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Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply
Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply
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Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply
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Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply
Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply

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Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply
Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply
Journal Article

Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply

2014
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Overview
Modern driver assistance systems make increasing use of auditory and tactile signals in order to reduce the driver's visual information load. This entails potential crossmodal interaction effects that need to be taken into account in designing an optimal system. Here we show that saccadic reaction times to visual targets (cockpit or outside mirror), presented in a driving simulator environment and accompanied by auditory or tactile accessories, follow some well-known spatiotemporal rules of multisensory integration, usually found under confined laboratory conditions. Auditory nontargets speed up reaction time by about 80 ms. The effect tends to be maximal when the nontarget is presented 50 ms before the target and when target and nontarget are spatially coincident. The effect of a tactile nontarget (vibrating steering wheel) was less pronounced and not spatially specific. It is shown that the average reaction times are well-described by the stochastic \"time window of integration\" model for multisensory integration developed by the authors. This two-stage model postulates that crossmodal interaction occurs only if the peripheral processes from the different sensory modalities terminate within a fixed temporal interval, and that the amount of crossmodal interaction manifests itself in an increase or decrease of second stage processing time. A qualitative test is consistent with the model prediction that the probability of interaction, but not the amount of crossmodal interaction, depends on target-nontarget onset asynchrony. A quantitative model fit yields estimates of individual participants' parameters, including the size of the time window. Some consequences for the design of driver assistance systems are discussed.