Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction

The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). Both external factors, such as input variability, and intern...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Silvia Radulescu, Areti Kotsolakou, Frank Wijnen, Sergey Avrutin, Ileana Grama
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	rule induction entropy channel capacity (information rate) generalization (psychology) category formation bit rate Psychology BF1-990
Acceso en línea:	https://doaj.org/article/8d5bce8bf4fc4782ba71a34eae8a46a6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8d5bce8bf4fc4782ba71a34eae8a46a6
record_format	dspace
spelling	oai:doaj.org-article:8d5bce8bf4fc4782ba71a34eae8a46a62021-11-11T06:43:40ZFast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction1664-107810.3389/fpsyg.2021.661785https://doaj.org/article/8d5bce8bf4fc4782ba71a34eae8a46a62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpsyg.2021.661785/fullhttps://doaj.org/toc/1664-1078The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon’s noisy-channel coding theory, which adds into the “formula” for rule induction the crucial dimension of time: the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity-based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity, the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization, so as to avoid exceeding the channel capacity. We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from item-bound to category-based generalization, it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the input entropy and the maximum rate of information transmission.Silvia RadulescuAreti KotsolakouFrank WijnenSergey AvrutinIleana GramaFrontiers Media S.A.articlerule inductionentropychannel capacity (information rate)generalization (psychology)category formationbit ratePsychologyBF1-990ENFrontiers in Psychology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rule induction entropy channel capacity (information rate) generalization (psychology) category formation bit rate Psychology BF1-990
spellingShingle	rule induction entropy channel capacity (information rate) generalization (psychology) category formation bit rate Psychology BF1-990 Silvia Radulescu Areti Kotsolakou Frank Wijnen Sergey Avrutin Ileana Grama Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction
description	The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon’s noisy-channel coding theory, which adds into the “formula” for rule induction the crucial dimension of time: the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity-based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity, the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization, so as to avoid exceeding the channel capacity. We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from item-bound to category-based generalization, it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the input entropy and the maximum rate of information transmission.
format	article
author	Silvia Radulescu Areti Kotsolakou Frank Wijnen Sergey Avrutin Ileana Grama
author_facet	Silvia Radulescu Areti Kotsolakou Frank Wijnen Sergey Avrutin Ileana Grama
author_sort	Silvia Radulescu
title	Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction
title_short	Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction
title_full	Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction
title_fullStr	Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction
title_full_unstemmed	Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction
title_sort	fast but not furious. when sped up bit rate of information drives rule induction
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/8d5bce8bf4fc4782ba71a34eae8a46a6
work_keys_str_mv	AT silviaradulescu fastbutnotfuriouswhenspedupbitrateofinformationdrivesruleinduction AT aretikotsolakou fastbutnotfuriouswhenspedupbitrateofinformationdrivesruleinduction AT frankwijnen fastbutnotfuriouswhenspedupbitrateofinformationdrivesruleinduction AT sergeyavrutin fastbutnotfuriouswhenspedupbitrateofinformationdrivesruleinduction AT ileanagrama fastbutnotfuriouswhenspedupbitrateofinformationdrivesruleinduction
_version_	1718439512069111808

Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction

Ejemplares similares