Visuomotor Associative Learning Research Articles

A cerebro-cerebellar network for learning visuomotor associations

Consensus is rapidly building to support a role for the cerebellum beyond motor function, but its contributions to non-motor learning remain poorly understood. Here, we provide behavioral, anatomical and computational evidence to demonstrate a causal role for the primate posterior lateral cerebellum in learning new visuomotor associations. Reversible inactivation of the posterior lateral cerebellum of male monkeys impeded the learning of new visuomotor associations, but had no effect on movement parameters, or on well-practiced performance of the same task. Using retrograde transneuronal transport of rabies virus, we identified a distinct cerebro-cerebellar network linking Purkinje cells in the posterior lateral cerebellum with a region of the prefrontal cortex that is critical in learning visuomotor associations. Together, these results demonstrate a causal role for the primate posterior lateral cerebellum in non-motor, reinforcement learning.

Noise exposure in early adulthood causes age-dependent and brain region-specific impairments in cognitive function.

Hearing loss is a chronic health condition that affects millions of people worldwide. In addition to age-related hearing impairment, excessive noise exposure is a leading cause of hearing loss. Beyond the devastating effects of hearing impairment itself, epidemiological studies have identified hearing loss as a major risk factor for age-related cognitive decline, including dementia. At present, we currently lack a full understanding of the brain regions and underlying molecular changes that are responsible for mediating the link between hearing loss and cognitive impairment across aging. In the present study, we exposed 6-month-old rats to an occupational-like noise (100 dB SPL, 4 h/day × 30 days) or sham exposure and investigated both hippocampal-dependent (i.e., spatial learning and memory, assessed using the Morris water maze) and striatal-dependent (i.e., visuomotor associative learning, assessed using an operant-conditioning task) cognitive function across aging at 7, 10, and 13 months of age. We also investigated brain region-specific changes in microglial expression following noise/sham exposure in order to assess the potential contribution of this cell type to noise-induced cognitive impairments. Consistent with human studies, the occupational-like noise exposure resulted in high-frequency hearing loss, evidenced by a significant increase in hearing thresholds at 20 kHz. Ultimately, our results suggest that not all higher-level cognitive tasks or their associated brain regions appear to be equally susceptible to noise-induced deficits during aging, as the occupational-like noise exposure caused an age-dependent deficit in spatial but not visuomotor associative learning, as well as altered microglial expression in the hippocampus but not the striatum. Interestingly, we found no significant relationships between spatial learning ability and the level of hearing loss or altered microglial density in the hippocampus following noise exposure, suggesting that other changes in the brain likely contribute to hippocampal-dependent cognitive dysfunction following noise exposure. Lastly, we found that a subset of younger animals also showed noise-induced deficits in spatial learning; findings which suggest that noise exposure may represent an increased risk for cognitive impairment in vulnerable subjects. Overall, our findings highlight that even a mild occupational-like noise exposure earlier in adulthood can have long lasting implications for cognitive function later in life.

Impact of Metacognitive and Psychological Factors in Learning-Induced Plasticity of Resting State Networks.

Simple SummaryConnections within the brain can reshape themselves to rapidly adapt to new learning. We aimed to demonstrate that these reconfigurations do not only reflect a memory trace but a more global response to other processes involved in learning. Furthermore, we investigated why individuals do not present the same ability both in learning and in connection plasticity. Present results indicate that brain rapid reconfiguration is not only linked to learning abilities but also to the process of confidence in learning. Factors such as age, education, and anxiety also appear to influence the brain’s response to learning and explain part of the variability observed between subjects. This study revealed important links between brain and psychological functioning and how they influence each other which highlights the need for considering psychological factors both in education and in psychiatric disorders.While resting-state networks are able to rapidly adapt to experiences and stimuli, it is currently unknown whether metacognitive processes such as confidence in learning and psychological temperament may influence this process. We explore the neural traces of confidence in learning and their variability by: (1) targeting rs-networks in which functional connectivity (FC) modifications induced by a learning task were associated either with the participant’s performance or confidence in learning; and (2) investigating the links between FC changes and psychological temperament. Thirty healthy individuals underwent neuropsychological and psychometric evaluations as well as rs-fMRI scans before and after a visuomotor associative learning task. Confidence in learning was positively associated with the degree of FC changes in 11 connections including the cerebellar, frontal, parietal, and subcortical areas. Variability in FC changes was linked to the individual’s level of anxiety sensitivity. The present findings indicate that reconfigurations of resting state networks linked to confidence in learning differ from those linked to learning accuracy. In addition, certain temperament characteristics appear to influence these reconfigurations.

