Learning to anticipate in a busy world
Cycling through a busy Amsterdam intersection illustrates the complexity of our visual world. We consciously watch traffic lights and cars, while advertisements or honking scooters automatically draw our attention. With experience, however, we learn to recognize patterns, such as how long the traffic light stays red or when pedestrians are likely to cross. This ability, known as visual statistical learning, enables us to anticipate important signals while filtering out irrelevant ones.
Xu examined how this process works across both space and time. Her studies show that people, without being aware of it, learn where and when relevant targets appear, as well as where and when distracting stimuli are likely to occur. This implicit knowledge allows the brain to direct attention flexibly: to the right place at the right time, and away from potential sources of interference.
Guiding attention and preparing action
Beyond visual attention, Xu also studied how people prepare motor responses based on temporal patterns. She demonstrated that even complex time–event associations can be unconsciously learned and retained, enabling us not only to identify where something will happen but also to respond faster and more accurately.
Practical applications
The insights from this research have direct societal relevance. In traffic design, signs and traffic lights can be positioned in ways that match people’s natural expectations, improving safety. In human–technology interaction, such as car dashboards or smartphones, information can be presented at the right time and place, reducing mental load and enhancing usability.
In the long term, these findings may contribute to the development of human-friendly cities and smarter devices that align more closely with our natural ability to learn and predict patterns.
Xu defended her PhD on October 6 at Vrije Universiteit Amsterdam.