Perceptual confidence is an evaluation of the validity of our perceptual decisions. We present here a complete generative model that describes how confidence judgments result from some confidence evidence. The model that generates confidence evidence has two main parameters, confidence noise and confidence boost. Confidence noise reduces the sensitivity to the confidence evidence, and confidence boost accounts for information used for confidence judgment which was not used for the perceptual decision. The opposite effect of these two parameters creates a problem of confidence parameters indeterminacy, where the confidence in a perceptual decision is the same in spite of differences in confidence noise and confidence boost. When confidence is estimated for multiple stimulus strengths, both of these parameters can be recovered, thus allowing us to estimate whether confidence is generated using the same primary information that was used for the perceptual decision or some secondary information. We also describe a novel measure of confidence efficiency relative to the ideal confidence observer, as well as the estimate of one type of confidence bias. Finally, we apply the model to the confidence forced-choice paradigm, a paradigm that provides objective estimates of confidence, and we discuss how each parameter of the model can be recovered using this paradigm.