TY - JOUR
T1 - Generalised exponential-Gaussian distribution
T2 - a method for neural reaction time analysis
AU - Marmolejo-Ramos, Fernando
AU - Barrera-Causil, Carlos
AU - Kuang, Shenbing
AU - Fazlali, Zeinab
AU - Wegener, Detlef
AU - Kneib, Thomas
AU - De Bastiani, Fernanda
AU - Martinez-Flórez, Guillermo
PY - 2023/2
Y1 - 2023/2
N2 - 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 RT’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).
AB - 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 RT’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).
KW - Cognitive neuroscience
KW - Exponential Gaussian distribution
KW - Generalised additive models for location, Scale and shape
KW - Neuronal response latency
KW - Reaction times
UR - http://www.scopus.com/inward/record.url?scp=85130253383&partnerID=8YFLogxK
U2 - 10.1007/s11571-022-09813-2
DO - 10.1007/s11571-022-09813-2
M3 - Article
AN - SCOPUS:85130253383
SN - 1871-4080
VL - 17
SP - 221
EP - 237
JO - Cognitive Neurodynamics
JF - Cognitive Neurodynamics
IS - 1
ER -