The psychological literature on mental health has shown that oppressions such as racism, sexism and classism can be extremely stressful. Thus individuals' identities, such as race, gender, and socioeconomic status, and the oppression these can lead to have clinical implications. The current research sought to investigate the intersection of Black Canadian women's identities and how it contributes to their unique experience of depression and coping. The aim of this study was to identify significant experiences of depression and coping of this population in order to develop a theory of healing.

Much like unpalatable foods, filthy restrooms and bloody wounds, sociomoral transgressions are often described as “disgusting”. This linguistic similarity suggests that there is a link between sociomoral disgust and more rudimentary forms of disgust associated with toxicity and disease. Critics have argued, however, that such references are purely metaphorical, or that sociomoral disgust may be limited to transgressions that remind us of more basic disgust stimuli. My aim was to provide more direct evidence that sociomoral transgressions do genuinely evoke disgust, and to explore factors that may influence how much disgust is evoked. I first searched for similarity in the facial expressions evoked by gustatory distaste (elicited by unpleasant tastes), physical disgust (elicited by photographs of contaminants), and moral disgust (elicited by unfair treatment in an economic game). I found that all three states evoked activation of the levator labii muscle region of the face, characteristic of an oral-nasal rejection response and consistent with an origin of sociomoral disgust in oral disgust. I next investigated whether individual differences in the tendency to experience physical disgust are related to variability in sociomoral judgement and emotion. In two different populations, heightened sensitivity toward physical disgust was related to more severe sociomoral judgements. A complementary neuroimaging study showed overlap between the neural correlates of physical disgust and sociomoral judgement, as well as highlighting brain regions that may underlie sociomoral hypersensitivity. Finally, I tested the idea that perceived differences in the causal stability of sociomoral transgressions may specifically affect levels of disgust. Although it was not possible to dissociate disgust from anger, the transgressions that were presented did evoke reliable self-reports of disgust. Taken together, these findings converge to support the conclusion that sociomoral transgressions can in fact elicit disgust, and accordingly that references to the disgusting nature of wrongdoing reflect biological reality rather than metaphor.

To date, limited research has been conducted to identify differences in children’s truthful and deceptive statements concerning stressful events. The present study uses automated linguistic software to detect linguistic patterns and objectively differentiate between the true and false stressful reports of bullying and non-stressful reports of sports events 7- to 14-year-olds. Results revealed that children displayed different linguistic patterns when reporting true and false stories, and between stressful and non-stressful stories. A discriminant analysis reliably differentiated between true and false stressful and non-stressful stories, though the veracity of non-stressful stories was more accurately classified than stressful stories. Experiment 2 revealed that adults were below chance levels in accurately identifying children’s true and false reports of stressful events (bullying), with confidence ratings and experience with children failing to improve accuracy scores. Taken together, results reveal that children are able to fabricate emotional and stressful stories that closely replicate their true reports.

Much like unpalatable foods, filthy restrooms and bloody wounds, sociomoral transgressions are often described as “disgusting”. This linguistic similarity suggests that there is a link between sociomoral disgust and more rudimentary forms of disgust associated with toxicity and disease. Critics have argued, however, that such references are purely metaphorical, or that sociomoral disgust may be limited to transgressions that remind us of more basic disgust stimuli. My aim was to provide more direct evidence that sociomoral transgressions do genuinely evoke disgust, and to explore factors that may influence how much disgust is evoked. I first searched for similarity in the facial expressions evoked by gustatory distaste (elicited by unpleasant tastes), physical disgust (elicited by photographs of contaminants), and moral disgust (elicited by unfair treatment in an economic game). I found that all three states evoked activation of the levator labii muscle region of the face, characteristic of an oral-nasal rejection response and consistent with an origin of sociomoral disgust in oral disgust. I next investigated whether individual differences in the tendency to experience physical disgust are related to variability in sociomoral judgement and emotion. In two different populations, heightened sensitivity toward physical disgust was related to more severe sociomoral judgements. A complementary neuroimaging study showed overlap between the neural correlates of physical disgust and sociomoral judgement, as well as highlighting brain regions that may underlie sociomoral hypersensitivity. Finally, I tested the idea that perceived differences in the causal stability of sociomoral transgressions may specifically affect levels of disgust. Although it was not possible to dissociate disgust from anger, the transgressions that were presented did evoke reliable self-reports of disgust. Taken together, these findings converge to support the conclusion that sociomoral transgressions can in fact elicit disgust, and accordingly that references to the disgusting nature of wrongdoing reflect biological reality rather than metaphor.

