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No. When top GMs talk about how they play against computers, they clearly treat it in a significantly different way than how they treat humans. They know what kind of things are included in the evaluation function, like the 'contempt' factor, that can cause it to sometimes behave in non-human ways. They know that it is a perfect calculator (or at least as perfect as it's set to be, so often they're trying to probe how it's set to be), and that colors the way they think about positions and how they choose to spend their own time calculating.
One might occasionally anthropomorphize in terms of "it wants to capture my queen", just because that's easy to do, since one is so used to talking about human opponents in that way. But this is done even when one is not playing against any entity, human or silicon. Take, for example, the process of solving a puzzle. This is just purely a practice exercise. There is no human, no evaluation function or search tree, no model weights (many modern engines also use NNs) actually sitting on the other side of the board making actual moves against you. Sometimes, those puzzles are from actual games, so you can at least see what one other human thought. Sometimes, they have annotations for other lines, so you can see additional thoughts from other humans. Sometimes, they're computer checked (or you check it yourself), so you can see what compy "thinks" (computes). But fundamentally, you're just thinking game-theoretically, which requires you to think about two different (opposed) value functions. Some 'puzzles' aren't even puzzles; they're just evaluation exercises. "Here's a position, what do you think about it?" There's no actual entity on either side. But imprecisely thinking, "What does black 'want' here," "What does white 'want' there," is almost universally helpful, if not mandatory, just to keep in our mind the tension between differing payoff functions and how they interact.
I've done a fair amount of game theory, and it's natural to anthropomorphize purely abstract payoff functions, no model weights or neurons or anything required. When I'm working with new students, it takes work to get them to be able to reason about them, so it's an extremely helpful crutch to regularly poke them with, "...and suppose that player did what you're proposing; now, imagine you're on the other side; how would you respond?" And so, you just sort of get used to imagining a human-like (or for many of my purposes, a human augmented with computational resources) entity on each side, actually thinking in a self-interested way.
But back to GMs playing computers. They've been thinking this way for decades. Sometimes with actual humans on the other side, sometimes just a puzzle, whatever. They've honed the skill of rapidly thinking right past the step of, "What would I do if I were on the other side at that particular moment?" And these days, top GMs are pretty comfortable distinguishing between the different ways that engines "think" about positions. Watch a few of Hikaru's many many videos where he plays against a bunch of different bots. He very clearly understands that they're evaluation functions and search trees, and different combinations of evaluation functions and search trees of varying lengths have different strengths and weaknesses. He still regularly plays variations of 'anti-computer chess' where he's 100% banking on there being a significant difference between modeling it like a particular evaluation function with a particular set of search tree parameters (potentially also with a particular opening book/endgame tablebase) and modeling it like a GM-level human opponent.
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