Cognitive effort and decision-making: integrating computational, behavioral, and psychophysiological approaches

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Everyday experiences are defined by decisions, from deciding what to eat for lunch to making career choices, but not all of our decisions come about the same way: some decisions are made with effort and are slow, while other decisions are easier and made quickly. The proposed research aims to elucidate when effortful processes are deployed the service of maximizing rewards. This aim will be met by addressing three fundamental questions: 1) how do people calculate and balance the relative costs and benefits of cognitive effort, 2) what are the psychophysiological markers of these costs and benefits and how do these signals influence the expenditure of cognitive effort as evidenced by behavior, and 3) how do individual differences in cognitive ability influence this cost-benefit calculus?******While the question of when and why people decide to expend—or withhold—cognitive effort has received recent theoretical treatment, little experimental progress has been made in understanding how either the task environment or factors at the level of the individual affects the allocation of such effortful processes. The proposed research leverages Reinforcement Learning (RL)—a computational framework for understanding how we learn to select actions in the service of maximizing rewards—to pinpoint computations that underpin our negotiation of the effort-reward tradeoff.******One influential RL-based account posits that the opportunity cost of time should dictate response speed or “vigor”: when delayed action is more expensive, actions should be made more quickly. Our reliance upon cognitively demanding strategies should shift accordingly: when time is expensive, we should employ cognitively ‘inexpensive' strategies to make decisions. To test this idea, I will use an experimental design that implements fluctuations in opportunity costs—operationalized as the average rate of reward per second—to carefully isolate the ‘cost' . This manipulation will be used with several well-characterized cognitive tasks, yielding an understanding of how costs and benefits dictate trial-to-trial adjustments of cognitive effort expenditure. Following this set of experiments that will characterize cognitive effort modulation, we will use pupillometry—a physiological marker of processing load—to elucidate how these putative costs and benefits are represented internally. In conjunction with behavioral baseline measures of cognitive ability, these biomarkers will afford quantification of moment-to-moment cognitive effort as well as individual variation in sensitivity to cognitive costs.