{"id":{"repo_id":"plymouth","oai_identifier":"oai:pearl.plymouth.ac.uk:psy-theses-1073"},"canonical_url":"https://search.dev.ndltd.org/etd/plymouth/oai:pearl.plymouth.ac.uk:psy-theses-1073","repository":{"repo_id":"plymouth","name":"University of Plymouth","base_url":"https://pearl.plymouth.ac.uk/do/oai"},"display":{"title":"Testing the adequacy of formal models of an irrational learning effect","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Dome, Lenard"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Andy Wills, Nadège Bault"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T03:50:32Z","subjects":["inverse base-rate effect","computational cognitive science","computational modelling"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10026.1/21341"],"render_values":[{"text":"10026.1/21341","href":null,"code":true}]}]},"links":{"outbound_url":"https://pearl.plymouth.ac.uk/psy-theses/74","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Andy Wills, Nadège Bault"]},{"key":"dc:creator","label":"Author","values":["Dome, Lenard"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023-01-01T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["inverse base-rate effect","computational cognitive science","computational modelling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10026.1/21341","https://pearl.plymouth.ac.uk/psy-theses/74"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Testing the adequacy of formal models of an irrational learning effect"]}]}],"canonical_facts":{"dc:contributor":["Andy Wills, Nadège Bault"],"dc:creator":["Dome, Lenard"],"dc:date.issued":["2023-01-01T08:00:00Z"],"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."],"dc:identifier":["10026.1/21341","https://pearl.plymouth.ac.uk/psy-theses/74"],"dc:language":["eng"],"dc:subject":["inverse base-rate effect","computational cognitive science","computational modelling"],"dc:title":["Testing the adequacy of formal models of an irrational learning effect"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:50:32Z"}