Theory of mind is the ability to have mental states about mental states. Among theories concerning the structure and role of theory of mind is the view that theory of mind is the cognitive component of empathy. It is proposed that there is partial dissociation within theory of mind between emotional state representation and non-emotional state representation. In trying to test this hypothesis, an instrument was developed and implemented in a pilot study. Current theory of mind tests are reviewed and design features discussed in relation to the new hypothesis. The instrument aims to measure emotional and non-emotional state representation on separate subscales, as well as coding representations from emotional stories and non-emotional stories separately. The instrument was administered to 33 third level or higher students from the University of KwaZulu-Natal. Groups were chosen from science major (n = 9) and humanities major (n = 24) students. The findings fail to show the group performance patterns reported in literature, for example that humanities students tend to score higher in ToM tests than science students. A number of factors might contribute to the finding, but principally, low sample size and unequal general cognitive ability between groups are proposed as vital. Problems with the pilot study are identified and improvements suggested for subsequent testing. / Thesis (M.A.)-University of KwaZulu-Natal, Durban, 2010.

This thesis examines both behavioral and anatomical effects of prenatal exposure of Valproic Acid (VPA) on Long Evans rats. Tactile stimulation (TS) is then used to investigate its’ effect on remediating any abnormalities VPA may produce. Several behavioral tests were done to assess the behavioral effects of VPA and TS. It was found that VPA had an effect of many of the tasks, whereas, TS had almost none with the exception of an effect on females in the elevated plus maze. However, anatomical data showed that TS had a profound effect on neuronal branch order, cell complexity, and spine density in pyramidal neurons in the medial prefrontal cortex, the orbitofrontal cortex and the amygdala. Where VPA decreased the above in all of these areas, TS increased neuronal complexity in the aforementioned structures. This study demonstrates that prenatal exposure to VPA is a viable model of autism in rats and that TS has significant anatomical effects in these animals as well as in control animals / xi, 98 leaves; 29 cm

Predicting the effect of missense variants is critically important in population and medical genetics. It is essential to interpret genetic variation in population screening and clinical diagnostic sequencing, to reach optimal statistical power of risk gene discovery in genetic studies of diseases and traits. A quantitative analysis of the fitness effect of all possible missense variants can provide a foundation for understanding how proteins evolve in humans and other species. In this thesis, I describe new methods to infer the effect of missense variants using various machine learning techniques. First, I worked on a ResNet-based supervised model to predict pathogenicity trained on curated databases. The curated clinical databases have uneven quality and uncertain bias across genes. To address this issue, I developed a new method, MisFit, to separately model the molecular effect and population fitness effect of missense variants, and to estimate them jointly using a probabilistic graphical model. The architecture of MisFit follows the biological causality of the variant effect, that is, for a missense variant, the protein sequence and structure context determine its molecular effect, which in turn determines its fitness effect given how the protein is involved in various conditions and traits. The latter is a latent factor encapsulated in a sigmoid-shaped function with gene-specific parameters. The fitness effect determines the expected allele counts in human populations. This model can be trained using large-scale population genome data without known pathogenicity labels. I investigated how informative allele counts are for inferring fitness effect using simulations with realistic demographic parameters. To take advantage of the latest deep learning techniques and large population genome data sets, I use a Poisson-Inverse-Gaussian distribution, which is differentiable, to approximate the probability of allele counts given fitness effect and sample size. We show that MisFit estimated heterozygous selection coefficient of missense variants is consistent with ratio of de novo mutations among observed variants in a population with child-parents trio data. Furthermore, de novo missense variants with selection coefficient >0.01 are significantly enriched in neurodevelopmental disorders cases, achieving the best performance in prioritization of de novos for new risk gene discovery compared to previous methods. We also show that the estimated molecular effect reached the state-of-the-art performance in the classification of damaging variants in deep mutational scanning assays, with improved consistency of the score scale across genes. Finally, I analyzed the transmission disequilibrium of inherited variants in autism using a new empirical Bayesian method to identify risk genes, which models relative risk as a continuous function of variant effect in each gene.