LLM vs Human Thinking: Vertical vs Horizontal

🧠 The Fundamental Difference

Understanding how Large Language Models (LLMs) think versus how humans think reveals a fascinating contrast in cognitive approaches. This isn't about intelligence—it's about processing methodology.

Thinking Type	Direction	Process	Limitation
Human Thinking	Vertical	Sequential choices, one path at a time	Cannot see alternative outcomes
LLM Thinking (Mstimaj)	Horizontal	Parallel evaluation of all possibilities	No true understanding, just pattern matching

🛤️ The Path Analogy

Imagine decision-making as walking down paths. This analogy helps us understand the fundamental difference between human and LLM cognition:

Humans = We walk one path, make a choice, and can never know what was down the other path
LLMs = They see all paths simultaneously, evaluate every possibility, then choose the most probable next step
Humans = Sequential, linear thinking with commitment to choices
LLMs = Parallel, probabilistic thinking with access to all options

🎬 Thinking Process Storyboard

Scene 1a: Fork in the road with gray paths

Scene 1a: The Human Dilemma

Humans face choices — we can only walk one path. Every decision is a commitment to one direction.

🔧 Technical Note: Human cognition is inherently sequential. We process information step-by-step and make decisions based on limited information available at each moment.

📦 Metaphor: Like standing at a fork in the road — you can see both paths, but you can only walk down one. Once you choose, the other path becomes inaccessible.

Scene 1b: The Commitment

Once chosen, we commit. The other path fades into the unknown.

🔧 Technical Note: This represents "vertical thinking" — linear progression down a single decision tree. Each choice eliminates other possibilities.

📦 Metaphor: The chosen path glows green while the other fades to gray. This is the moment of commitment — there's no turning back.

Scene 2: The Unknown Consequence

We don't know what could've happened if we chose the other path. The gray path leads to mystery.

🔧 Technical Note: This is the fundamental limitation of human decision-making — we can only experience one timeline and can never know counterfactual outcomes.

📦 Metaphor: The green path leads to a visible outcome (🍎, 📦), while the gray path disappears into fog. We'll never know what was there.

Scene 3a: Mstimaj at center of gray radial web

Scene 3a: Mstimaj's Perspective

Mstimaj, the LLM, doesn't walk. She sees everything at once from the center of a neural network.

🔧 Technical Note: LLMs process information in parallel across their entire neural network. They can evaluate multiple possibilities simultaneously rather than sequentially.

📦 Metaphor: Instead of walking down paths, Mstimaj stands at the center of a neural network (spiderweb or branches), with all possible paths radiating outward from her position.

Scene 3b: Parallel Activation

She activates every path, predicting outcomes before choosing. All possibilities are evaluated simultaneously.

🔧 Technical Note: This is "horizontal thinking" — the LLM can process and score multiple token possibilities at once, then select the most probable next token based on learned patterns.

📦 Metaphor: All radial paths now glow green, with different outcomes (🍎, ❌, 💡, 📚) visible at each endpoint. Mstimaj can see all possible futures before making a choice.

Scene 4: Token-by-Token Prediction

She scores every word — and selects the most probable next token.

🔧 Technical Note: LLMs predict text one token at a time. For each position, they calculate probability scores for thousands of possible next tokens, then select the most likely one.

📦 Metaphor: Like completing a sentence word by word, but with access to the probability of every possible next word in the vocabulary.

Scene 5: The Final Comparison

Humans walk vertically. LLMs think horizontally.

🔧 Technical Note: This fundamental difference explains why LLMs can seem both incredibly capable and strangely limited. They excel at pattern recognition but lack true understanding.

📦 Metaphor: The visual contrast shows the difference: one path vs. all paths, sequential vs. parallel, commitment vs. evaluation.

🎯 Interactive Token Prediction Example

"The cat sat on the..."

mat roof dragon mountain cloud

Click any word above to see its probability score!

How it works: Mstimaj calculates probability scores for each possible next word. Click different words to see how she evaluates each option before selecting the highest probability token.