Punishment Feedback Impairs Memory and Changes Cortical Feedback-Related Potentials During Motor Learning

The effects of reward and punishment on the performance of ping-pong ball bouncing

Introduction

Reward and punishment modulate behavior. In real-world motor skill learning, reward and punishment have been found to have dissociable effects on optimizing motor skill learning, but the scientific basis for these effects is largely unknown.

Methods

In the present study, we investigated the effects of reward and punishment on the performance of real-world motor skill learning. Specifically, three groups of participants were trained and tested on a ping-pong ball bouncing task for three consecutive days. The training and testing sessions were identical across the three days: participants were trained with their right (dominant) hand each day under conditions of either reward, punishment, or a neutral control condition (neither). Before and after the training session, all participants were tested with their right and left hands without any feedback.

Results

We found that punishment promoted early learning, while reward promoted late learning. Reward facilitated short-term memory, while punishment impaired long-term memory. Both reward and punishment interfered with long-term memory gains. Interestingly, the effects of reward and punishment transferred to the left hand.

Discussion

The results show that reward and punishment have different effects on real-world motor skill learning. The effects change with training and transfer readily to novel contexts. The results suggest that reward and punishment may act on different learning processes and engage different neural mechanisms during real-world motor skill learning. In addition, high-level metacognitive processes may be enabled by the additional reinforcement feedback during real-world motor skill learning. Our findings provide new insights into the mechanisms underlying motor learning, and may have important implications for practical applications such as sports training and motor rehabilitation.

Reinforcement feedback impairs locomotor adaptation and retention

Introduction

Locomotor adaptation is a motor learning process used to alter spatiotemporal elements of walking that are driven by prediction errors, a discrepancy between the expected and actual outcomes of our actions. Sensory and reward prediction errors are two different types of prediction errors that can facilitate locomotor adaptation. Reward and punishment feedback generate reward prediction errors but have demonstrated mixed effects on upper extremity motor learning, with punishment enhancing adaptation, and reward supporting motor memory. However, an in-depth behavioral analysis of these distinct forms of feedback is sparse in locomotor tasks.

Methods

For this study, three groups of healthy young adults were divided into distinct feedback groups [Supervised, Reward, Punishment] and performed a novel locomotor adaptation task where each participant adapted their knee flexion to 30 degrees greater than baseline, guided by visual supervised or reinforcement feedback (Adaptation). Participants were then asked to recall the new walking pattern without feedback (Retention) and after a washout period with feedback restored (Savings).

Results

We found that all groups learned the adaptation task with external feedback. However, contrary to our initial hypothesis, enhancing sensory feedback with a visual representation of the knee angle (Supervised) accelerated the rate of learning and short-term retention in comparison to monetary reinforcement feedback. Reward and Punishment displayed similar rates of adaptation, short-term retention, and savings, suggesting both types of reinforcement feedback work similarly in locomotor adaptation. Moreover, all feedback enhanced the aftereffect of locomotor task indicating changes to implicit learning.

Discussion

These results demonstrate the multi-faceted nature of reinforcement feedback on locomotor adaptation and demonstrate the possible different neural substrates that underly reward and sensory prediction errors during different motor tasks.

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Deep learning methods are increasingly being applied to raw electroencephalogram (EEG) data. However, if these models are to be used in clinical or research contexts, methods to explain them must be developed, and if these models are to be used in research contexts, methods for combining explanations across large numbers of models must be developed to counteract the inherent randomness of existing training approaches. Model visualization-based explainability methods for EEG involve structuring a model architecture such that its extracted features can be characterized and have the potential to offer highly useful insights into the patterns that they uncover. Nevertheless, model visualization-based explainability methods have been underexplored within the context of multichannel EEG, and methods to combine their explanations across folds have not yet been developed. In this study, we present two novel convolutional neural network-based architectures and apply them for automated major depressive disorder diagnosis. Our models obtain slightly lower classification performance than a baseline architecture. However, across 50 training folds, they find that individuals with MDD exhibit higher β power, potentially higher δ power, and higher brain-wide correlation that is most strongly represented within the right hemisphere. This study provides multiple key insights into MDD and represents a significant step forward for the domain of explainable deep learning applied to raw EEG. We hope that it will inspire future efforts that will eventually enable the development of explainable EEG deep learning models that can contribute both to clinical care and novel medical research discoveries.