Mixed Selectivity in the Cerebellar Purkinje-Cell Response during Visuomotor Association Learning.

Although the cerebellum has been traditionally considered to be exclusively involved in motor control, recent anatomic and clinical studies show that it also has a role in reward-processing. However, the way in which the movement-related and the reward-related neural activity interact at the level of the cerebellar cortex and contribute toward learning is still unclear. Here, we studied the simple spike activity of Purkinje cells in the mid-lateral cerebellum when 2 male monkeys learned to associate a right or left-hand movement with one of two visual symbolic cues. These cells had distinctly different discharge patterns between an overtrained symbol-hand association and a novel symbol-hand association, responding in association with the movement of both hands, although the kinematics of the movement did not change between the two conditions. The activity change was not related to the pattern of the visual symbols, the movement kinematics, the monkeys' reaction times, or the novelty of the visual symbols. The simple spike activity changed throughout the learning process, but the concurrent complex spikes did not instruct that change. Although these neurons also have reward-related activity, the reward-related and movement-related signals were independent. We suggest that this mixed selectivity may facilitate the flexible learning of difficult reinforcement learning problems.SIGNIFICANCE STATEMENT The cerebellum receives both motor-related and reward-related information. However, it is unclear how these two signals interact at the level of cerebellar cortex and contribute to learning nonmotor skills. Here we show that in the mid-lateral cerebellum, the reward information is encoded independently from the motor information such that during reward-based learning, only the reward information carried by the Purkinje cells inform learning while the motor information remains unchanged with learning.

Post-error Slowing During Instrumental Learning is Shaped by Working Memory-based Choice Strategies

Post-error slowing (PES) – a relative increase in response time for a decision on trialtgiven an error on trialt − 1 – is a well-known effect in studies of human decision-making. Post-error processing is reflected in neural signatures such as reduced activity in sensorimotor regions and increased activity in medial prefrontal cortex. PES is thought to reflect the deployment of executive resources to get task performance back on track. This provides a general account of PES that cuts across perceptual decision-making, memory, and learning tasks. With respect to PES and learning, things are complicated by the fact that learning often reflectsmultiple qualitatively different processes with distinct neural correlates. It is unclear if multiple processes shape PES during learning, or if PES reflects a policy for reacting to errors generated by one particular process (e.g., cortico-striatal reinforcement learning). Here we provide behavioral and computational evidence that PES is influenced by the operation of multiple distinct processes. Human subjects learned a simple visuomotor skill (arbitrary visuomotor association learning) under low load conditionsmore amenable to simple working memory-based strategies, and high load conditions that were putatively more reliant on trial-by-trial reinforcement learning. PES decreased withload, even when the progress of learning (i.e., reinforcement history) was accounted for. This result suggested that PES during learning is influenced by the recruitment of working memory. Indeed, observed PES effects were approximated by a computational model with parallel working memory and reinforcement learning systems that are differentially recruited according to cognitive load.

Modulation of associative learning in the hippocampal-striatal circuit based on item-set similarity

