{"title":"Bayesian Modeling of the Mnemonic Similarity Task Using Multinomial Processing Trees.","authors":"Michael D Lee, Craig E L Stark","doi":"10.1007/s41237-023-00193-3","DOIUrl":null,"url":null,"abstract":"<p><p>The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a \"similar\" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.</p>","PeriodicalId":39640,"journal":{"name":"Behaviormetrika","volume":"1 1","pages":"517-539"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936565/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behaviormetrika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41237-023-00193-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0
Abstract
The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.
期刊介绍：
Behaviormetrika is issued twice a year to provide an international forum for new theoretical and empirical quantitative approaches in data science. When Behaviormetrika was launched in 1974, the journal advocated data science, as an interdisciplinary field that included the use of statistical methods to extract meaningful knowledge from data in its various forms: structured or unstructured. Behaviormetrika is the oldest journal addressing the topic of data science. The first editor-in-chief of Behaviormetrika, Dr. Chikio Hayashi, described data science in this way:“Data science is not only a synthetic concept to unify statistics, data analysis, and their related methods; it also comprises its results. Data science is intended to analyze and understand actual phenomena with ‘data.’ In other words, the aim of data science is to reveal the features or the hidden structure of complicated natural, human, and social phenomena using data from a different perspective from the established or traditional theory and method.” Behaviormetrika is a fully refereed international journal, which publishes original research papers, notes, and review articles. Subject areas suitable for publication include but are not limited to the following methodologies and fields. Methodologies Data scienceMathematical statisticsSurvey methodologiesArtificial intelligence Information theoryMachine learning Knowledge discovery in databases (KDD)Graphical modelsComputer scienceAlgorithms FieldsMedicinePsychologyEducationEconomicsMarketingSocial scienceSociologyPolitical sciencePolicy scienceCognitive scienceBrain science
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
