Ben Baker

Dance plays a vital role in human societies across time and culture, with different communities having invented different systems for artistic expression through movement (genres). Differences between genres can be described by experts in words and movements, but these descriptions can only be appreciated by people with certain background abilities. Existing dance notation schemes could be applied to describe genre-differences, however they fall substantially short of being able to capture the important details of movement across a wide spectrum of genres. Our knowledge and practice around dance would benefit from a general, quantitative and human-understandable method of characterizing meaningful differences between aspects of any dance style; a computational kinematics of dance. Here we introduce and apply a novel system for encoding bodily movement as 17 macroscopic, interpretable features, such as expandedness of the body or the frequency of sharp movements. We use this encoding to analyze Hip Hop Dance genres, in part by building a low-cost machine-learning classifier that distinguishes genre with high accuracy. Our study relies on an open dataset (AIST++) of pose-sequences from dancers instructed to perform one of ten Hip Hop genres, such as Breakdance, Popping, or Krump. For comparison we evaluate moderately experienced human observers at discerning these sequence's genres from movements alone (38% where chance = 10%). The performance of a baseline, Ridge classifier model was fair (48%) and that of the model resulting from our automated machine learning pipeline was strong (76%). This indicates that the selected features represent important dimensions of movement for the expression of the attitudes, stories, and aesthetic values manifested in these dance forms. Our study offers a new window into significant relations of similarity and difference between the genres studied. Given the rich, complex, and culturally shaped nature of these genres, the interpretability of our features, and the lightweight techniques used, our approach has significant potential for generalization to other movement domains and movement-related applications.

Neuroscientists often describe neural activity as a representation of something, or claim to have found evidence for a neural representation, but there is considerable ambiguity about what such claims entail. Here we develop a thorough account of what 'representation' does and should do for neuroscientists in terms of three key aspects of representation. (i) Correlation: a neural representation correlates to its represented content; (ii) causal role: the representation has a characteristic effect on behavior; and (iii) teleology: a goal or purpose served by the behavior and thus the representation. We draw broadly on literature in both neuroscience and philosophy to show how these three aspects are rooted in common approaches to understanding the brain and mind. We first describe different contexts that 'representation' has been closely linked to in neuroscience, then discuss each of the three aspects in detail.

The rapid development of autonomous systems, some of which emulate animal abilities, raises a question about how to understand and compare their complexity. Taking an “agent” to be any system that can be well-explained by attributing goals and intentional states to it, in what sense should we understand some agents as more “complex” than others? There is a sizable literature about complexity in biology, notably in comparative psychology, where an animal’s complexity informs predictions about its abilities and guides experimental design. In engineering and artificial intelligence, complexity is often assumed to be a sign of progress – we want to build more complex, capable machines. While the goals of researchers in these fields are different, they plausibly overlap in common conceptions of what a “more complex agent” entails. Existing work on “environmental complexity” and its relationship to “complex cognition” inquires into the origins of complexity, but not what it means to be a complex agent. Recognizing that complex behavior comes not just from complex cognition but from the interplay of body, environment, and cognition, we articulate an account of agent complexity in terms of Gibson’s (The Ecological Approach to Visual Perception (Classic), Houghton Mifflin, Boston, 1979) notion of affordances. We distinguish between cognitive and behavioral dimensions of affordances, and offer a framework for comparing agents based on the range of affordances they can exploit. Our approach ecologically grounds the sense in which some agents are more complex than others, while also clarifying why the answer to “which agent is more complex?” is sometimes indeterminate. It enables systematic comparison across diverse agent types, as we demonstrate through a detailed analysis of the nematode C. elegans and the hexapod robot RHex. We hope our account can advance discourse around the complexity of animals and guide the development of complex, agential machines.

This piece offers a critique of what is commonly the structure of introductory philosophy textbooks, syllabi, and courses. The basic criticism is that this structure perpetuates the systematic devaluing of the views of historically marginalized and exploited people. The form my critique takes is that of a referee report on a hypothetical manuscript for an introductory philosophy textbook, authored by “Dr. Unspecified.” I examine what the manuscript chooses to focus on and what it chooses to omit from discussion. I thereby outline much of the content typically used to introduce newcomers to philosophy, while illustrating that presenting exclusively that content supports a prejudiced view of philosophy. I try to show how this representation of philosophy marginalizes the concerns and insights of many and reinforces the disproportionate extent to which those who can do philosophy for a living are white, straight, men with typical body morphology. My report also identifies various ways that the content of an introductory philosophy textbook or course could be modified or supplemented in light of the sort of critique my report makes.

Biological and cognitive sciences rely heavily on the idea of information transmitted between natural events or processes. This paper critically assesses some current philosophical views of natural information and defends a view of natural information as Nomic and Factive. Dretske offered a Factive view of information, and recent work on the topic has tended to reject this aspect of his view in favor of a non-Factive, probabilistic approach. This paper argues that the reasoning behind this move to non-Factivity is flawed and mixes up different problems with Dretske’s original view. I show that one of these problems—strictness—has to do with Nomicity rather than Factivity. The other problem—reference class ambiguity—is not solvable just in terms of a theory of natural information. On the Dretske-inspired view I defend, natural information is Factive and Nomic but is insufficient to determine the cognitive or biological content of a natural process. In sum I present an examination what natural information is and what role it can play in our understanding of living and thinking things.