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Luiz Henrique M. Canto-Pereira, Marcelo M. Rocha, Ronald Ranvaud; Is Stochastic Simulation a Suitable Geostatistical Method for the Study of Visual Attention?. Journal of Vision 2005;5(8):687. doi: 10.1167/5.8.687.
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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).
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