Coordination of Arctic Research in the U.S.A.

An Interagency Arctic Research Coordinating Committee was established in 1968, with members from the departments of Agriculture, Air Force, Army, Commerce, Health, Education and Welfare, Interior, Transportation and Navy, also the Atomic Energy Commission, National Aeronautics and Space Administration and National Science Foundation. The Committee's function is to coordinate basic, unclassified research, promote cooperative use of available logistics among research groups, maintain a current survey of foreign arctic research, and to encourage international meetings and cooperative fieldwork, data exchange and research analysis

Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference

Meta-awareness refers to the capacity to explicitly notice the current content of consciousness and has been identified as a key component for the successful control of cognitive states, such as the deliberate direction of attention. This paper proposes a formal model of meta-awareness and attentional control using hierarchical active inference. To do so, we cast mental action as policy selection over higher-level cognitive states and add a further hierarchical level to model meta-awareness states that modulate the expected confidence (precision) in the mapping between observations and hidden cognitive states. We simulate the example of mind-wandering and its regulation during a task involving sustained selective attention on a perceptual object. This provides a computational case study for an inferential architecture that is apt to enable the emergence of these central components of human phenomenology, namely, the ability to access and control cognitive states. We propose that this approach can be generalized to other cognitive states, and hence, this paper provides the first steps towards the development of a computational phenomenology of mental action and more broadly of our ability to monitor and control our own cognitive states. Future steps of this work will focus on fitting the model with qualitative, behavioural, and neural data

Parallel languages in the history of language ideology in Norway and the lesson for Nordic higher education

This chapter compares recent policy on the use of English and Norwegian in Higher Education with earlier policies on the relationship between the two standard varieties of Norwegian, and it charts how and why English became a policy issue in Norway. Based on the experience of over a century of language planning, a highly interventionist approach is today being avoided and language policies in the universities of Norway seek to nurture a situation where English and Norwegian may be used productively side-by-side. However, there remain serious practical challenges to be overcome. This paper also builds on a previous analysis (Linn 2010b) of the metalanguage of Nordic language policy and seeks to clarify the use of the term ‘parallelingualism’

From generative models to generative passages: a computational approach to (Neuro) phenomenology

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience