The research in this dissertation investigates the consistency of juror decision models when evaluating incriminating and exonerating evidence. Many stochastic and psychological models indicate that an interaction between a person’s prior beliefs and their evaluation of the evidence contribute to their verdict decision. However, less is known regarding how this interaction occurs for different forms of evidence. In particular, a pervasive assumption is that jurors use the same or similar models to evaluate exonerating and incriminating evidence. The data from this dissertation indicates that this may not be the case. Participants adjust estimates of probability of guilt in a Bayesian inference problem more when case specific evidence is incriminating versus exonerating. Further, their response patterns and reported and observed measures of the type and variety of information they are using to produce probability of guilt estimates indicate that they engage in a process of mental estimation more often than they report that they do. The findings indicate that jurors may potentially use different decision models to evaluate different forms of evidence. Further, the framing of the search for culpability provides a plausible explanation for differences in the decision models that are used. Specifically, a juror’s selection criteria and perceived importance of a given piece of evidence will vary depending on its relevance to their decision task. Thus, asking jurors to estimate likelihoods of guilt may lead to their underutilization of evidence implying innocence.