The Effects of Body Posture on Emotion Perception: A Developmental and Theoretical Analysis

Previously, studies investigating emotional face perception - regardless of whether they involved adults or children - presented participants with static photos of faces in isolation. In the natural world, faces are rarely encountered in isolation. In the few studies that have presented faces in context, the perception of emotional facial expressions is altered when paired with an incongruent context. For both adults and 8- year-old children, reaction times increase and accuracy decreases when facial expressions are presented in an incongruent context depicting a similar emotion (e.g., sad face on a fear body) compared to when presented in a congruent context (e.g., sad face on a sad body; Meeren, van Heijnsbergen, & de Gelder, 2005; Mondloch, 2012). This effect is called a congruency effect and does not exist for dissimilar emotions (e.g., happy and sad; Mondloch, 2012). Two models characterize similarity between emotional expressions differently; the emotional seed model bases similarity on physical features, whereas the dimensional model bases similarity on underlying dimensions of valence an . arousal. Study 1 investigated the emergence of an adult-like pattern of congruency effects in pre-school aged children. Using a child-friendly sorting task, we identified the youngest age at which children could accurately sort isolated facial expressions and body postures and then measured whether an incongruent context disrupted the perception of emotional facial expressions. Six-year-old children showed congruency effects for sad/fear but 4-year-old children did not for sad/happy. This pattern of congruency effects is consistent with both models and indicates that an adult-like pattern exists at the youngest age children can reliably sort emotional expressions in isolation. In Study 2, we compared the two models to determine their predictive abilities. The two models make different predictions about the size of congruency effects for three emotions: sad, anger, and fear. The emotional seed model predicts larger congruency effects when sad is paired with either anger or fear compared to when anger and fear are paired with each other. The dimensional model predicts larger congruency effects when anger and fear are paired together compared to when either is paired with sad. In both a speeded and unspeeded task the results failed to support either model, but the pattern of results indicated fearful bodies have a special effect. Fearful bodies reduced accuracy, increased reaction times more than any other posture, and shifted the pattern of errors. To determine whether the results were specific to bodies, we ran the reverse task to determine if faces could disrupt the perception of body postures. This experiment did not produce congruency effects, meaning faces do not influence the perception of body postures. In the final experiment, participants performed a flanker task to determine whether the effect of fearful bodies was specific to faces or whether fearful bodies would also produce a larger effect in an unrelated task in which faces were absent. Reaction times did not differ across trials, meaning fearful bodies' large effect is specific to situations with faces. Collectively, these studies provide novel insights, both developmentally and theoretically, into how emotional faces are perceived in context.

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.