While cognitive theory has advanced several candidate frameworks to explain attentional entrainment, the neural basis for the temporal allocation of attention is unknown. Here we present a new model of attentional entrainment guided by empirical evidence obtained using a cohort of 50 artificial brains. These brains were evolved in silico to perform a duration judgment task similar to one where human subjects perform duration judgments in auditory oddball paradigms. We found that the artificial brains display psychometric characteristics remarkably similar to those of human listeners and exhibit similar patterns of distortions of perception when presented with out-of-rhythm oddballs. A detailed analysis of mechanisms behind the duration distortion suggests that attention peaks at the end of the tone, which is inconsistent with previous attentional entrainment models. Instead, the new model of entrainment emphasizes increased attention to those aspects of the stimulus that the brain expects to be highly informative.

Zipser (1991) showed that the hidden unit activity of a fully recurrent neural network model, trained on a simple memory task, matched the temporal activity patterns of memory-associated neurons in monkeys performing delayed saccade or delayed match-to-sample tasks. When noise, simulating random fluctuations in neural firing rate, is added to the unit activations of this model, the effect on the memory dynamics is to slow the rate of information loss. In this paper, we show that the dynamics of the iterated sigmoid function, with gain and bias parameters, is qualitatively very similar to the tonic response properties of Zipser's multiunit model. Analysis of the simpler system provides an explanation for the effect of noise that is missing from the description of the multiunit model.