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Attentional mechanisms allow the brain to selectively allocate its resources to stimuli of interest within the huge amount of information reaching its sensory systems. The voluntary component of attention, endogenous attention, can be allocated in a flexible manner depending on the goals and strategies of the observer. On the other hand, the reflexive component, exogenous attention, is driven by the stimulus. Here, we investigated how exogenous attention is deployed to moving stimuli that form distinct perceptual groups. We showed that exogenous attention is deployed according to a reference frame that moves along with the stimulus. Moreover, in addition to the cued stimulus, exogenous attention is deployed to all elements forming a perceptual group. These properties provide a basis for the efficient deployment of exogenous attention under ecological viewing conditions.

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In an online shopping platform, a detailed classification of the products facilitates user navigation. It also helps online retailers keep track of the price fluctuations in a certain industry or special discounts on a specific product category. Moreover, an automated classification system may help to pinpoint incorrect or subjective categories suggested by an operator. In this study, we focus on product title classification of the grocery products. We perform a comprehensive comparison of six different text classification models to establish a strong baseline for this task, which involves testing both traditional and recent machine learning methods. In our experiments, we investigate the generalizability of the trained models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the benefits of incorporating product titles in multiple languages. Our numerical results indicate that dynamic masking of subcategories is effective in improving prediction accuracy. In addition, we observe that using bilingual product titles is generally beneficial, and neural network-