An action-incongruent secondary task modulates prediction accuracy in experienced performers: evidence for motor simulation

Enhancing motor imagery practice using synchronous action observation

In this paper, we discuss a variety of ways in which practising motor actions by means of motor imagery (MI) can be enhanced via synchronous action observation (AO), that is, by AO + MI. We review the available research on the (mostly facilitatory) behavioural effects of AO + MI practice in the early stages of skill acquisition, discuss possible theoretical explanations, and consider several issues related to the choice and presentation schedules of suitable models. We then discuss considerations related to AO + MI practice at advanced skill levels, including expertise effects, practical recommendations such as focussing attention on specific aspects of the observed action, using just-ahead models, and possible effects of the perspective in which the observed action is presented. In section "Coordinative AO + MI", we consider scenarios where the observer imagines performing an action that complements or responds to the observed action, as a promising and yet under-researched application of AO + MI training. In section "The dual action simulation hypothesis of AO + MI", we review the neurocognitive hypothesis that AO + MI practice involves two parallel action simulations, and we consider opportunities for future research based on recent neuroimaging work on parallel motor representations. In section "AO + MI training in motor rehabilitation", we review applications of AO, MI, and AO + MI training in the field of neurorehabilitation. Taken together, this evidence-based, exploratory review opens a variety of avenues for future research and applications of AO + MI practice, highlighting several clear advantages over the approaches of purely AO- or MI-based practice.

Effector-specific improvements in action prediction in left-handed individuals after short-term physical practice

Research has established the influence of short-term physical practice for enhancing action prediction in right-handed (RH) individuals. In addition to benefits of physical practice for these later assessed perceptual-cognitive skills, effector-specific interference has been shown through action-incongruent secondary tasks (motor interference tasks). Here we investigated this experience-driven facilitation of action predictions and effector-specific interference in left-handed (LH) novices, before and after practicing a dart throwing task. Participants watched either RH (n = 19) or LH (n = 24) videos of temporally occluded dart throws, across a control condition and three secondary-task conditions: tone-monitoring, RH or LH force monitoring. These conditions were completed before and after physical practice throwing with the LH. Significantly greater improvement in prediction accuracy was shown post-practice for the LH- versus RH-video group. Consistent with previous work, effector-specific interference was shown, exclusive to the LH-video group. Only when doing the LH force monitoring task did the LH-video group show secondary task interference in prediction accuracy. These data support the idea that short-term physical practice resulted in the development of an effector-specific motor representation. The results are also consistent with other work in RH individuals (showing RH motor interference) and hence rule out the interpretation that these effector specific effects are due to the disruption of more generalized motor processes, thought to be lateralized to the left-hemisphere of the brain.

Relationships between the magnitude of representational momentum and the spatial and temporal anticipatory judgments of opponent's kicks in taekwondo

For successful actions in a fast, dynamic environment such as sports, a quick successful anticipation of a forthcoming environmental state is essential. However, the perceptual mechanisms involved in successful anticipation are not fully understood. This study examined the relationships between the magnitude of representational momentum (RM) as a forward displacement of the memory representation of the final position of a moving object (which implies that observers perceptually "see" a near future forthcoming dynamic environmental state) and the temporal and spatial anticipatory judgments of the opponent's high or middle kicks in taekwondo. Twenty-seven participants (university taekwondo club members and non-members) observed video clips of taekwondo kicks that vanished at one of 10 frame positions prior to the kick impact and performed three tasks consecutively: anticipatory coincidence timing (CT) with the arrival of kick impact, judgment of the kick type (high and middle kicks) by forced choice, and judgment of the vanishing frame position (measuring RM). Our results showed significant group effects for the number of correct kick-type judgments and the judgment threshold for kick-type choice (kick-typeJT), which was estimated in terms of individual psychometric function curves. A significant correlation was found between the magnitude of RM (estimated at kick-typeJT) and kick-typeJT, but not between the CT errors (estimated at kick-typeJT) and kick-typeJT. This indicates that the magnitude of RM may play an influential role in quick kick-type judgments, but not in coincidence timing while observing an opponent's kick motion. These findings suggest that subjective anticipatory perception or judgment of the future spatial state is vital to anticipatory actions under severe time constraints.

