The relationship between habits and motor skills in humans

Role of dopamine in reward expectation and predictability during execution of action sequences

Reward-associated cues serve different functions depending on whether they precede or terminate action sequences. Cues that precede action sequences and signal opportunity for reward could serve as GO signals to initiate the sequence, whereas sequence termination cues could serve as response feedback by signaling reward delivery. Reward expectation during sequence execution depends on these cues and might condition whether behavior is habitual or goal-directed. However, it remains unknown how sequence initiation and termination cues differentially affect reward expectation and contribute to habit learning. Further, while mesolimbic dopamine plays a key role in cue-induced reward expectation and sequence learning, how dynamic changes in dopamine signals differ depending on the response strategy is unclear. Here, we determined how mesolimbic DA signals change over training during cue-mediated sequence learning, depending on the type of cue and the nature of behavioral control. We found sequence initiation and termination cues differentially affect reward expectation during action sequences, with the termination cue contributing to habit and automaticity. Distinct response strategies induced by sequence initiation and termination cues induced differential changes in mesolimbic DA signals that captured variations in reward expectation along sequence execution. Notably, habit-like behavior induced by the sequence termination cue was associated with a rapid shift in DA signals from reward retrieval to the cue. This habit-like behavior was reflected in behavioral inflexibility and attenuated DA reward prediction error signals. Finally, using optogenetics, we provide evidence that phasic DA activity elicited by the sequence termination cue is critical for the development of habit-like behavior.

Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement

Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

De novo sensorimotor learning through reuse of movement components

From tying one's shoelaces to driving a car, complex skills involving the coordination of multiple muscles are common in everyday life; yet relatively little is known about how these skills are learned. Recent studies have shown that new sensorimotor skills involving re-mapping familiar body movements to unfamiliar outputs cannot be learned by adjusting pre-existing controllers, and that new task-specific controllers must instead be learned "de novo". To date, however, few studies have investigated de novo learning in scenarios requiring continuous and coordinated control of relatively unpractised body movements. In this study, we used a myoelectric interface to investigate how a novel controller is learned when the task involves an unpractised combination of relatively untrained continuous muscle contractions. Over five sessions on five consecutive days, participants learned to trace a series of trajectories using a computer cursor controlled by the activation of two muscles. The timing of the generated cursor trajectory and its shape relative to the target improved for conditions trained with post-trial visual feedback. Improvements in timing transferred to all untrained conditions, but improvements in shape transferred less robustly to untrained conditions requiring the trained order of muscle activation. All muscle outputs in the final session could already be generated during the first session, suggesting that participants learned the new task by improving the selection of existing motor commands. These results suggest that the novel controllers acquired during de novo learning can, in some circumstances, be constructed from components of existing controllers.

Acquiring musculoskeletal skills with curriculum-based reinforcement learning

Efficient musculoskeletal simulators and powerful learning algorithms provide computational tools to tackle the grand challenge of understanding biological motor control. Our winning solution for the inaugural NeurIPS MyoChallenge leverages an approach mirroring human skill learning. Using a novel curriculum learning approach, we trained a recurrent neural network to control a realistic model of the human hand with 39 muscles to rotate two Baoding balls in the palm of the hand. In agreement with data from human subjects, the policy uncovers a small number of kinematic synergies, even though it is not explicitly biased toward low-dimensional solutions. However, selectively inactivating parts of the control signal, we found that more dimensions contribute to the task performance than suggested by traditional synergy analysis. Overall, our work illustrates the emerging possibilities at the interface of musculoskeletal physics engines, reinforcement learning, and neuroscience to advance our understanding of biological motor control.