Mounting evidence suggests that the medial temporal lobe (MTL) and striatal learning systems support different forms of learning, which can be competitive or cooperative depending on task demands. We have previously shown how activity in these regions can be modulated in a conditional visuomotor associative learning task based on the consistency of response mappings or reward feedback (Mattfeld & Stark, 2015). Here, we examined the shift in learning towards the MTL and away from the striatum by placing strong demands on pattern separation, a process of orthogonalizing similar inputs into distinct representations. Mnemonically, pattern separation processes have been shown to rely heavily on processing in the hippocampus. Therefore, we predicted modulation of hippocampal activity by pattern separation demands, but no such modulation of striatal activity. Using a variant of the conditional visuomotor associative learning task that we have used previously, we presented participants with two blocked conditions: items with high and low perceptual overlap during functional magnetic resonance imaging (fMRI). As predicted, we observed learning-related activity in the hippocampus, which was greater in the high than the low overlap condition, particularly in the dentate gyrus. In contrast, the associative striatum also showed learning related activity, but it was not modulated by overlap condition. Using functional connectivity analyses, we showed that the correlation between the hippocampus and dentate gyrus with the associative striatum was differentially modulated by high vs. low overlap, suggesting that the coordination between these regions was affected when pattern separation demands were high. These findings contribute to a growing literature that suggests that the hippocampus and striatal network both contribute to the learning of arbitrary associations that are computationally distinct and can be altered by task demands.

Strength of resting-state functional connectivity associated with performance-adjustment ability

Erroneous behavior is usually, although not always, inhibited following a negative outcome (e.g., a penalty), although this adjusting behavior is highly varied. Here we aimed to identify brain regions associated with successful behavioral adjustment to negative feedback, and the intrinsic functional connectivity associated with individual variability in such adjustments, using combined task-based and resting-state functional magnetic resonance imaging (MRI). Functional MRI data were obtained from 28 young adults performing a visuomotor associative learning task, wherein participants learned by trial and error to make one of four key responses to each of 24 English letters. All preceding error response trials were sorted post hoc, based on whether the error response was repeated (Error-Repeated) or not (Error-Changed) for the subsequent trial with the same stimulus, and the rate of error adjustment for each individual was computed as the number of Error-Changed trials divided by all error trials. We identified two brain regions, the right dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC), whose brain response was significantly greater for Error-Changed than Error-Repeated trials. Stronger anti-correlation between the right dACC seed and right amygdala and between the DLPFC seed and the paracentral gyrus and inferior temporal region extending to the hippocampus was associated with better adjustment ability. These results suggest that the stronger anticorrelated relationship between the error monitoring region and emotional processing and that between the executive control region with those involved in memory or default mode network reflect individual variability in error adjustment.

Neural oscillations during conditional associative learning

Associative learning requires mapping between complex stimuli and behavioural responses. When multiple stimuli are involved, conditional associative learning is a gradual process with learning based on trial and error. It is established that a distributed network of regions track associative learning, however the role of neural oscillations in human learning remains less clear. Here we used scalp EEG to test how neural oscillations change during learning of arbitrary visuo-motor associations. Participants learned to associative 48 different abstract shapes to one of four button responses through trial and error over repetitions of the shapes. To quantify how well the associations were learned for each trial, we used a state-space computational model of learning that provided a probability of each trial being correct given past performance for that stimulus, that we take as a measure of the strength of the association. We used linear modelling to relate single-trial neural oscillations to single-trial measures of association strength. We found frontal midline theta oscillations during the delay period tracked learning, where theta activity was strongest during the early stages of learning and declined as the associations were formed. Further, posterior alpha and low-beta oscillations in the cue period showed strong desynchronised activity early in learning, while stronger alpha activity during the delay period was seen as associations became well learned. Moreover, the magnitude of these effects during early learning, before the associations were learned, related to improvements in memory seen on the next presentation of the stimulus. The current study provides clear evidence that frontal theta and posterior alpha/beta oscillations play a key role during associative memory formation.

BDNF Val66Met Polymorphism Influences Visuomotor Associative Learning and the Sensitivity to Action Observation.

Motor representations in the human mirror neuron system are tuned to respond to specific observed actions. This ability is widely believed to be influenced by genetic factors, but no study has reported a genetic variant affecting this system so far. One possibility is that genetic variants might interact with visuomotor associative learning to configure the system to respond to novel observed actions. In this perspective, we conducted a candidate gene study on the Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism, a genetic variant linked to motor learning in regions of the mirror neuron system, and tested the effect of this polymorphism on motor facilitation and visuomotor associative learning. In a single-pulse TMS study carried on 16 Met (Val/Met and Met/Met) and 16 Val/Val participants selected from a large pool of healthy volunteers, Met participants showed significantly less muscle-specific corticospinal sensitivity during action observation, as well as reduced visuomotor associative learning, compared to Val homozygotes. These results are the first evidence of a genetic variant tuning sensitivity to action observation and bring to light the importance of considering the intricate relation between genetics and associative learning in order to further understand the origin and function of the human mirror neuron system.

