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Updated: Jan 12, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
Published on: May 10, 2019
CART: The Comprehensive Analysis of Reaction Times - GUI for Multisensory Processes and Race Models
David A Tovar1, Marcus R Watson1, David J Lewkowicz2
1Department of Psychology, 5718Vanderbilt University, Nashville, TN, USA.
None:
Multisensory integration (MSI) is a core neurobehavioral operation that enhances our ability to perceive, decide, and act by combining information from different sensory modalities. This integrative capability is essential for efficiently navigating complex environments and responding to their multisensory nature. One of the powerful behavioral benefits of MSI is in speeding responses. To evaluate this speeding, traditional research in MSI often relies on so-called race models, which predict reaction times (RTs) based on the assumption that information from the different sensory modalities is initially processed independently. When observed RTs are faster than those predicted by these models, it indicates the presence of true convergence and integration of multisensory information prior to the initiation of the motor response. Despite the strong applicability of race models in MSI research, analysis of multisensory RT data often poses challenges for researchers, particularly in managing, interpreting and modeling large datasets or a collection of datasets. To surmount these challenges, we developed a user-friendly graphical user interface (GUI) packaged into a freely available software application that is compatible with both Windows and Mac and that requires no programming expertise. This tool simplifies the processes of data loading, filtering, and statistical analysis. It allows the calculation and visualization of RTs across different sensory modalities, the performance of robust statistical tests, and the testing of race model violations. By integrating these capabilities into a single platform, the CART-GUI facilitates MSI analyses and makes it accessible to a wider range of users, from novice researchers to experts in the field. The GUI's user-friendly design and advanced analytical features will allow for valuable insights into the mechanisms underlying MSI and contribute to the advancement of research in this domain.
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