Given its conspicuous nature, gravity has been acknowledged by several research lines as a prime factor in structuring the spatial perception of one's environment. One such line of enquiry has focused on errors in spatial localization aimed at the vanishing location of moving objects - it has been systematically reported that humans mislocalize spatial positions forward, in the direction of motion (representational momentum) and downward in the direction of gravity (representational gravity). Moreover, spatial localization errors were found to evolve dynamically with time in a pattern congruent with an anticipated trajectory (representational trajectory). The present study attempts to ascertain the degree to which vestibular information plays a role in these phenomena. Human observers performed a spatial localization task while tilted to varying degrees and referring to the vanishing locations of targets moving along several directions. A Fourier decomposition of the obtained spatial localization errors revealed that although spatial errors were increased "downward" mainly along the body's longitudinal axis (idiotropic dominance), the degree of misalignment between the latter and physical gravity modulated the time course of the localization responses. This pattern is surmised to reflect increased uncertainty about the internal model when faced with conflicting cues regarding the perceived "downward" direction.
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http://dx.doi.org/10.1016/j.visres.2014.10.024 | DOI Listing |
Phys Rev Lett
December 2024
University of Oregon, Department of Physics and Materials Science Institute, Eugene, Oregon 97403, USA.
We consider many-particle diffusion in one spatial dimension modeled as "random walks in a random environment." A shared short-range space-time random environment determines the jump distributions that drive the motion of the particles. We determine universal power laws for the environment's contribution to the variance of the extreme first passage time and extreme location.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Massachusetts Institute of Technology, Research Laboratory of Electronics, Cambridge, Massachusetts 02139, USA.
Classical transport of electrons and holes in nanoscale devices leads to heating that severely limits performance, reliability, and efficiency. In contrast, recent theory suggests that interband quantum tunneling and subsequent thermalization of carriers with the lattice results in local cooling of devices. However, internal cooling in nanoscale devices is largely unexplored.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
MSC, CNRS, Université Paris Cité, UMR 7057, F-75013 Paris, France.
We report on the dynamics of a soliton propagating on the surface of a fluid in a 4-m-long canal with a random or periodic bottom topography. Using a full space-and-time resolved wave field measurement, we evidence, for the first time experimentally, how the soliton is affected by the disorder, in the context of Anderson localization, and how localization depends on nonlinearity. For weak soliton amplitudes, the localization length is found in quantitative agreement with a linear shallow-water theory.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Université de Mons, Laboratoire Interfaces & Fluides Complexes, 20 Place du Parc, B-7000 Mons, Belgium.
The phase separation that occurs in two-temperature mixtures, which are driven out of equilibrium at the local scale, has been thoroughly characterized, but much less is known about the depletion interactions that drive it. Using numerical simulations in dimension 2, we show that the depletion interactions extend beyond two particle diameters in dilute systems, as expected at equilibrium, and decay algebraically with an exponent -4. Solving for the N-particle distribution function in the stationary state, perturbatively in the interaction potential, we show that algebraic correlations with an exponent -2d arise from triplets of particles at different temperatures in spatial dimension d.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
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