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Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample
Despite the widespread use of the delay discounting task in clinical and non-clinical contexts, several task versions are available in the literature, making it hard to compare results across studies. Moreover, normative data are not available to evaluate individual performances. The present study a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872280/ https://www.ncbi.nlm.nih.gov/pubmed/35206238 http://dx.doi.org/10.3390/ijerph19042049 |
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author | Garofalo, Sara Degni, Luigi A. E. Sellitto, Manuela Braghittoni, Davide Starita, Francesca Giovagnoli, Sara di Pellegrino, Giuseppe Benassi, Mariagrazia |
author_facet | Garofalo, Sara Degni, Luigi A. E. Sellitto, Manuela Braghittoni, Davide Starita, Francesca Giovagnoli, Sara di Pellegrino, Giuseppe Benassi, Mariagrazia |
author_sort | Garofalo, Sara |
collection | PubMed |
description | Despite the widespread use of the delay discounting task in clinical and non-clinical contexts, several task versions are available in the literature, making it hard to compare results across studies. Moreover, normative data are not available to evaluate individual performances. The present study aims to propose a unified version of the delay discounting task based on monetary rewards and it provides normative values built on an Italian sample of 357 healthy participants. The most used parameters in the literature to assess the delay discount rate were compared to find the most valid index to discriminate between normative data and a clinical population who typically present impulsivity issues, i.e., patients with a lesion to the medial orbitofrontal cortex (mOFC). In line with our hypothesis, mOFC patients showed higher delay discounting scores than the normative sample and the normative group. Based on this evidence, we propose that the task and indexes here provided can be used to identify extremely high (above the 90th percentile for hyperbolic k or below the 10th percentile for AUC) or low (below the 10th percentile for hyperbolic k or above the 90th percentile for AUC) delay discounting performances. The complete dataset, the R code used to perform all analyses, a free and modifiable version of the delay discounting task, as well as the R code that can be used to extract all indexes from such tasks and compare subjective performances with the normative data here presented are available as online materials. |
format | Online Article Text |
id | pubmed-8872280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88722802022-02-25 Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample Garofalo, Sara Degni, Luigi A. E. Sellitto, Manuela Braghittoni, Davide Starita, Francesca Giovagnoli, Sara di Pellegrino, Giuseppe Benassi, Mariagrazia Int J Environ Res Public Health Article Despite the widespread use of the delay discounting task in clinical and non-clinical contexts, several task versions are available in the literature, making it hard to compare results across studies. Moreover, normative data are not available to evaluate individual performances. The present study aims to propose a unified version of the delay discounting task based on monetary rewards and it provides normative values built on an Italian sample of 357 healthy participants. The most used parameters in the literature to assess the delay discount rate were compared to find the most valid index to discriminate between normative data and a clinical population who typically present impulsivity issues, i.e., patients with a lesion to the medial orbitofrontal cortex (mOFC). In line with our hypothesis, mOFC patients showed higher delay discounting scores than the normative sample and the normative group. Based on this evidence, we propose that the task and indexes here provided can be used to identify extremely high (above the 90th percentile for hyperbolic k or below the 10th percentile for AUC) or low (below the 10th percentile for hyperbolic k or above the 90th percentile for AUC) delay discounting performances. The complete dataset, the R code used to perform all analyses, a free and modifiable version of the delay discounting task, as well as the R code that can be used to extract all indexes from such tasks and compare subjective performances with the normative data here presented are available as online materials. MDPI 2022-02-11 /pmc/articles/PMC8872280/ /pubmed/35206238 http://dx.doi.org/10.3390/ijerph19042049 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Garofalo, Sara Degni, Luigi A. E. Sellitto, Manuela Braghittoni, Davide Starita, Francesca Giovagnoli, Sara di Pellegrino, Giuseppe Benassi, Mariagrazia Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample |
title | Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample |
title_full | Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample |
title_fullStr | Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample |
title_full_unstemmed | Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample |
title_short | Unifying Evidence on Delay Discounting: Open Task, Analysis Tutorial, and Normative Data from an Italian Sample |
title_sort | unifying evidence on delay discounting: open task, analysis tutorial, and normative data from an italian sample |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8872280/ https://www.ncbi.nlm.nih.gov/pubmed/35206238 http://dx.doi.org/10.3390/ijerph19042049 |
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