Personalized Model-Driven Interventions for Decisions From Experience.

IF 2.9 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Topics in Cognitive Science Pub Date : 2024-10-16 DOI:10.1111/tops.12758
Edward A Cranford, Christian Lebiere, Cleotilde Gonzalez, Palvi Aggarwal, Sterling Somers, Konstantinos Mitsopoulos, Milind Tambe
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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.

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个性化模型驱动的经验决策干预。
代表个体的认知模型为理解人类的各种行为提供了许多益处。个体差异的一种表现形式是知识差异。在动态情况下,决策是根据经验做出的,建立在经验选择理论(基于实例的学习理论;IBLT)基础上的模型可以准确预测人类个体的学习和对不断变化的环境的适应能力。在此,我们展示了在认知架构(自适应控制思维--理性)中实施的基于实例学习(IBL)的认知模型如何用于模拟个人在网络安全防御任务中的决策,同时考虑到群体平均值和个体差异。相同的 IBL 模型结构具有相同的架构参数,通过随机记忆检索过程的运行和独特经验的贡献,可生成人类的各种行为。通过递归量化分析,我们可以超越个体之间和个体内部的平均行为,观察试验到试验行为的连续模式。我们展示了如何利用模型追踪和知识追踪技术将模型与个体实时匹配,从而为网络安全防御系统提供自适应和个性化的信号算法。我们还介绍了一种对认知模型进行内省的方法,以进一步深入了解个人决策中考虑的特征的认知显著性。这些技术的结合为个人个性化建模提供了蓝图。我们将讨论这种自适应和个性化方法的结果和对网络安全防御的影响,以及在人机协作等领域针对个体差异量身定制智能人工智能的影响。
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来源期刊
Topics in Cognitive Science
Topics in Cognitive Science PSYCHOLOGY, EXPERIMENTAL-
CiteScore
8.50
自引率
10.00%
发文量
52
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.
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