Have you ever seen accurate bar graphs portrayed for percent correct data? For other bounded quantities, such as average scores from an ordinal scale (for instance a 19 Likert scale)? It is entirely possible that you have never seen accurate bar graphs of these quantities, because most of these graphs rely on the wrong tools: typically, the mean +/ SD or SEM is shown, or a classic confidence interval of the mean. Why are these techniques wrong? First, they use the mean, which is a nonrobust estimator of central tendency; second, they use the variance, a nonrobust estimator of dispersion; third, they assume symmetry; fourth, the results are not bounded, such that they can span impossible values, for instance percent correct beyond 100%. This is simply impossible: participants cannot be more than 100% correct. Yet, I regularly see articles with error bars beyond 100% correct, and authors, reviewers and editors seem to be ok with that.
How do we fix the problem? They are fourĀ simple answers, and one more elaborate:
 Do not use bar graphs, use scatterplots instead. There is absolutely no reason why you should have to report means + error bars and hide your data.

Use a percentile bootstrap confidence interval – it will not produce boundaries with impossible values and will accommodate asymmetric distributions. If there is skewness or outliers, the mean will produce misleading results – use a robust estimator of central tendency instead, for instance the median or a trimmed mean (Wilcox & Keselman, 2003).

Use a binomial proportion confidence interval such as the Jeffreys interval. A quick google search indicates it is available in several R packages.

Compute d’ instead of percent correct: you will get a measure of sensitivity independent of bias, and on a continuous scale amenable to regularĀ confidence interval calculations.

Use a generalised mixed model, for instance a logit mixed model (Jaeger, 2008).
References
Jaeger, T.F. (2008) Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. J Mem Lang, 59, 434446.
Wilcox, R.R. & Keselman, H.J. (2003) Modern Robust Data Analysis Methods: Measures of Central Tendency. Psychological Methods, 8, 254274.