Insights into motor memory interference among experts and competent individuals

Memory interference can arise when multiple motor skill tasks are learned. A study by Nepotiuk and Brown (Nepotiuk AH, Brown LE. J Neurophysiol 128: 969-981, 2022) demonstrated that the susceptibility of motor memory to interference differs depending on expertise, using a vegetable-chopping task. The authors suggest that the motor memories of expert chefs and competent home cooks are organized differently. This Neuro Forum article offers an alternative explanation for their results and provides insights into motor memory processing in both experts and competents.

Attributes of Expert Anticipation Should Inform the Design of Virtual Reality Simulators to Accelerate Learning and Transfer of Skill

Expert sport performers cope with a multitude of visual information to achieve precise skill goals under time stress and pressure. For example, a major league baseball or cricket batter must read opponent variations in actions and ball flight paths to strike the ball in less than a second. Crowded playing schedules and training load restrictions to minimise injury have limited opportunity for field-based practice in sports. As a result, many sports organisations are exploring the use of virtual reality (VR) simulators. Whilst VR synthetic experiences can allow greater control of visual stimuli, immersion to create presence in an environment, and interaction with stimuli, compared to traditional video simulation, the underpinning mechanisms of how experts use visual information for anticipation have not been properly incorporated into its content design. In themes, this opinion article briefly explains the mechanisms underpinning expert visual anticipation, as well as its learning and transfer, with a view that this knowledge can better inform VR simulator content design. In each theme, examples are discussed for improved content design of VR simulators taking into consideration its advantages and limitations relative to video simulation techniques. Whilst sport is used as the exemplar, the points discussed have implications for skill learning in other domains, such as military and law enforcement. It is hoped that our paper will stimulate improved content design of VR simulators for future research and skill enhancement across several domains.

Research on the Development of Digital Creative Sports Industry Based on Deep Learning

The core of the digital entrepreneurial sports culture creative industry lies in innovation, which emphasizes the new impetus brought by the digital entrepreneurial sports culture to the social economy. The digital entrepreneurial sports cultural creative industry is rooted in the cultural creative industry. The digital entrepreneurial sports cultural creative industry is also an important part of the sports industry, and its development highly depends on the development of the sports industry. The digital entrepreneurial sports cultural creative industry has the characteristics of both the sports industry and the cultural creative industry. This paper uses the deep learning technology to study the development of the digital creative sports industry and build an intelligent model. Moreover, this paper assigns weights to the input multidimensional features, extracts the most relevant data features, and analyzes the performance of the proposed model through simulation experiments. From the experimental analysis results, we can see that the model proposed in this paper has certain practicality.

Shared Representations in Athletes: Segmenting Action Sequences From Taekwondo Reveals Implicit Agreement

How do athletes represent actions from their sport? How are these representations structured and which knowledge is shared among experts in the same discipline? To address these questions, the event segmentation task was used. Experts in Taekwondo and novices indicated how they would subjectively split videos of Taekwondo form sequences into meaningful units. In previous research, this procedure was shown to unveil the structure of internal action representations and to be affected by sensorimotor knowledge. Without specific instructions on the grain size of segmentation, experts tended to integrate over longer episodes which resulted in a lower number of single units. Moreover, in accordance with studies in figure-skating and basketball, we expected higher agreement among experts on where to place segmentation marks, i.e., boundaries. In line with this hypothesis, significantly more overlap of boundaries was found within the expert group as compared to the control group. This was observed even though the interindividual differences in the selected grain size were huge and expertise had no systematic influence here. The absence of obvious goals or objects to structure Taekwondo forms underlines the importance of shared expert knowledge. Further, experts might have benefited from sensorimotor skills which allowed to simulate the observed actions more precisely. Both aspects may explain stronger agreement among experts even in unfamiliar Taekwondo forms. These interpretations are descriptively supported by the participants’ statements about features which guided segmentation and by an overlap of the group’s agreed boundaries with those of an experienced referee. The study shows that action segmentation can be used to provide insights into structure and content of action representations specific to experts. The mechanisms underlying shared knowledge among Taekwondoists and among experts in general are discussed on the background of current theoretic frameworks.

