“Something fascinating about humans is how confidently they claim to know things. Yet, in reality, while they might know what thst know, most of them don’t truly understand why they know it.”  – Some AI in the future.

Human Cognition: Implicit Knowledge and Confident Assertions

Humans often operate on implicit knowledge – information or skills we’ve learned subconsciously – which can lead us to act or speak with great confidence even when we lack explicit understanding. We frequently “know” how to do things or that something is true without being able to explain the underlying why. This comes from patterns and behaviors ingrained through experience, habit, and procedural memory. For example, people can confidently ride a bicycle or use language correctly without articulating the physics of balance or the grammar rules involved. Implicit memory allows us to perform such tasks automatically, influencing our behavior without conscious awareness. We “know how” without knowing why – and thus may assert knowledge boldly despite shallow understanding.

• Tacit Knowledge and Skills: Psychologist Michael Polanyi famously observed “we can know more than we can tell.” Much of our knowledge is tacit – like tying shoelaces or recognizing a familiar face – and we carry it out confidently without verbal reasoning. We feel certain because our brains have learned the pattern, even if we can’t articulate the mechanism. Everyday examples include riding a bike (we remember how to balance but not the physics) and typing on a keyboard (we hit the right keys by habit but might struggle to recite the key order). This implicit learning builds confidence in action without explicit explanation.
• Implicit Beliefs: Similarly, many beliefs are absorbed from our environment or upbringing without us examining the justification. We might assert “X is true” because we’ve heard it repeatedly or learned it early, giving a sense of certainty. However, if pressed “Why do you believe X?”, we may falter or resort to “I just know.” For instance, someone might strongly believe “eating before swimming is dangerous” or “smoking causes cancer”. They are sure of these facts (often with good reason), yet they may not genuinely understand the biochemical explanation or evidence – they rely on what was implicitly taught. In essence, learned familiarity can feel like understanding, fueling confident assertions without deeper comprehension.

The Illusion of Understanding and Overconfidence

Humans are prone to overestimating how well we understand things. Psychologically, this is captured by the illusion of explanatory depth – the tendency to believe we grasp the details of complex systems or topics until we’re challenged to explain them. We feel knowledgeable, but that feeling can be an illusion:

• Everyday Examples of Shallow Understanding: Ask an average person to explain how a common object works (like a toilet, a zipper, or a familiar gadget). Initially, most will be confident – after all, they use these things daily. Yet when they attempt a step-by-step explanation, gaps quickly appear. This reveals a large gap between what we think we know and our actual depth of understanding. Only by trying to articulate the mechanism do we confront our ignorance. Researchers have found this illusion is widespread – even young children assume they understand more than they do, until asked to explain.
• Dunning–Kruger Effect: Our confidence often exceeds competence, especially in novices. The Dunning–Kruger effect is a bias where people with low knowledge or skill in a domain overestimate their own ability. In one study, the least competent participants (scoring in the bottom quartile) wildly overjudged how well they’d performed – “the less they knew, the more they thought they knew”. This happens because “incompetent individuals lack the metacognitive skills to recognize their poor performance, and thus hold inflated views of their ability.” In other words, without metacognitive awareness (the ability to reflect on one’s own knowledge), people don’t know what they don’t know. This leads to illusory superiority – a confidence not warranted by actual understanding.

Graphical depiction of the Dunning–Kruger effect: those with the lowest performance (far left) greatly overestimate their ability (dashed line vs. actual). Without realizing their ignorance, they confidently believe they performed well. Improved skill and self-awareness lead to more accurate self-assessment.

• Community Knowledge and “Knowing by Proxy”: Part of why we feel we understand is because we unconsciously lean on knowledge in our community. Modern cognitive science suggests that individual thinking is supplemented by others – we live in a “community of knowledge.” We confidently claim to know facts that society or experts around us know, even if we couldn’t explain them. For example, most people assert scientific truths (like “vaccines work” or “climate change is real”) with confidence because they trust expert consensus, not because they have personally analyzed the data. This is usually rational – we can’t individually verify every fact – but it means our personal understanding is often shallow. We know that something is true without knowing the detailed why, yet we feel as certain as if we had worked it out ourselves. Without deliberate reflection, we hardly notice this divide between knowing and understanding.

