Experiments with humans indicate that decision accuracy drives the evolution of niche width.

One theory to explain the high incidence of niche specialization in many animals is that it reduces attentional load during resource-seeking behaviour and thus leads to more accurate resource selection. A recent neural network model refined the predictions of this theory, indicating that a cognitive advantage in specialists is likely to occur under realistic ecological conditions, namely when 'mistakes' (i.e. selection of non-host resources) contribute moderately but positively to fitness. Here, we present a formal empirical test of the predictions of this model. Using a human-computer interactive, we demonstrate that the central prediction of the model is supported: specialist humans are more accurate decision-makers than generalists when their mistakes are rewarded, but not when mistakes are punished. The idea that increased decision accuracy drives the evolution of niche width in animals has been supported in almost all empirical systems in which it has been investigated. Theoretical work supports the idea, and now the predictions of a key theoretical model have been demonstrated in a real biological information-processing system. Considering these interlocking pieces of evidence, we argue that specialization through increased decision accuracy may contribute significantly, along with other mechanisms, to promote niche specialization in animals.