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Edward A Cranford1, Christian Lebiere1, Cleotilde Gonzalez2

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This study models individual decision-making in cybersecurity using instance-based learning (IBL) within a cognitive architecture. It shows how personalized models can adapt to changing environments and individual differences for better defense systems.

Keywords:
ACT‐RCognitive salienceCyber deceptionIndividual differencesInstance‐based learningModel tracingPersonalization

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Individual differences in knowledge influence behavior.
  • Instance-based learning theory (IBLT) models decision-making from experience.
  • Cognitive architectures integrate learning and decision processes.

Purpose of the Study:

  • To demonstrate an instance-based learning (IBL) cognitive model for predicting individual behavior in dynamic environments.
  • To account for population averages and individual variances in decision-making.
  • To develop personalized signaling algorithms for cybersecurity defense.

Main Methods:

  • Implementation of an IBL cognitive model within the Adaptive Control of Thought-Rational (ACT-R) architecture.
  • Utilizing recurrence quantification analyses to examine sequential trial-to-trial behavior.
  • Applying model-tracing and knowledge-tracing for real-time individual alignment.

Main Results:

  • The IBL model, with identical parameters, generated diverse human behaviors via stochastic memory retrieval.
  • Personalized modeling successfully aligned with individual decision-making in a cybersecurity task.
  • Cognitive model introspection revealed salient features influencing individual choices.

Conclusions:

  • The combined techniques offer a blueprint for personalized cognitive modeling.
  • This adaptive, personalized approach has implications for cybersecurity defense and intelligent systems.
  • Tailoring intelligent artifacts to individual differences is crucial for domains like human-machine teaming.