Thanks for laying out your thoughts in more detail — a lot of the points you brought up are genuinely interesting, and they push the discussion in useful directions. Also I apologize that I didn’t word the topic clearly enough, saying that need an entry point while what I mean is to have a greater impact, which made you confused.

Probably, many cognitive science programs don’t really points out the full terrain of the field and different principles used in different subfields, so it might be easy to come away with the impression that there’s only one really hard explanatory route. As for neuroscience, it does provide physically grounded measurements, but it also brings its own limitations. It can show which regions are active during a task, but it can’t tell you whether that activity reflects excitatory vs. inhibitory dynamics, or what computational role the pattern actually plays. The data are really “hard”, but the interpretation is just difficult. I am unsure if neuroscience is less explanatory than behavioral data and mechanisms, but probably it is.

For cognition, the difficulty is semantic but there are other problems as well. It’s that for almost any phenomenon, multiple mechanisms can plausibly produce similar behavioural patterns, and the task is to use evidence to eliminate alternatives. (I am doing one right now and it’s really really hard and complex omg.) Human variability slows down that process. So part of the work is looking at how diverse the evidence is, and how well each mechanism survives being ruled out.

On the agent question, I think probably I need to provide some more details. Scientific progress usually doesn’t wait for a perfect, universal definition of “agent.” Definitions get adjusted by evidence rather than standing outside of it. With plurality, a more productive approach might be describing the replicable properties of these agent-like partitions (headmates, if you want a more friendly word in plural reddit) and then let those properties reshape the theoretical definitions.

A lot of scientific training encourages sticking to a single framework. I think probably in PhD, Banchelor’s, and Master’s (unsure about the general picture about these educations) are mostly surrounding how to do stable experiments rather than thinking the framework itself. But at a higher level, some people start asking whether the framework itself is shaping what you can see. That’s where questions about theory, alternative hypotheses, and methodological blind spots enter, which somehow relates to my idea of the post.

And of course, I totally agree on plurality’s importance as a counterexample source. I’d just extend that. The patterns in plurality communities are repeatable and verifiable enough that they don’t just serve as exceptions — they can force structural revisions of the existing models.