Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty.

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.

Optimal control: when redundancy matters.

A new experiment provides support for optimal feedback control as a theoretical basis of how the motor system responds to perturbations in a context-dependent manner.

Autonomous extraction of optimal flame fronts in OH planar laser-induced fluorescence images.

The location of a flame front is often taken as the point of maximum OH gradient. Planar laser-induced fluorescence of OH can be used to obtain the flame front by extracting the points of maximum gradient. This operation is typically performed using an edge detection algorithm. The choice of operating parameters a priori poses significant problems of robustness when handling images with a range of signal-to-noise ratios. A statistical method of parameter selection originating in the image processing literature is detailed, and its merit for this application is demonstrated. A reduced search space method is proposed to decrease computational cost and render the technique viable for large data sets. This gives nearly identical output to the full method. These methods demonstrate substantial decreases in data rejection compared to the use of a priori parameters. These methods are viable for any application where maximum gradient contours must be accurately extracted from images of species or temperature, even at very low signal-to-noise ratios.

Learning optimal adaptation strategies in unpredictable motor tasks.

Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.

Coupling ecology and economy: modeling optimal release scenarios for summer flounder (Paralichthys dentatus) stock enhancement

Increasing interest in the use of stock enhancement as a management tool necessitates a better understanding of the relative costs and benefits of alternative release strategies. We present a relatively simple model coupling ecology and economic costs to make inferences about optimal release scenarios for summer flounder (Paralichthys dentatus), a subject of stock enhancement interest in North Carolina. The model, parameterized from mark-recapture experiments, predicts optimal release scenarios from both survival and economic standpoints for varyious dates-of-release, sizes-at-release, and numbers of fish released. Although most stock enhancement efforts involve the release of relatively small fish, the model suggests that optimal results (maximum survival and minimum costs) will be obtained when relatively large fish (75–80 mm total length) are released early in the nursery season (April). We investigated the sensitivity of model predictions to violations of the assumption of density-independent mortality by including density-mortality relationships based on weak and strong type-2 and type-3 predator functional responses (resulting in depensatory mortality at elevated densities). Depending on postrelease density, density-mortality relationships included in the model considerably affect predicted postrelease survival and economic costs associated with enhancement efforts, but do not alter the release scenario (i.e. combination of release variables) that produces optimal results. Predicted (from model output) declines in flounder over time most closely match declines observed in replicate field sites when mortality in the model is density-independent or governed by a weak type-3 functional response. The model provides an example of a relatively easy-to-develop predictive tool with which to make inferences about the ecological and economic potential of stock enhancement of summer flounder and provides a template for model creation for additional species that are subjects of stock enhancement interest, but for which limited empirical data exist.