To date, limited research has been conducted to identify differences in children’s truthful and deceptive statements concerning stressful events. The present study uses automated linguistic software to detect linguistic patterns and objectively differentiate between the true and false stressful reports of bullying and non-stressful reports of sports events 7- to 14-year-olds. Results revealed that children displayed different linguistic patterns when reporting true and false stories, and between stressful and non-stressful stories. A discriminant analysis reliably differentiated between true and false stressful and non-stressful stories, though the veracity of non-stressful stories was more accurately classified than stressful stories. Experiment 2 revealed that adults were below chance levels in accurately identifying children’s true and false reports of stressful events (bullying), with confidence ratings and experience with children failing to improve accuracy scores. Taken together, results reveal that children are able to fabricate emotional and stressful stories that closely replicate their true reports.

Languages, like species, evolve. Just like biologists, historical linguists infer relationships between the lineages they study by analysing heritable features. For linguists, these features can be words, grammar and phonemes. This linguistic evidence of descent with modification plays an important role in our understanding of human prehistory. However, conventional methods in historical linguistics do not employ an explicit optimality criterion to evaluate evolutionary language trees. These methods cannot quantify uncertainty in the inferences nor provide an absolute chronology of divergence events. Previous attempts to estimate divergence times from lexical data using glottochronological methods have been heavily criticized, particularly for the assumption of constant rates of lexical replacement. Computational phylogenetic methods from biology can overcome these problems and allow divergence times to be estimated without the assumption of constant rates. Here these methods are applied to lexical data to test hypotheses about human prehistory. First, divergence time estimates for the age of the Indo-European language family are used to test between two competing theories of Indo- European origin - the Kurgan hypothesis and the Anatolian farming hypothesis. The resulting age estimates are consistent with the age range implied by the Anatolian farming theory. Validation exercises using different models, data sets and coding procedures, as well as the analysis of synthetic data, indicate these results are highly robust. Second, the same methodology was applied to Mayan lexical data to infer historical relationships and divergence times within the Mayan language family. The results highlight interesting uncertainties in Mayan language relationships and suggest that the family may be older than previously thought. Finally, returning to biology, similar tree-building and model validation techniques are used to draw inferences about human origins and dispersal from human mitochondrial DNA sequence data. These analyses support a human origin 150,000-250,000 years ago and reveal time dependency in rates of mitochondrial DNA evolution. Population size estimates generated using a coalescent approach suggest a twophase human population expansion from Africa. Potential correlations between human genetic and linguistic diversity are highlighted. I conclude that there is much to be gained by linguists and biologists using the same methods and speaking the same language.

Languages, like species, evolve. Just like biologists, historical linguists infer relationships between the lineages they study by analysing heritable features. For linguists, these features can be words, grammar and phonemes. This linguistic evidence of descent with modification plays an important role in our understanding of human prehistory. However, conventional methods in historical linguistics do not employ an explicit optimality criterion to evaluate evolutionary language trees. These methods cannot quantify uncertainty in the inferences nor provide an absolute chronology of divergence events. Previous attempts to estimate divergence times from lexical data using glottochronological methods have been heavily criticized, particularly for the assumption of constant rates of lexical replacement. Computational phylogenetic methods from biology can overcome these problems and allow divergence times to be estimated without the assumption of constant rates. Here these methods are applied to lexical data to test hypotheses about human prehistory. First, divergence time estimates for the age of the Indo-European language family are used to test between two competing theories of Indo- European origin - the Kurgan hypothesis and the Anatolian farming hypothesis. The resulting age estimates are consistent with the age range implied by the Anatolian farming theory. Validation exercises using different models, data sets and coding procedures, as well as the analysis of synthetic data, indicate these results are highly robust. Second, the same methodology was applied to Mayan lexical data to infer historical relationships and divergence times within the Mayan language family. The results highlight interesting uncertainties in Mayan language relationships and suggest that the family may be older than previously thought. Finally, returning to biology, similar tree-building and model validation techniques are used to draw inferences about human origins and dispersal from human mitochondrial DNA sequence data. These analyses support a human origin 150,000-250,000 years ago and reveal time dependency in rates of mitochondrial DNA evolution. Population size estimates generated using a coalescent approach suggest a twophase human population expansion from Africa. Potential correlations between human genetic and linguistic diversity are highlighted. I conclude that there is much to be gained by linguists and biologists using the same methods and speaking the same language.

