Many researchers have discovered FaceReader as a tool for their research. These 3 recent studies with FaceReader show how emotion data helps you to better understand human-human, human-machine, and human-product interactions.
As emotions run through everyday life, facial expression analysis is often used in consumer and behavior research. With FaceReader 7.1 you can now detect affective attitudes as well.
What does ‘nice’ actually mean in relation to psychological variables? And does it positively correlate with self-reported levels of health, happiness, and wellbeing?
Studies show that people with anorexia nervosa have reduced facial expressivity of emotions while viewing emotionally provoking stimuli. Researcher Leppanen and her team used FaceReader to investigate this.
Many researchers are interested in the relationship between facial expressions and music preference. This is the first study that uses FaceReader to investigate this.
José Chavaglia Neto and José António Filipe investigated the effect of one commercial on consumer emotion. They asked consumers to watch this commercial related to a specific brand.
Learn all about gamification in marketing, facial expression analysis, and the difference between self-report, qualitative research, and unobtrusive observations.
Are you interested in using automatic facial expression analysis in a standardized lab setting? Here are 5 tips to get you started!
Real time measurement techniques like FaceReader might be the key to measuring flow in real-time.
David Schindler and colleagues developed a software, µCap (muCap), which is capable of creating a link between video footage and phases of the experiment, suitable for automated analysis in FaceReader.