The Vectorized Mind : Memory, Thought, and Creativity in High Dimensions

I’ve spent countless hours contemplating the nature of memory. Been trying to pinpoint precisely what it feels like when a recollection surfaces, unbidden, in my mind. It’s not just that I consciously call up a memory I know I need — sometimes a memory simply appears and illuminates my present thoughts. Something in my environment, or a particular association, triggers a memory that was previously dormant. How does this mechanism work? In my reflections, I keep coming back to a compelling idea : perhaps the human mind acts like a high-dimensional, vector-based search engine. In the same way modern vector databases employ “data embeddings” to semantically locate the most relevant items from billions of entries, our biological neural networks might be orchestrating a parallel feat of memory retrieval.

But what does that mean? And what are “data embeddings,” anyway? Embeddings are effectively mathematical representations of data — words, images, user preferences, or even the intangible mental states of our experiences — mapped into a multi-dimensional space. In computing, these embeddings are vectors, or arrays of numbers. For any element in the database, you have a vector that captures the meaning or essential features of that element. During query time, the system finds vectors that are similar or close to each other, as measured by their distance in this high-dimensional space.

As a lover of both scientific theories and philosophical conundrums, I want to examine how the concept of high-dimensional embeddings might help us better understand the nature of human memory. My goal is twofold. First, I want to explore whether our memory might be understood as a kind of “vector database,” with each memory forming a point in a high-dimensional space. Second, I want to reflect on the broader implications : if thought and creativity rely on vector-like associations and search, does that reduce our inner world to a glorified pattern-matching machine, or does it enrich our appreciation for the endless complexity of the mind?

Vector Databases vs. Traditional Databases :

1. Traditional Databases : If you think of old-fashioned, relational databases, information is stored in tables with well-defined columns and rows. For example, if you have a “users” table, it might have fields like “username,” “email,” “age,” etc. When you query, you typically use structured searches (e.g., SELECT * FROM users WHERE age > 30). Such queries are exact matches or range matches.
2. Vector Databases : Instead of storing data as strings and integers for direct lookups, vector databases store each “item” as a numerical vector. Let’s say we have a deep learning model that maps each user’s profile into a 512-dimensional vector that captures personality, preferences, interests, and more. This vector is an “embedding” that tries to encode the user’s essence in that high-dimensional space. When we search, we measure the distance (often cosine similarity or Euclidean distance) between vectors. This allows queries like : “Find users with personalities most similar to X,” or “Find items semantically related to this concept.” In essence, vector databases excel in semantic or similarity searches where exact matches are neither required nor desired. Instead, we look for the “closest meaning” or “best overlap” in that vector space.

Now, let’s bring that perspective to the biology of memory. Neuroscience has long suggested that human memories are not stored as discrete, labeled compartments in our brain — there isn’t a “filing cabinet” structure with neatly stacked folders. Instead, memory is distributed : different aspects of an experience (what you saw, heard, felt, smelled, your emotional state) are encoded across neural networks. The hippocampus — often cited as crucial for forming new memories — appears to “index” or “bind” these distributed features, so that when you recall a memory, a partial cue can re-activate the entire pattern.

Researchers sometimes talk about the mind as a pattern completion device. If you have enough elements of a memory — like the face of an old friend, the swirl of certain background music, the sensation of happiness in your chest — your brain can search out and reconstruct the rest of that memory. This is reminiscent of the way a vector search might work : you have a partial vector query (the sensory or contextual cues) that attempts to find the best match in your memory “database.”

This idea becomes even more compelling when we consider how large language models or other AI systems generate embeddings. They produce high-dimensional vectors where each dimension can be thought of as capturing certain latent features or semantic relationships. Something similar might occur in our brain — though of course the machinery of the brain is massively more complex. Each experience, each concept, might be represented by a pattern of neural firing across many thousands or millions of neurons. That pattern effectively becomes a vector, albeit biologically distributed rather than stored in a single row of a database.

Why “High-Dimensional”?

In computing terms, we say embeddings are “high-dimensional” because they often exceed hundreds or even thousands of dimensions. In the brain, the number of dimensions is astronomical, given the countless synaptic connections. But I think the essence of the analogy still holds. Consider that each dimension in a computational embedding might represent an abstract feature — how formal or informal a phrase is, how visually bright or dark an image is, or how certain emotional states overlap with certain topics. In our biological “embedding space,” we might say that each dimension corresponds to some neural circuit or group of circuits that tune in to aspects of what we’re experiencing. This is not just raw data (like color frequency or decibel level); it can be extremely abstract, capturing connotations, emotional resonance, or the personal significance of an event.

