General
From Cortex to Code: The Neural Basis of Answering Questions
Apr 9, 2025
In my previous article, I explored the fascinating parallels between Large Language Models (LLMs) and the human brain. Building on that foundation, this article dives deeper into a specific question: what actually happens — in the brain and in an LLM — when a question is asked? From perception and memory to inference and expression, we’ll examine how the brain and LLMs each tackle this seemingly simple task — and what it reveals about intelligence, awareness, and the mechanics of thought.
What happens when a question is asked to us?
The human brain’s response to a question involves a dynamic interplay of cognitive processes and neural pathways. The process begins with an inward search through memory and, when needed, transitions to exploring external sources for answers.
Hugging Face
Here’s how the process unfolds:
1. Perception & Understanding
When a question is asked, sensory input — whether auditory (processed in the auditory cortex) or visual (in the visual cortex) — is converted into neural signals.
Wernicke’s area, along with other temporal and parietal regions, contributes to decoding language into meaningful concepts.
The Prefrontal Cortex filters distractions, maintains attention, and connects the question to prior knowledge (e.g., linking “Aurora Borealis” to physics or astronomy rather than history).
This “biological preprocessing” helps the brain interpret not just the words, but their intent and relevance.
Wernicke’s area is a region in the brain (typically in the left temporal lobe) involved in language comprehension — understanding both spoken and written language. It doesn’t just “transcribe” sounds into text; it interprets meaning from language. You can think of it as speech-to-text (STT) systems.
2. Memory Retrieval (Search for Known Information)
When tasked with answering a question, the brain first turns inward.
The hippocampus — a small, curved structure vital for learning and memory — springs into action, searching episodic memory (personal experiences) and semantic memory (facts and concepts) for relevant knowledge
If the answer exists in memory— say, a fact like “water boils at 100°C” — relevant neural pathways activate, retrieving the stored data. Broca’s area then helps structure the retrieved knowledge into a coherent verbal or written response. This seamless process represents the “happy path”, where the brain effortlessly answers from its existing repository.
But what happens when the answer isn’t there? The hippocampus signals a dead end, triggering a shift to problem-solving mode — a cascade of adaptive strategies to bridge the knowledge gap.
Broca’s area is primarily responsible for speech production and language processing, especially grammar and syntax. It helps us formulate grammatically correct sentences and plan the motor actions necessary for speaking
3. Metacognition
If the answer isn’t in memory, the anterior cingulate cortex flags uncertainty, triggering a Metacognitive alarm. The prefrontal cortex then evaluates confidence levels: “Do I know this? If not, where can I find it?” This self-awareness separates humans from most species — we not only recognize ignorance but actively plan to resolve it.
Metacognition refers to the awareness and regulation of one’s own cognitive processes. It involves “thinking about thinking,” enabling individuals to plan, monitor, and evaluate their learning and problem-solving strategies.
4. Problem-Solving & External Search
The prefrontal cortex activates executive functions to strategize potential actions, such as:
Do I have a book or article that talks about this?
Do I know people (teachers, experts) who might know about the subject?
Have I bookmarked something online?
Simultaneously, semantic memory retrieves schemas about external knowledge sources (e.g., libraries or the internet) based on prior understanding of where information might reside. Episodic memory then contributes by recalling past experiences — like successfully using Google — to guide the selection of an effective strategy
5. Accessing External Information
Executing the plan requires sensorimotor coordination
Reading a book activates the visual cortex to process text;
typing a query involves the motor cortex to press keys.
Once external information is acquired, the prefrontal cortex in the brain evaluates and integrates this new data, filtering relevance and connecting it to existing knowledge. The hippocampus then encodes the processed information into long-term memory, updating neural pathways to reflect this novel understanding.
This dual-phase process — capturing external knowledge and consolidating it through learning (primarily during sleep and repetition) — ensures that newly acquired knowledge becomes a retrievable part of the brain’s cognitive toolkit for future use.
The brain treats tools like books or smartphones as external ‘hard drives’ — Step 4 & 5 are analogous to a Retrieval-Augmented Generation (RAG) pipeline in language models, where the LLM decides to query external data sources to enhance its internal knowledge and generate a more informed response.
