They 100% are not those things... but they also approximate them well-enough to be functionally useful.
I.e. the high-dimensional curve-fitting / compression conceptualization of ML, which intuitively expresses both its strengths and weaknesses.
If "it" is represented in the data set (explicitly or implicitly), the "curve" will fit to that property.
Simultaneously, the "curve" is approximating and smoothing out disjoint data steps to pack high-fidelity data features into a more space-efficient model. Hence some features disappear, others are tortured beyond intuitive correspondence, and others become linked to non-obvious proxies. But some strongly-expressed ones remain.
It's fascinating but not surprising that responsiveness to emotion is encoded in model weights, given that all conversational training data had emotional impetus, given that it came from humans.