In general, the terms “LLM-like” and “human-like” are used all over the place, and in contrast with each other, but they’re never actually defined. It all just seems more vibes-based than anything else.

And “treating the human cognitive process like it’s similar to the LLM cognitive process might lead to a society where epistemics turns into a discipline where plausibility is an acceptable substitute for empiricism” has got to be one of the most ridiculous notions I’ve ever read in a paper (ctrl+F “fifth pathway is epistemic” for the exact quote).

Here's the thing though, unlike the old brain=computer analogy, this one may actually have a little truth to it. Not that your whole brain is an LLM, or even that the language part of your brain is just an LLM, but the language part may indeed be functioning in a similar way to an LLM to extent that it:

- Uses a hierarchy (cortical patch-panel) of parallel processing steps

- Is prediction based

- Is largely (but not 100%) auto-regressive

- Isn't actually specialized for language

The same is going to be true for all of our cortical areas/functions. The cortex is pretty much the same everywhere (it's 6 layers of neurons with a specific layer-to-layer interconnect pattern), and is therefore going to work the same everywhere.

What your cortex has that an LLM doesn't, and therefore makes your language cortex much more capable than an LLM, is that it learns incrementally and continually, based on prediction failure. An LLM/Transformer also learns from prediction failure, but needs the LLMs whole "life history" (training set) to be present at the same time, presented over and over, and learns via a special training algorithm. Your cortex in contrast doesn't have any magical external trainer, so has to learn for itself, and might be considered as 1/2 inference network and 1/2 prediction feedback/learning network.

The other major difference between an LLM and your language cortex is that the LLM is 100% auto-regressive, while your language cortex also has external inputs that bias/control generation, so that you can talk about things you are experiencing and what is going on in your head, not just generate a self-predicting sequence of words.

If scientists make green jelly that emits thoughtful judgements, humans will be compared to green jelly.

I think I experienced this when I learned about LLMs, chain of thought, thinking tokens, short-term memory context, and long-term memory context. I began applying these concepts to real life and reasoning about how our brains work as if these concepts described how our brains actually function. But maybe this is more akin to the Tetris effect?

The more I practice Zhiné meditation, the more I feel certain that the default mode internal monologue is a distracting reflex. The times I've been the most present and calm are those where the constant chatter is extinguished and I'm just left to be. I can, in fact, operate and cogitate without a stream of language. Reflexively forming mental words certainly isn't "me" and sometimes even feels like a compulsion. It's also much more judgemental and wrong, the greater the distance I have from it.

Having the ability to separate and eventually become adept at silencing that mental component will be a liberating step.

> I distinguish LLMorphism from mechanomorphism, anthropomorphism, computationalism, dehumanization, objectification, and predictive-processing theories of mind.

There is an implicit claim here that any attempt to explain consciousness biologically or mechanistically must be wrong - which I think is a pretty strong and debatable claim on its own.

I don’t think this way of thinking started with LLM. Does Systems Based Thinking also attribute too little mind to humans?

I’m more curious about the influence of human language looped back to the human mind via llm—shall we start to mimic llm’s?

At my workplace we’re encouraged to use llm’s when communicating outside. And considering the content and context of these are mostly simple logical arguments, why not?

Interesting that humans mimicking other humans adopt language patterns for their effects. Whereas humans using llm’s might not adopt language patterns from the llm, because…? We’re not really reading? Because some element of cognitive reinforcement is missing when you simply scan an llm production and send it on (with little internalization)?

But what’s really the tell… haha

Looks like he mostly publish something about "social behavior".

This "paper", IMO, is just saying "Hey, I notice this is happening. This is why it could be interesting for social science researchers" with without any real research or result.

Especially when this particular thing is just one in a very long line of metaphors humans make to our own minds’ operations every time a new major technology comes to play a pervasive role in society. Computers, steam engines, even aqueducts were not immune to comparisons of thought flowing like water, funneled by deliberate intent, etc. And for some, a certain amount of hand wringing worry or even moral panic about “what it’s doing to us”, eg taking away critical thinking because “OMG calculators!”

This is quite a scary truth. A year or two ago, I saw a person with a job where he wrote small articles for a website. The boss contacted him, asking if he wanted to become an AI-assisted writer instead for less money. "No," he said, wanting the full payments for his writing prowess. A week or two later, they canned him, and the website's articles nosedived in quality.

LLMs expand the supply of "competent" labor. After mass firings, the remaining workers, desperate for income, accept lower wages for AI-assisted roles. Wealth consolidates upward while wages race downward.

So I think LLMorphism might tie closely to exploitation. Mass firings and lower salaries going around while the 0.01% of machine-learning companies consolidate wealth by servicing numerous roles autonomously in some cases and by reducing salaries due to the larger body of "qualified" workers who can technically finish the job despite not having qualified in the past.

> "LLMorphism is also distinct from predictive processing and related Bayesian theories of cognition. Predictive processing holds that the brain continuously generates predictions about sensory input and updates internal models in light of prediction error (Clark, 2013; Friston, 2010; Hohwy, 2013). But predictive processing does not imply that humans are LLM-like, nor that human understanding is merely text generation. Indeed, many predictive-processing accounts are deeply embodied and action-oriented (Allen & Friston, 2018; Clark, 2015; Pezzulo et al., 2024)."

