Effects of feedback delay on learning from positive and negative feedback in patients with Parkinson's disease off medication

Prediction-error-dependent processing of immediate and delayed positive feedback

Learning often involves trial-and-error, i.e. repeating behaviours that lead to desired outcomes, and adjusting behaviour when outcomes do not meet our expectations and thus lead to prediction errors (PEs). PEs have been shown to be reflected in the reward positivity (RewP), an event-related potential (ERP) component between 200 and 350 ms after performance feedback which is linked to striatal processing and assessed via electroencephalography (EEG). Here we show that this is also true for delayed feedback processing, for which a critical role of the hippocampus has been suggested. We found a general reduction of the RewP for delayed feedback, but the PE was similarly reflected in the RewP and the later P300 for immediate and delayed positive feedback, while no effect was found for negative feedback. Our results suggest that, despite processing differences between immediate and delayed feedback, positive PEs drive feedback processing and learning irrespective of delay.

Learning bias predicts fear acquisition under stress but not cognitive flexibility

Individuals differ in their ability to learn from reinforcement and in avoiding punishment, which can be measured by the Probabilistic Selection Task (PST). Recently, some studies have demonstrated that this learning bias is regulated by the dopaminergic system, and that stress can differentially affect the use of positive (i.e., reinforcement) and negative (i.e., avoiding punishment) feedback. The current two studies examined whether performance on the PST can predict measures of goal-directed behaviour as assessed by a cognitive flexibility task (Wisconsin Card Sorting Test) and the acquisition of fear responses, when individuals are exposed to a stressor (Socially Evaluated Cold Pressor Test). A total of 26 and 59 healthy participants completed Experiments I and II, respectively. In those who were best at learning from reinforcement, stress increased the processing (i.e., higher skin conductance responses) of non-threatening stimuli during fear acquisition compared to the non-stressful condition, which was not recapitulated in those who were best at avoiding punishment. Additionally, PST performance did not interact with stress to modulate cognitive flexibility, although stress negatively impaired this domain, consistent with previous findings. Furthermore, independent of stress, both positive and negative learning biases were correlated with cognitive flexibility errors. Our results demonstrate that the PST has predictive value for better understanding the determinants of reinforcement and avoidance learning.

Learning new words via feedback-Association between feedback-locked ERPs and recall performance-An exploratory study

Feedback learning is thought to involve the dopamine system and its projection sites in the basal ganglia and anterior cingulate cortex (ACC), regions associated with procedural learning. Under certain conditions, such as when feedback is delayed, feedback-locked activation is pronounced in the medial temporal lobe (MTL), which is associated with declarative learning. In event-related potential research, the feedback-related negativity (FRN) has been linked to immediate feedback processing, while the N170, possibly reflecting MTL activity, has been related to delayed feedback processing. In the current study, we performed an exploratory investigation on the relation between N170 and FRN amplitude and memory performance in a test for declarative memory (free recall), also exploring the role of feedback delay. To this end, we adapted a paradigm in which participants learned associations between non-objects and non-words with either immediate or delayed feedback, and added a subsequent free recall test. We indeed found that N170, but not FRN amplitudes, depended on later free recall performance, with smaller amplitudes for later remembered non-words. In an additional analysis with memory performance as dependent variable, the N170, but not the FRN amplitude predicted free recall, modulated by feedback timing and valence. This finding shows that the N170 reflects an important process during feedback processing, possibly related to expectations and their violation, but is distinct from the process reflected by the FRN.

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined with mobile communication which led to the artificial intelligence online sports games teaching, pattern recognition, and virtual technology combined with innovative teaching interaction and experience. Promoting the development of green PE teaching machine learning can identify the types of PE activities and realize efficient PE learning diagnosis. Intelligent decision support system can identify sports talents and improve the effect of personalized PE teaching evaluation. From the perspective of psychological development and education, the key problems to be solved in the integration of artificial intelligence and physical education are examined. Then, the consistent model predictive control for feedback delay of nonlinear sports learning multiagent system with network induced delay and random communication protocol is studied. Under the communication waiting mechanism designed, each agent has a certain tolerance of delay, and this tolerance can be determined by ensuring the stability of the system. At the same time, a random communication protocol is designed to ensure the ordered communication of the multiagent system. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation. To solve the channel competition access problem of the sports intelligent learning system with special structure feedback delay model predictive control, a dual channel awareness scheduling strategy under the model predictive control framework was proposed, and the distributed threshold strategy of sensors and the priority threshold strategy of controllers were designed. It is proved that the sensor will eventually work at Nash equilibrium point under the policy updating mechanism, and the priority threshold strategy of the controller is better than the traditional independent and identically distributed access strategy. By avoiding the data transmission when the channel status is poor, the channel access of the system is efficient and saves energy.

Reward timing matters in motor learning

Reward timing, that is, the delay after which reward is delivered following an action is known to strongly influence reinforcement learning. Here, we asked if reward timing could also modulate how people learn and consolidate new motor skills. In 60 healthy participants, we found that delaying reward delivery by a few seconds influenced motor learning. Indeed, training with a short reward delay (1 s) induced continuous improvements in performance, whereas a long reward delay (6 s) led to initially high learning rates that were followed by an early plateau in the learning curve and a lower performance at the end of training. Participants who learned the skill with a long reward delay also exhibited reduced overnight memory consolidation. Overall, our data show that reward timing affects the dynamics and consolidation of motor learning, a finding that could be exploited in future rehabilitation programs.

Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task

Behavioral data, despite being a common index of cognitive activity, is under scrutiny for having poor reliability as a result of noise or lacking replications of reliable effects. Here, we argue that cognitive modeling can be used to enhance the test-retest reliability of the behavioral measures by recovering individual-level parameters from behavioral data. We tested this empirically with the Probabilistic Stimulus Selection (PSS) task, which is used to measure a participant’s sensitivity to positive or negative reinforcement. An analysis of 400,000 simulations from an Adaptive Control of Thought-Rational (ACT-R) model of this task showed that the poor reliability of the task is due to the instability of the end-estimates: because of the way the task works, the same participants might sometimes end up having apparently opposite scores. To recover the underlying interpretable parameters and enhance reliability, we used a Bayesian Maximum A Posteriori (MAP) procedure. We were able to obtain reliable parameters across sessions (intraclass correlation coefficient ≈ 0.5). A follow-up study on a modified version of the task also found the same pattern of results, with very poor test-retest reliability in behavior but moderate reliability in recovered parameters (intraclass correlation coefficient ≈ 0.4). Collectively, these results imply that this approach can further be used to provide superior measures in terms of reliability, and bring greater insights into individual differences.

The impact of social anxiety on feedback-based go and nogo learning

The term “Pavlovian” bias describes the phenomenon that learning to execute a response to obtain a reward or to inhibit a response to avoid punishment is much easier than learning the reverse. The present study investigated the interplay between this learning bias and individual levels of social anxiety. Since avoidance behavior is a hallmark feature of social anxiety and high levels of social anxiety have been associated with better learning from negative feedback, it is conceivable that the Pavlovian bias is altered in individuals with high social anxiety, with a strong tendency to avoid negative feedback, especially (but not only) in a nogo context. In addition, learning may be modulated by the individual propensity to learn from positive or negative feedback, which can be assessed as a trait-like feature. A sample of 84 healthy university students completed an orthogonalized go/nogo task that decoupled action type (go/nogo) and outcome valence (win/avoid) and a probabilistic selection task based upon which the individual propensity to learn from positive and negative feedback was determined. Self-reported social anxiety and learning propensity were used as predictors in linear mixed-effect model analysis of performance accuracy in the go/nogo task. Results revealed that high socially anxious subjects with a propensity to learn better from negative feedback showed particularly pronounced learning for nogo to avoid while lacking significant learning for nogo to win as well as go to avoid. This result pattern suggests that high levels of social anxiety in concert with negative learning propensity hamper the overcoming of Pavlovian bias in a win context while facilitating response inhibition in an avoidance context. The present data confirm the robust Pavlovian bias in feedback-based learning and add to a growing body of evidence for modulation of feedback learning by individual factors, such as personality traits. Specifically, results show that social anxiety is associated with altered Pavlovian bias, and might suggest that this effect could be driven by altered basal ganglia function primarily affecting the nogo pathway.

Modeling the effects of motivation on choice and learning in the basal ganglia

Decision making relies on adequately evaluating the consequences of actions on the basis of past experience and the current physiological state. A key role in this process is played by the basal ganglia, where neural activity and plasticity are modulated by dopaminergic input from the midbrain. Internal physiological factors, such as hunger, scale signals encoded by dopaminergic neurons and thus they alter the motivation for taking actions and learning. However, to our knowledge, no formal mathematical formulation exists for how a physiological state affects learning and action selection in the basal ganglia. We developed a framework for modelling the effect of motivation on choice and learning. The framework defines the motivation to obtain a particular resource as the difference between the desired and the current level of this resource, and proposes how the utility of reinforcements depends on the motivation. To account for dopaminergic activity previously recorded in different physiological states, the paper argues that the prediction error encoded in the dopaminergic activity needs to be redefined as the difference between utility and expected utility, which depends on both the objective reinforcement and the motivation. We also demonstrate a possible mechanism by which the evaluation and learning of utility of actions can be implemented in the basal ganglia network. The presented theory brings together models of learning in the basal ganglia with the incentive salience theory in a single simple framework, and it provides a mechanistic insight into how decision processes and learning in the basal ganglia are modulated by the motivation. Moreover, this theory is also consistent with data on neural underpinnings of overeating and obesity, and makes further experimental predictions.

Stress effects on learning and feedback-related neural activity depend on feedback delay

Depending on feedback timing, the neural structures involved in learning differ, with the dopamine system including the dorsal striatum and anterior cingulate cortex (ACC) being more important for learning from immediate than delayed feedback. As stress has been shown to promote striatum-dependent learning, the current study aimed to explore if stress differentially affects learning from and processing of immediate and delayed feedback. One group of male participants was stressed using the socially evaluated cold pressor test, and another group underwent a control condition. Subsequently, participants performed a reward learning task with immediate (500 ms) and delayed (6,500 ms) feedback while brain activity was assessed with electroencephalography (EEG). While stress enhanced the accuracy for delayed relative to immediate feedback, it reduced the feedback-related negativity (FRN) valence effect, which is the amplitude difference between negative and positive feedback. For the P300, a reduced valence effect was found in the stress group only for delayed feedback. Frontal theta power was most pronounced for immediate negative feedback and was generally reduced under stress. Moreover, stress reduced associations of FRN and theta power with trial-by-trial accuracy. Associations between stress-induced cortisol increases and EEG components were examined using linear mixed effects analyses, which showed that the described stress effects were accompanied by associations between the stress-induced cortisol increases and feedback processing. The results indicate that stress and cortisol affect different aspects of feedback processing. Instead of an increased recruitment of the dopamine system and the ACC, the results may suggest enhanced salience processing and reduced cognitive control under stress.