Do multimodal search cues help or hinder teleoperated search and rescue missions?

In search and rescue missions, teleoperated rovers equipped with sensor technology are deployed into harsh environments to search for targets. To support the search task, unimodal/multimodal cues can be presented via visual, acoustic and/or haptic channels. However, human operators often perform the search task in parallel with the driving task, which can cause interference of attentional resources based on multiple resource theory. Navigating corners can be a particularly challenging aspect of remote driving, as described with the Cornering Law. Therefore, search cues should not interfere with cornering. The present research explores how unimodal/multimodal search cues affect cornering performance, with typical communication delays of 50 ms and 500 ms. One-hundred thirty-one participants, distributed into two delay groups, performed a target search task with unimodal/multimodal search cues. Search cues did not interfere with cornering performance with 50 ms delays. For 500 ms delays, search cues presented via the haptic channel significantly interfered with the driving task. Practitioner summary: Teleoperated rovers can support search and rescue missions. Search cues may assist the human operator, but they may also interfere with the task of driving. The study examined interference of unimodal and multimodal search cues. Haptic cues should not be implemented for systems with a delay of 500 ms or more.

Object affordances from the perspective of an avatar.

Humans often interact with avatars in video gaming, workplace, or health applications, for instance. The present research studied object affordances from an avatar's perspective. In two experiments, participants responded to objects with a left/right keypress, indicating whether the objects were upright or inverted. Task-irrelevant objects' handles were aligned with either the left or right hand of the actor and/or avatar. We hypothesized that actors respond faster when the handles are aligned, as compared to non-aligned, with the respective avatar hand (spatial alignment effect or object-based Simon effect). In Experiment 1, the spatial alignment effect was increased through the presentation of avatar hands as compared to when no hands were presented. In Experiment 2, the avatar perspective was rotated by 90° to the right and left of the actor's view. Here, the spatial alignment effect was guided by the avatar, suggesting that the actors took its perspective when perceiving objects' affordances.

Know-how or know-why? The role of hybrid electric vehicle drivers' acquisition of eco-driving knowledge for eco-driving success.

Hybrid electric vehicles (HEVs) can contribute to sustainable transport. Yet, their real-world energy efficiency depends on HEV drivers' eco-driving behaviour. Eco-driving knowledge is key for successful eco-driving. The present research focused on the role of perceived strategy knowledge (know-how) versus technical system knowledge (know-why) in a study with 121 HEV drivers. The relationship between knowledge components and knowledge acquisition processes, as well as fuel efficiency, were examined. Structural equation modelling results indicated that perceived strategy knowledge was related to acquisition by testing (i.e., interacting with the vehicle and its interfaces) and reading (i.e., manuals, books and websites) while technical system knowledge was only related to acquisition by reading. In contrast to technical system knowledge, perceived strategy knowledge was no significant predictor of fuel efficiency. The results indicated that emphasis should be put into promoting technical system knowledge (e.g., by tutoring systems) to support motivated drivers' in achieving higher fuel efficiency.

Statistical power in two-level models: A tutorial based on Monte Carlo simulation.

The estimation of power in two-level models used to analyze data that are hierarchically structured is particularly complex because the outcome contains variance at two levels that is regressed on predictors at two levels. Methods for the estimation of power in two-level models have been based on formulas and Monte Carlo simulation. We provide a hands-on tutorial illustrating how a priori and post hoc power analyses for the most frequently used two-level models are conducted. We describe how a population model for the power analysis can be specified by using standardized input parameters and how the power analysis is implemented in SIMR, a very flexible power estimation method based on Monte Carlo simulation. Finally, we provide case-sensitive rules of thumb for deriving sufficient sample sizes as well as minimum detectable effect sizes that yield a power ≥ .80 for the effects and input parameters most frequently analyzed by psychologists. For medium variance components, the results indicate that with lower level (L1) sample sizes up to 30 and higher level (L2) sample sizes up to 200, medium and large fixed effects can be detected. However, small L2 direct- or cross-level interaction effects cannot be detected with up to 200 clusters. The tutorial and guidelines should be of help to researchers dealing with multilevel study designs such as individuals clustered within groups or repeated measurements clustered within individuals. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Role of Interaction Patterns with Hybrid Electric Vehicle Eco-Features for Drivers' Eco-Driving Performance.

OBJECTIVE: The objective of the present research was to understand drivers' interaction patterns with hybrid electric vehicles' (HEV) eco-features (electric propulsion, regenerative braking, neutral mode) and their relationship to fuel efficiency and driver characteristics (technical system knowledge, eco-driving motivation). BACKGROUND: Eco-driving (driving behaviors performed to achieve higher fuel efficiency) has the potential to reduce CO2 emissions caused by road vehicles. Eco-driving in HEVs is particularly challenging due to the systems' dynamic energy flows. As a result, drivers are likely to show diverse eco-driving behaviors, depending on factors like knowledge and motivation. The eco-features represent an interface for the control of the systems' energy flows. METHOD: A sample of 121 HEV drivers who had constantly logged their fuel consumption prior to the study participated in an online questionnaire. RESULTS: Drivers' interaction patterns with the eco-features were related to fuel efficiency. A common factor was identified in an exploratory factor analysis, characterizing the intensity of actively dealing with electric energy, which was also related to fuel efficiency. Driver characteristics were not related to this factor, yet they were significant predictors of fuel efficiency. CONCLUSION: From the perspective of user-energy interaction, the relationship of the aggregated factor to fuel efficiency emphasizes the central role of drivers' perception of and interaction with energy conversions in determining HEV eco-driving success. APPLICATION: To arrive at an in-depth understanding of drivers' eco-driving behaviors that can guide interface design, authors of future research should be concerned with the psychological processes that underlie drivers' interaction patterns with eco-features.