We present two openly accessible databases related to the assessment of implicit motives using Picture Story Exercises (PSEs): (a) A database of 183,415 German sentences, nested in 26,389 stories provided by 4,570 participants, which have been coded by experts using Winter's (1994) coding system for the implicit affiliation/intimacy, achievement, and power motives, and (b) a database of 54 classic and new pictures which have been used as PSE stimuli. Updated picture norms are provided which can be used to select appropriate pictures for PSE applications.Based on an analysis of the relations between raw motive scores, word count, and sentence count, we give recommendations on how to control motive scores for story length, and validate the recommendation with a meta-analysis on gender differences in the implicit affiliation motive that replicates existing findings. We discuss to what extent the guiding principles of the story length correction can be generalized to other content coding systems for narrative material. Several potential applications of the databases are discussed, including (un)supervised machine learning of text content, psychometrics, and better reproducibility of PSE research.

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Psychologists have long theorized that people use music to create auditory environments matching their personality traits. While there is initial evidence relating self-reported musical style preferences to the Big Five dimensions, little is known about day-to-day music listening behavior and the intrinsic attributes of music that give rise to personality patterns. The present study (N = 330) proposes a personality computing approach to fill these gaps with new insights from ecologically valid music listening records from smartphones. We provided a holistic account of individual differences in music listening by integrating everyday preferences for various musical attributes with habitual listening behaviors. More specifically, we quantified music preferences at fine granularity via technical audio features from Spotify and via lyrical attributes obtained through natural language processing. Using machine learning algorithms, these behavioral variables served to predict Big Five personality on domain and facet level. Our out-of-sample prediction accuracies revealed that the Openness dimension was most strongly related to music listening, while several other traits (most notably Conscientiousness facets) also showed moderate effects. Thereby, preferences for audio and lyrics characteristics were distinctly predictive of personality, hinting at the incremental value of both musical components. Furthermore, variable importance metrics displayed generally trait-congruent relationships between personality outcomes and music listening behaviors prompting us to discuss possible mechanisms underlying these interactions. Overall, our study contributes to the development of a detailed cumulative theory on music listening in personality science, which may be extended in numerous ways in future studies leveraging the computational framework proposed here.

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people’s thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this development, the present study demonstrates how smartphones can help establish associations between the psychological perception and the physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective situation cues obtained via smartphone sensing. To account for the complexity of real-world situations, we extracted a total of 1,356 granular situation cues from different sensing modalities. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight DIAMONDS, finding significant out-of-sample predictions for the five dimensions capturing the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. Analyses of (grouped) feature importance revealed that these predictions relied on complex constellations of cues representing various situational information about the Persons/Interactions and Objects present, the Events/Activities happening, and the current Location and Time. Furthermore, a nomological network analysis provided evidence for the construct validity of our cue-based DIAMONDS predictions. We conclude by discussing how smartphone-based situational snapshots, in general, and our prediction models, in particular, advance psychological research on situations.

Practically all user activities on a smartphone depend on self-contained software applications, so-called apps. Due to the large number and diversity of available apps, the analysis of app usage behaviour in social science research requires elaborate preprocessing of app data. Therefore, we present a categorisation scheme and a dataset of 3,091 manually categorised apps used by a representative quota sample within a large-scale smartphone sensing study conducted in Germany over several months in 2020. For the categorisation, we report values for inter-rater agreement between two independent raters. We provide the freely available dataset as a CSV and we invite other researchers to use and modify the categorisation for their specific research questions and to extend it for the mobile sensing research community.