Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
Résumé
The fastACI toolbox is a set of MATLAB routines that allows to prepare and perform behavioural listening experiments. The main feature of this toolbox is the inclusion of a reverse correlation (revcorr) post-processing module. The revcorr method allows the assessment of acoustic cues used by a listener with only minimal a priori knowledge of the actual listener's strategy during the experiments. Another special fastACI feature is the possibility to replicate experiments using an artificial listener, consisting of a hearing-inspired model and a decision module which convert trial-by-trial waveforms into a decision variable (e.g., discriminated or not discriminated). In this contribution we explain how to set an artificial listener using an auditory model from the AMT toolbox, combined with one of the decision back-ends that are available in the fastACI toolbox. The selected decision back-ends are all based on a template-matching approach. We provide a list of assumptions and the steps required to derive templates to mimic the performance of human listeners. The emphasis of this contribution is on showing the flexibility of the fastACI framework where any new experiment can be tested using either human or artificial listeners and how to strategically compare collected or simulated results.
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