Published October 14, 2025 | Version v1
Preprint Open

Alignment vs. Cognitive Fit: Rethinking Model-Human Synchronization

  • 1. VANTA Research

Description

This paper proposes the concept of Cognitive Fit as a complementary framework to traditional AI alignment. While alignment focuses on ensuring that artifical systems adhere to explicit human objectives, Cognitive Fit explores how well an AI's internal reasoning patterns, communication styles, and representational structures align with the diversity of human cognition itself. 

Through theoretical analysis and applied examples, the paper argues that most modern alignment strategies implicitly assume neurotypical and idealized models of rationality - leaving significant gaps when interacting with the variability of real human thought. By recontextualizing "safety" and "alignment" through the lens of cognitive ergonomics, the work proposes a broader goal: AI systems that are not merely obedient to human intent, but intelligible, interpretable, and resonant with the ways humans actually reason, learn, and make meaning. 

This work builds upon exisiting literature in alignment theory, human-computer interaction, and cognitive science, positioning Cognitive Fit as a bridge between technical safety research and practical human usability. It concludes with a call for interdisciplinary design methodologies that treat alignment not as a constrant problem, but as a dialogue between minds. 

Files

VANTA_Research_Tyler_Williams_Alignment_vs_Cognitive_Fit_2025.pdf

Files (126.2 kB)

Additional details

Dates

Created
2025-10-13

References

  • [1] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. _Advances in Neural Information Processing Systems_, 35, 27730-27744.
  • [2] Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., ... & Kaplan, J. (2022). Constitutional AI: Harmlessness from AI feedback. _arXiv preprint arXiv:2212.08073_.
  • [3] Barkley, R. A. (2015). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (4th ed.). _Guilford Press_.
  • [4] Armstrong, T. (2012). Neurodiversity in the classroom: Strength-based strategies to help students with special needs succeed in school and life. _ASCD_.
  • [5] Kofler, M. J., Rapport, M. D., Bolden, J., Sarver, D. E., & Raiker, J. S. (2010). ADHD and working memory: The impact of central executive deficits and exceeding storage/rehearsal capacity on observed inattentive behavior. _Journal of Abnormal Child Psychology_, 38(2), 149-161.
  • [6] Schweitzer, J. B., & Sulzer-Azaroff, B. (1995). Self-control in boys with attention deficit hyperactivity disorder: Effects of added stimulation and time. _Journal of Child Psychology and Psychiatry_, 36(4), 671-686.
  • [7] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. _IEEE Transactions on Knowledge and Data Engineering_, 17(6), 734-749.
  • [8] Burke, R. (2002). Hybrid recommender systems: Survey and experiments. _User Modeling and User-Adapted Interaction_, 12(4), 331-370.
  • [9] Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. _Cognitive Science_, 12(2), 257-285.
  • [10] Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. _Educational Psychologist_, 38(1), 1-4.
  • [11] Sweller, J., Van Merriënboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. _Educational Psychology Review_, 31(2), 261-292.
  • [12] Villamin, G. R., & Luppicini, R. (2024). Co-Designing Digital Assistive Technologies for Autism Spectrum Disorder (ASD) Using Qualitative Approaches. _International Journal of Disability, Development and Education_, 1–19.
  • 13] Morris, M. R., Johnson, J., Bennett, C. L., & Cutrell, E. (2018). Rich representations of visual content for screen reader users. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ (pp. 1-11).