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Logarithmic encoding of ensemble time intervals
Although time perception is based on the internal representation of time, whether the subjective timeline is scaled linearly or logarithmically remains an open issue. Evidence from previous research is mixed: while the classical internal-clock model assumes a linear scale with scalar variability, th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584664/ https://www.ncbi.nlm.nih.gov/pubmed/33097781 http://dx.doi.org/10.1038/s41598-020-75191-6 |
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author | Ren, Yue Allenmark, Fredrik Müller, Hermann J. Shi, Zhuanghua |
author_facet | Ren, Yue Allenmark, Fredrik Müller, Hermann J. Shi, Zhuanghua |
author_sort | Ren, Yue |
collection | PubMed |
description | Although time perception is based on the internal representation of time, whether the subjective timeline is scaled linearly or logarithmically remains an open issue. Evidence from previous research is mixed: while the classical internal-clock model assumes a linear scale with scalar variability, there is evidence that logarithmic timing provides a better fit to behavioral data. A major challenge for investigating the nature of the internal scale is that the retrieval process required for time judgments may involve a remapping of the subjective time back to the objective scale, complicating any direct interpretation of behavioral findings. Here, we used a novel approach, requiring rapid intuitive ‘ensemble’ averaging of a whole set of time intervals, to probe the subjective timeline. Specifically, observers’ task was to average a series of successively presented, auditory or visual, intervals in the time range 300–1300 ms. Importantly, the intervals were taken from three sets of durations, which were distributed such that the arithmetic mean (from the linear scale) and the geometric mean (from the logarithmic scale) were clearly distinguishable. Consistently across the three sets and the two presentation modalities, our results revealed subjective averaging to be close to the geometric mean, indicative of a logarithmic timeline underlying time perception. |
format | Online Article Text |
id | pubmed-7584664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75846642020-10-27 Logarithmic encoding of ensemble time intervals Ren, Yue Allenmark, Fredrik Müller, Hermann J. Shi, Zhuanghua Sci Rep Article Although time perception is based on the internal representation of time, whether the subjective timeline is scaled linearly or logarithmically remains an open issue. Evidence from previous research is mixed: while the classical internal-clock model assumes a linear scale with scalar variability, there is evidence that logarithmic timing provides a better fit to behavioral data. A major challenge for investigating the nature of the internal scale is that the retrieval process required for time judgments may involve a remapping of the subjective time back to the objective scale, complicating any direct interpretation of behavioral findings. Here, we used a novel approach, requiring rapid intuitive ‘ensemble’ averaging of a whole set of time intervals, to probe the subjective timeline. Specifically, observers’ task was to average a series of successively presented, auditory or visual, intervals in the time range 300–1300 ms. Importantly, the intervals were taken from three sets of durations, which were distributed such that the arithmetic mean (from the linear scale) and the geometric mean (from the logarithmic scale) were clearly distinguishable. Consistently across the three sets and the two presentation modalities, our results revealed subjective averaging to be close to the geometric mean, indicative of a logarithmic timeline underlying time perception. Nature Publishing Group UK 2020-10-23 /pmc/articles/PMC7584664/ /pubmed/33097781 http://dx.doi.org/10.1038/s41598-020-75191-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ren, Yue Allenmark, Fredrik Müller, Hermann J. Shi, Zhuanghua Logarithmic encoding of ensemble time intervals |
title | Logarithmic encoding of ensemble time intervals |
title_full | Logarithmic encoding of ensemble time intervals |
title_fullStr | Logarithmic encoding of ensemble time intervals |
title_full_unstemmed | Logarithmic encoding of ensemble time intervals |
title_short | Logarithmic encoding of ensemble time intervals |
title_sort | logarithmic encoding of ensemble time intervals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584664/ https://www.ncbi.nlm.nih.gov/pubmed/33097781 http://dx.doi.org/10.1038/s41598-020-75191-6 |
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