Punishment Leads to Greater Sensorimotor Learning But Less Movement Variability Compared to Reward

When a musician practices a new song, hitting a correct note sounds pleasant while striking an incorrect note sounds unpleasant. Such reward and punishment feedback has been shown to differentially influence the ability to learn a new motor skill. Recent work has suggested that punishment leads to greater movement variability, which causes greater exploration and faster learning. To further test this idea, we collected 102 participants over two experiments. Unlike previous work, in Experiment 1 we found that punishment did not lead to faster learning compared to reward (n = 68), but did lead to a greater extent of learning. Surprisingly, we also found evidence to suggest that punishment led to less movement variability, which was related to the extent of learning. We then designed a second experiment that did not involve adaptation, allowing us to further isolate the influence of punishment feedback on movement variability. In Experiment 2, we again found that punishment led to significantly less movement variability compared to reward (n = 34). Collectively our results suggest that punishment feedback leads to less movement variability. Future work should investigate whether punishment feedback leads to a greater knowledge of movement variability and or increases the sensitivity of updating motor actions.

Differential effects of reward and punishment on reinforcement-based motor learning and generalization

Reward and punishment have long been recognized as potent modulators of human behavior. Although reinforcement learning is a significant motor learning process, the exact mechanisms underlying how the brain learns movements through reward and punishment are not yet fully understood. Beyond the memory of specific examples, investigating the ability to generalize to new situations offers a better understanding of motor learning. This study hypothesizes that reward and punishment engage qualitatively different motivational systems with different neurochemical and neuroanatomical substrates, which would have differential effects on reinforcement-based motor learning and generalization. To test this hypothesis, two groups of participants learn a motor task in one direction and then relearn the same task in a new direction, receiving only performance-based reward or punishment score feedback. Our findings support our hypothesis, showing that reward led to slower learning but promoted generalization. On the other hand, punishment led to faster learning but impaired generalization. These behavioral differences may be due to different tendencies of movement variability in each group. The punishment group tended to explore more actively than the reward group during the initial learning phase, possibly due to loss aversion. In contrast, the reward group tended to explore more actively than the initial learning phase during the generalization test phase, seemingly recalling the strategy that led to the reward. These results suggest that reward and punishment may engage different neural mechanisms during reinforcement-based motor learning and generalization, with important implications for practical applications such as sports training and motor rehabilitation.NEW & NOTEWORTHY Although reinforcement learning is a significant motor learning process, the mechanisms underlying how the brain learns movements through reward and punishment are not fully understood. We modified a well-established motor adaptation task and used savings (faster relearning) to measure generalization. We found reward led to slower learning but promoted generalization, whereas punishment led to faster learning but impaired generalization, suggesting that reward and punishment may engage different neural mechanisms during reinforcement-based motor learning and generalization.

Using EEG to study sensorimotor adaptation

Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.

Visuomotor errors drive step length and step time adaptation during 'virtual' split-belt walking: the effects of reinforcement feedback

