Cross-cultural invariances in the architecture of shame.

Human foragers are obligately group-living, and their high dependence on mutual aid is believed to have characterized our species' social evolution. It was therefore a central adaptive problem for our ancestors to avoid damaging the willingness of other group members to render them assistance. Cognitively, this requires a predictive map of the degree to which others would devalue the individual based on each of various possible acts. With such a map, an individual can avoid socially costly behaviors by anticipating how much audience devaluation a potential action (e.g., stealing) would cause and weigh this against the action's direct payoff (e.g., acquiring). The shame system manifests all of the functional properties required to solve this adaptive problem, with the aversive intensity of shame encoding the social cost. Previous data from three Western(ized) societies indicated that the shame evoked when the individual anticipates committing various acts closely tracks the magnitude of devaluation expressed by audiences in response to those acts. Here we report data supporting the broader claim that shame is a basic part of human biology. We conducted an experiment among 899 participants in 15 small-scale communities scattered around the world. Despite widely varying languages, cultures, and subsistence modes, shame in each community closely tracked the devaluation of local audiences (mean r = +0.84). The fact that the same pattern is encountered in such mutually remote communities suggests that shame's match to audience devaluation is a design feature crafted by selection and not a product of cultural contact or convergent cultural evolution.

Deep learning for Chilean native flora classification: a comparative analysis.

The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed.

Did a 3800-year-old Mw ~9.5 earthquake trigger major social disruption in the Atacama Desert?

Early inhabitants along the hyperarid coastal Atacama Desert in northern Chile developed resilience strategies over 12,000 years, allowing these communities to effectively adapt to this extreme environment, including the impact of giant earthquakes and tsunamis. Here, we provide geoarchaeological evidence revealing a major tsunamigenic earthquake that severely affected prehistoric hunter-gatherer-fisher communities ~3800 years ago, causing an exceptional social disruption reflected in contemporary changes in archaeological sites and triggering resilient strategies along these coasts. Together with tsunami modeling results, we suggest that this event resulted from a ~1000-km-long megathrust rupture along the subduction contact of the Nazca and South American plates, highlighting the possibility of Mw ~9.5 tsunamigenic earthquakes in northern Chile, one of the major seismic gaps of the planet. This emphasizes the necessity to account for long temporal scales to better understand the variability, social effects, and human responses favoring resilience to socionatural disasters.