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Paper   IPM / Cognitive Sciences / 17837
School of Cognitive Sciences
  Title:   Generalised exponential-Gaussian distribution: a method for neural reaction time analysis
  Author(s): 
1.  F. Marmolejo-Ramos
2.  C. Barrera-Causil
3.  S. Kuang
4.  Z. Fazlali
5.  D. Wegener
6.  T. Kneib
7.  F. De Bastiani
8.  G. Martinez-Flórez
  Status:   Published
  Journal: Cognitive Neurodynamics
  Vol.:  17
  Year:  2023
  Pages:   221Ã?¢??237
  Supported by:  IPM
  Abstract:
Reaction times (RTs) are an essential metric used for understanding the link between brain and behaviour. As research is reaffirming the tight coupling between neuronal and behavioural RTs, thorough statistical modelling of RT data is thus essential to enrich current theories and motivate novel findings. A statistical distribution is proposed herein that is able to model the complete RTâ??s distribution, including location, scale and shape: the generalised-exponential-Gaussian (GEG) distribution. The GEG distribution enables shifting the attention from traditional means and standard deviations to the entire RT distribution. The mathematical properties of the GEG distribution are presented and investigated via simulations. Additionally, the GEG distribution is featured via four real-life data sets. Finally, we discuss how the proposed distribution can be used for regression analyses via generalised additive models for location, scale and shape (GAMLSS).

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