Can AI agents produce behavioral data that passes as human? This question carries direct consequences for any field that relies on online reaction time (RT) experiments. Recent proposals for bot detection emphasize distributional shape, mean-variance scaling, and trial-wise autocorrelation of RTs, though the sufficiency of these markers has been challenged. We report the iterative development and empirical evaluation of an autonomous AI agent that completes the Attention Network Test (ANT) on a live Pavlovia experiment, producing behavioral data in real time. Across seven code revisions, each informed by analysis of the agent's output, the bot achieved attention network scores within published human norms (alerting = 65.1ms, orienting = 52.1ms, executive = 72.6ms), 95.8% accuracy, and an RT distribution exhibiting positive skew and trial-to-trial autocorrelation. We evaluated the agent against 796 human participants who completed the same ANT implementation across three university sites. The bot fell within the human range on QQ normality (z = -0.09), skewness (z = -0.77), and all three network scores, but showed elevated autocorrelation and a bimodal RT distribution from intermittent detection failures. Building this agent was technically feasible but required substantial iterative effort, experiment-specific reverse engineering, and repeated access to behavioral output. These constraints make widespread deployment unlikely for complex RT tasks in the near term, though the barrier will lower as agentic AI tools mature.

For over a decade the media multitasking index (MMI) has been the de facto standard measure for media multitasking. Thousands of studies in communication and adjacent disciplines have used the measure to investigate the relationship between media multitasking and cognitive functioning. In this paper we pose one central question: Is it time to abandon the MMI as a valid measure of media multitasking? To answer this question, we highlight a selection of practical, methodological, and theoretical concerns regarding the validity and usefulness of the MMI for indexing individual differences in media multitasking. Thereafter, we outline a research agenda to evaluate the veracity of these concerns and to advance the development of new measures of media multitasking and the adoption of new research designs. In doing so, we aim to integrate disjointed perspectives and stimulate new, improved theoretical and methodological practices in media multitasking research.

The literature on how smartphone and social media use affects adolescent mental health is highly fragmented. To synthesize the evidence, we convened over 120 researchers with diverse perspectives to evaluate 26 commonly cited claims using a Delphi process. A large majority agree that: Adolescent mental health has declined in several Western countries; heavy smartphone and social media use can cause sleep problems; such use correlates with attention problems and behavioural addiction; among girls, social media use may be associated with body dissatisfaction, perfectionism, exposure to mental disorders, harassment and predation. Most other claims were judged to have insufficient evidence due to limited, inconsistent, or non-causal data. Researchers also raised broader concerns, including challenges in measuring mental health and establishing causality, geographic bias in existing evidence, and the need for policies that account for diverse risks and avoid unintended harms. This collective review offers a foundation for future research and policy.

Punishment of moral norm violators is instrumental for human cooperation. Yet, social and affective neuroscience research has primarily focused on second- and third-party norm enforcement, neglecting the neural architecture underlying observed (vicarious) punishment of moral wrongdoers. We used naturalistic television drama as a sampling space for observing outcomes of morally-relevant behaviors to assess how individuals cognitively process dynamically evolving moral actions and their consequences. Drawing on Affective Disposition Theory, we derived hypotheses linking character morality with viewers’ neural processing of characters’ rewards and punishments. We used functional magnetic resonance imaging (fMRI) to examine neural responses of 28 female participants while free-viewing 15 short story summary video clips of episodes from a popular US television soap opera. Each summary included a complete narrative structure, fully crossing main character behaviors (moral/immoral) and the consequences (reward/punishment) characters faced for their actions. Narrative engagement was examined via intersubject correlation and representational similarity analysis. Highest cortical synchronization in 9 specifically selected regions previously implicated in processing moral information was observed when characters who act immorally are punished for their actions with participants’ empathy as an important moderator. The results advance our understanding of the moral brain and the role of normative considerations and character outcomes in viewers’ engagement with popular narratives.

Family members of a loved-one with an alcohol use disorder (AUD) experience much stress and other adverse impacts, especially those that are frontline caregivers and therefore most proximal to AUD. Previous research has shown such family members experience altered functioning of the prefrontal cortex in response to images of their loved-one, and these responses have similarities to brain responses to alcohol cues for a person with AUD. The current study aimed to expand this research by examining whole-brain functional activation of family members’ brains. Functional magnetic resonance imaging (fMRI) was used to measure activation responses of 10 family members with a loved-one with diagnosed AUD, as well as that of 10 control group participants, during an event-related research paradigm. Results from Generalized Linear Modeling (GLM) indicated significant activation in the left hippocampus and left amygdala for family members of an AUD loved-one, and this activation was significantly greater than that of a control group. These two subcortical regions play a role in the reward network and their activation found in this study may be associated with a reward-based “approach” response – drawing further parallels between the functioning of the impacted family member’s brain and that of the brain of someone with AUD. This understanding influences how clinicians might provide services to family members of those with AUD.

