Neurotechnology to accelerate learning: during marksmanship training.

This article explores the psychophysiological metrics during expert and novice performances in marksmanship, combat deadly force judgment and decision making (DFJDM), and interactions of teams. Electroencephalography (EEG) and electrocardiography (ECG) are used to characterize the psychophysiological profiles within all categories. Closed-loop biofeedback was administered to accelerate learning during marksmanship training in which the results show a difference in groups that received feedback compared with the control. During known distance marksmanship and DFJDM scenarios, experts show superior ability to control physiology to meet the demands of the task. Expertise in teaming scenarios is characterized by higher levels of cohesiveness than those seen in novices.

Modeling the potentiality of spinal-like circuitry for stabilization of a planar arm system.

The design of control systems for limb prostheses seems likely to benefit from an understanding of how sensorimotor integration is achieved in the intact system. Traditional BMIs guess what movement parameters are encoded by brain activity and then decode them to drive prostheses directly. Modeling the known structure and emergent properties of the biological decoder itself is likely to be more effective in bridging from normal brain activity to functionally useful limb movement. In this study, we have extended a model of spinal circuitry (termed SLR for spinal-like regulator; see Raphael, G., Tsianos, G. A., & Loeb G. E. 2010, Spinal-like regulator facilitates control of a two-degree-of-freedom wrist. The Journal of Neuroscience, 30(28), 9431-9444.) to a planar elbow-shoulder system to investigate how the spinal cord contributes to the control of a musculoskeletal system with redundant and multiarticular musculature and interaction (Coriolis) torques, which are common control problems for multisegment linkages throughout the body. The SLR consists of a realistic set of interneuronal pathways (monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory, Ib-inhibitory, and propriospinal) that are driven by unmodulated step commands with learned amplitudes. We simulated the response of a planar arm to a brief, oblique impulse at the hand and investigated the role of cocontraction in learning to resist it. Training the SLR without cocontraction led to generally poor performance that was significantly worse than training with cocontraction. Further, removing cocontraction from the converged solutions and retraining the system achieved better performance than the SLR responses without cocontraction. Cocontraction appears to reshape the solution space, virtually eliminating the probability of entrapment in poor local minima. The local minima that are entered during learning with cocontraction are favorable starting points for learning to perform the task when cocontraction is abruptly removed. Given the control system's ability to learn effectively and rapidly, we hypothesize that it will generalize more readily to the wider range of tasks that subjects must learn to perform, as opposed to BMIs mapped to outputs of the musculoskeletal system.

Spinal-like regulator facilitates control of a two-degree-of-freedom wrist.

The performance of motor tasks requires the coordinated control and continuous adjustment of myriad individual muscles. The basic commands for the successful performance of a sensorimotor task originate in "higher" centers such as the motor cortex, but the actual muscle activation and resulting torques and motion are considerably shaped by the integrative function of the spinal interneurons. The relative contributions of brain and spinal cord are less clear for reaching movements than for automatic tasks such as locomotion. We have modeled a two-axis, four-muscle wrist joint with realistic musculoskeletal mechanics and proprioceptors and a network of regulatory circuitry based on the classical types of spinal interneurons (propriospinal, monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory, and Ib-inhibitory pathways) and their supraspinal control (via biasing activity, presynaptic inhibition, and fusimotor gain). The modeled system has a very large number of control inputs, not unlike the real spinal cord that the brain must learn to control to produce desired behaviors. It was surprisingly easy to program this model to emulate actual performance in four very different but well described behaviors: (1) stabilizing responses to force perturbations; (2) rapid movement to position target; (3) isometric force to a target level; and (4) adaptation to viscous curl force fields. Our general hypothesis is that, despite its complexity, such regulatory circuitry substantially simplifies the tasks of learning and producing complex movements.

I-NET: interactive neuro-educational technology to accelerate skill learning.

The learning of a novel task currently rely heavily on conventional classroom instruction with qualitative assessment and observation. Introduction of individualized tutorials with integrated neuroscience-based evaluation techniques could significantly accelerate skill acquisition and provide quantitative evidence of successful training. We have created a suite of adaptive and interactive neuro-educational technologies (I-NET) to increase the pace and efficiency of skill learning. It covers four major themes: 1) Integration of brain monitoring into paced instructional tutorials, 2) Identifying psychophysiological characteristics of expertise using a model population, 3) Developing sensor-based feedback to accelerate novice-to-expert transition, 4) Identifying neurocognitive factors that are predictive of skill acquisition to allow early triage and interventions. We selected rifle marksmanship training as the field of application. Rifle marksmanship is a core skill for the Army and Marine Corps and it involves a combination of classroom instructional learning and field practice involving instantiation of a well-defined set of sensory, motor and cognitive skills. The instrumentation that incorporates the I-NET technologies is called the Adaptive Peak Performance Trainer (APPT). Preliminary analysis of pilot study data for performance data from a novice population that used this device revealed an improved learning trajectory.