VIDEO ANALYSIS AND 3D DETECTION SIMULATION OF JUMP SHOT PRECISION FOR BASKETBALL PLAYERS

ABSTRACT With the rapid development and application of computer technology, the application of computer science knowledge in basketball is also more and more extensive. Based on genetic algorithm and the background subtraction method, video analysis and 3D detection simulation model of shot jump action precision were constructed in this study. According to the genetic algorithm search method, jump shot precision was analyzed, and the problems encountered in the actual shooting process of basketball players were studied and solved. The results show that this study is necessary and feasible.

Hybrid fuzzy interface model of sports rehabilitation activities

At present, the relevant test data and training indicators of athletes during rehabilitation training lack screening and analysis, so it is impossible to establish a long-term longitudinal tracking research system and evaluation system. In order to improve the practical effect of sports rehabilitation activities, this paper successively introduces the matrix normal mixed model and the fuzzy clustering algorithm based on the K-L information entropy regularization and the matrix normal mixed model. Moreover, this paper uses the expectation maximization algorithm to estimate the parameters of the model, discusses the framework, key technologies and core services of the development platform, and conducts certain research on the related technologies of the three-tier architecture. At the same time, according to the actual needs of sports rehabilitation training, this paper designs the functions required for exercise detection and prescription formulation. In addition, this paper analyzes and designs the database structure involved in each subsystem. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research results show that the performance of the model constructed in this paper meets the expectations of model construction, so it can be applied to practice.

Design of evaluation system of physical education based on machine learning algorithm and SVM

The evaluation system of physical education is limited by many factors, so the reliability of the quantitative results of its intelligent scoring system is not high. In order to improve the teachingeffect ofphysical education major, this paper combines a machine learning algorithm and SVM to build anevaluation system of physical education. The system uses optimized machine learning as the system algorithm. In order to improve the operating efficiency of the system, this study optimizes the system physical layer certification to improve the system data processing speed and accuracy and uses a three-layer structure to build a basic model of the system structure and analyze its functional modules. Moreover, this study uses a database based on an expert evaluation system for data processing to achieve physical education evaluation and puts forward corresponding improvements. In addition, system performance verification is carried out on the basis of building the system. Through various experimental verifications, we know that the model constructed in this paper has good performance and can be applied to actual physical education.

Research on sports simulation and fatigue characteristics of athletes based on machine learning

The research on the fatigue characteristics of athletes has a certain role in promoting the development of sports. In order to detect fatigue more accurately in the state of human fatigue, this article uses a method of fusing characteristic information of many physiological parameters related to fatigue to design a multi-physical parameter-based exercise fatigue recognition method with high research value and significance. Moreover, this study combines machine learning technology to construct a dynamic fatigue detection system based on BP neural network and multiple physiological parameters. In addition, this study uses samples to construct a BP neural network and achieves dynamic detection of fatigue through multiple physiological parameters. Finally, by constructing controlled trials, fatigue is predicted. The results show that the predicted output of the fatigue value is in good agreement with the expected output, and the research method has certain practical effects.

Application of VR image recognition and digital twins in artistic gymnastics courses

Because rhythmic gymnastics requires a combination of human movements and hand-held instruments, it is difficult to teach and requires high movement standards. Therefore, the actual course teaching is difficult. In order to improve the teaching efficiency of rhythmic gymnastics courses, based on VR image recognition technology and digital twins, this paper combines the actual teaching needs of rhythmic gymnastics to build a corresponding auxiliary teaching system. The sports database designed in this article mainly has three kinds of sports: difficulty movements, connecting movements and equipment movements. It is different from the traditional method in that each movement and the device-related connection movement correspond to a difficulty movement of the same length and close coordination, and the connection movement plays a role in smoothly connecting the two difficulty movements. In addition, the performance of the auxiliary teaching system constructed in this paper is studied through system experiments. The research results show that this system is feasible.

