Ooo, very good questions! :) I think there are a few different reasons why.… one small clarification, though, I don’t think ACT-R shrunk to a small group—I’d say more that it gradually grew from a small group (starting out of John Anderson’s lab at CMU) up to about 100 active researchers around the world, and then sort of stabilized at that level for the last decade or two.
But, as for why it didn’t take over everything or at least get more widely known, I’d say one big reason is that the tasks it historically focused on were very specific—usually things involving looking at letters and numbers on a screen and pressing keys on a keyboard or moving a mouse. So lots of the early examples were that sort of specific experimental psychology task. It’s been expanded a lot since then (car driving, for example), but that’s where its history was, and so for people who are interested in different sorts of tasks, I can see them maybe initially feeling like it’s not relevant. And even now, lots of the tasks in the paper you provided are so far away from even modern ACT-R that I can see people believing that they can just ignore ACT-R and try to develop completely new theories instead.
Another more practical reason, however, is that there’s a pretty high barrier to entry to getting into ACT-R, partly due to the fact that the reference implementation is in Lisp. Lisp made tons of sense as being the language to use when ACT-R was first developed, but it’s very hard to find students with experience in Lisp now. There’s been a big movement in the last decade to make alternate implementations of ACT-R (Python ACT-R, ACT-Up, jACT-R), and the latest version of ACT-R has interfaces to other languages which I think will help to make it more accessible. But, even with a more common programming language, there’s still a lot of teaching/training/learning required to get new people used to the core ideas. And even to get people used to the idea of sticking with the constraints of ACT-R. For example, I can remember a student building a model that needed to do mental arithmetic, and it took a while to explain why they couldn’t just say “c = a + b” and have the computer do the math (after all, you’re implementing these models on a computer, and computers are good at math, so why not just do the math that way?). Forcing yourself to break that addition down into steps (e.g. trying to recall the result from memory, or trying to do a memory recall of some similar addition fact and then doing counting to adjust to this particular question, or just doing counting right from the beginning, or doing the manual column-wise addition method in your head) gets pretty complicated, and it can be hard to adjust to that sort of mind-set.
I will note that this high-barrier-to-entry problem is probably true of all other cognitive architectures (e.g. Sigma, Clarion, Soar, Dynamic Field Theory, Semantic Pointer Architecture, etc: https://en.wikipedia.org/wiki/Comparison_of_cognitive_architectures ). But one thing ACT-R did really well is to address this by regularly running a 2-week summer-school (since 1994 http://act-r.psy.cmu.edu/workshops/ ). That seems to me to be a big reason why ACT-R got much more widely used (and thus much more widely tested and evaluated) than the other architectures that are out there. There was an active effort to teach the system and to spread it into new domains, and to combat the common approach in computational cognitive modelling of people sticking with the one model that they (or their supervisor) invented. It’s much more fun to build my own model from scratch and to evaluate it on the one particular task that I had in mind when I was inventing the model. But that just leads to a giant proliferation of under-tested models. :( To really test these theories, we need a community, and ACT-R is the biggest and most stable cognitive architecture community so far. It’d be great to have more such communities, but they’re hard to grow.
I agree that there isn’t an overarching theory at the level of specificity of ACT-R that covers all the different aspects of the mind that cognitive science researchers wish it would cover. And so yes, I can see cognitive scientists saying that there is no such theory, or (more accurately) saying that even though ACT-R is the best-validated one, it’s not validated on the particular types of tasks that they’re interested in, so therefore they can ignore it.
However, I do think that there’s enough of a consensus about some aspects of ACT-R (and other theories) that there are some broader generalizations that all cognitive scientists should be aware of. That’s the point of the two papers listed in the original post on the “Common Model of Cognition”. They dig through a whole bunch of different cognitive architectures and ideas over the decades and point out that there are some pretty striking commonalities and similarities across these models. (ACT-R is just one of the theories that they look at, and they point out that there are a set of commonalities across all the theories, and that’s what they call the Common Model of Cognition). The Common Model of Cognition is much more loosely specified and is much more about structural organization rather than being about the particular equations used, though, so I’d still say that ACT-R is the best-validated model. But CMC is surprisingly consistent with a lot of models, and that’s why the community is getting together to write papers like that. The whole point is to try to show that there are some things that we can say right now about an overarching theory of the mind, even if people don’t want to buy into the particular details of ACT-R. And if people are trying to build overarching theories, they should at least be aware of what there is already.
(Full disclosure: I was at the 2017 meeting where this community came together on this topic and started the whole CMC thing. The papers from that meeting are at https://www.aaai.org/Library/Symposia/Fall/fs17-05.php and that’s a great collection of short papers of people talking about the various challenges of expanding the CMC. The general consensus from that meeting is that it was useful to at least have an explicit CMC to help frame that conversation, and it’s been great to see that conversation grow over the last few years. Note: at the time we were calling it the Standard Model of the Mind, but that got changed to Common Model of Cognition).