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A. Ross Otto
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Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2022) 34 (11): 2113–2126.
Published: 01 October 2022
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People tend to avoid exerting cognitive effort, and findings from recent behavioral studies suggest that effort allocation is in part determined by the opportunity cost of slothful responding—operationalized as the average reward rate per unit time. When the average rate of reward is high, individuals make more errors in cognitive control tasks, presumably owing to a withdrawal of costly cognitive processing. An open question remains whether the presumed modulations of cognitively effortful control processes are observable at the neural level. Here, we measured EEG while participants completed the Simon task, a well-known response conflict task, while the experienced average reward rate fluctuated across trials. We examined neural activity associated with the opportunity cost of time by applying generalized eigendecomposition, a hypothesis-driven source separation technique, to identify a midfrontal component associated with the average reward rate. Fluctuations in average reward rate modulated not only component amplitude but also, most importantly, component theta power (4–8 Hz). Higher average reward rate was associated with reduced theta power, suggesting that the opportunity of time modulates effort allocation. These neural results provide evidence for the idea that people strategically modulate the amount of cognitive effort they exert based on the opportunity cost of time.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2018) 30 (10): 1433–1441.
Published: 01 October 2018
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When choosing between options that vary in risk, we often rely on our experience with options—our episodic memories—to make that choice. Although episodic memory has been demonstrated to be critically involved in value-based decision-making, it is not clear how these memory processes contribute to decision-making that involves risk. To investigate this issue, we tested a group of participants on a repeated-choice risky decision-making task. Before completing this task, half of the participants were given a well-validated episodic induction task—a brief training procedure in recollecting the details of a past experience—known to engage episodic memory processes, and the other half were given a general impressions induction task. Our main finding was that risk-taking following the general impressions induction task was significantly lower than following the episodic induction task. In a follow-up experiment, we tested risk-taking in another group of participants without any prior induction task and found that risk-taking from this no-induction (baseline) group was more similar to the episodic induction than to the general impression group. Overall, these findings suggest engaging episodic memory processes when learning about decision outcomes can alter apparent risk-taking behavior in decision-making from experience.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2015) 27 (2): 319–333.
Published: 01 February 2015
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Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information—in the service of overcoming habitual, stimulus-driven responses—in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior.