Explain: Extinct

Associate Professor Joe Wilkins and his family spent the summer and fall of 2015 living in an off-the-grid cabin in the Klamath Mountains above the Rogue River of southwest Oregon. His poem Explain: Extinct grew out of that time on the Rogue

How to Explain Miscomputation

Just as theory of representation is deficient if it can’t explain how misrepresentation is possible, a theory of computation is deficient if it can’t explain how miscomputation is possible. Nonetheless, philosophers have generally ignored miscomputation. My primary goal in this paper is to clarify both what miscomputation is and how to adequately explain it. Miscomputation is a special kind of malfunction: a system miscomputes when it computes in a way that it shouldn’t. To explain miscomputation, you must provide accounts of computational behavior, computational norms, and how computational behavior can deviate from computational norms. A secondary goal of this paper is to defend an (quasi-)individualist, mechanistic theory of miscomputation. Computational behavior is narrowly individuated. Computational norms are widely individuated. A system miscomputes when its behavior manifests a narrow computational structure that the widely individuated norms say that it should not have

Using an extended food metaphor to explain concepts about pedagogy

It is anathema for educators to describe pedagogy as having a recipe - it is tantamount to saying it is a technicist process rather than a professional one requiring active, informed decision-making. But if we are to help novice teachers understand what pedagogy is and how it can be understood, there must be a starting point for pedagogical knowledge to shape both the understanding and design of appropriate curriculum learning. In order to address this challenge, I argue that food preparation processes and learning how to competently cook are analogous to understanding how pedagogy - also about process, design, and making knowledge knowable - facilitates learning about teaching specific curriculum knowledge. To do so, I use evidence from an ITE cohort lecture on pedagogy as a case study. In essence, viewing pedagogy through the lens of food and recipes may help make some abstractions of pedagogy more concrete and make some principles of pedagogy more accessible to novice teachers as they learn to design learning

Does Evolution Explain Human Nature?

Compiles short essays by twelve scientists and scholars on how well the theory of evolution explains human nature. Separate link includes video of a panel discussion with three scientists

Causation does not explain contextuality

Realist interpretations of quantum mechanics presuppose the existence of elements of reality that are independent of the actions used to reveal them. Such a view is challenged by several no-go theorems that show quantum correlations cannot be explained by non-contextual ontological models, where physical properties are assumed to exist prior to and independently of the act of measurement. However, all such contextuality proofs assume a traditional notion of causal structure, where causal influence flows from past to future according to ordinary dynamical laws. This leaves open the question of whether the apparent contextuality of quantum mechanics is simply the signature of some exotic causal structure, where the future might affect the past or distant systems might get correlated due to non-local constraints. Here we show that quantum predictions require a deeper form of contextuality: even allowing for arbitrary causal structure, no model can explain quantum correlations from non-contextual ontological properties of the world, be they initial states, dynamical laws, or global constraints.Comment: 18+8 pages, 3 figure

Dark antiatoms can explain DAMA

We show that the existence of a sub-dominant form of dark matter, made of dark antiatoms of mass and size of the order of 1 TeV and 30 fm respectively, can explain the results of direct detection experiments, with a positive signal in DAMA/NaI and DAMA/LIBRA and no signal in other experiments. The signal comes from the binding of the dark antiatoms to thallium, a dopant in DAMA, and is not present for the constituent atoms of other experiments. The dark antiatoms are made of two particles oppositely charged under a dark U(1) symmetry and can bind to terrestrial atoms because of a kinetic mixing between the photon and the massless dark photon, such that the dark particles acquire an electric millicharge of the order of 0.0005e. This millicharge enables them to bind to high-Z atoms via radiative capture, after they thermalize in terrestrial matter through elastic collisions.Comment: 18 pages, 5 figure

An exemplar model should be able to explain all syntactic priming phenomena : a commentary on Ambridge (2020)

The authors argue that Ambridge’s radical exemplar account of language cannot clearly explain all syntactic priming evidence, such as inverse preference effects (greater priming for less frequent structures), and the contrast between short-lived lexical boost and long-lived abstract priming. Moreover, without recourse to a level of abstract syntactic structure, Ambridge’s account cannot explain abstract priming in amnesia patients or cross-linguistic priming. Instead, the authors argue that abstract representations remain the more parsimonious account for the wide variety of syntactic priming phenomena

How to Explain Individual Classification Decisions

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.Comment: 31 pages, 14 figure

Can Science Explain Consciousness?

For diverse reasons, the problem of phenomenal consciousness is persistently challenging. Mental terms are characteristically ambiguous, researchers have philosophical biases, secondary qualities are excluded from objective description, and philosophers love to argue. Adhering to a regime of efficient causes and third-person descriptions, science as it has been defined has no place for subjectivity or teleology. A solution to the “hard problem” of consciousness will require a radical approach: to take the point of view of the cognitive system itself. To facilitate this approach, a concept of agency is introduced along with a different understanding of intentionality. Following this approach reveals that the autopoietic cognitive system constructs phenomenality through acts of fiat, which underlie perceptual completion effects and “filling in”—and, by implication, phenomenology in general. It creates phenomenality much as we create meaning in language, through the use of symbols that it assigns meaning in the context of an embodied evolutionary history that is the source of valuation upon which meaning depends. Phenomenality is a virtual representation to itself by an executive agent (the conscious self) tasked with monitoring the state of the organism and its environment, planning future action, and coordinating various sub- agencies. Consciousness is not epiphenomenal, but serves a function for higher organisms that is distinct from that of unconscious processing. While a strictly scientific solution to the hard problem is not possible for a science that excludes the subjectivity it seeks to explain, there is hope to at least psychologically bridge the explanatory gulf between mind and matter, and perhaps hope for a broader definition of science