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The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words

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While many studies have shown that toddlers are able to detect syntactic regularities in speech, the learning mechanism allowing them to do this is still largely unclear. Perrine Brusini discusses computational modeling to assess the plausibility of a context-based learning mechanism for the acquisition of nouns and verbs.

Image courtesy of interviewee. May 1, 2024

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This video video is a transcript from a presentation discussing the complexity of human language, particularly focusing on syntax, which is the ability to combine words following certain rules, often unconsciously. The speaker emphasizes that this ability is unique to humans and is referred to as generative capacity, allowing individuals to understand and produce sentences they have never encountered before.

The discussion touches on the development of language in infants, concerning when they start using these complex rules. The speaker references Noam Chomsky’s theory that linguistic abilities are biologically innate, which has sparked much debate in the field. Extensive research has been conducted to determine at what age infants can manipulate syntax, with challenges in differentiating between grammatical sentences and those that simply have low probabilities of word co-occurrence.

The research presented involves modeling experiments to assess how infants might acquire understanding of nouns and verbs through the input they receive from caregivers and their environment. The modeling approach uses naturalistic data from caregiver-infant interactions, analyzing vocabulary and the relationship between context and word categories.

Results from these models show that they can effectively categorize nouns and verbs, with relatively high precision rates, demonstrating a potential method through which infants might learn syntax using limited vocabulary and semantic cues. The audience is informed about future research directions, including verifying if infants can indeed recognize semantic categories in a way similar to adults and how they apply this understanding when encountering new words. Overall, the presentation explores significant questions around language acquisition and the cognitive processes involved in learning syntax.

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Here are some articles that explore similar themes related to language acquisition, syntax, and cognitive processes in infants:

1. **"The Innateness Hypothesis: A Review and Recent Developments"**
- This article reviews Noam Chomsky's innateness hypothesis and its implications in linguistic theory. It discusses the biological foundations of language and examines various studies regarding the innate capabilities of children in language learning.

2. **"Understanding Language Development: The Role of Caregiver Interaction"**
- This piece explores how interactions between caregivers and infants contribute to language development, emphasizing the importance of convideo and social cues in acquiring vocabulary and understanding syntax.

3. **"The Development of Syntax in Early Childhood: A Cross-Cultural Perspective"**
- This article reviews studies on when and how children across different cultures begin to understand and use complex syntactic structures, reflecting on the universal patterns of language acquisition.

4. **"Modeling Language Learning in Infants: Insights from Computational Approaches"**
- A discussion about computational models that simulate how infants learn language, focusing on the acquisition of nouns and verbs, and examining the effectiveness of models which mimic naturalistic input.

5. **"From Sounds to Syntax: The Infancy of Language Development"**
- This article reviews the stages of language acquisition in infants, specifically focusing on how they learn to tell apart grammatical sentences from ungrammatical ones, including discussions on probability and word co-occurrence.

6. **"Language Acquisition and Semantic Memory in Infants"**
- A study that looks specifically at how infants develop semantic understanding and how this relates to later syntax learning, featuring experimental results that demonstrate skills in categorizing words before forming complex sentences.

7. **"Cognitive Processes in Language Learning: Insights from Infant Studies"**
- This article summarizes current research findings on cognitive mechanisms underlying language learning in infants, emphasizing syntactic understanding and the role of innate structures as proposed by leading linguists.

These articles delve into the intricacies of language development in infants, the theoretical foundations of syntax, and the empirical research surrounding these topics, paralleling the subject matter of your transcript.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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When we use language, we, we don't really realize it

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because it's very automatic, for us adults,

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but we use many different units.

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And one of, the important one,

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and that has been argued to be, the facts

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that we are specific language is specific to human

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is, syntax.

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So syntax is our capacity to combine war together.

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And what is interesting is that we combine,

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words together according to rules

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that we are not totally aware.

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But there is sentence that you will never produce

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as a speaker of your own language.

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Some things like, flower on the,

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yellow, that doesn't make any sense.

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You cannot do this and you will never produce it.

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What is interesting also is that

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because you know, some of the rule of your language,

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you are able to integrate sentences

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that you have never heard before.

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So if I say something like, the cat is mm-hmm.

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Under the mm-Hmm.

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Even if you don't know at all what those non-word that is,

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-mm are you start to understand what is happening.

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You have a fairly good idea of the action

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that the cat is doing and where is doing it.

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So, this capacity seems to be very specific to human.

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It's called generative.