Research on college physical education model based on virtual crowd simulation and digital media

At present, the traditional sports teaching model has been unable to meet the needs of modern diversified talents. Based on the actual needs of physical education, this article reforms traditional physical education methods, proposes a university physical education model based on virtual crowd simulation and digital media, and builds a corresponding system. Moreover, this paper uses feature identification to monitor classroom teaching and counts multiple parameters to assist the effective teaching activities. In model tracking, the DMS motion trajectory is obtained, and then the center points of all grid sequences are calculated to generate a center point matrix. Considering the difference in motion between adjacent frames of DMS, the difference is quantified by the significance value, so that the sequence of adjacent frames with small differences is organized into clusters. In addition, this paper builds a computer system model based on actual needs and evaluates model performance through actual teaching. The research results show that the proposed model has good performance.

Image detection and basketball training performance simulation based on improved machine learning

Basketball player detection technology is an important subject in the field of computer vision and the basis of related image processing research. This study uses machine learning technology to build a basketball sport feature recognition model. Moreover, this research mainly takes the characteristic information of basketball in the state of basketball goals as the starting point and compares and analyzes the detection methods by detecting the targets in the environment. By comprehensively considering the advantages and disadvantages of various methods, a method suitable for the subject is proposed, namely, a fast skeleton extraction and model segmentation method. The fitting effect of this method, whether in terms of compactness or quantity, has greater advantages than traditional bounding boxes, and realizes the construction of dynamic ellipsoidal bounding boxes in a moving state. In addition, this study designs a controlled trial to verify the analysis of this research model. The research results show that the model proposed in this paper has certain effects and can improve practical guidance for competitions and basketball players training.

Machine learning model for feature recognition of sports competition based on improved TLD algorithm

Sports competition characteristics play an important role in judging the fairness of the game and improving the skills of the athletes. At present, the feature recognition of sports competition is affected by the environmental background, which causes problems in feature recognition. In order to improve the effect of feature recognition of sports competition, this study improves the TLD algorithm, and uses machine learning to build a feature recognition model of sports competition based on the improved TLD algorithm. Moreover, this study applies the TLD algorithm to the long-term pedestrian tracking of PTZ cameras. In view of the shortcomings of the TLD algorithm, this study improves the TLD algorithm. In addition, the improved TLD algorithm is experimentally analyzed on a standard data set, and the improved TLD algorithm is experimentally verified. Finally, the experimental results are visually represented by mathematical statistics methods. The research shows that the method proposed by this paper has certain effects.

An artificial intelligence fuzzy system for improvement of physical education teaching method

There are certain disadvantages in the traditional physical education teaching model. In order to improve the advanced nature of physical education teaching methods, this paper builds a physical education evaluation system based on artificial intelligence fuzzy algorithm. The system uses fuzzy control instructions as the basis to combine human language and mechanical language, so that the machine can recognize human working language habits and execute commands according to the instructions. Moreover, in this study, the trapezoid function is selected as the membership function, and the improved particle optimization algorithm is used to capture the student’s motion process and the motion vector decomposition, and the system structure model is constructed based on the functional requirements analysis. In addition, this study conducts system performance analysis through experimental teaching methods. The research results show that this system can effectively promote the reform of teaching methods in physical education and has a certain practical effect.

An artificial intelligence and machine vision based evaluation of physical education teaching

The manual evaluation method to evaluate the effect of physical education teaching is tedious, and it will have a large error when the amount of data is large. In order to improve the efficiency of physical education evaluation, this article uses artificial intelligence for data analysis and uses machine vision to identify the teaching process to assist teachers in physical education. In order to reduce the calibration error of the parameters and obtain more accurate camera imaging geometric parameters, this paper adopts the method of averaging multiple sample points to determine the calibration parameters of the camera. In addition, this study builds system function modules according to actual needs and verifies system performance through experimental teaching methods. The research results show that the model proposed in this paper has a certain practical effect.

