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Immersive environments such as Virtual Reality (VR) are now a main area of interactive digital entertainment. The challenge to design personalized interactive VR systems is specifically to guide and adapt to the user's attention. Understanding the connection between the visual content and the human attentional process is therefore key. In this article, we investigate this connection by first proposing a new head motion predictor named HeMoG. HeMoG is a white-box model built on physics of rotational motion and gravitation. Second, we compare HeMoG with existing reference Deep Learning models. We show that HeMoG can achieve similar or better performance and provides insights on the inner workings of these black-box models. Third, we study HeMoG parameters in terms of video categories and prediction horizons to gain knowledge on the connection between visual saliency and the head motion process.