Attenuation of dopamine-modulated prefrontal value signals underlies probabilistic reward learning deficits in old age

Boer, Lieke de; Axelsson, Jan; Riklund, Katrine; Nyberg, Lars; Dayan, Peter; Bäckman, Lars; Guitart-Masip, Marc

doi:10.7554/elife.26424

Cited by 48 publications

(72 citation statements)

References 86 publications

(158 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Chowdhury et al , (2013 a ) demonstrated that older adults do not show a representation of expected value in the ventral striatum when performing a probabilistic reward learning under basal conditions and that an expected value representation was only observed after boosting the dopaminergic system with L-DOPA. Furthermore, older adults performing a probabilistic reward learning task show an attenuation of value anticipation in the ventromedial prefrontal cortex (vmPFC) that predicts performance in the probabilistic learning task (de Boer et al , 2017). These findings suggest that whilst general reward processing may be intact in older adults, they are impaired in learning the predictive value of probabilistic reward cues.…”

Section: Discussionmentioning

confidence: 99%

Learning in anticipation of reward and punishment: Perspectives across the human lifespan

Betts

Richter

Boer

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Pavlovian biases influence the interaction between action and valence by coupling reward seeking to action invigoration and punishment avoidance to action suppression. In this study we used an orthogonalised go/no-go task to investigate learning in 247 individuals across the human lifespan (7-80 years) to demonstrate that all participants, independently of age, demonstrated an influence of Pavlovian control. Computational modeling revealed peak performance in young adults was attributable to greater sensitivity to both rewards and punishment. However in children and adolescents an increased bias towards action but not reward sensitivity was observed. In contrast, reduced learning in midlife and older adults was accompanied with decreased reward sensitivity and especially punishment sensitivity. These findings reveal distinct learning capabilities across the human lifespan that cannot be probed using conventional go/reward no-go/punishment style paradigms that have important implications in life-long education.

show abstract

Section: Discussionmentioning

confidence: 99%

Learning in anticipation of reward and punishment: Perspectives across the human lifespan

Betts

Richter

Boer

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Christakou et al ( 2013 ) report the neural representation of state-action values to become stronger from adolescence to adulthood; however, they did not find this to be related to behavioral differences. In older adults, state-action value signals are reduced (Tobia et al, 2016 ; de Boer et al, 2017 ) and signal strength predicts performance in a probabilistic decision-making task (de Boer et al, 2017 ), suggesting that the age-related deterioration of value signals in the vmPFC may explain the behavioral deficits.…”

Section: Modeling Analyses Beyond Parameter Estimationmentioning

confidence: 99%

Developmental Changes in Learning: Computational Mechanisms and Social Influences

2017

View full text Add to dashboard Cite

Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.

show abstract

“…Dopamine D1 receptors (D1R) dominate in the direct striatal "go" pathway and in prefrontal cortex (6). Prefrontal D1R availability was related to reward learning (7), and prefrontal D1R activation may enhance cue-triggered goal-directed behavior by strengthening prefrontal goal representations (8). Likewise, according to a recent theoretical model on striatal dopamine (9), striatal D1R activity both facilitates the updating of cue-outcome associations after unexpected positive feedback and increases instrumental behavior in response to reward-predicting cues.…”

Section: Introductionmentioning

confidence: 99%

Activation of D1 receptors affects human reactivity and flexibility to valued cues

Soutschek

Kozak

Martinis

et al. 2020

Neuropsychopharmacol.

View full text Add to dashboard Cite

Reward-predicting cues motivate goal-directed behavior, but in unstable environments humans must also be able to flexibly update cue-reward associations. While the capacity of reward cues to trigger motivation ('reactivity') as well as flexibility in cue-reward associations have been linked to the neurotransmitter dopamine in humans, the specific contribution of the dopamine D1 receptor family to these behaviors remained elusive. To fill this gap, we conducted a randomized, placebo-controlled, doubleblind pharmacological study testing the impact of three different doses of a novel D1 agonist (relative to placebo) on reactivity to reward-predicting cues (Pavlovian-to-instrumental transfer) and flexibility of cue-outcome associations (reversal learning). We observed that the impact of the D1 agonist crucially depended on baseline working memory functioning, which has been identified as a proxy for baseline dopamine synthesis capacity. Specifically, increasing D1 receptor stimulation strengthened Pavlovianto-instrumental transfer in individuals with high baseline working memory capacity. In contrast, higher doses of the D1 agonist improved reversal learning only in individuals with low baseline working memory functioning. Our findings suggest a crucial and baseline-dependent role of D1 receptor activation in controlling both cue reactivity and the flexibility of cue-reward associations.

show abstract

Attenuation of dopamine-modulated prefrontal value signals underlies probabilistic reward learning deficits in old age

Cited by 48 publications

References 86 publications

Learning in anticipation of reward and punishment: Perspectives across the human lifespan

Learning in anticipation of reward and punishment: Perspectives across the human lifespan

Developmental Changes in Learning: Computational Mechanisms and Social Influences

Activation of D1 receptors affects human reactivity and flexibility to valued cues

Contact Info

Product

Resources

About