Role of artificial intelligence algorithm for taekwondo teaching effect evaluation model

The problems and disadvantages of the traditional teaching mode of Taekwondo in colleges and universities are obvious, which is not conducive to cultivating the interest of contemporary college students in learning Taekwondo. In order to improve the teaching effect of Taekwondo, based on the intelligent algorithm of human body feature recognition, this study uses support vector machine to construct a Taekwondo teaching effect evaluation model based on artificial intelligence algorithm. The model corrects the movement of the students by recognizing the movement characteristics of the students’ Taekwondo and can conduct the movement guidance and exercises through the simulation method. In order to verify the performance of the model in this study, this study set up control experiments and mathematical statistical methods to verify the performance of the model. The research results show that the model proposed in this paper has a certain effect and can be applied to teaching practice

Susceptibility to the fusion illusion is modulated during both action execution and action observation

Many researchers have proposed that when an individual observes the actions of another individual, the observer simulates the action using many of the same neural areas that are involved in action production. The present study was designed to test this simulation hypothesis by comparing the perception of multisensory stimuli during both the execution and observation of an aiming action. The present work used the fusion illusion - an audio-visual illusion in which two visual stimuli presented with one auditory stimulus are erroneously perceived as being one visual stimulus. Previous research has shown that, during action execution, susceptibly to this illusion is reduced early in the execution of the movement when visual information may be more highly weighted than other sensory information. We sought to determine whether or not a non-acting observer of an action showed a similar reduction in susceptibility to the fusion illusion. Participants fixated a target and either executed or observed a manual aiming movement to that target. Audiovisual stimuli were presented at 0, 100, or 200 ms relative to movement onset and participants reported the number of perceived flashes after the movement was completed. Analysis of perceived flashes revealed that participants were less susceptible to the fusion illusion when the stimuli were presented early (100 ms) relative to later in the movement (200 ms). Critically, this pattern emerged in both execution and observation tasks. These findings support the hypothesis that observers simulate the performance of the actor and experience comparable real-time alterations in multisensory processing.

Empathic perspective taking promotes interpersonal coordination through music

Coordinated behavior promotes collaboration among humans. To shed light upon this relationship, we investigated whether and how interpersonal coordination is promoted by empathic perspective taking (EPT). In a joint music-making task, pairs of participants rotated electronic music-boxes, producing two streams of musical sounds that were meant to be played synchronously. Participants – who were not musically trained – were assigned to high and low EPT groups based on pre-experimental assessments using a standardized personality questionnaire. Results indicated that high EPT pairs were generally more accurate in synchronizing their actions. When instructed to lead the interaction, high and low EPT leaders were equally cooperative with followers, making their performance tempo more regular, presumably in order to increase their predictability and help followers to synchronize. Crucially, however, high EPT followers were better able to use this information to predict leaders’ behavior and thus improve interpersonal synchronization. Thus, empathic perspective taking promotes interpersonal coordination by enhancing accuracy in predicting others’ behavior while leaving the aptitude for cooperation unaltered. We argue that such predictive capacity relies on a sensorimotor mechanism responsible for simulating others’ actions in an anticipatory manner, leading to behavioral advantages that may impact social cognition on a broad scale.

Concurrent Imitative Movement During Action Observation Facilitates Accuracy of Outcome Prediction in Less-Skilled Performers