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder associated with suboptimal outcomes throughout the life-span. Extant work suggests that ADHD-related deficits in task performance may be magnified under high cognitive load and minimized under high perceptual load, but these effects have yet to be systematically examined, and the neural mechanisms that undergird these effects are as yet unknown. Herein, we report results from three experiments investigating how performance in ADHD is modulated by cognitive load and perceptual load during a naturalistic task. Results indicate that cognitive load and perceptual load influence task performance, reaction time variability (RTV), and brain network topology in an ADHD-specific fashion. Increasing cognitive load resulted in reduced performance, greater RTV, and reduced brain network efficiency in individuals with ADHD relative to those without. In contrast, increased perceptual load led to relatively greater performance, reduced RTV, and greater brain network efficiency in ADHD. These results provide converging evidence that brain network efficiency and intraindividual variability in ADHD are modulated by both cognitive and perceptual load during naturalistic task performance.

Media multitasking has become nearly ubiquitous in the developed world. Higher self-reported media multitasking has consistently been shown to relate to self-reported attention problems, including symptoms of attention deficit/hyperactivity disorder (ADHD), but the magnitude of this relationship is small and heterogeneous across studies. These findings have motivated calls for increased specificity in media multitasking research, moving beyond aggregated summaries of multitasking behavior in favor of an approach that considers how specific combinations of media behaviors relate to cognitive outcomes of interest. Herein, we take a data-driven (Jack et al., 2018), computational approach to uncover the network structure of media multitasking behaviors in a sample of 2542 young adults in the United States. Results indicate that those with greater severity of ADHD symptoms tend to have more densely connected multitasking networks overall, as well as differing patterns of node centrality within the network. These results provide increased understanding of how individual differences in media multitasking habits relate to attention and cognition, and point to the promise of network-based analyses developing a fuller understanding within this topic domain.

Moral foundations theory (MFT) holds that moral judgements are driven by modular and ideologically variable moral foundations but where and how these foundations are represented in the brain and shaped by political beliefs remains an open question. Using a moral vignette judgement task (n = 64), we probed the neural (dis)unity of moral foundations. Univariate analyses revealed that moral judgement of moral foundations, versus conventional norms, reliably recruits core areas implicated in theory of mind. Yet, multivariate pattern analysis demonstrated that each moral foundation elicits dissociable neural representations distributed throughout the cortex. As predicted by MFT, individuals’ liberal or conservative orientation modulated neural responses to moral foundations. Our results confirm that each moral foundation recruits domain-general mechanisms of social cognition but also has a dissociable neural signature malleable by sociomoral experience. We discuss these findings in view of unified versus dissociable accounts of morality and their neurological support for MFT. Hopp et al. probe the neural (dis)unity of moral foundations theory and report that each moral foundation recruits domain-general mechanisms of social cognition but also has a dissociable neural signature malleable by sociomoral experience.

Emotions have long been regarded as a central component of the entertainment media experience. The present study combines computational analysis and quantitative content analysis to analyze the emotional arcs of inspirational media content and associated elicitors of self-transcendent emotions in order to develop a more comprehensive understanding of inspirational media. Results demonstrate that inspirational movies can be characterized by a positive shift in sentiment at the end of the narrative and multiple shifts between relatively negative and relatively positive sentiment throughout the story. Furthermore, the "peaks" and "valleys" in the emotional arcs are associated with the presence of differing inspirational elicitors, and the presence of certain elicitors (such as kindness/moral goodness) within a scene predicted positive shifts in the emotional arcs in the scenes following the elicitor.

Successful media processing requires that an individual attend to relevant information embedded among numerous competing stimuli. In communication research, this process is often referred to as resource allocation. Although the factors that drive individuals to allocate resources toward or away from a single message are relatively well characterized, there is a lack of understanding regarding how resource allocation proceeds in the presence of multiple media tasks. In four experiments, we show that resource allocation is contingent on features of the “primary task” but also on other available tasks. Furthermore, we show that “secondary” tasks elicit more attention when they are more rewarding, and that the attention-capturing influence of these tasks is magnified when the “primary” task is more effortful. These results provide support for recent theoretical advancements in communication research and point to promising future directions using models of motivated attention to predict resource allocation across multiple media tasks.

