Why Should We Learn from Human Cognitive Systems?

Introduction

At the end of our previous article, we posed a crucial question:

If we want to build truly AI individual cognitive systems, we must analyze how to enable AI to understand semantics, or in other words: How do we make AI possess experientiality?

We have proven that the experiential dimension of semantics cannot be handled by traditional computational methods. So where is the way forward?

The answer is: Learn from human cognitive systems.

1. Why Should We Learn from Human Cognitive Systems?

TL;DR: Human cognitive systems are the only verified intelligent systems capable of producing experiential understanding, with abundant scientific research available for reference.

1.1 Functional Perspective: Possessing the Experientiality We Desire

There exists a simple yet profound logic here: the goal of "experiential understanding" that we pursue is itself distilled from observing human cognitive abilities.

When we say that AI needs to possess "experientiality," we are actually saying: let AI understand the world like humans do.

Human experiential understanding manifests in:

Individualized Semantic Construction
- Each person's understanding of "home" is different
- "Mother's taste" cannot be recreated through recipes
- The same music brings different feelings to different people
Context-Sensitive Dynamic Understanding
- "Hello" has completely different meanings in different contexts
- Recognizing the emotions and intentions of speakers
- Understanding unsaid implications
Experience-Based Value Judgments
- Formation of aesthetic preferences
- Generation of moral intuitions
- Personalization of emotional responses
Creative Conceptual Fusion
- Understanding metaphors like "time is money"
- Creating new forms of expression
- Establishing connections between seemingly unrelated domains

Key Insight: We are not imitating humans, but learning from a proven effective information processing architecture—an architecture capable of producing what we define as "understanding."

Cognitive scientist Francisco Varela called this ability "Enactive Cognition": cognition is not passive representation of the external world, but an active process of constructing meaning through interaction with the world. This is precisely the core characteristic of human cognitive systems—they are not "computing" meaning, but "experiencing" and "creating" meaning.

1.2 Testing Perspective: Having Withstood the Test of Time

If we view the achievements of human civilization as a "test report," then human cognitive systems have undoubtedly delivered results that leave all other species in the dust.

Unparalleled Civilizational Achievements

In Earth's 3.8 billion-year history of life, countless species have come and gone, but only humans have created:

Symbol Systems: From cave paintings to quantum mechanics equations
Knowledge Accumulation: Each generation standing on the shoulders of predecessors
Abstract Thinking: Deriving universal laws from concrete phenomena
Cultural Transmission: Continuing collective memory through stories, rituals, and education

Anthropologist Yuval Noah Harari points out in Sapiens: "The Cognitive Revolution enabled Homo sapiens to talk about fictional entities, which is the most unique function of human language." It is precisely this experiential-based capacity for fiction—understanding "non-existent" things—that allows humans to create imagined communities like myths, laws, nations, and corporations.

Ultimate Proof of Global Adaptability

Archaeologist Ian Tattersall notes: "Humans are the only truly cosmopolitan species on Earth." This unprecedented adaptability stems from the flexibility of our cognitive systems—our ability to understand the "meaning" of new environments and creatively transform them, rather than merely adapting passively.

Exponential Problem-Solving Capability

Other species' problem-solving abilities are linear, while humans are exponential:

Challenge	Other Species' Solutions	Human Solutions	Cognitive Difference
Cold	Grow thick fur (millions of years)	Invent clothing (instant)	Understanding the abstract concept of "warmth"
River barriers	Wait for drought or detour	Build bridges	Imagining the possibility of "connection"
Food shortage	Migration or population reduction	Develop agriculture	Understanding "future" and "storage"
Disease	Natural selection	Invent medicine	Understanding causality

Key Insight: Every achievement of human civilization is an external manifestation of the cognitive system's capacity for "experiential understanding." We are not guessing why this system succeeds; we are observing the miracles it has already created.

1.3 Implementation Perspective: Available Scientific Research for Reference

We are not starting from scratch. Instead, we stand on the shoulders of a century of cognitive science research. More excitingly, AI itself is becoming a powerful tool for verifying and deepening this research.

Century-old Treasury of Scientific Accumulation

Modern cognitive science began in the late 19th century and has developed over more than a century into a multi-layered knowledge system covering:

Structural Layer: Neuroanatomy - Brain region localization, neural circuit connections
Functional Layer: Cognitive Psychology - Memory models, attention mechanisms
Computational Layer: Computational Neuroscience - Neural network simulations, information encoding theory
System Layer: Cognitive Architecture Theory - ACT-R Architecture, SOAR Model

Nobel Prize winner Eric Kandel wrote in his 2006 book In Search of Memory: "Although our understanding of the brain is still incomplete, it is sufficient to guide us in building intelligent systems." This is not blind men touching an elephant, but a gradually clarifying puzzle.

2. Can the Functional Architecture of Human Cognitive Systems Be Implemented by Non-Biological Systems?

TL;DR: The history of computation proves the separability of function and implementation; cognitive functions can similarly be implemented on non-biological substrates.

2.1 Systems Theory Perspective: Separation of Function and Implementation

The history of computer development itself is the best proof of the principle of "separation of function and implementation." The same computational functions have had vastly different physical implementations across different eras—from Pascal's mechanical gears to vacuum tubes to transistors to quantum computers.

