Maarten A Hogervorst | PubMed Health

PubMed2004

Combining cues while avoiding perceptual conflicts.

A common assumption in cue combination models is that small discrepancies between cues are due to the limited resolution of the individual cues. Whenever this assumption holds, information from the separate cues can best be combined to give a single, more accurate estimate of the property of interest. We examined whether information about the discrepancy itself is lost when this is done. In our experiments, subjects were required to combine cues to match certain properties while avoiding perceptual conflicts. In part 1, they combined expansion and change in disparity to estimate motion in depth; and in part 2, they combined perspective and binocular disparities to estimate slant. We compared the pattern in the way that subjects set the two cues with the patterns predicted by models of cue combination with and without a loss of information about the discrepancy. From this comparison we conclude that little information about the discrepancies between cues is lost when the cues are combined.

Cues↗

PubMed2003

Pooling speed information in complex tasks: estimation of average speed and detection of nonplanarity.

To gain insight into how speeds are combined in structure-from-motion, we compared performance for estimating the mean speed and performance for detecting deviations from planarity. The stimuli showed a center dot surrounded by an annulus of dots. In one (plane) condition, the stimuli simulated a rotating plane. In a two alternative forced choice (2AFC) task, the subject had to choose in which of two stimuli the center dot moved in the plane. In another (cloud) condition, the same dot locations and speeds were used but now assigned to different dots. Such a stimulus resembles a translating and rotating cloud of dots. In this case, the subject had to choose the stimulus in which the center dot moved with the mean speed of the surrounding dots. Performance was measured as a function of deformation/slant. Although location and speeds were the same in both conditions, performance was much poorer in the cloud condition. Subsequent experiments and an ideal observer model point to a plausible explanation: in detecting deviations from planarity, the visual system can focus on the most reliable pieces of information (the slower dots, closest to the test dot). Although performance could benefit by taking more dots into account, performance barely improved with an increase in the number of dots. This may reflect a limited processing capacity of the visual system.

Humans↗