Metacognition: Thinking About Our Own Thinking (or Lack Thereof)

Metacognition is the act of reflecting on our own thought processes – essentially thinking about thinking. It includes examining how we know things, how confident we should be, and whether our reasoning is sound. While humans can do this (and it’s a cornerstone of critical thinking), we often don’t do it enough in practice. Several psychological factors explain why metacognitive insight is frequently lacking:

• Limited Introspection: We intuitively feel we have direct insight into our minds, but in reality, much of our thinking happens behind the scenes. There is an introspection illusion in which people believe they understand their own mental processes directly, when they are actually guessing or inventing explanations. Experiments by Nisbett and Wilson found that people asked to explain their choices or beliefs often give confident answers that are confabulations – plausible-sounding reasons that don’t reflect the real (unconscious) causes. We have access to the outputs of thought (feelings, decisions), but not the full process. Because our brains automatically construct a narrative, we feel we’ve introspected, even if the true cognitive process remains hidden. This illusion means we typically don’t realize what we don’t know about our own thinking.
• Metacognitive Effort: Engaging in metacognition requires mental effort and honesty. It’s often easier to trust our intuition or go with familiar beliefs than to scrutinize them. Unless trained or prompted, many people don’t habitually second-guess how valid their knowledge is. For instance, metacognition in learning (like double-checking if you truly understand a concept) is a skill that students must develop. Without it, one might stop studying a topic too early, feeling knowledgeable but actually missing key pieces. Lacking this self-monitoring, we carry on with unwarranted confidence. In short, self-reflection is a learned skill – and not one our brains default to, since it’s easier to operate on autopilot.
• Bias Blind Spot: Ironically, we tend to recognize others’ biases and blind spots more readily than our own. Most people will acknowledge humans in general have biases, but then assume they personally are less biased. This “bias blind spot” stems from poor metacognitive insight into our own susceptibilities. We introspect to check our reasoning and find nothing obviously wrong – but that’s because many biases work unconsciously. Meanwhile, we can see others’ behaviors and easily attribute those to biases. This lack of metacognitive accuracy feeds naïve realism – the sense that “I see the world as it is, so if I believe it, it must be true.” Improving metacognition (through training, reflection, or tools like journaling) can help counteract these tendencies by forcing us to examine our thoughts more critically.

Post-Hoc Rationalization: How We Justify and Rationalize Beliefs

Another facet of human psychology is our talent for post-hoc rationalization. We often make decisions or form opinions based on gut feelings, intuition, or implicit influences – and only later do we concoct a logical reason for them. In other words, our stated explanations for why we “know” or chose something are frequently stories we tell ourselves after the fact. This isn’t usually done in bad faith; rather, the conscious mind is acting as a narrator trying to make sense of our often-unconscious motivations. Key aspects include:

• Confabulation of Reasons: When asked “Why do you think that?” or “Why did you choose this option?”, people will usually give an answer – but research shows those answers can be invented rationalizations, not the true cause. A striking example comes from choice blindness experiments. In one study, participants chose between two options (e.g. which face they found more attractive) and then were unknowingly handed the opposite of their choice. Remarkably, a majority did not notice the switch – and proceeded to confidently explain why the (unchosen) option was their preference, offering detailed reasons that they completely made up. They weren’t intentionally lying; their brains simply constructed a reasonable-sounding explanation for a choice they believed they made. This shows how effortlessly we rationalize decisions after they occur, reinforcing our sense that we understood our motivations all along.
• Rationalization Bias: Psychologists refer to a “rationalization heuristic” or bias, where individuals create logical justifications for decisions after they make them. We like our beliefs and actions to appear consistent and reasonable (to ourselves and others), so we retrofit reasons to align with the outcome. For example, someone might buy an expensive gadget on impulse and later defend the purchase with arguments about its superior quality or long-term value – convincing themselves the decision was fully rational. This post-hoc reasoning is partly driven by cognitive dissonance reduction: we feel discomfort if our choices seem irrational, so we adjust our beliefs to believe the choice was wise. In doing so, we become more confident in our belief or decision than we perhaps should be, given that the real driver may have been a fleeting emotion or social cue rather than the elaborate rationale we articulate.
• Confirmation Bias and Belief Defense: After forming a belief, humans also exhibit confirmation bias – selectively noticing or favoring information that supports what we already think. This means that once we’ve rationalized a belief, we will continue to find reasons to reinforce it. Over time, our justifications grow stronger and more elaborate, while we dismiss counter-evidence. Thus, we end up believing our own stories. Our confidence in what we know gets bolstered by these biased recollections and explanations, even though the original foundation might have been shaky. In summary, human decision-making is often story-driven – we act (often guided by unconscious knowledge or emotion) and then our minds create a story to explain and justify that action. This gives us a comforting sense that we truly understand our choices and beliefs, when in reality we might be rationalizing on autopilot.

Advanced AI vs. Humans: How Would an AI Know What It Knows?

