Neurometric analysis has proven to be a powerful tool for studying links between neural activity and perception, especially in visual and somatosensory cortices, but conventional neurometrics are based on a simplistic rate-coding hypothesis that is clearly at odds with the rich and complex temporal spiking patterns evoked by many natural stimuli. In this study, we investigated the possible relationships between temporal spike pattern codes in the primary auditory cortex (A1) and the perceptual detection of subtle changes in the temporal structure of a natural sound. Using a two-alternative forced-choice oddity task, we measured the ability of human listeners to detect local time reversals in a marmoset twitter call. We also recorded responses of neurons in A1 of anesthetized and awake ferrets to these stimuli, and analyzed these responses using a novel neurometric approach that is sensitive to temporal discharge patterns. We found that although spike count-based neurometrics were inadequate to account for behavioral performance on this auditory task, neurometrics based on the temporal discharge patterns of populations of A1 units closely matched the psychometric performance curve, but only if the spiking patterns were resolved at temporal resolutions of 20 msec or better. These results demonstrate that neurometric discrimination curves can be calculated for temporal spiking patterns, and they suggest that such an extension of previous spike count-based approaches is likely to be essential for understanding the neural correlates of the perception of stimuli with a complex temporal structure.