scholarly journals Individual differences in successful self-regulation of the dopaminergic midbrain

2019 ◽  
Author(s):  
Lydia Hellrung ◽  
Matthias Kirschner ◽  
James Sulzer ◽  
Ronald Sladky ◽  
Frank Scharnowski ◽  
...  

AbstractThe dopaminergic midbrain is associated with brain functions, such as reinforcement learning, motivation and decision-making that are often disturbed in neuropsychiatric disease. Previous research has shown that activity in the dopaminergic midbrain can be endogenously modulated via neurofeedback, suggesting potential for non-pharmacological interventions. However, the robustness of endogenous modulation, a requirement for clinical translation, is unclear. Here, we examined how self-modulation capability relates to regulation transfer. Moreover, to elucidate potential mechanisms underlying successful self-regulation, we studied individual prediction error coding, and, during an independent monetary incentive delay (MID) task, individual reward sensitivity. Fifty-nine participants underwent neurofeedback training either in a veridical or inverted feedback group. Successful self-regulation was associated with post-training activity within the cognitive control network and accompanied by decreasing prefrontal prediction error signals and increased prefrontal reward sensitivity in the MID task. The correlative link of dopaminergic self-regulation with individual differences in prefrontal prediction error and reward sensitivity suggests that reinforcement learning contributes to successful self-regulation. Our findings therefore provide new insights in the control of dopaminergic midbrain activity and pave the way to improve neurofeedback training in neuropsychiatric patients.

2021 ◽  
Author(s):  
Kenway Louie

Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral implications of nonlinear RL are unknown. Here, we show that nonlinear RL incorporating the canonical divisive normalization computation introduces an intrinsic and tunable asymmetry in prediction error coding. At the behavioral level, this asymmetry explains empirical variability in risk preferences typically attributed to asymmetric learning rates. At the neural level, diversity in asymmetries provides a computational mechanism for recently proposed theories of distributional RL, allowing the brain to learn the full probability distribution of future rewards. This behavioral and computational flexibility argues for an incorporation of biologically valid value functions in computational models of learning and decision-making.


2019 ◽  
Vol 33 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Elizabeth M. Stoakley ◽  
Karen J. Mathewson ◽  
Louis A. Schmidt ◽  
Kimberly A. Cote

Abstract. Resting respiratory sinus arrhythmia (RSA) is related to individual differences in waking affective style and self-regulation. However, little is known about the stability of RSA between sleep/wake stages or the relations between RSA during sleep and waking affective style. We examined resting RSA in 25 healthy undergraduates during the waking state and one night of sleep. Stability of cardiac variables across sleep/wake states was highly reliable within participants. As predicted, greater approach behavior and lower impulsivity were associated with higher RSA; these relations were evident in early night Non-REM (NREM) sleep, particularly in slow wave sleep (SWS). The current research extends previous findings by establishing stability of RSA within individuals between wake and sleep states, and by identifying SWS as an optimal period of measurement for relations between waking affective style and RSA.


NeuroImage ◽  
2014 ◽  
Vol 88 ◽  
pp. 113-124 ◽  
Author(s):  
Emma J. Lawrence ◽  
Li Su ◽  
Gareth J. Barker ◽  
Nick Medford ◽  
Jeffrey Dalton ◽  
...  

2019 ◽  
Vol 180 ◽  
pp. 104-112 ◽  
Author(s):  
Sanne B. Geeraerts ◽  
Roy S. Hessels ◽  
Stefan Van der Stigchel ◽  
Jorg Huijding ◽  
Joyce J. Endendijk ◽  
...  

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