"Generalization" is rather unspecific.
Analogy-making points to more specific mechanism: ability to identify features within representations, and modulating associative lookup using those features. I.e. it's not as simple as D(x, y) i.e. distance between embeddings, but something like D(f(x), f(y)) where f projects representation to a specific feature space.
The evidence can be found e.g. in LLM interpretability - people were able to identify features as something concrete. Also in our cognition - we can tell _how_ two things are similar, or find Y similar to Y in context of F.
"Prediction" by itself seems like a black box: if we are trying to predict sensory input, that gives no explanation to our ability to focus on specific details, or explains why some prediction failures are more likely, etc. OTOH if we say that cortex might be 'disassembling' sensory signal into high-level features and is trying to predict those (features themselves might be identified as "something useful for prediction", i.e. something which explains a lot of variance), it's much easier to connect low-level "prediction" to our high-level cognition.
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 identities, especially to basic survival
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 coincidence that sabre-tooths are often 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.