Abstract
The inverse base-rate effect (IBRE) is an irrational phenomenon in predictive learning characterized by as a preference for rare, unlikely outcomes in the face of ambiguity. This thesis investigates the adequacy of formal explanations for this puzzling phenomenon. In the first project, I will focus on mechanisms of learning that mathematical models posit underlie this preference. A class of attentional explanation produces a counter-intuitive prediction: the effect disappears under concurrent load. I confirm the prediction, but only when participants were under an obvious time constraint -- irrationality reduces under increased task demands. This suggests that multiple learning mechanisms operate independently and are differentially affected by concurrent load. In the second project, I test basic assumptions of the most prominent theories: this irrational bias depends on prediction error. Here, I gradually removed elements of a predictive learning design to test the extent to which error-driven processes underlie this bias. Throughout my attempts, the inverse base-rate effect persisted and remained robust. This outcome suggests that this irrational bias is independent of supervised learning procedures - a big change in the problem structures of the IBRE. In the third project, I look for the most adequate formal computational model of the canonical IBRE. In addition to group-level accommodation, I also incorporate heterogeneity into the benchmark. To accomplish this, I developed g-distance, which incorporates the extent to which models exhibit a similar range of behaviors to the humans they model. Applying it to five models of the IBRE reveals that none of the models outperform a random model. While analyzing the human data, I also discovered that the group-level result was observed in less than 1% of individuals. These projects provide new insight into the IBRE and how we should approach building and evaluating models of the IBRE and associated phenomena. I will discuss these insights in detail and how they influence future research on the IBRE.
Keywords
inverse base-rate effect, computational cognitive science, computational modelling
Document Type
Thesis
Publication Date
2023
Recommended Citation
Dome, L. (2023) Testing the adequacy of formal models of an irrational learning effect. Thesis. University of Plymouth. Retrieved from https://pearl.plymouth.ac.uk/psy-theses/74
