Long‐range correlation is a general class of coordination pattern found to be common to the intrinsic dynamics of complex systems, including human behavior. Balasubramaniam, Hove, and Médé investigate intrinsic dynamics in repeated finger movements, and they find that different measures of movement dynamics yield different long‐range correlations. Results shed light on the way that coordination patterns are expressed as a function of measurement context.

Grush has proposed a fairly comprehensive version of the idea of internal models within the framework of the emulation theory of representation. However, the formulation suffers from assumptions that render such models biologically infeasible. Here I present some problems from physiological principles of human movement production to illustrate why. Some alternative views to emulation are presented.

It is proposed here that Webb's ideas about robots as possible models of animals need some rethinking. In our view, even though widely used biorobotics strategies are fairly successful at reproducing the macroscopic behavior of biological systems, there are still several problems unresolved on the side of robotics as well as biology. Both mathematical and hardware-like robotics models should be feasible physiologically. Control principles elaborated in robotics are not necessarily applied to biological control systems. Although observations of flying birds inspired (...) aerodynamics and thus modern airplanes, little knowledge has been added to the neurophysiological principles underlying flight in birds. Chess playing computers might outperform most chess players, but they cannot be considered as physiologically feasible models of human thinking. (shrink)

Rich sensorimotor interaction facilitates language learning and is presumed to ground conceptual representations. Yet empirical support for early stages of embodied word learning is currently lacking. Finding evidence that sensorimotor interaction shapes learned linguistic representations would provide crucial support for embodied language theories. We developed a gamified word learning experiment in virtual reality in which participants learned the names of six novel objects by grasping and manipulating objects with either their left or right hand. Participants then completed a word–color match (...) task in which they were tested on the same six words and objects. Participants were faster to respond to stimuli in the match task when the response hand was compatible with the hand used to interact with the named object, an effect we refer to as affordance compatibility. In two follow up experiments, we found that merely observing virtual hands interact with the objects was sufficient to acquire a smaller affordance compatibility effect, and we found that the compatibility effect was driven primarily by responses with a compatible hand and not by responses in a compatible spatial location. Our results support theoretical views of language which ground word representations in sensorimotor experiences, and they suggest promising future routes to explore the sensorimotor foundations of higher cognition through immersive virtual experiments. (shrink)

When multiple individuals interact in a conversation or as part of a large crowd, emergent structures and dynamics arise that are behavioral properties of the interacting group rather than of any individual member of that group. Recent work using traditional signal processing techniques and machine learning has demonstrated that global acoustic data recorded from a crowd at a basketball game can be used to classify emergent crowd behavior in terms of the crowd's purported emotional state. We propose that the description (...) of crowd behavior from such global acoustic data could benefit from nonlinear analysis methods derived from dynamical systems theory. Such methods have been used in recent research applying nonlinear methods to audio data extracted from music and group musical interactions. In this work, we used nonlinear analyses to extract features that are relevant to the behavioral interactions that underlie acoustic signals produced by a crowd attending a sporting event. We propose that recurrence dynamics measured from these audio signals via recurrence quantification analysis (RQA) reflect information about the behavioral dynamics of the crowd itself. We analyze these dynamics from acoustic signals recorded from crowds attending basketball games, and that were manually labeled according to the crowds' emotional state across six categories: angry noise, applause, cheer, distraction noise, positive chant, and negative chant. We show that RQA measures are useful to differentiate the emergent acoustic behavioral dynamics between these categories, and can provide insight into the recurrence patterns that underlie crowd interactions. (shrink)