Cognitive Science

Cognitive Science is the interdisciplinary study of the mind and intelligence, focusing on how humans (and other systems) perceive, think, learn, reason, communicate, and act. It combines knowledge from psychology, neuroscience, linguistics, philosophy, computer science, and anthropology to understand how cognition — the process of knowing — actually works.

How Simple Brain-Like Systems Learn and Remember

~ Manuel Castañón-Puga

Imagine you’re trying to remember the name of a song. You don’t recall the whole thing—just a fragment of the melody or a single lyric. But somehow your brain fills in the rest, and the entire song suddenly pops into your mind. This everyday moment shows something powerful: even small bits of information can trigger complete memories. The document by Hopfield explains how elementary networks, made of many tiny “on/off” units, can behave in surprisingly brain-like ways and perform tasks like this without needing complicated programming.

Hopfield and colleagues describe how a network of simple neurons—each capable of switching only between “on” and “off”—can work together to store memories and retrieve them when given partial hints. For example, if the network had learned several patterns, showing it only part of one pattern could make the whole system automatically “flow” toward the full version. This happens because the system creates stable states, like resting spots, that it naturally falls into. It’s similar to how a marble dropped on a bumpy surface always ends up in one of the low dips. If your starting point is close enough to a dip, the system finishes the job for you and returns the full memory.

What’s especially interesting is that these networks can correct small mistakes, sort confusing inputs into categories, and even recognize when something is unfamiliar. For instance, if the system is shown a pattern that doesn’t match any of the stored memories, it settles into a special “unknown” state, acting almost like a built-in warning that the input doesn’t fit anything it has seen before. The document also shows that the network continues to function even if some of its connections fail or if many memories are stored simultaneously; its performance slowly degrades rather than collapsing suddenly. This “fail-soft” behavior is rare in ordinary computer circuits but everywhere in biological systems.

The most surprising part is how all these smart behaviors don’t come from any single neuron being clever. Instead, they arise from the collective behavior of many simple units acting together. This idea matters beyond neuroscience. It suggests that powerful abilities—such as recognizing faces, learning patterns, or making quick decisions—can emerge from surprisingly simple parts working in parallel. For young people learning about technology and the brain, this demonstrates that intelligence doesn’t always require complexity at the most fundamental level. Sometimes, it’s the connections, the cooperation, and the way the whole system behaves that create something much more potent than the pieces alone.

Reference:
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

How Your Brain Runs Its Own Belief Network

~ Manuel Castañón-Puga

Imagine you are at university, sitting in the library, when three things happen almost simultaneously. A friend messages you, “Huge storm coming, buses might stop.” At the same time, you see a dark cloud through the window, and then you read a post online saying, “Public transport strike today!” In a few seconds, you decide whether to pack up and leave or keep studying. You do not write down equations, but you quickly combine these bits of information, ignoring some while trusting others more, and end up with a single decision. This everyday moment is precisely the kind of situation that Pearl describes when he talks about “belief networks” and how we fuse and spread information in our minds.

Pearl describes a belief network as a web of small questions about the world, each one represented as a node, with arrows indicating which ideas directly influence which. A node might be “there is a storm,” another “the bus is late,” another “I see dark clouds,” and so on. Instead of trying to track every possible combination of all these ideas, the network only stores simple, local relationships: how strongly one thing affects another. Pearl explains this using examples like suspects, fingerprints, and lab reports, where each piece of evidence is linked to a possible cause. The key insight is that our mind does not handle one giant, impossible table of chances; it uses many small links between related ideas, which is much closer to how we actually think when we ask, “If this is true, how likely is that?”

Once the network is in place, new information has to move through it, and this is where things become very practical. Pearl shows that each link can carry two kinds of support: one coming from “causes” (what usually leads to this) and one from “effects” (what we have seen that points back to it). When something changes—say you get a new lab report, or in your life, a new message, a news alert, or a friend’s opinion—that update first affects the nearby node and then spreads step by step through the network. Importantly, each node only communicates with its neighbors, so the process is local and easy to manage, yet the final picture remains globally consistent. Pearl even warns that we must avoid counting the same clue twice, like when a rumor appears on several accounts that all secretly copy each other. His method keeps “upward” and “downward” flows of belief apart so they do not get stuck in loops of self-reinforcement.