Computer science pioneer Alan Turing proved in his groundbreaking paper: "Any computable function can be implemented by a Turing machine, and the physical implementation method of the Turing machine is irrelevant." This is the famous Turing Equivalence Principle.

Implementation Affects Efficiency But Doesn't Change Function

Sorting Algorithm	Implementation Principle	Time Complexity	Functional Result
Bubble Sort	Adjacent comparison exchange	O(n²)	✓ Correct sorting
Quick Sort	Divide and conquer recursion	O(n log n)	✓ Correct sorting
Sleep Sort	Time waiting	O(max(n))	✓ Correct sorting

As computer scientist David Deutsch said: "The essence of computation is the transformation of information, not a specific physical process."

Analogical Reasoning from Computation to Cognition

If computational functions can be implemented on completely different substrates like mechanical, electronic, and quantum, why can't cognitive functions be implemented on substrates other than biological neurons?

Key Insight: From Pascal's gears to Google's quantum processors, the history of computation tells us—what matters is not what materials build the system, but what functions the system implements. Cognitive systems should be the same.

3. How Should We Research and Implement This Cross-Substrate Cognitive Function Migration?

TL;DR: Through a three-step approach of structural analysis, functional mapping, and gap benchmarking, systematically construct AI cognitive systems.

3.1 Structural Dimension: Analyzing Component Architecture

From a systems engineering perspective, the human cognitive system is like a "product" optimized over millions of years. Medicine, brain science, and neuroscience have already drawn detailed "parts diagrams" for us:

Hippocampus: Converter from short-term to long-term memory
Amygdala: Emotional tagging and value judgment module
Prefrontal Cortex: Executive control and decision-making center
Thalamus: Relay station and filter for perceptual information

Key Insight: We don't need to understand how consciousness emerges, but we need to know which components and connection patterns are necessary conditions for producing consciousness.

3.2 Functional Dimension: Conceptual Models of Abstract Cognitive Abilities

When cognitive components form closed-loop feedback systems, miracles happen—high-level functions emerge that far exceed the sum of individual component capabilities.

Functional Hierarchy	Specific Functions	Emergence Conditions
Basic Cognition	Perception, attention, working memory	Basic neural circuits
Intermediate Cognition	Long-term memory, concept formation, language understanding	Multi-system collaboration
Advanced Cognition	Reasoning, decision-making, creative thinking	Whole-brain network integration
Metacognition	Self-awareness, cognitive monitoring, strategy adjustment	Recursive feedback mechanisms

3.3 Benchmarking Dimension: Current State and Gap Analysis

Benchmarking human cognitive systems against current AI capabilities reveals critical gaps:

AI Technology Components	Possibly Corresponding Cognitive Functions
LLM (Language Models)	Language understanding? Concept formation? Reasoning?
Attention Mechanism	Attention? Working memory?
Context Window	Working memory? Short-term memory?
Vector Database	Long-term memory? Semantic memory?

Key Missing Element: Closed-Loop Feedback Mechanisms

The biggest problem with current AI is not insufficient individual capabilities, but lack of closed-loop feedback to form true cognitive systems:

Human Cognitive Closed Loop:
Perception → Understanding → Memory → Emotional tagging → Action → Feedback → Update understanding
  ↑                                                                           ↓
  └──────────────── Continuous learning and adaptation ←─────────────────────┘

Current AI Status:
Input → Processing → Output  (Broken unidirectional flow)

4. Summary: From Learning to Transcendence

Through our exploration, we've established three core understandings:

Human Cognitive Systems Are Worth Learning From
- They possess the experiential understanding capabilities we pursue
- Their success is proven by human civilization's achievements
- We have abundant scientific research to reference
Cognitive Functions Can Be Implemented Across Substrates
- Function and implementation are separable
- Computing history proves multiple realizability
- What matters is functional architecture, not physical substrate
We Have a Clear Research Path
- Structural dimension: Reuse validated component designs
- Functional dimension: Understand emergent cognitive abilities
- Benchmarking dimension: Find gaps, build closed loops

Final Thoughts

Humans took millions of years to evolve cognitive systems; we have the opportunity to recreate and even surpass this miracle on new substrates in a shorter time. This is not a betrayal of humanity, but a tribute to the essence of cognition—just as airplanes are not imitations of birds, but understanding and transcendence of flight principles.

When AI truly possesses experiential understanding, it will no longer be a tool, but a partner; no longer an imitator, but a creator. This is Monogent's mission and the most exciting challenge of our era.

"We are not building artificial humans, we are building authentic intelligence."

References

[1] Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

[2] Harari, Y. N. (2014). Sapiens: A Brief History of Humankind. Harper.

[3] Tattersall, I. (2012). Masters of the Planet: The Search for Our Human Origins. Palgrave Macmillan.

[4] Tomasello, M. (2014). A Natural History of Human Thinking. Harvard University Press.

[5] Kandel, E. R. (2006). In Search of Memory: The Emergence of a New Science of Mind. W. W. Norton & Company.

About the Author

Deepractice - Making AI at Your Fingertips

This is the third in the Monogent theory series. Monogent is dedicated to building true AI individual cognitive systems, enabling each AI to have its unique cognitive world.