Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science

Abstract

Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.

The DECIDE Framework: Describing Ethical Choices in Digital-Behavioral-Data Explorations

“…The data psychologists collect is already laden with information about people, meaning the field arguably has the hardest job when it comes to data privacy and sharing. Digital research further exacerbates this difficulty, as whenever a person uses a piece of technology, they leave behind a behavioral trace containing identifying information such as where they were, what they were doing, and can be used to infer a person's interests and personality traits (Hinds et al, 2022;Hinds & Joinson, 2019;Rafaeli et al, 2019). These details afford great opportunities to learn about human behavior with fine grained resolution, however this comes with the caveat that more personal and identifying information resides in extracted datasets.…”

Digital traces of offline mobilization.

“…We aimed to show that incorporating the data-driven approaches into psychological research offers new opportunities to test theoretical propositions, and doing so may improve the predictive ability of psychological theories (Hinds et al, 2022; Yarkoni & Westfall, 2017). Concurrently, we aimed to show that psychological theories can enhance the explanatory power of data-driven algorithms (Hinds et al, 2022). In the following sections, we first describe theories that explain how and why online interactions might be related to offline mobilization, and then we suggest how using digital trace data and data science techniques can help test those theories.…”

Response Durations: A Flexible, No-Cost Tool for Psychological Science

“…The neglect of motor execution in many cognitive approaches stands in stark contrast to a range of fields that do not rely heavily on response time measurements. In fact, some experimental setups obviously invite the study of how movements are enacted, as in the case of reaching, grasping, or pointing actions (Fitts, 1954); movements of the mouse cursor and swiping on a touch screen (Wirth et al, 2020); and data on mobility in everyday life (Hinds et al, 2022). The same is true for measures of syllable duration in psycholinguistic studies (e.g., Kawamoto et al, 1998) and for dwell times in eye-tracking research (e.g., Sauter et al, 2021).…”