Abstract
Emotional experience has a pervasive impact on choice behavior, yet the underlying mechanism remains unclear. Introducing facial-expression primes into a probabilistic learning task, we investigated how affective arousal regulates reward-related choice based on behavioral, model fitting, and feedback-related negativity (FRN) data. Sixty-six paid subjects were randomly assigned to the Neutral-Neutral (NN), Angry-Neutral (AN), and Happy-Neutral (HN) groups. A total of 960 trials were conducted. Subjects in each group were randomly exposed to half trials of the pre-determined emotional faces and another half of the neutral faces before choosing between two cards drawn from two decks with different assigned reward probabilities. Trial-by-trial data were fit with a standard reinforcement learning model using the Bayesian estimation approach. The temporal dynamics of brain activity were simultaneously recorded and analyzed using event-related potentials. Our analyses revealed that subjects in the NN group gained more reward values than those in the other two groups; they also exhibited comparatively differential estimated model-parameter values for reward prediction errors. Computing the difference wave of FRNs in reward vs. non-reward trials, we found that, compared to the NN group, subjects in the AN and HN groups had larger “General” FRNs (i.e., FRNs in no-reward trials minus FRNs in reward trials) and “Expected” FRNs (i.e., FRNs in expected reward-omission trials minus FRNs in expected reward-delivery trials), indicating an interruption in predicting reward. Further, both AN and HN groups appeared to be more sensitive to negative outcomes than the NN group. Collectively, our study suggests that affective arousal negatively regulates reward-related choice, probably through overweighting with negative feedback.
Highlights
Our daily life is flooded with making decisions, such as having milk or decaf latte for the morning, driving through the main road or taking a shortcut to the office, and holding or selling the declining stocks in hand
It has been investigated under the reinforcement-learning framework (Sutton and Barto, 1998), in which the goal of optimal behavior of an organism is to maximize its reward through the minimization of the reward prediction error (RPE)
To the best of our knowledge, this is the first study to integrate model-based analysis and event-related potentials (ERP) components to simultaneously examine the effects of affective primes with both positive and negative facial expressions on the regulation of reward-based decision making
Summary
Our daily life is flooded with making decisions, such as having milk or decaf latte for the morning, driving through the main road or taking a shortcut to the office, and holding or selling the declining stocks in hand. Further evidence has shown that RPE signaling is expressed by mid-brain dopaminergic neurons (Schultz et al, 1997; Bayer and Glimcher, 2005) and is carried out by an extensive fronto-subcortical network, including the orbitofrontal cortex, ventromedial pre-frontal cortex, anterior cingulate cortex, striatum, and amygdala (Hare et al, 2008; Haber and Knutson, 2010; Glimcher, 2011; Shenhav et al, 2013) In complementary to these imaging works disclosing neural circuits engaged in the decision process, activities of event-related potentials (ERP) are considered to be especially suitable for the capture of covert mental operations and dynamic changes because of the high temporal resolution and electrophysiological nature. Researchers further identified a component termed feedbackrelated negativity (FRN) as a potential electrophysiological signature for coding RPE signals during reinforcement learning (Holroyd and Coles, 2002; Holroyd et al, 2003, 2009; Yasuda et al, 2004; Warren et al, 2014)
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