Fig 1.
Measurement of reaction times.
The random dot stereograms at the top of the figure can be viewed using simple red-green goggles. The cyclopean checkerboard appears in near disparity if the red and green filters are in front of the left and right eyes, respectively. Disparity turns into far if the filters are reversed. Note that this is just an illustration of concept for the reader, in the real experiment, left and right channels were separated by circularly polarizing filters and the pattern of random dots was updated at 60 Hz frequency. The images show one frame each of the background (left) and the target (right) condition. Reaction time was measured with millisecond accuracy from the first frame of the target (red tick marks) until the response button was pressed.
Table 1.
Distribution of the number of trials with valid RTs for near disparities.
Table 2.
Distribution of the number of trials with valid RTs for far disparities.
Fig 2.
Mean reaction times for near disparity values at two (10% and 90%) contrast levels.
Each data point represents the mean of 15 participants (at least 133 RTs); error bars show ±SEM. RTs formed statistically homogeneous groups for each contrast level. While the RTs (filled circles) were not significantly different from the shortest mean RT (370 ms for 10% and 317 ms for 90%), RTs signed open circles were not significantly different from the longest mean RTs (495 ms for 10% and 373 ms for 90% contrast), except 7.3 arc min at 10% contrast. Solid black curves show best fit 2nd order polynomial functions (R2 = 0.867, min. value = 16.3 arc min, equation = 186 * x2 − 450 * x + 654 or 10% and R2 = 0.833, min. value = 20.2 arc min, equation = 79 * x2 − 206 * x + 464 for 90% contrast).
Fig 3.
Mean reaction times for far disparity values at two contrast levels (10% and 90%).
Each data point represents the mean of 15 participants (at least 141 RTs); error bars show ±SEM. Asterisk marks significant difference (p<0.05) found in pairwise comparisons between the lowest disparity and disparity with the shortest mean RT at both contrast. The mean RTs for near disparities (from Fig 2) are superimposed for comparison by gray lines. Second order polynomial fits are represented by solid black curves (R2 = 0.592, min. value = 19.8 arc min, equation = for 10% and R2 = 0.422, min. value = 19 arc min, equation =
for 90% contrast).
Fig 4.
The difference between reaction times to near and far disparity DRDS checkerboards at 10% and at 90% contrast.
(A) Median reaction times of participant MAG for near (solid lines) and far (broken lines) disparity DRDS checkerboards at 10% contrast. Each data point represents the median of up to 10 valid responses. This participant responded slower to far targets of all disparity magnitudes except for the largest one. (B) The difference between reaction times to near and far disparity DRDS checkerboards at 10% contrast for all participants. Data of participant MAG (shown in A) are plotted by solid black line and open circles; the remaining participants are plotted by gray lines. Note that reaction times to near disparities were always shorter in the middle of the tested disparity range. (C) The same as B for 90% contrast.
Fig 5.
The difference between ΔRTs to near and far disparity.
(A) Differences of mean RT values between 10% and 90% contrast (ΔRT) for near disparities. The data points represent means of 15 participants, error bars show ±SEM. The best fit 2nd order polynomial function (solid black curve) is shown (R2 = 0.821). (B) The same as A for far disparities, (R2 = 0.62).
Fig 6.
Representation of the RT-based contrast gain k-1 for near and far disparities.
Solid line shows the near and the dashed line the far disparities. Asterisks mark disparity values where the contrast gains for near and far stimuli were significantly different (*p<0.05, paired t-test of log transformed data). The data points represent means of 15 participants, error bars show ±SEM.