Human Attention Measure
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A Human Attention Measure is an attention measure for a human experience.
- Example(s):
- a Mindfulness Measure (of mindfulness).
- a Nielsen Total Audience.
- It can detect gazing towards surprising events while watching television.
- …
- Counter-Example(s):
- See: Attention Economy, Attention Economy, Eyeball Tracking.
References
2019
- https://nielsen.com/news-center/2019/nielsen-ready-to-bring-total-audience-measurement/
- QUOTE: ... In this digital era where consumers live a seamless lifestyle (not differentiating online/offline activities), media companies and advertisers are facing the challenge to make the right decision, from how to spread advertising budget (changing from conventional to digital) to the ‘monetization’ metric to reach an effective target audience. To help media companies and advertisers penetrate and attract consumers’ attention in this new media landscape, Nielsen connects all online/offline measurement capabilities into a comprehensive framework, which measures “Total Audience.” ...
2009
- (Itti & Baldi, 2009) ⇒ Laurent Itti, and Pierre Baldi. (2009). “Bayesian Surprise Attracts Human Attention.” Vision research 49, no. 10
- ABSTRACT: We propose a formal Bayesian definition of surprise to capture subjective aspects of sensory information. Surprise measures how data affects an observer, in terms of differences between posterior and prior beliefs about the world. Only data observations which substantially affect the observer’s beliefs yield surprise, irrespectively of how rare or informative in Shannon’s sense these observations are. We test the framework by quantifying the extent to which humans may orient attention and gaze towards surprising events or items while watching television. To this end, we implement a simple computational model where a low-level, sensory form of surprise is computed by simple simulated early visual neurons. Bayesian surprise is a strong attractor of human attention, with 72% of all gaze shifts directed towards locations more surprising than the average, a figure rising to 84% when focusing the analysis onto regions simultaneously selected by all observers. The proposed theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.