Fig. 16.7  Reading accuracies (top) and speeds (bottom) for two stabilization (free-locked) and two contrast (low-high) conditions [23]. Trials were presented in six sets (1–6), each composed of three consecutive blocks of 16 trials (A–C). Each set took between one and three 1-h sessions to complete. Error bars denote the between-subject standard deviation among five subjects. The “low vision” points represent the performance of the one subject with severely reduced visual acuity and contrast sensitivity. Notice the effect of practice in on gaze-locked performance

prosthesis-wearer can perceive [5]. Without any dropout, arrays producing about 600 distinct percepts should be enough to allow read rates of about 50–70 words/ min.

16.4  Face and Object Recognition

Dagnelie et al. published the first study involving a recognition task in 2001 [14]. In their experiment, subjects were asked to identify pixelized faces 12° wide among four options. The number of dots in the grid simulation, percentage of dot dropout, and the number of gray levels strongly affected subjects’ ability to recognize these faces. Instances where grid parameters reduced the sampling frequency below 8

cycles per face width, grid size fell below 256 dots and 7 deg2, dropout exceeded 50%, or when fewer than four gray levels were used, caused face recognition abilities to fall to chance. Recognition accuracy of 80% or greater was consistently possible only in 98% contrast conditions, specifically with a grid size of 625 dots spanning 11° × 11°, or a grid of 256 dots about 70¢ wide each.

Thompson et al., of the same Johns Hopkins University group, with collaborators from the University of Southern California, published a very similar study to the one above in 2003 [30]. In their 2001 experiment [14], however, they used a dot size of 23¢ of visual field as a baseline parameter, whereas the base dot size was increased to 31.5¢ in their 2003 experiment. Among other factors, this increased the basic grid size from 7° × 7° to 9.6°× 9.6°. While no parameter combination generated an average accuracy of 80% or more when contrast was set to 12.5%, several parameter conditions with 99% contrast did allow facial recognition with 80% or more accuracy. Based on their results, it appeared that a dot density around 1 dots/deg with 4.5-arcmin dot spacing was optimal among their parameter combinations. Increasing the grid size from 256 dots to 625 and 1,024 dots also consistently improved recognition accuracy.

Hayes et al. [21] also reported on object recognition using prosthetic vision simulations. Using grids with 4 × 4, 6 × 10, or 16 × 16 dots, with various levels of contrast and dynamic noise, subjects were asked to visually describe and, if possible, identify common objects without touching them. Contrast and noise did not appear to have significant effects, but grid size did. The 4 × 4 and 6 × 10 grids had the same dot size and spacing, but the 6 × 10 grid provided a significant advantage over the 4 × 4 grid. This would suggest that grid size, measured by dot count or visual span, is important for recognition tasks. The 16 × 16 grid was significantly more useful for object recognition than the 6 × 10 grid, but this could also be an effect of increased dot density.

Dagnelie et al. published a study on visual discrimination of white target squares on a black background (“modified checkerboard”) in 2006 [15]. While this study did not ask subjects to recognize specific features of these targets, it did evaluate the subjects’ abilities to discern and count these targets when gaze-locking was employed. For most of the subjects, the time to count all the targets on a modified checkerboard did not change with the number of targets, as a result of their counting strategies, but the addition of gaze-locking did significantly increase counting time, particularly before practice. Srivastava et al., of the Illinois Institute of Technology in cooperation with Dagnelie et al., published similar experiments with this counting task in 2009 by simulating a cortical prosthesis [29]. In these experiments, gazelocking was enforced consistently and levels of dropout varied so that 325–650 dots were used. Search times were comparable with the 2006 study [15], after practice, and levels of dropout did not seem to affect the performance of most subjects.

Zhao et al., of Shanghai Jiao Tong and Peking Universities in China, reported results of testing subjects with object and scene recognition tasks in 2008 [34] and 2010 [33]. Subjects could freely view 4.5° × 4.5° grids of either square or circular dots with various dot densities and two different methods of image processing: binary (black–white) output through contrast enhancement, and edge detection.