Mindful Machine Learning
Using Machine Learning Algorithms to Predict the Practice of Mindfulness
Abstract
Abstract. Mindfulness refers to a stance of nonjudgmental awareness of present-moment experiences. A growing body of research suggests that mindfulness may increase cognitive resources, thereby buffering stress. However, existing models have not achieved a consensus on how mindfulness should be operationalized. As the sound measurement of mindfulness is the foundation needed before substantial hypotheses can be supported, we propose a novel way of gauging the psychometric quality of a mindfulness measurement instrument (the Freiburg Mindfulness Inventory; FMI). Specifically, we employed 10 predictive algorithms to scrutinize the measurement quality of the FMI. Our criterion of measurement quality was the degree to which an algorithm separated mindfulness practitioner from nonpractitioners in a sample of N = 276. A high predictive accuracy of class membership can be taken as an indicator of the psychometric quality of the instrument. In sum, two findings are of interest. First, over and above some items of the FMI were able to reliably predict class membership. However, some items appeared to be uninformative. Second, from an applied methodological point of view, it appears that machine learning algorithms can outperform traditional predictive methods such as logistic regression. This finding may generalize to other branches of research.
References
1994). Diagnostic tests. 1: Sensitivity and specificity. BMJ: British Medical Journal, 308, 1552
(2011). Measuring mindfulness. Contemporary Buddhism, 12, 241–261.
(2004). Mindfulness: A proposed operational definition. Clinical Psychology Science and Practice, 11, 230–241.
(2011). Psychometric properties of the five facet mindfulness questionnaire in depressed adults and development of a short form. Assessment, 18, 308–320. doi: 10.1177/1073191111408231.
(2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84, 822–848. doi: 10.1037/0022-3514.84.4.822.
(2011). Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clinical Psychology Review, 31, 449–464. doi: 10.1016/j.cpr.2010.11.003.
(2013). Applied multiple regression/correlation analysis for the behavioral sciences. London, UK: Routledge.
(2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97, 77–87. doi: 10.1198/016214502753479248.
(2012). The effects of mindfulness meditation: A meta-analysis. Mindfulness, 3, 174–189. doi: 10.1007/s12671-012-0101-x.
(2008). Mindfulness-based cognitive therapy for treatment-resistant depression: A pilot study. Psychotherapy and Psychosomatics, 77, 319–320.
(2012). The mindful brain and emotion regulation in mood disorders. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 57, 70–77.
(1973). The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and Psychological Measurement, 33, 613–619.
(2014). The potential effects of meditation on age-related cognitive decline: A systematic review. Annals of the New York Academy of Sciences, 1307, 89–103. doi: 10.1111/nyas.12348.
(2008). On measuring mindfulness in psychosomatic and psychological research. Journal of Psychosomatic Research, 64, 405–408.
(2011). Defining mindfulness by How poorly I think I pay attention during everyday awareness and other intractable problems for psychology’s (re)invention of mindfulness: Comment on Brown et al. (2011). Psychological Assessment, 23, 1034–1040.
(2009). The elements of statistical learning. Elements (Vol. 1), New York City, NY: Springer.
(2011). Mindfulness or Mindlessness? European Journal of Psychological Assessment, 27, 59–64. doi: 10.1027/1015-5759/a000045.
(2010). The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. Journal of Consulting and Clinical Psychology, 78, 169–183.
(2009). Facets of mindfulness – results of an online study investigating the Freiburg mindfulness inventory. Personality and Individual Differences, 46, 224–230.
(2008). Building predictive models in R using the Caret package. Journal of Statistical Software, 28, 1–26.
(1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 33, 363–374. doi: 10.2307/2529786.
(2000). The construct of mindfulness. Journal of Social Issues, 56, 1–9.
(2008). Personality psychology. New York City, NY: Mac Graw-Hill.
(2005). Spirituality, mindfulness and substance abuse. Addictive Behaviors, 30, 1335–1341. doi: 10.1016/j.addbeh.2005.01.010.
(2010). Mindfulness meditation practise as a healthcare intervention: A systematic review. International Journal of Osteopathic Medicine, 13, 56–66. doi: 10.1016/j.ijosm.2009.07.005.
(2012). How long is now for mindfulness meditators? Personality and Individual Differences, 52, 750–754.
(2010). Gray’s Behavioural Inhibition System as a mediator of mindfulness towards well-being. Personality and Individual Differences, 50, 506–551.
(2011). Measuring mindfulness: A Rasch analysis of the Freiburg mindfulness inventory. Religions, 2, 693–706.
(2013). Assessment of mindfulness: review on state of the art. Mindfulness, 4, 3–17.
(2013). Specific objectivity of mindfulness – a Rasch analysis of the Freiburg Mindfulness Inventory. Mindfulness, 4, 45–54. doi: 10.1007/s12671-012-0145-y.
(2012). The psychological effects of meditation: A meta-analysis. Psychological Bulletin, 138, 1139–1171. doi: 10.1037/a0028168.
(2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine, 8, 68–74. doi: 10.1038/nm0102-68.
(2013). Tools of the trade: Theory and method in mindfulness neuroscience. Social Cognitive and Affective Neuroscience, 8, 118–120. doi: 10.1093/scan/nss112.
(2013). Meditation, mindfulness and executive control: The importance of emotional acceptance and brain-based performance monitoring. Social Cognitive and Affective Neuroscience, 8, 85–92. doi: 10.1093/scan/nss045.
(2010). Measuring mindfulness? An Item Response Theory analysis of the Mindful Attention Awareness Scale. Personality and Individual Differences, 49, 805–810. doi: 10.1016/j.paid.2010.07.020.
(2003). Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nature Medicine, 9, 416–423. doi: 10.1038/nm843.
(2013). The efficacy of mindfulness-based stress reduction on mental health of breast cancer patients: A meta-analysis. Psycho-Oncology, 22, 1457–1465. doi: 10.1002/pon.3171.
(