Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.

Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.

The open science movement, although not new to social science broadly, has gained momentum recently within communication science. In response, journals in our field have begun encouraging open science practices, from data and materials sharing to submitting preregistered research reports. However, this momentum has also led to some confusion over what is and is not considered open science and what the value of open sciences practices is. In this editorial we lay out an "onion model" of open science that describes increasing levels of transparency and suggests how open science practices can be understood less as a revolutionary concept but more as a logical extension of some of the historical pillars of scientific norms. Through this model, we provide tangible steps for how scholars may begin thinking about how to introduce open science practices into their current and future empirical efforts.

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An experiment investigates the impact of fan identification on the cognitive and emotional processing of sports-related news media. Two coaches were featured; one conceptualized as negatively valenced the other positively. Participants completed a fan identification scale before stimuli presentation. While watching the press conferences, heart rate, skin conductance, and corrugator muscle activity were recorded as indices of cognitive resource allocation, emotional arousal, and aversive motivation activation respectively. Self-report measures were collected after each stimulus. Results show that highly identified fans process sports-related news content differently than moderate fans, allocating more cognitive resources and exhibiting greater aversive reactions to the negatively valenced coach. Comparisons between the self-report and psychophysiology data suggest that the latter may be less susceptible to social desirability response bias when emotional reaction to sports messages are concerned.

Although scholars have generated much research examining enjoyment of mediated sports, much of it has failed to explore how visual production elements shape viewer response. This study examines the impact of one increasingly common technique, subjective camera, on viewer arousal and enjoyment of game play. Participants viewed multiple plays from a college football game R. Glenn Cummins (Ph.D., University of Alabama, 2005) is an Assistant Professor in the College of Mass Communications at Texas Tech University. His research interests include the impact of various structural, content, and user characteristics on the enjoyment of media entertainment. Justin R. Keene (M.A., Texas Tech University, 2009) is a dual-Ph.D. student in the Department of Telecommunications and the Cognitive Science program at Indiana University. His research interests include motivated cognition, differential cognitive processing of sports fans, and cognitive modeling. Brandon H. Nutting (M.A., Texas Tech University, 2009) is a doctoral candidate in the College of Mass Communications at Texas Tech University. His research interests include cognitive processing of mediated messages and time perception.

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