Generalised exponential-Gaussian distribution: a method for neural reaction time analysis | |
Marmolejo-Ramos, Fernando1; Barrera-Causil, Carlos2; Kuang, Shenbing3![]() | |
第一作者 | Fernando Marmolejo-Ramos |
通讯作者邮箱 | fernando.marmolejo-ramos@unisa.edu.au (fernando marmolejo-ramos ) |
心理所单位排序 | 4 |
摘要 | 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). |
关键词 | Exponential Gaussian distribution Reaction times Neuronal response latency Cognitive neuroscience Generalised additive models for location Scale and shape |
2022-05-17 | |
语种 | 英语 |
DOI | 10.1007/s11571-022-09813-2 |
发表期刊 | COGNITIVE NEURODYNAMICS
![]() |
ISSN | 1871-4080 |
页码 | 17 |
期刊论文类型 | 综述 |
收录类别 | SCI |
资助项目 | Instituto Tecnologico Metropolitano (ITM) ; German Research Foundation (Deutsche Forschungsgemeinschaft ; DFG)[KN 922/9-1] ; German Research Foundation (Deutsche Forschungsgemeinschaft ; DFG)[WE 5469/2-1] ; National Council for Scientific and Technological Development (CNPq)[310050/2019-7] |
出版者 | SPRINGER |
WOS关键词 | FRACTIONAL ORDER-STATISTICS ; NEURONAL RESPONSE LATENCY ; POWER ; PATHWAYS ; MODELS |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neurosciences |
WOS记录号 | WOS:000796789400002 |
WOS分区 | Q3 |
资助机构 | Instituto Tecnologico Metropolitano (ITM) ; German Research Foundation (Deutsche Forschungsgemeinschaft ; DFG) ; National Council for Scientific and Technological Development (CNPq) |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.psych.ac.cn/handle/311026/42706 |
专题 | 认知与发展心理学研究室 |
通讯作者 | Marmolejo-Ramos, Fernando |
作者单位 | 1.Univ South Australia, Ctr Change & Complex Learning, Adelaide, SA 5000, Australia 2.Inst Tecnol Metropolitano ITM, Fac Ciencias Exactas & Aplicadas, Medellin 050034, Colombia 3.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China 4.Inst Res Fundamental Sci IPM, Sch Cognit Sci, Tehran, Iran 5.Columbia Univ, Dept Psychiat, Div Integrat Neurosci, New York, NY USA 6.New York State Psychiat Inst & Hosp, New York, NY 10032 USA 7.Univ Bremen, Ctr Cognit Sci, Brain Res Inst, Bremen, Germany 8.Georg August Univ Gottingen, Campus Inst Data Sci CIDAS, Gottingen, Germany 9.Georg August Univ Gottingen, Chair Stat, Gottingen, Germany 10.Univ Fed Pernambuco, Stat Dept, Recife, PE, Brazil 11.Univ Cordoba, Fac Ciencias, Dept Matemat & Estadist, Cordoba 2300, Colombia 12.Univ Fed Ceara, Programa Posgrad Modelagem & Metodos Quantitativo, Fortaleza, Ceara, Brazil |
推荐引用方式 GB/T 7714 | Marmolejo-Ramos, Fernando,Barrera-Causil, Carlos,Kuang, Shenbing,et al. Generalised exponential-Gaussian distribution: a method for neural reaction time analysis[J]. COGNITIVE NEURODYNAMICS,2022:17. |
APA | Marmolejo-Ramos, Fernando.,Barrera-Causil, Carlos.,Kuang, Shenbing.,Fazlali, Zeinab.,Wegener, Detlef.,...&Martinez-Florez, Guillermo.(2022).Generalised exponential-Gaussian distribution: a method for neural reaction time analysis.COGNITIVE NEURODYNAMICS,17. |
MLA | Marmolejo-Ramos, Fernando,et al."Generalised exponential-Gaussian distribution: a method for neural reaction time analysis".COGNITIVE NEURODYNAMICS (2022):17. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Generalised exponent(577KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论