Estimation of individual characteristics through interaction with models based on cognitive architecture

Abstract

Driven by recent advancements in sophisticated machine learning, integrated AI systems show increasing promise for real-world deployment. Nonetheless, explainability is essential for systems that function alongside human users, as it underpins trust and transparent decision processes. This study introduces a theoretical foundation anchored in ACT-R, serving as a blueprint for implementing cognitive models in applied settings. Focusing on phonological awareness in language development, it investigates how such models capture individual cognitive characteristics. A key premise is that participants may fail to detect errors in models mirroring their traits. Building on this assumption, we conducted an experiment wherein participants interacted with an immature phonological awareness model, analyzing erroneous responses to infer user-specific attributes and parameters. We also deployed an audio filter to emulate personal traits and examined its effect on model preferences. Findings corroborate our hypothesis and highlight cognitive modeling’s potential for interpretability and adaptation.