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Perception and action are the result of an integration of various sources of information, such as current sensory input, prior experience, or the context in which a stimulus occurs. Often, the interpretation is not trivial hence needs to be learned from the co-occurrence of stimuli. Yet, how do we combine such diverse information to guide our action? Here we use a distance production-reproduction task to investigate the influence of auxiliary, symbolic cues, sensory input, and prior experience on human performance under three different conditions that vary in the information provided. Our results indicate that subjects can (1) learn the mapping of a verbal, symbolic cue onto the stimulus dimension and (2) integrate symbolic information and prior experience into their estimate of displacements. The behavioral results are explained by to two distinct generative models that represent different structural approaches of how a Bayesian observer would combine prior experience, sensory input, and symbolic cue information into a single estimate of displacement. The first model interprets the symbolic cue in the context of categorization, assuming that it reflects information about a distinct underlying stimulus range (categorical model). The second model applies a multi-modal integration approach and treats the symbolic cue as additional sensory input to the system, which is combined with the current sensory measurement and the subjects’ prior experience (cue-combination model). Notably, both models account equally well for the observed behavior despite their different structural assumptions. The present work thus provides evidence that humans can interpret abstract symbolic information and combine it with other types of information such as sensory input and prior experience. The similar explanatory power of the two models further suggest that issues such as categorization and cue-combination could be explained by alternative probabilistic approaches.

We study the critical dynamics of three-dimensional ferromagnets with uniaxial anisotropy by taking into account exchange and dipole-dipole interaction. The dynamic spin correlation functions and the transport coefficients are calculated within a mode coupling theory. It is found that the crossover scenario is determined by the subtle interplay between three length scales: the correlation length, the dipolar and uniaxial wave vector. We compare our theoretical findings with hyperfine interaction experiments on Gd and find quantitative agreement. This analysis allows us to identify the universality class for Gd. It also turns out that the $\mu$SR relaxation rate can be best fitted if it is assumed that muons occupy octahedral interstitials sites within the Gd lattice.Comment: 21 pages, 23 figure

The biophysical and biochemical properties of live tissues are important in the context of development and disease. Methods for evaluating these properties typically involve destroying the tissue or require specialized technology and complicated analyses. Here, we present a novel, noninvasive methodology for determining the spatial distribution of tissue features within embryos, making use of nondirectionally migrating cells and software we termed “Landscape,” which performs automatized high-throughput three-dimensional image registration. Using the live migrating cells as bioprobes, we identified structures within the zebrafish embryo that affect the distribution of the cells and studied one such structure constituting a physical barrier, which, in turn, influences amoeboid cell polarity. Overall, this work provides a unique approach for detecting tissue properties without interfering with animal’s development. In addition, Landscape allows for integrating data from multiple samples, providing detailed and reliable quantitative evaluation of variable biological phenotypes in different organisms.