Adolescents exhibit reduced Pavlovian biases on instrumental learning

Raab, Hillary A.; Hartley, Catherine A.

doi:10.1038/s41598-020-72628-w

Cited by 25 publications

(57 citation statements)

References 116 publications

(143 reference statements)

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“…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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Section: Discussionmentioning

confidence: 99%

Dissociable influences of reward and punishment on adaptive cognitive control

et al. 2021

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“…For example, adolescents are more sensitive to the effects of social stress, such as social isolation, on mental health, and are more capable of recovering from those same social stressors compared to children and adults ( Fuhrmann et al 2015 ). Recent work suggests that adolescents, more so than children or adults, rely on learning strategies that are specifically suited to exploring novel opportunities and challenges in the environment ( Raab and Hartley 2020 ).…”

Section: Introductionmentioning

confidence: 99%

An evolutionary model of sensitive periods when the reliability of cues varies across ontogeny

2021

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Sensitive periods are widespread in nature, but their evolution is not well understood. Recent mathematical modeling has illuminated the conditions favoring the evolution of sensitive periods early in ontogeny. However, sensitive periods also exist at later stages of ontogeny, such as adolescence. Here, we present a mathematical model that explores the conditions that favor sensitive periods at later developmental stages. In our model, organisms use environmental cues to incrementally construct a phenotype that matches their environment. Unlike in previous models, the reliability of cues varies across ontogeny. We use stochastic dynamic programming to compute optimal policies for a range of evolutionary ecologies and then simulate developmental trajectories to obtain mature phenotypes. We measure changes in plasticity across ontogeny using study paradigms inspired by empirical research: adoption and cross-fostering. Our results show that sensitive periods only evolve later in ontogeny if the reliability of cues increases across ontogeny. The onset, duration, and offset of sensitive periods—and the magnitude of plasticity—depend on the specific parameter settings. If the reliability of cues decreases across ontogeny, sensitive periods are favored only early in ontogeny. These results are robust across different paradigms suggesting that empirical findings might be comparable despite different experimental designs.

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“…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 individualspecific [58,59] and context-specific [60] differences in learning from these positive and negative outcomes.…”

mentioning

confidence: 99%

Dissociable influences of reward and punishment on adaptive cognitive control

Leng

Yee

Ritz

et al. 2020

Preprint

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When deciding how to allocate cognitive control to a given task, people must consider both positive outcomes (e.g., praise) and negative outcomes (e.g., admonishment). However, it is unclear how these two forms of incentives differentially influence the amount and type of cognitive control a person chooses to allocate to achieve task goals. To address this question, we had participants perform a self-paced incentivized cognitive control task, varying the magnitude of reward for a correct response and punishment for an incorrect response. Formalizing control allocation as a process of adjusting parameters of a drift diffusion model (DDM), we show that participants engaged in different strategies in response to reward (primarily adjusting drift rate) versus punishment (primarily adjusting response threshold). We demonstrate that this divergent set of strategies is optimal for maximizing reward rate while minimizing effort costs. Finally, we show that these dissociable patterns of behavior enable us to infer the motivational salience of positive versus negative incentives for a given individual. These results provide a normative and mechanistic account for how reward and punishment differentially influence the adaptive allocation of cognitive control.

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Adolescents exhibit reduced Pavlovian biases on instrumental learning

Cited by 25 publications

References 116 publications

Dissociable influences of reward and punishment on adaptive cognitive control

Dissociable influences of reward and punishment on adaptive cognitive control

An evolutionary model of sensitive periods when the reliability of cues varies across ontogeny

Dissociable influences of reward and punishment on adaptive cognitive control

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