First we assessed the absolute color error for the objects in the post-test. Color error in the post-test varied across similarity conditions (F(2,44) = 4.03, p = 0.025).  Color error was higher for low similarity objects compared to  color error in the moderate similarity (t(22) = 2.44, p=0.023) and high similarity (t(22) = 2.13, p = 0.044) conditions. Color error did not differ between the moderate and high similarity conditions (t(22) = 0.18, p = 0.86). However, we were most interested in whether there was a bias in color errors relative to the competing color. Critically, color bias highly varied by similarity condition (F(2,44) = 10.11, p = 0.0002). While there was a strong repulsion bias away from the competing color in the high similarity condition (t(22) = 4.31, p = 0.0003), there was no bias in either the moderate similarity (t(22) = -0.67, p = 0.51) or low similarity (t(22) = -0.03, p = 0.97) conditions. Thus participants systematically exaggerated the color of the object away form the competing color when competition between the colors was highest.  This repulsion effect persisted after a 24 hour delay. During the Day 2 post-test,  color bias still varied by similarity condition (F(2,42) = 9.82, p = 0.0003) with a selective repulsion bias in the high similarity condition (t(22) = 4.31, p = 0.0002; moderate and low similarity ps>0.51).  
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Next we turned to the critical post test to assess if and how color memory for the objects was distorted over learning. After completing all 8 rounds of study, color error in the post-test marginally varied across similarity conditions (p=0.073).  Color error was lower for moderate similarity objects compared to low similarity (p=0.037) and high similarity color error did not differ from either moderate (p=0 .62) or low similarity (p-0.13)  color error. However, we were most interested in whether there was a bias in color errors relative to the competing color. We quantified the bias in color estimates two ways. First, we signed the distance of each error depending on whether the color estimate was positioned towards the competing color (positive error) or away from the competing color (negative error). Thus, any deviation in this measure from zero would indicate a bias in color memory relative to the competing color with positive values reflecting an attraction towards and negative values reflecting a repulsion away from the competing color. Second, we calculated the percentage of trials in which the estimate fell away from the competing color. Similarly, any deviations in the measure from 50% would reflect a bias in color memory. While signing the error allowed for an estimate of the effect size (in degrees) of the distortion of the competing colors in color space,  the percent error estimate was more robust to outliers in color memory as this measure was less influenced by extreme values in color error. Critically, both measures of color bias scores highly varied by similarity condition (percent away: p=0.0002, signed error: p=0.0004). While there was a strong color bias away from the competing color in the high similarity condition (percent away: signed error: p=0.002), there was no bias in either the moderate (percent away: p=0.33 , signed error: p=0.22) or low similarity (percent away: p=0.92, signed error: p= 0.20) conditions. Thus participants systematically exaggerated the color of the object away form the competing color when competition between the colors was highest.  This effect persisted after a 24 hour delay. Color bias still varied by similarity condition (STAT) with a selective repulsion bias in the high similarity condition on the Day2 post-test. 
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Next we tested the extent to which this repulsion bias in color estimates was adaptive to memory performance. Specifically we asked if the degree of exaggeration between the competing colors in the high similarity condition was related to improved discrimination performance on the association test. To test this we correlated each subject's repulsion bias during the post test with their average target selection rate in the last 3 rounds of the associative memory test (Figure 2.2E). In support of an adaptive benefit of the repulsion, we found a positive correlation between the repulsion bias in the post-test and performance at discriminating between the objects during the association test (r = 0.62, p = 0.002). Thus subjects who showed greater repulsion of the similar colors in memory demonstrated less interference between those corresponding objects. We next wanted to rule out the possibility that repulsion of the colors didn't improve participants discrimination performance over learning but rather the subjects that initially perceived the competing colors as being more different due to perceptual noise were the ones who exhibited better discrimination performance. If difference in perception explain variability in discrimination performance then we would expect the repulsion bias to predict discrimination performance at the start of learning. However, we found no correlation between the repulsion bias and discrimination performance in the association test averaged across the first three rounds of learning (r = 0.23, p = 0.29). 
further confirming that the learned distortion in colorspace explained the improvement in discrimination performance.  

The limits of repulsion

In Experiment 1 we found maximal repulsion in the condition with the greatest similarity between colors (24o). However, the degree of repulsion might not monotonically increase as similarity between the colors increases. If similarity between the colors becomes too great there might be a point where repulsion dissipates. To test wether there is a limit to repulsion we ran another experiment in which we introduced an ultra similarity condition in which the colors of the object pairs were only separated by 6(Figure 3A). In addition to the ultra similarity condition we kept the high and moderate similarity conditions as comparison points. This also gave us another opportunity to replicate the repulsion bias in the high similarity condition found in Experiment 1. 
In the associative memory test subjects learned to choose the target face over the learning rounds (F(1,37) = 326.9, p <0.0000001; Figure 2.3A) such that by the end of learning they reliably chose the target faces over the competitor faces in all similarity conditions (ps <0.0000001). However, as in Experiment 1, discrimination difficulty between the colors varied by similarity condition (F(2,74) = 129.9, p <0.00000001). Average target selection rates were lower in the ultra similarity condition compared to both the high similarity (t(37) = -11.39, p <0.00000001)and moderate similarity conditions (t(37) = -16.26, p <0.00000001). And average target selection rate was lower in the high similarity condition compared to the moderate similarity condition (t(37) = -4.34, p = 0.0001). Thus, increasing the similarity between colors increased the discrimination difficulty between them. Turning toward the color memory test, color error for the objects steadily decreased over learning across all conditions (F(1,37) = 186.5, p <0.0000001; Figure 2B) suggesting participants were able to learn each object's associated color. 
COLOR REPULSION PARAGRAPH