UC San Diego

The computations that link our perception to action, human cognition, define a multifaceted, dynamic and complex system. In the center is a body housing a brain that develops and evolves over our lifespan. Ever unfolding processes towards individuality are guided by temporally and spatially varying expression of inherited programs, pushed and prodded by experience and embedded in rich cultural contexts. One’s journey through this landscape injects individuality into cognition and may be observable in our actions. Because cognition is latent, individual differences are amorphous, taking refuge in factors localized or distributed. Despite this, for over a century we have attempted to characterize the relative importance of inherited factors in cognitive individuality. Often cognitive differences are operationalized, particularly those thought to emerge from internal factors, as variability in neuropsychological performance or in the presence of psychiatric diagnoses. Although these instruments capture restricted aspects of human cognition, genetic analyses suggest individuals sharing more inherited factors index more similarly on these measures. To me, this recurrent trend suggests cognitive systems involving genetically similar individuals integrate similar percepts into more similar actions. Insights into the nature of these genetic factors becomes an interesting aim for cognitive scientists interested in parameters that may define human cognition.

This thesis is a series of works that attempt to make concrete an approach for characterizing features of genetic factors involved in aspects of human cognition. I focus on data resources from genome-wide association studies (GWAS). GWAS make available surveys of the involvement of millions of common single nucleotide polymorphisms (SNPs) in numerous neuropsychological and psychiatric metrics for hundreds of thousands of individuals. These data were rapidly assembled assuming one particular analytic framework and are controversial with regards their success. Here I follow emerging trends that conceptually challenge this original design, suggesting alternative approaches might increase the utility of these data. Specifically, I propose that the traditional approach is biased a lens that does not allow for many thousands of genetic factors underlying cognitive traits. Readjusting our view of existing data resources uncovers new insights and demonstrate a potential for polygenic studies of human cognition.