Internationally agreed sustainability goals are being missed. Here, we conduct global meta‐analyses to assess how the extent to which humans see themselves as part of nature—known as human–nature connectedness (HNC)—can be used as a leverage point to reach sustainability. A meta‐analysis of 147 correlational studies shows that individuals with high HNC had more pronature behaviours and were significantly healthier than those with low HNC. A meta‐analysis of 59 experimental studies shows significant increases in HNC after manipulations involving contact with nature and mindfulness practices. Surprisingly, this same meta‐analysis finds no significant effect of environmental education on HNC. Thus, HNC is positively linked to mind‐sets that value sustainability and behaviours that enhance it. Further, we argue that HNC can be enhanced by targeted practices, and we identify those most likely to succeed. Our results suggest that enhancing HNC, via promotion of targeted practices, can improve sustainability and should be integrated into conservation policy.

Behavioural and cognitive processes play important roles in mediating an individual's interactions with its environment. Yet, while there is a vast literature on repeatable individual differences in behaviour, relatively little is known about the repeatability of cognitive performance. To further our understanding of the evolution of cognition, we gathered 44 studies on individual performance of 25 species across six animal classes and used meta-analysis to assess whether cognitive performance is repeatable. We compared repeatability ( R ) in performance (1) on the same task presented at different times (temporal repeatability), and (2) on different tasks that measured the same putative cognitive ability (contextual repeatability). We also addressed whether R estimates were influenced by seven extrinsic factors (moderators): type of cognitive performance measurement, type of cognitive task, delay between tests, origin of the subjects, experimental context, taxonomic class and publication status. We found support for both temporal and contextual repeatability of cognitive performance, with mean R estimates ranging between 0.15 and 0.28. Repeatability estimates were mostly influenced by the type of cognitive performance measures and publication status. Our findings highlight the widespread occurrence of consistent inter-individual variation in cognition across a range of taxa which, like behaviour, may be associated with fitness outcomes. This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.

Previous magnetoencephalography/electroencephalography (M/EEG) studies have suggested that face processing is extremely rapid, indeed faster than any other object category. Most studies, however, have been performed using centered, cropped stimuli presented on a blank background resulting in artificially low interstimulus variability. In contrast, the aim of the present study was to assess the underlying temporal dynamics of face detection presented in complex natural scenes.We recorded EEG activity while participants performed a rapid go/no-go categorization task in which they had to detect the presence of a human face. Subjects performed at ceiling (94.8% accuracy), and traditional event-related potential analyses revealed only modest modulations of the two main components classically associated with face processing (P100 and N170). A multivariate pattern analysis conducted across all EEG channels revealed that face category could, however, be readout very early, under 100 ms poststimulus onset. Decoding was linked to reaction time as early as 125 ms. Decoding accuracy did not increase monotonically; we report an increase during an initial 95–140 ms period followed by a plateau ∼140–185 ms–perhaps reflecting a transitory stabilization of the face information available–and a strong increase afterward. Further analyses conducted on individual images confirmed these phases, further suggesting that decoding accuracy may be initially driven by low-level stimulus properties. Such latencies appear to be surprisingly short given the complexity of the natural scenes and the large intraclass variability of the face stimuli used, suggesting that the visual system is highly optimized for the processing of natural scenes.

Face recognition is supposed to be fast. However, the actual speed at which faces can be recognized remains unknown. To address this issue, we report two experiments run with speed constraints. In both experiments, famous faces had to be recognized among unknown ones using a large set of stimuli to prevent pre-activation of features which would speed up recognition. In the first experiment (31 participants), recognition of famous faces was investigated using a rapid go/no-go task. In the second experiment, 101 participants performed a highly time constrained recognition task using the Speed and Accuracy Boosting procedure. Results indicate that the fastest speed at which a face can be recognized is around 360–390 ms. Such latencies are about 100 ms longer than the latencies recorded in similar tasks in which subjects have to detect faces among other stimuli. We discuss which model of activation of the visual ventral stream could account for such latencies. These latencies are not consistent with a purely feed-forward pass of activity throughout the visual ventral stream. An alternative is that face recognition relies on the core network underlying face processing identified in fMRI studies (OFA, FFA, and pSTS) and reentrant loops to refine face representation. However, the model of activation favored is that of an activation of the whole visual ventral stream up to anterior areas, such as the perirhinal cortex, combined with parallel and feed-back processes. Further studies are needed to assess which of these three models of activation can best account for face recognition.