de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Poster

Is Stochastic Simulation a Suitable Geostatistical Method for the Study of Visual Attention?

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83847

Canto-Pereira,  LH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Canto-Pereira, L., Rocha, M., & Ranvaud, R. (2005). Is Stochastic Simulation a Suitable Geostatistical Method for the Study of Visual Attention?. Poster presented at Fifth Annual Meeting of the Vision Sciences Society (VSS 2005), Sarasota, FL, USA.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D467-F
Zusammenfassung
Visual attention has been the subject of different metaphors including a spotlight (Posner, 1980), a zoom lens (Ericksen and St.James, 1986), and a gradient field (LaBerge, 1995; Downing and Pinker, 1985). This study proposes a novel paradigm to investigate the spatial distribution of visual attention. Simple reaction times (SRTs) to dots presented over the visual field were used to assess attentional allocation in space. We analyzed the data with several geostatistical methods. One of these, stochastic simulation, has been used in various fields, such as petroleum geology, hydrogeology, meteorology, and oceanography and seems to be particularly suitable for our purposes because it emphasizes spatial continuity patterns. Geostatistical stochastic simulation has the advantage of global precision, in other words it reproduces both the spatial variance and the statistical distribution characteristics of the phenomenon under study. As in any geostatistical method the basic tool is the variogram, which is used to predict data at any point within the domain. Simulations provide several different scenarios of equal probability, with the same spatial statistics of the original data. We used 5 different tasks and through SRTs we assessed attention (shorter or longer RTs were taken to indicate, respectively, higher or lower attentional focus). In experiment 1 participants were asked not to attend to any particular region, but rather try to spread their attention as uniformly as possible over the computer screen (diffuse attention). In the remaining experiments, subjects were instructed to direct their visual attention covertly to the center (experiment 2), to the left (experiment 3), to the right (experiment 4) or to both right and left, but not to the center, characterizing a divided attention situation (experiment 5).