Seizing the opportunity: Lifespan differences in the effects of the opportunity cost of time on cognitive control

Devine, Sean; Neumann, Cassandra; Otto, A. Ross; Bolenz, Florian; Reiter, Andrea; Eppinger, Ben

doi:10.1016/j.cognition.2021.104863

Cited by 13 publications

(16 citation statements)

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“…Thus, our findings seem to suggest that the development of model‐based decision making may be independent from the development of stakes‐based metacontrol. This is in line with findings that varying the difficulty of cognitive‐control tasks does not necessarily affect the adaptation of cognitive effort (Devine et al, 2021) suggesting that the adaptation of effort is to some degree independent from one's capacity for the task.…”

Section: Discussionsupporting

confidence: 90%

Valence bias in metacontrol of decision making in adolescents and young adults

Bolenz

Eppinger

2021

Child Development

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During adolescence, the ability to engage in more complex decision-making strategies increases (Raab & Hartley, 2019). However, the successful use of a given decision-making strategy does not only depend on the mere ability to engage in it-it also depends on how flexible individuals are in adjusting their reliance on decision-making strategies to changes in internal and external demands. In this study, we ask how the ability for metacontrol of decision making (i.e. the dynamic adaptation of decision-making strategies; Eppinger et al., 2021;Ruel, Devine, et al., 2021) develops from adolescence into young adulthood and whether framing effects differentially affect the flexible usage of decision-making strategies in adolescents as compared to young adults.To study metacontrol, we draw on previous work that dissociates two major decision-making strategies: modelbased and model-free decision making (Daw et al., 2011;Dayan & Niv, 2008). Model-based decision making represents a deliberative, prospective strategy that evaluates different choice options by means of forward planning based on knowledge about the structure of the environment (a cognitive model). In contrast, model-free decision making represents a more reflexive, retrospective strategy that relies on previously experienced actionreward contingencies. Previous developmental research shows that the reliance on model-based decision making (but not model-free decision making) becomes more pronounced from childhood to adulthood

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Section: Discussionsupporting

confidence: 90%

Valence bias in metacontrol of decision making in adolescents and young adults

Bolenz

Eppinger

2021

Child Development

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“…While this assumption was reasonable given the parameters of our task (i.e., where incentives were explicitly cued and pseudorandomized), a crucial next step will be to examine how people dynamically reconfigure control as they learn from feedback that the expected rewards and penalties in their environment are changing. Research has shown that people dynamically adjust their response threshold in both decision-making tasks [ 56 ] and cognitive control tasks [ 30 , 57 ] as they learn to expect greater rewards. It remains to be tested how these cognitive control adjustments are distributed across both threshold and drift rate with changes in both reward and punishment, as well as with individual-specific [ 58 , 59 ] and context-specific [ 60 ] differences in learning from these positive and negative outcomes.…”

Section: Discussionmentioning

confidence: 99%

Dissociable influences of reward and punishment on adaptive cognitive control

et al. 2021

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To invest effort into any cognitive task, people must be sufficiently motivated. Whereas prior research has focused primarily on how the cognitive control required to complete these tasks is motivated by the potential rewards for success, it is also known that control investment can be equally motivated by the potential negative consequence for failure. Previous theoretical and experimental work has yet to examine how positive and negative incentives differentially influence the manner and intensity with which people allocate control. Here, we develop and test a normative model of control allocation under conditions of varying positive and negative performance incentives. Our model predicts, and our empirical findings confirm, that rewards for success and punishment for failure should differentially influence adjustments to the evidence accumulation rate versus response threshold, respectively. This dissociation further enabled us to infer how motivated a given person was by the consequences of success versus failure.

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confidence: 88%

“…individuals appeared to modulate (presumably effortful) controlled cognitive processing, which resulted in more errors on difficult, incongruent trials in the Simon task. More recently, this effect was replicated in a structurally similar Flanker task (Eriksen & Eriksen, 1974), which also requires cognitive control to inhibit distracting information (Devine et al, 2021).In short, these behavioral studies suggest that moment-to-moment varying average rate of reward influences individual's strategic allocation of cognitive resources, which could be similar to the observed adjustments to cognitive control in accordance with, for example, recently experienced response conflict (Ridderinkhof, 2002) or cues signaling upcoming conflict (Gratton et al, 1992). However, it remains unclear whether this behavioral signature of average reward rate-evoked effort modulation (i.e., increased error rates on incongruent trials) is also accompanied by well-characterized neural signatures of cognitive control modulation-in particular, midfrontal theta oscillations (4-8 Hz) measured using electroencephalography (EEG;…”

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confidence: 89%

“…To manipulate the average reward rate, we induced random fluctuations in the reward available for making a correct response (Figure 1B), which we used to compute a time-varying "experienced" average rate of reward per second (Figure 1C). As participants had 5 minutes per block to complete as many trials as they could, the average reward rate effectively imposed an opportunity cost for slow responding (Beierholm et al, 2013;Guitart-Masip et al, 2011), which we have previously observed (Devine et al, 2021;Otto & Daw, 2019) increases error rates on difficult, incongruent trials. In other words, because this task is self-paced (with a fixed amount of time per block), a reward-maximizing strategy is to make responses as quickly as possible to ensure as many trials as possible-and this is especially true when the average rate of reward is high (versus low).…”

Section: Introductionmentioning

confidence: 99%

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The Average Reward Rate Modulates Behavioral and Neural Indices of Effortful Control Allocation

Lin

Ristic

Inzlicht

et al. 2022

Journal of Cognitive Neuroscience

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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 reduction in cognitive control. 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.

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Seizing the opportunity: Lifespan differences in the effects of the opportunity cost of time on cognitive control

Cited by 13 publications

References 75 publications

Valence bias in metacontrol of decision making in adolescents and young adults

Valence bias in metacontrol of decision making in adolescents and young adults

Dissociable influences of reward and punishment on adaptive cognitive control

The Average Reward Rate Modulates Behavioral and Neural Indices of Effortful Control Allocation

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