Edward A Cranford, Christian Lebiere, Cleotilde Gonzalez, Palvi Aggarwal, Sterling Somers, Konstantinos Mitsopoulos, Milind Tambe
{"title":"Personalized Model-Driven Interventions for Decisions From Experience.","authors":"Edward A Cranford, Christian Lebiere, Cleotilde Gonzalez, Palvi Aggarwal, Sterling Somers, Konstantinos Mitsopoulos, Milind Tambe","doi":"10.1111/tops.12758","DOIUrl":null,"url":null,"abstract":"<p><p>Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance-based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance-based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought-Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial-to-trial behavior. We show how model-tracing and knowledge-tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human-machine teaming.</p>","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topics in Cognitive Science","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1111/tops.12758","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance-based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance-based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought-Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial-to-trial behavior. We show how model-tracing and knowledge-tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human-machine teaming.
期刊介绍:
Topics in Cognitive Science (topiCS) is an innovative new journal that covers all areas of cognitive science including cognitive modeling, cognitive neuroscience, cognitive anthropology, and cognitive science and philosophy. topiCS aims to provide a forum for: -New communities of researchers- New controversies in established areas- Debates and commentaries- Reflections and integration The publication features multiple scholarly papers dedicated to a single topic. Some of these topics will appear together in one issue, but others may appear across several issues or develop into a regular feature. Controversies or debates started in one issue may be followed up by commentaries in a later issue, etc. However, the format and origin of the topics will vary greatly.