<p>Here's something I dug up for language: a Computer Science Thesis from Boulder:</p>

<p><a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.3216" rel="nofollow">The Sensorimotor Foundations of Phonology: A Computational Model of Early Childhood Articulatory and Phonetic Development (1994)</a></p>

<p>it discusses what it calls HABLAR (Hierarchical Articulatory Based Language
Acquisition by Reinforcement learning).  </p>

<p>From the <strong>reductionist/biology side</strong>, developing brains have a much higher population of gap junctions.  I would speculate that this would create a much more analog brain, as gap junctions are modeled diffusively, unlike synaptic connections, which can be modeled in a more binary way.  I would speculate that the analog driving of the environmental and genetic signals leads to the formation and orgnaization of the synaptic connectivity.</p>

<p>I wonder if one could find a correlation between plasticity and gap junctions.  They are found to <a href="http://www.pnas.org/content/104/30/12548.full" rel="nofollow">connect axons laterally in hippocampal cells</a> (a region of brain that remains plastic through adulthood)</p>

CC BY-SA 3.0
2012-07-03T23:00:16.937Z
2012-07-03T23:51:59.160Z
2012-07-03T23:51:59.160Z
Keegan Keplinger
<blockquote>
<p>What are currently used biologically plausible computational models/frameworks of early learning in children?</p>
</blockquote>
<p>Personally, I have used <a href="https://doi.org/10.1007/s12559-010-9050-7" rel="nofollow noreferrer" title="Kaznatcheev, A. A Connectionist Study on the Interplay of Nouns and Pronouns in Personal Pronoun Acquisition. Cogn Comput 2, 280–284 (2010)">cascade correlation neural nets to model pronoun acquisition in children</a>, but in hindsight, I consider this framework to be <a href="https://cogsci.stackexchange.com/q/214/29">not neurobiologically plausible</a>.</p>
<p><strong>Are there existing computational frameworks that have been used for early-learning and development in children that have a reasonable biologically justification?</strong> For example: just being a neural net model is not a reasonable justification, but a neural net model witch has local update rules (like <a href="https://en.wikipedia.org/wiki/Hebbian_theory" rel="nofollow noreferrer">Hebbian learning</a>) and models neurons more realistically (instead of just saying they are a sigmoid function without any further biological justification) would be fine. General non-connectionist frameworks with empirical justification are also of interest, even if they don't have a solid reductionist account.</p>
<h3>Notes</h3>
<p>I have asked an <a href="https://linguistics.stackexchange.com/q/1385/33">alternate version of this question on Linguistics.SE</a> that focuses on language acquisition instead of general learning, but considers both children and adults.</p>

CC BY-SA 3.0
2012-01-31T04:19:23.980Z
What does 'early learning' mean is this context? It sounds way too broad, but perhaps that is due to my relative ignorance of developmental psychology.
zergylord
CC BY-SA 3.0
2012-01-31T04:21:52.650Z
@zergylord I guess by early learning I would refer mostly to the learning when the child's brain is still undergoing massive neurogenesis... i.e. before it settles down to adult levels of plasticity.
Artem Kaznatcheev
CC BY-SA 3.0
2012-01-31T04:27:24.313Z
In that case I'd specify a domain of interest (e.g. Computational models of *pronoun acquisition* early learning in children), since a general model is currently quite out of reach.
zergylord
CC BY-SA 3.0
2012-01-31T04:30:50.923Z
Alternatively, you could the term 'plausible algorithms' instead of 'computational models', since the latter tends to be task dependent whilst the former is a paradigm in which to build models.
zergylord
CC BY-SA 3.0
2012-01-31T04:31:43.997Z
then I will get my own paper (and the ones it references) back as an answer. I don't want to restrict to a single task, because then the models you get back have very little meaning. The goal is to have models and frameworks that generalize across many related tasks (like CC NN is used across all parts of developmental psychology, for instance). I was hoping that by leaving the domain more open ended, I could get answers from areas I am not as familiar with. However, if it is too broad I could restrict it to models to do with linguistic/language-acquisition.
Artem Kaznatcheev
CC BY-SA 3.0
2012-01-31T04:32:28.317Z
@zergylord I will replace the words computational model by framework or theory, then. Thanks for the suggestion!
Artem Kaznatcheev
<learning><developmental-psychology><computational-modeling><theoretical-neuroscience>
Computational models of early learning in children
Artem Kaznatcheev
# Computational models of early learning in children

