Visual motion provides a rich source of information about the movement of objects in the world and about the observer's own movement (Koenderink & van Doorn,
1991; Longuet-Higgins & Prazdny,
1980). In order to correctly extract this information, motion signals must be correctly segmented and integrated (Braddick,
1993). At a local level, even the motion of a single object becomes ambiguous due to the aperture problem (Marr & Ullman,
1981; Wallach,
1996). This ambiguity arises as any motion of an extended contour or luminance gradient parallel to its orientation will produce no change in the image. The component of motion parallel to the orientation is rendered undetectable, and so we can refer to this local, ambiguous motion as a 1-D motion. Although 1-D movement does not determine the underlying 2-D translation, it does impose certain geometrical constraints on the set of potential solutions to the 2-D velocity (Adelson & Movshon,
1982). The set of all possible 2-D motions that could generate a given 1-D motion lies on a line in velocity space, and the set of all 1-D local motions that could arise from a rigidly translating 2-D contour lies on a circle through the origin in velocity space.
Amano, Edwards, Badcock, and Nishida (
2009) have shown that human observers can integrate motion signals from multi-Gabor arrays to produce a robust and accurate estimation of global motion. Each Gabor element has a different orientation and hence a different 1-D motion but references the same underlying 2-D motion. Extracting the correct underlying motion given the geometry of the array becomes a simple matter of finding the intersection of constraints for multiple 1-D signals. It is still a matter of debate whether the visual system makes full use of the geometric constraint. Other methods of motion integration have been proposed based on the vector average or sum (Bowns & Alais,
2006; Yo & Wilson,
1992), feature tracking (Bowns,
1996), or mechanisms based on combining information from first- and second-order motion channels (Derrington, Badcock, & Holroyd,
1992). A more recent analysis points to the harmonic vector average as a viable integration scheme (Johnston & Scarfe,
2013). Although the exact method of integration is still under investigation, the need for motion integration over space is undeniable. It should be noted that, here, by motion integration, we are generally referring to the process by which the inherent ambiguity of 1-D signals is resolved to recover the underlying 2-D motion. This is different from motion integration used in reference to the unambiguous motion of 2-D stimuli, such as dots, by which it describes the process of assigning one overall 2-D motion to a set of moving elements, which may or may not have the same physical 2-D motion (Dakin, Mareschal, & Bex,
2005; Webb, Ledgeway, & Rocchi,
2011).
For rigid frontoparallel translations, the motion constraints depend solely on the speed and direction of the 1-D velocity components and are independent of location in the visual field (Jasinschi, Rosenfeld, & Sumi,
1992; Schunck,
1989), i.e., we can throw away position information and still compute the global motion. However, for more complex situations, such as multiple objects, global rotations and expansions, and transparent motion, the spatial arrangement of motion signals must be taken into account (Durant, Donoso-Barrera, Tan, & Johnston,
2006; Zemel & Sejnowski,
1998). Accordingly, a number of motion-processing models make explicit use of the spatial distribution of motion signals (Grossberg, Mingolla, & Viswanathan,
2001; Liden & Pack,
1999). These models tend to assume a small, isotropic pooling region in which motion signals from both line segments and line terminators are grouped primarily on the basis of their location.
The effect of stimulus configuration on detection of contours has been extensively studied (Field, Hayes, & Hess,
1993). For static Gabor elements, detection performance increases with the number of elements, element alignment, and similarity of phase and spatial frequency (Bex, Simmers, & Dakin,
2003). Hayes (
2000) showed that detection was based on perceived alignment rather than physical alignment by introducing motion-based position shifts into the elements. The detection of these contours is thought to be accomplished by biased connections between local detectors favoring spatially aligned neurons tuned to similar orientations often referred to as an association field (Field et al.,
1993). However, longer range interactions also need to be considered (Loffler,
2008).
Bex et al. (
2003) have shown that the human visual system can correctly integrate local direction signals into moving contours. Their array elements consisted of Laplacian dots to ensure the grouping cue was provided by the direction of motion rather than the contour orientation. They found that the direction of motion of each local element, relative to the spatial arrangement of the contour, had little effect on the contour's detectability. However, because the stimuli used in these experiments were band-pass filtered dots, which have well-defined 2-D movements, the visual system can, in theory, extract the direction and speed of motion of each element unambiguously. Amano et al. (
2009) have shown global motion integration can be quite different for 1-D (Gabor) and 2-D (plaid) elements. In particular, 2-D plaid elements chosen to have the same velocity as the normal component of a corresponding set of Gabor array elements do not appear to cohere effectively into a single moving surface, and the perceived speed is much lower. The expected average normal component velocity of a global Gabor array is half the speed of the true global velocity. If the local motion of a Gabor array with a single global speed is integrated perfectly, for example, by means of an intersection of constraints (IOC) operation, the perceived speed should match the true global speed. If perceived speed is less than the true global velocity, this implies less than perfect integration.
We examined 1-D motion integration over space by measuring the perceived 2-D motion of multielement Gabor arrays by comparison with locally unambiguous plaid arrays. In the first experiment, we found that the perceived speeds of Gabor arrays depended on the spatial arrangement of the individual elements relative to their single global motion direction. When global motion was parallel to the direction of spatial orientation of linear Gabor arrays, the perceived speed of motion was greater than when the global motion was orthogonal to the spatial orientation of the array. In the second experiment, we found similar results with more complex motion, i.e., rotation and expansion. For circular Gabor arrays, the perceived speed of global rotation was seen as faster than expanding Gabor arrays when measured against the corresponding plaid arrays. This is again consistent with motion orthogonal to the spatial arrangement being perceived as slower than motion parallel to the global contour, indicating enhanced motion integration for global motion aligned with the spatial configuration.
Other studies have found a difference in perceived speed between translation, rotation, and expansion (Bex & Makous,
1997; Bex, Metha, & Makous,
1998; Geesaman & Qian,
1996). In a final control experiment, we used larger multielement arrays and found no integration-dependent reductions in perceived speed for these global motion patterns. The results of Experiments 1 and 2 therefore point toward a reduction in the degree of motion integration when the direction of global motion is orthogonal to the spatial arrangement of the 1-D motions. We conclude that a higher-order property, namely global motion, that is not available to neurons early in the visual pathway, must be influencing how the information from these neurons is integrated.