Images have low priority in the study of Islam, despite their ubiquitous proximity to lived experience. This chapter argues for an exploration of images in contemporary Islam. It proposes a dynamic... Show moreImages have low priority in the study of Islam, despite their ubiquitous proximity to lived experience. This chapter argues for an exploration of images in contemporary Islam. It proposes a dynamic approach towards the relationship between Islam and the image by engaging with the concept of provocation. The chapter proposes that provocation helps us to draw attention to a multiplicity of emotions that images may engender, from feelings of joy and enlightenment to terror and rage, and from mixed feelings and feelings of indifference to a sense of shame. The chapter suggests that provocation helps to map how Muslims navigate and make sense of the overwhelming abundance and multiplicity of sounds and images in the religious public sphere today. Show less
Woo, C.W.; Schmidt, L.; Krishnan, A.; Jepma, M.; Roy, M.; Lindquist, M.A.; ... ; Wager, T.D. 2017
Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods... Show moreMultivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or ‘decode’ psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction—based on population-level predictive maps from prior groups—and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N = 180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker—in this case, the Neurologic Pain Signature (NPS)—improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Show less
