Contemplative Approaches to Reading and Writing: Cultivating Choice, Connectedness, and Wholeheartedness in the Critical Humanities

This article describes an approach in which two humanities instructors use reading and writing as a means to help students connect to their minds as objects of contemplation, experience alternative ways of being and relating, and consider how they make meaning from experience. To derive conclusions from this approach, they analyze student work and student feedback from a 3000 level elective comparative literature course, “Spiritual Journeys in Young Adult Fiction.” The results show that students cherish the opportunity to inquire into their habitual ways of relating to their academic work and to each other. They find a greater sense of choice, connectedness, and wholeheartedness, and rediscover their love for reading and writing

A Generic Approach for Escaping Saddle points

A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly deteriorating their performance. Typically, to escape from saddles one has to use second-order methods. However, most works on second-order methods rely extensively on expensive Hessian-based computations, making them impractical in large-scale settings. To tackle this challenge, we introduce a generic framework that minimizes Hessian based computations while at the same time provably converging to second-order critical points. Our framework carefully alternates between a first-order and a second-order subroutine, using the latter only close to saddle points, and yields convergence results competitive to the state-of-the-art. Empirical results suggest that our strategy also enjoys a good practical performance

Dynamic causal modeling of spontaneous fluctuations in skin conductance

Spontaneous fluctuations (SF) in skin conductance are often used to index sympathetic arousal and emotional states. SF are caused by sudomotor nerve activity (SNA), which is a direct indicator of sympathetic arousal. Here, we describe a dynamic causal model (DCM) of how SNA causes SF, and apply variational Bayesian model inversion to infer SNA, given empirically observed SF. The estimated SNA bears a relationship to the number of SF as derived from conventional (semi-visual) analysis. Crucially, we show that, during public speaking induced anxiety, the estimated number of SNA bursts is a better predictor of the (known) psychological state than the number of SF. We suggest dynamic causal modeling of SF potentially allows a more precise and informed inference about arousal than purely descriptive methods