Understanding traffic participants’ behaviour is crucial for predicting their future trajectories, enabling autonomous vehicles to better assess the environment and consequently anticipate possible dangerous situations at an early stage. While the integration of cognitive processes and machine learning models has demonstrated promise in various domains, its application in trajectory forecasting of multiple traffic agents in large-scale autonomous driving datasets remains lacking. This work investigates the state-of-the-art trajectory forecasting model Trajectron++ which we enhance by incorporating a smoothing term in its attention module. This attention mechanism mimics human attention inspired by cognitive science research indicating limits to attention switching. We evaluate the performance of the resulting Smooth- Trajectron++ model and compare it to the original model on various benchmarks. Our results show improved performance on the large-scale nuScenes dataset, revealing the potential of incorporating insights from human cognition into trajectory prediction models.