Due to the methodological challenges inherent in studying social media use (SMU), as well as the methodological choices that have shaped research into the effects of SMU on well-being, clear conclusions regarding relationships between SMU and well-being remain elusive. We provide a review of five methodological developments poised to provide increased understanding in this domain: (a) increased use of longitudinal and experimental designs; (b) the adoption of behavioural (rather than self-report) measures of SMU; (c) focusing on more nuanced aspects of SMU; (d) embracing effect heterogeneity; and (e) the use of formal modelling and machine learning. We focus on how these advances stand to bring us closer to understanding relations between SMU and well-being, as well as the challenges associated with these developments.

Media entertainment frequently elicits rapt attention, loss of self-consciousness, high levels of enjoyment along with a sense that time is passing more quickly or more slowly than usual. This state is often referred to as flow. For over two decades, scholars have sought to understand when and why media produce flow experiences in audiences. The synchronization theory of flow (STF) advanced flow research by specifying the neural underpinnings of flow experiences and how these neural substrates are influenced by entertaining media. In the intervening years, research from a variety of fields has provided support for the core predictions of STF and has highlighted promising areas for future investigation. This chapter reviews the current state and future directions of STF, focusing on its potential for creating a bridge between media entertainment research and other vibrant research domains including motivated decision-making and neuroaesthetics.

Media psychology researchers seek to understand both why people choose certain media over others and how media influence cognitive, emotional, social, and psychological processes. A burgeoning body of literature has emerged in recent years describing media selection and media effects as reciprocally linked dynamic processes, but research approaches empirically investigating them as such have been sparse. In parallel, technological developments like algorithmic personalization and mobile computing have served to blur the lines between media selection and media effects, highlighting novel problems at their intersection. Herein, we propose an integrative approach for building an understanding of these processes rooted in decision theory, a formal framework describing how organisms (and nonbiological agents) select and optimize behaviors in response to their environment.

ABSTRACT The increasing adoption of brain imaging methods has greatly augmented our understanding of the neural underpinnings of communication processes. Enabled by recent advancements in mathematics and computational infrastructure, researchers have begun to move beyond traditional univariate analytic techniques in favor of methods that consider the brain in terms of evolving networks of interactions between brain regions. This network neuroscience approach is a potential boon to communication and media psychology research but also requires a careful look at the complications inherent in adopting a novel (and complex) methodological tool. In this manuscript, we provide an overview of network neuroscience in view of the needs of communication neuroscientists, discussing considerations that must be taken into account when constructing networks from neuroimaging data and conducting statistical tests on these networks. Throughout the manuscript, we highlight research domains in which network neuroscience is likely to be particularly useful for increasing theoretical clarity in communication and media psychology research.

There is widespread public and academic interest in understanding the uses and effects of digital media. Scholars primarily use self-report measures of the quantity or duration of media use as proxies for more objective measures, but the validity of these self-reports remains unclear. Advancements in data collection techniques have produced a collection of studies indexing both self-reported and log-based measures. To assess the alignment between these measures, we conducted a meta-analysis of this research. Based on 106 effect sizes, we found that self-reported media use only moderately correlates with logged measurements, that self-reports were rarely an accurate reflection of logged media use, and that measures of problematic media use show an even smaller association with usage logs. These findings raise concerns about the validity of findings relying solely on self-reported measures of media use. The materials needed to reproduce the analysis and an article preprint are available at: https://osf.io/dhx48/.

In the last 10 years, many canonical findings in the social sciences appear unreliable. This so-called “replication crisis” has spurred calls for open science practices, which aim to increase the reproducibility, replicability, and generalizability of findings. Communication research is subject to many of the same challenges that have caused low replicability in other fields. As a result, we propose an agenda for adopting open science practices in Communication, which includes the following seven suggestions: (1) publish materials, data, and code; (2) preregister studies and submit registered reports; (3) conduct replications; (4) collaborate; (5) foster open science skills; (6) implement Transparency and Openness Promotion Guidelines; and (7) incentivize open science practices. Although in our agenda we focus mostly on quantitative research, we also reflect on open science practices relevant to qualitative research. We conclude by discussing potential objections and concerns associated with open science practices.

The limited capacity model of motivated mediated message processing (LC4MP) is a model for understanding the dynamic interactions between mediated messages and the human information processing system. At the time of its original writing, the LC4MP was unique in the landscape of media psychology research in that it eschewed rote investigation of message effects and instead focused on investigating how message structure and content interact with and influence various biological systems of the brain and body. In the last twenty years, the LC4MP has catalyzed much progress in our understanding of the biological dimensions of message processing. In this entry, we review the assumptions of the LC4MP, along with key concepts and methods used in the LC4MP literature. In addition, we discuss the core propositions of the model, highlighting ways in which they have evolved as the model has developed. Finally, we introduce a few areas in which the model is still under active development, pointing out promising areas for future research.