Visual intensity-dependent response latencies predict perceived audio-visual simultaneity - Data

Published: 06-11-2020| Version 1 | DOI: 10.17632/5742kchy9b.1
Ryan Horsfall,
Sophie Wuerger,
Georg Meyer


Simultaneity judgement (SJ) and temporal order judgement (TOJ) tasks are commonly used to investigate the temporal processes involved in multisensory perception. From these tasks, it is possible to estimate a point of subjective simultaneity (PSS), which refers to the audio-visual offset where simultaneity is perceived. Despite the tasks usually involving identical stimuli, the PSSs have been shown to be uncorrelated. The multisensory correlation detector (MCD) model (Parise & Ernst, 2016) predicts this lack of correlation, by proposing that different decisional weightings are applied to common, early audio-visual processing mechanisms. In the included files, we evaluate an additional component to the MCD model: an intensity-dependent processing delay that was estimated from simple reaction times. The Matlab codes titled 'M1' to 'M4' will load .mat files that contain behavioural data and run the various models which we compared. Our behavioural data was obtained using the SJ (titled p1_sj_intensGenfile to p20_sj_intensGenfile) and TOJ tasks (titled p1_tj_intensGenfile to p20_tj_intensGenfile), measured at four visual intensities, with a fixed auditory intensity. Each file contains 4 matrices, titled 'dataIntensity1' to 'dataIntensity4', with 1 being the lowest visual intensity. Each matrix contains 2 columns: column 1 represents the stimulus onset asynchrony (with negative values indicating an audio-leading stimulus), whereas column 2 represents the relative frequency of 'simultaneous' and 'light leading' responses in the simultaneity judgement and temporal order judgement tasks, respectively. ‘pssSJ’ and ‘pssTOJ’ contain the PSSs estimated for each observer using atheoretical fits. The results show that the added early processing delay predicts the intensity-dependence of perceived simultaneity, without the need for additional decisional factors. References: Parise, C. V., & Ernst, M. O. (2016). Correlation detection as a general mechanism for multisensory integration. Nature Communications, 7.