Expert Action Research Articles

Observing an expert's action swapped with an observer's face increases corticospinal excitability during combined action observation and motor imagery.

This study aimed to examine whether observing an expert's action swapped with an observer's face increases corticospinal excitability during combined action observation and motor imagery (AOMI). Twelve young males performed motor imagery of motor tasks with different difficulties while observing the actions of an expert performer and an expert performer with a swapped face. Motor tasks included bilateral wrist dorsiflexion (EASY) and unilateral two-ball rotating motions (DIFF). During the AOMI of EASY and DIFF, single-pulse transcranial magnetic stimulation was delivered to the left primary motor cortex, and motor-evoked potentials (MEPs) were obtained from the extensor carpi ulnaris and first dorsal interosseous muscles of the right upper limb, respectively. Visual analogue scale (VAS) assessed the subjective similarity of the expert performer with the swapped face in the EASY and DIFF to the participants themselves. The MEP amplitude in DIFF was larger in the observation of the expert performer with the swapped face than that of the expert performer (P = 0.012); however, the corresponding difference was not observed in EASY (P = 1.000). The relative change in the MEP amplitude from observing the action of the expert performer to that of the expert performer with the swapped face was positively correlated with VAS only in DIFF (r = 0.644, P = 0.024). These results indicate that observing the action of an expert performer with the observer's face enhances corticospinal excitability during AOMI, depending on the task difficulty and subjective similarity between the expert performer being observed and the observer.

ВПЛИВ ФУНКЦІЙ МЕТОДИКИ НА ЯКІСТЬ ВИРІШЕННЯ ЕКСПЕРТНОГО ЗАВДАННЯ

The publication considers forensic expertise as an integrated system with interconnected elements, one of which is methodological support. The interrelation of the main structural elements in organizing expert research is provided. The impact of methodological functions on the quality resolution of expert tasks through the application of methodology in the process of expert cognition is substantiated. It is suggested that when developing an expert methodology, an additional consideration should be given to the optimization function of the expert's conclusion, which implies the ability to concisely describe the methodology in the research part of the expert's conclusion for a full understanding by investigators, prosecutors, judges, and other participants in the judicial process of the specificity of research in a certain field of knowledge and to evaluate the expert's conclusion in terms of reliability. The development of society constantly increases the requirements for the quality and speed of conducting forensic examinations and research. This requires regular updating of methodologies. Especially, this concerns specialized methodologies for conducting examinations of intellectual property objects, which are unique and multifaceted. The role and significance of a specific methodology in the process of expert cognition can be determined through an analysis of its functions, the main ones being: decisive (or guiding), systematizing, prognostic, pragmatic, managerial, evaluative, substantiating, and cognitive. It is noted that expert research as a cognitive process has two levels of cognition: the first level is empirical; the second level is theoretical. The result of expert cognition should be the solution of an expert task – establishing facts with evidentiary value. General expert knowledge is decisive in conducting qualitative forensic expertise – a dynamic category that is constantly improving through transformation and growth, integrated into the process of expert cognition through the competence of the forensic expert. It is proposed that in organizing the work of a forensic expert to optimize time and labor costs in conducting expert research, the simplex method be used as an algorithm for solving optimization problems. This method involves making directed movements along reference plans to find the optimal solution. The idea of applying the method for expert research lies in finding an expert's action plan according to a certain rule, which is checked against the optimality criterion – time and quality. If the optimality criterion is met, the task is considered solved. In case of non-compliance, a new iteration takes place, using the minimally calculated step, which needs to be recorded in the form of tables.

Safe and efficient imitation learning by clarification of experienced latent space

Behavioral cloning from observation (BCO) allows the robot to learn the policy without the expert's action information. However, it requires a few interactions with the environment to infer expert's action with risk of robot failures. In addition, BCO assumes that the inferred action is of accurate, causing wrong and inefficient updates of the policy. Both problems can be resolved by outlier detection whether the faced state is experienced or not. This paper addresses such outlier detection mechanisms using variational autoencoder (VAE) to improve safety and efficiency of the standard BCO. For the first safety problem, we suppose that the expert's demonstrations only visited the safe states, and then, VAE is learned by the expert's state data to detect inexperienced and dangerous scenes. For the second efficiency problem, another VAE is trained with the state data safely collected by the imitator's policy to detect the scenes where the inferred actions are not accurate. In handwriting robot experiments, the proposed mechanisms succeeded in improving the standard BCO in terms of both the safety (roughly 64%) and the efficiency (roughly 44%). The high versatility of the proposed mechanisms is verified from learning various alphabets.

Deep imitation reinforcement learning with expert demonstration data

In recent years, deep reinforcement learning (DRL) has made impressive achievements in many fields. However, existing DRL algorithms usually require a large amount of exploration to obtain a good action policy. In addition, in many complex situations, the reward function cannot be well designed to meet task requirements. These two problems will make it difficult for DRL to learn a good action policy within a relatively short period. The use of expert data can provide effective guidance and avoid unnecessary exploration. This study proposes a deep imitation reinforcement learning (DIRL) algorithm that uses a certain amount of expert demonstration data to speed up the training of DRL. In the proposed method, the learning agent imitates the expert's action policy by learning from demonstration data. After imitation learning, DRL is used to optimise the action policy in a self-learning way. By experimental comparison on a video game called the Mario racing game, it is shown that the proposed DIRL algorithm with expert demonstration data can obtain much better performance than previous DRL algorithms without expert guidance.

Costly transparency

We consider whether a career-minded expert would make better decisions if the principal could observe the consequences of the expert's action. The previous literature has found that this “transparency of consequence” can only improve the efficacy of the expert's decision making. We show, however, that this conclusion is very sensitive to the specified cost structure: if learning the consequences of the expert's action makes the expert more likely to choose the action most likely to correspond to the true state of the world, when costs are asymmetric, this can be associated with a decrease in the principal's expected welfare. In addition, we show that, when the prior on the state of the world is sufficiently strong, if the principal benefits from learning the consequences of the expert's action, her utility is higher if she observes only the consequences and not the action taken. For such priors, the optimal transparency regime will involve either the principal observing only the expert's action or only the consequences of the expert's action: it will never be optimal to observe both. We illustrate these results with examples from finance and public policymaking.