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Across-object relationships in perceived object orientation.

Abstract

An orientation discrimination paradigm was used to determine whether the perceived orientation of extended objects is based on the distribution of edge-orientations or on the response of mechanisms that encode relationships across the object; specifically we considered large, second-stage filters and cores (the perceived middle of the object) as encoders of the across-object relationship. The stimuli were "rectangles" with sinusoidally modulated long edges. Manipulating the frequency and relative phase of the edge modulation allowed us to assess the importance of the across-object relationships. Evidence was found for the importance of such relationships in determining perceived orientation. No evidence was found for direct use of the distribution of responding edge detectors.

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BibTeXRIS

C A Burbeck, G S Zauberman. 1997. Across-object relationships in perceived object orientation.. https://doi.org/10.1016/s0042-6989(96)00232-5

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Direct evidence for the existence of energy-based texture mechanisms.

Two classes of models have been proposed to explain how the visual system processes texture modulations. In 'feature models', abstract representations of the featural properties of local texture regions (eg orientation, spatial frequency, contrast) are first generated, after which differences in individual feature properties across space are detected. In 'energy models', on the other hand, differences across space in the response energies of linear simple-cell-like filters are detected. This model thus processes the existing differences between texture regions directly without generating a full representation of the individual texture regions. We provide here direct evidence for the existence of the second, energy model, using an adaptation paradigm in conjunction with textures simultaneously modulated in two dimensions--orientation and spatial frequency. We found that the mechanism that processed the conjoint modulation was tuned to orientations and spatial frequencies that could not be predicted by any feature model, but which were precisely predicted by the energy model.

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