Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory (OMCT), as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed – a motor command. This paper aims to show the limitations of such instructionist approaches to skillful performance. We specifically address the question of whether the assumption of control-theoretic models is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists of the execution of theoretical instructions harnessed in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from OMCT. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from OMCT – and argue that the instructionist, control-theoretic assumptions are ill-motivated in light of new developments in active inference

New Pharmacological Agents to Aid Smoking Cessation and Tobacco Harm Reduction: What has been Investigated and What is in the Pipeline?

A wide range of support is available to help smokers to quit and aid attempts at harm reduction, including three first-line smoking cessation medications: nicotine replacement therapy, varenicline and bupropion. Despite the efficacy of these, there is a continual need to diversify the range of medications so that the needs of tobacco users are met. This paper compares the first-line smoking cessation medications to: 1) two variants of these existing products: new galenic formulations of varenicline and novel nicotine delivery devices; and 2) twenty-four alternative products: cytisine (novel outside of central and eastern Europe), nortriptyline, other tricyclic antidepressants, electronic cigarettes, clonidine (an anxiolytic), other anxiolytics (e.g. buspirone), selective 5-hydroxytryptamine (5-HT) reuptake inhibitors, supplements (e.g. St John’s wort), silver acetate, nicobrevin, modafinil, venlafaxine, monoamine oxidase inhibitors (MAOI), opioid antagonist, nicotinic acetylcholine receptors (nAChR) antagonists, glucose tablets, selective cannabinoid type 1 receptor antagonists, nicotine vaccines, drugs that affect gamma-aminobutyric acid (GABA) transmission, drugs that affect N-methyl-D-aspartate receptors (NMDA), dopamine agonists (e.g. levodopa), pioglitazone (Actos; OMS405), noradrenaline reuptake inhibitors, and the weight management drug lorcaserin. Six criteria are used: relative efficacy, relative safety, relative cost, relative use (overall impact of effective medication use), relative scope (ability to serve new groups of patients), and relative ease of use (ESCUSE). Many of these products are in the early stages of clinical trials, however, cytisine looks most promising in having established efficacy and safety and being of low cost. Electronic cigarettes have become very popular, appear to be efficacious and are safer than smoking, but issues of continued dependence and possible harms need to be considered

Mental health problems among medical students in Brazil: a systematic review and meta-analysis

Objective: To provide a comprehensive picture of mental health problems (MHPs) in Brazilian medical students by documenting their prevalence and association with co-factors. Methods: We systematically searched the MEDLINE/PubMed, SciELO, LILACS, and PsycINFO databases for cross-sectional studies on the prevalence of MHPs among medical students in Brazil published before September 29, 2016. We pooled prevalences using a random-effects meta-analysis, and summarized factors associated with MHP. Results: We included 59 studies in the analysis. For meta-analyses, we identified the summary prevalence of different MHPs, including depression (25 studies, prevalence 30.6%), common mental disorders (13 studies, prevalence 31.5%), burnout (three studies, prevalence 13.1%), problematic alcohol use (three studies, prevalence 32.9%), stress (six studies, prevalence 49.9%), low sleep quality (four studies, prevalence 51.5%), excessive daytime sleepiness (four studies, prevalence 46.1%), and anxiety (six studies, prevalence 32.9%). Signs of lack of motivation, emotional support, and academic overload correlated with MHPs. Conclusion: Several MHPs are highly prevalent among future physicians in Brazil. Evidence-based interventions and psychosocial support are needed to promote mental health among Brazilian medical students