Defining distinctiveness: A computational and experimental analysis
| dc.contributor.author | Spear, Jackie | |
| dc.contributor.examiningcommittee | Singer, Murray (Psychology) | en_US |
| dc.contributor.examiningcommittee | Ghomeshi, Jila (Linguistics) | en_US |
| dc.contributor.supervisor | Jamieson, Randy (Psychology) | en_US |
| dc.date.accessioned | 2020-12-23T17:05:19Z | |
| dc.date.available | 2020-12-23T17:05:19Z | |
| dc.date.copyright | 2020-12-18 | |
| dc.date.issued | 2020 | en_US |
| dc.date.submitted | 2020-12-18T21:27:48Z | en_US |
| dc.degree.discipline | Psychology | en_US |
| dc.degree.level | Master of Arts (M.A.) | en_US |
| dc.description.abstract | Distinctiveness is a fundamental principle of human memory. However, definitions of distinctiveness have largely remained intuitive and imprecise (Hunt & Worthen, 2006). In Experiments 1 and 2, participants studied critical, distinctive words that were embedded in eight different categorized lists. At test, three different types of lures were presented: distinct related lures, categorical related lures, and unrelated lures. Vector-based representations of word meaning were derived using distributional models of semantics to fit the data. Namely, the Bound Encoding of the Language Environment (Jones & Mewhort, 2007) and Latent Semantic Analysis (Landauer & Dumais, 1997) were employed to derive word meaning from written text. These representations were coupled with an instance-based model of human memory, MINERVA 2 (Hintzman, 1988) to model recognition. The same experimental design as in Experiments 1 and 2 was used in Experiments 3 and 4, where DRM materials replaced the old and new category words, and where LSA and BEAGLE were used to derive the distinctive words, the lures related to those distinctive words, and the subsequent unrelated lures. Lastly, Experiment 5 aimed to formulate a priori predictions for recognition when words were sampled at random. | en_US |
| dc.description.note | February 2021 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/35178 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | computational modelling, semantic distinctiveness, natural language processing, semantic space models, computational linguistics, semantic memory, vector space models, word recognition | en_US |
| dc.title | Defining distinctiveness: A computational and experimental analysis | en_US |
| dc.type | master thesis | en_US |