EXPLORING DUAL-PROCESS ARCHITECTURES IN MODERN AI SYSTEMS: A REVIEW OF BICAMERAL MIND THEORY APPLICATIONS

Mukhitdinova Munavvarkhon Hayot kizi

This paper examines the emerging application of Julian Jaynes’ bicameral mind theory to modern artificial intelligence systems, particularly in reinforcement learning (RL) and large language models (LLMs). The dual-process structure proposed by Jaynes—consisting of "speaking" and "listening" components—has shown remarkable parallels with observation-action cycles in RL and thinking-writing processes in contemporary language models. Through a systematic review of recent research and analysis of prominent AI systems including OpenAI's CoinRun, RainMazes models, and advanced LLMs (Claude, Gemini, ChatGPT), this study evaluates the potential of bicameral principles in enhancing AI system efficiency and adaptability. The evidence suggests that dual-component architectures may represent a universal organizational principle for AI systems, offering new pathways for developing more robust and adaptive artificial intelligence. This review contributes to the growing interdisciplinary dialogue between cognitive science and AI development, proposing a conceptual framework for future research directions.

Журнал номиRaqamli iqtisodiyot
Нашр номи12-son
Кўришлар сони 15

Internet ҳавола https://infocom.uz/magazine/19

DOI

UzSCI тизимида яратилган сана 25-11-2025

Ўқишлар сони 15

Нашр санаси 30-09-2025

Мақола тилиIngliz

Саҳифалар сони129-139

Калит сўзлар

artificial intelligence

Reinforcement Learning

large language models

bicameral mind theory

dual-process architecture

English

This paper examines the emerging application of Julian Jaynes’ bicameral mind theory to modern artificial intelligence systems, particularly in reinforcement learning (RL) and large language models (LLMs). The dual-process structure proposed by Jaynes—consisting of "speaking" and "listening" components—has shown remarkable parallels with observation-action cycles in RL and thinking-writing processes in contemporary language models. Through a systematic review of recent research and analysis of prominent AI systems including OpenAI's CoinRun, RainMazes models, and advanced LLMs (Claude, Gemini, ChatGPT), this study evaluates the potential of bicameral principles in enhancing AI system efficiency and adaptability. The evidence suggests that dual-component architectures may represent a universal organizational principle for AI systems, offering new pathways for developing more robust and adaptive artificial intelligence. This review contributes to the growing interdisciplinary dialogue between cognitive science and AI development, proposing a conceptual framework for future research directions.

Калит сўзлар

artificial intelligence

Reinforcement Learning

large language models

bicameral mind theory

dual-process architecture

№ Муаллифнинг исми Лавозими Ташкилот номи

1 Mukhitdinova M.H. PhD, Senior Lecturer «Digital economy» department of TSUE

№ Ҳавола номи

1 Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.

2 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

3 Jaynes, J. (1976). The Origin of Consciousness in the Breakdown of the Bicameral Mind (1st ed.). Houghton Mifflin.

4 Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement learning, fast and slow. Trends in Cognitive Sciences, 23(5), 408-422.

5 Gershman, S. J., & Uchida, N. (2021). The computational architecture of value-based decision making. Nature Neuroscience, 24(4), 458-466.

6 Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285.

7 Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6), 26-38.

8 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.

9 Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.

10 Cavanna, A. E., Trimble, M., Cinti, F., & Monaco, F. (2007). The "bicameral mind" 30 years on: A critical reappraisal of Julian Jaynes’ hypothesis. Functional Neurology, 22(1), 11-15.

11 Block, N. (1978). Review of Julian Jaynes’s Origins of Consciousness in the Breakdown of the Bicameral Mind. Cognitive Brain Theory, 1, 295-306.

12 Baars, B. J. (1988). A Cognitive Theory of Consciousness. Cambridge University Press.

13 Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181-204.

14 Gazzaniga, M. S. (2005). Forty-five years of split-brain research and still going strong. Nature Reviews Neuroscience, 6(8), 653-659.

15 Cobbe, K., Klimov, O., Hesse, C., Kim, T., & Schulman, J. (2019). Quantifying generalization in reinforcement learning. arXiv preprint arXiv:1812.02341.

16 Cobbe, K., Hesse, C., Hilton, J., & Schulman, J. (2019). Leveraging procedural generation to benchmark reinforcement learning. arXiv preprint arXiv:1912.01588.

17 Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.

18 Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.

19 OpenAI. (2023). GPT-4 Technical Report. Retrieved from https://openai.com/index/gpt-4-research

20 Anthropic. (2024). Claude 3.7 Sonnet. Retrieved from https://www.anthropic.com/claude

21 Anthropic. (2024). Introducing the Next Generation of Claude. Retrieved from https://www.anthropic.com/news/claude-3-family

22 Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., ... & Sifre, L. (2022). Training compute-optimal large language models. arXiv preprint arXiv:2203.15556.

23 François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. arXiv preprint arXiv:1811.12560.

24 Google DeepMind. (2025). Gemini 2.5: Our Most Intelligent AI Model. Retrieved from https://blog.google/technology/googledeepmind/gemini-model-thinking-updates-march-2025

25 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.

26 Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.

27 Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253.

28 Marcus, G. (2018). Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631.

Кутилмоқда

№	Ҳавола номи
1	Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
2	LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
3	Jaynes, J. (1976). The Origin of Consciousness in the Breakdown of the Bicameral Mind (1st ed.). Houghton Mifflin.
4	Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement learning, fast and slow. Trends in Cognitive Sciences, 23(5), 408-422.
5	Gershman, S. J., & Uchida, N. (2021). The computational architecture of value-based decision making. Nature Neuroscience, 24(4), 458-466.
6	Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285.
7	Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6), 26-38.
8	Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
9	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
10	Cavanna, A. E., Trimble, M., Cinti, F., & Monaco, F. (2007). The "bicameral mind" 30 years on: A critical reappraisal of Julian Jaynes’ hypothesis. Functional Neurology, 22(1), 11-15.
11	Block, N. (1978). Review of Julian Jaynes’s Origins of Consciousness in the Breakdown of the Bicameral Mind. Cognitive Brain Theory, 1, 295-306.
12	Baars, B. J. (1988). A Cognitive Theory of Consciousness. Cambridge University Press.
13	Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181-204.
14	Gazzaniga, M. S. (2005). Forty-five years of split-brain research and still going strong. Nature Reviews Neuroscience, 6(8), 653-659.
15	Cobbe, K., Klimov, O., Hesse, C., Kim, T., & Schulman, J. (2019). Quantifying generalization in reinforcement learning. arXiv preprint arXiv:1812.02341.
16	Cobbe, K., Hesse, C., Hilton, J., & Schulman, J. (2019). Leveraging procedural generation to benchmark reinforcement learning. arXiv preprint arXiv:1912.01588.
17	Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
18	Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
19	OpenAI. (2023). GPT-4 Technical Report. Retrieved from https://openai.com/index/gpt-4-research
20	Anthropic. (2024). Claude 3.7 Sonnet. Retrieved from https://www.anthropic.com/claude
21	Anthropic. (2024). Introducing the Next Generation of Claude. Retrieved from https://www.anthropic.com/news/claude-3-family
22	Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., ... & Sifre, L. (2022). Training compute-optimal large language models. arXiv preprint arXiv:2203.15556.
23	François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. arXiv preprint arXiv:1811.12560.
24	Google DeepMind. (2025). Gemini 2.5: Our Most Intelligent AI Model. Retrieved from https://blog.google/technology/googledeepmind/gemini-model-thinking-updates-march-2025
25	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
26	Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
27	Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253.
28	Marcus, G. (2018). Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631.