Abstract
Computational models of reward processing suggest that foregone or fictive outcomes serve as important information sources for learning and augment those generated by experienced rewards (e.g. reward prediction errors). An outstanding question is how these learning signals interact with top-down cognitive influences, such as cognitive reappraisal strategies. Using a sequential investment task and functional magnetic resonance imaging, we show that the reappraisal strategy selectively attenuates the influence of fictive, but not reward prediction error signals on investment behavior; such behavioral effect is accompanied by changes in neural activity and connectivity in the anterior insular cortex, a brain region thought to integrate subjective feelings with high-order cognition. Furthermore, individuals differ in the extent to which their behaviors are driven by fictive errors versus reward prediction errors, and the reappraisal strategy interacts with such individual differences; a finding also accompanied by distinct underlying neural mechanisms. These findings suggest that the variable interaction of cognitive strategies with two important classes of computational learning signals (fictive, reward prediction error) represent one contributing substrate for the variable capacity of individuals to control their behavior based on foregone rewards. These findings also expose important possibilities for understanding the lack of control in addiction based on possibly foregone rewarding outcomes. Hum Brain Mapp 35:3738–3749, 2014.
Highlights
Recent computational models and experimental probes support the notion of multiple learning mechanisms in healthy individuals [Chiu et al, 2008; Daw et al, 2011; Glascher et al, 2010; Lohrenz et al, 2007; Montague et al, 2004, 2006; Pagnoni et al, 2002; Simon and Daw, 2011]
There was no significant effect of site for any of these behavioral coefficients. These results suggest the reappraisal strategy significantly attenuated the influence of fictive errors on investment behavior
We provide human neuroimaging evidence demonstrating that fictive errors are more amenable to cognitive strategies such as reappraisal, when compared with reward prediction error signals; and that these learning signals and their interaction with cognitive influences vary among individual decision makers
Summary
Recent computational models and experimental probes support the notion of multiple learning mechanisms in healthy individuals [Chiu et al, 2008; Daw et al, 2011; Glascher et al, 2010; Lohrenz et al, 2007; Montague et al, 2004, 2006; Pagnoni et al, 2002; Simon and Daw, 2011]. One central physical substrate supporting these mechanisms is dopaminergic signaling in the brain [Niv et al, 2005; Rangel et al, 2008]. In healthy individuals, both fictive [Lohrenz et al, 2007] and reward prediction errors [Montague et al, 2002] activate the striatum [Chiu et al, 2008; Montague et al, 2002], a dopaminoceptive structure that is commonly implicated in decisionmaking tasks and works closely with a network of brain regions such as the anterior insular cortex (AIC), orbitofrontal cortex (OFC), and the amygdala [Hsu et al, 2005; Li et al, 2011; Seymour et al, 2004]
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