Congruence of BOLD response across intertemporal choice conditions: fictive and real money gains and losses

Abstract

Intertemporal choice is predicated on the valuation of commodities with respect to delay until their receipt. Subjective value of a future outcome decreases, or is discounted, as a function of that delay (Bickel and Johnson, 2003). Although behavioral studies suggest no difference between the devaluation of real and fictive outcomes, no neuroimaging studies have investigated potential differences in the underlying deliberative process. Here, we compare behavioral and neural correlates of intertemporal valuation of real and hypothetical monetary gains as well as hypothetical losses, which have been posited to involve different mechanisms. Behavioral and neuroimaging sessions were conducted in which participants made intertemporal choice decisions in a gains condition using both real and hypothetical $100 money and in a loss condition using a fictive $100 money. Within-subject comparison of behavioral data revealed no significant difference between levels of discounting across the three conditions. Random-effects analysis of functional magnetic resonance imaging (fMRI) data of each of the three discounting conditions independently revealed significant signal change in limbic (anterior cingulate, striatum, posterior cingulate) and executive functioning areas (lateral prefrontal cortex), whereas a repeated-measures ANOVA failed to detect differences in signal change across the three discounting conditions after correcting for multiple comparisons. These data support a concordance between real and hypothetical conditions from delay-discounting studies and further suggest a congruence of the fMRI blood oxygen level-dependent signal across brain regions associated with the deliberative process of different forms of intertemporal choice.

Figures

Figure 1.

Indifference points for delays of 1, 7, 30, and 180 d were collected from each individual before scanning in each of three discounting conditions and were subsequently fitted to a hyperbolic curve using Mazur's (1987) nonlinear function. Estimated ks were then log transformed to address skewed distributions and then entered into a repeated-measures ANOVA to test for differences in discounting between the conditions. Within-subject differences between discounting conditions were determined not to be significant (F(2,28) = 0.21, p = 0.81): RMG–HMG = 0.196 ± 0.68, HMG–HML = −0.014 ± 1.34, and HML–RMG = −0.18 ± 1.17 (A). Indifference points were also used to calculate AUC measures which were subsequently used in a repeated-measures ANOVA to detect within-subject difference between discounting conditions. AUC allows for freedom from the theoretical framework of the hyperbolic discounting function and problems associated with estimated parameters (Myerson et al., 2001). Results indicate no statistically significant difference between average normalized AUC measures (F(2,28) = 0.00, p = 0.99) (B): RMG–HMG = 0.001 ± 0.05, HMG–HML = −0.003 ± 0.10, and HML–RMG = 0.002 ± 0.07.

Figure 2.

fMRI random effects results for real money gains (A), hypothetical money gains (B), and hypothetical money losses (C) discounting conditions, contrasting the discounting trials where individuals responded to their preference for smaller sooner or larger later rewards/losses, with control events where participants responded to their preference for gaining or losing dollar amounts where the temporal delay component has been removed (FDR: p < 0.05). Resulting areas of signal change occurred in similar regions across all three conditions: left and right lateral prefrontal cortex (LPFC/RPFC), PMA and SMA, posterior cingulate cortex (PCC), bilateral insula (seen lateralized primarily to the left hemisphere above), precuneus (PCu), visual cortex (VCtx), striatum (STR), and left and right parietal (data not shown).

Figure 3.

fMRI correlation analysis of random effects contrasts (discounting trials > control trials) in combined RMG and HMG conditions. Log transformed k values (ln(k)) estimated from data collected before scanning used to predict areas correlated with discounting across subjects. Increased activity in the right middle frontal gyrus, BA9 (A), and left medial frontal gyrus, BA10 (B), are both shown here to be significantly correlated with increasing levels of discounting using an uncorrected threshold of p < 0.001 and an extent threshold of five contiguous voxels. Results are displayed at p < 0.005. Additional areas of significant correlation are listed in Table 1.