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Modeling the Overalternating Bias with an Asymmetric Entropy Measure

Psychological research has found that human perception of randomness is biased. In particular, people consistently show the overalternating bias: they rate binary sequences of symbols (such as Heads and Tails in coin flipping) with an excess of alternation as more random than prescribed by the norma...

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Autores principales: Gronchi, Giorgio, Raglianti, Marco, Noventa, Stefano, Lazzeri, Alessandro, Guazzini, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934134/
https://www.ncbi.nlm.nih.gov/pubmed/27458418
http://dx.doi.org/10.3389/fpsyg.2016.01027
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author Gronchi, Giorgio
Raglianti, Marco
Noventa, Stefano
Lazzeri, Alessandro
Guazzini, Andrea
author_facet Gronchi, Giorgio
Raglianti, Marco
Noventa, Stefano
Lazzeri, Alessandro
Guazzini, Andrea
author_sort Gronchi, Giorgio
collection PubMed
description Psychological research has found that human perception of randomness is biased. In particular, people consistently show the overalternating bias: they rate binary sequences of symbols (such as Heads and Tails in coin flipping) with an excess of alternation as more random than prescribed by the normative criteria of Shannon's entropy. Within data mining for medical applications, Marcellin proposed an asymmetric measure of entropy that can be ideal to account for such bias and to quantify subjective randomness. We fitted Marcellin's entropy and Renyi's entropy (a generalized form of uncertainty measure comprising many different kinds of entropies) to experimental data found in the literature with the Differential Evolution algorithm. We observed a better fit for Marcellin's entropy compared to Renyi's entropy. The fitted asymmetric entropy measure also showed good predictive properties when applied to different datasets of randomness-related tasks. We concluded that Marcellin's entropy can be a parsimonious and effective measure of subjective randomness that can be useful in psychological research about randomness perception.
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spelling pubmed-49341342016-07-25 Modeling the Overalternating Bias with an Asymmetric Entropy Measure Gronchi, Giorgio Raglianti, Marco Noventa, Stefano Lazzeri, Alessandro Guazzini, Andrea Front Psychol Psychology Psychological research has found that human perception of randomness is biased. In particular, people consistently show the overalternating bias: they rate binary sequences of symbols (such as Heads and Tails in coin flipping) with an excess of alternation as more random than prescribed by the normative criteria of Shannon's entropy. Within data mining for medical applications, Marcellin proposed an asymmetric measure of entropy that can be ideal to account for such bias and to quantify subjective randomness. We fitted Marcellin's entropy and Renyi's entropy (a generalized form of uncertainty measure comprising many different kinds of entropies) to experimental data found in the literature with the Differential Evolution algorithm. We observed a better fit for Marcellin's entropy compared to Renyi's entropy. The fitted asymmetric entropy measure also showed good predictive properties when applied to different datasets of randomness-related tasks. We concluded that Marcellin's entropy can be a parsimonious and effective measure of subjective randomness that can be useful in psychological research about randomness perception. Frontiers Media S.A. 2016-07-06 /pmc/articles/PMC4934134/ /pubmed/27458418 http://dx.doi.org/10.3389/fpsyg.2016.01027 Text en Copyright © 2016 Gronchi, Raglianti, Noventa, Lazzeri and Guazzini. 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.
spellingShingle Psychology
Gronchi, Giorgio
Raglianti, Marco
Noventa, Stefano
Lazzeri, Alessandro
Guazzini, Andrea
Modeling the Overalternating Bias with an Asymmetric Entropy Measure
title Modeling the Overalternating Bias with an Asymmetric Entropy Measure
title_full Modeling the Overalternating Bias with an Asymmetric Entropy Measure
title_fullStr Modeling the Overalternating Bias with an Asymmetric Entropy Measure
title_full_unstemmed Modeling the Overalternating Bias with an Asymmetric Entropy Measure
title_short Modeling the Overalternating Bias with an Asymmetric Entropy Measure
title_sort modeling the overalternating bias with an asymmetric entropy measure
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4934134/
https://www.ncbi.nlm.nih.gov/pubmed/27458418
http://dx.doi.org/10.3389/fpsyg.2016.01027
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