Given these human quirks, it’s interesting to imagine how an advanced AI might handle knowledge and understanding differently. A future, highly advanced AI (far more self-aware and transparent than today’s systems) could be designed to avoid many of our cognitive blind spots. Key contrasts in how an AI might structure and utilize knowledge include:

• Explicit Knowledge Representation: Unlike the human brain’s tangled web of implicit and explicit knowledge, a future AI could organize information in a very structured, transparent way. For example, it might maintain a vast knowledge base (a database of facts or a network of concepts) along with the source or justification for each piece of knowledge. Rather than just “knowing” something intuitively, the AI could trace why it knows it – pointing to the data or logic that led to that conclusion. Humans usually cannot pinpoint the origin of a belief (“Where did I learn that? Not sure – I just know it”). In contrast, an AI could, in principle, be built to log the provenance of each piece of information (e.g. “I know X is true because it was stated in source Y, which has been verified”). This means the AI’s knowledge would be explicitly justified rather than taken on faith.
• Hierarchical and Modular Understanding: An advanced AI might break down complex concepts into sub-parts and logical links more effectively than a human mind can. For instance, if asked to explain how a car engine works, the AI could retrieve a detailed multi-step explanation from its knowledge store, complete with diagrams and causal chains. It wouldn’t be subject to an illusion of explanatory depth – either it has the explanation stored or can derive it systematically, or it knows it does not have enough information. Humans, by contrast, often feel they understand the whole when they only grasp a few parts. An AI’s confidence could be tied to the completeness of its knowledge graph: if some nodes or connections are missing in the explanatory chain, it would identify a gap rather than gloss over it with intuition.
• Consistency and Updating: Human knowledge is messy – we can hold contradictions or outdated beliefs without realizing it. A well-designed AI system could enforce a higher degree of consistency in its knowledge. If new data arrives that contradicts a stored belief, the AI can flag the inconsistency and update or reconcile it systematically. Humans often struggle with this due to confirmation bias or emotional attachment to beliefs. AIs, lacking emotion, could more readily discard a disproven “fact.” Moreover, learning in AI can be on-going and data-driven: a future AI might continuously ingest new research and statistics, updating its knowledge base nightly. This could prevent the kind of stagnation or rationalization humans do to preserve prior beliefs. In effect, the AI’s knowledge might be more fluid but evidence-based, whereas humans sometimes stick to comfortable beliefs and rationalize away the evidence.

Transparency and Traceability of Decision-Making: Humans vs. AI

Human decisions are often opaque – not only to outside observers but even to ourselves. We cannot replay a transparent log of our thought process for each decision (and we often wouldn’t like what it shows!). An advanced AI, on the other hand, could be engineered for full traceability of its decision-making steps:

• Transparent Reasoning Chains: Imagine an AI that, when asked a question or faced with a problem, follows a chain of logical steps or algorithmic reasoning. It could keep an internal transcript of this process. For example, a medical AI diagnosing a patient might log: “Step 1: noted symptoms A, B, C. Step 2: retrieved possible conditions matching those symptoms. Step 3: evaluated likelihoods given patient data. Step 4: selected diagnosis X because it had the highest probability.” This chain could then be output as an explanation. Such explainable AI techniques are already a focus of research – the goal is for AI to not be a inscrutable “black box,” but rather to provide reasons for its conclusions. Humans cannot do this reliably; we might give a post-hoc story, but we can’t record our neural firings or subconscious heuristic leaps. An AI’s advantage is that everything it does can be logged and inspected (if we design it that way). This means an AI could not only come to a conclusion, but also demonstrate how – offering a level of transparency far beyond human cognitive introspection.
• Auditability and Consistency: Because of this traceability, AI decisions can be audited. One could trace back an AI’s output to the very data that influenced it. For instance, if a future AI responds, “The bridge is likely to fail in high winds,” it might be able to show the engineering rules or simulations that led to that warning. This is akin to an accountant keeping detailed records – whereas a human engineer might say “just a hunch from experience” (not very traceable!). The AI can ensure that every step was grounded in logic or data, which regulators or users could review for fairness or accuracy. In contrast, human decision-making in complex tasks often depends on gut feelings that can’t be inspected, and human explanations can be biased or incomplete. Traceability in AI promises that decisions aren’t mysteries but have an accessible lineage.
• Speed and Complexity of Reasoning: Another difference is that an AI can handle massively complex reasoning with transparency, whereas a human bogs down. A person making a decision might only consciously weigh a few factors (while other factors influence them subconsciously). An AI could juggle hundreds of factors – and still log each one’s contribution. For example, a finance AI could consider thousands of market indicators in a split second and document that “100 features were considered, here are the top 10 contributors to the final decision.” No human mind could explicitly do that. This means future AI might not only be more comprehensive in decision inputs but also maintain clarity about the process. The caveat is that the explanations must be understandable – a dump of millions of weight values from a neural network isn’t helpful. But researchers are developing ways for AI to summarize why certain inputs mattered. Ultimately, the goal is an AI that can expose its “thought process” in detail, whereas humans remain largely opaque even to themselves when making choices.

