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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-09-01
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| Series: | Frontiers in Behavioral Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnbeh.2016.00177/full |
| _version_ | 1818175071802359808 |
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| author | Andrea Alamia Oleg Solopchuk Etienne Olivier Alexandre Zénon |
| author_facet | Andrea Alamia Oleg Solopchuk Etienne Olivier Alexandre Zénon |
| author_sort | Andrea Alamia |
| collection | DOAJ |
| 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 |
| first_indexed | 2024-12-11T19:54:28Z |
| format | Article |
| id | doaj.art-1a7cecc2aa044769a0079b983540c82a |
| institution | Directory Open Access Journal |
| issn | 1662-5153 |
| language | English |
| last_indexed | 2024-12-11T19:54:28Z |
| publishDate | 2016-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Behavioral Neuroscience |
| spelling | doaj.art-1a7cecc2aa044769a0079b983540c82a2022-12-22T00:52:41ZengFrontiers Media S.A.Frontiers in Behavioral Neuroscience1662-51532016-09-011010.3389/fnbeh.2016.00177190726Non-parametric algorithm to isolate chunks in response sequencesAndrea Alamia0Oleg Solopchuk1Etienne Olivier2Alexandre Zénon3Université Catholique de LouvainUniversité Catholique de LouvainUniversité Catholique de LouvainUniversité Catholique de LouvainChunking 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-sequenceshttp://journal.frontiersin.org/Journal/10.3389/fnbeh.2016.00177/fullsequence learningworking memorychunkingsegmentationconcatenation |
| spellingShingle | Andrea Alamia Oleg Solopchuk Etienne Olivier Alexandre Zénon Non-parametric algorithm to isolate chunks in response sequences Frontiers in Behavioral Neuroscience sequence learning working memory chunking segmentation concatenation |
| title | 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_short | Non-parametric algorithm to isolate chunks in response sequences |
| title_sort | non parametric algorithm to isolate chunks in response sequences |
| topic | sequence learning working memory chunking segmentation concatenation |
| url | http://journal.frontiersin.org/Journal/10.3389/fnbeh.2016.00177/full |
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