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

概述：最近对动物认知的研究表明，非人类动物群体在特定的认知表现特征上可能存在大量的个体差异。然而，这种变异是如何通过遗传和/或非遗传因素产生的，以及它影响适应性的途径在很大程度上是未知的。这个博士项目的主要目的是利用圈养的脊椎动物模型（孔雀鱼，Poecilia reticulata）来研究这种变异的遗传原因和下游后果（对个性、生活史和最终的适应性）。它将采用定量遗传方法，将认知和行为测试与育种实验以及数学和统计建模相结合。这将允许对一些关于动物智力适应性进化的限制和潜力的假设进行新的测试。具体的目标必然取决于学生的兴趣，但可能包括测试将认知与人格联系起来的假设途径的遗传基础，以及仔细检查认知表现的“一般智力”因素(g)模型的充分性。方法：采用孔雀鱼，因为它们易于在实验室中维护，繁殖力高，繁殖时间短，并且已经有成熟的人格和认知行为测试。此外，威尔逊小组最近的研究表明，性格差异很大，部分是由遗传因素驱动的（未发表的数据）。学生将使用半自动行为表型方法收集跨多个认知领域（如联想条件反射、辨别学习、反转学习、空间记忆）的个体（和基因型）的高重复测量数据。这样的数据收集需要精心设计的心理测量测试，能够量化个体之间的认知差异，第二主管桑顿在这方面具有特殊的专长。它还需要复杂的、多元的分析方法，目前在认知研究中尚未广泛使用。威尔逊最近强调了在数量遗传学中发展起来的混合模型策略如何转化为行为研究，以解决后一种需求。这些模型跨越多种表型成分（即认知领域、个性、生活史），并且可以分层指定，同时在已知谱系结构的群体中模拟个体和遗传效应。结构方程模型将用于正式比较认知和人格之间联系的不同因果模型。定量遗传“动物模型”将用于估计g（和/或特定领域的认知表现）的遗传性，并确定其对其他性状中更广泛的遗传（co）方差矩阵(g)的贡献。

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work in Wilson's group has recently shown personality variation is strong and driven in part by genetic factors (unpublished data).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.