits on localization of concurrent sources.

The resulting model localized accurately in realistic conditions with noise and reverberation. To better understand real-world localization, we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment. Localization in real-world conditions is challenging, as echoes provide erroneous information and noises mask parts of target sounds.

Mammals localize sounds using information from their two ears.

Human voice wave files