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1. Debates—Does Information Theory Provide a New Paradigm for Earth Science?

   https://doi.org/10.1029/2019WR026398  

   Kumar, Praveen; Gupta, Hoshin V. (February 2020, Water Resources Research)

   Abstract The basis for all knowledge is “information” that we compile about the world, expressed through models that support understanding, prediction, and decision making. This overview paper provides a contextual basis for the four papers that make up the “debate series” compiled under the above title. We briefly introduce Information Theory, discuss how “information” can be considered to be both a “physical” quantity and a “probabilistic” basis for representing incompleteness in knowledge, discuss the core motivation for this debate series, and briefly summarize the major arguments advanced by each of the debate papers. Our purpose is to facilitate an understanding of how these papers are related and how they approach the debate series question from different perspectives, while pointing to future directions for research. Finally, we invite further discourse and debate to advance the understanding and prediction of natural system dynamics using Information Theory, including the assessment of its limitations and complementarity to existing physics and machine learning approaches. Ultimately, our goal is to press for the development of philosophical and methodological advances that will enable the Earth science community to address some of the compelling unsolved problems in our field. 

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2. Divisive language

   https://doi.org/10.1017/S0140525X23003047  

   Dillon, Moira R (January 2024, Behavioral and Brain Sciences)

   Abstract What language devises, it might divide. By exploring the relations among the core geometries of the physical world, the abstract geometry of Euclid, and language, I give new insight into both the persistence of core knowledge into adulthood and our access to it through language. My extension of Spelke's language argument has implications for pedagogy, philosophy, and artificial intelligence. 

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3. The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables

   https://doi.org/10.1093/jrsssb/qkad071  

   Agrawal, Raj; Squires, Chandler; Prasad, Neha; Uhler, Caroline (July 2023, Journal of the Royal Statistical Society Series B: Statistical Methodology)

   Abstract Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particular, pervasive confounding is commonly encountered and has been utilised for consistent causal estimation in linear causal models. In this article, we present a provably consistent method to estimate causal relationships in the nonlinear, pervasive confounding setting. The core of our procedure relies on the ability to estimate the confounding variation through a simple spectral decomposition of the observed data matrix. We derive a DAG score function based on this insight, prove its consistency in recovering a correct ordering of the DAG, and empirically compare it to previous approaches. We demonstrate improved performance on both simulated and real datasets by explicitly accounting for both confounders and nonlinear effects. 

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4. Physical Physics – Getting students Active in Learning Materials Science

   https://doi.org/10.1557/adv.2017.61  

   Kavanagh, Yvonne; O’Hara, Noel; Palmer, Ross; Lowe, Peter; Raftery, Damien (January 2017, MRS Advances)

   ABSTRACT Physics forms the core of any Materials Science Programme at undergraduate level. Knowing the properties of materials is fundamental to developing and designing new materials and new applications for known materials. “Physical Physics” is a physics education approach which is an innovative and promising instruction model that integrates physical activity with mechanics and material properties. It aims to significantly enhance the learning experience and to illustrate how physics works, while allowing students to be active participants and take ownership of the learning process. It has been successfully piloted with undergraduate students studying mechanics on a Games Development Programme. It is a structured guided learning approach which provides a scaffold for learners to develop their problem solving skills. The objective of having applied physics on a programme is to introduce students to the mathematical world. Today students view the world through smart devices. By incorporating student recorded videos into the laboratory experience the student can visualise the mathematical world. Sitting in a classroom learning about material properties does not easily facilitate an understanding of mathematical equations as mapping to a physical reality. In order to get the students motivated and immersed in the real mathematical and physical world, an approach which makes them think about the cause and effect of actions is used. Incorporating physical action with physics enables students to assimilate knowledge and adopt an action problem solving approach to the physics concept. This is an integrated approach that requires synthesis of information from various sources in order to accomplish the task. As a transferable skill, this will ensure that the material scientists will be visionary in their approach to real life problems. 

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5. Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts

   https://doi.org/10.1038/s41539-020-0065-x  

   Cetron, Joshua S.; Connolly, Andrew C.; Diamond, Solomon G.; May, Vicki V.; Haxby, James V.; Kraemer, David J. (December 2020, npj Science of Learning)

   null (Ed.)

   Abstract How does STEM knowledge learned in school change students’ brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their “novice” peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices’ when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world. 

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