Awakening From the Meaning Crisis by John Vervaeke, Ep. 26 — Cognitive Science (Summary & Notes)

“I often say ‘meaning-making’ but as I’ll argue we don’t just make meaning the way the Romantics said, nor do we just receive meaning from the world the way the empiricists in the Enlightenment argued. We’re going to see that it’s neither one of those — it’s another dichotomy that we have to transcend.”

  • “I often say ‘meaning-making’ but as I’ll argue we don’t just make meaning the way the Romantics said, nor do we just receive meaning from the world the way the Empiricists in the Enlightenment argued. We’re going to see that it’s neither one of those — it’s another dichotomy that we have to transcend.”
  • The science of cognition: the machinery of meaning realization, and the cognitive processes at work within it
  • The term ‘mind’ has become equivocal. Neuroscience studies it at the brain level, computer scientists working on A.G.I. and machine learning are studying it at the information processing level, psychologists study it at the behavior level, linguists study it at the language level, anthropologists study the networking of minds at the cultural level…
  • Equivocation is when you fall into confusion because you do not keep track of the meaning of your terms.
  • “If you don’t get sensitive to the meaning of terms you’ll fall into equivocation, which is a disastrous way to reason about anything.”
  • One of the consequences of this is fragmentation. Each level of understanding of the term ‘mind,’ each of those disciplines, causally impact and constrain one another. This is why there’s a constant need to create hybrids like “psycho-linguistics.”
  • To study the mind we need to get the different disciplines to integrate in some way. Philosophy can help us get integration, because it is the discipline that has us take conceptual care to try and articulate the meaning of our terms and bridge between different vocabularies, ontologies, methodologies, etc.
  • The discipline that tries to come up with a philosophically astute integration between these disciplines so that we can avoid equivocation and deal with fragmentation and overcome the ignorance of the causal relationship between the levels is: cognitive science. It’s an interdisciplinary science.
  • Some people refer to the cognitive sciences, which is an example of generic nominalism (e.g. anthropology is one of the cognitive sciences, machine learning, neuroscience, etc.). But this use of the term doesn’t help us in our attempt to integrate, avoid equivocation, overcome our ignorance, etc. So we should reject this as the sole meaning of what cognitive science is doing.
  • Some people understand “cognitive science” as a kind of interdisciplinary eclecticism. That it lets people pick and chooses from different disciplines to use as needed. An analogy for this could be an interfaith dialogue. (e.g. Christians and Buddhists talk, exchange interesting ideas, but aren’t trying to significantly transform one another or integrate to come up with a new religion.) The problem with this is it’s either too weak or too strong. Strong and transformative insights aren’t being passed between the disciplines.
  • The third (and Vervaeke argues best) vision of cognitive science is synoptic integration. It says: we need to build something between the disciplines that addresses the equivocation, deals with the fragmentation, and fills in the ignorance. It acknowledges that they’re not all saying the same thing, but their also not saying different things. It uses a bridging vocabulary.
  • We use metaphor to bridge between domains (note: this is not an argument that science is a metaphor). Metaphors are expressions that refer to different things, but are used to communicate a shared quality or resemblance. It allows you to see things in a different ways and alters what you consider salient.
  • Metaphor has a duality about it, and should be balanced in an appropriate way. When this is the case we refer to a metaphor being apt. (e.g. if you say “bees are hornets” it doesn’t work, because thinking of bees as hornets doesn’t get you enough distance to reconsider bees from a different angle. But if you emphasize the distance between things too much, e.g. “arguments are chairs,” then you lose sight of things and the comparison becomes vague.)
  • “Cognitive science attempts to create constructs with multi-aptness. A balance between identity and difference that affords and provokes insightful transformation of the theorizing from one discipline to another.”
  • What is constraining us in this? Plausibility. The word ‘plausible’ has two meanings: 1.) as a synonym for high probability, which is not what we mean here, but rather 2.) as a synonym for reasonable: making good sense; deserving to be taken seriously.
  • We want a construct that is elegant, which refers to more than just simplicity and is about producing a variety of explanations i.e. multi-aptness. But we also want convergence: a construct that has been created by many convergent, independent lines of investigation too. When information comes in from multiple channels (e.g. seeing someone as they speak, so you get visual information + auditory information) it helps eliminate bias and distortion. That the relative biases and distortions cancel each other out. This is why we instinctively prefer information coming from a variety of channels. “By doing convergences I get bias reduction. i.e. trustworthiness”
  • Trustworthiness is not truth, or certainty. Numbers are a good example of this. (You can hear 3, touch 3, see 3…) Scientists like numbers because they afford convergence, boost trustworthiness, and help to reduce bias. The scientific method is all about trying to reduce bias.
  • If you have an explanation that is elegant and produces lots of different explanations and is multi-apt but doesn’t have convergence, so there’s lots of bias and lack of trustworthiness, what do you have? Conspiracy theories. They’re a form of bullshitting. It’s far-fetched.
  • What about the opposite? Where you have a lot of convergence but very little insight or integration being produced? Triviality. It’s not false, it just has no transformative power, it makes no difference.
  • Notice the ways e can equivocate between these too. Daniel Dennett calls this deepity — things that sound deep but are not deep at all. e.g. “Love is only a four letter word.” On one level it’s a triviality, just talking about the word “love.” But the concept of “love” is a very elegant one and gets us into lots of different meanings, uses etc. This expression “Love is only a four letter word” gives us no insight into “love” as a concept. It just pretends to give you multi-aptness when in fact it’s giving you triviality.
  • So we can abuse this machinery and bullshit ourselves, but this also tells us how we can improve it: to try to balance both sides, the elegance and the convergence. When you achieve that you’ve made a construct that’s both trustworthy and powerful, and affords you a new pattern of intelligibility. This is what we mean when we say something is profound. It’s the opposite of a deepity.
  • “Being profound doesn’t mean it’s true, it means it’s very reasonable and it should be taken very seriously.”
  • Now we’re going to shift into doing the cognitive science of meaning-making + meaning-seeking. i.e. Metaphor. In other words: meaning cultivation.
  • “Meaning is something between us, the way you cultivate a plant.”
  • Intelligence is the capacity that makes you a cognitive agent, whose cognition is working with meaning.
  • One way to frame intelligence is in terms of being a general problem-solver. That when we’re either making or measuring intelligence we’re making or measuring a general problem-solver. The ability to solve a wide variety of problems in a wide variety of domains.
  • Intelligence is not a synonym for being rational, “and what you ultimately should care about is not how intelligent you are, but how rational you can become.”
  • We want to keep intelligence separate from knowledge. If you make them synonymous, then you can’t use knowledge to explain intelligence. It becomes circular, non-explanatory.
  • What is it to solve a problem? A problem is when there’s a difference between the state you’re in (initial state) and the state you want to be in (goal state). So when you solve a problem you need to be able to represent the initial state and the goal state and then lay out actions/operations that can be performed that change the initial state to some other state. And then from those states create new operators that can transform one state into another state (visually this is described as a problem space or search space diagram, with nodes and lines branching off into different possibilities). There are also path constraints: e.g. you wouldn’t consider burning your house down to cook your lunch because that wouldn’t just solve your problem but also create a lot of new ones, and in the process you lose your intelligence.
  • “To solve a problem is to have a sequence of operations that can transform the initial state into the goal state while obeying the path constraints, preserving me as a general problem solver.”
  • Daniel Dennett — Intuition Pumps and Other Tools for Thinking
  • Elijah Millgram — Practical Induction

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Mark Mulvey

Mark Mulvey

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