Generalization is necessary as a way to sense the world for a bunch of reasons:
- The never-same-twice nature of sensory perception
- The need to recognize objects from different directions, distances, partially obscured, etc
- Because the nature of the world is that predictive categories are equally or more important than individual identities, especially to basic survival, and for basic organisms
Rather than "generalization" being unspecific, I'd say that as far as perception goes there is a very concrete realization of generalization in our brain - embeddings and associative recall by partial embeddings (see half a face, recall the whole face).
Analogies don't seem to offer any benefit over prediction in terms of what to focus on - there is always a need to separate foreground (object) from background, and to learn what is instance-specific vs generalizable. Is is irrelevant or coincidence that sabre-tooths are near the water hole, or is this part of a predictive pattern (or analogy of some sort?!) that should usefully be learnt?
When we are not sure about what we are looking at, we often then switch from the gestalt "looks familiar" to looking for specific points of confirmation. Is that car in the parking lot really ours? Does it have our air-freshener hanging from the mirror?
Needing to focus on details can also be part of generalization and exception making - when we recognize that two situations that seem similar are in fact different, and we are trying to isolate what the differences are.
I think that the strongest argument for our brains being built for prediction, not analogy making, are that:
1) Prediction is exactly what is needed to survive and thrive - to take advantage of the external world, and not be killed by it. We directly need to predict. We do not need to make analogies, unless analogies can also be used as a way to predict.
2) Our brain appears to be built for prediction, and prediction gives rise to the very obvious learning signal of prediction failure. One of the lessons of LLMs is confirmation of the power of prediction as all you need to drive learning.
The overall way our perception seems to work is indeed as much by prediction as sensory input - in a way prediction really comes first, with sensory inputs narrowing/correcting the prediction (and when you turn it off then we predict/dream without the constraint of sensory reality). The environment you are in already leads you to predict what might be there, and memory of what specifically was there previously will shape those predictions. Sensory feedback corrects/narrows those predictions, and then - if necessary - we look for specific memorized features to confirm what we are seeing/sensing.
Considerations of evolution, and what would be the sensory and cognitive "needs" of organisms of ever increasing complexity, all the way up to ourselves, can also point to what brain capabilities are older and more fundamental, and would have been there to be refined, adapted and built upon later. Even the most simple organism needs to never-same-twice generalize, but will be stuck being reactive until evolution has either equipped it with "reactive predictions" such as the fly being startled by the fast-moving visual input (predictive of being swatted by the horse's tail), or eventually developing a predictive brain like our own, especially useful to an animal that is a generalist living in an environment that is more difficult to predict.