Interpreting Practice: Dilthey, Epistemology, and the Hermeneutics of Historical Life

This paper explores Dilthey’s radical transformation of epistemology and the human sciences through his projects of a critique of historically embodied reason and his hermeneutics of historically mediated life. Answering criticisms that Dilthey overly depends on epistemology, I show how for Dilthey neither philosophy nor the human sciences should be reduced to their theoretical, epistemological, or cognitive dimensions. Dilthey approaches both immediate knowing and theoretical knowledge in the context of a hermeneutical phenomenology of historical life. Knowing is not an isolated activity but an interpretive and self-interpretive practice oriented by situated reflexive awareness and self-reflection. As embedded in an historical relational context, knowing does not only consist of epistemic validity claims about representational contents but is fundamentally practical, involving all of human existence. Empirically informed Besinnung, with its double reference to sense as meaning and bodily awareness, orients Dilthey’s inquiry rather than the “irrationalism” of immediate intuition or the “rationalism” of abstract epistemological reasoning

Factorial graphical lasso for dynamic networks

Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. There are many aspects of dynamical networks that require statistical considerations. In this paper we focus on determining network structure. Estimating dynamic networks is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is bigger than the number of observations. However, a characteristic of many networks is that they are sparse. For example, the molecular structure of genes make interactions with other components a highly-structured and therefore sparse process. Penalized Gaussian graphical models have been used to estimate sparse networks. However, the literature has focussed on static networks, which lack specific temporal constraints. We propose a structured Gaussian dynamical graphical model, where structures can consist of specific time dynamics, known presence or absence of links and block equality constraints on the parameters. Thus, the number of parameters to be estimated is reduced and accuracy of the estimates, including the identification of the network, can be tuned up. Here, we show that the constrained optimization problem can be solved by taking advantage of an efficient solver, logdetPPA, developed in convex optimization. Moreover, model selection methods for checking the sensitivity of the inferred networks are described. Finally, synthetic and real data illustrate the proposed methodologies.Comment: 30 pp, 5 figure

Measuring the World: Olfaction as a Process Model of Perception

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes