Evolutionary genetics of animal "intelligence"The evolution of animal "intelligence": Among-individual differences and the heritable basis of cognitiv

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Overview: Recent studies of animal cognition have demonstrated that populations of non-human animals can harbour large amounts of among-individual variation in specific cognitive performance traits. However, how this variation arises through genetic and/or non-genetic factors, and the pathways by which it influences fitness are largely unknown. The broad aim of this PhD project will be to investigate the genetic causes and downstream consequences (for personality, life history and ultimately fitness) of this variation using a captive vertebrate model (the guppy, Poecilia reticulata). It will take a quantitative genetic approach, combining cognitive and behavioural testing with breeding experiments and mathematical and statistical modelling. This will allow novel tests of a number of hypotheses concerning constraints on, and potential for, the adaptive evolution of animal intelligence. Specific objectives will necessarily depend on the student's interests but are likely to include testing the genetic basis of hypothesised pathways linking cognition to personality, and scrutinising the adequacy of the "general intelligence" factor (g) model of cognitive performance. Methods: Guppies will be used as they are easy to maintain in the lab, have high fecundity and short generation time, and well developed behavioural tests of personality and cognition are already available. In addition work in Wilson's group has recently shown personality variation is strong and driven in part by genetic factors (unpublished data). A student will use semi-automated behavioural phenotyping approaches to collect high repeated measures data informative for individuals (and genotypes) across multiple cognitive domains (e.g., associative conditioning, discrimination learning, reversal learning, spatial memory). Such data collection requires carefully designed psychometric tests capable of quantifying cognitive differences between individuals in which second supervisor Thornton has particular expertise. It also requires sophisticated, multivariate methods of analysis not widely used in cognition research at present. Wilson has recently highlighted how mixed model strategies, developed in quantitative genetics, can be translated to behavioural studies to address this latter need. These models scale across multiple phenotypic components (i.e., cognitive domains, personality, life history), and can be specified hierarchically to model individual and genetic effects simultaneously in population of known pedigree structure. Structural equation modelling will be used to formally compare different causal models of the links between cognition and personality. Quantitative genetic "animal models" will be applied to estimate heritability of g (and/or domain-specific cognitive performance) and determine its contribution to the wider genetic (co)variance matrix (G) among other traits.