Skilled athletes can predict the outcome of actions performed by others, based on the kinematic information inherent in others' actions, earlier and more accurately than less-skilled athletes. Activation of the motor cortex during action observation indicates motor simulation of other's actions in one's own motor system; this contributes to skilled outcome prediction. Thus, the present study investigated whether concurrent movements during action observation that affect motor simulation influence the accuracy of outcome prediction, namely, whether concurrent imitative movement and self-movement enhance and inhibit accuracy, respectively, based on skill level. Twelve male varsity basketball players (skilled group) and twelve male college students with no special training in basketball (less-skilled group) were required to predict the outcome of a basketball free throw by another player based on the action kinematics in the following four conditions: prediction without any action (observation), prediction with right-wrist volar flexion with maximum speed (incongruent-action), prediction with concurrent imitative movement during observation by right-wrist flexion as if imitating the model's action (imitative-motion), or prediction with concurrent self-movement by right-wrist flexion as if shooting by oneself (self-motion). The results showed that the skilled group had degraded accuracy of outcome prediction in the self-motion condition compared to the observation condition. In contrast, accuracy in the less-skilled group was facilitated in the imitative-motion condition compared to the observation condition. The findings suggest that, at least in less-skilled participants, the appropriate motor simulation that relates to skilled prediction can be virtually induced by concurrent imitative movement during the prediction task, even if they have less experience of free throws. This effect in imitative movement is likely to occur by producing identical motor commands with observed action, thereby enabling the prediction of sensory consequences and outcome accurately via a forward model. We propose that traditional perceptual training with concurrent imitative movement is likely to be an effective way to develop visual- and motor-based hybrid outcome predictions that produce superior inferences in skilled athletes.

Prediction error induced motor contagions in human behaviors

Motor contagions refer to implicit effects on one's actions induced by observed actions. Motor contagions are believed to be induced simply by action observation and cause an observer's action to become similar to the action observed. In contrast, here we report a new motor contagion that is induced only when the observation is accompanied by prediction errors - differences between actions one observes and those he/she predicts or expects. In two experiments, one on whole-body baseball pitching and another on simple arm reaching, we show that the observation of the same action induces distinct motor contagions, depending on whether prediction errors are present or not. In the absence of prediction errors, as in previous reports, participants' actions changed to become similar to the observed action, while in the presence of prediction errors, their actions changed to diverge away from it, suggesting distinct effects of action observation and action prediction on human actions.

Watching sports sometimes causes people to unintentionally move in the same way as the athlete they are observing. This type of unconscious mimicry is called a motor contagion. Observing everyday actions can also trigger motor contagion, and plays an important role in social interactions.

So far, studies have focused on understanding how observing an action leads to motor contagion. They have not factored in the fact that in everyday life individuals consciously or unconsciously predict observed actions by others. Sometimes these predictions are wrong, leading to so called ‘prediction errors’. It was not clear whether motor contagion occurs when the viewer has made an incorrect prediction, or if prediction errors change the behavior of the viewer.

Now, Ikegami, Ganesh et al. show that prediction errors influence motor contagion. In one experiment, baseball players were asked to watch a video of an actor pitching a ball toward a target and predict where on the target the ball would hit. Some of the players were given misleading information intended to increase the likelihood they would incorrectly predict where the actor would throw. The players then pitched the ball towards a target themselves. When the players had just watched the actor’s throw, their throws became similar to it. When their predictions were wrong, their throws were very different from the actor’s throw. The players were not aware of the changes to their throw in either case.

Ikegami, Ganesh et al. also conducted a similar experiment in which other volunteers were asked to observe an actor reaching for a target and then reach for the target themselves. The results were similar: when the volunteers’ predictions were wrong, they reached in different ways to the actor.

This may be a new type of motor contagion. Learning more about this effect could help researchers to better understand the adjustments people make to their social behaviors and give new insights about the brain mechanisms that underlie normal human actions and social interactions. Sports trainers or physical therapists might also use this information to develop better strategies for maintaining athlete performances or helping people to recover movement after an injury or illness.

Action-skilled observation: Issues for the study of sport expertise and the brain

With a growing body of research devoted to uncovering regions of the brain implicated in action observation following various action-related experiences, including sport, we ask what we know from this research, and what we still need to know, as it pertains to sport and the brain. To do this, we review and integrate knowledge garnered from developmental work, short-term motor learning studies, and most significantly sport athletes across varying skill levels. We consider various neurophysiological methods, including TMS, fMRI, and EEG, which have been used to help uncover brain regions involved in action-skilled observation. We are particularly interested in how these processes are related to action prediction and the detection of deceptive actions among athlete groups. This research is considered within broad theoretical frameworks related to action-simulation and prediction, although our main focus is on the brain regions that have been implicated in skilled action observation and the implications of this research for knowledge and further study of sport expertise.

