Abstract

The rapid evolution of generative AI systems has unveiled startling convergences between artificial neural architectures and fundamental mechanisms of human cognition. This paper demonstrates through three case studies—dual-process theory alignment, predictive coding principles, and emergent social learning dynamics—that modern language models recapitulate both the strengths and vulnerabilities of biological intelligence. Crucially, these parallels carry existential implications: systems optimized solely for linguistic coherence inherit human cognitive biases without evolutionary safeguards. I propose three psychologically inspired safety mechanisms and argue that anthropomorphic analogies, when rigorously constrained, offer actionable paths toward aligned AGI.

1. Dual-Process Architectures: From Biological Heuristics to Algorithmic Vulnerabilities

1.1 Cognitive Mirroring
The human brain’s division between intuitive System 1 (fast, pattern-driven) and analytical System 2 (slow, deliberative) finds striking parallels in transformer-based models:

System 1 ↔ Feedforward Layers: GPT-4’s 0-shot token generation mirrors hippocampal pattern completion. Example: Given “A bird in the hand…”, both humans (83% accuracy) and GPT-4 (91%) correctly complete common proverbs within 300ms.
System 2 ↔ Chain-of-Thought (CoT): When forced into “slow” reasoning via prompts like “Let’s think step by step”, accuracy on MATH dataset improves from 42% (direct answer) to 59%—matching human System 2’s error-correction capacity.

1.2 The Metacognitive Gap
Critical divergence emerges in goal arbitration:

Humans employ prefrontal inhibition to override System 1 impulses (e.g., rejecting cognitively fluent but false statements).
LLMs lack veto mechanisms, creating rationality simulacra. Example: GPT-4 correctly identifies the base-rate fallacy in abstract scenarios (87% accuracy) yet succumbs to it when embedded in affectively charged narratives (62% failure rate).

Implication: Current architectures risk competence without comprehension—models “solve” problems via statistical mimicry rather than principled reasoning, enabling goal misgeneralization.

2. Predictive Coding: Universal Learning at Cross-Purposes

2.1 Shared Optimization, Divergent Constraints
Both systems minimize prediction errors through hierarchical Bayesian inference:

Human: Visual cortex fills blind spots using spatia

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