A Computational Basis of Natural Intelligence

Alireza Karami, Veronica Chelu, Po-Chen Kuo, Lindsay M. Smith, Mia Whitefield, Jonathan D. Cohen
Proceedings of the Analytical Connectionism Schools 2023--2024, PMLR 320:151-176, 2026.

Abstract

Lecture notes from Professor Jonathan Cohen at the summer school ’Analytical Connectionism’ at the Flatiron Institute in 2024. The notes discuss a computational basis for understanding natural intelligence. The notes are mainly focused on four theoretical frameworks that help explain the basis of natural intelligence: the Relational Bottleneck, the Rational Boundedness of Cognitive Control, Miller’s Law, and Episodic Generalization and Optimization.

Cite this Paper

BibTeX
@InProceedings{pmlr-v320-karami26a, title = {A Computational Basis of Natural Intelligence}, author = {Karami, Alireza and Chelu, Veronica and Kuo, Po-Chen and Smith, Lindsay M. and Whitefield, Mia and Cohen, Jonathan D.}, booktitle = {Proceedings of the Analytical Connectionism Schools 2023--2024}, pages = {151--176}, year = {2026}, editor = {Sarao Mannelli, Stefano and Mignacco, Francesca and Chou, Chi-Ning and Chung, SueYeon and Saxe, Andrew}, volume = {320}, series = {Proceedings of Machine Learning Research}, month = {01 Jan--31 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v320/main/assets/karami26a/karami26a.pdf}, url = {https://proceedings.mlr.press/v320/karami26a.html}, abstract = {Lecture notes from Professor Jonathan Cohen at the summer school ’Analytical Connectionism’ at the Flatiron Institute in 2024. The notes discuss a computational basis for understanding natural intelligence. The notes are mainly focused on four theoretical frameworks that help explain the basis of natural intelligence: the Relational Bottleneck, the Rational Boundedness of Cognitive Control, Miller’s Law, and Episodic Generalization and Optimization.} }
Endnote
%0 Conference Paper %T A Computational Basis of Natural Intelligence %A Alireza Karami %A Veronica Chelu %A Po-Chen Kuo %A Lindsay M. Smith %A Mia Whitefield %A Jonathan D. Cohen %B Proceedings of the Analytical Connectionism Schools 2023--2024 %C Proceedings of Machine Learning Research %D 2026 %E Stefano Sarao Mannelli %E Francesca Mignacco %E Chi-Ning Chou %E SueYeon Chung %E Andrew Saxe %F pmlr-v320-karami26a %I PMLR %P 151--176 %U https://proceedings.mlr.press/v320/karami26a.html %V 320 %X Lecture notes from Professor Jonathan Cohen at the summer school ’Analytical Connectionism’ at the Flatiron Institute in 2024. The notes discuss a computational basis for understanding natural intelligence. The notes are mainly focused on four theoretical frameworks that help explain the basis of natural intelligence: the Relational Bottleneck, the Rational Boundedness of Cognitive Control, Miller’s Law, and Episodic Generalization and Optimization.
APA
Karami, A., Chelu, V., Kuo, P., Smith, L.M., Whitefield, M. & Cohen, J.D.. (2026). A Computational Basis of Natural Intelligence. Proceedings of the Analytical Connectionism Schools 2023--2024, in Proceedings of Machine Learning Research 320:151-176 Available from https://proceedings.mlr.press/v320/karami26a.html.

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