Two analytic traditions characterize fMRI language research. One relies on averaging activations across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, any given voxel/vertex in a common brain space is part of the language network in some individuals but in others, may belong to a distinct network. An alternative approach relies on identifying language areas in each individual using a functional ‘localizer’. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. To bridge these disjoint approaches, we created a probabilistic functional atlas using fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common space belongs to the language network, and thus can help interpret group-level activation peaks and lesion locations, or select voxels/electrodes for analysis. More meaningful comparisons of findings across studies should increase robustness and replicability in language research.
Language decoding studies have identified word representations which can be used to predict brain activity in response to novel words and sentences (Anderson et al., 2016; Pereira et al., 2018). The unspoken assumption of these studies is that, during processing, linguistic information is transformed into some shared semantic space, and those semantic representations are then used for a variety of linguistic and non-linguistic tasks. We claim that current studies vastly underdetermine the content of these representations, the algorithms which the brain deploys to produce and consume them, and the computational tasks which they are designed to solve. We illustrate this indeterminacy with an extension of the sentence-decoding experiment of Pereira et al. (2018), showing how standard evaluations fail to distinguish between language processing models which deploy different mechanisms and which are optimized to solve very different tasks. We conclude by suggesting changes to the brain decoding paradigm which can support stronger claims of neural representation.
Some metallographic studies performed on the basis of the massive forging steel static ingot, on its cross-section, allowed to reveal the following morphological zones: a/ columnar grains (treated as the austenite single crystals), b/ columnar into equiaxed grains transformation, c/ equiaxed grains at the ingot axis. These zones are reproduced theoretically by the numerical simulation. The simulation was based on the calculation of both temperature field in the solidifying large steel ingot and thermal gradient field obtained for the same boundary conditions. The detailed analysis of the velocity of the liquidus isotherm movement shows that the zone of columnar grains begins to disappear at the first point of inflection and the equiaxed grains are formed exclusively at the second point of inflection of the analyzed curve. In the case of the continuously cast brass ingots three different morphologies are revealed: a/ columnar structure, b/ columnar and equiaxed structure with the CET, and c/ columnar structure with the single crystal formation at the ingot axis. Some forecasts of the temperature field are proposed for these three revealed morphologies. An analysis / forecast of the behavior of the operating point in the mold is delivered for the continuously cast ingot. A characteristic delay between some points of breakage of the temperature profile recorded at the operating point and analogous phenomena in the solidifying alloy is postulated.
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