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So we can generate

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and decode sentences that we have never heard.

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And so one of the question that is crucial for my work is

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to know from when infants can do that, when do we start

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to use this fairly abstract, fairly complex, rules

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that allow us to exchange in our day-to-day basis.

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So there have been a lot of work,

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toward this finding at which age infants can do this kind

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of very complex abstract processing.

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And it has also be a, a lot of debate in the field.

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I don't know if, you, you know, about this,

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man Noam Shamsky,

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but at some point he put forward the idea

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that our linguistic capacity was, biologically based

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and our capacity to manipulate, words

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and category of words like non verb determiner, ter

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and ter was innate.

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And there, there have been then a lot of debate

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because obviously if it's innate, it means

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that we can do it from when we are born.

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And there have been people that were not very

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happy with this idea.

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And so there have been a, a, a gigantic amount of data

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trying to find out if infants can manipulate syntax from

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fairly early on.

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And, my work, is a bit of a continuation of this.

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So while I wanted

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to know is at which age infants can do this manipulation?

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And one of the thing

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that has been difficult in the past few years for,

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developmentalists, for people working in the field

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of language development is to make the difference between

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a sentence that is grammatical.

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As I said, you will never produce some sentence

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and a sentence that,

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where the probability of words to appear together

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are super low.

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So one of the things that you, you need to understand is

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that we have a confirmed here.

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When I say that you are able to decode the sentence

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that you have never heard, it means that if I use correctly

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words that you, you you have never heard together,

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you will be able to decode even.

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So you have never been arbitrated to the sequence,

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specific sequence of the world.

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Obviously, it means that there is some,

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what we call transition of probability.

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So the probability that towards co-occur equal zero,

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and sometimes it's equals zero because it's ungrammatical.

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Sometimes it's equals zero

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because it's, it's just not used in your,

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in your input.

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And so this is a confirm that is very complicated

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to manipulate because it's possible that the infant react

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to the fact that there is a zero probability.

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And so it's not because it's grammatical, but it's

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because he has the, the, the, the children,

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the infants never heard the sentence

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before that he react,

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but not using the syntax.

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And it's very difficult to do this manipulation with,

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with infants because obviously they don't know a lot

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of words and they don't have a big span of attention.

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So there have been a lot of work around this.

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And, what we have decided to do, it was, a,

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a modalization so a feasibility experiment.

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And the goal here was to say, okay, so let's say that,

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early infants, no few words,

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he ha the infants has a she somewhere

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because, he, he has his, input from,

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you know, the caretakers.

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He has also the environment and some pragmatic ideas.

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So for example, if you show the bottle with the mic

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with the infants, this is an object that is fairly ate.

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Maybe at some point he will learn the meaning of bottle.

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Maybe he will also understand, the verb eat

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because he is eating sometimes

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and there is some correlation between the action

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and the, and the word.

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So we decided to do a, a, a, a model

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that will know some words

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and we wanted to see if there were this,

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this little amount of information

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that was bring from another part of our linguistic capacity,

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which is Symantec, how it can potentially help

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infant to start a syntactic calculation.

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What we did, again, it's a modernization.

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So what we wanted to know is how an infant will be able to,

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Uh, acquire New non

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and new verb, given the handful of non

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and verb that this infant will

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Have learned.

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So what we did is to,

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use a very helpful, source

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Of information. It's called

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Shy Death. So Child

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Death is a website

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That is open to everybody.

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And on this website

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There is Transcription of,

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Uh, Caretakers and infants, exchange.

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So basically scientists go to the home of people,

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they record the parents

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and their infants when they're together

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and their natural exchange,

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and we have the transcription. So

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What we did with that, it was to,

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First clean it a bit,

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and then we, selected the most often nouns

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and the most often verbs that the infants

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Have, heard.

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And we decided that those

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Words will be the words that the

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Infants. So,

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or model, no. So

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Basically that was the words that the,

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That the model Have,

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Uh, been, have been able to

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Acquire, just by The, the input.

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And what we decided is that our,

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our model will have some kind of semantic

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Basis. So

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What the model is doing is

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that it's separating from one side

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The verb.

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So the action, it's kind of an

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Approximation Because

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There is some known that, Uh,

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Also refer an action.

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I can say, for example, a

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Jump, A jump is an action, but it

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Is a known, but when you Look

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At the global vocabulary that we have, generally

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We can approximate that most of the action are used

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As Verb and most

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Of the objects are used, as

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Referring, to a norm.

