Developing and validating attention bias tools for assessing trait and state affect in animals: A worked example with Macaca mulatta

Attention bias is a new approach to assessing animal affect that has shown promising results in several animal species. It describes a tendency to preferentially attend to emotional compared to neutral cues and is influenced by underlying affect. It is important in the early days of this new field that we develop widely utilisable methods and incorporate lessons from the human literature from which tasks are adapted. This fundamental knowledge is critical to the development of standardised and sensitive tools, and the validation of experimental protocols to ensure best practice. Here, we describe protocols for two preferential-looking attention bias tasks. Study 1 involved a manual task using freely available low-cost materials. Study 2 used an automated task requiring specialist equipment and programming, but presumably less prone to noisy data. Tasks were tested with 109 socially housed rhesus macaques, Macaca mulatta, who had been trained to sit by a target, but received no other training. Tasks involved showing animals emotional face pairs (threat-neutral), and subsequent blind coding of video for duration of looking at either face. Three measures of social attention were examined: time spent looking at the threat face (THR), total time looking at the threat-neutral face pair overall (TL), and attention bias difference score (ABD) calculated as time spent looking at the neutral face subtracted from time spent looking at the threat face. Based on the human literature and early primate work, the influence of five potential confounding factors on attention was assessed: trial number, stimulus ID, previous testing experience, time of day and visual field to which the threat face was presented; as were several life history factors: sex, age, and social rank. Both tasks revealed stable individual differences in baseline social attention (THR and TL: effect sizes = 0.15−0.31; repeatabilities = 0.12−0.26; suggesting sensitivity to trait affect), but not ABD (which may be more sensitive to brief shifts in emotion state). All potential confounding factors had a significant effect on at least one measure of social attention. For a subset of monkeys who took part in both Study 1 and Study 2 several years apart (n = 18), there was significant reproducibility between tasks for all three measures (R = 0.15−0.63), supporting an argument for stable individual differences in baseline attention bias, and validating the two tasks for measuring the same trait. The attention bias method shows promise for further development of standardised protocols with animals. We provide framework and recommendations for future method development

A machine learning approach to predict perceptual decisions: an insight into face pareidolia

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using differential hemispheric asymmetry features based on large-scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain activities could achieve a classification accuracy, discriminating face from no-face perception, of 75% across trials. The time–frequency features representing hemispheric asymmetry yielded the best classification performance, and prestimulus alpha oscillations were found to be mostly involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision making

Primates in the Urban Mosaic: Terminology, Flexibility, and Management

Continuous human expansion is affecting landscape composition, in particular through urbanisation. Wildlife persistence in the urban-mosaic is generally negatively affected; however, many primate species show behavioural plasticity and thrive in the urban-mosaic. Urban primates often show selective behaviours in the urban-mosaic, e.g. responses to anthropogenic food resources. However, as the urban-mosaic becomes more prominent and important for biodiversity conservation and management, clearer definitions and terminology used to describe the urban-mosaic are needed. Therefore, we use this chapter to review current definitions and suggest using the term ‘mosaic’ to discuss urban landscape ecology moving forward. Throughout our review, we consider the urban-mosaic complexity and emphasise the value of considering quantified anthropogenic disturbance and species-specific knowledge in urban primate ecology. We suggest that management focus on the multiple facets of the urban-mosaic, both human and primate derived, and discuss the benefits of biodiversity, conservation and human-primate coexistence

Energy balance in patients with advanced NSCLC, metastatic melanoma and metastatic breast cancer receiving chemotherapy - A longitudinal study

Chemotherapy exerts a variable effect on nutritional status. It is not known whether loss of body fat or fat-free mass (FFM) during chemotherapy relates to diminished dietary intake, failure to meet elevated energy requirements, or to the presence of an acute-phase response. We sought to determine prospective measurements of body mass and composition, resting energy expenditure, energy and protein intake, and C-reactive protein over a course of chemotherapy in 82 patients with advanced cancer. There was a large dropout from the study. Prospective measurements were obtained in 19 patients with non-small-cell lung cancer (NSCLC), 12 with metastatic melanoma and 10 with metastatic breast cancer. There were significant increases in energy intake among patients with metastatic breast cancer, 873 (266–1480) kJ (mean 95% CI; P<0.01), and metastatic melanoma, 2513 (523–4503) kJ (P<0.01). Breast cancer patients gained percentage body fat over the course of treatment, 2.1 (0.8–3.5%). Gain or loss of body fat correlated to mean energy intake throughout chemotherapy in patients with NSCLC (Rs=0.751; P<0.01) and metastatic breast cancer (Rs=0.617; P<0.05). The ability to meet or exceed energy requirements led to gains in body fat among patients with metastatic breast cancer and NSCLC, but did not prevent loss of FFM in these groups