Practice Reshapes the Geometry and Dynamics of Task-tailored Representations

Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (e.g., low-level features, stimulus-response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (e.g., power law of practice). To resolve this controversy, we built on recent theoretical advances in understanding computations through neural population dynamics. Specifically, we hypothesized that practice optimizes the neural representational geometry of task representations to minimally separate the highest-level task contingencies needed for successful performance. This involves efficiently reaching conjunctive neural states that integrate task-critical features nonlinearly while abstracting over non-critical dimensions. To test this hypothesis, human participants (n = 40) engaged in extensive practice of a simple, context-dependent action selection task over 3 days while recording EEG. During initial rapid improvement in task performance, representations of the highest-level, context-specific conjunctions of task-features were enhanced as a function of the number of successful episodes. Crucially, only enhancement of these conjunctive representations, and not lower-order representations, predicted the power-law improvement in performance. Simultaneously, over sessions, these conjunctive neural states became more stable earlier in time and more aligned, abstracting over redundant task features, which correlated with offline performance gain in reducing switch costs. Thus, practice optimizes the dynamic representational geometry as task-tailored neural states that minimally tesselate the task space, taming their high-dimensionality.

The role of training variability for model-based and model-free learning of an arbitrary visuomotor mapping

A fundamental feature of the human brain is its capacity to learn novel motor skills. This capacity requires the formation of vastly different visuomotor mappings. Using a grid navigation task, we investigated whether training variability would enhance the flexible use of a visuomotor mapping (key-to-direction rule), leading to better generalization performance. Experiments 1 and 2 show that participants trained to move between multiple start-target pairs exhibited greater generalization to both distal and proximal targets compared to participants trained to move between a single pair. This finding suggests that limited variability can impair decisions even in simple tasks without planning. In addition, during the training phase, participants exposed to higher variability were more inclined to choose options that, counterintuitively, moved the cursor away from the target while minimizing its actual distance under the constrained mapping, suggesting a greater engagement in model-based computations. In Experiments 3 and 4, we showed that the limited generalization performance in participants trained with a single pair can be enhanced by a short period of variability introduced early in learning or by incorporating stochasticity into the visuomotor mapping. Our computational modeling analyses revealed that a hybrid model between model-free and model-based computations with different mixing weights for the training and generalization phases, best described participants' data. Importantly, the differences in the model-based weights between our experimental groups, paralleled the behavioral findings during training and generalization. Taken together, our results suggest that training variability enables the flexible use of the visuomotor mapping, potentially by preventing the consolidation of habits due to the continuous demand to change responses.

Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval

Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.

Rethinking dopamine-guided action sequence learning

As opposed to those requiring a single action for reward acquisition, tasks necessitating action sequences demand that animals learn action elements and their sequential order and sustain the behaviour until the sequence is completed. With repeated learning, animals not only exhibit precise execution of these sequences but also demonstrate enhanced smoothness and efficiency. Previous research has demonstrated that midbrain dopamine and its major projection target, the striatum, play crucial roles in these processes. Recent studies have shown that dopamine from the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) serve distinct functions in action sequence learning. The distinct contributions of dopamine also depend on the striatal subregions, namely the ventral, dorsomedial and dorsolateral striatum. Here, we have reviewed recent findings on the role of striatal dopamine in action sequence learning, with a focus on recent rodent studies.

Influence of conventional driving habits on takeover performance in joystick-controlled autonomous vehicles: A low-speed field experiment

Takeover is a critical factor in the safety of autonomous driving. Takeover refers to the action of a human driver assuming control of an autonomous vehicle from its automated driving system. This can occur when the vehicle encounters a situation it cannot handle, when the system requests the driver to take control, or when the driver chooses to intervene for safety or other reasons. This study explored how traditional steering-wheel driving habits affect takeover performance in joystick-controlled autonomous vehicles. We conducted an experiment using a joystick-controlled Dongfeng Sharing-VAN autonomous vehicle in a low-speed campus environment. The participants were divided into three groups based on their driving experience: the individuals who have no licence and no experience (NN Group), the drivers who have licence but not experienced (HN Group), and the drivers who have licence and have been experienced (HH Group), representing varying levels of driving habits. The experiment focused on two takeover tasks: passive takeover and active takeover. We evaluated takeover performance using takeover time and takeover quality as key metrics. The results from the passive takeover task indicated that traditional driving habits had a significant negative impact on takeover performance. The HH Group took 2.65 s longer to complete the task compared to the NN Group, while the HN Group took 3.78 s longer. When we analyzed takeover time in stages, the initial stage showed the most significant difference in takeover time among the three groups. In the active takeover task, driving habits did not significantly affect takeover braking in front of obstacles in a low-speed driving environment. These findings suggest that conventional driving habits can hinder passive takeover in joystick-controlled autonomous vehicles. This insight can be valuable for developing training programs and guidelines for drivers transitioning from conventional to autonomous driving.

Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila

Background

Motor learning is central to human existence, such as learning to speak or walk, sports moves, or rehabilitation after injury. Evidence suggests that all forms of motor learning share an evolutionarily conserved molecular plasticity pathway. Here, we present novel insights into the neural processes underlying operant self-learning, a form of motor learning in the fruit fly Drosophila.

Methods

We operantly trained wild type and transgenic Drosophila fruit flies, tethered at the torque meter, in a motor learning task that required them to initiate and maintain turning maneuvers around their vertical body axis (yaw torque). We combined this behavioral experiment with transgenic peptide expression, CRISPR/Cas9-mediated, spatio-temporally controlled gene knock-out and confocal microscopy.

Results

We find that expression of atypical protein kinase C (aPKC) in direct wing steering motoneurons co-expressing the transcription factor FoxP is necessary for this type of motor learning and that aPKC likely acts via non-canonical pathways. We also found that it takes more than a week for CRISPR/Cas9-mediated knockout of FoxP in adult animals to impair motor learning, suggesting that adult FoxP expression is required for operant self-learning.

Conclusions

Our experiments suggest that, for operant self-learning, a type of motor learning in Drosophila, co-expression of atypical protein kinase C (aPKC) and the transcription factor FoxP is necessary in direct wing steering motoneurons. Some of these neurons control the wing beat amplitude when generating optomotor responses, and we have discovered modulation of optomotor behavior after operant self-learning. We also discovered that aPKC likely acts via non-canonical pathways and that FoxP expression is also required in adult flies.

Motor Learning Following Stroke: Mechanisms of Learning and Techniques to Augment Neuroplasticity

Sensorimotor impairments are common after stroke requiring stroke survivors to relearn lost motor skills or acquire new ones in order to engage in daily activities. Thus, motor skill learning is a cornerstone of stroke rehabilitation. This article provides an overview of motor control and learning theories that inform stroke rehabilitation interventions, discusses principles of neuroplasticity, and provides a summary of practice conditions and techniques that can be used to augment motor learning and neuroplasticity in stroke rehabilitation.

Dual-tasking modulates movement speed but not value-based choices during walking

Value-based decision-making often occurs in multitasking scenarios relying on both cognitive and motor processes. Yet, laboratory experiments often isolate these processes, thereby neglecting potential interactions. This isolated approach reveals a dichotomy: the cognitive process by which reward influences decision-making is capacity-limited, whereas the influence of motor cost is free of such constraints. If true, dual-tasking should predominantly impair reward processing but not affect the impact of motor costs. To test this hypothesis, we designed a decision-making task in which participants made choices to walk toward targets for rewards while navigating past an obstacle. The motor cost to reach these rewards varied in real-time. Participants either solely performed the decision-making task, or additionally performed a secondary pitch-recall task. Results revealed that while both reward and motor costs influenced decision-making, the secondary task did not affect these factors. Instead, dual-tasking slowed down participants' walking, thereby reducing the overall reward rate. Hence, contrary to the prediction that the added cognitive demand would affect the weighing of reward or motor cost differentially, these processes seem to be maintained at the expense of slowing down the motor system. This slowdown may be indicative of interference at the locomotor level, thereby underpinning motor-cognitive interactions during decision-making.

