Visual motion integration is mediated by directional ambiguities in local motion signals - Rocchi, Francesca; Ledgeway, Timothy; Webb, Ben S. (Frontiers in Computational Neuroscience, 2013)
The output of primary visual cortex (V1) is a piecemeal representation of the visual scene and the response of any one cell cannot unambiguously guide sensorimotor behavior. It remains unsolved how subsequent stages of cortical processing combine (‘pool’) these early visual signals into a coherent representation. We (Webb et al., 2007, 2011) have shown that responses of human observers on a pooling task employing broadband, random dot motion can be accurately predicted by decoding the maximum likelihood direction from a population of motion-sensitive neurons. Whereas Amano et al. (2009) found that the vector average velocity of arrays of narrowband, two-dimensional (2-d) plaids predicts perceived global motion. To reconcile these different results, we designed two experiments in which we used 2-d noise textures moving behind spatially distributed apertures and measured the point of subjective equality between pairs of global noise textures. Textures in the standard stimulus moved rigidly in the same direction, whereas their directions in the comparison stimulus were sampled from a set of probability distributions. Human observers judged which noise texture had a more clockwise global direction. In agreement with Amano and colleagues, observers’ perceived global motion coincided with the vector average stimulus direction. To test if directional ambiguities in local motion signals governed perceived global direction, we manipulated the fidelity of the texture motion within each aperture. A proportion of the apertures contained texture that underwent rigid translation and the remainder contained dynamic (temporally uncorrelated) noise to create locally ambiguous motion. Perceived global motion matched the vector average when the majority of apertures contained rigid motion, but with increasing levels of dynamic noise shifted towards the maximum likelihood direction. A class of population decoders utilizing power-law nonlinearities can accommodate this flexible pooling.