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So we decided that basically the

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Lexicon of our modern, We have this

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Semantic information. It will,

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It'll know that,

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Few nos are object and few nos are action,

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and they are already a separation between

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Those Two element of the

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Lexicon.

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Once you have this, you can start to do a

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Distribution analysis.

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So what we decided was A very simple algorithm

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Is that The infants are able

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To look at all, Three grams.

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So three grams is

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Three wards, two, And learn all of them.

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Among the three grams that our model have learned,

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There is those seeds.

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So the nouns and the verbs that the model know.

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And what we have done is that we have

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Decided To run many model a model

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That was, very poor

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In lexicon because it has only four noun and two verbs,

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and we have double this amount each time

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to run different model until we have, until a,

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a model that is not,

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Uh, a, a very good

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Modernization of a, of a child

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because, the model knew all the non and all the verb,

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but it was just a way to see

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what this method could do if it was at the maximum capacity.

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And so we have look recorded all the sequence

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of three words, and then we have kind of request

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or model to do a categorization task.

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So we give a new part of this transcription,

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so a transcription that the model have not been trained on,

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and we ask the model to give the category of the third words

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that, the, that, the model encounter into this,

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new corpus.

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What the model is doing is that it's taking the two world

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of context before, guessing the third one.

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So for example, if the model encounter the little cat,

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it will, it'll have the, and little to guess the next word.

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And what we have done is that we have required a model

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to give exactly the, answer that is expected.

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So here you have to remember that a model has some kind

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of a mix flu

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because there is some words that,

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we expect a category that's the word that the model know.

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So we expect the model to answer non

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or verb all the words.

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They are just themselves that they sta as they are.

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So for example, the model doesn't know any adjective,

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so little skeletal, and there is some words non

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and verb that the model doesn't know.

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So again, they stay themself and we count true

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or correct only when the model gives exact category of the,

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of the words

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that in encounter the result were really surprising

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because I don't know if you realize how simple the model is,

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is just collecting sequence of three words.

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And what we have found is that the model is very precise.

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So the precision is when you say, for example, none

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how many times where you Correct.

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So the precision were, were excellent.

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It was around 80% for the non, a bit lower for the verb,

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around, 70% of the time.

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Correct. And what we have done

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to estimate if the model was good,

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because it's something that is difficult in development,

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we don't want a perfect model.

11:23.165 --> 11:24.705
We don't want a model that is able

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to categorize all the non and all the verb.

11:27.005 --> 11:30.425
We want a model that is able to model the infants

11:30.425 --> 11:32.625
and obviously the infants doesn't know all

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the non and all the verb.

11:34.125 --> 11:37.385
So then you have to estimate how good is your model.

11:37.445 --> 11:41.025
So what we have done is a very simple little manipulation.

11:41.485 --> 11:43.545
We have run a chance model.

11:43.965 --> 11:46.465
So a model that will just say non

11:46.465 --> 11:48.625
and verb adds the proportion of the non

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and verb of the training corpus.

11:51.405 --> 11:54.965
So it's a model that has no training compared

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to our other model that have learned something

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from the distribution.

11:59.385 --> 12:03.005
And, the, chance model was very below

12:03.385 --> 12:07.285
or the train model, especially on the precision.

12:07.285 --> 12:08.525
And then we use another measure

12:08.525 --> 12:10.405
that's a bit harder to understand.

12:10.475 --> 12:13.445
It's called recall, but, it's,

12:13.675 --> 12:15.245
it's not very hard to understand.

12:15.395 --> 12:18.365
It's basically when you need to find some things,

12:18.755 --> 12:20.285
were you able to find the things?

12:20.385 --> 12:24.365
So when, does the model have swe,

12:24.605 --> 12:26.725
a non at the level of the norm

12:26.725 --> 12:28.765
that was present in, in its target?

12:29.905 --> 12:32.285
And for this, it was also interesting

12:32.285 --> 12:36.445
because the, the recall was, a have a slop.

12:36.985 --> 12:40.925
So basically it was going up with a level of non

12:40.985 --> 12:44.165
and verb that the model knew from the beginning.

12:45.465 --> 12:48.205
Why is that? Uh, well, it's, or

12:48.205 --> 12:50.285
because we have given a mixed flu.

12:50.705 --> 12:54.445
So if we look at what the model was doing bad,

12:54.445 --> 12:56.805
especially in the low vocabulary,

12:57.115 --> 13:01.445
what the model was doing is that sometimes he encounter,

13:01.465 --> 13:06.285
it encounters a noun that, he guess was known.