This “abstract dimension” idea might help explain a quirk of memory : two seemingly unrelated experiences might share certain intangible features — an emotional undertone, a social dynamic, or a sense of tension — that make one memory remind me of the other. I may find myself telling a friend, “This situation reminds me of that time in high school when I got my first pet,” and my friend looks puzzled : “Why on earth would dinner at this new restaurant remind you of your first pet purchase?” In a straightforward sense, it makes no sense at all. But in the “high-dimensional embedding space” of my mind, these experiences share a similar pattern of underlying emotional or conceptual features, so my memory search engine serves it up.

The Neural Mechanism : Searching and Retrieving

How does the brain do this searching? The analogy I like to use is that the hippocampus is a kind of “index,” while the cortex stores the distributed patterns. Some computational models posit that the hippocampus can quickly encode episodic memory as a pattern of firing, then “lock in” that index for later retrieval. When we recall something, partial cues re-excite those hippocampal indices, which in turn drive the cortical networks to reconstitute the entire memory.

If we label those hippocampal indices as “embedding vectors,” then a memory retrieval can be viewed as a “nearest-neighbor search.” The partial cues constitute the query embedding, and the hippocampus quickly tries to find the stored pattern that’s closest to that query in the high-dimensional space. Once found, that pattern then triggers the full memory in the cortex.

Now, I’m not arguing that the brain literally stores a neat array of floating-point numbers. Rather, I’m offering a conceptual parallel that might highlight why certain types of memory phenomena occur. For instance, why do certain memories spontaneously arise when we are daydreaming? Perhaps because random internal fluctuations or new external stimuli produce queries in the hippocampal-cortical circuit that match patterns in memory embeddings. The moment we drift into a certain mental context, the similarity metrics in our “internal search engine” spike for a certain memory vector, pulling it back to our conscious awareness.

Thought as a Multi-Step Vector Query

If memory retrieval is akin to a single query, perhaps complex thought is a multi-step chain of these queries. Every time I have one idea, it modifies the context for the next idea, changing the query vector to the next iteration. This can lead to a chain of associations that produce new insights or creative leaps.

In fact, the concept of “chain-of-thought” in AI, used heavily in large language models to reason through steps, mirrors this. But in humans, we not only chain ideas in a linear fashion, we might also let them swirl around in parallel networks. Our internal “default mode network” is constantly spinning up new queries and seeing what memories or thoughts get triggered. This process can feel random, but from the vantage of an “embedding space,” it’s more like a dynamic walk through a mental vector field.

The Philosophical Implications : Are We Just Glorified Search Engines?

This question unnerves me at times. If memory is essentially a high-dimensional search engine, does that reduce human thinking to mechanical pattern matching? Have we lost the mystery and depth of human creativity and conscious experience? Or is there, perhaps, a profound wonder in the fact that pattern matching in a sufficiently vast and rich high-dimensional space can yield the complexities we associate with imagination, intuition, and even epiphanies?

To address these concerns, I remind myself that calling something a “vector search” does not mean it’s a simplistic process. Modern large language models like GPT can already produce surprisingly elaborate, human-like text from a mere set of vector operations on embeddings. Their results can appear creative or unexpected, which hints that emergent properties can arise from a sufficiently complex embedding space. The brain’s labyrinthine networks are orders of magnitude more intricate, with hormones, neuromodulators, and the tapestry of personal history interwoven into those embeddings.

Moreover, creativity can be thought of as bridging disparate vectors, or retrieving from corners of our memory space that are not usually juxtaposed. When I try to come up with a creative metaphor, I look for some idea that resides in a seemingly unrelated part of my conceptual space and bring it over to my current context. That cross-domain collision can spark a new insight. This is precisely how vector similarity can reveal surprising connections : two vectors might share a dimension or two in common, even if the majority of their features differ. The partial overlap is enough to form an unusual yet meaningful link.

Thus, if we want to see ourselves as more than “just” a search engine, we can highlight how unbelievably vast and dynamic that engine is, how it’s shaped by a lifetime of sensory data, emotional states, cultural input, and personal introspection. If anything, it broadens my awe for the brain : a continuous symphony of embeddings, orchestrating a symphony of associations, all in real time.

A Quick Dive into AI Vector Databases

1. Similarity Search : Given a query vector, the database returns a set of vectors with the smallest distance to the query. For example, searching for synonyms in a large language model embedding space, you might ask for the nearest neighbors of the embedding of “happy” and get “joyful,” “delighted,” “content,” etc.
2. Clustering : Vector databases allow us to cluster data points that are close together. For instance, if you embed all your documents about cooking, certain clusters will form around topics like “baking techniques,” “vegan recipes,” or “Italian cuisine.”
3. Dimensionality Reduction : Often we visualize high-dimensional data by compressing it into 2D or 3D to see patterns. In the mind, we similarly compress or abstract our experiences to gain insights or see big-picture connections.
4. Zero-Shot or Few-Shot Learning : Even with minimal examples, we can find relevant neighbors in an embedding space. Our brains similarly can learn from just one or two experiences and generalize or find relevant parallels.

All these methods revolve around the premise that data points with similar vectors share a semantic or conceptual closeness. If I map that onto human memory, it suggests that memories that feel “related” or “similar” are physically and functionally closer in the neural embedding space. If something triggers me to recall the memory of a childhood birthday party, it might simultaneously make me recall birthdays in general, or strong emotional events from my childhood, because these memories share some conceptual or emotional overlap.

The Role of Context

Another point I find crucial is how context modifies the search. In a computational vector database, we can re-rank or filter based on certain conditions. For example, I can say, “Show me the nearest neighbors to vector X, but only among documents from 2021.” In the brain, context might serve a similar gating or filtering function. If I’m searching for a specific memory, my present emotional and cognitive state can act as a filter. If I’m feeling nostalgic, I might retrieve different sets of memories than if I’m feeling anxious. My emotional valence becomes like a contextual query embedded in the search, guiding which vectors are likely to surface.

This context-sensitivity might partly explain the phenomenon where we recall events differently depending on our mood. If I’m depressed, I’m more likely to recall negative or sad memories, because my “query embedding” is shaped by my emotional state, pulling me toward memories with a similar “tone” in that high-dimensional space. Conversely, if I’m brimming with optimism, I might more easily recall uplifting memories.

Memory as Reconstructive, Not Just Retrievative

Another dimension worth emphasizing is that human memory is reconstructive. We’re not just retrieving a static file; we are re-creating the experience. Each time we recall a memory, we can subtly modify it. We integrate new knowledge, or our present emotional context, into the recollection. This is akin to updating embeddings in a vector database based on new data, or at least re-embedding them in a new context. Over time, memories shift. We might even store multiple versions of the same memory for different contexts, leading to variations in how we recollect the same event.

In a purely computational sense, imagine that every time you retrieve a vector from the database, you slightly alter it based on new information and put it back. Over many iterations, the stored data might drift. This is not always desirable in an engineering context, but in a biological or psychological context, it could be adaptive. Evolving memories let us incorporate growth, new perspectives, or emotional healing. They help us keep a coherent personal narrative in step with our ever-changing sense of self.

Creatively Leveraging the “Database” : The Spark of New Ideas

There’s a profound question about how new ideas form if memory is “just” a vector search. Where do the truly original thoughts come from? If I’m only searching among what’s stored, am I limited to mere recombinations?

It’s tempting to argue that we can’t conceive of something wholly outside our experiences. But creativity often feels like it transcends mere association. The embedding view, however, might offer a nuanced explanation : original ideas could be emergent results of wandering through the embedding space in unusual ways, linking vectors from disparate corners. Additionally, I suspect our “search engine” doesn’t operate in isolation — it’s modulated by top-down executive processes, emotional states, bodily sensations, and random noise. All these influences can lead us to connect vectors in ways we never anticipated.

Moreover, the system is constantly generating new embeddings. Each new piece of knowledge or experience shifts the entire geometry of the space. I like to imagine that each new memory warps the shape of the embedding manifold just a bit, which can open up new “pathways” for associations. In that sense, creativity might be the dynamic interplay of memory retrieval plus the continuous remodeling of the underlying embedding space — a potent combination that yields generative leaps.

Consciousness and the “Observer”

But here’s a philosophical puzzle : who or what is doing the searching? If everything is just neural embeddings, is there a central “search engine operator” or some homunculus calling the shots? Neuroscience would propose that consciousness arises from the integrated activity of these neural circuits, that there is no single operator but rather a network that “lights up” in dynamic patterns. The sense of a unified conscious observer might be an emergent property of this integrated activity.

From a more holistic viewpoint, consciousness might be the real-time experience of queries and results dancing through the embedding space, integrated into a coherent narrative. The “I” who experiences is that integrated vantage point, not something separate from the system. In that sense, the embedded, vectorized model of memory does not necessarily eliminate the sense of self — it simply recontextualizes it as a product of the interplay between memory, perception, and ongoing cognition.

