Transfer of perceptual learning of depth discrimination between local and global stereograms

Abstract

Several previous studies reported differences when stereothresholds are assessed with local-contour stereograms vs. complex random-dot stereograms (RDSs). 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 between local and 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 previous results, we chose RDSs with element densities of 0.17% and 28.3% 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 distributed over 10 sessions. Stereothresholds for the trained condition improve for approximately 3000 trials, by an average of 0.36+/-0.08 for local and 0.29+/-0.10 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.

Copyright 2010 Elsevier Ltd. All rights reserved.

Figures

Figure 1. Stimuli used in the transfer of perceptual learning experiment

Two half views of a pair of upper and lower 223’ × 223’ (a) 28.3% density (b) 0.17% density RDSs, presented side-by-side on the laptop monitor. The upper stereogram in each panel is always a zero disparity reference. The lower test stereogram in each panel contains an offset in the dot locations in the right vs. left half view.

Figure 2. Normalized stereothresholds as a function of the number of training trials

For each observer, stereothresholds measured during training were normalized with respect to the pretraining stereothreshold measurement for the trained condition. The average normalized stereothresholds across subjects for each training session for the sparse (unfilled circles) and dense (filled circles) RDSs were plotted as a function of the number of training trials, and fit with an exponential equation, as shown in 2a. Panels in 2b and 2c present non-normalized stereothresholds plotted as a function of the number of trials for individual subjects who trained using sparse (2b) or dense (2c) RDSs.

Figure 2. Normalized stereothresholds as a function of the number of training trials

For each observer, stereothresholds measured during training were normalized with respect to the pretraining stereothreshold measurement for the trained condition. The average normalized stereothresholds across subjects for each training session for the sparse (unfilled circles) and dense (filled circles) RDSs were plotted as a function of the number of training trials, and fit with an exponential equation, as shown in 2a. Panels in 2b and 2c present non-normalized stereothresholds plotted as a function of the number of trials for individual subjects who trained using sparse (2b) or dense (2c) RDSs.

Figure 2. Normalized stereothresholds as a function of the number of training trials

For each observer, stereothresholds measured during training were normalized with respect to the pretraining stereothreshold measurement for the trained condition. The average normalized stereothresholds across subjects for each training session for the sparse (unfilled circles) and dense (filled circles) RDSs were plotted as a function of the number of training trials, and fit with an exponential equation, as shown in 2a. Panels in 2b and 2c present non-normalized stereothresholds plotted as a function of the number of trials for individual subjects who trained using sparse (2b) or dense (2c) RDSs.

Figure 3. Bar graph demonstrating the average change in the logarithms of the stereothresholds (±1 SE) before and after training for the trained and untrained stimulus conditions

Figure 4. Global pre- and post-training stereothresholds as a function of local pre- and post-training stereothresholds

Pre- (filled circles) and post- (unfilled triangles) training stereothresholds measured with global stereograms are plotted as a function of the stereothresholds measured with local stereograms. Pre-training data are not significantly correlated, whereas the post-training data show a significant correlation. The dotted 1:1 line marks perfect agreement.