Task-specific prioritization of reward and effort information: Novel insights from behavior and computational modeling

Abstract

Efficient integration of environmental information is critical in goal-directed behavior. Motivational information regarding potential rewards and costs (such as required effort) affects performance and decisions whether to engage in a task. While it is generally acknowledged that costs and benefits are integrated to determine the level of effort to be exerted, how this integration occurs remains an open question. Computational models of high-level cognition postulate serial processing of task-relevant features and demonstrate that prioritizing the processing of one feature over the other can affect performance. We investigated the hypothesis that motivationally relevant task features also may be processed serially, that people may prioritize either benefit or cost information, and that artificially controlling prioritization may be beneficial for performance (by improving task-accuracy) and decision-making (by boosting the willingness to engage in effortful trials). We manipulated prioritization by altering order of presentation of effort and reward cues in two experiments involving preparation for effortful performance and effort-based decision-making. We simulated the tasks with a recent model of prefrontal cortex (Alexander & Brown in Neural Computation, 27(11), 2354–2410, 2015). Human behavior was in line with model predictions: prioritizing reward vs. effort differentially affected performance vs. decision. Prioritizing reward was beneficial for performance, showing striking increase in accuracy, especially when a large reward was offered for a difficult task. Counterintuitively (yet predicted by the model), prioritizing reward resulted in a blunted reward effect on decisions. Conversely, prioritizing effort increased reward impact on decision to engage. These results highlight the importance of controlling prioritization of motivational cues in neuroimaging studies.