Science must be simple, yet the human brain has a structure that gives it the capacity for relating to the world in its undivided complexity in ways that are not logical, though they are effective. Aesthetic interest aroused by observation and half-formed perception seems usually, perhaps always, to precede exact analysis.
Conventional tech industry product practice will not produce deep enough subject matter insights to create transformative tools for thought.
...The aspiration is for any team serious about making transformative tools for thought. It’s to create a culture that combines the best parts of modern product practice with the best parts of the (very different) modern research culture. You need the insight-through-making loop to operate, whereby deep, original insights about the subject feed back to change and improve the system, and changes to the system result in deep, original insights about the subject.
The details are fascinating, but the central argument — that the birth of modernity can be traced to a meta-crisis spawned by the 0.1s problem — is worth understanding and appreciating whether or not you’re a time nerd like me.
There is no convenient leitmotif, comparable to the 0.1s problem, for our contemporary version of the rhyming conditions, but something very similar to the “tenth of a second crisis” is going on today. I suspect our Great Weirding too involves some sort of limiting factor on human cognition that we haven’t yet properly wrapped our minds around. It isn’t reaction time, but something analogous.
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.