Fig 1.
A logistic function framework to model indifference points data.
Illustration of the transformation from a linear scale to a logarithmic scale of the classic hyperbolic curve; and illustration of the transformation from a logarithmic scale to a linear scale of a logistic function curve. The logistic curve is characterized by parameters a (the slope at the inflection point) and b (the log-delay at the inflection point). The hyperbolic curve is a particular type of logistic function with parameters a = 1 and b = −log k, where k is the classic discounting rate.
Fig 2.
Experiential intertemporal choice task.
The task required making a series of decisions between viewing a partially occluded photograph immediately or a non-occluded photograph after a delay. Outcomes unfolded in real-time following each decision. Every five trials, participants were asked about the pleasantness of their experience and waited through a “Processing Data” screen, with duration designed to equalize experimental time across participants.
Fig 3.
Participants were presented with a series of novel photographs with varying occlusion levels and asked to rate the pleasantness of their experience.
Fig 4.
Hypothetical intertemporal choice task.
The task required making a series of hypothetical decisions between receiving an amount of money varying between $1 and $99 now or receiving $100 after a delay.
Table 1.
Goodness of fit of the experiential and hypothetical indifference points, comparing fit by the hyperbolic model, logistic function model, and null model (defined as the mean of the indifference points for each participant).
Fig 5.
Fitting indifference point data.
Example of indifference points obtained for two participants during the experiential intertemporal choice task (squared markers), and comparison of fit using a logistic function (black solid line) and a hyperbolic function (blue solid line). Delay is presented using a logarithmic scale.
Fig 6.
Logistic temporal discounting curves.
Logistic temporal discounting curves resulting from fitting indifference point data for the experiential (left) and hypothetical (right) intertemporal choice tasks. Temporal discounting curves are depicted for each participant and each task. Delays are presented using logarithmic scales.
Fig 7.
Relation between pleasantness ratings and level of occlusion of the photographs.
An example of a dataset obtained for one participant is presented in the left panel. Regression lines obtained for all participants are presented in the right panel. Absolute values of correlations between pleasantness ratings and occlusion levels are used as input in subsequent analyses to represent individual differences in reward sensitivity.
Fig 8.
Absence of significant correlation between experiential AUC and hypothetical AUC.
AUC stands for Area Under the Curve, with greater AUC implying greater willingness to wait for the delayed reward.
Fig 9.
Results of the path analysis without mediation (left panel) and path analysis with mediation (right panel). AUC stands for Area Under the Curve, with greater AUC implying greater willingness to wait for the delayed reward. Parameter estimates are standardized with respect to the variance of state anxiety and trait anxiety. Significance level is indicated by number of stars (p < .05*, p < .01**, p < .001***).
Fig 10.
Illustration of the interaction between state anxiety and trait anxiety on AUC during the experiential intertemporal choice task.
A split-half method was employed over the trait anxiety distribution. AUC stands for Area Under the Curve, with greater AUC implying greater willingness to wait for the delayed reward. Solid black circles correspond to data for individuals with lower trait anxiety; and blue squares to data for individuals with higher trait anxiety.