(Asked by: username_0 on 2012-01-31)

> What are currently used biologically plausible computational models/frameworks of early learning in children?

Personally, I have used [cascade correlation neural nets to model pronoun acquisition in children](https://doi.org/10.1007/s12559-010-9050-7 "Kaznatcheev, A. A Connectionist Study on the Interplay of Nouns and Pronouns in Personal Pronoun Acquisition. Cogn Comput 2, 280–284 (2010)"), but in hindsight, I consider this framework to be [not neurobiologically plausible](https://cogsci.stackexchange.com/q/214/29).

**Are there existing computational frameworks that have been used for early-learning and development in children that have a reasonable biologically justification?** For example: just being a neural net model is not a reasonable justification, but a neural net model witch has local update rules (like [Hebbian learning](https://en.wikipedia.org/wiki/Hebbian_theory)) and models neurons more realistically (instead of just saying they are a sigmoid function without any further biological justification) would be fine. General non-connectionist frameworks with empirical justification are also of interest, even if they don't have a solid reductionist account.

### Notes

I have asked an [alternate version of this question on Linguistics.SE](https://linguistics.stackexchange.com/q/1385/33) that focuses on language acquisition instead of general learning, but considers both children and adults.

**Tags:** learning, developmental-psychology, computational-modeling, theoretical-neuroscience

> **Comments:**
> **username_1**: What does 'early learning' mean is this context? It sounds way too broad, but perhaps that is due to my relative ignorance of developmental psychology.  
> **username_0**: @username_1 I guess by early learning I would refer mostly to the learning when the child's brain is still undergoing massive neurogenesis... i.e. before it settles down to adult levels of plasticity.  
> **username_1**: In that case I'd specify a domain of interest (e.g. Computational models of *pronoun acquisition* early learning in children), since a general model is currently quite out of reach.  
> **username_1**: Alternatively, you could the term 'plausible algorithms' instead of 'computational models', since the latter tends to be task dependent whilst the former is a paradigm in which to build models.  
> **username_0**: then I will get my own paper (and the ones it references) back as an answer. I don't want to restrict to a single task, because then the models you get back have very little meaning. The goal is to have models and frameworks that generalize across many related tasks (like CC NN is used across all parts of developmental psychology, for instance). I was hoping that by leaving the domain more open ended, I could get answers from areas I am not as familiar with. However, if it is too broad I could restrict it to models to do with linguistic/language-acquisition.  
> **username_0**: @username_1 I will replace the words computational model by framework or theory, then. Thanks for the suggestion!  

---

## 1 answers

### Answer by username_2 on 2012-07-03. Score: 5

Here's something I dug up for language: a Computer Science Thesis from Boulder:

[The Sensorimotor Foundations of Phonology: A Computational Model of Early Childhood Articulatory and Phonetic Development (1994)](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.3216)

it discusses what it calls HABLAR (Hierarchical Articulatory Based Language
Acquisition by Reinforcement learning).

From the **reductionist/biology side**, developing brains have a much higher population of gap junctions. I would speculate that this would create a much more analog brain, as gap junctions are modeled diffusively, unlike synaptic connections, which can be modeled in a more binary way. I would speculate that the analog driving of the environmental and genetic signals leads to the formation and orgnaization of the synaptic connectivity.

I wonder if one could find a correlation between plasticity and gap junctions. They are found to [connect axons laterally in hippocampal cells](http://www.pnas.org/content/104/30/12548.full) (a region of brain that remains plastic through adulthood)


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