A State Space Approach to Dynamic Modeling of Mouse-Tracking Data

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

Data collection explicitness as a micro-suasor: Its effect on sensitivity judgment and safety decisions

It has been found that transparency implemented in the name of the users' safety can instead encourage the users to trust the system and to disclose their personal data. In this work we consider whether the transparency of the data collection technique can work in this way. The study (N = 40) compares an explicit technique (questionnaires) with an implicit one (eye-tracker). The actual sensitivity of the data collected was also varied, sensitive (popularity) vs. non- sensitive (usability).The results suggest that, when judging general data sensitivity, the transparency of the data collection procedure tends to work as a heuristic; this is not the case when more specific judgments or decisions are asked

Transparency Heuristic: Effect of Implicitness of Online Data Acquisition on Sensitivity Perception

We present a study that investigates whether the transparency of the data acquisition technique can work as a heuristic when making evaluations about data protection and sensitivity. The study (N\u2009=\u200940) compares an explicit data acquisition technique (questionnaires) with an implicit one (eye-tracker) and varies also the actual sensitivity of the data collected (popularity evaluation vs. usability evaluation). The results suggest that, when judging general data sensitivity, the transparency of the data collection procedure might work as a heuristic; instead if more specific judgments or decisions are asked this effect is not observed. Implications are discussed

Reducing Elevated Gravitational-Force Events through visual feedback: a simulator study

Road traffic accidents represent one of the leading causes of death across all age groups globally. Most of these accidents can be directly attributed to drivers\u2019 failure to select the correct driving speed. Thus, actions aimed to mitigate inappropriate driving performance, including speeding, are needed. Here, we used a dynamic driving simulator to investigate the effects of different real-time coaching programs on driving performance, specifically on the occurrence of Elevated Gravitational-Force Events (EGFEs). Forty-three drivers underwent a two-day evaluation. On the first day, participants \u2013after an initial screening and depending on their driving style\u2013 were divided into two groups: defensive vs. aggressive drivers. On the second day, they received a different type of real time visual feedback based on their driving performance. For each of the two driving style groups, half of the drivers received contingent positive feedback (when smooth driving events occurred), the other half received contingent negative feedback (when harsh driving events occurred). Thus, there were four groups based on driving style and feedback. Overall, results showed that among aggressive drivers contingent feedback \u2013independently from its type\u2013 reduces the occurrence of EGFEs. Potential applications of the proposed methodology include its use for Pay-how-you-drive programs aimed to improve driver speed control