aspg-25-4392-1792226190 20261017083630 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cognitive Human-Computer Interaction JCHCI 2771-1471 2771-1463 2026 11 2 Aiswan Aumanti Institut Bakti Nusantara, Lampung, Indonesia Citra Dewi Universitas Lampung, Indonesia Human-AI collaboration can improve decision quality only when users know when to rely on an AI recommendation and when to resist it. Explanations are often proposed as a remedy, but explanation content can also intensify automation bias or reinforce a user’s initial belief. This paper presents a cognitive explanation selection model for mitigating over-reliance and under-reliance in AI-assisted decision tasks. The study compares no explanation, feature-based, contrastive, example-driven, and hybrid explanations across simulated novice, intermediate, and expert decision makers using a public medical decision dataset as the task substrate. The analysis focuses on reliance behaviour rather than on model accuracy alone. The proposed model estimates when the user is likely to accept a wrong recommendation, reject a correct recommendation, or accept advice simply because it confirms an initial judgment. The results indicate that contrastive and hybrid explanations are more effective for reducing automation bias, while example-driven explanations preserve trust for lower-expertise users. The paper concludes with a transparent interface loop for high-stakes environments in which explanation style is selected according to user expertise, AI confidence, and human-AI agreement. 2026 22 32 4392 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCHCI.110205 https://americaspg.com/journal/25/article/4392 https://americaspg.com/storage/articles/manuscripts/01KTD71E1D8GVDPW1J62PF3W9V.pdf