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Combination of texture and object motion in slant discrimination

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84925

Rosas,  P
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rosas, P. (2003). Combination of texture and object motion in slant discrimination. Poster presented at Third Annual Meeting of the Vision Sciences Society (VSS 2003), Sarasota, FL, USA.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DB5B-F
Abstract
We have previously observed systematic differences in slant discrimination performance for different types of synthetic texture (Rosas et al., VSS-2002, abstract 300). These results allowed a rank-order of textures according to their “helpfulness” — that is, how easy the slant discrimination is when a particular texture is mapped on the surface. Textures composed of circles tended to allow the best slant discrimination performance, followed by a leopard-skin like pattern, then by a “coherent” noise, and finally a fractal noise inducing the worst performance. For large slants, 66 degrees or more, the discrimination was almost independent of the particular texture. In the present slant discrimination experiment these texture types were combined with two types of (rigid) motion: translation of the planes in the vertical direction (parallel to the viewing direction) and horizontal direction (orthogonal to the viewing direction). In terms of cue combination, one subject showed a discrimination enhanced by the addition of motion in most conditions, consistent with an accumulation of both cues. However, for a second observer there was a significant fraction of observations with equal performance for both types of stimuli (texture with motion and texture only), and for a third subject equal performance was observed more often than discrimination enhanced by motion. This evidence discards a simple summation rule of independent cues in all conditions tested. A combination rule sensitive to the cue reliability could explain the lack of accumulation by nullifying the influence of motion for the more helpful textures. However, such a rule does not explain our data because the cues did not accumulate for the less helpful textures (noises) at low slants, that is, in situations when the texture cue was very unreliable for the task. These data provide a challenge to a simple cue combination model. Alternatively, they can indicate the dependence of the motion cue from texture type.