Characterizing Human Habits in the Lab

Habits pose a fundamental puzzle for those aiming to understand human behavior. They pervade our everyday lives and dominate some forms of psychopathology but are extremely hard to elicit in the lab. In this Registered Report, we developed novel experimental paradigms grounded in computational models, which suggest that habit strength should be proportional to the frequency of behavior and, in contrast to previous research, independent of value. Specifically, we manipulated how often participants performed responses in two tasks varying action repetition without, or separately from, variations in value. Moreover, we asked how this frequency-based habitization related to value-based operationalizations of habit and self-reported propensities for habitual behavior in real life. We find that choice frequency during training increases habit strength at test and that this form of habit shows little relation to value-based operationalizations of habit. Our findings empirically ground a novel perspective on the constituents of habits and suggest that habits may arise in the absence of external reinforcement. We further find no evidence for an overlap between different experimental approaches to measuring habits and no associations with self-reported real-life habits. Thus, our findings call for a rigorous reassessment of our understanding and measurement of human habitual behavior in the lab.

Making and breaking habits: Revisiting the definitions and behavioral factors that influence habits in animals

Habits have garnered significant interest in studies of associative learning and maladaptive behavior. However, habit research has faced scrutiny and challenges related to the definitions and methods. Differences in the conceptualizations of habits between animal and human studies create difficulties for translational research. Here, we review the definitions and commonly used methods for studying habits in animals and humans and discuss potential alternative ways to assess habits, such as automaticity. To better understand habits, we then focus on the behavioral factors that have been shown to make or break habits in animals, as well as potential mechanisms underlying the influence of these factors. We discuss the evidence that habitual and goal-directed systems learn in parallel and that they seem to interact in competitive and cooperative manners. Finally, we draw parallels between habitual responding and compulsive drug seeking in animals to delineate the similarities and differences in these behaviors.

A goal-centric outlook on learning

Goals play a central role in human cognition. However, computational theories of learning and decision-making often take goals as given. Here, we review key empirical findings showing that goals shape the representations of inputs, responses, and outcomes, such that setting a goal crucially influences the central aspects of any learning process: states, actions, and rewards. We thus argue that studying goal selection is essential to advance our understanding of learning. By following existing literature in framing goal selection within a hierarchy of decision-making problems, we synthesize important findings on the principles underlying goal value attribution and exploration strategies. Ultimately, we propose that a goal-centric perspective will help develop more complete accounts of learning in both biological and artificial agents.

Model-free decision making resists improved instructions and is enhanced by stimulus-response associations

Human behaviour may be thought of as supported by two different computational-learning mechanisms, model-free and model-based respectively. In model-free strategies, stimulus-response associations are strengthened when actions are followed by a reward and weakened otherwise. In model-based learning, previous to selecting an action, the current values of the different possible actions are computed based on a detailed model of the environment. Previous research with the two-stage task suggests that participants' behaviour usually shows a mixture of both strategies. But, interestingly, a recent study by da Silva and Hare (2020) found that participants primarily deploy model-based behaviour when they are given detailed instructions about the structure of the task. In the present study, we reproduce this essential experiment. Our results confirm that improved instructions give rise to a stronger model-based component. Crucially, we also found a significant effect of reward that became stronger under conditions that favoured the development of strong stimulus-response associations. This suggests that the effect of reward, often taken as indicator of a model-free component, is related to stimulus-response learning.

