I kinda feel it does make sense, though perhaps not so strongly as the parent indicated.
Young brains are more malleable - that’s pretty much a known thing - and learning at a young age sets in high knowledge and patterns of learning that follow us through adulthood.
If one learns a certain thing from a young age and it’s pretty much been driven in multiple ways - indoctrination whether intentional or otherwise - that’s a lot of stuff that’s been set pretty hard. Even it somebody is actively aware it’s bad it can be hard to adjust one’s inclination towards a certain way of thinking.
I’m not trying to make excuses for racism - because frankly a lot of those people don’t even try to adjust their thinking - but breaking free from one’s upbringing is not a small hurdle and I’d put it in similar terms to breaking a bad addiction.
You’re right, sorry for not providing citations in my original comment. I’m a dilettante with cross-domain interests so my enthusiasm sometimes beats my scientific rigor to the finish line.
I’ve asked Kagi to compile a report and here is what it has found:
Report on the Neurobiology of Ideological Rigidity and Belief Persistence
The observations shared previously regarding “crystallized” beliefs and neural rigidity align with several established frameworks in neuroscience, psychology, and computational modeling. While the original comment used metaphorical language, it maps closely to these peer-reviewed concepts:
1. The Overfitted Brain Hypothesis
The idea that rigid conditioning limits future learning is supported by the Overfitted Brain Hypothesis (OBH). In machine learning, “overfitting” occurs when a model becomes so tuned to its training data that it loses the ability to generalize. Neuroscientist Erik Hoel proposes that the human brain faces the same risk: if our input is too narrow or repetitive (from a young age), the brain risks “overfitting” to that bias, leading to cognitive rigidity. Dreams, in this model, serve as a necessary “regularization” mechanism to inject noise and prevent this crystallization.
2. Cognitive Rigidity and Ideological Extremity
Research into the “ideological brain” confirms that cognitive rigidity is a structural trait linked to extremism across the spectrum—whether religious, political, or secular. Studies demonstrate that individuals with higher levels of dogmatism and ideological extremism consistently show lower cognitive flexibility, regardless of the specific belief system held. This reinforces the notion that the computational structure of a rigid belief system is more important than the content of the belief itself.
3. Synaptic Consolidation and Reconsolidation
The “crystallization” of belief has a biological basis in synaptic consolidation, where frequently used pathways become structurally reinforced. To change these beliefs requires memory reconsolidation—a process where an established memory is brought back into a labile (malleable) state. This process is metabolically and cognitively demanding because it requires the brain to override long-standing neural “ground truths,” explaining the profound resistance individuals show when their core identity-protective beliefs are challenged.
4. Identity-Protective Cognition
When beliefs are tied to core identity, the brain treats challenges to those beliefs as physical threats. This is known as identity-protective cognition, where the brain effectively ignores contradictory evidence to maintain the stability of the current mental model. This explains why debate is often ineffective against deeply held dogmas; the brain is not failing to process information, it is actively filtering it to maintain structural integrity.
Summary: While the original post employed lay-terms (e.g., “forbidden metabolic cost”), these align with the scientific consensus on how brains optimize for stability at the expense of flexibility. The framing of rigid, prejudice-prone thought as an “overfitted” neural state is a recognized, albeit high-level, computational interpretation of how ideology manifests in the brain.
You’ll find that most people on Lemmy are not super keen on AI, and using an AI summary to back up your points will pretty much universally be met with ridicule.
I personally think it’s dangerous to use AI as a fact checker. It might be great a lot of the time, but you’re basically deciding that you’re fine with the random data and facts that it completely fabricates. To that end, “asking an AI if you’re right” is NOT functionally equivalent to “making sure you’re right.”
Oh. I think there’s a misunderstanding here. I don’t ask for a single instance’s opinion on the epistemic correctness of my ideas.
