Personality Research and Assessment in the Era of Machine Learning

The increasing availability of high-dimensional, fine-grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.     

Mission for/as Reconciliatory Emancipation

Since Christian mission in the way of Jesus Christ is best understood and experienced in the fringes of the society, it is imperative that we listen to and learn from marginalized voices. Keeping this in mind, this article looks at the relevance of the theology of mission of the Dalit theologian and activist Masilamani Azariah, who served as a Bishop of the Church of South India in the 1990s. It seeks to highlight his pioneering work, which was discomforting for some dominant caste Indian Christians, in challenging the Indian church as well as the global ecumenical movement to speak up and act against untouchability and caste discrimination. Using Azariah's radical perspectives of mission as a lens, and employing the framework of reconciliatory emancipation, a theological concept explicated by the American political theologian Mark Taylor, this essay proposes that the mission of the church that endeavours to be holistic and Christ-centred would and should be committed to the empowerment and healing of the oppressed, driven by a burning prophetic rage against injustice, even while retaining the space for forgiveness and repentance, with the ultimate goal of building the kingdom of God that transcends divisive and discriminating boundaries.     

Rhythms of life: antecedents and outcomes of work-family balance in employed parents

This study examined antecedents and outcomes of a fourfold taxonomy of work-family balance in terms of the direction of influence (work-family vs. family-work) and type of effect (conflict vs. facilitation). Respondents were full-time employed parents in India. Confirmatory factor analysis results provided evidence for the discriminant validity of M. R. Frone's (2003) fourfold taxonomy of work-family balance. Results of moderated regression analysis revealed that different processes underlie the conflict and facilitation components. Furthermore, gender had only a limited moderating influence on the relationships between the antecedents and the components of work-family balance. Last, work-family facilitation was related to the work outcomes of job satisfaction and organizational commitment.     

Categories in the pigeon brain: A reverse engineering approach

Pigeons are well known for their visual capabilities as well as their ability to categorize visual stimuli at both the basic and superordinate level. We adopt a reverse engineering approach to study categorization learning: Instead of training pigeons on predefined categories, we simply present stimuli and analyze neural output in search of categorical clustering on a solely neural level. We presented artificial stimuli, pictorial and grating stimuli, to pigeons without the need of any differential behavioral responding while recording from the nidopallium frontolaterale (NFL), a higher visual area in the avian brain. The pictorial stimuli differed in color and shape; the gratings differed in spatial frequency and amplitude. We computed representational dissimilarity matrices to reveal categorical clustering based on both neural data and pecking behavior. Based on neural output of the NFL, pictorial and grating stimuli were differentially represented in the brain. Pecking behavior showed a similar pattern, but to a lesser extent. A further subclustering within pictorial stimuli according to color and shape, and within gratings according to frequency and amplitude, was not present. Our study gives proof‐of‐concept that this reverse engineering approach—namely reading out categorical information from neural data—can be quite helpful in understanding the neural underpinnings of categorization learning.     

Luck for Moral Luck?

Philosophia - In their article “No Luck for Moral Luck” (Kneer and Machery Cognition, 182, 331-348 2019), the authors claim to have dissolved the philosophical puzzle of resultant moral...     

Inter-group alliance dynamics in Indo-Pacific bottlenose dolphins (Tursiops aduncus)

Animal Cognition - The social intelligence hypothesis holds that complex social relationships are the major selective force underlying the evolution of large brain size and intelligence. Complex...