Neural correlates of effort-based behavioral inconsistency

According to the theory of value-based decision making, subjects tend to choose the most valuable among a set of options. However, agents may not be consistent when facing the same decision several times. In this paper, Shannon's entropy (H) is employed as a measure of behavioral inconsistency: it is a central measure of information theory that, applied to decision making, allows the estimation of behavioral preferences among a set of options. We scanned (functional magnetic resonance imaging, fMRI) 24 young (18–25 year) subjects (14 female) while performing a decision-making task, where monetary rewards were devalued by physical effort (minutes running in the treadmill) and risk. Twenty different pairs of options were presented nine times each, and H was calculated for each pair and subject. Behavioral analyses showed that subjective value (SV) significantly explained agents' preferences only in pairs with a low inconsistent response. Averaged response time positively correlated with H, confirming entropy as an indicator of choice difficulty. Group analyses on fMRI data revealed a cluster in the paracingulate cortex as the neural correlate of H. Besides, BOLD signal in the posterior cingulate correlated with the SV of the pair only in consistent decisions, confirming that SV loses its explanatory power on highly inconsistent decisions. Finally, the anterior and central cingulate were especially recruited when predicting a secured effortless reward, compared with a secured reward that involved a maximum effort. Our study shows that different regions of the cingulate cortex are involved in choice inconsistency, SV and processing effort costs.