Limits...
A computational model associating learning process, word attributes, and age of acquisition.

Hidaka S - PLoS ONE (2013)

Bottom Line: Simply put, this model formulates how different learning processes, characterized by change in learning rate over time and/or by the number of exposures required to acquire a word, likely result in different AoA distributions depending on word type.The first analysis showed that the proposed model accounts for empirical AoA distributions better than a standard alternative.We further discuss the theoretical implications of our model-based approach.

View Article: PubMed Central - PubMed

Affiliation: Japan Advanced Institute of Science and Technology (JAIST), Ishikawa, Japan.

ABSTRACT
We propose a new model-based approach linking word learning to the age of acquisition (AoA) of words; a new computational tool for understanding the relationships among word learning processes, psychological attributes, and word AoAs as measures of vocabulary growth. The computational model developed describes the distinct statistical relationships between three theoretical factors underpinning word learning and AoA distributions. Simply put, this model formulates how different learning processes, characterized by change in learning rate over time and/or by the number of exposures required to acquire a word, likely result in different AoA distributions depending on word type. We tested the model in three respects. The first analysis showed that the proposed model accounts for empirical AoA distributions better than a standard alternative. The second analysis demonstrated that the estimated learning parameters well predicted the psychological attributes, such as frequency and imageability, of words. The third analysis illustrated that the developmental trend predicted by our estimated learning parameters was consistent with relevant findings in the developmental literature on word learning in children. We further discuss the theoretical implications of our model-based approach.

Show MeSH

Related in: MedlinePlus

Schematic images illustrating the cumulative, rate-based, and cumulative-and-rate-based learning models (a–c) and the alternative logistic model (d).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3815221&req=5

pone-0076242-g001: Schematic images illustrating the cumulative, rate-based, and cumulative-and-rate-based learning models (a–c) and the alternative logistic model (d).

Mentions: We consider a hypothetical case where a child acquires a word after accumulating a particular number of relevant events (Figure 1a). In Figure 1a, the child acquires the word when he/she is exposed to four relevant events: this is represented by the accumulator counting up to four. The occurrence of the relevant event follows a particular probabilistic process in which probability per unit time, called learning rate, is constant over time (the equal learning rate for events is reflected in the y-axis). The model assumes that every hypothetical child starts with the same initial accumulator reading, with zero instances of the event, and that a word is acquired after its N-th observation. Despite the same initial state (i.e., accumulator reading and learning rate) for every child, their ages of acquisition for the same word need not be equivalent. Due to randomness in the sampling, each child may have a different AoA for the same word. For a large enough population of children, such a probabilistic process would make their AoA follow a gamma distribution. In general, learning modeled using an N-accumulator and under a constant learning rate leads to a gamma distribution for AoA (see Appendix S1). We call this type of learning cumulative learning.


A computational model associating learning process, word attributes, and age of acquisition.

Hidaka S - PLoS ONE (2013)

Schematic images illustrating the cumulative, rate-based, and cumulative-and-rate-based learning models (a–c) and the alternative logistic model (d).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3815221&req=5

pone-0076242-g001: Schematic images illustrating the cumulative, rate-based, and cumulative-and-rate-based learning models (a–c) and the alternative logistic model (d).
Mentions: We consider a hypothetical case where a child acquires a word after accumulating a particular number of relevant events (Figure 1a). In Figure 1a, the child acquires the word when he/she is exposed to four relevant events: this is represented by the accumulator counting up to four. The occurrence of the relevant event follows a particular probabilistic process in which probability per unit time, called learning rate, is constant over time (the equal learning rate for events is reflected in the y-axis). The model assumes that every hypothetical child starts with the same initial accumulator reading, with zero instances of the event, and that a word is acquired after its N-th observation. Despite the same initial state (i.e., accumulator reading and learning rate) for every child, their ages of acquisition for the same word need not be equivalent. Due to randomness in the sampling, each child may have a different AoA for the same word. For a large enough population of children, such a probabilistic process would make their AoA follow a gamma distribution. In general, learning modeled using an N-accumulator and under a constant learning rate leads to a gamma distribution for AoA (see Appendix S1). We call this type of learning cumulative learning.

Bottom Line: Simply put, this model formulates how different learning processes, characterized by change in learning rate over time and/or by the number of exposures required to acquire a word, likely result in different AoA distributions depending on word type.The first analysis showed that the proposed model accounts for empirical AoA distributions better than a standard alternative.We further discuss the theoretical implications of our model-based approach.

View Article: PubMed Central - PubMed

Affiliation: Japan Advanced Institute of Science and Technology (JAIST), Ishikawa, Japan.

ABSTRACT
We propose a new model-based approach linking word learning to the age of acquisition (AoA) of words; a new computational tool for understanding the relationships among word learning processes, psychological attributes, and word AoAs as measures of vocabulary growth. The computational model developed describes the distinct statistical relationships between three theoretical factors underpinning word learning and AoA distributions. Simply put, this model formulates how different learning processes, characterized by change in learning rate over time and/or by the number of exposures required to acquire a word, likely result in different AoA distributions depending on word type. We tested the model in three respects. The first analysis showed that the proposed model accounts for empirical AoA distributions better than a standard alternative. The second analysis demonstrated that the estimated learning parameters well predicted the psychological attributes, such as frequency and imageability, of words. The third analysis illustrated that the developmental trend predicted by our estimated learning parameters was consistent with relevant findings in the developmental literature on word learning in children. We further discuss the theoretical implications of our model-based approach.

Show MeSH
Related in: MedlinePlus