Executive functioning, motor difficulties and developmental coordination disorder

The current study assessed a comprehensive range of executive functions (EFs) in children with poor motor skills, comparing profiles of children with a diagnosis of developmental coordination disorder (DCD) and those identified with motor difficulties (MD). Children in both groups performed more poorly than typically-developing controls on nonverbal measures of working memory, inhibition, planning and fluency, but not on tests of switching. The similar patterns of strengths and weaknesses in children with MD and DCD have important implications for parents, teachers and clinicians, as children with MD may struggle with EF tasks even though their motor difficulties are not identified

Identifying Engagement in Children's Interaction whilst Composing Digital Music at Home

Identifying points of engagement from a person’s interaction with computers could be used to assess their experience and to adapt user interfaces in real-time. However, it is difficult to identify points of engagement unobtrusively; HCI studies typically use retrospective protocols or rely on cumbersome sensors for real-time analysis. We present a case study on how children compose digital music at home in which we remotely identify points of engagement from patterns of interaction with a musical interface. A mixed-methods approach is contributed in which video recordings of children’s interactions whilst composing are labelled for engagement and linked to i) interaction logs from the interface to identify indicators of engagement in interaction, and ii) interview data gathered using a remote video-cued recall technique to understand the experiential qualities of engaging interactions directly from users. We conclude by speculating on how the suggested indicators of engagement inform the design of adaptive music systems

Technology-Rich Ethnography for Examining the Transition to Authentic Problem-Solving in a High School Computer Programming Class

This study utilized elements of technology-rich ethnography to create a rich description of a multi-user virtual environment in a high school computer programming class. Of particular interest was the transition that took place in classroom culture from one characterized by a well-defined problem solving approach to one more indicative of open learning environment. Using technology, high school students created learning activities and resources for use by younger students in the virtual environment. Evidence supported that high school students initially benefitted from the new open environment; however, some immutable elements of the classroom environment presented barriers to peer collaboration and motivation for high-level, creative work. Our findings lend support to the argument that teachers in high school computer programming classes should incorporate the following features in their curricula: open-ended problem solving, real-world clients, group work, student autonomy and ample opportunities for student creative expression.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Drivers of Dyadic Cofeeding Tolerance in Pan: A Composite Measure Approach

This study aimed to construct a composite model of Dyadic Cofeeding Tolerance (DCT) in zoo-housed bonobos and chimpanzees using a validated experimental cofeeding paradigm and to investigate whether components resulting from this model differ between the two species or vary with factors such as sex, age, kinship and social bond strength. Using dimension reduction analysis on five behavioral variables from the experimental paradigm (proximity, aggression, food transfers, negative food behavior, participation), we found a two-factor model: "Tolerant Cofeeding" and "Agonistic Cofeeding". To investigate the role of social bond quality on DCT components alongside species effects, we constructed and validated a novel relationship quality model for bonobos and chimpanzees combined, resulting in two factors: Relationship Value and Incompatibility. Interestingly, bonobos and chimpanzees did not differ in DCT scores, and sex and kinship effects were identical in both species but biased by avoidance of the resource zone by male-male dyads in bonobos. Social bonds impacted DCT similarly in both species, as dyads with high Relationship Value showed more Tolerant Cofeeding, while dyads with higher Relationship Incompatibility showed more Agonistic Cofeeding. We showed that composite DCT models can be constructed that take into account both negative and positive cofeeding behavior. The resulting DCT scores were predicted by sex, kinship and social bonds in a similar fashion in both Pan species, likely reflecting their adaptability to changing socio-ecological environments. This novel operational measure to quantify cofeeding tolerance can now be applied to a wider range of species in captivity and the wild to see how variation in local socio-ecological circumstances influences fitness interdependence and cofeeding tolerance at the dyadic and group levels. This can ultimately lead to a better understanding of how local environments have shaped the evolution of tolerance in humans and other species

Chimpanzees organize their social relationships like humans

Human relationships are structured in a set of layers, ordered from higher (intimate relationships) to lower (acquaintances) emotional and cognitive intensity. This structure arises from the limits of our cognitive capacity and the different amounts of resources required by different relationships. However, it is unknown whether nonhuman primate species organize their affiliative relationships following the same pattern. We here show that the time chimpanzees devote to grooming other individuals is well described by the same model used for human relationships, supporting the existence of similar social signatures for both humans and chimpanzees. Furthermore, the relationship structure depends on group size as predicted by the model, the proportion of high-intensity connections being larger for smaller groups

Fostering appropriate behaviour in rehabilitant orangutans (Pongo pygmaeus)

Rehabilitation centres in Indonesia and Malaysia accommodate displaced orangutans (Pongo pygmaeus and P. abelii) and aim to facilitate their release into the wild by developing in them the skills that are necessary for survival. Regular forest excursions are provided but their efficacy in improving learning of appropriate behaviours is unknown. We observed forty rehabilitating orangutans from the Orangutan Care and Quarantine Centre during three forest excursions each to determine whether their behaviour fostered the development of survival skills. In total 38% of their time was spent in locomotion, particularly quadrupedal arboreal travel (13%), walking (8%), climbing (7%) and vine-swinging (4%). 26.5% of their time was spent 5 m or more from the ground, at heights up to 25 m. Arboreal activities were more 2 common early in the excursions and interaction with c are-givers more common later (hour 1: 0.3% of time; hour 5: 0.9% of time). Animals of lower body weight were significantly more likely to engage in arboreal movement, locomotion in general, eating of bark and leaves, and social play, and less likely to eat insects. Those that had been at the Centre the longest were less likely to perform arboreal activities and significantly more likely to be found standing and at ground level, than those that were there for a shorter time. During this study, many forest food items were consumed, particularly leaves and fruit, but also invertebrates and bark. Little time was spent in sexual behaviour, tool use, nest building or socially-mediated learning, but social play occupied almost 6% of their time. We conclude that regular excursions into the forest are likely to assist in the development of locomotion and feeding skills for survival in rehabilitating orangutans, but special attention is needed to encourage nest building, social activities and arboreal activity. Animals least likely to benefit are heavy animals and those that have been captive for a long time

Robust ecological analysis of camera trap data labelled by a machine learning model

1. Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time‐consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. 2. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case‐study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. 3. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out‐of‐sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. 4. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user‐community with a multi‐platform, multi‐language graphical user interface that can be used to run our model offline.Additional co-authors: Cisquet Kiebou Opepa, Ross T. Pitman, Hugh S. Robinso

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. We modified an off-the-shelf camera trap (Bushnell) and customised existing open-source hardware to rapidly create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding 'uncertain' labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas