50 Years of Reverse Correlation: Replicating Ahumada et al.’s Pioneering Study
Résumé
In 1975, Ahumada, Marken & Sandusky addressed the fundamental question of tone-in-noise perception using an innovative psychoacoustic approach. They conducted a detection task involving a 500-Hz pure-tone target in white noise, and analyzed participants' trial-by-trial responses ('tone present' or 'tone absent') by correlating them with the spectrotemporal configuration of the noise presented on each trial. This approach enabled them to identify the acoustic cues essential for detection, demonstrating that listeners rely on the presence of energy at the target frequency, but also that the absence of energy in adjacent bands or immediately before the signal facilitates detection. Beyond these findings, the article marks an important milestone in psychophysical research as it introduced for the first time the "psychophysical reverse correlation" method. This technique, which consists in analyzing the influence of noise on perception on a trial-by-trial basis, has since gained considerable momentum in psychoacoustics. The increasing interest in the approach is reflected in the recent development of fastACI, an open-source Matlab toolbox for designing, conducting and analysing reverse correlation experiments with any type of acoustic signal.
Here, we present a replication of the original study by Ahumada et al. as an illustration of the possibilities of fastACI. The experiment was reimplemented within the toolbox, and data were collected from nine participants. These subjects completed the task under conditions as close as possible to those of the original experiment, except from the SNR that was adjusted to match the performance levels reported by Ahumada et al. Four of them then repeated the task using the SNR from the original study. The results are broadly consistent with the original findings. However, the comparison between the two SNR conditions provide valuable insights into the type of mental templates that are used in such detection-in-noise tasks.
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