{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:21:42Z","timestamp":1760059302709,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This study explores the potential of Julian Jaynes\u2019 bicameral mind theory in enhancing reinforcement learning (RL) algorithms and large language models (LLMs) for artificial intelligence (AI) systems. By drawing parallels between the dual-process structure of the bicameral mind, the observation\u2013action cycle in RL, and the \u201cthinking\u201d\/\u201dwriting\u201d processes in LLMs, we hypothesize that incorporating principles from this theory could lead to more efficient and adaptive AI. Empirical evidence from OpenAI\u2019s CoinRun and RainMazes models, together with analysis of Claude, Gemini, and ChatGPT functioning, supports our hypothesis, demonstrating the universality of the dual-component structure across different types of AI systems. We propose a conceptual model for integrating bicameral mind principles into AI architectures capable of guiding the development of systems that effectively generalize knowledge across various tasks and environments.<\/jats:p>","DOI":"10.3390\/computers14060218","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T06:21:51Z","timestamp":1748931711000},"page":"218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the Potential of the Bicameral Mind Theory in Reinforcement Learning Algorithms"],"prefix":"10.3390","volume":"14","author":[{"given":"Munavvarkhon","family":"Mukhitdinova","sequence":"first","affiliation":[{"name":"Tashkent State University of Economics, Islom Karimov 49, Tashkent 100066, Uzbekistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1531-4312","authenticated-orcid":false,"given":"Mariana","family":"Petrova","sequence":"additional","affiliation":[{"name":"Department of Information Technologies, St. Cyril and St. Methodius University of Veliko Tarnovo, 5003 Veliko Tarnovo, Bulgaria"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"ref_1","unstructured":"Jaynes, J. (1976). The Origin of Consciousness in the Breakdown of the Bicameral Mind, Houghton Mifflin. [1st ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.tics.2019.02.006","article-title":"Reinforcement learning, fast and slow","volume":"23","author":"Botvinick","year":"2019","journal-title":"Trends Cogn. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1038\/nrn1723","article-title":"Forty-five years of split-brain research and still going strong","volume":"6","author":"Gazzaniga","year":"2005","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_4","first-page":"458","article-title":"The computational architecture of value-based decision making","volume":"24","author":"Gershman","year":"2021","journal-title":"Nat. Neurosci."},{"key":"ref_5","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","article-title":"Reinforcement learning: A survey","volume":"4","author":"Kaelbling","year":"1996","journal-title":"J. Artif. Intell. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep reinforcement learning: A brief survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_8","unstructured":"Cobbe, K., Klimov, O., Hesse, C., Kim, T., and Schulman, J. (2019). Quantifying generalization in reinforcement learning. arXiv."},{"key":"ref_9","unstructured":"Cobbe, K., Hesse, C., Hilton, J., and Schulman, J. (2019). Leveraging procedural generation to benchmark reinforcement learning. arXiv."},{"key":"ref_10","unstructured":"Li, Y. (2017). Deep reinforcement learning: An overview. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1037\/h0080053","article-title":"Consciousness and the voices of the mind","volume":"27","author":"Jaynes","year":"1986","journal-title":"Can. Psychol.\/Psychol. Can."},{"key":"ref_12","unstructured":"Kuijsten, M. (2006). Reflections on the Dawn of Consciousness: Julian Jaynes\u2019s Bicameral Mind Theory Revisited, Julian Jaynes Society."},{"key":"ref_13","first-page":"11","article-title":"The \u201cbicameral mind\u201d 30 years on: A critical reappraisal of Julian Jaynes\u2019 hypothesis","volume":"22","author":"Cavanna","year":"2007","journal-title":"Funct. Neurol."},{"key":"ref_14","first-page":"295","article-title":"Review of Julian Jaynes\u2019s Origins of Consciousness in the Breakdown of the Bicameral Mind","volume":"1","author":"Block","year":"1978","journal-title":"Cogn. Brain Theory"},{"key":"ref_15","first-page":"206","article-title":"The social-emotional basis of creative production in children with and without autism","volume":"28","author":"Jarrold","year":"2016","journal-title":"Creat. Res. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_17","unstructured":"Emilio, G. (2019, January 7). The Conscious Bicameral Mind. Proceedings of the Conscious Bicameral Mind, Granada, Spain."},{"key":"ref_18","unstructured":"Baars, B.J. (1988). A Cognitive Theory of Consciousness, Cambridge University Press."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fran\u00e7ois-Lavet, V., Henderson, P., Islam, R., Bellemare, M.G., and Pineau, J. (2018). An introduction to deep reinforcement learning. arXiv.","DOI":"10.1561\/9781680835397"},{"key":"ref_20","unstructured":"Mill, J.S. (1884). A System of Logic, Ratiocinative and Inductive: Being a Connected View of the Principles of Evidence and the Methods of Scientific Investigation, Longmans, Green, and Company."},{"key":"ref_21","unstructured":"Dema, D., Fabiha, I., Zulfikar, A., Zeyar, A., and Mohammad, A. (2021, January 23\u201325). Reinforcement Learning: A Friendly Introduction. Proceedings of the International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021), Virtual Event."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/s42256-019-0025-4","article-title":"Reinforcement learning in artificial and biological systems","volume":"1","author":"Neftci","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rodionov, A.A., Fayziev, R.A., and Gulyamov, S.S. (2022, January 15). Experience in using big data technology for digitalization of information. Proceedings of the 6th International Conference on Future Networks & Distributed Systems (ICFNDS \u201822), Tashkent, Uzbekistan.","DOI":"10.1145\/3584202.3584266"},{"key":"ref_24","unstructured":"Majumder, A. (2022, January 26\u201327). Introduction to Reinforcement Learning. Proceedings of the 2022 2nd International Conference on Technology Enhanced Learning in Higher Education (TELE), Lipetsk, Russia."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cao, L., and Min, Z. (2019, January 19\u201321). An Overview of Deep Reinforcement Learning. Proceedings of the CACRE2019: Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering, Shenzhen, China.","DOI":"10.1145\/3351917.3351989"},{"key":"ref_26","unstructured":"Juliani, A. (2016). Simple Reinforcement Learning with Tensorflow Part 8: Asynchronous Actor-Critic Agents (A3C), Medium."},{"key":"ref_27","unstructured":"Saidakhror, G., Rabim, F., Andrey, R., and Munavvarkhon, M. (2022, January 26\u201327). The Introduction of Artificial Intelligence in the Study of Economic Disciplines in Higher Educational Institutions. Proceedings of the 2022 2nd International Conference on Technology Enhanced Learning in Higher Education (TELE), Lipetsk, Russia."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fodor, J.A. (1983). The Modularity of Mind: An Essay on Faculty Psychology, MIT Press.","DOI":"10.7551\/mitpress\/4737.001.0001"},{"key":"ref_29","unstructured":"OpenAI (2025, February 10). GPT-4 Technical Report. Available online: https:\/\/openai.com\/index\/gpt-4-research."},{"key":"ref_30","unstructured":"OpenAI (2025, February 10). Hello GPT-4o. Available online: https:\/\/openai.com\/index\/hello-gpt-4o."},{"key":"ref_31","unstructured":"Anthropic (2025, February 10). Introducing the Next Generation of Claude. Available online: https:\/\/www.anthropic.com\/news\/claude-3-family."},{"key":"ref_32","unstructured":"Anthropic (2025, February 10). Claude 3.7 Sonnet. Available online: https:\/\/www.anthropic.com\/claude."},{"key":"ref_33","unstructured":"Google DeepMind (2025, February 12). Gemini 2.5: Our Most Intelligent AI Model. Available online: https:\/\/blog.google\/technology\/google-deepmind\/gemini-model-thinking-updates-march-2025."},{"key":"ref_34","unstructured":"Google DeepMind (2025, February 15). Gemini 2.5 Pro. Available online: https:\/\/deepmind.google\/models\/gemini\/pro."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/6\/218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:46:32Z","timestamp":1760031992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/6\/218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["computers14060218"],"URL":"https:\/\/doi.org\/10.3390\/computers14060218","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2025,6,3]]}}}