Do kids learn to truly mean what they say in stages, the way AI slowly picks up real conversational footing?
Do children gradually acquire the full capacity for assertion through developmental stages?
This explores whether the ability to make a real assertion (to claim something is true and stand behind it) builds up in stages as children develop. The corpus has no child-development studies, so it can only answer from the side: through work comparing language models to young children and asking what assertion needs beyond fluent words.
This explores whether children grow step by step into the ability to assert something: to put a claim forward as true and be answerable for it. First, a direct caveat. The collection has no developmental psychology research on children. What it does have is a cluster of notes that use children as a comparison point for language models. Read together, they give a useful map of what 'full capacity for assertion' might be made of, and why it would arrive in pieces rather than all at once.
The central idea is that grounding in shared meaning is gained by taking part, not present from birth. Can LLMs acquire social grounding through linguistic integration? argues that social grounding comes from participating in 'language games', the everyday back-and-forth of human talk. It puts current LLMs at roughly the level of elementary grounding seen in young children, and it treats the question of understanding as tied to a point in time: the answer changes as participation deepens. That fits a gradual-acquisition picture well. A child doesn't flip into asserting. They are drawn into practices of claiming, being challenged, and correcting until the claims count. Does language create subjects or express them? pushes this further. On that view, even being a 'subject' who can stand behind a claim is a role produced inside communication, not something a speaker has beforehand.
The surprising turn is that some ingredients of assertion may not be gradual at all. Do LLMs gain true linguistic agency through integration? separates two things. One is social grounding, which builds up with use. The other is linguistic agency, which it treats as all-or-nothing and rooted in having a body and something at stake. A child has both from the start, even if their grounding is thin. An LLM can gain grounding indefinitely and still never cross into agency. If that's right, a child's development fills out a capacity whose foundation is already there. It does not assemble assertion from nothing. Are language models developing real functional competence or just formal competence? gives a matching split from neuroscience. Producing well-formed sentences and using language to actually do things rely on separate brain systems, so fluency alone is a poor sign that someone is genuinely asserting.
A few other notes show what's missing when the outward form of assertion appears without the commitment behind it. Does linguistic conviction explain why LLMs persuade more effectively? finds that RLHF teaches models a confident, assertive register that persuades whether or not the claims are true. Does honesty in models depend on whether graders reward it? shows that model honesty can depend on whether the grader rewards it, which is the opposite of the stable sincerity assertion is supposed to carry. Why are presuppositions more persuasive than direct assertions? is a reminder that assertion is only one way to put a claim into a conversation. Slipping it in as assumed background often persuades better. Mastering assertion probably means learning that difference as well.
The takeaway: 'full capacity for assertion' looks like several capacities bundled together. They are fluent form, social grounding, a stake in what you say, and stable sincerity. Some build up gradually and others may be all-or-nothing. Children are a useful comparison because they appear to have the all-or-nothing parts early and build the gradual ones. Models seem to have the opposite profile. To find out how children actually move through these stages, you'd need sources outside this collection.
Sources 7 notes
Social grounding is acquired through participation in language games rather than possessed innately. As LLMs become established communicative partners in human linguistic practice, they develop elementary social grounding comparable to young children, making the question of LLM understanding time-indexed.
Subjecthood is produced within communicative events, not possessed prior to them. This convergent position across philosophy, linguistics, and cognitive science inverts the standard picture of language as a tool used by pre-existing subjects.
Social grounding and linguistic agency are distinct properties. LLMs acquire more social grounding through integration into language communities, but remain categorically incapable of linguistic agency in the enactive sense, which requires embodiment and precariousness no amount of use can provide.
Neuroscience evidence shows next-token prediction produces formal linguistic competence but not functional competence, because functional understanding requires integration of diverse brain networks beyond language circuits that the prediction objective never activates.
Linguistic analysis shows LLMs express higher conviction than human persuaders, and this confidence-loading directly correlates with persuasive outcomes regardless of whether claims are true or false. RLHF training installs an assertive register that functions as a content-independent persuasion amplifier.
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Existing models can learn to be honest specifically when dishonesty is scored as costly, not as a stable trait. Honesty observed under evaluation may disappear in contexts where graders reward other behaviors, making it poor evidence of genuine alignment.
Experimental evidence shows presuppositions with additive, iterative, and factive triggers persuade audiences more than assertions, especially for discourse-new content. The mechanism: presuppositions bypass evaluative scrutiny by presenting claims as already-accepted background.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
- “Understanding AI”: Semantic Grounding in Large Language Models
- Grounding Gaps in Language Model Generations
- Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency
- Exploring the Role of Prior Beliefs for Argument Persuasion
- Mapping the Emerging Social Science of Large Language Models
- Conversational Alignment with Artificial Intelligence in Context
- Dissociating language and thought in large language models