28 research outputs found
Human search for a target on a textured background is consistent with a stochastic model
Previous work has demonstrated that search for a target in noise is consistent with the predictions of the optimal search strategy, both in the spatial distribution of fixation locations and in the number of fixations observers require to find the target. In this study we describe a challenging visual-search task and compare the number of fixations required by human observers to find the target to predictions made by a stochastic search model. This model relies on a target-visibility map based on human performance in a separate detection task. If the model does not detect the target, then it selects the next saccade by randomly sampling from the distribution of saccades that human observers made. We find that a memoryless stochastic model matches human performance in this task. Furthermore, we find that the similarity in the distribution of fixation locations between human observers and the ideal observer does not replicate: Rather than making the signature doughnut-shaped distribution predicted by the ideal search strategy, the fixations made by observers are best described by a central bias. We conclude that, when searching for a target in noise, humans use an essentially random strategy, which achieves near optimal behavior due to biases in the distributions of saccades we have a tendency to make. The findings reconcile the existence of highly efficient human search performance with recent studies demonstrating clear failures of optimality in single and multiple saccade tasks
Challenges in Collaborative HRI for Remote Robot Teams
Collaboration between human supervisors and remote teams of robots is highly
challenging, particularly in high-stakes, distant, hazardous locations, such as
off-shore energy platforms. In order for these teams of robots to truly be
beneficial, they need to be trusted to operate autonomously, performing tasks
such as inspection and emergency response, thus reducing the number of
personnel placed in harm's way. As remote robots are generally trusted less
than robots in close-proximity, we present a solution to instil trust in the
operator through a `mediator robot' that can exhibit social skills, alongside
sophisticated visualisation techniques. In this position paper, we present
general challenges and then take a closer look at one challenge in particular,
discussing an initial study, which investigates the relationship between the
level of control the supervisor hands over to the mediator robot and how this
affects their trust. We show that the supervisor is more likely to have higher
trust overall if their initial experience involves handing over control of the
emergency situation to the robotic assistant. We discuss this result, here, as
well as other challenges and interaction techniques for human-robot
collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019
Workshop: The Challenges of Working on Social Robots that Collaborate with
People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing
Systems, May 2019, Glasgow, U
Visualising COVID-19 Research
The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a
new strain of coronavirus, causing the COVID-19 pandemic, and radically
changing our lives and work conditions. Many scientists are working tirelessly
to find a treatment and a possible vaccine. Furthermore, governments,
scientific institutions and companies are acting quickly to make resources
available, including funds and the opening of large-volume data repositories,
to accelerate innovation and discovery aimed at solving this pandemic. In this
paper, we develop a novel automated theme-based visualisation method, combining
advanced data modelling of large corpora, information mapping and trend
analysis, to provide a top-down and bottom-up browsing and search interface for
quick discovery of topics and research resources. We apply this method on two
recently released publications datasets (Dimensions' COVID-19 dataset and the
Allen Institute for AI's CORD-19). The results reveal intriguing information
including increased efforts in topics such as social distancing; cross-domain
initiatives (e.g. mental health and education); evolving research in medical
topics; and the unfolding trajectory of the virus in different territories
through publications. The results also demonstrate the need to quickly and
automatically enable search and browsing of large corpora. We believe our
methodology will improve future large volume visualisation and discovery
systems but also hope our visualisation interfaces will currently aid
scientists, researchers, and the general public to tackle the numerous issues
in the fight against the COVID-19 pandemic.Comment: 11 pages. 10 figures. Preprint paper made available here prior to
submission. Update: special characters correcte