Another idea from Pearl that fits daily life is the concept of multiple explanations competing. In one story, an alarm can be triggered by either a burglary or an earthquake. Hearing that the alarm went off increases your belief in both causes. Still, once you also hear a reliable earthquake report, the “earthquake” explanation makes the “burglary” explanation less likely, because one clear cause can “explain away” the same event. The same pattern appears when you feel tired before an exam: you might blame stress, lack of sleep, or getting sick. A positive COVID test, for instance, suddenly shifts most of your belief toward one cause and away from the others. Pearl and colleagues also discuss “hidden causes,” extra nodes that we do not directly see but that help explain why several things tend to happen together, such as a shared background reason for your friends’ moods or repeated delays on your train line. Thinking in terms of these networks can help young people make better choices: check where your information really comes from, notice when two pieces of “news” are actually the same source, and remember that one good explanation can reduce the need to invent many others. In short, your mind is already running a belief network; learning to see it that way can make your everyday reasoning clearer, calmer, and more honest.

Reference:
Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29(3), 241–288. https://doi.org/10.1016/0004-3702(86)90072-X

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

How We Actually Make Good Decisions (and Why the Bar Gets Crowded)

~ Manuel Castañón-Puga

You’ve checked Maps and your favorite café looks “busy.” Should you go anyway? You text a friend: “Last Thursday it was packed, so today might be fine.” That’s you doing what most of us do when things are uncertain. Not perfect math. Just pattern-spotting and a best guess. Economist W. Brian Arthur says that expecting people to use flawless, step-by-step logic in real life is unrealistic, especially when situations become complicated or when other people’s choices continually shift the game. In messy problems, strict logic runs out of road, and we fall back on simpler ways of thinking. We look for patterns, try a plan, see how it goes, and adjust. That’s normal, not lazy. It’s how humans cope when full information and crystal-clear rules aren’t available.

Arthur calls this inductive reasoning. Think of it like building little “if-this-then-that” mini-models in your head. You notice a pattern, form a quick hypothesis, act on it, and then update based on feedback. Chess players do this all the time: they spot familiar shapes on the board, guess what the opponent is aiming for, test a few moves in their head, and then keep or ditch their plan depending on what happens next. We do the same in everyday life—studying, dating, and job hunting. We try something that worked before, keep score in our minds, and switch tactics when it stops paying off. It’s learning on the fly, not waiting for the “perfect” answer that rarely exists in the wild.

To illustrate this, Arthur shares a simple story: a bar with 100 potential customers. It’s fun only if fewer than 60 show up. Nobody can know attendance for sure. Each person looks at past weeks and uses a small rule to predict next week: “same as last week,” “average of the last four,” “two-week cycle,” and so on. If your rule says it won’t be crowded, you go; if it says it will, you stay home. No secret coordination. Just lots of small, private guesses. Now the cool part: across time, people’s rules “learn,” and the whole crowd stabilizes around an average of 60—yet the specific rules people rely on keep changing. It’s like a forest with a stable shape but trees that come and go. Expectations can’t all match because if everyone believes “it’ll be empty,” then everyone goes and it’s crowded; if everyone believes “it’ll be packed,” no one goes and it’s empty. As a result, people end up holding different views, and the mix keeps things balanced.

Why should you care? Because life is that bar. Group projects, trending restaurants, sneaker drops, and even pricing a side hustle—all are moving targets shaped by other people’s guesses. Arthur’s point is practical: don’t wait for perfect logic. Build simple rules from real signals, keep track of what works, and be prepared to adjust strategies when they stop delivering results. Small, adaptable rules often outperform rigid “one true plan” in social settings that are constantly evolving. That’s how markets, negotiations, poker nights, and product launches often behave—cycling through temporary patterns instead of settling into one eternal formula. Use patterns, measure results, and iterate. That’s not second-best thinking. It’s the kind that actually wins when everyone else is also deciding at the same time.

Reference:
Arthur, W. B. (1994). Inductive Reasoning and Bounded Rationality. Papers and Proceedings of the Hundred and Sixth Annual Meeting of the American Economic Association, 84(2), 406–411. https://www.jstor.org/stable/2117868

Privacy Notice & Disclaimer:
This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.