Information-optimal local features automatically attract covert and overt attention

Saccadic trajectories deviate toward or away from optimally informative visual features

The saccades' path is influenced by visual distractors, making their trajectory curve away or toward them. Previous research suggested that the more salient the distractor, the more pronounced is the curvature. We investigate the saliency of spatial visual features, predicted by a constrained maximum entropy model to be optimal or non-optimal information carriers in fast vision, by using them as distractors in a saccadic task. Their effect was compared to that of luminance-based control distractors. Optimal features evoke a larger saccadic curvature compared to non-optimal features, and the magnitude and direction of deviation change as a function of the delay between distractor and saccade onset. Effects were similar to those found with high-luminance versus low-luminance distractors. Therefore, model-predicted information optimal features interfere with target-oriented saccades, following a dynamic attraction-repulsion pattern. This suggests that the visuo-oculomotor system rapidly and automatically processes optimally informative features while programming visually guided eye movements.

Fast discrimination of fragmentary images: the role of local optimal information

In naturalistic conditions, objects in the scene may be partly occluded and the visual system has to recognize the whole image based on the little information contained in some visible fragments. Previous studies demonstrated that humans can successfully recognize severely occluded images, but the underlying mechanisms occurring in the early stages of visual processing are still poorly understood. The main objective of this work is to investigate the contribution of local information contained in a few visible fragments to image discrimination in fast vision. It has been already shown that a specific set of features, predicted by a constrained maximum-entropy model to be optimal carriers of information (optimal features), are used to build simplified early visual representations (primal sketch) that are sufficient for fast image discrimination. These features are also considered salient by the visual system and can guide visual attention when presented isolated in artificial stimuli. Here, we explore whether these local features also play a significant role in more natural settings, where all existing features are kept, but the overall available information is drastically reduced. Indeed, the task requires discrimination of naturalistic images based on a very brief presentation (25 ms) of a few small visible image fragments. In the main experiment, we reduced the possibility to perform the task based on global-luminance positional cues by presenting randomly inverted-contrast images, and we measured how much observers' performance relies on the local features contained in the fragments or on global information. The size and the number of fragments were determined in two preliminary experiments. Results show that observers are very skilled in fast image discrimination, even when a drastic occlusion is applied. When observers cannot rely on the position of global-luminance information, the probability of correct discrimination increases when the visible fragments contain a high number of optimal features. These results suggest that such optimal local information contributes to the successful reconstruction of naturalistic images even in challenging conditions.

A Bio-Inspired Endogenous Attention-Based Architecture for a Social Robot

A robust perception system is crucial for natural human–robot interaction. An essential capability of these systems is to provide a rich representation of the robot’s environment, typically using multiple sensory sources. Moreover, this information allows the robot to react to both external stimuli and user responses. The novel contribution of this paper is the development of a perception architecture, which was based on the bio-inspired concept of endogenous attention being integrated into a real social robot. In this paper, the architecture is defined at a theoretical level to provide insights into the underlying bio-inspired mechanisms and at a practical level to integrate and test the architecture within the complete architecture of a robot. We also defined mechanisms to establish the most salient stimulus for the detection or task in question. Furthermore, the attention-based architecture uses information from the robot’s decision-making system to produce user responses and robot decisions. Finally, this paper also presents the preliminary test results from the integration of this architecture into a real social robot.