The rapid growth in complex datasets within the field of psychology poses challenges for integrating observations into quantitative models of human information processing. Other fields of research, such as physics, proposed equation discovery techniques as a way of automating data-driven discovery of interpretable models. One such approach is the Bayesian Machine Scientist (BMS), which employs Bayesian inference to derive mathematical equations linking input variables to an output variable. While BMS has shown promise, its application has been limited to a small subset of scientific domains. This study examines the utility of BMS for model discovery in psychology. In Experiment 1, we compare BMS in recovering four models of human information processing against two common psychological benchmark models---linear/logit regression and a black-box neural network---across a spectrum of noise levels. BMS outperformed the benchmark models on the majority of noise levels and demonstrated at least equivalent performance when considering higher levels of noise. These findings demonstrate BMS’s potential for discovering psychological models of human information processing. In Experiment 2, we investigated the impact of informed priors on BMS recovery, comparing domain-specific function priors against a benchmark uniform prior. Specifically, we investigated four priors across research domains spanning their specificity to psychology. We observe that informed priors robustly enhanced BMS performance for only one of the four models of human information processing. In summary, our findings demonstrate the effectiveness of BMS in recovering computational models of human information processing across a range of noise levels; however, whether integrating expert knowledge into the BMS framework improves performance remains a subject of further inquiry.