| Title |
Individual differences in long-range time representation
|
|---|---|
| Published in |
Attention, Perception, & Psychophysics, January 2017
|
| DOI | 10.3758/s13414-017-1286-9 |
| Pubmed ID | |
| Authors |
Camila S. Agostino, Marcelo S. Caetano, Fuat Balci, Peter M. E. Claessens, Yossi Zana |
| Abstract |
On the basis of experimental data, long-range time representation has been proposed to follow a highly compressed power function, which has been hypothesized to explain the time inconsistency found in financial discount rate preferences. The aim of this study was to evaluate how well linear and power function models explain empirical data from individual participants tested in different procedural settings. The line paradigm was used in five different procedural variations with 35 adult participants. Data aggregated over the participants showed that fitted linear functions explained more than 98% of the variance in all procedures. A linear regression fit also outperformed a power model fit for the aggregated data. An individual-participant-based analysis showed better fits of a linear model to the data of 14 participants; better fits of a power function with an exponent β > 1 to the data of 12 participants; and better fits of a power function with β < 1 to the data of the remaining nine participants. Of the 35 volunteers, the null hypothesis β = 1 was rejected for 20. The dispersion of the individual β values was approximated well by a normal distribution. These results suggest that, on average, humans perceive long-range time intervals not in a highly compressed, biased manner, but rather in a linear pattern. However, individuals differ considerably in their subjective time scales. This contribution sheds new light on the average and individual psychophysical functions of long-range time representation, and suggests that any attribution of deviation from exponential discount rates in intertemporal choice to the compressed nature of subjective time must entail the characterization of subjective time on an individual-participant basis. |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Puerto Rico | 1 | 3% |
| Unknown | 28 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 23% |
| Student > Master | 4 | 13% |
| Researcher | 4 | 13% |
| Student > Bachelor | 4 | 13% |
| Professor > Associate Professor | 3 | 10% |
| Other | 2 | 7% |
| Unknown | 6 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 9 | 30% |
| Philosophy | 3 | 10% |
| Neuroscience | 3 | 10% |
| Environmental Science | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 10 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 October 2017.
All research outputs
#21,500,614
of 24,003,070 outputs
Outputs from Attention, Perception, & Psychophysics
#1,661
of 1,773 outputs
Outputs of similar age
#362,128
of 425,242 outputs
Outputs of similar age from Attention, Perception, & Psychophysics
#28
of 42 outputs
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