13:06.785 --> 13:10.685
For example, after Al, Al is a context of noun,

13:10.805 --> 13:12.285
a very easy context of noun.

13:12.285 --> 13:14.165
So it was answering noun,

13:14.385 --> 13:15.845
but sometimes it was noun

13:15.845 --> 13:18.925
that the model didn't learn like elephant, a very,

13:19.225 --> 13:20.565
you know, uncommon nouns.

13:20.865 --> 13:23.885
But we expect that the model answer elephant here.

13:24.185 --> 13:26.685
So most of the time when we look at

13:26.685 --> 13:30.205
what the model was doing, it was not trying to mix non

13:30.205 --> 13:32.365
and verb, which will be very bad,

13:33.145 --> 13:37.525
but it was throwing an item instead of the category

13:37.865 --> 13:39.885
or the category instead of the item.

13:40.155 --> 13:43.285
With this model, we can see that the very fairly easy

13:44.445 --> 13:49.365
learning method can be used in fact to learn from the input.

13:49.495 --> 13:52.325
Where are your norms and where are your your verb.

13:53.305 --> 13:56.205
Uh, obviously this doesn't demonstrate

13:56.515 --> 13:59.045
that this is the method that the infants use

13:59.305 --> 14:00.765
to learn syntax.

14:01.665 --> 14:03.325
It could be useful to learn it,

14:03.785 --> 14:08.405
but we have made some, decision, some,

14:08.435 --> 14:10.725
arbitrary decision that we have to demonstrate

14:10.725 --> 14:14.845
that they are true to then be able to say, guy,

14:14.865 --> 14:17.045
we have found out how infants learn syntax.

14:17.585 --> 14:20.685
So in my future research, I want to,

14:21.065 --> 14:25.205
to study if it's, if this, semantic seed,

14:25.345 --> 14:30.125
if the way we have said, in fact, infants are able to say

14:30.125 --> 14:33.445
that the non are object and that the verb are action

14:33.545 --> 14:36.565
and those elements are very separating.

14:36.565 --> 14:40.605
The lexicon is true from the very first word that you learn.

14:42.205 --> 14:44.485
Interestingly, it's something that is true for adults.

14:44.585 --> 14:48.925
So if you do a MRI and a scan of a adult brain

14:49.345 --> 14:51.405
and you, present norms

14:51.405 --> 14:54.725
and your present ferb, you will see very fairly distinct

14:54.725 --> 14:56.045
region that will activate.

14:56.585 --> 14:58.805
So it's not a totally crazy idea,

14:59.105 --> 15:01.205
but it hasn't been checked in infants.

15:01.585 --> 15:04.685
Do they learn the kind of semantic that is attached

15:04.685 --> 15:05.725
to a world directly?

15:05.745 --> 15:08.685
And would it affect the way they anchor the

15:08.685 --> 15:09.885
world in the lexicon?

15:10.265 --> 15:13.925
We have to check that. We have to check also if once

15:14.555 --> 15:17.205
they have learned the distribution of the word,

15:17.595 --> 15:19.525
they can transmit this information.

15:19.585 --> 15:22.925
So let's say that the infants know that cat is an object

15:23.305 --> 15:25.805
and cat appear with oli.

15:26.385 --> 15:29.285
Are they able, when they encounter elephant

15:29.575 --> 15:33.285
after the little to say, in this case this object,

15:33.285 --> 15:35.325
this word should be an object?

15:36.355 --> 15:37.485
That is another question.

15:37.625 --> 15:39.965
And that's something that I would like also to test.

15:40.265 --> 15:44.845
So if it's true, it'll give a real cognitive, cognitive,

15:45.715 --> 15:49.405
cognitive assessment of, of, this model that was just

15:49.405 --> 15:50.445
for no affordability.

00:00 - When we use language, we, we don't really realize it because it's very automatic, for us adults, but we use many different units. And one of, the important one, and that has been argued to be, the facts that we are specific language is specific to human

00:19 - is, syntax. So syntax is our capacity to combine war together. And what is interesting is that we combine, words together according to rules that we are not totally aware. But there is sentence that you will never produce as a speaker of your own language. Some things like, flower on the, yellow, that doesn't make any sense. You cannot do this and you will never produce it.

00:45 - What is interesting also is that because you know, some of the rule of your language, you are able to integrate sentences that you have never heard before. So if I say something like, the cat is mm-hmm. Under the mm-Hmm. Even if you don't know at all what those non-word that is, -mm are you start to understand what is happening. You have a fairly good idea of the action that the cat is doing and where is doing it.

