Left gaze bias in humans, rhesus monkeys and domestic dogs

While viewing faces, human adults often demonstrate a natural gaze bias towards the left visual field, that is, the right side of the viewee’s face is often inspected first and for longer periods. Using a preferential looking paradigm, we demonstrate that this bias is neither uniquely human nor limited to primates, and provide evidence to help elucidate its biological function within a broader social cognitive framework. We observed that 6-month-old infants showed a wider tendency for left gaze preference towards objects and faces of different species and orientation, while in adults the bias appears only towards upright human faces. Rhesus monkeys showed a left gaze bias towards upright human and monkey faces, but not towards inverted faces. Domestic dogs, however, only demonstrated a left gaze bias towards human faces, but not towards monkey or dog faces, nor to inanimate object images. Our findings suggest that face- and species-sensitive gaze asymmetry is more widespread in the animal kingdom than previously recognised, is not constrained by attentional or scanning bias, and could be shaped by experience to develop adaptive behavioural significance

Face processing limitation to own species in primates: a comparative study in brown capuchins, Tonkean macaques and humans

Most primates live in social groups which survival and stability depend on individuals' abilities to create strong social relationships with other group members. The existence of those groups requires to identify individuals and to assign to each of them a social status. Individual recognition can be achieved through vocalizations but also through faces. In humans, an efficient system for the processing of own species faces exists. This specialization is achieved through experience with faces of conspecifics during development and leads to the loss of ability to process faces from other primate species. We hypothesize that a similar mechanism exists in social primates. We investigated face processing in one Old World species (genus Macaca) and in one New World species (genus Cebus). Our results show the same advantage for own species face recognition for all tested subjects. This work suggests in all species tested the existence of a common trait inherited from the primate ancestor: an efficient system to identify individual faces of own species only

Negative Results in Computer Vision: A Perspective

A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

The primate visual system achieves remarkable visual object recognition performance even in brief presentations and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations such as the amount of noise, the number of neural recording sites, and the number trials, and computational limitations such as the complexity of the decoding classifier and the number of classifier training examples. In this work we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.Comment: 35 pages, 12 figures, extends and expands upon arXiv:1301.353

Computational ethology for primate sociality: a novel paradigm for computer-vision-based analysis of animal behaviour

Research in the biological and wildlife sciences is increasingly reliant on video data for measuring animal behaviour, however large-scale analysis is often limited by the time and resources it takes to process video archives. Computer vision holds serious potential to unlock these datasets to analyse behaviour at an unprecedented level of scale, depth and reliability, however thus far a framework for processing and analysing behaviour from large-scale video datasets is lacking. This thesis attempts to solve this problem by developing the theory and methods for capturing long-term sociality of animal populations from longitudinal video archives, laying the foundations for an emerging field; computational ethology of animals in the wild. It makes several key contributions by a) establishing the first unified longitudinal video dataset of wild chimpanzee stone tool use across a 30 year period, and building a framework for collaborative research using cloud-technology b) developing a set of computational tools to allow for processing of large volumes of video data for automated individual identification and behaviour recognition c) applying these automated methods to validate use for social network analysis and d) measuring the social dynamics and behaviour of a group of wild chimpanzees living in the forest of Bossou, Guinea, West Africa. In Chapter 1 I introduce the theoretical and historical context for the thesis, and outline the novel methodological framework for using computer vision to measure animal social behaviour in video. In Chapter 2 I introduce the methodology for processing and managing a longitudinal video archive, and future directions for a new framework for collaborative research workflows in the wildlife sciences using cloud technology. In Chapter 3 I lay the foundations of this framework for analysing behaviour and unlocking video datasets, using deep learning and face recognition. In Chapter 4 I evaluate the robustness of the method for modelling long-term sociality and social networks at Bossou and test whether life history variables predict individual-level sociality patterns. In Chapter 5 I introduce the final component to this framework for measuring long-term animal behaviour, through audiovisual behavioural recognition of chimpanzee nut-cracking. In my final chapter (6) I discuss the main contributions, limitations and future directions for research. Overall this thesis integrates a diverse range of interdisciplinary methods and concepts from primatology, ethology, engineering, and computer vision, to build the foundations for further exploration of cognition, ecology and evolution in wild animals using automated methods

What does the amygdala contribute to social cognition?

The amygdala has received intense recent attention from neuroscientists investigating its function at the molecular, cellular, systems, cognitive, and clinical level. It clearly contributes to processing emotionally and socially relevant information, yet a unifying description and computational account have been lacking. The difficulty of tying together the various studies stems in part from the sheer diversity of approaches and species studied, in part from the amygdala's inherent heterogeneity in terms of its component nuclei, and in part because different investigators have simply been interested in different topics. Yet, a synthesis now seems close at hand in combining new results from social neuroscience with data from neuroeconomics and reward learning. The amygdala processes a psychological stimulus dimension related to saliency or relevance; mechanisms have been identified to link it to processing unpredictability; and insights from reward learning have situated it within a network of structures that include the prefrontal cortex and the ventral striatum in processing the current value of stimuli. These aspects help to clarify the amygdala's contributions to recognizing emotion from faces, to social behavior toward conspecifics, and to reward learning and instrumental behavior

Does gaze direction modulate facial expression processing in children with autism spectrum disorder?

Two experiments investigated whether children with autism spectrum disorder (ASD) integrate relevant communicative signals, such as gaze direction, when decoding a facial expression. In Experiment 1, typically developing children (9–14 years old; n = 14) were faster at detecting a facial expression accompanying a gaze direction with a congruent motivational tendency (i.e., an avoidant facial expression with averted eye gaze) than those with an incongruent motivational tendency. Children with ASD (9–14 years old; n = 14) were not affected by the gaze direction of facial stimuli. This finding was replicated in Experiment 2, which presented only the eye region of the face to typically developing children (n = 10) and children with ASD (n = 10). These results demonstrated that children with ASD do not encode and/or integrate multiple communicative signals based on their affective or motivational tendency

Automated face recognition using deep neural networks produces robust primate social networks and sociality measures

Longitudinal video archives of behaviour are crucial for examining how sociality shifts over the lifespan in wild animals. New approaches adopting computer vision technology hold serious potential to capture interactions and associations between individuals in video at large scale; however, such approaches need a priori validation, as methods of sampling and defining edges for social networks can substantially impact results.Here, we apply a deep learning face recognition model to generate association networks of wild chimpanzees using 17 years of a video archive from Bossou, Guinea. Using 7 million detections from 100 h of video footage, we examined how varying the size of fixed temporal windows (i.e. aggregation rates) for defining edges impact individual-level gregariousness scores.The highest and lowest aggregation rates produced divergent values, indicating that different rates of aggregation capture different association patterns. To avoid any potential bias from false positives and negatives from automated detection, an intermediate aggregation rate should be used to reduce error across multiple variables. Individual-level network-derived traits were highly repeatable, indicating strong inter-individual variation in association patterns across years and highlighting the reliability of the method to capture consistent individual-level patterns of sociality over time. We found no reliable effects of age and sex on social behaviour and despite a significant drop in population size over the study period, individual estimates of gregariousness remained stable over time.We believe that our automated framework will be of broad utility to ethology and conservation, enabling the investigation of animal social behaviour from video footage at large scale, low cost and high reproducibility. We explore the implications of our findings for understanding variation in sociality patterns in wild ape populations. Furthermore, we examine the trade-offs involved in using face recognition technology to generate social networks and sociality measures. Finally, we outline the steps for the broader deployment of this technology for analysis of large-scale datasets in ecology and evolution.info:eu-repo/semantics/publishedVersio