Functional contributions and interactions between the human hippocampus and subregions of the striatum during arbitrary associative learning and memory.

The hippocampus and striatum are thought to have different functional roles in learning and memory. It is unknown under what experimental conditions their contributions are dissimilar or converge, and the extent to which they interact over the course of learning. In order to evaluate both the functional contributions of as well as the interactions between the human hippocampus and striatum, the present study used high-resolution functional magnetic resonance imaging (fMRI) and variations of a conditional visuomotor associative learning task that either taxed arbitrary associative learning (Experiment 1) or stimulus-response learning (Experiment 2). In the first experiment, we observed changes in activity in the hippocampus and anterior caudate that reflect differences between the two regions consistent with distinct computational principles. In the second experiment, we observed activity in the putamen that reflected content specific representations during the learning of arbitrary conditional visuomotor associations. In both experiments, the hippocampus and ventral striatum demonstrated dynamic functional coupling during the learning of new arbitrary associations, but not during retrieval of well-learned arbitrary associations using control variants of the tasks that did not preferentially tax one system versus the other. These findings suggest that both the hippocampus and subregions of the dorsal striatum contribute uniquely to the learning of arbitrary associations while the hippocampus and ventral striatum interact over the course of learning.

Advanced Parkinson's disease effect on goal-directed and habitual processes involved in visuomotor associative learning

The present behavioral study re-addresses the question of habit learning in Parkinson's disease (PD). Patients were early onset, non-demented, dopa-responsive, candidates for surgical treatment, similar to those we found earlier as suffering greater dopamine depletion in the putamen than in the caudate nucleus. The task was the same conditional associative learning task as that used previously in monkeys and healthy humans to unveil the striatum involvement in habit learning. Sixteen patients and 20 age- and education-matched healthy control subjects learned sets of 3 visuo-motor associations between complex patterns and joystick displacements during two testing sessions separated by a few hours. We distinguished errors preceding vs. following the first correct response to compare patients' performance during the earliest phase of learning dominated by goal-directed actions with that observed later on, when responses start to become habitual. The disease significantly retarded both learning phases, especially in patients under 60 years of age. However, only the late phase deficit was disease severity-dependent and persisted on the second testing session. These findings provide the first corroboration in Parkinson patients of two ideas well-established in the animal literature. The first is the idea that associating visual stimuli to motor acts is a form of habit learning that engages the striatum. It is confirmed here by the global impairment in visuo-motor learning induced by PD. The second idea is that goal-directed behaviors are predominantly caudate-dependent whereas habitual responses are primarily putamen-dependent. At the advanced PD stages tested here, dopamine depletion is greater in the putamen than in the caudate nucleus. Accordingly, the late phase of learning corresponding to the emergence of habitual responses was more vulnerable to the disease than the early phase dominated by goal-directed actions.

Visuomotor Representations within the Human Primary Motor Cortex: The Elusive Markers of Visuomotor Associative Learning

What is the nature of representations within the human primary motor cortex (M1)? Early primate studies proposed that M1 is mainly involved in encoding muscle force, i.e., intrinsic aspects of movement ([Evarts, 1968][1]). Later studies involving single-cell recordings in primates suggested that

Striatal and Medial Temporal Lobe Functional Interactions during Visuomotor Associative Learning

A network of regions including the medial temporal lobe (MTL) and the striatum are integral to visuomotor associative learning. Here, we evaluated the contributions of the structures of the striatum and the MTL, as well as their interactions during an arbitrary associative learning task. We hypothesized that activity in the striatum would correlate with the rate of learning, while activity in the MTL would track how well associations were learned. Further, we expected functional correlations to show both facilitative as well as competitive relationships depending on the regions involved. Results showed that activity throughout the striatum was modulated by the rate of learning, while the sensorimotor and ventral striatum were also modulated by probability correct. Across the MTL, activity correlated with the probability of being correct, while the perirhinal cortex and right parahippocampal cortex were modulated by the rate of learning. The activity in the ventral striatum robustly coupled with activity in the MTL during learning, while interactions between the associative striatum and the MTL showed the opposite pattern. These findings suggest dissociable computational roles for different subregions of the striatum and MTL. These subregions interact in distinct ways, perhaps forming functionally integrated networks during the learning of arbitrary associations.

