Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

It is well established that not only vision but also other sensory modalities affect drivers’ control of their vehicles, and that drivers adapt over time to persistent changes in sensory cues (for example in driving simulators), but the mechanisms underlying these behavioural phenomena are poorly understood. Here, we consider the existing literature on how driver steering in slalom tasks is affected by down-scaling of vestibular cues, and propose, for the first time, a computational model of driver behaviour that can, based on neurobiologically plausible mechanisms, explain the empirically observed effects, namely: decreased task performance and increased steering effort during initial exposure, followed by a partial reversal of these effects as task exposure is prolonged. Unexpectedly, the model also reproduced another previously unexplained empirical finding: a local optimum for motion down-scaling, where path-tracking is better than when one-to-one motion cues are available. Overall, our findings suggest that: (1) drivers make direct use of vestibular information as part of determining appropriate steering actions, and (2) motion down-scaling causes a yaw rate underestimation phenomenon, where drivers behave as if the simulated vehicle is rotating more slowly than it is. However, (3) in the slalom task, a certain degree of such underestimation brings a path-tracking performance benefit. Furthermore, (4) behavioural adaptation in simulated slalom driving tasks may occur due to (a) down-weighting of vestibular cues, and/or (b) increased sensitivity in timing and magnitude of steering corrections, but (c) seemingly not in the form of a full compensatory rescaling of the received vestibular input. The analyses presented here provide new insights and hypotheses about simulated driving and simulator design, and the developed models can be used to support research on multisensory integration and behavioural adaptation in both driving and other task domains

Towards harmonizing competing models: Russian forests' net primary production case study

This paper deals with the issue of reconciling competing stochastic estimates provided by independent sources. We employ an integration method based on a principle of mutual compatibility of prior estimates. The method does not take into account credibility of the sources of the estimates, including their past performance. The quality of integration is evaluated in terms of change in the probability distribution. We use the method to integrate two types of estimates of the annual Net Primary Production (NPP) of the forest ecosystems in seven bioclimatic zones in Russia. The estimates are generated based on an empirical landscape-ecosystem approach and on an ensemble of dynamic global vegetation models; the gaps in thei estimates reach 23%. Elimination of the gaps may help better quantify the input of the terrestrial ecosystems to the global carbon cyce. The main result of this paper is the evidence of applicability of the method for selection a set of candidates for credible integrated estimates of uncertain ecological parameters (like forest NPP) integrating prior estimates