RESUMEN
The use of assistance systems aimed at reducing road fatalities is spreading, especially for car drivers, but less effort has been devoted to developing and testing similar systems for powered two-wheelers (PTWs). Considering that over speeding represents one of the main causal factors in road crashes and that riders are more vulnerable than drivers, in the present study we investigated the effectiveness of an assistance system which signaled speed limit violations during a simulated moped-driving task, in optimal and poor visibility conditions. Participants performed four conditions of simulated riding: one baseline condition without Feedback, one Fog condition in which visual feedback was provided so as to indicate to the participants when a speed limit (lower than that indicated by the traffic signals) was exceeded, and two post-Feedback conditions with and without Fog, respectively, in which no feedback was delivered. Results showed that participants make fewer speeding violations when the feedback is not provided, after 1 month, and regardless of the visibility condition. Finally, the feedback has been proven effective in reducing speed violations in participants with an aggressive riding style, as measured in the baseline session.
RESUMEN
Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and lexical decision tasks. Mouse-tracking data are commonly analyzed using a two-step procedure which first summarizes individuals' hand trajectories with independent measures, and then applies standard statistical models on them. However, this approach can be problematic in many cases. In particular, it does not provide a direct way to capitalize the richness of hand movement variability within a consistent and unified representation. In this article we present a novel, unified framework for mouse-tracking data. Unlike standard approaches to mouse-tracking, our proposal uses stochastic state-space modeling to represent the observed trajectories in terms of both individual movement dynamics and experimental variables. The model is estimated via a Metropolis-Hastings algorithm coupled with a non-linear recursive filter. The characteristics and potentials of the proposed approach are illustrated using a lexical decision case study. The results highlighted how dynamic modeling of mouse-tracking data can considerably improve the analysis of mouse-tracking tasks and the conclusions researchers can draw from them.