1,720,967 research outputs found
The SNARC effect: a preregistered study on the interaction of horizontal, vertical, and sagittal spatial–numerical associations
Full text linkSmall numbers are processed faster through left-sided than right-sided responses, whereas large numbers are processed faster through right-sided than left-sided responses [i.e., the Spatial–Numerical Association of Response Codes (SNARC) effect]. This effect suggests that small numbers are mentally represented on the left side of space, whereas large numbers are mentally represented on the right side of space, along a mental number line. The SNARC effect has been widely investigated along the horizontal Cartesian axis (i.e., left–right). Aleotti et al. (Cognition 195:104111, 2020), however, have shown that the SNARC effect could also be observed along the vertical (i.e., small numbers-down side vs. large numbers-up side) and the sagittal axis (i.e., small numbers-near side vs. large numbers-far side). Here, we investigated whether the three Cartesian axes could interact to elicit the SNARC effect. Participants were asked to decide whether a centrally presented Arabic digit was odd or even. Responses were collected through an ad hoc-made response box on which the SNARC effect could be compatible for one, two, or three Cartesian axes. The results showed that the higher the number of SNARC-compatible Cartesian axes, the stronger the SNARC effect. We suggest that numbers are represented in a three-dimensional number space defined by interacting Cartesian axes
Fractional graph Laplacian for image reconstruction
Full text linkImage reconstruction problems, like image deblurring and computer tomography, are usually ill-posed and require regularization. A popular approach to regularization is to substitute the original problem with an optimization problem that minimizes the sum of two terms, an
term and an
term with
. The first penalizes the distance between the measured data and the reconstructed one, the latter imposes sparsity on some features of the computed solution.
In this work, we propose to use the fractional Laplacian of a properly constructed graph in the
term to compute extremely accurate reconstructions of the desired images. A simple model with a fully automatic method, i.e., that does not require the tuning of any parameter, is used to construct the graph and enhanced diffusion on the graph is achieved with the use of a fractional exponent in the Laplacian operator. Since the fractional Laplacian is a global operator, i.e., its matrix representation is completely full, it cannot be formed and stored. We propose to replace it with an approximation in an appropriate Krylov subspace. We show that the algorithm is a regularization method under some reasonable assumptions. Some selected numerical examples in image deblurring and computer tomography show the performance of our proposal
Order versus chaos: The impact of structure on number-space associations
No full textThe Spatial-Numerical Association of Response Codes (SNARC) effect has been observed with different stimuli, beside Arabic numerals, such as written/spoken number words, sequences of acoustic stimuli, and groups of elements. Here we investigated how the enumeration of sets of elements can be affected by the spatial configuration of the displayed stimuli with regard to the emergence of the SNARC effect. To this aim, we asked participants to perform a magnitude comparison task with structured (i.e., dice-like) and unstructured (i.e., random) patterns of rectangles. With this manipulation, we sought to explore the presence of the SNARC effect in relation to the structure of the displayed visual stimuli. The results showed that the spatial arrangement of rectangles does not impact visual enumeration processes leading to the SNARC effect. An unexpected reversal of the size effect for unstructured stimuli was also observed. We speculate that the presence of a similar SNARC effect, both with structured and unstructured stimuli, indicates the existence of a common access to the mental number line
A nested primal–dual iterated Tikhonov method for regularized convex optimization
Full text linkProximal-gradient methods are widely employed tools in imaging that can be accelerated by adopting variable metrics and/or extrapolation steps. One crucial issue is the inexact computation of the proximal operator, often implemented through a nested primal-dual solver, which represents the main computational bottleneck whenever an increasing accuracy in the computation is required. In this paper, we propose a nested primal-dual method for the efficient solution of regularized convex optimization problems. Our proposed method approximates a variable metric proximal-gradient step with extrapolation by performing a prefixed number of primal-dual iterates, while adjusting the steplength parameter through an appropriate backtracking procedure. Choosing a prefixed number of inner iterations allows the algorithm to keep the computational cost per iteration low. We prove the convergence of the iterates sequence towards a solution of the problem, under a relaxed monotonicity assumption on the scaling matrices and a shrinking condition on the extrapolation parameters. Furthermore, we investigate the numerical performance of our proposed method by equipping it with a scaling matrix inspired by the Iterated Tikhonov method. The numerical results show that the combination of such scaling matrices and Nesterov-like extrapolation parameters yields an effective acceleration towards the solution of the problem
Correction to: Order versus chaos: The impact of structure on number-space associations (Attention, Perception, & Psychophysics, (2019), 81, 6, (1781-1788), 10.3758/s13414-019-01768-7)
No full textIn the Results section (pp.1785, left column), please replace the sentences “In particular, RTs for the large-unstructured condition were significantly faster than RTs on the small-unstructured condition
Numbers around Descartes: A preregistered study on the three-dimensional SNARC effect
No full textThe Spatial-Numerical Association of Response Codes (SNARC) effect suggests that numbers are represented along a horizontal left-to-right oriented, mental number line, with small numbers on the left and large numbers on the right. Much less evidence exists for vertical (down-to-up) and sagittal (near-to-far) SNARC effects. This might be due to the employment of different experimental paradigms among studies and to the, sometimes, inexact definition of the vertical and sagittal axes. We investigated for the first time the SNARC effect along the horizontal, vertical, and sagittal axes, by means of a classic SNARC task. Our results suggest the presence of three equally-strong SNARC effects. Our findings can be considered as evidence in favor of a three-dimensional, mental representation of numbers, in the form of a mental number space, defined by Cartesian coordinates
A Fractional Graph La+Ψ Approach to Image Reconstruction
No full textWe investigate a variational method for ill-posed problems, which embeds the fractional power of the standard graph Laplacian operator in the regularization term. We explore the dependence of the regularizer on a preliminary approximation of the solution, which is obtained using various existing reconstruction methods Ψ from the literature. As a result, the regularization term is both dependent on and adaptive to the observed data, noise, and the choice of the fractional exponent. We present a selected numerical example problem on 2D computerized tomography, for which we consider various reconstruction techniques Ψ, including Filtered Back Projection, Total Variation, and a trained deep neural network. Incorporating the fractional power of the graph Laplacian operator into the regularization term significantly enhances the quality of the approximated solutions for each method Ψ. Additionally, we show that our proposal behaves as a regularization method and is also stable with respect to variations in the noise level
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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