{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/34245"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/34245","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"Modelling a Fractionated System of Deductive Reasoning over Categorical Syllogisms","abstract":"The study of deductive reasoning has been a major research paradigm in psychology for decades. Recent additions to this literature have focused heavily on neuropsychological evidence. Such a practice is useful for identifying regions associated with particular functions, but fails to clearly define the specific interactions and timescale of these functions. Computational modelling provides a method for creating different cognitive architectures for simulating deductive processes, and ultimately determining which architectures are capable of modelling human reasoning. This thesis details a computational model for solving categorical syllogisms utilizing a fractionated system of brain regions. Lesions are applied to formal and heuristic systems to simulate accuracy and reaction time data for bi-lateral parietal and frontotemporal patients. The model successfully combines belief-bias and other known cognitive biases with a mental models formal approach to recreate the congruency by group effect present in the human data. Implications are drawn to major theories of reasoning.","abstract_html":"The study of deductive reasoning has been a major research paradigm in psychology for decades. Recent additions to this literature have focused heavily on neuropsychological evidence. Such a practice is useful for identifying regions associated with particular functions, but fails to clearly define the specific interactions and timescale of these functions. Computational modelling provides a method for creating different cognitive architectures for simulating deductive processes, and ultimately determining which architectures are capable of modelling human reasoning. This thesis details a computational model for solving categorical syllogisms utilizing a fractionated system of brain regions. Lesions are applied to formal and heuristic systems to simulate accuracy and reaction time data for bi-lateral parietal and frontotemporal patients. The model successfully combines belief-bias and other known cognitive biases with a mental models formal approach to recreate the congruency by group effect present in the human data. Implications are drawn to major theories of reasoning.","abstract_has_math":false,"creators":["Giovannini, Gregory"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Goel, Vinod"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-01","date_published":"2018-03-01","updated_at":"2026-07-24T06:33:36Z","subjects":["Computer science"],"languages":["en"],"rights":["Author owns copyright, except where explicitly noted. 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This thesis details a computational model for solving categorical syllogisms utilizing a fractionated system of brain regions. Lesions are applied to formal and heuristic systems to simulate accuracy and reaction time data for bi-lateral parietal and frontotemporal patients. The model successfully combines belief-bias and other known cognitive biases with a mental models formal approach to recreate the congruency by group effect present in the human data. Implications are drawn to major theories of reasoning."]},{"key":"dc:title","label":"Title","values":["Modelling a Fractionated System of Deductive Reasoning over Categorical Syllogisms"]}]}],"canonical_facts":{"dc:contributor.advisor":["Goel, Vinod"],"dc:creator":["Giovannini, Gregory"],"dc:date.accessioned":["2018-03-01T13:43:34Z"],"dc:date.available":["2018-03-01T13:43:34Z"],"dc:date.issued":["2018-03-01"],"dc:description.abstract":["The study of deductive reasoning has been a major research paradigm in psychology for decades. Recent additions to this literature have focused heavily on neuropsychological evidence. Such a practice is useful for identifying regions associated with particular functions, but fails to clearly define the specific interactions and timescale of these functions. Computational modelling provides a method for creating different cognitive architectures for simulating deductive processes, and ultimately determining which architectures are capable of modelling human reasoning. This thesis details a computational model for solving categorical syllogisms utilizing a fractionated system of brain regions. Lesions are applied to formal and heuristic systems to simulate accuracy and reaction time data for bi-lateral parietal and frontotemporal patients. The model successfully combines belief-bias and other known cognitive biases with a mental models formal approach to recreate the congruency by group effect present in the human data. Implications are drawn to major theories of reasoning."],"dc:identifier.uri":["http://hdl.handle.net/10315/34245"],"dc:language.iso":["en"],"dc:rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"dc:subject":["Computer science"],"dc:title":["Modelling a Fractionated System of Deductive Reasoning over Categorical Syllogisms"],"dc:type":["Electronic Thesis or Dissertation"]},"updated_at":"2026-07-24T06:33:36Z"}