Clemens Stachl

University of St.Gallen · Institute of Behavioral Science and Technology

The present study investigates to what degree individual differences can predict frequency and duration of actual behaviour, manifested in mobile application (app) usage on smartphones. In particular, this work focuses on the identification of stable associations between personality on the factor and facet level, fluid intelligence, demography and...

Sociability as a disposition describes a tendency to affiliate with others (vs. be alone). Yet, we know relatively little about how much social behavior people engage in during a typical day. One challenge to documenting social behavior tendencies is the broad number of channels over which socializing can occur, both in-person and through digital m...

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...

Significance Smartphones are sensor-rich computers that can easily be used to collect extensive records of behaviors, posing serious threats to individuals’ privacy. This study examines the extent to which individuals’ personality dimensions (assessed at broad domain and narrow facet levels) can be predicted from six classes of behavior: 1) communi...

Student attrition poses a major challenge to academic institutions, funding bodies and students. With the rise of Big Data and predictive analytics, a growing body of work in higher education research has demonstrated the feasibility of predicting student dropout from readily available macro-level (e.g., socio-demographics or early performance metr...

It is a long-held belief in psychology and beyond that individuals’ music preferences reveal information about their personality traits. While initial evidence relates self-reported preferences for broad musical styles to the Big Five dimensions, little is known about day-to-day music listening behavior and the intrinsic attributes of melodies and...

It is a long-held belief in psychology and beyond that individuals’ music preferences reveal information about their personality traits. While initial evidence relates self-reported preferences for broad musical styles to the Big Five dimensions, little is known about day-to-day music listening behavior and the intrinsic attributes of melodies and...

Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects an...

Decisions such as which movie to watch next, which song to listen to, or which product to buy online, are increasingly influenced by recommender systems and user models that incorporate information on users' past behaviours, preferences, and digitally created content. Machine learning models that enable recommendations and that are trained on user...

Digital technologies play an important role in our daily lives. Smartphones and tablet computers are very common worldwide and are available for everybody from a very early age. This trend offers the opportunity to track digital usage data for psychological and educational research purposes. The current paper introduces two research projects, the P...

Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretability. Most work in this area has been focused on the interpretation of single features in a model. However, for researchers and practitioners, it is often equally important...

Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects an...

The ubiquity of location-data-enabled devices provides novel avenues for psychology researchers to incorporate spatial analytics into their studies. Spatial analytics use global positioning system (GPS) data to assess and understand mobility behavior (e.g., locations visited, movement patterns). In this tutorial, we provide a practical guide to ana...

Supervised machine learning (ML) is becoming an influential research method in psychology and other social sciences. However, theoretical ML concepts and predictive modeling techniques are not yet widely taught in psychology programs. This tutorial is intended to provide a low-barrier, non-technical entrance to supervised ML for psychologists in fo...

Psychology's tendency to focus on confirmatory analyses before ensuring constructs are clearly defined and accurately measured is exacerbating the generalizability crisis. Our growing use of digital behaviors as predictors has revealed the fragility of subjective measures and the latent constructs they scaffold. However, new technologies can provid...

App usage data provide some of the most psychologically rich information one can collect using mobile sensing methods. Here, we discuss how data from the applications ( “apps”) people use to enhance the functionality of their mobile devices can advance research in all subdisciplines of psychology. First, we describe prior psychological work on app...

Text is one of the most prevalent types of digital data that people create as they go about their lives. Digital footprints of people’s language usage in social media posts were found to allow for inferences of their age and gender. However, the even more prevalent and potentially more sensitive text from instant messaging services has remained lar...

Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but contin...

Computational methods have increased the objectivity of measures of human behavior and positioned personality science to benefit from the ongoing digital revolution. In this review, we define and discuss computational personality assessment (CPA), a measurement process that uses computational technologies to obtain estimates of personality. We brie...

The ubiquity of location data-enabled devices provides novel avenues for psychology researchers to incorporate spatial analytics into their studies. Spatial analytics use GPS data to assess and understand mobility behavior (e.g., locations visited, movement patterns). This tutorial provides a practical guide to using GPS data in R, introducing rese...

An institution, be it a body of government, commercial enterprise, or a service, cannot interact directly with a person. Instead, a model is created to represent us. We argue the existence of a new high-fidelity type of person model which we call a digital voodoo doll. We conceptualize it and compare its features with existing models of persons. Di...

Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretability. Most work in this area has been focused on the interpretation of single features in a model. However, for researchers and practitioners, it is often equally important...

Psychology’s tendency to focus on confirmatory analyses before ensuring constructs are clearly defined and accurately measured is exacerbating the generalizability crisis. Our growing use of digital behaviors as predictors has revealed the fragility of subjective measures and the latent constructs they scaffold. However, new technologies can provid...

Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytic...

Computational methods for the representation and analysis of data have drastically increased the objectivity, reliability, and the practical implications of research conducted throughout most scientific pursuits. Our rapidly-emerging potential to transform digital data into objective measures of human behavior, thoughts, and feelings has perfectly...

People's thoughts, feelings, and behaviors can vary a great deal over situations and time. Such dynamic patterns are difficult to assess using traditional survey- and lab-based methods. However, new digital media technologies (e.g., smartphones, wearable devices, smart-home devices) offer a powerful approach to capturing fine-grained records of peo...

Text is one of the most prevalent types of digital data that people create as they go about their lives. Digital footprints of people’s language usage in social media posts were found to allow for inferences of their age and gender. However, the even more prevalent and potentially more sensitive text from instant messaging services has remained lar...

People around the world own digital media devices that mediate and are in close proximity to their daily behaviours and situational contexts. These devices can be harnessed as sensing technologies to collect information from sensor and metadata logs that provide fine‐grained records of everyday personality expression. In this paper, we present a co...

For decades, day–night patterns in behaviour have been investigated by asking people about their sleep–wake timing, their diurnal activity patterns, and their sleep duration. We demonstrate that the increasing digitalization of lifestyle offers new possibilities for research to investigate day–night patterns and related traits with the help of beha...

We present the first systematic analysis of personality dimensions developed specifically to describe the personality of speech-based conversational agents. Following the psycholexical approach from psychology, we first report on a new multi-method approach to collect potentially descriptive adjectives from 1) a free description task in an online s...

The intentional distortion of test results presents a fundamental problem to self-report-based psychiatric assessment, such as screening for depressive symptoms. The first objective of the study was to clarify whether depressed patients like healthy controls possess both the cognitive ability and motivation to deliberately influence results of comm...

Theories derived from lab-based research emphasize the importance of mentalizing for social interaction and propose a link between mentalizing, autistic traits, and social behavior. We tested these assumptions in everyday life. Via smartphone-based experience sampling and logging of smartphone usage behavior we quantified mentalizing and social int...

This chapter discusses main opportunities and challenges of assessing and utilizing personality traits in personalized interactive systems and services. This unique perspective arises from our long-term collaboration on research projects involving three groups on human-computer interaction (HCI), psychology, and statistics. Currently, personalizati...

The increasing availability of high-dimensional and fine-grained data about human behaviorin the form of digital footprints from online repositories and data traces from high-frequencymobile sensing studies, is about to drastically alter the way personality psychologists performresearch and personality assessment. The new opportunities to collect t...

The understanding, quantification and evaluation of individual differences in behavior, feelings and thoughts have always been central topics in psychological science. An enormous amount of previous work on individual differences in behavior is exclusively based on data from self-report questionnaires. To date, little is known about how individuals...

Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In this paper, we introduce an algor...

Theories derived from lab-based research emphasize the importance of mentalizing for social interaction and propose a link between mentalizing, autistic traits, and social behavior. We took social cognitive research outside the lab to test these assumptions in everyday life. Via smartphone-based experience sampling and logging of smartphone usage b...

We propose Information Transmission as a novel perspective on the mobile Experience Sampling Method (ESM) to frame a research agenda with a sharpened focus on increasing data quality in ESM studies. In this view, good experience sampling transmits valid, relevant, and "noise-free" information from users' in-situ experiences to remote researchers. W...

Previous research suggests that men outperform women when they are required to use Euclidean information such as distances for orientation tasks, whereas women are superior in the use of landmarks. Our study examines whether this finding stands up to a test if it is put into an application context. Besides comparing self-reported wayfinding strateg...

The increasing usage of new technologies implies changes for personality research. First, human behavior becomes measurable by digital data, and second, digital manifestations to some extent replace conventional behavior in the analog world. This offers the opportunity to investigate personality traits by means of digital footprints. In this contex...

Drivers' emotional and physical states have a big impact on their driving performance. New technological sensing methods are currently investigated and will soon allow to automatically detect the driver's state. Yet, how to communicate the detected state to the driver is less well understood. In an iterative design process, we developed two concept...

Personal Visualizations (PV) provide visual feedback on personal data, e.g., regarding physical activity or energy consumption. They are a vital part of many behavior change technologies (BCT) and Personal Informatics tools. Feedback can be presented in various ways, for example using counts and graphs, stylized displays, metaphoric displays, narra...

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification vers...

The recognition and utilization of user-specific information is of increasing importance in relation to modern recommender systems and adaptive user interfaces. Associated with this trend is the increased need for privacy protecting measures in personalized systems. This work demonstrates the possibility to recognize user-gender from automotive dri...