The Impact of Emotions on Habitual Inhibition

Emotional information prioritizes human behavior. How much emotions influence ongoing behavior critically depends on the extent of executive control functions in a given context. One form of executive control is based on stimulus-stop associations (i.e., habitual inhibition) that rapidly and effortlessly elicits control over the interruption of ongoing behavior. So far, no behavioral accounts have explored the emotional impact on habitual inhibition. We aimed to examine the emotional modulation on habitual inhibition and associated psycho-physiological changes. A go/no-go association task asked participants to learn stimulus-stop and stimulus-response associations during 10-day training to form habitual inhibition (without emotional interference). Probabilistic feedback guided learning with varying probabilities of congruent feedback, generating stronger versus weaker pairings. A reversal test measured habitual inhibition strength counteracted by emotional cues (high-arousal positive and negative stimuli compared with neutral ones). Our training protocol induced stable behavioral and psycho-physiological responses compatible with habitual behavior. At reversal, habitual inhibition was evident as marked by significant speed costs of reversed no-go trials for strongly associated stimuli. Positive and negative emotional cues produced larger impact on habitual inhibition. We report first evidence on a cognitive control mechanism that is vulnerable to emotional stimuli and suggest alternative explanations on how emotions may boost or counteract certain behavioral abnormalities mediated by habitual inhibition.

Conscious awareness of others' actions during observational learning does not benefit motor skill performance

The conscious awareness of motor success during motor learning has recently been revealed as a learning factor. In these studies, participants had to learn a motor sequence and to detect when they assumed the execution had reached a maximal fluidity. The consciousness groups showed better motor performance during a delayed post-training test than the non-consciousness control groups. Based on the "similar mechanism" hypothesis between observational and physical practice, we tested this beneficial effect of the conscious awareness of action in an observational learning context. In the present study, two groups learned a motor sequence task by observing a videotaped human model performing the task. However, only the consciousness group had to detect the maximal fluidity of the learning human model during observational practice. Unpredictably, no difference was detected between groups during the post-training test. However, the consciousness group outperformed the non-consciousness control group for tests that assessed the motor knowledges.

Revealing abrupt transitions from goal-directed to habitual behavior

A fundamental tenet of animal behavior is that decision-making involves multiple 'controllers.' Initially, behavior is goal-directed, driven by desired outcomes, shifting later to habitual control, where cues trigger actions independent of motivational state. Clark Hull's question from 1943 still resonates today: "Is this transition abrupt, or is it gradual and progressive?"1 Despite a century-long belief in gradual transitions, this question remains unanswered2,3 as current methods cannot disambiguate goal-directed versus habitual control in real-time. Here, we introduce a novel 'volitional engagement' approach, motivating animals by palatability rather than biological need. Offering less palatable water in the home cage4,5 reduced motivation to 'work' for plain water in an auditory discrimination task when compared to water-restricted animals. Using quantitative behavior and computational modeling6, we found that palatability-driven animals learned to discriminate as quickly as water-restricted animals but exhibited state-like fluctuations when responding to the reward-predicting cue-reflecting goal-directed behavior. These fluctuations spontaneously and abruptly ceased after thousands of trials, with animals now always responding to the reward-predicting cue. In line with habitual control, post-transition behavior displayed motor automaticity, decreased error sensitivity (assessed via pupillary responses), and insensitivity to outcome devaluation. Bilateral lesions of the habit-related dorsolateral striatum7 blocked transitions to habitual behavior. Thus, 'volitional engagement' reveals spontaneous and abrupt transitions from goal-directed to habitual behavior, suggesting the involvement of a higher-level process that arbitrates between the two.

Reflecting on what is "skill" in human motor skill learning

Humans have an exceptional ability to execute a variety of skilled movements. Researchers have been long interested in understanding behavioral and neurophysiological basis of human motor skill learning for advancing both fundamental neuroscientific knowledge and clinical outcomes. However, despite decades of work in this field there is a lack of consensus about what is meant by "skill" in skill learning. With an advent of various task paradigms testing human motor behavior and increasing heterogeneity in motor learning assessments methods, it is very crucial to identify key features of skill in order to avoid any ambiguity that may result in misinterpretation or over-generalization of findings, which could have serious implications for replication and translational research. In this review, we attempt to highlight the features of skill following a historical approach, considering the seminal work that led to the first definitions of skill and including some contemporary concepts emerging from human motor learning research. Overall, based on this literature, we emphasize that skill has some fundamental characteristics, such as- (i) optimal movement selection and execution, (ii) improved movement speed and accuracy, and (iii) reduced movement variability and error. These features of skill can emerge as a consequence of extensive practice/training/learning, thus resulting in an improved performance state beyond baseline levels. Finally we provide some examples of model tasks that can appropriately capture these features of skill, and conclude that any neuroscientific endeavor aimed at understanding the essence of skill in human motor skill learning should focus on these aspects.

