The "wisdom of the crowd" phenomenon is when an aggregated group answer to a problem is more accurate than the answer of individuals in the group. Traditionally, aggregation is done with simple statistical methods such as the mean or median of all people's answers. While these methods can be effective, they don't allow us to learn about how people make their decisions. Such cognitive processes are not evident from summary statistics. However, we can use cognitive modeling to infer the mechanisms that lead to a person's decision. In this dissertation, I show how cognitive models can be a powerful tool in accounting for individual differences and biases as well as in helping generate better crowd decisions. I first discuss a hierarchical model for aggregating people's estimates of probabilities, in an environment for which we know the true answers as well as in a predictive environment for which we don't know the truth. I demonstrate how using this model, we can identify the experts in the crowd and account for biases in probability perception. I move on to applying the "wisdom of the crowd" idea to the field of category learning. First, I establish that taking the modal response in a categorization task is an effective and accurate crowd measure. I then implement two prominent cognitive models of categorization into a Bayesian framework and apply them to existing category learning data sets. I show how we can learn about individual differences in strategy use and apply these inferences to development of a novel, latent-mixture cognitive model that allows for people to use multiple types of categorization strategies within the same data set. I conclude by discussing the implications of this research for the study of aggregation in the "wisdom of the crowd" effect and in the study of individual differences in decision-making.