: This paper represents the authors' attempt to provide a useful framework for discussing and investigating the links between the apparently disparate disciplines of neuroscience and dance. This attempt arose from an interdisciplinary course offering on this topic. A clear need apparent in preparing for an exploration of such uncharted territory was for some definition of the relevant landmarks in the form of a conceptual framework. The current status of that developing framework is presented here, as we consider the historical (...) context that contributed to the cultural distance between neuroscience and dance as disciplines; the conceptual and technical obstacles to collaborative work between these disciplines; and the recent developments, both conceptual and technological, that make the interface between neuroscience and dance a particularly fruitful source of inspiration not only for dancers and neuroscientists but potentially for a wide variety of disciplines touching on health and education in general. (shrink)

The problem of Time is one of the most fascinating and yet most difficult of those questions to which the human mind applies itself in philosophical thought. Dean Inge, in his Philosophy of Plotinus, has referred to this problem as ‘the hardest in metaphysics,’ and we know that “from the time of Parmenides and Zeno to that of Mr. Bradley and M. Bergson, there has been no other problem that has seemed so baffling as that of Time.”.

Knowledge, like other things of value, can be faked. According to Hawley (2011), know-how is harder to fake than knowledge-that, given that merely apparent propositional knowledge is in general more resilient to our attempts at successful detection than are corresponding attempts to fake know-how. While Hawley’s reasoning for a kind of detection resilience asymmetry between know-how and know-that looks initially plausible, it should ultimately be resisted. In showing why, we outline different ways in which know-how can be faked even when (...) a given performance is successful; and in doing so, we distinguish how know-how can be faked (no less than know-that) via upstream and downstream indicators of its presence, and within each of these categories, we’ll distinguish (in connection with detection resilience) both faking symptoms and (various kinds of) criteria. The unappreciated resilience of faked knowledge-how to successful detection highlights a largely overlooked dimension of social-epistemic risk – risk we face not just in our capacity as recipients of testimony, but in our capacity as both (would-be) apprentices and clients of knowledge-how. (shrink)

In Charles Renouvier we have one of the lone, stern, and indefatigable workers in philosophy in the nineteenth century. His powerful mind, moral earnestness, and intellectual vigour command respect and attention and place him high in the ranks of the philosophical thinkers of his century. He differed profoundly from his English contemporary Spencer and his German contemporary Lotze, both of whom have received more attention than Renouvier. His long and immensely active life fell into periods which coincide with, and partly (...) reflect, the political and intellectual fortunes of his country from the Battle of Waterloo, through the Revolution of 1830, the Second Republic of 1848, the Second Empire, the War and the Commune of 1871, into the Third Republic, with its Dreyfus struggles and its Educational and Disestablishment problems in the early years of the present century. (shrink)

It is difficult within the space of an article such as this to do more than indicate the principal features of Renouvier's philosophy, and it is, of course, impossible to give in detail a discussion of the immense wealth of thought and argument contained in his writings. Of his thought before 1854, the most important piece of work was the article on “Philosophie” written for the Encyclopédic Nouvelle. This in some respects shows his own thought developing in the direction.

Reductive intellectualists (e.g., Stanley & Williamson 2001; Stanley 2011a; 2011b; Brogaard 2008; 2009; 2011) hold that knowledge-how is a kind of knowledge-that. If this thesis is correct, then we should expect the defeasibility conditions for knowledge-how and knowledge-that to be uniform—viz., that the mechanisms of epistemic defeat which undermine propositional knowledge will be equally capable of imperilling knowledge-how. The goal of this paper is twofold: first, against intellectualism, we will show that knowledge-how is in fact resilient to being undermined by (...) the very kinds of traditional (propositional) epistemic defeaters which clearly defeat the items of propositional knowledge which intellectualists identify with knowledge-how. Second, we aim to fill an important lacuna in the contemporary debate, which is to develop an alternative way in which epistemic defeat for knowledge-how could be modelled within an anti-intellectualist framework. (shrink)

The stir caused in the civilised world by the writings of Bergson, particularly during the past decade, is evidenced by the volume of the stream of exposition and comment which has flowed and is still flowing. If the French were to be tempted to set up, after the German manner, a Bergson-Archiv they would be in no embarrassment for material, as the Appendix to this book - limited though it wisely is - will show. Mr. Gunn, undaunted by all this, (...) makes a further, useful contribution in his unassuming but workmanlike and well-documented account of the ideas of the distinguished French thinker. It is designed to serve as an introduction to Bergson's philosophy for those who are making their first approach to it, and as such it can be commended. (shrink)

Most growth models for complex networks consider networks comprising a single connected block or island, which contains all the nodes in the network. However, it has been demonstrated that some large complex networks have more than one island, with an island size distribution obeying a power-law function Is~s-α. This paper introduces a growth model that considers the emergence of islands as the network grows. The proposed model addresses the following two features: the probability that a new island is generated decreases (...) as the network grows and new islands are created with a constant probability at any stage of the growth. In the first case, the model produces an island size distribution that decays as a power-law Is~s-α with a fixed exponent α=1 and in-degree distribution that decays as a power-law Qi~i-γ with γ=2. When the second case is considered, the model describes island size and in-degree distributions that decay as a power-law with 2<α<∞ and 2<γ<∞, respectively. (shrink)

Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection method that efficiently obtains a selection of the (...) most relevant features of the model. However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability of the selection. Neural networks are an example of a model that does not support embedded feature selection. As neural networks have shown to provide remarkable results in multiple scenarios such as classification and regression, sometimes in an ensemble with a model that includes an embedded feature selection, we attempt to embed a feature selection process with a general-purpose methodology. In this work, we propose a novel general-purpose layer for neural networks that removes the influence of irrelevant features. The Feature-Aware Drop Layer is included at the top of the neural network and trained during the backpropagation process without any additional parameters. Our methodology is tested with 17 datasets for classification and regression tasks, including data from different fields such as Health, Economic and Environment, among others. The results show remarkable improvements compared to three different feature selection approaches, with reliable, efficient and effective results. (shrink)

This book presents a comprehensive overview of what the criminal law would look like if organised around the principle that those who deserve punishment should receive punishment commensurate with, but no greater than, that which they deserve. Larry Alexander and Kimberly Kessler Ferzan argue that desert is a function of the actor's culpability, and that culpability is a function of the risks of harm to protected interests that the actor believes he is imposing and his reasons for acting in (...) the face of those risks. The authors deny that resultant harms, as well as unperceived risks, affect the actor's desert. They thus reject punishment for inadvertent negligence as well as for intentions or preparatory acts that are not risky. Alexander and Ferzan discuss the reasons for imposing risks that negate or mitigate culpability, the individuation of crimes, and omissions. (shrink)

Appeals to human nature are ubiquitous, yet historically many have proven ill-founded. Why? How might frequent errors be remedied towards building a more robust and reliable scientific study of human nature? Our aim is neither to advance specific scientific or philosophical claims about human nature, nor to proscribe or eliminate such claims. Rather, we articulate through examples the types of errors that frequently arise in this field, towards improving the rigor of the scientific and social studies. We seek to analyze (...) such claims rhetorically, cognitively, and epistemically. Namely, how do we think about human nature? Claims about human nature, we show, are susceptible to widely exhibited deficits in cognitive tendencies such as framing, confirmation bias, satisficing, and teleological perspectives, as well as motivated reasoning. Such missteps foster methodological, empirical, and psychological mistakes and biases. Specifically, they promote the naturalizing error, whereby cultural ideology and values are projected onto an apparently objective description of nature. Concrete remedies are offered to aid scientists in conducting and reporting their research goals and findings more responsibly and effectively (relevant also to educators and other communicators who convey these findings publicly). Recommendations include acknowledging that human nature claims are often context-dependent, seeking multiple critical perspectives, and explicitly labeling uncertainties. (shrink)

The reductive, as opposed to deductive, view of logic is the form of logic that is, perhaps, most widely employed in practical reasoning. In particular, it is the basis of logic programming. Here, building on the idea of uniform proof in reductive logic, we give a treatment of logic programming for BI, the logic of bunched implications, giving both operational and denotational semantics, together with soundness and completeness theorems, all couched in terms of the resource interpretation of BI’s semantics. We (...) use this set-up as a basis for exploring how coalgebraic semantics can, in contrast to the basic denotational semantics, be used to describe the concrete operational choices that are an essential part of proof-search. The overall aim, toward which this paper can be seen as an initial step, is to develop a uniform, generic, mathematical framework for understanding the relationship between the deductive structure of logics and the control structures of the corresponding reductive paradigm. (shrink)

The extent to which we generalize a novel property from a sample of familiar instances to novel instances depends on the sample composition. Previous property induction experiments have only used samples consisting of novel types (unique entities). Because real‐world evidence samples often contain redundant tokens (repetitions of the same entity), we studied the effects on property induction of adding types and tokens to an observed sample. In Experiments 1–3, we presented participants with a sample of birds or flowers known to (...) have a novel property and probed whether this property generalized to novel items varying in similarity to the initial sample. Increasing the number of novel types (e.g., new birds with the target property) in a sample produced tightening, promoting property generalization to highly similar stimuli but decreasing generalization to less similar stimuli. On the other hand, increasing the number of tokens (e.g., repeated presentations of the same bird with the target property) had little effect on generalization. Experiment 4 showed that repeated tokens are encoded and can benefit recognition, but appear to be given little weight when inferring property generalization. We modified an existing Bayesian model of induction (Navarro, Dry, & Lee, 2012) to account for both the information added by new types and the discounting of information conveyed by tokens. (shrink)

Throughout the 20th century, many doctors did not view pain management as an important aspect of their practice. Because pain cannot be objectively measured by the doctor, many doctors found it dif...

Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ‘‘sampling’’ assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, (...) data are assumed to have been generated without any restrictions. We develop a more general account of sampling that allows for an intermediate mixture of these two extremes, and we test its usefulness. In two experiments, we show that most people complete simple one-dimensional generalization tasks in a way that is consistent with their believing in some mixture of strong and weak sampling, but that there are large individual differences in the relative emphasis different people give to each type of sampling. We also show experimentally that the relative emphasis of the mixture is influenced by the structure of the available information. We discuss the psychological meaning of mixing strong and weak sampling, and possible extensions of our modeling approach to richer problems of inductive generalization. (shrink)