The metaphorical alignment between Large Language Models (LLMs) and human language processing offers a transformative lens for bridging artificial intelligence and neuroscience, revealing profound insights about both systems and catalyzing reciprocal advancement. Despite their fundamentally different substrates—biochemical neural circuits versus engineered tensor networks—LLMs and the human brain share core computational principles manifest in attention, predictive processing, memory, and hierarchical representation dynamics.
At a functional level, LLM tensors and attention mechanisms can be meaningfully mapped to neurotransmitter modulation and receptor gating. Both systems employ weighted operations to selectively amplify or suppress signals, gating contextual information critical for language comprehension (Schain & Goldstein, 2025; Li et al., 2025). The selective attention mechanisms in LLMs, such as multi-head attention, mirror specialized neurotransmitter pathways modulating attention and memory retrieval in distinct brain regions, supporting a modular functional architecture (Mischler et al., 2025; Yan et al., 2025).
Human language cognition relies on unconscious, continuous next-word prediction—a biological analogue to the autoregressive training objective underlying LLMs. Neural and behavioral research confirms striking parallels between anticipatory language mechanisms in the brain and LLM operations, emphasizing prediction and error correction as fundamental to fluency and comprehension (Gauthier et al., 2022; Google Research, 2025). Although the human brain processes language sequentially with recurrent temporal dynamics, advances in LLM architectures now incorporate sequential decision frameworks and multimodal processing that approximate these temporal aspects, narrowing functional gaps (Ouyang et al., 2024; Raschka, 2024; Wei et al., 2024).
Importantly, LLMs have surfaced latent biases embedded in human language with unprecedented speed and clarity, revealing systemic language patterns often opaque to human awareness. Serving as both mirrors and mentors, LLMs enable the diagnosis and mitigation of these biases, fostering more equitable, conscientious communication (Zhang et al., 2024; HolisticAI, 2024). This dual role exemplifies the bidirectional benefit of AI models that not only emulate cognition but help humans evolve their linguistic practices.
Extending beyond language function to brain physiology, the organization of tensor flows and attention heads in LLMs has inspired testable hypotheses about neurotransmitter signaling regulating selective gating and memory encoding in neural circuits (Burns, Fukai, & Earls, 2003; Li et al., 2025). The dynamic predictive filtering observed in LLM layers parallels brain predictive coding theories, where neurotransmitters modulate synaptic weights to optimize error minimization during learning (Mischler et al., 2025). Sparse subnetworks in LLMs model efficient neural activation and resource allocation shaped by neurochemical plasticity (Yan et al., 2025). Thus, AI architectures offer mechanistic templates to probe brain function more deeply.
Historically, mental models of human-computer interaction produced the “windows, icons, menus, and pointers” (WIMP) paradigm, which revolutionized computing and enriched cognitive science understanding. Similarly, current advances in LLMs, rooted in computational neuroscience principles, inaugurate a new cycle: models reflecting brain processes that, in turn, inspire better AI designs and deepen neuroscientific insight. This recursive loop facilitates richer comprehension of language, attention, and memory—empowering us to harness language more consciously and inclusively.
In conclusion, Large Language Models and human brains share foundational language processing principles—selective attention, prediction, hierarchical integration—despite distinct substrates. The dialogue between neuroscience and AI reveals how LLM attention and tensor management inform neurotransmitter and memory research, while LLMs expose subtle linguistic biases, guiding us toward more effective communication. This synergy marks a frontier where AI not only emulates cognition but teaches us more about ourselves, echoing the transformative impact of earlier mental models.
References
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