01:15 - So, this capacity seems to be very specific to human. It's called generative. So we can generate and decode sentences that we have never heard. And so one of the question that is crucial for my work is to know from when infants can do that, when do we start to use this fairly abstract, fairly complex, rules that allow us to exchange in our day-to-day basis. So there have been a lot of work, toward this finding at which age infants can do this kind

01:48 - of very complex abstract processing. And it has also be a, a lot of debate in the field. I don't know if, you, you know, about this, man Noam Shamsky, but at some point he put forward the idea that our linguistic capacity was, biologically based and our capacity to manipulate, words and category of words like non verb determiner, ter and ter was innate. And there, there have been then a lot of debate

02:21 - because obviously if it's innate, it means that we can do it from when we are born. And there have been people that were not very happy with this idea. And so there have been a, a, a gigantic amount of data trying to find out if infants can manipulate syntax from fairly early on. And, my work, is a bit of a continuation of this. So while I wanted to know is at which age infants can do this manipulation?

02:51 - And one of the thing that has been difficult in the past few years for, developmentalists, for people working in the field of language development is to make the difference between a sentence that is grammatical. As I said, you will never produce some sentence and a sentence that, where the probability of words to appear together are super low. So one of the things that you, you need to understand is

03:22 - that we have a confirmed here. When I say that you are able to decode the sentence that you have never heard, it means that if I use correctly words that you, you you have never heard together, you will be able to decode even. So you have never been arbitrated to the sequence, specific sequence of the world. Obviously, it means that there is some, what we call transition of probability. So the probability that towards co-occur equal zero,

03:51 - and sometimes it's equals zero because it's ungrammatical. Sometimes it's equals zero because it's, it's just not used in your, in your input. And so this is a confirm that is very complicated to manipulate because it's possible that the infant react to the fact that there is a zero probability. And so it's not because it's grammatical, but it's because he has the, the, the, the children, the infants never heard the sentence

04:19 - before that he react, but not using the syntax. And it's very difficult to do this manipulation with, with infants because obviously they don't know a lot of words and they don't have a big span of attention. So there have been a lot of work around this. And, what we have decided to do, it was, a, a modalization so a feasibility experiment. And the goal here was to say, okay, so let's say that, early infants, no few words,

04:51 - he ha the infants has a she somewhere because, he, he has his, input from, you know, the caretakers. He has also the environment and some pragmatic ideas. So for example, if you show the bottle with the mic with the infants, this is an object that is fairly ate. Maybe at some point he will learn the meaning of bottle. Maybe he will also understand, the verb eat because he is eating sometimes and there is some correlation between the action

05:23 - and the, and the word. So we decided to do a, a, a, a model that will know some words and we wanted to see if there were this, this little amount of information that was bring from another part of our linguistic capacity, which is Symantec, how it can potentially help infant to start a syntactic calculation. What we did, again, it's a modernization. So what we wanted to know is how an infant will be able to,

05:57 - acquire New non and new verb, given the handful of non and verb that this infant will Have learned. So what we did is to, use a very helpful, source Of information. It's called Shy Death. So Child Death is a website That is open to everybody.

06:17 - And on this website There is Transcription of, Caretakers and infants, exchange. So basically scientists go to the home of people, they record the parents and their infants when they're together and their natural exchange, and we have the transcription. So What we did with that, it was to, First clean it a bit,

06:37 - and then we, selected the most often nouns and the most often verbs that the infants Have, heard. And we decided that those Words will be the words that the Infants. So, or model, no. So Basically that was the words that the, That the model Have, been, have been able to

06:58 - Acquire, just by The, the input. And what we decided is that our, our model will have some kind of semantic Basis. So What the model is doing is that it's separating from one side The verb. So the action, it's kind of an Approximation Because There is some known that,

07:22 - Also refer an action. I can say, for example, a Jump, A jump is an action, but it Is a known, but when you Look At the global vocabulary that we have, generally We can approximate that most of the action are used As Verb and most Of the objects are used, as Referring, to a norm. So we decided that basically the

07:46 - Lexicon of our modern, We have this Semantic information. It will, It'll know that, Few nos are object and few nos are action, and they are already a separation between Those Two element of the Lexicon. Once you have this, you can start to do a Distribution analysis. So what we decided was A very simple algorithm