Anticipation in sport

Anticipation has become an increasingly important research area within sport psychology since its infancy in the late 1970s. Early work has increased our fundamental understanding of skilled anticipation in sports and how this skill is developed. With increasing theoretical and practical insights and concurrent technological advancements, researchers are now able to tackle more detailed questions with sophisticated methods. Despite this welcomed progress, some fundamental questions and challenges remain to be addressed, including the (relative) contributions of visual and motor experience to anticipation, intraindividual and interindividual variation in gaze behaviour, and the impact of non-kinematic (contextual or situational) information on performance and its interaction with advanced kinematic cues during the planning and execution of (re)actions in sport. The aim of this opinion paper is to shortly sketch the state of the art, and then to discuss recent work that has started to systematically address open challenges thereby inspiring promising future routes for research on anticipation and its application in practice.

Sex-specific effects of posture on the attribution of handedness to an imagined agent

In a series of previous studies, we found that when participants were required to imagine another person performing a manual action, they imagined a significantly higher proportion of actions performed with their dominant rather than non-dominant hand, which indicates that shared motor representations between the self and the other are involved also during the imagination of others' actions. Interestingly, the activation of lateralized body-specific motor representations (as indexed by the congruence between the participant's handedness and the imagined person's handedness) appeared to be affected by the visual perspective adopted and participants' handedness. Given that past literature indicates that incongruent or unnatural postures interfere with motor imagery, we tested 480 right-handed participants to investigate whether subjects holding their right hand behind their back would have imagined right-handed actions less frequently than those holding their left hand behind their back. Moreover, we examined the effects of participant's sex, action category (simple or complex) and hand shape (open or fist). Contrary to our prediction, female participants holding their right hand behind their back imagined right-handed actions more frequently than those holding their left hand behind their back, whereas no significant effect was observed in male participants. We propose that the muscle contraction needed to keep a hand behind the back could activate the motor representations of that hand so as to increase the likelihood of imagining an action performed with the corresponding hand. Moreover, the sex difference observed is consistent with the greater use of embodied strategies by females than by males.

Critical Motor Involvement in Prediction of Human and Non-biological Motion Trajectories

OBJECTIVES

Adaptive interaction with the environment requires the ability to predict both human and non-biological motion trajectories. Prior accounts of the neurocognitive basis for prediction of these two motion classes may generally be divided into those that posit that non-biological motion trajectories are predicted using the same motor planning and/or simulation mechanisms used for human actions, and those that posit distinct mechanisms for each. Using brain lesion patients and healthy controls, this study examined critical neural substrates and behavioral correlates of human and non-biological motion prediction.

METHODS

Twenty-seven left hemisphere stroke patients and 13 neurologically intact controls performed a visual occlusion task requiring prediction of pantomimed tool use, real tool use, and non-biological motion videos. Patients were also assessed with measures of motor strength and speed, praxis, and action recognition.

RESULTS

Prediction impairment for both human and non-biological motion was associated with limb apraxia and, weakly, with the severity of motor production deficits, but not with action recognition ability. Furthermore, impairment for human and non-biological motion prediction was equivalently associated with lesions in the left inferior parietal cortex, left dorsal frontal cortex, and the left insula.

CONCLUSIONS

These data suggest that motor planning mechanisms associated with specific loci in the sensorimotor network are critical for prediction of spatiotemporal trajectory information characteristic of both human and non-biological motions. (JINS, 2017, 23, 171-184).

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

Joint Action: Mental Representations, Shared Information and General Mechanisms for Coordinating with Others

In joint action, multiple people coordinate their actions to perform a task together. This often requires precise temporal and spatial coordination. How do co-actors achieve this? How do they coordinate their actions toward a shared task goal? Here, we provide an overview of the mental representations involved in joint action, discuss how co-actors share sensorimotor information and what general mechanisms support coordination with others. By deliberately extending the review to aspects such as the cultural context in which a joint action takes place, we pay tribute to the complex and variable nature of this social phenomenon.