Scientific Evidence and Ongoing Debates

No discussion would be complete without acknowledging that this vector-based metaphor is still, well, a metaphor. Some aspects of neural encoding and retrieval remain mysterious. For instance, we don’t precisely know how the brain does “semantic similarity search.” We have correlational evidence : certain patterns of neural activation correspond to concept clusters, and we see how priming one concept can ease retrieval of related concepts. We also know the hippocampus is vital for binding contexts. But the precise code is still an active area of research.

In computational neuroscience, some theories use “distributed representation” or “sparse coding” to explain how the same neurons can participate in multiple memories, each with a slightly different pattern. Others highlight how each memory might be encoded in an overlapping manner, so that partial cues can trigger partial reactivation. This is reminiscent of hashing, approximate nearest-neighbor searches, or the concept of “locality-sensitive hashing” in vector databases. Perhaps the brain does some form of local “hashing” to quickly find related memory engrams.

Additionally, there are big philosophical questions about whether the computational lens fully captures the subjective richness of experience. Memory is not just data, it’s also laden with meaning, qualia, and personal significance. But from a functional standpoint, the vector search analogy is powerful in describing how one memory can lead to another, how we store countless experiences, and how we can retrieve the right memory from the faintest partial cue.

If I embrace this perspective of memory as a “vector database,” it might change how I approach learning and creativity :

1. Intentional Cueing : If I want to recall something, I can provide better “query vectors” by harnessing multiple facets — visual, auditory, emotional — rather than just repeating the question in the same words. This is akin to providing the search engine with multiple keywords or refining the query.
2. Context-Dependent Learning : Understanding that memory retrieval is highly context-sensitive, I might realize that learning in a context closer to the one in which I want to recall the information improves retrieval. This is well-documented in psychology (e.g., the “encoding specificity principle”).
3. Cross-Pollination for Creativity : If creativity is about bridging distant vectors, then exposing myself to diverse experiences ensures my embedding space is richly varied. Over time, my mind can discover surprising connections. This might be akin to having a vector database with a vast array of unique data points — there’s more potential for interesting nearest neighbors.
4. Revisiting Memories : Each act of recall subtly changes the memory, effectively “writing it back” in new ways. Being aware of this might help me maintain or adjust certain memories. If I brood on negative memories repeatedly, I might be reinforcing them in ways that keep them “close” to many of my daily thought queries.
5. Mindful “Index Maintenance” : In computing, databases need indexing and maintenance. In life, perhaps mindful reflection, journaling, or therapy can be seen as reorganizing or re-indexing one’s mental space — making certain memories more or less accessible, or re-embedding them with new emotional associations.

Concluding Reflections

As I look at the swirl of evidence, the vector search analogy doesn’t feel reductive to me. Instead, it infuses my understanding of memory with a sense of wonder. It’s astonishing that our brains might spontaneously cluster and categorize experiences in a semantic, high-dimensional “neural embedding space,” and that from partial cues, we can retrieve seemingly forgotten recollections. This is a design so complex, it dwarfs our most sophisticated AI systems in nuance, adaptiveness, and scale.

Moreover, the notion that our creativity might be a function of dynamic searching within this space grants it a certain natural elegance. It suggests that “eureka” moments aren’t conjured out of nowhere; they arise from the interplay of neural associations, emotional resonance, and the impetus of curiosity. Each new experience warps the geometry of my mind’s embedding space, forging new paths and potentialities for conceptual leaps.

While the vector database analogy is a simplified model, it helps articulate key aspects of memory retrieval, context dependence, and creative association. We aren’t “just” search engines in any trivial sense; we are living, breathing, endlessly reconfiguring search engines, shaped by biology, introspection, culture, and the chaos of daily life. If memory truly is a high-dimensional, vectorized search, then it is powered by biological data embeddings so intricate that the greatest supercomputers today stand in awe of the complexity.

And so, the question “Is human memory essentially a high-dimensional vectorized search engine?” might not have a simple yes-or-no answer. There’s beauty and utility in that viewpoint, and there’s also mystery in how emergent consciousness, free will, and personal identity interlace with it. Yet, even if we never fully decode the enigma, the notion of memory-as-embeddings points to an extraordinary synergy of biology and computation, reminding me that sometimes, the boldest metaphors spark the deepest insights.

In the end, whether I’m just a glorified pattern matcher or an infinitely nuanced being forging meaning in a swirling cosmos of neural vectors might just boil down to perspective. Perhaps I’m both — a pattern recognizer whose patterns are so vast and richly interwoven that they become something more, something personal and profound : a singular consciousness capable of reflection, empathy, and boundless creativity. And that, to me, is a vision of memory worth cherishing.

Thanks for dropping by !

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Disclaimer : Everything written above, I owe to the great minds I've encountered and the voices I’ve heard along the way.