Temporal context and conditional associative learning

BackgroundWe investigated how temporal context affects the learning of arbitrary visuo-motor associations. Human observers viewed highly distinguishable, fractal objects and learned to choose for each object the one motor response (of four) that was rewarded. Some objects were consistently preceded by specific other objects, while other objects lacked this task-irrelevant but predictive context.ResultsThe results of five experiments showed that predictive context consistently and significantly accelerated associative learning. A simple model of reinforcement learning, in which three successive objects informed response selection, reproduced our behavioral results.ConclusionsOur results imply that not just the representation of a current event, but also the representations of past events, are reinforced during conditional associative learning. In addition, these findings are broadly consistent with the prediction of attractor network models of associative learning and their prophecy of a persistent representation of past objects.

Neurodynamics of the Prefrontal Cortex during Conditional Visuomotor Associations

The prefrontal cortex is believed to be important for cognitive control, working memory, and learning. It is known to play an important role in the learning and execution of conditional visuomotor associations, a cognitive task in which stimuli have to be associated with actions by trial-and-error learning. In our modeling study, we sought to integrate several hypotheses on the function of the prefrontal cortex using a computational model, and compare the results to experimental data. We constructed a module of prefrontal cortex neurons exposed to two different inputs, which we envision to originate from the inferotemporal cortex and the basal ganglia. We found that working memory properties do not describe the dominant dynamics in the prefrontal cortex, but the activation seems to be transient, probably progressing along a pathway from sensory to motor areas. During the presentation of the cue, the dynamics of the prefrontal cortex is bistable, yielding a distinct activation for correct and error trails. We find that a linear change in network parameters relates to the changes in neural activity in consecutive correct trials during learning, which is important evidence for the underlying learning mechanisms.

Neurodynamics of the Prefrontal Cortex during Conditional Visuomotor Associations

The prefrontal cortex is believed to be important for cognitive control, working memory, and learning. It is known to play an important role in the learning and execution of conditional visuomotor associations, a cognitive task in which stimuli have to be associated with actions by trial-and-error learning. In our modeling study, we sought to integrate several hypotheses on the function of the prefrontal cortex using a computational model, and compare the results to experimental data. We constructed a module of prefrontal cortex neurons exposed to two different inputs, which we envision to originate from the inferotemporal cortex and the basal ganglia. We found that working memory properties do not describe the dominant dynamics in the prefrontal cortex, but the activation seems to be transient, probably progressing along a pathway from sensory to motor areas. During the presentation of the cue, the dynamics of the prefrontal cortex is bistable, yielding a distinct activation for correct and error trails. We find that a linear change in network parameters relates to the changes in neural activity in consecutive correct trials during learning, which is important evidence for the underlying learning mechanisms.

Is the cerebellum involved in the visuo-locomotor associative learning?

The role played by cerebellar circuits in visuo-motor associative learning is still unclear. The aim of the present study was to analyse cerebellar involvement using a visuo-locomotor associative learning paradigm that did not require spatial competences. Hemicerebellectomized (HCbed) and Control rats were tested in a visual discrimination task. First, both groups of rats had to learn that a reward was associated with an object that had a specific colour and shape (Experiment 1). Then, the shape but not the colour of the rewarded object was modified to verify whether the animals were able to transfer the rule of rewarding or whether they had to acquire a new association (Experiment 2). In the first sessions of the Experiment 1, HCbed animals displayed a tendency toward peripheral circling and a delay of about three sessions in reaching the criterion of correct choices compared to Controls. This delay has to be correlated to the need to overcome the procedural impairment elicited by the HCb. Once the HCbed animals put efficient procedural abilities into action, they exhibited a similar increase in percentages of successes from the fourth session onward as Controls. The results of Experiment 2 confirm the intact associative abilities of HCbed animals, as demonstrated by their progressive increase in successful associative responses, which, at the end of the transfer phase, were not significantly different from those of the Control group. The present findings indicate that the presence of a cerebellar lesion delays but does not prevent visuo-locomotor associative learning and that stimulus generalisation is performed without difficulty even in the presence of a cerebellar lesion.