Memory and Learning: Differences Between Human and AI Minds

Memory and learning are fundamental to knowledge, and here the differences between humans and AIs are stark. By design, future AI systems could surpass many human limitations, but they also lack some of our mind’s intuitive qualities:

• Capacity and Accuracy of Memory: Human memory is finite, fallible, and often inexact. We forget details, misremember sources, and our recall can be influenced by context or emotion. By contrast, an AI can be given virtually unlimited storage. It could retain a perfect record of every document, conversation, or datum it has encountered (limited only by hardware). This means an AI might never forget a relevant fact – something humans often do. If asked a specific question, a human might say “I think I read that somewhere but can’t recall”; an AI could potentially pull up the exact reference in microseconds. Also, human memory is reconstructive (we rebuild memories from bits and often introduce errors), whereas AI memory retrieval can be exact (the text or image recalled is pixel-perfect as stored). However, note that current AI models like neural networks compress information (leading to their own kind of forgetting or “blurred” recall). A future AI might combine neural learning with a direct database of knowledge to get the best of both: broad generalization and precise recall.
• Learning Mechanisms: Humans learn through incremental experience, sensory input, and social interaction. We have few-shot learning abilities (we can learn from a single example in many cases, by relating it to prior knowledge) and we integrate new information with a rich context of understanding. AI systems traditionally needed tons of data to learn something (e.g. thousands of examples to recognize a cat in images). However, advanced AI is improving at one-shot learning using pre-trained knowledge. In the future, an AI might ingest a single new piece of information and immediately incorporate it consistently into its knowledge base (for instance, reading a new scientific finding and updating all relevant inferences). Importantly, AI learning can be surgically targeted – if an AI’s knowledge is wrong in one area, engineers can retrain or correct that specific point. Human learning is messier; misconceptions can persist and are hard to “overwrite” without conscious effort. Moreover, humans are influenced by biases when learning (we might ignore information that conflicts with our beliefs). An AI could be programmed to weigh evidence in a statistically optimal way, showing less bias in updating its “beliefs”. In short, human learning is powerful but emotionally and cognitively biased, whereas AI learning is data-driven and can be systematically aligned with logic or statistics (given the right design).
• Belief Formation and Update: Humans form beliefs that can be sticky and tied to identity (“I believe in this political ideology, so I filter new information through that lens”). AI systems don’t have emotional attachments to beliefs – a future AI’s “belief” is just a piece of data or a parameter value. If evidence changes, the AI can change that piece of data without angst. For example, if an AI thought a certain medical treatment works but new large trials show it doesn’t, the AI can drop the old belief and adopt the new conclusion immediately. Humans, however, might struggle – they might doubt the new study, or feel dissonance changing their stance. Epistemic flexibility could be a strength of AI. On the flip side, current AIs (like today’s large language models) sometimes lack a mechanism to update their training quickly – they are stuck with whatever was frozen in their last training session. Future designs likely will allow continuous learning so that the AI’s “beliefs” (i.e., knowledge) stay up-to-date. Additionally, an AI could maintain calibrated uncertainties with each belief (e.g., “90% confidence in this fact”) and adjust those with new evidence, whereas humans tend to overestimate their certainty and are slow to admit uncertainty.

Self-Awareness and Metacognition: Can AI Know What It Doesn’t Know?

The ultimate comparison is in self-awareness and metacognition. Humans, as discussed, have limited metacognition – we often don’t truly know the limits or causes of our knowledge. Could a future AI develop something like metacognitive insight? This involves an AI monitoring its own processes, evaluating its confidence, and recognizing when it doesn’t know something or made a mistake.