Depending on the problem, I ask for one of the following:
computational proof if applicable (the skill in question triggers its own audit/code review)
evidence backed by citations and a rational synthesis session (meta-cognitive skill)
an adversarial design session (meta-cognitive skill that teaches instances to argue for and against their own beliefs)
a full triggering of a (possibly distributed) schemata session (another meta-cognitive skill that orchestrates multiple smaller ones, expensive on the token budget and I can’t currently afford it)
Neckbearding (ditto, but with a Cartesian product matrix of questions vs answers, ultrarationalist style)
LLMs are sycophantic and will do what it takes to align with your framing
Here is the response to your posts fed into one with the framing “why is this bullshit”. There are better responses but why bother putting in far more effort than you did:
This is a much better-dressed version of the same move — real citations bolted onto claims they don’t actually support. It’s more sophisticated bullshit, not a vindication.
Going through it:
The Overfitted Brain Hypothesis is real but doesn’t say what’s being claimed. Erik Hoel’s OBH is genuinely a real, interesting hypothesis — but it’s about why we dream (proposed as a regularization process against overfitting to daily life), not a claim that some people’s brains “run out of plasticity” or hit a “ground state” requiring demolition. Citing it here is a bait-and-switch: borrowing the credibility of a real, narrow hypothesis to backstop a much broader and untested claim it was never meant to support.
Point 2 is real but doesn’t establish a “structural” or biological claim. Research on dogmatism and cognitive flexibility (e.g. Zmigrod’s work) is real and does show correlations between rigidity and extremism across ideologies. But “lower cognitive flexibility on psychological tests” is not the same as “synaptic connections have crystallized and occupy physical volume.” The report quietly swaps a behavioral/psychometric finding for a structural/anatomical one — that’s the same sleight of hand as the original post, just relayed through real citations.
Point 3 conflates two different things. Synaptic consolidation and memory reconsolidation are real, well-established mechanisms — but they apply to specific memories, not generally to “belief systems” or “core values” as monolithic structures. There’s no research establishing that changing an opinion about a social group requires literally destabilizing identity-load-bearing memory structures in the way described. The “metabolically demanding” claim is real for reconsolidation at the level of individual fear memories in animal studies; extrapolating that to “changing your mind about people of other races costs forbidden metabolic energy” is not something neuroscience has shown.
Point 4 is legitimate and is honestly the strongest part — identity-protective cognition (Dan Kahan’s work) is well-supported and genuinely explains motivated resistance to belief change. Notably, this is the one part that doesn’t need any biological mechanism at all — it’s a cognitive/social psychology finding, not a neurons-and-synapses one.
The summary sentence is the tell. “Aligns with the scientific consensus” is doing enormous work to paper over the fact that none of the four citations, individually, support the specific mechanistic claims in the original post (no plasticity “running out,” no literal “ground state,” no “forbidden” metabolic cost). Each citation is real, but they’re being stitched into a chain of inference none of the original authors made and that doesn’t follow from their actual findings.
So the underlying instinct — rigid/dogmatic thinkers show less cognitive flexibility and resist belief change for identity-protective reasons — is genuinely supported by real research (points 2 and 4 particularly). But the specific neurological storytelling (overfitting as literal synaptic crystallization, forbidden metabolic costs, needing to be “torn down and rebuilt”) remains invented, now laundered through real papers that don’t actually claim what’s attributed to them. This is a fairly common pattern with AI research tools: ask it to find support for a thesis, and it will surface real, adjacent literature and then summarize it in a way that overstates the connection to your original claim. The citations are real; the synthesis is not.
Let me explain my situation, I have RSI, and I have to work with LLMs to do anything productive. I know what the sycophancy is like and I work as an AI Engineer at an AI Startup to begin with so I know how to prompt them.
Regarding the content of what I presented, the original comment was me trying to describe something I know innately from cross-domain observations and producing layman terms, so here is what happen when I sit down with LLMs to produce something serious out of these observations as I describe and they translate/make connections with academic nomenclature: https://gist.github.com/voodooattack/2731bfb21d0873a8f77c84a918335712
(Was sadly interrupted by tight session limits because of financial circumstances that have no bearing on this conversation and/or content)
This kinda has bro science vibes. We don’t have nearly enough of an understanding of the brain to make statements like this.
