by godelski | 2025-10-19 21:23:15+00:00
> there's actual voice heard with your ears, there's the internal monologue, and then there's a hallucination.

This needs no explaining. I think I sufficiently made it clear that we agree with these distinctions.

>> I hear my voice in my head. I can differentiate this from a voice outside my head, but yes, I do "hear" it.

Though to be more precise I would say that a hallucination appears to come from outside the head, even if you are aware that it is coming from inside. Still, clearly distinct from an internal monologue, which is always clearly internal.

> And you did not dig in deeper? >>>> I know some of these people. ***We've had deep conversations about what is going on in our thought processes.***

Yes. Multiple hours long conversations. One of these people I know now studies psychology. I research intelligence and minds from an artificial standpoint and they from a biological. Yeah, we have gotten pretty deep and have the skills and language to do so far more than the average person.

I think you need to consider that you may just be wrong. You are trying very hard to defend your belief, but why? The strengths of our beliefs should be proportional to the evidence that supports them. I am not trying to say that your logic is bad, let's make that clear. But I think your logic doesn't account for additional data. If you weren't previously aware of this data then how could you expect the logic to reach the correct conclusion? I want to make this clear because I want to distinguish correctness from intelligence (actually relevant to the conversation this stemmed from). You can be wrong without being dumb, but you can also be right and dumb. I think on this particular issue you fall into the former, not the latter. I respect that you are defending your opinion and beliefs, but this is turning as you are rejecting data. Your argument now rests on the data being incorrect, right? Because that's the point. Either the data is wrong or your model is wrong (and let's distinguish that a model is derived through logic to explain data).

I want to remind you that this idea is testable too. I told you this because it is a way to convince yourself and update the data up have available to you. You can train yourself to do this in some cases. Not all and obviously it won't be an identical experience to these people, but you can get yourself to use lower amounts of language when thinking through problems. You had also mentioned that people with aphantasia couldn't function, but think about that too. These topics are quite related actually, considering how we've discussed anendophasia you should be able to reason that these people are really likely to have low aphantasia. Notice I said low, as this is a spectrum. You can train the images in your mind to be stronger too. The fact that some images are stronger than others should lead you to believe that this is a spectrum and that it is likely people operate at different base levels. It should also lead you to reason that this is likely trainable in an average person. The same goes for anendophasia. Don't make this binary, consider it a spectrum. That's how the scientific literature describes the topic too. But if you pigeonhole it to being binary and only true in the extreme cases then your model isn't flexible enough as it also isn't considering the variances in people.

Go talk with your friends. Get detailed. When you imagine an apple in your head how much do you see? As the person if their process involves words or if it is purely imagery. If words, how many? Is it a red apple? Green? Yellow? Can they smell it? Can they taste it? What's it smell and taste like? I will bet you every single person you talk to will answer these differently. I will even wager that each time you do the exercise you yourself will answer differently, even if the variance is much smaller. But that's data, and your model needs to be able to explain that data too. While I think you have the right thought process I don't think you are accounting for this variance, instead treating it as noise. But noise can be parameterized and modeled too. Noise is just the statistical description of uncertainty.