How social learning amplifies moral outrage expression in online social networks

Moral outrage shapes fundamental aspects of social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two preregistered observational studies on Twitter (7331 users and 12.7 million total tweets) and two preregistered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. In addition, users conform their outrage expressions to the expressive norms of their social networks, suggesting norm learning also guides online outrage expressions. Norm learning overshadows reinforcement learning when normative information is readily observable: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to affect moral discourse in digital public spaces.

How do humans learn about other people? Incorporating social knowledge structures into reinforcement learning

To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture such complex social learning processes are currently lacking. In this study, we specified and tested potential strategies that humans could employ for learning about others. Standard reinforcement learning (RL) models only capture part of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalized two social knowledge structures and implemented them in novel hybrid RL models to test their usefulness across different social learning tasks. We named these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results indicated that participants combined the concepts of granularity and reference points in a rather optimal fashion—with the specific optimal combinations in models depending on the people and trait items that participants learned about. Overall, our experiments demonstrate that variants of RL algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.

The Psychological Reach of Culture in Animals’ Lives

Culture—the totality of traditions acquired in a community by social learning from other individuals—has increasingly been found to be pervasive not only in humans’ but in many other animals’ lives. Compared with learning on one’s own initiative, learning from others can be very much safer and more efficient, as the wisdom already accumulated by other individuals is assimilated. This article offers an overview of often surprising recent discoveries charting the reach of culture across an ever-expanding diversity of species, as well as an extensive variety of behavioral domains, and throughout an animal’s life. The psychological reach of culture is reflected in the knowledge and skills an animal thus acquires, via an array of different social learning processes. Social learning is often further guided by a suite of adaptive psychological biases, such as conformity and learning from optimal models. In humans, cumulative cultural change over generations has generated the complex cultural phenomena observed today. Animal cultures have been thought to lack this cumulative power, but recent findings suggest that elementary versions of cumulative culture may be important in animals’ lives.

Sustainability in Higher Education

Since sustainable development has emerged as a normative guiding idea at the global level, it has been perceived as a “moving target” that requires deliberation and social learning processes. Consequently, the notion of learning for sustainability figures prominently in both academia and policy, and learning and education are increasingly considered important features in this regard. Education for sustainable development was first introduced in 1992 at the United Nations Conference on Sustainable Development and has since developed into a well-established educational field. Additional momentum has been gained through the UN Decade of Education for Sustainable Development (2005–2014). This is even more so with the ongoing follow-up program “ESD for 2030” in which a direct link to the Sustainable Development Goals (SDGs) has been made. While the implementation of this vision is supported in all educational sectors, it is higher education that has a key role to play in the overall process of striving for sustainable development. Universities generate and transfer relevant knowledge, as well as enable future change agents to contribute to a sustainable future. In addition, they act in their own organizational practice as a role model and contribute through societal outreach and service. This bibliography contains a selection of papers and volumes on themes discussed in the area of sustainability in higher education. It covers the Historical Context, various areas of universities activities and different disciplinary and regional approaches. Emphasis is given on educational activities and thus the research and practice of teaching and learning on the micro- level (courses) and the macro-level (programs).

The burgeoning reach of animal culture

Culture can be defined as all that is learned from others and is repeatedly transmitted in this way, forming traditions that may be inherited by successive generations. This cultural form of inheritance was once thought specific to humans, but research over the past 70 years has instead revealed it to be widespread in nature, permeating the lives of a diversity of animals, including all major classes of vertebrates. Recent studies suggest that culture’s reach may extend also to invertebrates—notably, insects. In the present century, the reach of animal culture has been found to extend across many different behavioral domains and to rest on a suite of social learning processes facilitated by a variety of selective biases that enhance the efficiency and adaptiveness of learning. Far-reaching implications, for disciplines from evolutionary biology to anthropology and conservation policies, are increasingly being explored.

Understanding of Developing and Setting Tasks in Geography Lessons by German and Dutch Student Teachers

Setting tasks plays a key role in geography lessons, as they enable students to engage with the subject content, guide lessons towards predefined learning outcomes, and are therefore important for assessment. At the same time, the use of tasks is complex as numerous aspects regarding the content and the students have to be taken into account. Based on theoretical and empirical literature, we identify seven quality criteria for tasks in geography education: motivating and engaging students; addressing the heterogeneity of students; structuring learning processes; comprehensible formulation; considering individual and social learning processes; making meaningful use of materials; and fostering the development of subject specific competences. These criteria were applied in observation of lessons, which were given during an exchange between student geography teachers from a Dutch and German university. Overall, it was found that student teachers recognize the defined quality criteria, but half of them focus on only one or two aspects. The difficulties student teachers face in task setting during their traineeship can partly be explained by their phase of apprenticeship and the context. The developed observation form was considered to be valuable for preparation and observation of and reflection on tasks in geography lessons, and the exchange enabled student teachers to gain an insight into their own teaching practice.

How social learning amplifies moral outrage expression in online social networks

Moral outrage shapes fundamental aspects of human social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two pre-registered observational studies of Twitter (7,331 users and 12.7 million total tweets) and two pre-registered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. We also find that outrage expressions are sensitive to expressive norms in users’ social networks, over and above users’ own preferences, suggesting that norm learning processes guide online outrage expressions. Moreover, expressive norms moderate social reinforcement of outrage: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to impact moral discourse in digital public spaces.

Machine learning for rediscovering revolutionary ideas of the past

The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.