In the domain of moral decision making, models in which emotion and deliberation constitute competing dual-systems have become increasingly popular. Currently, the favored explanation of this interaction is what Evans (2008) termed a “default-interventionist” (DI) process where moral decisions are the result of a prepotent emotional response, which can be overridden with substantial deliberative effort. Although this “emotion-then-deliberation” sequence is often assumed, existing methods have lacked the requisite process resolution to clearly depict the nature of this interaction. The present work utilized continuous mouse tracking, or response dynamics, to develop and test predictions of these DI models of moral decision making. Study 1 utilized previously published moral dilemmas to validate the method for use with such complex stimuli. Although the data replicated typical choice and RT patterns, the process metrics provided by the response trajectories did not demonstrate the online preference reversals predicted by DI models. Study 2 utilized more rigorously constructed stimuli and an alternative presentation format to provide the strongest possible test of DI predictions, but again failed to show the predicted reversals. In summary, neither experiment provided data in accordance with the predictions of popular DI dual-systems models, which suggests that researchers should consider models allowing for concurrent activation of deliberative and emotional systems, or reconceptualize moral decisions within the typical multiattribute decision framework.

The history of judgment and decision making is defined by a trend toward increasingly nuanced explanations of the decision making process. Recently, process models have become incredibly sophisticated, yet the tools available to directly test these models have not kept pace. These increasingly complex process models require increasingly complex process data by which they can be adequately tested. We propose a new class of data collection that will facilitate evaluation of sophisticated process models. Tracking mouse paths during a continuous response provides an implicit measure of the growth of preference that produces a choice—rather than the current practice of recording just the button press that indicates that choice itself. Recent research in cognitive science (Spivey & Dale, 2006) has shown that cognitive processing can be revealed in these dynamic motor responses. Unlike current process methodologies, these response dynamics studies can demonstrate continuous competition between choice options and even online preference reversals. Here, in order to demonstrate the mechanics and utility of the methodology, we present an example response dynamics experiment utilizing a common multi-alternative decision task.

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

The Jet Propulsion Laboratory (JPL) is embarking on a series of exploration missions to Mars. Long-range rovers with several science instruments on board will traverse many kilometers on the surface. This paper describes some of the research underway at JPL as they pertain to navigation of these long-range rovers. A brief description is provided of the Sojourner rover which recently completed a short-range, near-lander Mars mission. A research rover called Rocky-7 under development at JPL implements a number of new technologies for long-range Mars traverses. The kinematic configuration of this vehicle is described as is tile sensor set. The paper then focuses on state estimation techniques used on-board Rocky-7. A brief review of rover-related state-estimation work is presented and an Extended Kalman Filter framework is introduced. This includes process models for translation, small-angle/quaternion based rotations, gyro bias and wheel contact as well as observation models for a gyro, sun-sensor, and accelerometer. A novel slip constraint technique is developed to allow incorporation into the filter of the highly non-linear kinematics resulting from the rover's rocker-bogey suspension mechanism and wheel-to-ground contact point variations. Simulation results are discussed to allow detailed comparisons with ground-truth data and experimental results from Rocky-7 are presented.