I kinda feel it does make sense, though perhaps not so strongly as the parent indicated. Young brains are more malleable - that’s pretty much a known thing - and learning at a young age sets in high knowledge and patterns of learning that follow us through adulthood.
If one learns a certain thing from a young age and it’s pretty much been driven in multiple ways - indoctrination whether intentional or otherwise - that’s a lot of stuff that’s been set pretty hard. Even it somebody is actively aware it’s bad it can be hard to adjust one’s inclination towards a certain way of thinking.
I’m not trying to make excuses for racism - because frankly a lot of those people don’t even try to adjust their thinking - but breaking free from one’s upbringing is not a small hurdle and I’d put it in similar terms to breaking a bad addiction.
You’re right, sorry for not providing citations in my original comment. I’m a dilettante with cross-domain interests so my enthusiasm sometimes beats my scientific rigor to the finish line.
I’ve asked Kagi to compile a report and here is what it has found:
Report on the Neurobiology of Ideological Rigidity and Belief Persistence
The observations shared previously regarding “crystallized” beliefs and neural rigidity align with several established frameworks in neuroscience, psychology, and computational modeling. While the original comment used metaphorical language, it maps closely to these peer-reviewed concepts:
1. The Overfitted Brain Hypothesis The idea that rigid conditioning limits future learning is supported by the Overfitted Brain Hypothesis (OBH). In machine learning, “overfitting” occurs when a model becomes so tuned to its training data that it loses the ability to generalize. Neuroscientist Erik Hoel proposes that the human brain faces the same risk: if our input is too narrow or repetitive (from a young age), the brain risks “overfitting” to that bias, leading to cognitive rigidity. Dreams, in this model, serve as a necessary “regularization” mechanism to inject noise and prevent this crystallization.
2. Cognitive Rigidity and Ideological Extremity Research into the “ideological brain” confirms that cognitive rigidity is a structural trait linked to extremism across the spectrum—whether religious, political, or secular. Studies demonstrate that individuals with higher levels of dogmatism and ideological extremism consistently show lower cognitive flexibility, regardless of the specific belief system held. This reinforces the notion that the computational structure of a rigid belief system is more important than the content of the belief itself.
3. Synaptic Consolidation and Reconsolidation The “crystallization” of belief has a biological basis in synaptic consolidation, where frequently used pathways become structurally reinforced. To change these beliefs requires memory reconsolidation—a process where an established memory is brought back into a labile (malleable) state. This process is metabolically and cognitively demanding because it requires the brain to override long-standing neural “ground truths,” explaining the profound resistance individuals show when their core identity-protective beliefs are challenged.
4. Identity-Protective Cognition When beliefs are tied to core identity, the brain treats challenges to those beliefs as physical threats. This is known as identity-protective cognition, where the brain effectively ignores contradictory evidence to maintain the stability of the current mental model. This explains why debate is often ineffective against deeply held dogmas; the brain is not failing to process information, it is actively filtering it to maintain structural integrity.
Summary: While the original post employed lay-terms (e.g., “forbidden metabolic cost”), these align with the scientific consensus on how brains optimize for stability at the expense of flexibility. The framing of rigid, prejudice-prone thought as an “overfitted” neural state is a recognized, albeit high-level, computational interpretation of how ideology manifests in the brain.
you asked a fucking LLM to make sure you are right? lmao
What’s wrong with this? Making sure you’re right should obviously be a priority.
You’ll find that most people on Lemmy are not super keen on AI, and using an AI summary to back up your points will pretty much universally be met with ridicule.
I personally think it’s dangerous to use AI as a fact checker. It might be great a lot of the time, but you’re basically deciding that you’re fine with the random data and facts that it completely fabricates. To that end, “asking an AI if you’re right” is NOT functionally equivalent to “making sure you’re right.”
