Abstract
Several studies reported that stereothresholds assessed with local-contour stereograms and complex random-dot stereograms (RDSs) are different. Dissimilar thresholds may be due to differences in the properties of the stereograms (e.g., spatial frequency content, contrast, inter-element separation, area) or to different underlying processing mechanisms. This study examined the transfer of perceptual learning of depth discrimination for local RDSs to global RDSs with similar properties, and vice versa. If global and local stereograms are processed by separate neural mechanisms, then the magnitude and rate of training for the two types of stimuli are likely to differ, and the transfer of training from one stimulus type to the other should be minimal. Based on the results of a previous study, we chose 3.7-deg RDSs with element densities of 1.15% and 15% to serve as the local and global stereograms, respectively. Fourteen inexperienced subjects with normal binocular vision were randomly assigned to either a local- or global-RDS training group. Stereothresholds for both stimulus types were measured before and after 7700 training trials (10 sessions X 10 blocks X 77 trials). Each subject's stereothresholds were normalized to the pre-training measurement for the trained condition and the average data were fit with an exponential equation. Stereothresholds for the trained condition improve for approximately 3000 trials, by approximately 0.23 log units for local and 0.15 log units for global RDSs, and level off thereafter. Neither the rate nor the magnitude of improvement differ statistically between the local- and global-training groups. Further, no significant difference exists in the amount of improvement on the trained vs. the untrained targets for either training group. These results are consistent with the operation of a single mechanism to process both local and global stereograms.
We thank Hope Queener, Scott Stevenson, and Girish Kumar for assistance with programming. Support was provided by P30 EY07551 and VRSG award from the University of Houston.