Use of wearable physiological sensors to predict cognitive workload in a visuospatial learning task.

BACKGROUND: Increased cognitive workload, sometimes known as mental strain or mental effort, has been associated with reduced performance. OBJECTIVE: The use of physiological monitoring was investigated to predict cognitive workload and performance. METHODS: Twenty-one participants completed a 10-minute seated rest, a visuospatial learning task modeled after crane operation, and the Stroop test, an assessment that measures cognitive interference. Heart rate, heart rate variability, electrodermal activity, skin temperature, and electromyographic activity were collected. RESULTS: It was found that participants' ability to learn the simulated crane operation task was inversely correlated with self-reported frustration. Significant changes were also found in physiological metrics in the simulation with respect to rest, including an increase in heart rate, electrodermal activity, and trapezius muscle activity; heart rate and muscle activity were also correlated with simulation performance. The relationship between physiological measures and self-reported workload was modeled and it was found that muscle activity and high frequency power, a measure of heart rate variability, were significantly associated with the workload reported. CONCLUSIONS: The findings support the use of physiological monitoring to inform real time decision making (e.g., identifying individuals at risk of injury) or training decisions (e.g., by identifying individuals that may benefit from additional training even when no errors are observed).

Dynamic motor tracking is sensitive to subacute mTBI.

Effective screening for mild traumatic brain injury (mTBI) is critical to accurate diagnosis, intervention, and improving outcomes. However, detecting mTBI using conventional clinical techniques is difficult, time intensive, and subject to observer bias. We examine the use of a simple visuomotor tracking task as a screening tool for mTBI. Thirty participants, 16 with clinically diagnosed mTBI (mean time since injury: 36.4 ± 20.9 days (95 % confidence interval); median = 20 days) were asked to squeeze a hand dynamometer and vary their grip force to match a visual, variable target force for 3 min. We found that controls outperformed individuals with mTBI; participants with mTBI moved with increased variability, as quantified by the standard deviation of the tracking error. We modeled participants' feedback response-how participants changed their grip force in response to errors in position and velocity-and used model parameters to classify mTBI with a sensitivity of 87 % and a specificity of 93 %, higher than several standard clinical scales. Our findings suggest that visuomotor tracking could be an effective supplement to conventional assessment tools to screen for mTBI and track mTBI symptoms during recovery.

Brevity of haptic force perturbations induces heightened adaptive sensitivity.

We have exposed human participants to both full-movement and pulsatile viscous force perturbations to study the effect of force duration on the incremental transformation of sensation into adaptation. Traditional views of movement biomechanics could suggest that pulsatile forces would largely be attenuated as stiffness and viscosity act as a natural low-pass filter. Sensory transduction, however, tends to react to changes in stimuli and therefore could underlie heightened sensitivity to briefer, pulsatile forces. Here, participants adapted within perturbation duration conditions in a manner proportionate to sensed force and positional errors. Across perturbation conditions, we found participants had greater adaptive sensitivity when experiencing pulsatile forces rather than full-movement forces. In a follow-up experiment, we employed error-clamped, force channel trials to determine changes in predictive force generation. We found that while participants learned to closely compensate for the amplitude and breadth of full-movement forces, they exhibited a persistent mismatch in amplitude and breadth between adapted motor output and experienced pulsatile forces. This mismatch could generate higher salience of error signals that contribute to heightened sensitivity to pulsatile forces.

Visual salience affects performance in a working memory task.

Many studies of bottom-up visual attention have focused on identifying which features of a visual stimulus render it salient--i.e., make it "pop out" from its background--and on characterizing the extent to which salience predicts eye movements under certain task conditions. However, few studies have examined the relationship between salience and other cognitive functions, such as memory. We examined the impact of visual salience in an object-place working memory task, in which participants memorized the position of 3-5 distinct objects (icons) on a two-dimensional map. We found that their ability to recall an object's spatial location was positively correlated with the object's salience, as quantified using a previously published computational model (Itti et al., 1998). Moreover, the strength of this relationship increased with increasing task difficulty. The correlation between salience and error could not be explained by a biasing of overt attention in favor of more salient icons during memorization, since eye-tracking data revealed no relationship between an icon's salience and fixation time. Our findings show that the influence of bottom-up attention extends beyond oculomotor behavior to include the encoding of information into memory.

Trial-by-trial motor adaptation: a window into elemental neural computation.

How does the brain compute? To address this question, mathematical modelers, neurophysiologists, and psychophysicists have sought behaviors that provide evidence of specific neural computations. Human motor behavior consists of several such computations [Shadmehr, R., Wise, S.P. (2005). MIT Press: Cambridge, MA], such as the transformation of a sensory input to a motor output. The motor system is also capable of learning new transformations to produce novel outputs; humans have the remarkable ability to alter their motor output to adapt to changes in their own bodies and the environment [Wolpert, D.M., Ghahramani, Z. (2000). Nat. Neurosci., 3: 1212-1217]. These changes can be long term, through growth and changing body proportions, or short term, through changes in the external environment. Here we focus on trial-by-trial adaptation, the transformation of individually sensed movements into incremental updates of adaptive control. These investigations have the promise of revealing important basic principles of motor control and ultimately guiding a new understanding of the neuronal correlates of motor behaviors.

Trial-by-trial transformation of error into sensorimotor adaptation changes with environmental dynamics.

Humans can rapidly change their motor output to make goal-directed reaching movements in a new environment. Theories that describe this adaptive process have long presumed that adaptive steps scale proportionally with error. Here we show that while performing a novel reaching task, participants did not adopt a fixed learning rule, but instead modified their adaptive response based on the statistical properties of the movement environment. We found that as the directional bias of the force distribution shifted from strongly biased to unbiased, participants transitioned from an adaptive process that scaled proportionally with error to one that adapted to the direction, but not magnitude, of error. Participants also modified their response as the likelihood of the perturbation changed; as the likelihood decreased from 80 to 20% of trials, participants adopted an increasingly disproportional strategy. We propose that people can rapidly switch between learning processes within minutes of experiencing a novel environment.

Motor adaptation to single force pulses: sensitive to direction but insensitive to within-movement pulse placement and magnitude.

Although previous experiments have identified that errors in movement induce adaptation, the precise manner in which errors determine subsequent control is poorly understood. Here we used transient pulses of force, distributed pseudo-randomly throughout a movement set, to study how the timing of feedback within a movement influenced subsequent predictive control. Human subjects generated a robust adaptive response in postpulse movements that opposed the pulse direction. Regardless of the location or magnitude of the pulse, all pulses yielded similar changes in predictive control. All current supervised and unsupervised theories of motor learning presume that adaptation is proportional to error. Current neural models that broadly encode movement velocity and adapt proportionally to motor error can mimic human insensitivity to pulse location, but cannot mimic human insensitivity to pulse magnitude. We conclude that single trial adaptation to force pulses reveals a categorical strategy that humans adopt to counter the direction, rather than the magnitude, of movement error.