Abstract
We present an alternate account of human 3D processing for perception and action. Traditional views postulate that visual mechanisms extract veridical metric properties of environmental objects. Instead, we argue that local affine information, which encodes non metric aspects of a 3D structure, like the depth-order of feature points, determines both our conscious perception of the world and our motor actions. Since only this information is accurately carried on from the earliest stages of 3D processing, our theory predicts large biases in perceptual and motor tasks that require veridical metric estimates. We describe empirical results that support this prediction and show the inadequacy of metric models as computational theories of 3D processing.
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Notes
- 1.
We arbitrarily chose a zero disparity value for the flankers, as if the observers were fixating the plane identified by the two flanking rods.
- 2.
In the specific case of disparity and velocity \(\mathit{SNR}_{c} = \sqrt{\mathit{SNR}_{d}^{2} + \mathit{SNR}_{v}^{2}}\).
- 3.
Consider binocular disparities: The relative disparity d is proportional to the depth Δz of the distal object through a scaling factor k d , related to the egocentric distance of the object (d=k d Δz) and, therefore, to the Signal to Noise Ratio \((\mathit{SNR}_{d} = \frac {k_{d}}{\sigma_{d}}\Delta z)\). For a fixed value of k d , SNR d increases with Δz. Thus, “on average”, larger values of SNR correspond to larger values of Δz.
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Domini, F., Caudek, C. (2013). Perception and Action Without Veridical Metric Reconstruction: An Affine Approach. In: Dickinson, S., Pizlo, Z. (eds) Shape Perception in Human and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5195-1_20
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