scholarly journals A cerebello-olivary signal for negative prediction error is sufficient to cause extinction of associative motor learning

2020 ◽  
Vol 23 (12) ◽  
pp. 1550-1554
Author(s):  
Olivia A. Kim ◽  
Shogo Ohmae ◽  
Javier F. Medina
Keyword(s):  
Motor Learning ◽  
Prediction Error ◽  
Negative Prediction

scholarly journals Dopamine reward prediction error coding

2016 ◽  
Vol 18 (1) ◽  
pp. 23-32 ◽  
Keyword(s):  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Negative Prediction ◽  
Baseline Activity ◽  
Error Coding ◽  
Reward Value ◽  
Dopamine Signal

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.

From Feedback- to Response-based Performance Monitoring in Active and Observational Learning

2014 ◽  
Vol 26 (9) ◽  
pp. 2111-2127 ◽  
Author(s):  
Christian Bellebaum ◽  
Marco Colosio
Keyword(s):  
Active Learning ◽  
Negative Feedback ◽  
Observational Learning ◽  
Prediction Error ◽  
Performance Monitoring ◽  
Learning Performance ◽  
Behavioral Adaptation ◽  
Prediction Errors ◽  
Error Related Negativity ◽  
Negative Prediction

Humans can adapt their behavior by learning from the consequences of their own actions or by observing others. Gradual active learning of action–outcome contingencies is accompanied by a shift from feedback- to response-based performance monitoring. This shift is reflected by complementary learning-related changes of two ACC-driven ERP components, the feedback-related negativity (FRN) and the error-related negativity (ERN), which have both been suggested to signal events “worse than expected,” that is, a negative prediction error. Although recent research has identified comparable components for observed behavior and outcomes (observational ERN and FRN), it is as yet unknown, whether these components are similarly modulated by prediction errors and thus also reflect behavioral adaptation. In this study, two groups of 15 participants learned action–outcome contingencies either actively or by observation. In active learners, FRN amplitude for negative feedback decreased and ERN amplitude in response to erroneous actions increased with learning, whereas observational ERN and FRN in observational learners did not exhibit learning-related changes. Learning performance, assessed in test trials without feedback, was comparable between groups, as was the ERN following actively performed errors during test trials. In summary, the results show that action–outcome associations can be learned similarly well actively and by observation. The mechanisms involved appear to differ, with the FRN in active learning reflecting the integration of information about own actions and the accompanying outcomes.

scholarly journals Positive and negative prediction error signals to violated expectations of face and place stimuli distinctively activate FFA and PPA

NeuroImage ◽  
2021 ◽  
pp. 118028
Author(s):  
Lena M. Schliephake ◽  
Ima Trempler ◽  
Marlen A. Roehe ◽  
Nina Heins ◽  
Ricarda I. Schubotz
Keyword(s):  
Prediction Error ◽  
Negative Prediction ◽  
Violated Expectations

scholarly journals Learning prediction error neurons in a canonical interneuron circuit

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Loreen Hertäg ◽  
Henning Sprekeler
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Prediction Error ◽  
Sensory Information ◽  
Prediction Errors ◽  
Negative Prediction ◽  
Inhibitory Plasticity ◽  
The Brain ◽  
Shed Light ◽  
Model Circuit

Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

scholarly journals The Neural Correlates of Cued Reward Omission

2021 ◽  
Vol 15 ◽  
Author(s):  
Jessica A. Mollick ◽  
Luke J. Chang ◽  
Anjali Krishnan ◽  
Thomas E. Hazy ◽  
Kai A. Krueger ◽  
...  
Keyword(s):  
Prediction Error ◽  
Conditioned Inhibition ◽  
Brain Regions ◽  
Dopamine Neurons ◽  
Sensory Stimulus ◽  
Prediction Errors ◽  
Significant Activity ◽  
Lateral Habenula ◽  
Negative Prediction ◽  
Reward Delivery

