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Measurement of Genuine Happiness
  • Arindam BasuOrcid,
  • russell taylor
Arindam Basu
Orcid
University of Canterbury
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russell taylor
University of Canterbury
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Abstract

It is believed that an 'unhappy state of mind' is a risk factor for suicide & homicide in all ages - a major public health problem worldwide; therefore interventions that enable happy mental states in individuals may lead to a possible mitigation of this problem. However, the linkage between happiness and deaths due to suicide and homicide is unclear, as there exists no objective measurement of happiness as a trait. Happiness is measured using subjective instruments, this leads to response and information bias; besides, there is no agreement between the various instruments. While it is believed that facial features such as smile indicate a happy state of mind, there has been no validation of facial features with happiness states identified with the various instruments. Traditionally, an intense practice of insight meditation is believed to increase "trait happiness"; hence  our goal in this research is to use insight meditation to study and develop objective measures of trait happiness in individuals, and validate such measurement with instruments used for subjective assessment of happiness. 
We will achieve this goal by first conducting a survey of trait happiness using the Oxford Happiness Survey instrument on a group of meditators with experience of more than seven years of insight meditation practice & also video-record their facial expressions over a period of nine days in a retreat. Such data will be our validation set. We will then compare the findings with 20 lay meditators in similar settings for six nine-day insight meditation retreats (training data set). This work will provide a basis for an objective measurement of the trait of genuine happiness. It will draw on the literature on happiness traits in positive psychology but also draw on the principles of machine learning artificial intelligence and analysis of digital images to match the traits identified by survey questionnaires and match with automated image processing to validate the questionnaires.