For the dynamic accommodation calibration experiments, monkeys viewed a 2° × 2° Maltese cross that was back-projected onto a tangent screen at a distance of 60 cm. During the experiments, one of the eyes was occluded with a visible-block infrared-pass filter. The fixating eye viewed the target through corrective trial lenses to correct for distance refractive error only. For each of the monkeys, baseline distance refractive error had been previously measured by an experienced retinoscopist using streak retinoscopy while the monkeys were anesthetized and cyclopleged. During the calibration runs, trial lenses of powers ranging from −2 D to +5.5 D were placed in front of the eye that was occluded with the IR-pass filter (Optical Cast IR Longpass Filter; Edmund Optics, Barrington, NJ). Therefore, the defocus from the trial lens provides no stimulus to the monkey to alter the accommodative state, but a change in the slope of pupil pixel intensity profile was induced. In experiments to determine whether calibration or measurement of refraction was affected by misalignment between the photorefractor induced first Purkinje image and actual center of the pupil, the fixation target was placed at one of nine horizontal or vertical locations (Left 20°, Left 10°, 0°, Right 10°, Right 20°, Up 10°, Up 20°, Down 10°, and Down 15°) to cause systematic misalignments (with respect to the photorefractor) of the eye being used for the calibration.
Processing of video images was performed offline using custom software developed in Matlab (Mathworks Inc., Natick, MA) that incorporated routines available via the image processing toolbox. Image analysis involved identifying the first Purkinje image in each video frame and drawing two vertical lines within the pupil boundary equidistant on either side of the Purkinje image. A linear regression was performed on the pixel intensity values across the vertical lines and the mean slope of the pixel intensity profile was calculated. The slope of the pixel intensity profile is the measure that has previously been shown to be proportional to the refractive state of the eye. Video clips for each fixation condition were approximately 5 to 10 seconds (150 to 300 video frames) long. These were analyzed with the Matlab code to obtain averages and standard deviations of pixel intensity slope values for each fixation condition. Even though the image analysis was automated, the investigator was able to view each stage of the analysis procedure and thereby visually verify that the automated analysis was appropriate. Finally, a linear regression of the mean pixel intensity slope and the dioptric power of the ophthalmic lens yielded the calibration coefficients that convert photorefraction pixel intensity slope measurements to dioptric values. The calibration coefficients developed could then be used in other experiments where the accommodative state was manipulated to obtain an accommodative response curve. Statistical analysis of data included using t-tests and ANOVA to compare calibration coefficients obtained on different days and compare calibration coefficients obtained during straight-ahead viewing and eccentric viewing. A significance value of 0.05 was used for all comparisons.