Spatial memory training in a citizen science context

Let's Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human-Robot Interaction at Work

OBJECTIVE

This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human-robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness).

BACKGROUND

Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing.

METHOD

Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study.

RESULTS

Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown.

CONCLUSION

Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features' effects.

APPLICATION

Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context.

Nudging and citizen science: The effectiveness of feedback in energy-demand management

Nudging is a framework for directing individuals toward better behavior, both for personal and societal benefits, through heuristics that drive the decision-making process but without preventing any available choice. Considering the Grand Challenges that our society faces today, nudging represents an effective framework to tackle some of these pressing issues. In this work, we assessed the effectiveness of informational nudges in the form of detailed, customized feedback, within an energy-demand-management project. The project aligns energy production and demand, thereby reducing greenhouse gases and pollutant emissions to mitigate climate change. We also offered evidence that this kind of feedback is efficacious in involving individuals as citizen scientists, who volunteer their efforts toward the success of the environmentally-related aim of the project. The results of this research - based on surveys, electroencephalography measurements and online participation measures - indicate that feedback can be an effective tool to steer participants' behavior under the libertarian paternalistic view of nudging, increase their motivation to contribute to citizen science, and improve their awareness about environmentally-related issues. In so doing, we provide evidence that nudging and citizen science can be jointly adopted toward the mitigation of pressing environmental issues.

Using demographics toward efficient data classification in citizen science: a Bayesian approach

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them.

Virtual Enactment Effect on Memory in Young and Aged Populations: a Systematic Review

BACKGROUND

Spatial cognition is a critical aspect of episodic memory, as it provides the scaffold for events and enables successful retrieval. Virtual enactment (sensorimotor and cognitive interaction) by means of input devices within virtual environments provides an excellent opportunity to enhance encoding and to support memory retrieval with useful traces in the brain compared to passive observation.

METHODS

We conducted a systematic review with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines concerning the virtual enactment effect on spatial and episodic memory in young and aged populations. We aim at giving guidelines for virtual enactment studies, especially in the context of aging, where spatial and episodic memory decline.

RESULTS

Our findings reveal a positive effect on spatial and episodic memory in the young population and promising outcomes in aging. Several cognitive factors (e.g., executive function, decision-making, and visual components) mediate memory performances. Findings should be taken into account for future interventions in aging.

CONCLUSIONS

The present review sheds light on the key role of the sensorimotor and cognitive systems for memory rehabilitation by means of a more ecological tool such as virtual reality and stresses the importance of the body for cognition, endorsing the view of an embodied mind.

Producing knowledge by admitting ignorance: Enhancing data quality through an "I don't know" option in citizen science

The "noisy labeler problem" in crowdsourced data has attracted great attention in recent years, with important ramifications in citizen science, where non-experts must produce high-quality data. Particularly relevant to citizen science is dynamic task allocation, in which the level of agreement among labelers can be progressively updated through the information-theoretic notion of entropy. Under dynamic task allocation, we hypothesized that providing volunteers with an "I don't know" option would contribute to enhancing data quality, by introducing further, useful information about the level of agreement among volunteers. We investigated the influence of an "I don't know" option on the data quality in a citizen science project that entailed classifying the image of a highly polluted canal into "threat" or "no threat" to the environment. Our results show that an "I don't know" option can enhance accuracy, compared to the case without the option; such an improvement mostly affects the true negative rather than the true positive rate. In an information-theoretic sense, these seemingly meaningless blank votes constitute a meaningful piece of information to help enhance accuracy of data in citizen science.

In Memoriam: Paolo Cappa

Prof. Paolo Cappa passed away on 26 August 2016, at the age of 59, after a long and courageous fight against cancer. Paolo Cappa was a Professor in Mechanical and Thermal Measurements and Experimental Biomechanics in the Department of Mechanical and Aerospace Engineering of Sapienza University of Rome, where he had also served as the Head of the Department, and a Research Professor in the Department of Mechanical and Aerospace Engineering of New York University Tandon School of Engineering. During his intense, yet short, career, he made several significant scientific contributions within the discipline of Mechanical and Thermal Measurements, pioneering fundamental applications to Biomechanics. He co-founded the Motion Analysis and Robotics Laboratory (MARLab) within the Neurorehabilitation Division of IRCCS Pediatric Hospital “Bambino Gesu”, in Rome, to fuel transitional research from the laboratory to clinical practice. Through collaboration with neurologists and physiatrists at MARLab, Prof. Cappa led the development of a powerful array of novel mechanical solutions to wearable robotics for pediatric patients, addressing dramatic needs for children’s health and contributing to the training of an entire generation of Mechanical Engineering students.

Measuring Cognitive Load in Embodied Learning Settings

In recent years, research on embodied cognition has inspired a number of studies on multimedia learning and instructional psychology. However, in contrast to traditional research on education and multimedia learning, studies on embodied learning (i.e., focusing on bodily action and perception in the context of education) in some cases pose new problems for the measurement of cognitive load. This review provides an overview over recent studies on embodied learning in which cognitive load was measured using surveys, behavioral data, or physiological measures. The different methods are assessed in terms of their success in finding differences of cognitive load in embodied learning scenarios. At the same time, we highlight the most important challenges for researchers aiming to include these measures into their study designs. The main issues we identified are: (1) Subjective measures must be appropriately phrased to be useful for embodied learning; (2) recent findings indicate potentials as well as problematic aspects of dual-task measures; (3) the use of physiological measures offers great potential, but may require mobile equipment in the context of embodied scenarios; (4) meta-cognitive measures can be useful extensions of cognitive load measurement for embodied learning.