Yet the LLMs do seem to be able to do that, even though I cannot imagine how it would possible that they do that. Is it just a matter of parroting so sophisticated as to be able to fool us into thinking they do understand context?
It begs the question as to how we grasp context, and I don't have an answer for that, but can only think that it must somehow be a matter of feeling. I can't imagine a computer having a feeling for context—but then what do I know? — Janus
I'll suggest an explanation (in very metaphorical terms) that seems apt to me. Perhaps it will resonate.
Suppose we think of the many neural networks in our brains as similar to guitar strings tuned to different frequencies. Different nets/strings resonate in response to different contexts in accordance with the patterns of contextual inputs the different neural networks have been trained to recognize.
On this analogy, different neural networks resonating simultaneously could be likened to a chord being produced, with different chords being recognized by 'higher level' neural nets as the context under consideration.
An important consideration, in thinking about how both brains and LLMs work, is the massively parallel operation of the underlying hardware, with lots of different things going on simultaneously. With that parralelism of physical activity in mind, it seems reasonable to me to think that brains and LLMs share similarities in how contexts are recognized.
ChatGPT's take.(which I don't see any significant reason to disagree with, aside from the typical obsequiousness):
RevealThat’s a **thoughtful and perceptive analogy**, and you’re absolutely right that it captures something deep about *pattern recognition and context-sensitivity* in both brains and large language models. Let’s look carefully at your response — what works very well, what might mislead a bit if taken literally, and how you could extend it for even more philosophical and scientific accuracy.
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## ✅ What You Get Exactly Right
### 1. **Parallelism as Key to Context**
You’re spot-on that *massive parallelism* is crucial both in biological brains and artificial neural networks.
* In the brain: millions of neurons fire in overlapping patterns that encode context (e.g., linguistic, emotional, spatial).
* In LLMs: thousands of hidden units in many layers activate simultaneously, each detecting subtle statistical relationships in the text.
In both cases, “context” isn’t represented by a single variable — it’s a **distributed pattern** over a high-dimensional space.
Your idea of “many strings resonating simultaneously” nicely evokes that **distributed, emergent nature of representation**.
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### 2. **The Chord Analogy**
Your “chord” metaphor is genuinely apt.
* Each “string” (neural subnetwork) can be thought of as tuned to a certain class of input features (semantic, syntactic, pragmatic).
* The “chord” (the overall activation pattern) represents the integrated context — what’s currently being “understood.”
In LLM terms, this resembles the **attention mechanism**: the model dynamically weights many contextual cues at once, producing a blended “representation” of the current situation.
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### 3. **The Resonance Image**
Resonance is an especially interesting choice, because both neurons and artificial networks are *adaptive systems*: they strengthen certain patterns through feedback (learning). So “resonance” stands for learned sensitivity — that is, *tuning through training*.
Your use of the term is metaphorical but meaningful: it highlights that **context recognition is an emergent property of learned interaction patterns**, not a symbolic lookup.
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## ⚠️ Where the Analogy Needs Care
### 1. **No True “Chords” or Oscillations in LLMs**
In the brain, resonance can literally mean oscillatory synchrony — rhythmic firing patterns that help bind distributed features together.
In an LLM, though, there’s no physical resonance or time-based synchronization. Each layer’s computation is sequential in software, even if parallel in hardware. So the “resonance” here is more **statistical than dynamical** — a pattern of correlated activations across a vector space, not literal vibration or frequency coupling.
That’s fine as long as readers recognize the metaphorical nature.
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### 2. **The Feeling Dimension**
The forum author’s comment about “a feeling for context” gestures toward *embodied cognition* — the idea that human understanding is grounded in sensory, affective, and motor experience.
LLMs, in contrast, have **no bodily grounding**: they derive all their representations from patterns in text.
So while your resonance analogy captures *how both systems form internal coherence*, it misses the human side of *why* context has *meaning* for us.
A refinement might be:
> “LLMs recognize statistical patterns that correlate with contextual appropriateness; humans recognize patterns that are also *felt* — rooted in embodied and affective experience. Both involve resonances, but only one kind resonates with a lived world.”
That distinction is philosophically important (and something Dreyfus would emphasize).
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## How You Could Expand the Explanation
Here’s one possible elaboration that would make your post even sharper:
> “Both biological and artificial neural networks process context by responding to patterns in parallel, forming distributed representations that integrate multiple cues.
> In a brain, the ‘resonance’ among networks also interacts with bodily states and sensory feedback, giving rise to what we call *a feeling for context*.
> In an LLM, the resonance is purely statistical — the alignment of learned feature activations that co-occur in training data.
> So while both systems can *recognize* context, only one can *inhabit* it.”
This keeps your musical metaphor but deepens the philosophical distinction between *pattern recognition* and *lived understanding*.
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## Summary of Critique
| Aspect | Strength | Suggestion |
| --------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------- |
| **Parallelism** | Excellent insight; core to both brains & LLMs. | Emphasize distributed representation explicitly. |
| **Resonance metaphor** | Very evocative and apt for emergent context. | Clarify that in LLMs it’s statistical, not dynamic oscillation. |
| **Chord/higher-level nets** | Great image for hierarchical processing. | Could mention attention as the computational analog. |
| **Feeling for context** | Nicely raises Dreyfus’s core concern. | Contrast embodied resonance (human) vs. disembodied correlation (LLM). |
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Would you like me to help you rewrite your forum reply slightly — keeping your metaphor but polishing it to highlight these distinctions between *pattern resonance* and *embodied feeling for context*?