Single neurons in auditory cortex display highly selective spectrotemporal properties: their receptive fields modulate over small fractions of an octave and integrate across temporal windows of 100-200 ms. We investigated how these characteristics impact auditory behavior. Human observers were asked to detect a specific sound frequency masked by broadband noise; we adopted an experimental design which required the engagement of frequency-selective mechanisms to perform above chance. We then applied psychophysical reverse correlation to derive spectrotemporal perceptual filters for the assigned task. We were able to expose signatures of neuronal-like spectrotemporal tuning on a scale of 1/10 octave and 50-100 ms, but detailed modeling of our results showed that observers were not able to rely on the explicit output of these channels. Instead, human observers pooled from a large bank of highly selective channels via a weighting envelope poorly tuned for frequency (on a scale of 1.5 octave) with sluggish temporal dynamics, followed by a highly nonlinear max-like operation. We conclude that human detection of specific frequencies embedded within complex sounds suffers from a high degree of intrinsic spectrotemporal uncertainty, resulting in low efficiency values (<1 %) for this perceptual ability. Signatures of the underlying neural circuitry can be exposed, but there does not appear to be a direct line for accessing individual neural channels on a fine scale.

Sound waveforms convey information largely via amplitude modulations (AM). A large body of experimental evidence has provided support for a modulation (bandpass) filterbank. Details of this model have varied over time partly reflecting different experimental conditions and diverse datasets from distinct task strategies, contributing uncertainty to the bandwidth measurements and leaving important issues unresolved. We adopt here a solely data-driven measurement approach in which we first demonstrate how different models can be subsumed within a common ‘cascade’ framework, and then proceed to characterize the cascade via system identification analysis using a single stimulus/task specification and hence stable task rules largely unconstrained by any model or parameters. Observers were required to detect a brief change in level superimposed onto random level changes that served as AM noise; the relationship between trial-by-trial noisy fluctuations and corresponding human responses enables targeted identification of distinct cascade elements. The resulting measurements exhibit a dynamic complex picture in which human perception of auditory modulations appears adaptive in nature, evolving from an initial lowpass to bandpass modes (with broad tuning, Q∼1) following repeated stimulus exposure.

Human sensory processing is inherently noisy: if a participant is presented with the same set of stimuli multiple times and is asked to perform a task related to some property of the stimulus by pressing one of two buttons, the set of responses generated by the participant will differ on different presentations even though the set of stimuli remained the same. This response variability can be used to estimate the amount of internal noise (i.e. noise that is not present in the stimulus but in the participant's decision making process). The procedure by which the same set of stimuli is presented twice is referred to as double-pass (DP) methodology. This procedure is well-established, but there is no accepted recipe for how the repeated trials may be delivered (e.g. in the same order as they were originally presented, or in a different order); more importantly, it is not known whether the choice of delivery matters to the resulting estimates. Our results show that this factor (as well as feedback) has no measurable impact. We conclude that, for the purpose of estimating internal noise using the DP method, the system can be assumed to have no inter-trial memory.