Feature extraction and integration underlying perceptual decision making during courtship behavior.

Traditionally, perceptual decision making is studied in trained animals and carefully controlled tasks. Here, we sought to elucidate the stimulus features and their combination underlying a naturalistic behavior--female decision making during acoustic courtship in grasshoppers. Using behavioral data, we developed a model in which stimulus features were extracted by physiologically plausible models of sensory neurons from the time-varying stimulus. This sensory evidence was integrated over the stimulus duration and combined to predict the behavior. We show that decisions were determined by the interaction of an excitatory and a suppressive stimulus feature. The observed increase of behavioral response with stimulus intensity was the result of an increase of the excitatory feature's gain that was not controlled by an equivalent increase of the suppressive feature. Differences in how these two features were combined could explain interindividual variability. In addition, the mapping between the two stimulus features and different parameters of the song led us to re-evaluate the cues underlying acoustic communication. Our framework provided a rich and plausible explanation of behavior in terms of two stimulus cues that were extracted by models of sensory neurons and combined through excitatory-inhibitory interactions. We thus were able to link single neuron's feature selectivity and network computations with decision making in a natural task. This data-driven approach has the potential to advance our understanding of decision making in other systems and can inform the search for the neural correlates of behavior.