Akrasia and the Aesthetic: Human Agency and the Site of Literature, 1760-1820

This study focuses on a series of foundational stylistic and formal innovations in eighteenth-century and Romantic literature, and argues that they can be cumulatively attributed to the distinct challenges authors faced in representing human action and the will. The study focuses in particular on cases of “acting against better judgment” or “failing to do what one knows one ought to do” – concepts originally theorized as “akrasia” and “weakness of the will” in ancient Greek and Scholastic thought. During the Enlightenment, philosophy increasingly conceives of human minds and bodies like systems and machines, and consequently fails to address such cases except as intractable or incoherent. Yet eighteenth-century and Romantic narratives and poetry consistently engage the paradoxes and ambiguities of action and volition in representations of akrasia. As a result, literature develops representational strategies that distinguish the epistemic capacities of literature as privileged over those of philosophy.

The study begins by centering on narratives of distempered selves from the 1760s. Jean-Jacques Rousseau’s Confessions and Laurence Sterne’s A Sentimental Journey narrate cases of knowingly and weakly acting against better judgment, and in so doing, reveal the limitations of the “philosophy of the passions” that famously informed sentimental literature at the time. These texts find that the interpretive difficulties of action demand a non-systematic and hermeneutic approach to interpreting a self through the genre of narrative. Rousseau’s narrative in particular informs William Godwin’s realist novels of distempered subjects. Departing from his mechanistic philosophy of mind and action, Godwin develops the technique of free indirect discourse in his third novel Fleetwood (1805) as a means of evoking the ironies and self-deceptions in how we talk about willing.

Romantic poetry employs the literary trope of weakness of will primarily through the problem of regretted inaction – a problem which I argue motivates the major poetic innovations of William Wordsworth and John Keats. While Samuel Taylor Coleridge sought to characterize his weakness of will in philosophical writing, Wordsworth turns to poetry with The Prelude (1805), revealing poetry itself to be a self-deceiving and disappointing form of procrastination. More explicitly than Wordsworth, John Keats identifies indolence as the prime symbol and basis of what he calls “negative capability.” In his letters and poems such as “On Seeing the Elgin Marbles” (1817) and “Ode on Indolence” (1819), Keats reveals how the irreducibly contradictory qualities of human agency speak to the particular privilege of “disinterested aesthetics” – a genre fitted for the modern era for its ability to disclose contradictions without seeking to resolve or explain them in terms of component parts.

Inter-site and inter-scanner diffusion MRI data harmonization.

We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.