A Neuro-Computational Model for Discrete-Continuous Dual-Task Process - Sadeghi Talarposhti, Maryam; Ahmadi-Pajouh, Mohammad Ali; Towhidkhah, Farzad (Frontiers in Computational Neuroscience, 2022)
The study of dual-task (DT) procedures in human behavior are important as it can offer great insights into the cognitive control system. Accordingly, a discrete-continuous auditory-tracking DT experiment was implemented in this study with different difficulty conditions, including a continuous mouse-tracking task concurrent with a discrete auditory task (AT). The behavioral results regarding 25 participants were investigated via different factors such as response time (RT), errors, and hesitations (pauses in tracking tasks). In DT, the synchronization of different targets neuron units has been 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 neural resource allocates for various ongoing stimuli. We proposed a black-box model based on the interactions and mesoscopic behaviors of the neural units. The model structure was implemented based on neurological studies and oscillator units to represent neural activities. Each unit represents one stimulus feature of the task concept. Comparing the model’s output behavior with the experiment results (RT) validates the model. The evaluation of the proposed model and data in 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 psychological research findings based on which the DT interference occurs 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 through 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, with increasing the similarity factor between the features, the model’s result shows automatic task performance in each task.