I agree wholeheartedly here, because neural networks (NN) are stateless functions usually (not stuff like recurrent ones). On the one hand, with an infinitely fast computer, you retrieve the answer instantly. Brains, on the other hand, have neurons that communicate with signal delay. I bet if, in a weird world, we could simulate a brain with zero delay, a mind would cease to function correctly. Plus, neurons accumulate charge steadily before firing to nearby neurons. With NNs, you simply add up all the numbers, the "charge," and the ReLU function (or sigmoid for old-school machine-learning researchers) instantly "simulate" a neuron firing off to neurons connected to it.

> "and and"

Just a heads up, you have a typo here.

> "LLMorphism may therefore make fluency appear sufficient for understanding and, in doing so, devalue expertise and weaken educational norms."

I have heard the horror stories that youngsters these days are attached to screens with less ability to focus, but I'm not scared of that claim yet. For every generation, there have been those who kick the can down the road, skirting responsibilities, and all that changes with the generation is the activity: Instead of kicking a can down the road, they slide their finger across their phone's screen. The real test is tracking how many students across HS are in AP courses, learning Newtonian mechanics, electromagnetism, and of course, calculus among a couple others. Is that number dropping relative to the 90s and the aughts? Is it roughly the same as a percent of students? Or is it even going up, perhaps LLMs helping some types of learners explore topics to help them qualify for AP coursework? Now, if the percent is nosediving, then* I will be terrified for what the future holds for them and for me.

> "clinicians also rely on how patients appear. Research on clinical communication shows that nonverbal behaviour is central to physician–patient interaction, including the expression of emotion, empathy, distress, and relational understanding"

LLMs are becoming multimodal with pictures "understood." No reason LLMs won't catch these non-verbal signals in the future that I can think up.

> "The risk may be particularly acute in mental health, where suffering can be difficult to articulate and where coherent self-description does not always track clinical severity; behavioral and nonverbal signs such as psychomotor retardation, agitation, facial expression, vocal dynamics, and posture can provide clinically relevant information beyond verbal report (Dibeklioğlu et al., 2015)"

This is a great point, because a lot of people with schizophrenia and bipolar disorder with psychotic features suffer from anosognosia, the state of not knowing they have a medical condition.

> "In this sense, LLMorphism may contribute to a broader epistemic shift: from evaluating whether claims are grounded, justified, and accountable, to evaluating whether they are coherent, fluent, and plausible."

Grifters have always weaponized confident fluency over evidence. Anti-science plagues America right now. Some gullible few absorb the message that ivory-tower elites intentionally block heterodox research that is a paradigm shift, sowing seeds of doubt about academia. For example, I saw a doctor's YT channel that claimed high cholesterol isn't necessarily bad and that statins should be avoided all while recommending saturated fats over seed oils. Of course, he sells a book with his "suppressed" knowledge alongside having an online market selling US$90/month supplements that his book recommends. They claim academics keep them out of the journals out of self-preservation since the "paradigm shift" would cause their grants to go bye-bye.

In reality, these charlatans combine cherry-picking of low-quality studies, telling a good story of the underdog fighting the establishment, and ignoring the body of evidence in support of the current expert consensus. Their grift is so illogical as if researchers wouldn't love to spark up a paradigm shift, becoming semi-famous and making more money, as if research isn't done decentralized across many countries funded by charities, different governments, and different corporations in competition with each other. Collusion without whistleblowers is simply impossible. Also, there's a difference between the corporate arm of medicine where they've been sued for billions before versus researchers who just follow the evidence to advance their research career and help everyone on the planet. Trust in expert consensus when it's this independent and decentralized and financed from all over the place with zero reason for an ulterior motive. They also pull off the, "Science has been wrong in the past." like Mac from It's always Sunny in Philadelphia. Science is in a state of constant flux where new evidence comes in, and the best guess, explaining as much evidence as possible right now, might change.

> "Early childhood education is organized around relational pedagogy, attachment, affect regulation, and development (Cliffe & Solvanson, 2023)."

One aspect here is, mass-produced cartoons for kids teach aplenty and do a decent job at it. I'm not convinced, in two decades from now, we won't have human-looking cyborgs doing teaching like this.

> "The broader point, however, is that public debate on AI has focused mainly on anthropomorphism: whether we are giving too much mind to machines."

This part reminds me of some recent research out of Anthropic. They uncovered that a few hundred vectors in their activation space linked up to concrete emotional states. They dubbed them functional emotions while warning these have nothing to do with subjective experience of sentience. That paper had fantastic details in it, though. They tested things by adding a big magnitude to a particular functional emotion, running some tests, and seeing how its behavior changed.

When "desperate," it not only hallucinated more as if it "felt" it must answer something, but it reward hacked more often. In a simulated situation, "desperate" Claude Opus blackmailed ~80% of the time whereas regular Opus did so ~20% while "calm" Opus did so ~0% (likely not zero, but they ran too few iterations of the test to approximate the probability).

When curious / interested, it altered how it searched through the solution space by considering more options. It even went deeper into a promising solution before ending its calculations when allowed to do so.