Exact(12)
This paper proposes a GIS tool which analyzes the visual exposure of a terrain from single or multiple observation points, starting from a digital elevation model (DEM).
The first is obtained from an extension to the visual exposure algorithm, and the second is evaluated through a weighting curve based on the ability to perceive details of the subject of interest.
The visual exposure values for each cell can be added in order to estimate the visual impact of landscape elements represented by a set of cells in the RSG.
Consequently, the visual exposure in the weapon and ball training conditions was highly similar; the difference between conditions lies in the sensorimotor experience of using the weapon for action versus holding a ball, watching passively.
For the visual exposure condition a significant effect of distance [F 3,9) = 4.56, p<.05] was obtained.
In the visual exposure condition, the proportion of same responses was slightly higher for within- as compared with between-category bird-pairs.
Similar(48)
In the visual-exposure condition performance hovered between 55% and 65%.
In addition, significantly smaller responses were found for the visual-exposure condition as compared with the no-training condition (t 9) = 3.00, p<.05).
Indeed, whereas sensitivity in category discrimination was high for the category-trained bird types, for the visual-exposure bird types category-discrimination ability was very poor.
Whereas before training, conditions did not differ significantly, after training responses were significantly decreased for both for the category-training and the visual-exposure condition, as compared with the no-training condition (Table S1).
The response to category-trained bird types was reduced in the post-training session compared to the pre-training session (t(11) = 2.90, p<.05, for the visual-exposure condition the response was also reduced but did not reach significance (t(11) = 2.00, p = .07).07
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