Valuing the future at different temporal points: The role of time framing on discounting.
Published In: Journal of the Experimental Analysis of Behavior, 2023, v. 120, n. 2. P. 214 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Reyes‐Huerta, Hugo E.; Robles, Elias; dos Santos, Cristiano V. 3 of 3
Abstract
The rate of delay discounting exhibited by individuals has been experimentally altered by manipulating the way in which time is described, a specific application of the framing effect. Previous research suggests that using specific dates to describe delays tends to lower temporal discounting and change the shape of the discounting function. The main purpose of this study was to assess the influence of framing on discounting in different temporal contexts. Participants chose between hypothetical monetary gains (gains group), or between hypothetical monetary losses (losses group). Each group completed eight discounting tasks over two sessions (two choice tasks [SmallNow/SmallSoon] by two time frames [dates/calendar units] by two magnitudes. The results indicate that Mazur's model adequately described the observed discounting functions in most conditions. However, the decrease in discounting rate when both consequences were delayed only occurred when calendar units (but not dates) were used for both gains and losses. These findings suggest that framing affects the influence of a shared delay instead of changing the shape of the discounting function. Our results support the idea that time influences behavior similarly in humans and nonhumans when they choose between two delayed consequences. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of the Experimental Analysis of Behavior. 2023/09, Vol. 120, Issue 2, p214
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:0022-5002
- DOI:10.1002/jeab.871
- Accession Number:171349619
- Copyright Statement:Copyright of Journal of the Experimental Analysis of Behavior is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.