6. Synthesizing & Delivering the Answer
Once external data is acquired, the brain integrates it. The prefrontal cortex and temporal lobes connect new facts to existing knowledge, creating a coherent response. For verbal answers, Broca’s area structures language, while the motor cortex controls speech or typing (basically step 2).
If explaining to others, the prefrontal cortex ensures the response is logical and context-appropriate. Remarkably, the brain also “tags” the source of new knowledge (e.g., “I read this in a study”), aiding future credibility checks.
7. Emotional Reinforcement
Finally, the brain’s reward system reinforces learning. Successfully answering a question triggers dopamine release in the ventral striatum, creating satisfaction — a motivator to seek knowledge again.
Conversely, unresolved uncertainty may activate the amygdala, fueling frustration but also persistence. Over time, this cycle strengthens neural pathways, making future problem-solving faster.
dopamine — a neurotransmitter in the brain, playing a vital role in mood, movement, motivation, and reward.
ventral striatum — a brain region crucial for reward processing and motivation, particularly related to the anticipation and detection of rewards
Example: Answering “What Causes Aurora Borealis?”
Perception: You hear the question; auditory cortex and Wernicke’s area decode it
Memory Search: Hippocampus fails to retrieve details — you only recall it’s “something with lights.”
Gap Detection: anterior cingulate cortex signals uncertainty; prefrontal cortex decides to Google it.
External Search: Motor cortex types the query; visual cortex reads about solar winds and magnetospheres.
Integration: prefrontal cortex links this to basic physics knowledge; hippocampus stores the new details.
Answer: Broca’s area helps you explain: “Charged particles from the sun collide with Earth’s magnetic field!”
Reward: Dopamine makes you feel accomplished — you’ll remember this next time.
What happens when a question is asked to an LLM?
Unlike humans, LLMs follow a very different — but fascinating — process:
Perception & Understanding
Large Language Models (LLMs) understand the question through embeddings and transformer encoders:
Embeddings: Words are converted into dense numerical vectors that represent their contextual meaning. For example, “king” and “queen” lie close in vector space, differing along interpretable dimensions like gender or role.
Encoder Layers: Transformer-based LLMs apply stacks of encoder layers that use self-attention to evaluate relationships between all input tokens. This allows the model to disambiguate context (e.g., distinguishing “bank” as a riverbank vs. a financial institution). While not biologically grounded, this mechanism computationally resembles how the prefrontal cortex filters relevant information and maintains focus.
The brain operates through dynamic, adaptive biological circuits shaped by evolution, experience, and sensory-motor feedback. LLMs, by contrast, use static mathematical architectures trained on large text datasets. Yet, both systems convert raw input — whether sound or text — into structured, context-aware representations: neural activations in the brain, and embeddings with attention in LLMs.
2, 3. Inferencing (Predicting Response)
Large Language Models (LLMs) do not have a hippocampus — or any analog of episodic memory.
Their “knowledge” is encoded in billions of parameters shaped by statistical patterns in training data. At inference time, this knowledge is generally static, unless the model is integrated with external tools or retrieval systems.
When prompted with a question, an LLM does not retrieve stored facts. Instead, it uses probabilistic modeling to predict the most likely next token(s) based on the input and learned patterns. There is no active memory search or factual lookup — just a forward pass through a neural network that produces a continuation of text.
Humans retrieve information from structured memory systems shaped by experience and context, often with metacognitive awareness of certainty or gaps. LLMs, by contrast, have no sense of knowing or not knowing; they generate plausible-sounding outputs based on statistical likelihood, not retrieval or reasoning.
When an LLM encounters a prompt that falls outside its training distribution, it still attempts to respond — because its architecture is designed to always produce output. This can lead to the generation of incorrect or fabricated content, a phenomenon known as Hallucination.
Hallucinations arise not from deception, but from the model continuing text in the most statistically consistent way it has learned — regardless of factual correctness.