Effective practice and instruction: A skill acquisition framework for excellence

We revisit an agenda that was outlined in a previous paper in this journal focusing on the importance of skill acquisition research in enhancing practice and instruction in sport. In this current narrative review, we reflect on progress made since our original attempt to highlight several potential myths that appeared to exist in coaching, implying the existence of a theory-practice divide. Most notably, we present five action points that would impact positively on coaches and practitioners working to improve skill learning across sports, as well as suggesting directions for research. We discuss the importance of practice quality in enhancing learning and relate this concept to notions of optimising challenge. We discuss how best to assess learning, the right balance between repetition and practice that is specific to competition, the relationship between practice conditions, instructions, and individual differences, and why a more "hands-off" approach to instruction may have advantages over more "hands-on" methods. These action points are considered as a broad framework for advancing skill acquisition for excellence (SAFE) in applied practice. We conclude by arguing the need for increased collaboration between researchers, coaches, and other sport practitioners.

Investigating and acquiring motor expertise using virtual reality

After just months of simulated training, on January 19, 2019 a 23-year-old E-sports pro-gamer, Enzo Bonito, took to the racetrack and beat Lucas di Grassi, a Formula E and ex-Formula 1 driver with decades of real-world racing experience. This event raised the possibility that practicing in virtual reality can be surprisingly effective for acquiring motor expertise in real-world tasks. Here, we evaluate the potential of virtual reality to serve as a space for training to expert levels in highly complex real-world tasks in time windows much shorter than those required in the real world and at much lower financial cost without the hazards of the real world. We also discuss how VR can also serve as an experimental platform for exploring the science of expertise more generally.

Embodied decision biases: individually stable across different tasks?

In everyday life, action and decision-making often run in parallel. Action-based models argue that action and decision-making strongly interact and, more specifically, that action can bias decision-making. This embodied decision bias is thought to originate from changes in motor costs and/or cognitive crosstalk. Recent research confirmed embodied decision biases for different tasks including walking and manual movements. Yet, whether such biases generalize within individuals across different tasks remains to be determined. To test this, we used two different decision-making tasks that have independently been shown to reliably produce embodied decision biases. In a within-participant design, participants performed two tasks in a counterbalanced fashion: (i) a walking paradigm for which it is known that motor costs systematically influence reward decisions, and (ii) a manual movement task in which motor costs and cognitive crosstalk have been shown to impact reward decisions. In both tasks, we successfully replicated the predicted embodied decision biases. However, there was no evidence that the strength of the biases correlated between tasks. Hence, our findings do not confirm that embodied decision biases transfer between tasks. Future research is needed to examine whether this lack of transfer may be due to different causes underlying the impact of motor costs on decisions and the impact of cognitive crosstalk or task-specific differences.

Decoding fMRI alcohol cue reactivity and its association with drinking behaviour

BACKGROUND

Cue reactivity, the enhanced sensitivity to conditioned cues, is associated with habitual and compulsive alcohol consumption. However, most previous studies in alcohol use disorder (AUD) compared brain activity between alcohol and neutral conditions, solely as cue-triggered neural reactivity.

OBJECTIVE

This study aims to find the neural subprocesses during the processing of visual alcohol cues in AUD individuals, and how these neural patterns are predictive for relapse.

