scholarly journals Attention neglects a stare-in-the-crowd: Unanticipated consequences of prediction-error coding

Cognition ◽  
2021 ◽  
Vol 207 ◽  
pp. 104519
Author(s):  
Nayantara Ramamoorthy ◽  
Maximilian Parker ◽  
Kate Plaisted-Grant ◽  
Alex Muhl-Richardson ◽  
Greg Davis
2010 ◽  
Vol 30 (34) ◽  
pp. 11447-11457 ◽  
Author(s):  
K. Oyama ◽  
I. Hernadi ◽  
T. Iijima ◽  
K.-I. Tsutsui

2016 ◽  
Vol 18 (1) ◽  
pp. 23-32 ◽  

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards—an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.


2019 ◽  
Vol 45 (Supplement_2) ◽  
pp. S284-S285
Author(s):  
Teresa Katthagen ◽  
Jakob Kaminski ◽  
Andreas Heinz ◽  
Ralph Buchert ◽  
Florian Schlagenhauf

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.


2014 ◽  
Vol 112 (5) ◽  
pp. 1021-1024 ◽  
Author(s):  
Joachim Morrens

Dopamine midbrain neurons are well known for prediction error coding in a reward context. A recent report by Christopher Fiorillo ( Science 341: 546–549, 2013), however, suggests that these neurons behave markedly different when subjects get confronted with aversive, rather than appetitive, stimuli. Despite his findings being in line with indications of appetitive and aversive stimuli being processed by distinct neurotransmitter systems, they should still be interpreted with some caution due to a potential issue of recording location.


2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S172-S173
Author(s):  
Teresa Katthagen ◽  
Jakob Kaminski ◽  
Andreas Heinz ◽  
Florian Schlagenhauf

2020 ◽  
Vol 46 (2) ◽  
pp. 386-393
Author(s):  
Inge Volman ◽  
Abbie Pringle ◽  
Lennart Verhagen ◽  
Michael Browning ◽  
Phil J. Cowen ◽  
...  

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.


2018 ◽  
Vol 83 (9) ◽  
pp. S434
Author(s):  
Teresa Katthagen ◽  
Jakob Kaminski ◽  
Andreas Heinz ◽  
Florian Schlagenhauf

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