08:12 - Is that The infants are able To look at all, Three grams. So three grams is Three wards, two, And learn all of them. Among the three grams that our model have learned, There is those seeds. So the nouns and the verbs that the model know. And what we have done is that we have Decided To run many model a model That was, very poor

08:40 - In lexicon because it has only four noun and two verbs, and we have double this amount each time to run different model until we have, until a, a model that is not, a, a very good Modernization of a, of a child because, the model knew all the non and all the verb, but it was just a way to see what this method could do if it was at the maximum capacity. And so we have look recorded all the sequence

09:09 - of three words, and then we have kind of request or model to do a categorization task. So we give a new part of this transcription, so a transcription that the model have not been trained on, and we ask the model to give the category of the third words that, the, that, the model encounter into this, new corpus. What the model is doing is that it's taking the two world of context before, guessing the third one. So for example, if the model encounter the little cat,

09:45 - it will, it'll have the, and little to guess the next word. And what we have done is that we have required a model to give exactly the, answer that is expected. So here you have to remember that a model has some kind of a mix flu because there is some words that, we expect a category that's the word that the model know. So we expect the model to answer non or verb all the words. They are just themselves that they sta as they are.

10:21 - So for example, the model doesn't know any adjective, so little skeletal, and there is some words non and verb that the model doesn't know. So again, they stay themself and we count true or correct only when the model gives exact category of the, of the words that in encounter the result were really surprising because I don't know if you realize how simple the model is, is just collecting sequence of three words. And what we have found is that the model is very precise.

10:56 - So the precision is when you say, for example, none how many times where you Correct. So the precision were, were excellent. It was around 80% for the non, a bit lower for the verb, around, 70% of the time. Correct. And what we have done to estimate if the model was good, because it's something that is difficult in development, we don't want a perfect model. We don't want a model that is able

11:24 - to categorize all the non and all the verb. We want a model that is able to model the infants and obviously the infants doesn't know all the non and all the verb. So then you have to estimate how good is your model. So what we have done is a very simple little manipulation. We have run a chance model. So a model that will just say non and verb adds the proportion of the non and verb of the training corpus.

11:51 - So it's a model that has no training compared to our other model that have learned something from the distribution. And, the, chance model was very below or the train model, especially on the precision. And then we use another measure that's a bit harder to understand. It's called recall, but, it's, it's not very hard to understand. It's basically when you need to find some things,

12:18 - were you able to find the things? So when, does the model have swe, a non at the level of the norm that was present in, in its target? And for this, it was also interesting because the, the recall was, a have a slop. So basically it was going up with a level of non and verb that the model knew from the beginning. Why is that? well, it's, or because we have given a mixed flu.

12:50 - So if we look at what the model was doing bad, especially in the low vocabulary, what the model was doing is that sometimes he encounter, it encounters a noun that, he guess was known. For example, after Al, Al is a context of noun, a very easy context of noun. So it was answering noun, but sometimes it was noun that the model didn't learn like elephant, a very, you know, uncommon nouns.

13:20 - But we expect that the model answer elephant here. So most of the time when we look at what the model was doing, it was not trying to mix non and verb, which will be very bad, but it was throwing an item instead of the category or the category instead of the item. With this model, we can see that the very fairly easy learning method can be used in fact to learn from the input. Where are your norms and where are your your verb. obviously this doesn't demonstrate

13:56 - that this is the method that the infants use to learn syntax. It could be useful to learn it, but we have made some, decision, some, arbitrary decision that we have to demonstrate that they are true to then be able to say, guy, we have found out how infants learn syntax. So in my future research, I want to, to study if it's, if this, semantic seed, if the way we have said, in fact, infants are able to say

14:30 - that the non are object and that the verb are action and those elements are very separating. The lexicon is true from the very first word that you learn. Interestingly, it's something that is true for adults. So if you do a MRI and a scan of a adult brain and you, present norms and your present ferb, you will see very fairly distinct region that will activate. So it's not a totally crazy idea, but it hasn't been checked in infants.

15:01 - Do they learn the kind of semantic that is attached to a world directly? And would it affect the way they anchor the world in the lexicon? We have to check that. We have to check also if once they have learned the distribution of the word, they can transmit this information. So let's say that the infants know that cat is an object and cat appear with oli. Are they able, when they encounter elephant

15:29 - after the little to say, in this case this object, this word should be an object? That is another question. And that's something that I would like also to test. So if it's true, it'll give a real cognitive, cognitive, cognitive assessment of, of, this model that was just for no affordability.

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