Lauren F. V. Scharff, Stephen F. Austin State University, Nacogdoches, TX
Albert J. Ahumada, NASA Ames Research Center, Moffett Field, CA
We have been developing an equivalent contrast metric for predicting the effects of textured backgrounds on text readability (Scharff, Ahumada, and Hill, 1999; Scharff, Hill, and Ahumada, 2000; Scharff and Ahumada, 2002; Scharff and Ahumada, 2003a). In the course of assessing the usefulness of the metric for transparent text on textured backgrounds, Scharff and Ahumada (2002) measured text readability for positive and negative contrast text on a plain background at two contrast levels, 30% and 45%. The observer’s task was to find a target word (triangle, circle, or square) in a text paragraph as illustrated in Figure 1.
Figure 1. 45% contrast texts from Scharff and Ahumada (2002), negative (left) and positive (right).
Scharff and Ahumada (2003a) measured the identifiability of the individual letters cut out from those target words. Both studies found better performance for negative contrast stimuli as shown in Figure 2. Scharff and Ahumada (2002) used a measure of contrast that included the text in the background luminance calculation and thus the measure did assign a slightly higher contrast to the negative polarity text. Scharff and Ahumada (2003a) found that the readability index fit the paragraph results significantly better if a polarity factor was included in the index and pointed out that the contrast polarity effect for single letters cannot be sensibly explained by adaptation differences.
Figure 2: Response latencies vs. contrast for the paragraph word search task data (left) of Scharff and Ahumada, (2002) and the letter identification task data (right) of Scharff and Ahumada (2003a). Error bars indicate 95% confidence intervals based on observer variance in each condition.
The underlying cause of the polarity effect could be the apparatus/stimulus or a visual mechanism. As part of our attempt to determine the underlying cause, here we report additional measurements of the polarity effect at lower contrasts than used by Scharff and Ahumada (2003a). Further, letters were presented using two pixel sizes and the display was more carefully calibrated for the specific monitor used (the previous studies each used several monitors). These results were presented to the Vision Sciences Society (Scharff and Ahumada, 2003b). For letters of the original pixel size, the results indicated that at contrasts of 20% and 40%, positive contrast letters are only as identifiable as negative contrast letters with 70 to 75% as much contrast. With the halved pixel size, the polarity effect was reduced, which could either be due to an apparatus/stimulus effect, or visual mechanism combined with a ceiling effect.
Some possible reasons for the improved performance for negative contrast text include the following. The first two address visual mechanisms, while the second two address apparatus/stimulus effects.
Separate gain hypothesis. The positive contrast system has lower gain than the negative contrast system (result opposite that reported by Chan and Tyler (1992)). Beginning with the retinal level, the positive and negative contrast systems have different anatomical structures (Kolb, Fernandez, and Nelson, 2001), thus there is no reason to expect them to have the same gain. Contrast discrimination data has been modeled by assuming that the positive and negative contrast discrimination functions have the same form, but different gains (Whittle, 1986, 1992; Belaïd and Martens, 1998). To fit the discrimination data for different background luminances, the relative gains needed to be a function of luminance.
Fechnerian brightness hypothesis. Another rationale for the separate gains hypothesis is that internal measures of contrast are computed from a “brightness” measure that is negatively accelerating with respect to luminance.
Dark screen matrix hypothesis. Pixels on the screen are made of a small number (~1) of dot triads on a dark matrix. The dark matrix and the blue dots form low luminance boundary regions that are assimilated to the negative contrast regions. This can only occur when the contrast regions are above threshold locally.
Display nonlinearity hypothesis. Single light pixels following a background pixel are more like the background than are dark pixels because video amplifiers have a slower rise time than fall time. This caused the old VT100 text with dim vertical strokes and bright horizontal strokes.
Figure 3: Illustrations of four possible causes of the polarity effect.
Under the separate gain hypothesis, but not the others, the polarity effect should persist at low contrasts, so we measured the effect at lower contrasts. If the polarity effects are not different for the two pixel sizes, then the matrix was not a factor.
Figure 4. The twelve letter images at –40% contrast.
The experiment was implemented on a Macintosh computer using the Psychophysics Toolbox (Brainard, 1997) under Matlab 5.2.1 (http://www.mathworks.com). Observers were asked to identify the Scharff & Ahumada (2003) twelve lower case letters (acegilnqrstu) on a uniform background (Figure 4). For the original, low-resolution stimuli, at their narrowest, the vertical strokes were one pixel wide. For the higher resolution stimuli, the letters were doubled in size and the viewing distance was doubled. A letter remained on screen until the participant typed a response. Within each block of 36 trials, each letter was presented at 3 contrast levels: 10%, 20%, and 40%. For the original, low-resolution stimuli, five observers ran the 5 negative contrast blocks first; six observers ran the 5 positive contrast blocks first. For the higher resolution stimuli, polarity blocks were alternated with ten participants starting with a positive contrast block, and ten starting with a negative contrast block.
