"Re-huminization": the role of human-animal similarity in predicting prejudice towards immigrants and non-human animals

Research implies that there ~ay be an association between attitudes toward margil1alized human outgroups and non-human animals. Very few studies, however, have specifically tested this relation empirically. The general purpose of the present research was to determine if such a relation exists and if perceptions of human-animal similarity avail as a common predictor of both types of attitudes. Ideological orientations associated with prejudiced attitudes (Social Dominance Orientation, Right-Wing Authoritarianism, and Universal Orientation) were also examined as individual differences in predicting perceptions of human-animal similarity. As predicted, people who endorsed prejudiced attitudes toward human outgroups (Study 1) and immigrants in particular (Studies 2 and 3), were more likely to endorse prejudiced attitudes toward non-human animals. In Study 2, perceptions that humans are superior (versus similar) to other animals directly predicted higher levels of prejudice toward non-human animals, whereas the effect of human superiority beliefs on immigrant prejudice was mediated by dehumanization. In other words, greater perceptions of humans as superior (versus similar) to other animals "allowed for" greater dehumanization of immigrants, which in turn resulted in heightened immigrant prejudice. Furthermore, people higher in Social Dominance Orientation or Right-Wing Authoritarianism were particularly likely to perceive humans as superior (versus similar) to other animals, whereas people characterized by a greater Universal Orientation were more likely to perceive humans and non-human animals as similar. Study 3 examined whether inducing perceptions of human-animal similarity through experimental manipulation would lead to more favourable attitudes toward non-human animals and immigrants. Participants were randomly assigned to read one of four 11 editorials designed to highlight either the similarities or differences between humans and other animals (i.e., animals are similar to humans; humans are similar to animals;~~nimals are inferior to humans; humans are superior to animals) or to a neutral control condition. Encouragingly, when animals were described as similar to humans, prejudice towards non-human animals and immigrants was significantly lower, and to some extent this finding was also true for people naturally high in prejudice (i.e., high in Social Dominance Orientation or Right-Wing Authoritarianism). Inducing perceptions that nonhuman animals are similar to humans was particularly effective at reducing the tendency to dehumanize immigrants ("re-humanization"), lowering feelings of personal threat regarding one's animal-nature, and at increasing inclusive intergroup representations and empathy, all of which uniquely accounted for the significant decreases in prejudiced attitudes. Implications for research, theory and prejudice interventions are considered.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.