Hi! I’m Andy Matuschak. You’ve stumbled upon my working notes. They’re kind of strange, so some context might help.
These notes are mostly written for myself: they’re roughly my thinking environment (Evergreen notes; My morning writing practice). But I’m sharing them publicly as an experiment (Work with the garage door up). If a note seems confusing or under-explained, it’s probably because I didn’t write it for you! Sorry—that’s sort of an essential tension of this experiment (Write notes for yourself by default, disregarding audience).
For now, there’s no index or navigational aids: you’ll need to follow a link to some starting point. You might be interested in §What’s top of mind.
👋 Andy (email, Twitter, main personal site)
PS: My work is made possible by a crowd-funded research grant from my Patreon community. You can become a member to support future work, and to read patron-only updates and previews of upcoming projects.
PS: Many people ask, so I’ll just note here: no, I haven’t made this system available for others to use. It’s still an early research environment, and Premature scaling can stunt system iteration.
In many fields, experts become experts mostly by developing more sophisticated mental representations (Mental representations, after Ericsson and Pool), which amounts to increasing the size of their mental chunks (“Chunks” in human cognition). This increases their information processing capacity (Human channel capacity increases with bits-per-chunk, Recoding can increase chunk size). This happens through practice: Good practice encodes more effective chunk recoding schemes
What sets expert performers apart from everyone else is the quality and quantity of their mental representations.
(Ericsson and Pool, 2016, p. 62, not well-cited)
For example, the model developed by Simon and Gilmartin (1973) suggests that chess masters have encoded order tens of thousands of chunks. (See also Chase and Simon - Perception in chess)
Knowledge work often requires solving search problems. Ericsson and Pool suggest that expert search performance comes from more complex chunk schemas (2016, p. 70-72). The argument’s not made very strongly, but because Human channel capacity increases with bits-per-chunk, this would seem to explain superior culling and feedback-uptake performance.
Simon, H. A., & Gilmartin, K. (1973). A simulation of memory for chess positions. Cognitive Psychology, 5(1), 29–46. https://doi.org/10.1016/0010-0285(73)90024-8
Ericsson, A., & Pool, R. (2016). Peak: Secrets from the New Science of Expertise (1 edition). Eamon Dolan/Houghton Mifflin Harcourt. Peak - Ericsson and Pool