The phrase "attention flows" is correct and usable in written English.
It can be used to describe the movement or direction of focus or concentration, often in contexts related to psychology, design, or communication.
Example: "In a well-designed presentation, attention flows seamlessly from one slide to the next, keeping the audience engaged."
Alternatives: "focus shifts" or "concentration moves".
Voyurl, made by a company of the same name, visualizes patterns that reflect where and how much your attention flows across categories while browsing the web during a workday.
Attention flows naturally to the outrageous, the harrowing, the intimate, and the recognizable, and the online personal essay began to harden into a form defined by identity and adversity — not in spite of how tricky it is to negotiate those matters in front of a crowd but precisely because of that fact.
A fair share of the demand and attention flows from Noma's anointment in April as the best restaurant in the world, at least according to an annual poll of food writers, prominent restaurateurs and other industry insiders conducted by San Pellegrino, the Italian mineral-water company.
Attention flows naturally to the outrageous, the harrowing, the intimate, and the recognizable, and the online personal essay began to harden into a form defined by identity and adversity not in spite of how tricky it is to negotiate those matters in front of a crowd but precisely because of that fact.
Whether it will reshape the TV attention flows the way it did with online media remains to be seen.
Other than Google, few sites loom larger today in brokering traffic and attention flows than Twitter and Facebook.
Its attention flowed more towards pictures of pretty young women in peril than stories about poverty in Indigenous communities.
This article presents a survey on the existing methods for the assessment of fun from its constituent elements – attention, flow, immersion and emotion.
These stream into and thereby constitute a fluctuating field of attention: flowing and broad, it is called "perception;" ebbing and concentrate, "apperception".
As a further step it could be helpful to analyze the egocentric networks around selected actors to get insights into their network ties, to observe the attention flow going from and to these actors, and to find out which other actors are closely connected.
Empirically we find that the Globally Normalized Reader (GNR), achieves the second highest single model performance (8th sept 2017) on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow.
