FastACI: a Toolbox for Investigating Auditory Perception using Reverse Correlation

The fastACI toolbox provides a compilation of tools for collecting and analyzing data from auditory reverse-correlation experiments. These experiments involve behavioral listening tasks including one or more target sounds presented with some random fluctuation, typically in the form of additive background noise. In turn, the paired stimulus-response data from each trial can be used to assess the relevant acoustic features that were effectively used by the listener while performing the task. The results are summarized as a matrix of perceptual weights termed auditory classification image. The framework provided by the toolbox is flexible and it has been so far used to probe different auditory mechanisms such as tone-in-noise detection, amplitude modulation detection, phoneme-in-noise categorization, and word segmentation. In this article, we present the structure of the toolbox, how it can be used to run existing experiments or design new ones, as well as the main options for analyzing the collected data. We then illustrate the capabilities of the toolbox through five case studies: a replication of a pioneering reverse correlation study from 1975, an example of reproduction of the analyses of one of our previous studies, a comparison of the results of three phoneme-categorization experiments, and a quantification of how noise type and estimation method affect the quality of the resulting auditory classification image.