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LLMs and Humans are Cognitive Cousins – Sean Carroll podcast w/Dr. Chandra Sripada
(preposterousuniverse.com)
this post was submitted on 19 Aug 2026
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LLMs are spicy auto-correct. Anthropomorphizing them isn't going to suddenly change reality...
Yeah, but it will make rich people even richer. So same difference.
*auto-complete
No more than thought terminating clishes do.
The burden of proof is very much on the people who say a computer is a person (and get paid for it).
Marketing does not care about the validity of its claims.
I can tell you're not up to date on cognitive psychology.
The principle of minimizing surprise -- of predicting the next thing -- is what most people who study this sort of thing have used as the basis for most of our cognitive capacity.
But, please do keep telling us your popular opinion, I'm sure it's well-informed by detailed published studies.
I can tell you're an AI shill...
You are objecting to an objectively debatable, scientifically study-able thesis with ad-hominem, tribalism, end emotional groupthink.
I don't see a lot of critiques about the notion of production systems in LLMs being phenomenologically similar to those in humans, nor to any of the other reasons why this particular expert -- which I am not -- has the opinion that neural net cognition is usefully describable as akin -- "cousin to" -- human cognition.
I personally have huge doubts about the methodological merits of phenomenology. Consider this as a starting point: https://en.wikipedia.org/wiki/Phenomenology_(psychology)
Oh, completely agree! In a way that was the point being made by the interviewee (Sripada)
That cognitive psych had been stuck for decades with a well-documented robust phenomenology, but no really good, biologically-plausible, mechanistic models.
And then suddenly LLMs show up -- an actual technological artifact non-trivially displaying much of the same phenomenology, biologically motivated, and completely mechanistic.
I was wrong. You're not an AI shill, you're just an AI that was told to justify its existence...
Cool unscientific story.
That's a very simplistic interpretation. A popular book that counters it is https://bookwyrm.social/book/29025/s/thinking-fast-and-slow
I can tell you didn't listen to the podcast. A lot of it is about how System I / production systems are very similar to simple one-pass LLM outputs, and how Reasoning models closely match System II
Which BTW has very little directly to do with the notion that minimizing surprise -- predicting the next thing -- is the basis of much neurology and psychology.
I wouldn't think that your advertising of the podcast is so successful as to make people listen to it.
But somehow you seem to be leaking the idea that reasoning models are something different than autoregressive LLMs. Yes, they do have different fine-tuning and system prompts, but little beyond that. Subbarao Kambhampati has worked a lot on this, e.g. https://doi.org/10.1111/nyas.15339 or https://doi.org/10.1111/nyas.15125