A saliency-based bottom-up visual attention model for dynamic scenes analysis

A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

Recent evidence has demonstrated that during visual spatial attention sampling, neural activity and behavioral performance exhibit large fluctuations. To understand the origin of these fluctuations and their functional role, here, we introduce a mechanism based on the dynamical activity pattern (attention spotlight) emerging from neural circuit models in the transition regime between different dynamical states. This attention activity pattern with rich spatiotemporal dynamics flexibly samples from different stimulus locations, explaining many key aspects of temporal fluctuations such as variable theta oscillations of visual spatial attention. Moreover, the mechanism expands our understanding of how visual attention exploits spatially complex fluctuations characterized by superdiffusive motion in space and makes experimentally testable predictions. We further illustrate that attention sampling based on such spatiotemporal fluctuations provides profound functional advantages such as adaptive switching between exploitation and exploration activities and is particularly efficient at sampling natural scenes with multiple salient objects.

A Neuro-Computational Model for Discrete-Continuous Dual-Task Process

Studies on dual-task (DT) procedures in human behavior are important, as they can offer great insight into the cognitive control system. Accordingly, a discrete-continuous auditory-tracking DT experiment was conducted in this study with different difficulty conditions, including a continuous mouse-tracking task concurrent with a discrete auditory task (AT). Behavioral results of 25 participants were investigated via different factors, such as response time (RT), errors, and hesitations (pauses in tracking tasks). In DT, synchronization of different target neuron units was observed in corresponding brain regions; consequently, a computational model of the stimulus process was proposed to investigate the DT interference procedure during the stimulus process. This generally relates to the bottom-up attention system that a neural resource allocates for various ongoing stimuli. We proposed a black-box model based on interactions and mesoscopic behaviors of neural units. Model structure was implemented based on neurological studies and oscillator units to represent neural activities. Each unit represents one stimulus feature of task concept. Comparing the model's output behavior with the experiment results (RT) validates the model. Evaluation of the proposed model and data on RT implies that the stimulus of the AT affects the DT procedure in the model output (84% correlation). However, the continuous task is not significantly changed (26% correlation). The continuous task simulation results were inconsistent with the experiment, suggesting that continuous interference occurs in higher cognitive processing regions and is controlled by the top-down attentional system. However, this is consistent with the psychological research finding of DT interference occurring in response preparation rather than the stimulus process stage. Furthermore, we developed the proposed model by adding qualitative interpretation and saving the model's generality to address various types of discrete continuous DT procedures. The model predicts a justification method for brain rhythm interactions by synchronization, and manipulating parameters would produce different behaviors. The decrement of coupling parameter and strength factor would predict a similar pattern as in Parkinson's disease and ADHD disorder, respectively. Also, by increasing the similarity factor among the features, the model's result shows automatic task performance in each task.