A Bayesian model of temporal frequency masking

The data of Anderson and Burr [1985. Vision Research, 25, 1147-1154] on the temporal-frequency (TF) specificity of noise maskers indicate that the effect of TF masking is broad and varies across spatial frequency (SF) channels. One subtle but significant feature of the data is that the TF at which the effect of masking is maximal falls continuously as the test TF falls. This continuous shift is hard to reconcile with models of detection in the literature that relate detection to the most sensitive filter, without resorting to a large number of temporal filters. We developed a new model, which relies on only three temporal filters and posits that detection is the result of a threshold decision based on the compound Bayesian probability of all filter responses, not just the most sensitive filter.