METHODS

Using cue reactivity and rating tasks, we separately modelled the patterns decoding the processes of visual object recognition and reward appraisal of alcohol cues with representational similarity analysis, and compared the decoding involvements (ie, distance between neural responses and hypothesised decoding models) between AUD and healthy individuals. We further explored connectivity between the identified neural systems and the whole brain and predicted relapse within 6 months using decoding involvements of the neural patterns.

FINDINGS

AUD individuals, compared with healthy individuals, showed higher involvement of motor-related brain regions in decoding visual features, and their reward, habit and executive networks were more engaged in appraising reward values. Connectivity analyses showed the involved neural systems were widely connected with higher cognitive networks during alcohol cue processing in AUD individuals, and decoding involvements of frontal eye fields and dorsolateral prefrontal cortex could contribute to relapse prediction.

CONCLUSIONS

These findings provide insight into how AUD individuals differently decode alcohol cues compared with healthy participants, from the componential processes of visual object recognition and reward appraisal.

CLINICAL IMPLICATIONS

The identified patterns are suggested as biomarkers and potential therapeutic targets in AUD.

Lack of action monitoring as a prerequisite for habitual and chunked behavior: Behavioral and neural correlates

We previously reported the rapid development of habitual behavior in a discrete-trials instrumental task in which lever insertion and retraction act as reward-predictive cues delineating sequence execution. Here we asked whether lever cues or performance variables reflective of skill and automaticity might account for habitual behavior in male rats. Behavior in the discrete-trials habit-promoting task was compared with two task variants lacking the sequence-delineating cues of lever extension and retraction. We find that behavior is under goal-directed control in absence of sequence-delineating cues but not in their presence, and that skilled performance does not predict goal-directed vs. habitual behavior. Neural activity recordings revealed an engagement of dorsolateral striatum and a disengagement of dorsomedial striatum during the sequence execution of the habit-promoting task, specifically. Together, these results indicate that sequence delineation cues promote habit and differential engagement of striatal subregions during instrumental responding, a pattern that may reflect cue-elicited behavioral chunking.

Inhibitory control hinders habit change

Our habits constantly influence the environment, often in negative ways that amplify global environmental and health risks. Hence, change is urgent. To facilitate habit change, inhibiting unwanted behaviors appears to be a natural human reaction. Here, we use a novel experimental design to test how inhibitory control affects two key components of changing (rewiring) habit-like behaviors in healthy humans: the acquisition of new habit-like behavior and the simultaneous unlearning of an old one. We found that, while the new behavior was acquired, the old behavior persisted and coexisted with the new. Critically, inhibition hindered both overcoming the old behavior and establishing the new one. Our findings highlight that suppressing unwanted behaviors is not only ineffective but may even further strengthen them. Meanwhile, actively engaging in a preferred behavior appears indispensable for its successful acquisition. Our design could be used to uncover how new approaches affect the cognitive basis of changing habit-like behaviors.

Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration

Animals can capitalize on invariance in the environment by learning and automating highly consistent actions; however, they must also remain flexible and adapt to environmental changes. It remains unclear how primary motor cortex (M1) can drive precise movements, yet also support behavioral exploration when faced with consistent errors. Using a reach-to-grasp task in rats, along with simultaneous electrophysiological monitoring in M1 and dorsolateral striatum (DLS), we find that behavioral exploration to overcome consistent task errors is closely associated with tandem increases in M1 and DLS neural variability; subsequently, consistent ensemble patterning returns with convergence to a new successful strategy. We also show that compared to reliably patterned intracranial microstimulation in M1, variable stimulation patterns result in significantly greater movement variability. Our results thus indicate that motor and striatal areas can flexibly transition between two modes, reliable neural pattern generation for automatic and precise movements versus variable neural patterning for behavioral exploration.

It is not fully understood how behavioral flexibility is established in the context of automatic performance of a complex motor skill. Here the authors show that corticostriatal activity can flexibly transition between two modes during a reach to-grasp task in rats: reliable neural pattern generation for precise, automatic movements versus variable neural patterning for behavioral exploration.