When you take large language models, their inner states at each step move from one emotional state to the next. This sequence of states could even be called "thoughts", and we even leverage it with "chain of thought" training/prompting where we explicitly encourage them, to not jump directly to the result but rather "think" about it a little more.
In fact one can even argue that neural network experience a purer form of feelings. They only care about predicting the next word/note, they weight-in their various sensations and memories they recall from similar context and generate the next note. But to generate the next note they have to internalize the state of mind where this note is likely. So when you ask them to generate sad music, their inner state can be mapped to a "sad" emotional state.
Current way of training large language models, don't let them enough freedom to experience anything other than the present. Emotionally is probably similar to something like a dog, or a baby that can go from sad to happy to sad in an instant.
This sequence of thought process is currently limited by a constant named the (time-)horizon which can be set to a higher value, or even be infinite like in recursive neural networks. And with higher horizon, they can exhibit some higher thought process like correcting themselves when they make a mistake.
One can also argue that this sequence of thoughts are just some simulated sequence of numbers but it's probably a Turing-complete process that can't be shortcut-ted, so how is it different from the real thing.
You just have to look at it in the plane where it exists to acknowledge its existence.