Truly learning a lesson is deeper than simply knowing the lesson. It’s possible to know a whole lot more than you’re capable of acting on with regularity. And it’s the act, not the mere knowing, th…
Works in Progress: The Long Journey to Doing Good Better
The more I learn, the more I realize how much I don’t know. — Albert Einstein
Why Some Students Learn Faster
A hypothesis about teaching and learning
Why Some Students Learn Faster
A hypothesis about teaching and learning
Excess Capacity Learning
How do humans learn from experience? Traditionally, cognitive scientists have assumed that discovering generalizable patterns requires that humans compress rich and noisy experiences. However, recent computer science results suggest otherwise — systems can learn by ‘overfitting’ and expanding all the details of their experiences. We offer a new perspective on learning based on a cognitive system’s representational capacity, which can be constrained (forcing the system to compress details of past experiences), sufficient (to memorize past experiences), or excess (allowing the system to expand on the details of past experiences). This framework has implications for understanding learning across cognitive, clinical, and developmental contexts.
How I Learned to Read Way, Way More
I had to rethink my relationship to attention
How Learning Happens | Seminal Works in Educational Psychology and Wha
How Learning Happens introduces 28 giants of educational research and their findings on how we learn and what we need to learn effectively, efficiently, and
things I should know by 30 (but don’t)
Talking with strangers is surprisingly informative
A meaningful amount of people’s knowledge comes from their conversations with others. The amount people expect to learn predicts their interest in having a conversation (pretests 1 and 2), suggesting that the presumed information value of conversations guides decisions of whom to talk with. The results of seven experiments, however, suggest that people may systematically underestimate the informational benefit of conversation, creating a barrier to talking with—and hence learning from—others in daily life. Participants who were asked to talk with another person expected to learn significantly less from the conversation than they actually reported learning afterward, regardless of whether they had conversation prompts and whether they had the goal to learn (experiments 1 and 2). Undervaluing conversation does not stem from having systematically poor opinions of how much others know (experiment 3) but is instead related to the inherent uncertainty involved in conversation itself. Consequently, people underestimate learning to a lesser extent when uncertainty is reduced, as in a nonsocial context (surfing the web, experiment 4); when talking to an acquainted conversation partner (experiment 5); and after knowing the content of the conversation (experiment 6). Underestimating learning in conversation is distinct from underestimating other positive qualities in conversation, such as enjoyment (experiment 7). Misunderstanding how much can be learned in conversation could keep people from learning from others in daily life.
Excess Capacity Learning
We introduce a new framework for understanding how cognitive systems (e.g., humans) learn from experience, based on the concept of representational capacity—the relative amount of representational resources devoted to encoding past experiences. Most paradigms in cognitive science have operated under the assumption that these resources are constrained, forcing cognitive systems to compress rich and noisy experiences to effectively generalize to new situations. We leverage recent advances in computer science to outline the implications of learning with excess capacity, or applying even more representational resources than needed to perfectly memorize all the details of one’s past experiences. In particular, we review evidence suggesting that excess capacity systems can exhibit many of the characteristics of human learning, such as the simultaneous ability to memorize individual experiences and generalize knowledge to new situations. We define and differentiate between constrained (not enough), sufficient (just enough), and excess (more than enough to perfectly capture all the details of one’s past experiences) capacity. We derive empirical properties of learning in each of these capacity regimes, and compare these predictions to effects documented for human learning. We highlight the broad implications of this framework for advancing theoretical and empirical work across cognitive, clinical, and developmental psychology.
The Best Tacit Knowledge Videos on Every Subject — LessWrong
TL;DR Tacit knowledge is extremely valuable. Unfortunately, developing tacit knowledge is usually bottlenecked by apprentice-master relationships. Ta…
Ever thought we acquire generalizable knowledge by discarding details and compressing our experiences? In a new BBS paper, @sabinasloman.bsky.social and I argue otherwise, proposing a novel way of studying human learning inspired by double descent in ML. Disagree? Propose a commentary by May 15 :)
This entire grift relies on convincing people that they don't know how to do the things they have always known how to do, and ironically, if it works, we will, in a very short amount of time, forget how to do all the things we have always known how to do.
More Perfect Union
Jimmy Fallon: "And do you use ChatGPT when raising your baby?" Sam Altman: "I cannot imagine figuring out how to raise a newborn without ChatGPT."