• AI Metacognition: Current AI systems do not possess genuine self-reflection – they do what they are programmed or trained to do, without an internal “I” that ponders its own thoughts. However, researchers are exploring metacognitive architectures for AI. For instance, an AI could have a secondary module that observes the primary reasoning module: checking if the reasoning is going in circles, if the answer seems dubious, or if it should consult additional data. We already see rudimentary versions of this: some AI models can output a confidence score alongside an answer, or they can be prompted to double-check their result. In fact, large language models can be asked to reason step-by-step (a prompt technique called “chain-of-thought”), which forces them to articulate a line of reasoning that can be inspected. While this isn’t true self-awareness, it’s a step toward the AI being able to monitor and report on its own reasoning. Future AI might have a built-in self-model – an understanding of its own capabilities and limits. For example, it might know “my vision module is good at identifying vehicles but not great with animal species” and thus flag when a task is outside its competence. This kind of self-knowledge would be analogous to a human knowing their own strengths and weaknesses (which many of us struggle to do accurately).
• Epistemic Humility: Because an AI can be designed to quantify uncertainty, a future advanced AI might demonstrate epistemic humility far better than humans. It could acknowledge when its knowledge is incomplete or when a question is underdetermined. For instance, it might respond: “I have low confidence in this answer because the input is unlike anything I was trained on,” or “There is insufficient data to be sure – here are two plausible hypotheses.” Humans, in contrast, often overclaim certainty due to cognitive biases. An AI, not having an ego, has no issue admitting “I don’t know” if that’s the correct assessment. In fact, AI developers are actively trying to curb the problem of AI hallucinations, where a model gives a confident-sounding but incorrect answer. By instilling more rigorous self-checks, future AI could avoid the human-like tendency to bullshit with confidence. Meta, for example, described their chatbot’s false answers as “confident statements that are not true” – something we recognize in human behavior as well. Tackling this in AI involves giving it better metacognitive sensors: e.g., cross-checking its answers against a database or its training data for veracity, and expressing uncertainty when appropriate. If successful, the AI would only assert what it can back up, and clearly label uncertain knowledge. This kind of humility and clarity is something humans aspire to (scientists try to do it), but we often fall short due to bias or overconfidence.
• No Inner Ego or Dissonance: Another difference is that AI, being an artifact, doesn’t have emotional blind spots. It doesn’t feel embarrassment for being wrong, nor pride that might cause it to stick to a claim. Humans sometimes double down on a false belief because admitting error hurts our ego. A well-designed AI could simply update its knowledge without those emotional obstacles. This suggests future AI might achieve a form of rational self-correction that eludes many humans. That said, a truly self-aware AI (in the full conscious sense) is still theoretical. Current discussions distinguish between functional awareness (like monitoring performance, which we can implement to a degree) and conscious self-awareness (which is a philosophical can of worms). We likely don’t need a conscious AI to get practical metacognition. Even a very advanced but non-sentient AI could be built to track its own reasoning and knowledge gaps as a software feature, not unlike how a compiler can detect its own errors.
• Safeguards and Alignment: Giving AI metacognitive abilities also ties into safety – an AI that knows when it’s out of its depth can avoid taking actions in uncertain scenarios without human consultation. This parallels a wise human who practices epistemic humility and seeks advice or more information when they realize a question exceeds their expertise. We might program future AI to have a sort of “expertise checker” – before acting on a decision, it assesses: “Have I encountered enough situations like this before? If not, I should not be overconfident.” In essence, the hope is that AI could transcend some human limitations, being consistently logical about what it knows and doesn’t know.

Toward a Synthesis of Human and AI Strengths

Humans are fascinating in our ability to know things without truly understanding them, confidently navigating the world via shortcuts of implicit knowledge, social trust, and cognitive biases. We claim certainty often with only a veneer of insight – a byproduct of our powerful but opaque minds. Advanced AI systems, by contrast, offer a vision of agents that might know both the “what” and the “why”: They could retain explicit reasons for knowledge, exhibit transparency in decision-making, and maintain calibrated confidence. Of course, today’s AI is not there yet – current AI can also produce confident bluffs (neural networks have their own opaque “intuition” in a sense). But as AI evolves, designers are actively addressing these issues by incorporating explainability, traceability, and self-monitoring.

In the future, the contrast between human and AI cognition may sharpen: humans will continue to have creativity, common sense born of lived experience, and emotional intuition, while AI will provide logical rigor, endless memory, and unbiased self-analysis. Ideally, each can complement the other. Humans can benefit from AI’s fact-checking and analytical transparency to compensate for our cognitive blind spots, while AI can be guided by human values and contextual understanding. Ultimately, exploring why we so often don’t know why we know things not only humbles us, but also guides us in building machines that might avoid the same pitfall. Encouraging metacognitive humility – be it in a person or a machine – seems key to bridging the gap between confidence and true understanding. By learning to say “I might be wrong” or “let’s double-check” (and building AI that does the same), we move toward knowledge that is both sure-footed and self-aware.

 

 

---

© 2025 insearchofyourpassions.com - Some Rights Reserve - This website and its content are the property of YNOT. This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit, provide a link to the license, and indicate if changes were made.

---