Theater Audience Can Actually Change The Chemical Composition Of The Air And Affects Your Feelings
It is possible for scientists to measure the way in which the chemicals exhaled by audiences when watching a film alters the air around the auditorium. When viewing films such as Captain America, it is hard to keep your heart from beating.
It is possible for scientists to measure the way in which the chemicals exhaled by audiences when watching a film alters the air around the auditorium. When viewing films such as Captain America, it is hard to keep your heart from beating. You may not realize it, but the entire audience is capable of changing the chemical composition around them as when they exhale there are hundreds of chemicals spilled out into the air.
Some of the chemicals can be used to monitor the way in which our bodies change as situations and feelings change. If there are emotional scenes, they will cause a reaction from within the autonomic nervous system, neuroendocrine and cardiovascular system, meaning that volatile products will be removed. This was the view that scientists have had for some time, so they wanted to test that the theory was accurate.
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Tests took place in Mainz Germany where 16 films were screened for a total of 108 performances, and 9,500 individuals watched screenings including Machete Kills and Walking with Dinosaurs. Tests were taken from the air vents as the films were being shown and there were some amazing discoveries made.
Certain films brought about the strongest reactions, and it was scenes with a lot of suspense or comedy that had the strongest reactions. If others in the room are able to pick up on the changes, these could be stand down/alert signals according to the researchers. The reaction of others could change a viewers’ concept of the film.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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