Testing the adequacy of formal models of an irrational learning effect
dc.contributor.supervisor | Wills, Andy | |
dc.contributor.author | Dome, Lenard | |
dc.contributor.other | School of Psychology | en_US |
dc.date.accessioned | 2023-09-25T13:49:04Z | |
dc.date.available | 2023-09-25T13:49:04Z | |
dc.date.issued | 2023 | |
dc.identifier | 10557964 | en_US |
dc.identifier.uri | https://pearl.plymouth.ac.uk/handle/10026.1/21341 | |
dc.description.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. | en_US |
dc.language.iso | en | |
dc.publisher | University of Plymouth | |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.subject | inverse base-rate effect | en_US |
dc.subject | computational cognitive science | en_US |
dc.subject | computational modelling | en_US |
dc.subject.classification | PhD | en_US |
dc.title | Testing the adequacy of formal models of an irrational learning effect | en_US |
dc.type | Thesis | |
plymouth.version | publishable | en_US |
dc.identifier.doi | http://dx.doi.org/10.24382/5096 | |
dc.identifier.doi | http://dx.doi.org/10.24382/5096 | |
dc.rights.embargoperiod | No embargo | en_US |
dc.type.qualification | Doctorate | en_US |
rioxxterms.funder | ESRC/SWDTP | en_US |
rioxxterms.identifier.project | £87510.62 | en_US |
rioxxterms.version | NA | |
plymouth.orcid.id | 0000-0001-7487-1974 | en_US |
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