However, in some cases, LLMs exhibit a learned version of the “better safe than sorry” heuristic. When a prompt contains unfamiliar or low-confidence patterns — such as a potentially made-up term — the model may lean toward a cautious response (e.g., “no such concept is widely recognized”) instead of fabricating details. This behavior emerges from pattern generalization during training and mimics a form of uncertainty handling, though it’s not driven by actual awareness or reasoning.
To mitigate hallucinations and improve factual accuracy, many modern LLMs are now extended with tool-use capabilities. These allow the LLMs to access external resources — vector databases, APIs, calculators, code interpreters, or web search engines — to supplement its static knowledge and respond more accurately. However, the LLM needs to be made aware about the available tools and when and how to invoke them. This can be accomplished using one of the following approaches:
a. Fine Tuning the LLM with list of available tools. This involves training the LLM on a custom dataset that includes examples of tool usage. For instance, the dataset could include conversational:
b. Prompt Engineering: the tool usage instructions can be embedded in the system prompt given to the model at inference time
Sample Prompt used in fine tuning for tool invocation
You are a helpful assistant. You can use the following tools:
– get_weather(location): returns current weather
– calculate(expression): evaluates a math expression
– search(query): performs a web search
– create_calendar_event(title, time): schedules an event in the user's calendar
– send_email(to, subject, body): sends an email
Always call the appropriate tool when needed, and present the response to the user.
Now when the LLM encounter a prompt like — “Schedule a call with Anurag at 5 PM”. It predicts something like:
create_calendar_event(“Call with Anurag”, “5 PM”)
Unlike the human brain, which can often sense uncertainty and then intentionally seek help or check. LLMs do not validate the correctness of their generated response before calling a tool. Instead, If a tool is available during inference, the LLM is typically fine-tuned or prompted to call it when certain patterns are detected in the input or task.
Tool usage looks smart because it mirrors reasoning, but it’s not backed by actual understanding or correctness evaluation. LLMs can simulate reasoning, but they do not “know” in the way humans do.
4–7. No Reinforcement, No Reflection: The Boundaries of LLM Inference
Unlike the human brain, which actively explores external sources, integrates new information, and reinforces learning through emotional feedback (step 5–7), Large Language Models do not engage in such processes during inference.
While some LLMs use tool calling to facilitate Retrieval-Augmented Generation (RAG) to access external data sources, this retrieval is orchestrated through engineered pipelines — it lacks genuine curiosity or understanding. Furthermore, LLMs do not learn or reinforce new knowledge at inference time.
Any form of “learning” or optimization, including reinforcement learning (such as RLHF), happens during the training phase. Once deployed, the model does not modify its internal representations based on retrieved information or feedback. In essence, LLMs can mimic aspects of knowledge acquisition, but they do not undergo continuous learning or self-improvement in the way biological systems do.
Conclusion: The Illusion of Knowing, The Mechanics of Intelligence
At first glance, answering a question seems like a trivial task — hear, think, respond. But whether it’s the human brain or a Large Language Model, what unfolds beneath the surface is anything but simple. Both systems engage in complex transformations: from raw input to contextual understanding, from abstract processing to concrete expression. But the how and why behind these processes reveal fundamental differences.
Human-made machines have achieved a remarkable feat: they can now answer questions with a fluency that mirrors human thought. Yet beneath the surface, they operate without understanding, intent, or awareness. The brain, shaped by nature’s evolution over millions of years, navigates uncertainty, learns through experience, and reflects on its own thinking. LLMs, by contrast, predict rather than know, and generate rather than understand. The gap is still vast — and while we’ve built something impressive, it’s only the beginning of a far more complex journey.
As we stand at the edge of increasingly human-like AI, the question is not just can machines think — but how should they? It’s up to us — scientists, engineers, thinkers, and citizens — to shape this technology with wisdom, responsibility, and humility. Understanding how we think is not just a curiosity — it’s a compass. If we want machines to serve humanity meaningfully, we must ground their development in a deeper understanding of the human mind. Because in learning how we answer questions, we uncover the questions that matter most.
If this made you nod, pause, or raise an eyebrow — give it some claps (you can hit it 50 times 😉).
Got a hot take or curious thought? Drop it in the comments — I’m all ears.