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ML models don't attempt to emulate human cognition, and they're solving a different problem than embodied cognition in the first place, with different constraints and different degrees of freedom.
If your input is a static image that you're trying to classify, that's a very different setup than being an embodied agent immersed in a dynamic world subject to cause and effect. In the former case, processing all the information available in one go is actually more effective
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How does that work with ambiguity in the signal, though? Even static images can be ambiguous and require active inference. For example, see this Figure from Bar (2004), where the same blob can be seen as a hairdryer or drill depending on active interpretation of the scene.pic.twitter.com/v4ZWzmCanP
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You can take context into account without active perception. Active perception only becomes really useful in a dynamic world where it's possible to formulate & test hypotheses (requires a time component)
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