Compared to our understanding of positive prediction error signals occurring due to unexpected reward outcomes, less is known about the neural circuitry in humans that drives negative prediction errors during omission of expected rewards. While classical learning theories such as Rescorla–Wagner or temporal difference learning suggest that both types of prediction errors result from a simple subtraction, there has been recent evidence suggesting that different brain regions provide input to dopamine neurons which contributes to specific components of this prediction error computation. Here, we focus on the brain regions responding to negative prediction error signals, which has been well-established in animal studies to involve a distinct pathway through the lateral habenula. We examine the activity of this pathway in humans, using a conditioned inhibition paradigm with high-resolution functional MRI. First, participants learned to associate a sensory stimulus with reward delivery. Then, reward delivery was omitted whenever this stimulus was presented simultaneously with a different sensory stimulus, the conditioned inhibitor (CI). Both reward presentation and the reward-predictive cue activated midbrain dopamine regions, insula and orbitofrontal cortex. While we found significant activity at an uncorrected threshold for the CI in the habenula, consistent with our predictions, it did not survive correction for multiple comparisons and awaits further replication. Additionally, the pallidum and putamen regions of the basal ganglia showed modulations of activity for the inhibitor that did not survive the corrected threshold.

scholarly journals Altered Dynamics of Cortical Beta-Oscillations during Motor Learning in Cerebellar Ataxia

2020 ◽  
Author(s):  
Jana Klimpke ◽  
Dorothea Henkel ◽  
Hans-Jochen Heinze ◽  
Max-Philipp Stenner
Keyword(s):  
Motor Learning ◽  
Implicit Learning ◽  
Cerebellar Ataxia ◽  
Prediction Error ◽  
Cognitive Strategies ◽  
Strategy Use ◽  
Healthy Controls ◽  
Healthy Individuals ◽  
Beta Oscillations ◽  
Sensory Prediction

AbstractCerebellar ataxia is associated with an implicit motor learning dysfunction, specifically, a miscalibration of internal models relating motor commands to state changes of the body. Explicit cognitive strategies could compensate for deficits in implicit calibration. Surprisingly, however, patients with cerebellar ataxia use insufficient strategies compared to healthy controls. We report a candidate physiological phenomenon of disrupted strategy use in cerebellar ataxia, reflected in an interaction of implicit and explicit learning effects on cortical beta oscillations. We recorded electroencephalography in patients with cerebellar ataxia (n=18), age-matched healthy controls (n=19), and young, healthy individuals (n=34) during a visuomotor rotation paradigm in which an aiming strategy was either explicitly instructed, or had to be discovered through learning. In young, healthy individuals, learning a strategy, but not implicit learning from sensory prediction error alone, decreased the post-movement beta rebound. Disrupted learning from sensory prediction error in patients, on the other hand, unmasked effects of explicit and implicit control that are normally balanced. Specifically, the post-movement beta rebound increased during strategy use when implicit learning was disrupted, i.e., in patients, but not controls. We conclude that a network disturbance due to cerebellar degeneration surfaces in imbalanced cortical beta oscillations normally involved in strategy learning.

The neural correlates of negative prediction error signaling in human fear conditioning

NeuroImage ◽  
2011 ◽  
Vol 54 (3) ◽  
pp. 2250-2256 ◽  
Author(s):  
V.I. Spoormaker ◽  
K.C. Andrade ◽  
M.S. Schröter ◽  
A. Sturm ◽  
R. Goya-Maldonado ◽  
...  
Keyword(s):  
Fear Conditioning ◽  
Prediction Error ◽  
Neural Correlates ◽  
Negative Prediction

scholarly journals Learning prediction error neurons in a canonical interneuron circuit

2020 ◽  
Author(s):  
Loreen Hertäg ◽  
Henning Sprekeler
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Prediction Error ◽  
Sensory Information ◽  
Prediction Errors ◽  
Negative Prediction ◽  
Inhibitory Plasticity ◽  
The Brain ◽  
Shed Light ◽  
Model Circuit

AbstractSensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

scholarly journals Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task

2020 ◽  
Author(s):  
Iku Tsutsui-Kimura ◽  
Hideyuki Matsumoto ◽  
Naoshige Uchida ◽  
Mitsuko Watabe-Uchida
Keyword(s):  
Decision Making ◽  
Prediction Error ◽  
Activity Level ◽  
Decision Task ◽  
Temporal Difference ◽  
Dorsolateral Striatum ◽  
Negative Prediction ◽  
Different Types ◽  
Dopamine Signal

SUMMARYDifferent regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed in a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, i.e. the temporal difference error. A major difference between these areas was the overall activity level of reward responses: reward responses in dorsolateral striatum (DLS) were positively shifted, lacking inhibitory responses to negative prediction error. Tenets of habit and skill can be explained by this positively biased dopamine signal in DLS.

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