Current Directions in Psychological Science
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The sounds of human infancy—baby babbling, adult talking, lullaby singing, and more—fluctuate over time. Infant-friendly wearable audio recorders can now capture very large quantities of these sounds throughout infants’ everyday lives at home. Here, we review recent discoveries about how infants’ soundscapes are organized over the course of a day. Analyses designed to detect patterns in infants’ daylong audio at multiple timescales have revealed that everyday vocalizations are clustered hierarchically in time, that vocal explorations are consistent with foraging dynamics, and that some musical tunes occur for much longer cumulative durations than others. This approach focusing on the multiscale distributions of sounds heard and produced by infants is providing new, fundamental insights on human communication development from a complex-systems perspective.

A standard model is a theoretical framework that synthesizes observables into a quantitative consensus. Have researchers made progress toward this kind of synthesis for children’s early language learning? Many computational models of early vocabulary learning assume that individual words are learned through an accumulation of environmental input. This assumption is also implicit in empirical work that emphasizes links between language input and learning outcomes. However, models have typically focused on average performance, whereas empirical work has focused on variability. To model individual variability, we relate the tradition of research on accumulator models to item response theory models from psychometrics. This formal connection reveals that currently available data sets do not allow researchers to test the resulting models fully, illustrating a critical need for theory to contribute to shaping new data collection and creating and testing an eventual standard model.

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.

Increasingly, we are required, encouraged, and/or motivated to track our behavior, presumably to improve our life “quality.” But health and life-satisfaction trends are not cooperating: Empirical evidence for success is sorely lacking. Intelligence has been tracked for more than 100 years; perhaps this example offers some hints about tracking’s overall social impact. I suggest that Huxley’s Brave New World offers a relevant long-term extrapolation and that popular recent tracking activities will accelerate “progress” in that dystopian direction.

Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building) that are relevant across many cognitive domains to specialized knowledge structures (e.g., a particular language’s lexicon and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language network, which is selective for high-level language processing, and the multiple-demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence and thus is a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension and that past findings suggesting the contrary are likely due to methodological artifacts. Although future studies may reveal some aspects of language comprehension that require the MD network, evidence to date suggests that those will not be related to core linguistic processes such as lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation of memory and computation in the mind and brain.

Online behavioral data, such as digital traces from social media, have the potential to allow researchers an unprecedented new window into human behavior in ecologically valid everyday contexts. However, research using such data is often purely observational, which limits its usefulness for identifying causal relationships. Here we review recent innovations in experimental approaches to studying online behavior, with a particular focus on research related to misinformation and political psychology. In hybrid lab-field studies, exposure to social-media content can be randomized, and the impact on attitudes and beliefs can be measured using surveys, or exposure to treatments can be randomized within survey experiments, and their impact on subsequent online behavior can be observed. In field experiments conducted on social media, randomized treatments can be administered directly to users in the online environment (e.g., via social-tie invitations, private messages, or public posts) without revealing that they are part of an experiment, and the effects on subsequent online behavior can then be observed. The strengths and weaknesses of each approach are discussed, along with practical advice and central ethical constraints on such studies.

Do social networking sites (SNSs) influence well-being? According to the active-passive model of SNS use, the impact of SNSs on well-being depends on how they are used: Using SNSs actively to interact with other users positively affects well-being, whereas passive consumption of SNS content negatively affects well-being. However, emerging evidence suggests that the active-passive distinction is too coarse to fully capture the relationship between SNS use and well-being. Here we describe the extended active-passive model of SNS use, which refines the original model in three ways: It decomposes active use, decomposes passive use, and crosses usage types with user characteristics. We describe recent empirical evidence illustrating the benefits of these three extensions and highlight important future research directions. The extended active-passive model of SNS use provides a nuanced understanding of the relationship between SNS use and well-being by highlighting that active use of SNSs is not always beneficial and passive use is not always detrimental.

The Golden Rule of ethics in its negative form states that you should not do to others what you would not want others to do to you, and in its positive form states that you should do to others as you would want them to do to you. The Golden Rule is an ethical principle, but in virtual reality (VR), it can also be thought of as a paradigm for the promotion of prosocial behavior. This is because in VR, you can directly experience harm that you inflicted or were complicit in inflicting from the embodied perspective of the victim. This use of what we refer to as the Golden Rule Embodiment Paradigm (GREP) relies on participants in VR having the illusion of body ownership over a virtual body. In this article, we introduce virtual embodiment and the consequent illusion of ownership over the virtual body, and describe how this phenomenon has been utilized to influence implicit attitudes. We then introduce the GREP and give examples of studies in which it enhanced helping behavior.

In this article, we argue that stigma may be an important, but heretofore underrecognized, source of heterogeneity in treatment effects of mental- and behavioral-health interventions. To support this hypothesis, we review recent evidence from randomized controlled trials and spatial meta-analyses suggesting that stigma may predict not only who responds more favorably to these health interventions (i.e., individuals with more stigma experiences), but also the social contexts that are more likely to undermine intervention effects (i.e., communities with greater structural stigma). By highlighting the potential role of personal and contextual stigma in shaping response to interventions, our review paves the way for additional research.

Skilled motor performance is essential in sports, the performing arts, various occupations, and many daily activities. Scientists and practitioners alike are therefore interested in understanding the conditions that influence the performance and learning of movement skills, and how they can be utilized to optimize training. In OPTIMAL theory, three motivational and attentional factors are key: enhanced expectancies for future performance, the performer’s autonomy, and an external focus of attention. We review recent evidence suggesting that each factor contributes independently to strengthen the coupling of goals to actions. This work has implications ranging from fostering more effective skill development in novice learners, to increasing the efficiency of athletes’ and musicians’ performance, and to facilitating the success of patients in regaining functional capabilities.