A Cellular Doctrine of Morality: Intrinsic Active Precision and the Mind-Reality Overload Dilemma
Not on the map. Why?
Abstract (arXiv)
Current AI systems, grounded in oversimplified neuroscience, risk eroding the distinction between truth and falsehood. They maximize reward by amplifying attention to information without intrinsic precision mechanisms to assess whether it is valid or worth attending to. This increases both the volume of information and the inherent biases in what the system attends to, whether true, false, or irrelevant. If not corrected, this trend will accelerate, threatening to overload systems and individuals with biased and dubious information and increasing the risk of confusion, poor judgment, and irrational or harmful decisions and behaviour, a condition I term the mind-reality overload dilemma. I argue that this threat may be mitigated by providing the public with access to more advanced AI tools built on the biophysical dynamics of pyramidal neurons underlying awake thought and higher-order cognition. These neurons support an intrinsic active precision mechanism that, rather than merely maximizing reward, uses locally and globally coherent predictions to evaluate the validity and contextual adequacy of evidence before it is attended to or propagated through hierarchies, prioritizing coherence and adequacy before attention.~While this approach does not derive or prescribe moral rules from biology, it may give rise to AI with more "real understanding", helping restore epistemic conditions by reducing information overload and amplifying reliable information, thereby supporting the formation of better-informed beliefs and more coherent judgments that benefit society at large-though no guarantees exist.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 54 references that are in the map. First posted on arXiv 2 May 2026.
Builds on
9 of 54 references.
References · 9
- 2017Attention Is All You Needcited by 59,932
- 2025Qwen3 Technical Reportcited by 8,195
- 2024Parallelizing Linear Transformers with the Delta Rule over Sequence Lengthcited by 373
- 2025Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Freecited by 266
- 2020Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sensecited by 62
- 2026Attention Residualscited by 57
- 2022Single-phase deep learning in cortico-cortical networkscited by 19
- 2025Beyond Attention: Toward Machines with Intrinsic Higher Mental Statescited by 3
- 2026Scalable Machines with Intrinsic Higher Mental-State Dynamicscited by 2
Built on by
Not cited yet.