Non-parametric Algorithm to Isolate Chunks in Response Sequences

Chunking consists in grouping items of a sequence into small clusters, named chunks, with the assumed goal of lessening working memory load. Despite extensive research, the current methods used to detect chunks, and to identify different chunking strategies, remain discordant and difficult to implem...

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Main Authors:	Alamia, Andrea, Solopchuk, Oleg, Olivier, Etienne, Zenon, Alexandre
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030762/

id	pubmed-5030762
recordtype	oai_dc
spelling	pubmed-50307622016-10-05 Non-parametric Algorithm to Isolate Chunks in Response Sequences Alamia, Andrea Solopchuk, Oleg Olivier, Etienne Zenon, Alexandre Neuroscience Chunking consists in grouping items of a sequence into small clusters, named chunks, with the assumed goal of lessening working memory load. Despite extensive research, the current methods used to detect chunks, and to identify different chunking strategies, remain discordant and difficult to implement. Here, we propose a simple and reliable method to identify chunks in a sequence and to determine their stability across blocks. This algorithm is based on a ranking method and its major novelty is that it provides concomitantly both the features of individual chunk in a given sequence, and an overall index that quantifies the chunking pattern consistency across sequences. The analysis of simulated data confirmed the validity of our method in different conditions of noise, chunk lengths and chunk numbers; moreover, we found that this algorithm was particularly efficient in the noise range observed in real data, provided that at least 4 sequence repetitions were included in each experimental block. Furthermore, we applied this algorithm to actual reaction time series gathered from 3 published experiments and were able to confirm the findings obtained in the original reports. In conclusion, this novel algorithm is easy to implement, is robust to outliers and provides concurrent and reliable estimation of chunk position and chunking dynamics, making it useful to study both sequence-specific and general chunking effects. The algorithm is available at: https://github.com/artipago/Non-parametric-algorithm-to-isolate-chunks-in-response-sequences. Frontiers Media S.A. 2016-09-21 /pmc/articles/PMC5030762/ /pubmed/27708565 http://dx.doi.org/10.3389/fnbeh.2016.00177 Text en Copyright © 2016 Alamia, Solopchuk, Olivier and Zenon. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Alamia, Andrea Solopchuk, Oleg Olivier, Etienne Zenon, Alexandre
spellingShingle	Alamia, Andrea Solopchuk, Oleg Olivier, Etienne Zenon, Alexandre Non-parametric Algorithm to Isolate Chunks in Response Sequences
author_facet	Alamia, Andrea Solopchuk, Oleg Olivier, Etienne Zenon, Alexandre
author_sort	Alamia, Andrea
title	Non-parametric Algorithm to Isolate Chunks in Response Sequences
title_short	Non-parametric Algorithm to Isolate Chunks in Response Sequences
title_full	Non-parametric Algorithm to Isolate Chunks in Response Sequences
title_fullStr	Non-parametric Algorithm to Isolate Chunks in Response Sequences
title_full_unstemmed	Non-parametric Algorithm to Isolate Chunks in Response Sequences
title_sort	non-parametric algorithm to isolate chunks in response sequences
description	Chunking consists in grouping items of a sequence into small clusters, named chunks, with the assumed goal of lessening working memory load. Despite extensive research, the current methods used to detect chunks, and to identify different chunking strategies, remain discordant and difficult to implement. Here, we propose a simple and reliable method to identify chunks in a sequence and to determine their stability across blocks. This algorithm is based on a ranking method and its major novelty is that it provides concomitantly both the features of individual chunk in a given sequence, and an overall index that quantifies the chunking pattern consistency across sequences. The analysis of simulated data confirmed the validity of our method in different conditions of noise, chunk lengths and chunk numbers; moreover, we found that this algorithm was particularly efficient in the noise range observed in real data, provided that at least 4 sequence repetitions were included in each experimental block. Furthermore, we applied this algorithm to actual reaction time series gathered from 3 published experiments and were able to confirm the findings obtained in the original reports. In conclusion, this novel algorithm is easy to implement, is robust to outliers and provides concurrent and reliable estimation of chunk position and chunking dynamics, making it useful to study both sequence-specific and general chunking effects. The algorithm is available at: https://github.com/artipago/Non-parametric-algorithm-to-isolate-chunks-in-response-sequences.
publisher	Frontiers Media S.A.
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030762/
_version_	1613656837401346048

Non-parametric Algorithm to Isolate Chunks in Response Sequences

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