Beeswax rock art from Limmen National Park (Northern Territory), northern Australia: new insights into technique-based patterning and absence in the rock art record

The known geographic distribution of beeswax rock art is largely restricted to the Arnhem Land plateau and Kimberley regions of northern Australia. While considerable research has focussed on the antiquity and meaning of beeswax rock art, much less attention has been directed to the nature and extent of the distribution pattern for this unique motif production technique. In this article, we present details of two beeswax motifs recently discovered in Marra Country at Limmen National Park (southwest Gulf of Carpentaria, Northern Territory). In the first instance, the motifs are explored in the context of their meaning, drawing on ethnography collected in the region over the last 40 years. The motifs are then used as a platform to engage with questions around the low frequency, and in some cases complete absence, of beeswax rock art across other areas of northern Australia. While it is highly unlikely there is one single, homogenous explanation for this in the Gulf country and northeastern Australia, we suggest that exploring the social, cultural and relational understandings of beeswax in these areas offers considerable potential to understand better how people engaged with and inscribed their cultural landscapes

Inferring user tasks in pedestrian navigation from eye movement data in real-world environments

Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing