Abstract
A crucial function of our visual system is to group local image fragments into coherent perceptual objects. Behavioral evidence has shown that this process is iterative and time-consuming. A simple theory suggested that visual neurons can solve this challenging task relying on recurrent processing: attending to an object could produce a gradual spread of enhancement across its representation in the visual cortex. Here, I will present results from a biologically plausible artificial neural network that can solve object segmentation by attention. This model was able to identify and segregate individual objects in cluttered scenes with extreme accuracy, only using modulatory top-down feedback as observed in visual cortical neurons. Then, I will present comparable results from large-scale electrophysiology recordings in the macaque visual cortex. We tested the effect of object attention with stimuli of increasing complexity, from lines to natural objects against cluttered backgrounds. Consistent with behavioral observations, the iterative model correctly predicted the spread of attentional modulation in visual neurons for simple stimuli. However, for more complex stimuli containing recognizable objects, we observed asynchronous but not iterative modulation. Thus, we produced a set of hybrid stimuli, combining local elements of two different objects, that we alternated with the presentation of stimuli of intact objects. By doing so, we made local information unreliable, forcing the monkey to solve the task iteratively. Indeed, we observed that this set of stimuli induced iterative attentional modulations. These results provide the first systematic investigation on object attention in both artificial and cortical neurons.