Languages, like species, evolve. Just like biologists, historical linguists infer relationships between the lineages they study by analysing heritable features. For linguists, these features can be words, grammar and phonemes. This linguistic evidence of descent with modification plays an important role in our understanding of human prehistory. However, conventional methods in historical linguistics do not employ an explicit optimality criterion to evaluate evolutionary language trees. These methods cannot quantify uncertainty in the inferences nor provide an absolute chronology of divergence events. Previous attempts to estimate divergence times from lexical data using glottochronological methods have been heavily criticized, particularly for the assumption of constant rates of lexical replacement. Computational phylogenetic methods from biology can overcome these problems and allow divergence times to be estimated without the assumption of constant rates. Here these methods are applied to lexical data to test hypotheses about human prehistory. First, divergence time estimates for the age of the Indo-European language family are used to test between two competing theories of Indo- European origin - the Kurgan hypothesis and the Anatolian farming hypothesis. The resulting age estimates are consistent with the age range implied by the Anatolian farming theory. Validation exercises using different models, data sets and coding procedures, as well as the analysis of synthetic data, indicate these results are highly robust. Second, the same methodology was applied to Mayan lexical data to infer historical relationships and divergence times within the Mayan language family. The results highlight interesting uncertainties in Mayan language relationships and suggest that the family may be older than previously thought. Finally, returning to biology, similar tree-building and model validation techniques are used to draw inferences about human origins and dispersal from human mitochondrial DNA sequence data. These analyses support a human origin 150,000-250,000 years ago and reveal time dependency in rates of mitochondrial DNA evolution. Population size estimates generated using a coalescent approach suggest a twophase human population expansion from Africa. Potential correlations between human genetic and linguistic diversity are highlighted. I conclude that there is much to be gained by linguists and biologists using the same methods and speaking the same language.

Languages, like species, evolve. Just like biologists, historical linguists infer relationships between the lineages they study by analysing heritable features. For linguists, these features can be words, grammar and phonemes. This linguistic evidence of descent with modification plays an important role in our understanding of human prehistory. However, conventional methods in historical linguistics do not employ an explicit optimality criterion to evaluate evolutionary language trees. These methods cannot quantify uncertainty in the inferences nor provide an absolute chronology of divergence events. Previous attempts to estimate divergence times from lexical data using glottochronological methods have been heavily criticized, particularly for the assumption of constant rates of lexical replacement. Computational phylogenetic methods from biology can overcome these problems and allow divergence times to be estimated without the assumption of constant rates. Here these methods are applied to lexical data to test hypotheses about human prehistory. First, divergence time estimates for the age of the Indo-European language family are used to test between two competing theories of Indo- European origin - the Kurgan hypothesis and the Anatolian farming hypothesis. The resulting age estimates are consistent with the age range implied by the Anatolian farming theory. Validation exercises using different models, data sets and coding procedures, as well as the analysis of synthetic data, indicate these results are highly robust. Second, the same methodology was applied to Mayan lexical data to infer historical relationships and divergence times within the Mayan language family. The results highlight interesting uncertainties in Mayan language relationships and suggest that the family may be older than previously thought. Finally, returning to biology, similar tree-building and model validation techniques are used to draw inferences about human origins and dispersal from human mitochondrial DNA sequence data. These analyses support a human origin 150,000-250,000 years ago and reveal time dependency in rates of mitochondrial DNA evolution. Population size estimates generated using a coalescent approach suggest a twophase human population expansion from Africa. Potential correlations between human genetic and linguistic diversity are highlighted. I conclude that there is much to be gained by linguists and biologists using the same methods and speaking the same language.

Languages, like species, evolve. Just like biologists, historical linguists infer relationships between the lineages they study by analysing heritable features. For linguists, these features can be words, grammar and phonemes. This linguistic evidence of descent with modification plays an important role in our understanding of human prehistory. However, conventional methods in historical linguistics do not employ an explicit optimality criterion to evaluate evolutionary language trees. These methods cannot quantify uncertainty in the inferences nor provide an absolute chronology of divergence events. Previous attempts to estimate divergence times from lexical data using glottochronological methods have been heavily criticized, particularly for the assumption of constant rates of lexical replacement. Computational phylogenetic methods from biology can overcome these problems and allow divergence times to be estimated without the assumption of constant rates. Here these methods are applied to lexical data to test hypotheses about human prehistory. First, divergence time estimates for the age of the Indo-European language family are used to test between two competing theories of Indo- European origin - the Kurgan hypothesis and the Anatolian farming hypothesis. The resulting age estimates are consistent with the age range implied by the Anatolian farming theory. Validation exercises using different models, data sets and coding procedures, as well as the analysis of synthetic data, indicate these results are highly robust. Second, the same methodology was applied to Mayan lexical data to infer historical relationships and divergence times within the Mayan language family. The results highlight interesting uncertainties in Mayan language relationships and suggest that the family may be older than previously thought. Finally, returning to biology, similar tree-building and model validation techniques are used to draw inferences about human origins and dispersal from human mitochondrial DNA sequence data. These analyses support a human origin 150,000-250,000 years ago and reveal time dependency in rates of mitochondrial DNA evolution. Population size estimates generated using a coalescent approach suggest a twophase human population expansion from Africa. Potential correlations between human genetic and linguistic diversity are highlighted. I conclude that there is much to be gained by linguists and biologists using the same methods and speaking the same language.