Múltiplos fluxos ópticos e o processamento atentivo da informação visual

The environment in which we live in, we constantly deal with a huge amount of dynamic information, thus, attention is an indispensable cognitive resource that allows an effective selection of stimuli for our survival. From this, investigating how we process our encouragement in movements and how the attention spreads into a space to serve more than one stimuli simultanously is something very important. The behavioural urgence hipothesis suggests that the encouragement in a movement of approaching shows a certain priority in the process related to objects which are in a movement away, but there are researches that point out that it might not happen in an attentive phase, but instead as a priorization of motor response. There are also many controversies found in researches about attentive focalization, in which some studies suggest that the focus of attention would work in a similar manner to a zoom lens, while some searches indicate that the focus of attention could be shared to answer some stimuli in non contiguous regions. This study tried to investigate through two experiments the effect of attentive priorization by encouragement in movements and how the attention is spread with distractors stimuli. The first experiment investigated if the amount of moving flows really influenced in the process of information. The results indicate an effect of priorization of the flows guided in relation to aleatory ones and also from the unique flow due to dual flow. The second experiment investigated how the distribution of attention is in a space with the use of flows as an exogenous cue. The results indicate that the focus of attention works as the one suggested in the zoom lens model.

Uma abordagem baseada em filtragem colaborativa integrada a mapas de saliência para a recomendação de imagens

Nowadays, the amount of customers using sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. The sites, differently from physical stores, can make anything available to customers. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of Collaborating Filtering (CF), whose main underlying idea is that products are recommended to a given user based on purchase information and evaluations of past, by a group of users similar to the user who is requesting recommendation. One of the main challenges faced by such a technique is the need of the user to provide some information about her preferences on products in order to get further recommendations from the system. When there are items that do not have ratings or that possess quite few ratings available, the recommender system performs poorly. This problem is known as new item cold-start. In this paper, we propose to investigate in what extent information on visual attention can help to produce more accurate recommendation models. We present a new CF strategy, called IKB-MS, that uses visual attention to characterize images and alleviate the new item cold-start problem. In order to validate this strategy, we created a clothing image database and we use three algorithms well known for the extraction of visual attention these images. An extensive set of experiments shows that our approach is efficient and outperforms state-of-the-art CF RS.