The gamma model analysis (GMA): Introducing a novel scoring method for the shape of components of the event-related potential

Background: Research using the event-related potential (ERP) method to investigate cognitive processes has usually focused on the analysis of either individual peaks or the area under the curve as components of interest. These approaches, however, do not analyse or describe the substantial variation in size and shape across the entire individual waveforms. New method: Here we show that the precision of ERP analyses can be improved by fitting gamma functions to components of interest. Gamma model analyses provide time-dependent and shape-related information about the component, such as the component's rise and decline. We demonstrated the advantages of the gamma model analysis in a simulation study and in a two-choice response task, as well as a force production task. Results: The gamma model parameters were sensitive to experimental variations, as well as variations in behavioural parameters. Comparison with existing methods: Gamma model analyses provide researchers with additional reliable indicators about the shape of an ERP component's waveform, which previous analytical techniques could not. Conclusion: This approach, therefore, provides a novel toolset to better understand the exact relationship between ERP components, behaviour and cognition.