75 research outputs found
Unveiling the temporal dynamics of emotional attentional capture with eye movements: from an automatic to a perceptual component
No full textHumans are natural emotion detectors and particularly predisposed to attend to emotions conveyed by facial expressions. The compulsory attraction to emotions, however, gets challenging when multiple emotional stimuli compete for attention, as in the emotion comparison task. In this task, participants are asked to choose which of two simultaneously presented faces displays a specific emotion (happiness/anger). When the two faces both express a different emotion intensity, the selection is driven by a purely perceptual attentional component: participants respond faster to the face displaying the strongest emotion (i.e., the emotional semantic-congruency effect, ESC), and this effect is stronger for face pairs that contain globally positive rather than negative emotional faces (i.e., the size-effect). In this experiment, we exploited the temporal dynamics of the emotion comparison task by tracking participants' eye movements using gaze-contingent displays. We expected that a perceptually driven attentional component like the ESC will rise over time following a purely automatic attentional component based on a left-to-right visual asymmetry. Analysis of fixation accuracy and dwell time fully corroborated this expectation. On the first fixation, participants were more accurate and dwell longer on the left face when it was the target, according to an automatic attentional component. The pattern of accuracy and dwell time on the second fixation was instead consistent with the ESC. Overall, our pattern of results indicates that attentional capture in emotion comparison task arises from the linear combination of purely automatic and perceptual (i.e., the ESC) attentional components over time [This research was supported by a founding for the research (RESRIC-FANTONI2018, Department of Life Sciences, University of Trieste) to CF and an international Fellowship within the European Social Fund 2014-2020 programme of Regione Autonoma Friuli Venezia Giulia to GB.
Methods to Reproduce In-Plane Deformability of Orthotropic Floors in the Finite Element Models of Buildings
No full textIn the modelling of reinforced concrete (RC) buildings, the rigid diaphragm hypothesis to represent the in-plane behavior of floors was and still is very commonly adopted because of its simplicity and computational cheapness. However, since excessive floor in-plane deformability can cause a very different redistribution of lateral forces on vertical resisting elements, it may be necessary to consider floor deformability. This paper investigates the classical yet intriguing question of modeling orthotropic RC floor systems endowed with lightening elements by means of a uniform orthotropic slab in order to describe accurately the building response under seismic loads. The simplified method, commonly adopted by engineers and based on the equivalence between the transverse stiffness of the RC elements of the real floor and those of the orthotropic slab, is presented. A case study in which this simplified method is used is also provided. Then, an advanced finite element (FE)-based method to determine the elastic properties of the equivalent homogenized orthotropic slab is proposed. The novel aspect of this method is that it takes into account the interaction of shell elements with frame elements in the 3D FE model of the building. Based on the results obtained from the application of this method to a case study, a discussion on the adequacy of the simplified method is also provided
Methods to Reproduce In-Plane Deformability of Orthotropic Floors in the Finite Element Models of Buildings
No full textIn the modelling of reinforced concrete (RC) buildings, the rigid diaphragm hypothesis to represent the in-plane behavior of floors was and still is very commonly adopted because of its simplicity and computational cheapness. However, since excessive floor in-plane deformability can cause a very different redistribution of lateral forces on vertical resisting elements, it may be necessary to consider floor deformability. This paper investigates the classical yet intriguing question of modeling orthotropic RC floor systems endowed with lightening elements by means of a uniform orthotropic slab in order to describe accurately the building response under seismic loads. The simplified method, commonly adopted by engineers and based on the equivalence between the transverse stiffness of the RC elements of the real floor and those of the orthotropic slab, is presented. A case study in which this simplified method is used is also provided. Then, an advanced finite element (FE)-based method to determine the elastic properties of the equivalent homogenized orthotropic slab is proposed. The novel aspect of this method is that it takes into account the interaction of shell elements with frame elements in the 3D FE model of the building. Based on the results obtained from the application of this method to a case study, a discussion on the adequacy of the simplified method is also provided
Effect of delayed artificial insemination on conception rate using a commercially available semen sexing agent in bovine
No full textSNARC like effect for musical tempo: An association between beat frequencies and the horizontal spatial axis
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Use of Commercially Available Bovine Semen Sexing Agent in Buffalo: Preliminary Report of the Effect on the Conception Rate
No full textLearning through atypical phase transitions in overparameterized neural networks
No full textCurrent deep neural networks are highly overparameterized (up to billions of connection weights) and nonlin-ear. Yet they can fit data almost perfectly through variants of gradient descent algorithms and achieve unexpected levels of prediction accuracy without overfitting. These are formidable results that defy predictions of statistical learning and pose conceptual challenges for nonconvex optimization. In this paper, we use methods from statistical physics of disordered systems to analytically study the computational fallout of overparameterization in nonconvex binary neural network models, trained on data generated from a structurally simpler but ???hidden??? network. As the number of connection weights increases, we follow the changes of the geometrical structure of different minima of the error loss function and relate them to learning and generalization performance. A first transition happens at the so-called interpolation point, when solutions begin to exist (perfect fitting becomes possible). This transition reflects the properties of typical solutions, which however are in sharp minima and hard to sample. After a gap, a second transition occurs, with the discontinuous appearance of a different kind of ???atypical??? structures: wide regions of the weight space that are particularly solution dense and have good generalization properties. The two kinds of solutions coexist, with the typical ones being exponentially more numerous, but empirically we find that efficient algorithms sample the atypical, rare ones. This suggests that the atypical phase transition is the relevant one for learning. The results of numerical tests with realistic networks on observables suggested by the theory are consistent with this scenario
Reward sharpens orientation coding independently on attention
No full textRewarding improves performance. Is it due to modulations of the output modules of the neural systems or are there mechanisms favoring more 'generous' inputs? Some recent study included V1 in the the circuitry of reward-based modulations, but the effects of reward can easily be confused with effects of attention. Here we address this issue with a psychophysical dual task to control attention while orientation sensitivity on targets associated to different levels of reward is measured. We found that different reward rates improve orientation discrimination and sharpen the internal response distributions. Data are unaffected by changing attentional load nor by dissociating the feature of the reward cue from the feature relevant for the task. This suggests that reward may act independently on attention by modulating the activity of early sensory stages, perhaps V1, through a SNR improvement of task-relevant channels. Reward acts like attention, but using separate channels
Entropic gradient descent algorithms and wide flat minima
No full textThe properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: the local entropy, which is useful for analysis and algorithm development, and the local energy, which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, entropy-stochastic gradient descent and replicated-stochastic gradient descent, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet)
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