Agnosticism and atheism are often grouped simply as nonreligious identities, yet emerging research highlights their distinct psychological profiles and social implications. Among these distinctions, collective narcissism—characterized by strong attachment to one’s group, exceptionalism, and grievance for recognition—offers a framework for understanding identity processes in both nonreligious groups. We examined whether agnostics and atheists exhibit collective narcissism and its forms (agentic—focused on exceptional effectiveness; communal—focused on exceptional morality) similarly to believers. We explored cross-denominational variance in agentic and communal collective narcissism levels relying on data from 77 countries (N = 3,570; 1227 agnostics, 2343 atheists). Agnostics and atheists from secular countries reported lower collective (particularly agentic) narcissism relative to their counterparts from religious countries. Further, agnostics and atheists were higher on communal than agentic collective narcissism. The results highlight the utility of the agency-communion model of collective narcissism among non-believers.

An important question in cognitive and evolutionary psychology is how the human mind anticipates the future and copes with stress and risk of disease. The parasite-stress model suggests that many patterns of human behavior and thought are adaptations to varying levels of exposure to parasites and pathogens. A growing body of health psychology research shows a link between positive future thinking and resiliency to various forms of disease. In this study, we investigate the link between historical pathogen prevalence in countries and in dividuals’ perception of the future of humanity. We surveyed 18,981 participants across 68 nations, examining their beliefs about how well humanity will be doing 1000 years from now compared to the present. We found that individuals residing in regions with higher historical disease risk tend to have more positive views about the future of humanity than individuals residing in areas with lower historical disease risk. The difference could not be attributed to several other stress-inducing factors, such as climate stress, population density or objective or subjective socioeconomic indicators. This research contributes to a growing body of evidence demonstrating how disease risk shapes human cognition and encourages future exploration of the evolution of temporal forecasting and consciousness.

Human activity recognition is an important functionality in any intelligent system designed to support human daily activities. While majority of human activity recognition systems use supervised learning, these systems lack the ability to detect new activities by themselves. In this paper, we report the results of our investigation of unsupervised human activity detection with features extracted from skeleton data obtained from RGBD sensor. Unlike activity recognition, activity detection does not provide the label however attempts to distinguish one activity from another. This paper demonstrates a suitable set of features to be used with K-means clustering to distinguish different activities from a pool of unlabeled observations. The results show 100% F0.5-score were achieved for six out of nine activities for one of the subjects at low frame rate, while F0.5-score of 71.9% was achieved on average for all activities by four subjects.

Human activity detection is usually achieved through supervised learning where a model is learned from given samples of each activity. With this approach, it is difficult for intelligent systems to automatically discover and learn new activities. We attempt to distinguish human activity using unsupervised learning. The approach relies on the fact that given appropriate features, the distinction between different activities can be observed from simple distance measurement. In this paper, we present our investigation to extract features from the coordinates of human joint positions based on human range of movement and the results of tests performed to check their effectiveness to distinguish sixteen (16) example activities are reported. Simple unsupervised learning, K means clustering was used to evaluate the effectiveness of the features. The results indicate that the features based on range of movement significantly improved clustering performance.

The ability to understand what humans are doing is crucial for any intelligent system to autonomously support human daily activities. Technologies to enable such ability, however, are still undeveloped due to the many challenges in human activity analysis. Among them are the difficulties in extracting human poses and motions from raw sensor data, either recorded from visual sensor or wearable sensor and the need to recognize activities not seen before using unsupervised learning. Furthermore, human activity analysis usually requires expensive sensors or sensing environment. With the availability of low-cost RGBD (RGB-depth) sensor, the new form of data can provide human posture data with high degree of confidence. In this paper, we present our approach to extract features directly from such data (joint positions) based on human range of movement and the results of tests performed to check their effectiveness to distinguish sixteen (16) example activities are reported. Simple unsupervised learning, K-means clustering was used to evaluate the effectiveness of the features. The results indicate that the features based on range of movement significantly improved clustering performance.

An approach for unsupervised human activity discovery has been proposed in this paper. The approach automatically discover unknown activities from unlabeled data and has the ability to reject random activities. This ability will enable intelligent systems to discover and learn new activities autonomously. K-means is used to cluster a pool of unlabeled activity observations into groups of different activities. The system requires no prior knowledge of how many activities to be discovered. It uses cluster validity indices to automatically estimate the required number of clusters and further evaluate cluster homogeneity to accept clusters with homogenous activity and reject clusters with random activities. Experimental results showed the potential of the approach and identified suitable validity indices to achieve unsupervised human activity discovery.