The letters were displayed on a NEC AccuSync 50 CRT monitor with a 0.28 mm dot trio pitch at a display resolution of approximately 0.26 mm per pixel (effectively one pixel per dot trio). The viewing distances were about 47 cm and 94 cm (forehead rest).
To test display linearity, we constructed three images and their polarity opposites, photometrically measured their average luminance, and computed their contrast with respect to the background luminance. Two were vertical line images: image pixel columns alternated between the background level (0% contrast) and the +40% contrast level (proportion of contrast pixels 1/2) and image pixel columns sandwiched 3 background pixels (1/4). The third image contained only +40% pixels (1/1). The resulting contrasts, shown below, do not reveal any appreciable nonlinearity.
image 1:1 2:1 4:1
+contrast +38.5% +19.1% +10.1%
-contrast -41.6% -22.0% -10.5%
As Figures 5 and 6 show (accuracy and latency, respectively), both performance measures for the original, low resolution pixel size, were better for the negative contrast conditions at 20% and 40% contrast, but were not different at 10% contrast, even though the accuracy performance was better than chance. However, when pixel size was halved, the polarity difference changed. For accuracy, there was still a significant difference at the 20% contrast, but it dropped at the 40% contrast. A similar trend is seen for the latency data. It is possible that the loss of a significant polarity effect at the highest contrast is the result of ceiling/floor effects for the negative contrast stimuli.
Figure 5: Letter identification accuracy plotted against the absolute value of letter contrast for the original, low-resolution stimuli (left) and the halved pixel size (right). Outer error bars are 95% confidence intervals using the standard deviation for that particular group to allow comparison with chance performance. Inner error bars are based on the pooled subject by treatment interaction for all points.
Figure 6: Letter identification latency plotted against the absolute value of letter contrast for the original, low-resolution stimuli (left) and the halved pixel size (right). Outer error bars are 95% confidence intervals using the standard deviation for that particular group to allow comparison with chance performance. Inner error bars are based on the pooled subject by treatment interaction for all points.
For the 20% and 40% conditions for the original pixel size, we used linear interpolation (in log contrast) to find the contrast of the other polarity with the equivalent accuracy (left graph). This opposite polarity contrast and the original contrast were used
to determine the ratio of the effectiveness of positive contrast to that of negative contrast:
+20%: 0.70, +40%: 0.74,
-20%: 0.75, -40%: 0.72.
The mean of the four estimates is 0.726 ± 0.036 (95% confidence interval).
Figure 7: Letter identification accuracy vs. latency (speed-accuracy trade-off) for the original, low-resolution stimuli. Square symbols indicate conditions run in first group of 5 blocks. Circles indicate second group of 5 blocks. Colors indicate the contrast in percent. Error bars are 95% confidence intervals based on the observer x treatment interaction.
For the original, low resolution stimuli, the speed accuracy trade-off graph (Figure 7) shows that while combined speed-accuracy performance was better in the second set of 5 blocks (circles), the observers did not improve in accuracy; they shortened their latencies instead. In the first blocks (squares), the observers given the more difficult positive contrast task (light squares) took more time to be more accurate, but when this task was second (light circles), observers gave it even less time than was given by the other observers doing the easier task second (dark circles).
For the original, low-resolution stimuli, the polarity effects were significant at the higher (20% and 40%) contrasts. When pixel resolution was doubled, the polarity effect dropped for the highest contrast and the interaction between the resolution and polarity was significant. The trend for better performance with the higher pixel resolution was not quite significant. These results support the dark screen matrix hypothesis, but are also consistent with the separate gains hypothesis and a ceiling effect in accuracy and a floor effect in latency.
The screen calibration results do not support the display nonlinearity hypothesis. We would like more direct measures of luminance modulation depth.
The speed-accuracy trade-off effects were strong, so we plan to redo the contrast ratios using a combined accuracy-latency measure.
As signal detection theory taught us that false alarms are necessary to measure detectability, speed-accuracy trade-off theory should remind us that errors are necessary to estimate performance in the presence of possible trade-off effects.
The Airspace Operations Systems (AOS) Project of NASA's Airspace Systems Program provided funding. NASA Ames Research Center cooperative agreement NCC 2-1095 with the San Jose State University Foundation provided support. We are grateful for the assistance of Ryan Smith, Robin Rustad and Lori Shird.
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