Reading the abstracts in order:
> The concept of surprise is central to sensory processing, adaptation, learning, and attention. Yet, no widely-accepted mathematical theory currently exists to quantitatively characterize surprise elicited by a stimulus or event, for observers that range from single neurons to complex natural or engineered systems. We describe a formal Bayesian definition of surprise that is the only consistent formulation under minimal axiomatic assumptions. Surprise quantifies how data affects a natural or artificial observer, by measuring the difference between posterior and prior beliefs of the observer. Using this framework we measure the extent to which humans direct their gaze towards surprising items while watching television and video games. We find that subjects are strongly attracted towards surprising locations, with 72% of all human gaze shifts directed towards locations more surprising than the average, a figure which rises to 84% when considering only gaze targets simultaneously selected by all subjects. The resulting theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.
Nothing to do with priming. Novelty seeking is not priming and I would expect this result to be true given what I know about people in general and bits and pieces of evolutionary psychology. Do ads try to be novel? Sure. I guess that is one way to grab attention but I don't see any connection between that and long-term manipulation that you talk about.
> A coercive monetization model depends on the ability to “trick” a person into making a purchase with incomplete information, or by hiding that information such that while it is technically available, the brain of the consumer does not access that information. Hiding a purchase can be as simple as disguising the relationship between the action and the cost as I describe in my Systems of Control in F2P paper.
Again, tricking someone is not the same as priming them for the long term. This is the same stuff casinos do so catering to short-term heuristics and tricking a person hardly qualifies as what you laid out in your original comment.
> The current study applied a “mixture-amount modeling” statistical approach—used most often in biology, agriculture, and food science—to measure the impact of advertising effort and allocation across different media. The authors of the current paper believe advertisers can use the mixture-amount model to detect optimal advertising-mix allocation changes as a function of their total advertising effort. The researchers demonstrated the use of the model by analyzing Belgian magazine and television data on 34 advertising campaigns for beauty-care brands. The goal is to help advertisers maximize desirable outcomes for campaign recognition and brand interest.
Sounds interesting but is more about optimizing exposure than anything else. No claims about long term cognitive effects and rightfully so.
Anyway, my bafflement should not be surprising.