Concurrent task performance enhances low-level visuomotor learning

Visuomotor association learning involves learning to make a motor response to an arbitrary visual stimulus. This learning is essential for visual search and discrimination performance and is reliant upon a well-defined neural circuit in the brain that includes the prefrontal cortex and the hippocampal formation. In the present study, we investigated the possible role of attentional processes during such learning using dual-task interference. A motor, verbal, or perceptual concurrent task was performed during the learning/training block of a simple visual discrimination task. Contrary to expectation, the dual-task groups showed improved learning and learning-dependent performance compared with untrained control and non-dual-task trained groups. A second experiment revealed that this effect did not appear to be due to increased arousal level; the inclusion of alerting tones during learning did not result in facilitation. These findings suggest that the engagement of attention, but not arousal, during the acquisition of a visuomotor association can facilitate this learning and its expression.

Neuronal Modifications During Visuomotor Association Learning Assessed by Electric Brain Tomography

In everyday life specific situations need specific reactions. Through repetitive practice, such stimulus-response associations can be learned and performed automatically. The aim of the present EEG study was the illustration of learning dependent modifications in neuronal pathways during short-term practice of visuomotor associations. Participants performed a visuomotor association task including four visual stimuli, which should be associated with four keys, learned by trial and error. We assumed that distinct cognitive processes might be dominant during early learning e.g., visual perception and decision making. Advanced learning, however, might be indicated by increased neuronal activation in integration- and memory-related regions. For assessment of learning progress, visual- and movement-related brain potentials were measured and compared between three learning stages (early, intermediate, and late). The results have revealed significant differences between the learning stages during distinct time intervals. Related to visual stimulus presentation, Low Resolution Electromagnetic Brain Tomography (LORETA) revealed strong neuronal activation in a parieto-prefrontal network in time intervals between 100-400 ms post event and during early learning. In relation to the motor response neuronal activation was significantly increased during intermediate compared to early learning. Prior to the motor response (120-360 ms pre event), neuronal activation was detected in the cingulate motor area and the right dorsal premotor cortex. Subsequent to the motor response (68-430 ms post event) there was an increase in neuronal activation in visuomotor- and memory-related areas including parietal cortex, SMA, premotor, dorsolateral prefrontal, and parahippocampal cortex. The present study has shown specific time elements of a visuomotor-memory-related network, which might support learning progress during visuomotor association learning.

Cue familiarity is represented in monkey medial prefrontal cortex during visuomotor association learning

To examine functional roles of the medial prefrontal cortex (mPFC) in visuomotor association learning, neuronal activity in the mPFC of a behaving monkey was recorded during this learning. The monkey was presented a cueing visual stimulus, and required to push, pull or turn a manipulator according to the cue following a delay period. Under the control condition, three cues (circle, triangle and square) instructed the monkey to the three responses in a block of trials. After 2 months of training the animal was familiar with these cue-response associations. Under the learning condition, two of the three familiar cues and one novel cue were presented in a block. The monkey initially did not know what the novel cue instructed at first and learned a new cue-response association by trial and error. Neurons in the mPFC showed marked responses to cue presentation, and cue responses changed depending on whether cues were familiar or novel. A group of mPFC neurons responded to novel cues, but not to familiar cues. Another group of neurons responded to familiar cues, but not to novel cues. In a subgroup of these familiar cue-selective neurons, cue response was increased under the learning condition compared to the control condition. These results suggest that mPFC neurons differentiate between familiar and novel instructions, and that the neurons responsive to familiar stimuli enhance their modulations when both familiar and novel instructions have to be processed during task performance.