Detection of Irregularities in Auditory Sequences: a Neural-network Approach to Temporal Processing *

Combining experiments and modeling, we study how the discrimination of time intervals depends both on the interval duration and on contextual stimuli. Participants had to judge the temporal regularity of a sequence of standard intervals that contained a deviant interval. We find that the performance to detect the deviant increases with the number of standards preceeding the deviant and decreases with the duration of the standard. While the effect of the standard duration can be explained by an neural network model that realizes the concept of multiple synfire chains, the position effect is incorporated into the model by an in-situ averaging process. Furthermore, experiments are discussed that are critical for the predictions of the model.