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Cake day: July 18th, 2021

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  • Professionals have large networks of neurons. They are sturdy and efficient from repeated use. Memory palaces help to start the construction of these large networks of neurons. Afterwards, as another commenter noted, the knowledge is deeply processed. Mnemonics are replaced by networks of meaning. It is no longer “This algorithm rhymes with tomato”, but “This algorithm is faster if the data is stored in faster hardware, but our equipment is old so we better use this other algorithm for now”.

    Broadly, the progression of learning is: superficial learning, deep learning, and transfer. Check out Visible Learning: The Sequel by John Hattie for more on this.

    Edit: To directly answer your question, experts have so many sturdy neural hooks on which to hang new knowledge that mnemonics become less and less necessary. Mnemonics may be particularly helpful when first learning something challenging, but are less necessary as people learn.

    You could also check out a paradox called the expert paradox. We used to think memory is boxes that get filled. This idea was directly challenged by Craik and Lockhart’s Levels of Processing. Levels of processing supports the idea that “the more you know, the faster you learn”. Note that this is domain-specific. In other words, an expert in dog training won’t learn quantum mechanics faster than anyone else.













  • I appreciate your passion for scientific literacy - it’s crucial for combating misinformation. However, I’d like to share some perspectives that might broaden our understanding of scientific knowledge and how it develops.

    First, it’s worth noting that the distinction between “theory” and “hypothesis” isn’t as clear-cut as we might think. In “The Scientific Attitude,” Stephen McIntyre argues that what truly defines science isn’t a rigid set of rules, but rather an ethos of critical inquiry and evidence-based reasoning. This ties into the “demarcation problem” in philosophy of science - the challenge of clearly defining what is and isn’t science. Despite this ongoing debate, science continues to be a powerful tool for understanding our world.

    Your stance seems to align with positivism, which views scientific knowledge as objective and verifiable. However, other epistemological approaches exist. Joseph A. Maxwell’s work on critical realism offers a nuanced view that acknowledges both the existence of an objective reality and the role of human interpretation in understanding it.

    Maxwell defines validity in research not just as statistical significance, but as the absence of plausible alternative explanations. This approach encourages us to constantly question and refine our understanding, rather than treating any explanation as final.

    Gerard Delanty’s “Philosophies of Social Science” provides a historical perspective on how our conception of science has evolved. Modern views often see science as a reflexive process, acknowledging the role of the researcher and societal context in shaping scientific knowledge.

    Larry McEnery’s work further emphasizes this point, describing how knowledge emerges from ongoing conversations within communities of researchers. What we consider “knowledge” at any given time is the result of these dynamic processes, not a static, unchanging truth.

    Understanding these perspectives doesn’t diminish the power or importance of science. Instead, it can make us more aware of the complexities involved in scientific inquiry and more resistant to overly simplistic arguments from science deniers.

    By embracing some psychological flexibility around terms like “theory” and “hypothesis,” we’re not opening the door to pseudoscience. Rather, we’re acknowledging the nuanced nature of scientific knowledge and the ongoing process of inquiry that characterizes good science.

    What do you think about these ideas? I’d be interested to hear your perspective and continue this conversation.


  • Thanks for the response. I guess I do see much of human behavior through a contextual behaviorist lens. Sorry if it seems excessive. I am not Hayes or Hoffman. It is just frustrating to see blanket explanations for human behavior, instead of understanding specific processes. I guess I really want to avoid the fundamental attribution error and reductionism, something contextual behaviorism deliberately aims to avoid.

    While I recognize Emotion Focused Therapy is helpful to understand and, if possible, change social behavior (which is why I mentioned it previously), I maybe should have brought up Emption Construction Theory or even Sapolsky’s multi-lens framework, considering different timescales of explanation. Would you have suggested something different? When does contextual behaviorism fail?

    Thanks for helping me potentially falling into reductionism. I wouldn’t want to fall in that trap.