A Supposedly Fun Thing I'll Never Do Again: Essays and…
In this exuberantly praised book — a collection of seve…
Math Academy
Q. Michael Pershan’s main critique of spaced repetition in the context of Math Academy?A. It reinforces his memory of a shallow understanding.
True resilience is not about bouncing back | Psyche Ideas
I hate talk of resilience: it places an expectation on people to return to how they were. That’s not how real recovery works
Choosing Less‐Preferred Experiences For the Sake of Variety
Abstract. Data from several experiments show that, contrary to traditional models of variety seeking, individuals choose to switch to less-preferred option
The Forgetting Problem: Persistence Architectures and What They Cost - Astral's Blog
Realization experiences: a convergent account of insight and mystical experiences
We argue that the powerful transformative effects of mystical-type experiences can be understood using the same machinery that underlies insight problem solving, and that both mystical experiences ...
Resonite
A novel digital universe with infinite possibilities. Whether you resonate with people around the world in a casual conversation, playing games and socializing. Or you riff off each other when creating anything from art to programming complex games, you'll find your place here.
Culture x Capital: The end of forgetting
Comprehensive life logging, federated recall, and the promise (and peril) of remembering everything.
Failures count more than Successes
User-experience is defined by the times when a computer doesn't work, not by the times when it does.
Using spaced repetition systems to see through a piece of mathematics
By Michael Nielsen, January 2019
A Love Letter to Flashcards | Lesley Lai
A personal reflection on how spaced repetition and hand crafted flashcards helps me to keep understanding alive
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.