Precise foot placement is dependent on changes in spatial and temporal coordination between two legs in response to a perturbation during walking. Here, we used a 'virtual' split-belt adaptation task to examine the effects of reinforcement (reward and punishment) feedback about foot placement on the changes in error, step length and step time asymmetry. Twenty-seven healthy adults (20 ± 2.5 years) walked on a treadmill with continuous feedback of the foot position and stepping targets projected on a screen, defined by a visuomotor gain for each leg. The paradigm consisted of a baseline period (same gain on both legs), visuomotor adaptation period (split: one high = 'fast', one low = 'slow' gain) and post-adaptation period (same gain). Participants were divided into 3 groups: control group received no score, reward group received increasing score for each target hit, and punishment group received decreasing score for each target missed. Re-adaptation was assessed 24 ± 2 h later. During early adaptation, the slow foot undershot and fast foot overshot the stepping target. Foot placement errors were gradually reduced by late adaptation, accompanied by increasing step length asymmetry (fast < slow step length) and step time asymmetry (fast > slow step time). Only the punishment group showed greater error reduction and step length re-adaptation on the next day. The results show that (1) explicit feedback of foot placement alone drives adaptation of both step length and step time asymmetry during virtual split-belt walking, and (2) specifically, step length re-adaptation driven by visuomotor errors may be enhanced by punishment feedback.

Punishments and rewards both modestly impair visuomotor memory retention

While the effects of rewards on memory appear well documented, the effects of punishments remain uncertain. Based on neuroimaging data, this study tested the hypothesis that, as compared to a neutral condition, a context allowing successful punishment avoidance would enhance memory to a similar extent as rewards. In a fully within-subject and counter-balanced design, participants (n = 18) took part in 3 distinct learning sessions during which the delivery of performance-contingent monetary punishments and rewards was manipulated. Specifically, participants had to reach towards visual targets while compensating for a gradually introduced visual deviation. Accuracy at achieving targets was either punished (Hit: "+0$"; Miss: "-0.5$), rewarded (Hit: "+0.5$"; Miss: "-0$"), or associated with neutral binary feedback (Hit: "Hit"; Miss: "Miss"). Retention was assessed through reach aftereffects both immediately and 24 h after initial acquisition. The results disconfirmed the hypothesis by showing that the punishment and reward learning sessions both impaired retention as compared to the neutral session, suggesting that both types of incentives similarly impaired memory formation and consolidation. Two alternative but complementary interpretations are discussed. One interpretation is that the presence of punishments and rewards induced a negative learning context, which - based on neurobiological data - could have been sufficient to interfere with memory formation and consolidation. Another interpretation is that punishments and rewards emphasized the disrupting effects of target hits on implicit learning processes, therefore yielding retention impairments. Altogether, these results suggest that incentives can have counter-productive effects on memory.

Reward boosts reinforcement-based motor learning

Besides relying heavily on sensory and reinforcement feedback, motor skill learning may also depend on the level of motivation experienced during training. Yet, how motivation by reward modulates motor learning remains unclear. In 90 healthy subjects, we investigated the net effect of motivation by reward on motor learning while controlling for the sensory and reinforcement feedback received by the participants. Reward improved motor skill learning beyond performance-based reinforcement feedback. Importantly, the beneficial effect of reward involved a specific potentiation of reinforcement-related adjustments in motor commands, which concerned primarily the most relevant motor component for task success and persisted on the following day in the absence of reward. We propose that the long-lasting effects of motivation on motor learning may entail a form of associative learning resulting from the repetitive pairing of the reinforcement feedback and reward during training, a mechanism that may be exploited in future rehabilitation protocols.

Acoustic stimulation increases implicit adaptation in sensorimotor adaptation

Sensorimotor adaptation is an important part of our ability to perform novel motor tasks (i.e., learning of motor skills). Efforts to improve adaptation in healthy and clinical patients using non-invasive brain stimulation methods have been hindered by inter-individual and intra-individual variability in brain susceptibility to stimulation. Here, we explore unpredictable loud acoustic stimulation as an alternative method of modulating brain excitability to improve sensorimotor adaptation. In two experiments, participants moved a cursor towards targets, and adapted to a 30º rotation of cursor feedback, either with or without unpredictable acoustic stimulation. Acoustic stimulation improved initial adaptation to sensory prediction errors in Study 1, and improved overnight retention of adaptation in Study 2. Unpredictable loud acoustic stimulation might thus be a potent method of modulating sensorimotor adaptation in healthy adults.