The movement of the eyes has been the subject of intensive research as a way to elucidate inner mechanisms of cognitive processes. A cognitive task that is rather frequent in our daily life is the visual search for hidden objects. Here we investigate through eye-tracking experiments the statistical properties associated with the search of target images embedded in a landscape of distractors. Specifically, our results show that the twofold process of eye movement, composed of sequences of fixations (small steps) intercalated by saccades (longer jumps), displays characteristic statistical signatures. While the saccadic jumps follow a log-normal distribution of distances, which is typical of multiplicative processes, the lengths of the smaller steps in the fixation trajectories are consistent with a power-law distribution. Moreover, the present analysis reveals a clear transition between a directional serial search to an isotropic random movement as the difficulty level of the searching task is increased.

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Neste artigo apresentamos os resultados de um experimento conduzido com o objetivo de investigar o efeito de cognatos triplos no acesso lexical de falantes de inglês (L3), alemão (L2), e português brasileiro (L1). Os participantes desempenharam uma tarefa de leitura, contendo 60 sentenças experimentais com as seguintes palavras críticas: cognatos triplos, cognatos duplos entre o português brasileiro e o inglês, e cognatos duplos entre o alemão e o inglês. Os movimentos dos olhos dos participantes foram monitorados enquanto eles desempenhavam a tarefa. As medidas de primeira fixação e tempo de primeira leitura foram analisadas. Os resultados sugerem que os cognatos triplos foram processados mais rapidamente do que seus respectivos controles para as medidas de primeira fixação (M: 264/311ms (cognato/controle); p=0,03) e primeira leitura (M: 407/448ms (cognato/controle); p=0,05), o que foi interpretado como evidência de um acesso lexical não seletivo e de um léxico integrado para as línguas do multilíngue. Adicionalmente, os resultados contribuem para a literatura sobre acesso lexical de multilíngues, favorecendo a visão de que todas as línguas do multilíngue se encontram ativadas, mesmo quando o falante tem a intenção de usar apenas uma dessas línguas.

Sentence complexity assessment is a relatively new task in Natural Language Processing. One of its aims is to highlight in a text which sentences are more complex to support the simplification of contents for a target audience (e.g., children, cognitively impaired users, non-native speakers and low-literacy readers ). This task is evaluated using datasets of pairs of aligned sentences including the complex and simple version of the same sentence. For Brazilian Portuguese, the task was addressed by (Leal et al., 2018), who set up the first dataset to evaluate the task in this language, reaching 87.8% of accuracy with linguistic features. The present work advances these results, using models inspired by (Gonzalez-Garduño and Søgaard, 2018), which hold the state-of-the-art for the English language, with multi-task learning and eyetracking measures. First-Pass Duration, Total Regression Duration and Total Fixation Duration were used in two moments; first to select a subset of linguistic features and then as an auxiliary task in the multi-task and sequential learning models. The best model proposed here reaches the new state-of-the-art for Portuguese with 97.5% accuracy 1 , an increase of almost 10 points compared to the best previous results, in addition to proposing improvements in the public dataset after analysing the errors of our best model. 1 Accuracy in our task is how close the model is to the true value, when assessing whether a given sentence is simple or complex, in a 10-fold cross-validation test.

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