{"title":"True and false recognition in MINERVA 2: Extension to sentences and metaphors","authors":"J. Nick Reid, Randall K. Jamieson","doi":"10.1016/j.jml.2022.104397","DOIUrl":null,"url":null,"abstract":"<div><p>Arndt and Hirshman (1998) used MINERVA 2 to simulate true and false recognition in DRM-style lists and found that the model was able to capture many features of the empirical data. Here, we first replicate their simulations, but using empirically structured vectors derived from Latent Semantic Analysis rather than the randomly generated vectors characteristic of MINERVA 2. We report that the model still captures the DRM effect with fewer free parameters. We then extend our analyses to true and false recognition for full sentences and metaphorical expressions. Using a simple bag-of-words representation for sentences, we find that the MINERVA 2 model captures classic sentence false recognition findings from Bransford and Frank (1971) and a more recent finding from Reid and Katz (2018a) that demonstrates false recognition of unstudied sentences that share a metaphorical but not literal theme to studied sentences. These simulations provide evidence that an instance-based memory model, when amalgamated with structured semantic representations from a distributional semantic model, can account for true and false recognition across different types of language experiences.</p></div>","PeriodicalId":16493,"journal":{"name":"Journal of memory and language","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of memory and language","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0749596X22000845","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LINGUISTICS","Score":null,"Total":0}
引用次数: 4
Abstract
Arndt and Hirshman (1998) used MINERVA 2 to simulate true and false recognition in DRM-style lists and found that the model was able to capture many features of the empirical data. Here, we first replicate their simulations, but using empirically structured vectors derived from Latent Semantic Analysis rather than the randomly generated vectors characteristic of MINERVA 2. We report that the model still captures the DRM effect with fewer free parameters. We then extend our analyses to true and false recognition for full sentences and metaphorical expressions. Using a simple bag-of-words representation for sentences, we find that the MINERVA 2 model captures classic sentence false recognition findings from Bransford and Frank (1971) and a more recent finding from Reid and Katz (2018a) that demonstrates false recognition of unstudied sentences that share a metaphorical but not literal theme to studied sentences. These simulations provide evidence that an instance-based memory model, when amalgamated with structured semantic representations from a distributional semantic model, can account for true and false recognition across different types of language experiences.
期刊介绍:
Articles in the Journal of Memory and Language contribute to the formulation of scientific issues and theories in the areas of memory, language comprehension and production, and cognitive processes. Special emphasis is given to research articles that provide new theoretical insights based on a carefully laid empirical foundation. The journal generally favors articles that provide multiple experiments. In addition, significant theoretical papers without new experimental findings may be published.
The Journal of Memory and Language is a valuable tool for cognitive scientists, including psychologists, linguists, and others interested in memory and learning, language, reading, and speech.
Research Areas include:
• Topics that illuminate aspects of memory or language processing
• Linguistics
• Neuropsychology.