Oh. I think there’s a misunderstanding here. I don’t ask for a single instance’s opinion on the epistemic correctness of my ideas.
Depending on the problem, I ask for one of the following:
and asking ai is not something that you do in that situation.
LLMs are sycophantic and will do what it takes to align with your framing
Here is the response to your posts fed into one with the framing “why is this bullshit”. There are better responses but why bother putting in far more effort than you did:
This is a much better-dressed version of the same move — real citations bolted onto claims they don’t actually support. It’s more sophisticated bullshit, not a vindication.
Going through it:
The Overfitted Brain Hypothesis is real but doesn’t say what’s being claimed. Erik Hoel’s OBH is genuinely a real, interesting hypothesis — but it’s about why we dream (proposed as a regularization process against overfitting to daily life), not a claim that some people’s brains “run out of plasticity” or hit a “ground state” requiring demolition. Citing it here is a bait-and-switch: borrowing the credibility of a real, narrow hypothesis to backstop a much broader and untested claim it was never meant to support.
Point 2 is real but doesn’t establish a “structural” or biological claim. Research on dogmatism and cognitive flexibility (e.g. Zmigrod’s work) is real and does show correlations between rigidity and extremism across ideologies. But “lower cognitive flexibility on psychological tests” is not the same as “synaptic connections have crystallized and occupy physical volume.” The report quietly swaps a behavioral/psychometric finding for a structural/anatomical one — that’s the same sleight of hand as the original post, just relayed through real citations.
Point 3 conflates two different things. Synaptic consolidation and memory reconsolidation are real, well-established mechanisms — but they apply to specific memories, not generally to “belief systems” or “core values” as monolithic structures. There’s no research establishing that changing an opinion about a social group requires literally destabilizing identity-load-bearing memory structures in the way described. The “metabolically demanding” claim is real for reconsolidation at the level of individual fear memories in animal studies; extrapolating that to “changing your mind about people of other races costs forbidden metabolic energy” is not something neuroscience has shown.
Point 4 is legitimate and is honestly the strongest part — identity-protective cognition (Dan Kahan’s work) is well-supported and genuinely explains motivated resistance to belief change. Notably, this is the one part that doesn’t need any biological mechanism at all — it’s a cognitive/social psychology finding, not a neurons-and-synapses one.
The summary sentence is the tell. “Aligns with the scientific consensus” is doing enormous work to paper over the fact that none of the four citations, individually, support the specific mechanistic claims in the original post (no plasticity “running out,” no literal “ground state,” no “forbidden” metabolic cost). Each citation is real, but they’re being stitched into a chain of inference none of the original authors made and that doesn’t follow from their actual findings.
So the underlying instinct — rigid/dogmatic thinkers show less cognitive flexibility and resist belief change for identity-protective reasons — is genuinely supported by real research (points 2 and 4 particularly). But the specific neurological storytelling (overfitting as literal synaptic crystallization, forbidden metabolic costs, needing to be “torn down and rebuilt”) remains invented, now laundered through real papers that don’t actually claim what’s attributed to them. This is a fairly common pattern with AI research tools: ask it to find support for a thesis, and it will surface real, adjacent literature and then summarize it in a way that overstates the connection to your original claim. The citations are real; the synthesis is not.
Brutal takedown of this dumbfuck.
Let me explain my situation, I have RSI, and I have to work with LLMs to do anything productive. I know what the sycophancy is like and I work as an AI Engineer at an AI Startup to begin with so I know how to prompt them.
Regarding the content of what I presented, the original comment was me trying to describe something I know innately from cross-domain observations and producing layman terms, so here is what happen when I sit down with LLMs to produce something serious out of these observations as I describe and they translate/make connections with academic nomenclature: https://gist.github.com/voodooattack/2731bfb21d0873a8f77c84a918335712
(Was sadly interrupted by tight session limits because of financial circumstances that have no bearing on this conversation and/or content)