dc.contributor.author | Medina, José M. | |
dc.contributor.author | Díaz Navas, José Antonio | |
dc.date.accessioned | 2020-10-21T10:13:15Z | |
dc.date.available | 2020-10-21T10:13:15Z | |
dc.date.issued | 2020-08-06 | |
dc.identifier.citation | Erratum: Noise-induced transition in human reaction times (2016 J. Stat. Mech.: Theory Exp. 9 093502) [https://doi.org/10.1088/1742-5468/aba0a8] | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/63841 | |
dc.description | We thank Kenneth H Norwich (University of Toronto, Canada) for his help and support
in the derivation of equations (10) and (11). | es_ES |
dc.description.abstract | The human reaction/response time can be defined as the time
elapsed from the onset of stimulus presentation until a response occurs in many
sensory and cognitive processes. A reaction time model based on Piéron’s law
is investigated. The model shows a noise-induced transition in the moments of
reaction time distributions due to the presence of strong additive noise. The
model also demonstrates that reaction times do not follow fluctuation scaling
between the mean and the variance but follow a generalized version between the
skewness and the kurtosis. The results indicate that noise-induced transitions
in the moments govern fluctuations in sensory–motor transformations and open
an insight into the macroscopic effects of noise in human perception and action.
The conditions that lead to extreme reaction times are discussed based on the
transfer of information in neurons. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IOP Publishing | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Fluctuation phenomena | es_ES |
dc.subject | Information processing | es_ES |
dc.subject | Noise models | es_ES |
dc.subject | Pattern formation | es_ES |
dc.title | Erratum: Noise-induced transition in human reaction times | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1088/1742-5468/aba0a8 | |
dc.type.hasVersion | VoR | es_ES |