Psychologists are navigating an unprecedented period of introspection about the credibility and utility of their discipline. Reform initiatives emphasize the benefits of transparency and reproducibility-related research practices; however, adoption across the psychology literature is unknown. Estimating the prevalence of such practices will help to gauge the collective impact of reform initiatives, track progress over time, and calibrate future efforts. To this end, we manually examined a random sample of 250 psychology articles published between 2014 and 2017. Over half of the articles were publicly available (154/237, 65%, 95% confidence interval [CI] = [59%, 71%]); however, sharing of research materials (26/183; 14%, 95% CI = [10%, 19%]), study protocols (0/188; 0%, 95% CI = [0%, 1%]), raw data (4/188; 2%, 95% CI = [1%, 4%]), and analysis scripts (1/188; 1%, 95% CI = [0%, 1%]) was rare. Preregistration was also uncommon (5/188; 3%, 95% CI = [1%, 5%]). Many articles included a funding disclosure statement (142/228; 62%, 95% CI = [56%, 69%]), but conflict-of-interest statements were less common (88/228; 39%, 95% CI = [32%, 45%]). Replication studies were rare (10/188; 5%, 95% CI = [3%, 8%]), and few studies were included in systematic reviews (21/183; 11%, 95% CI = [8%, 16%]) or meta-analyses (12/183; 7%, 95% CI = [4%, 10%]). Overall, the results suggest that transparency and reproducibility-related research practices were far from routine. These findings establish baseline prevalence estimates against which future progress toward increasing the credibility and utility of psychology research can be compared.

Psychologists are navigating an unprecedented period of introspection about the credibility and utility of their discipline. Reform initiatives have emphasized the benefits of several transparency and reproducibility-related research practices; however, their adoption across the psychology literature is unknown. To estimate their prevalence, we manually examined a random sample of 250 psychology articles published between 2014-2017. Over half of the articles were publicly available (154/237, 65% [95% confidence interval, 59%-71%]); however, sharing of research materials (26/183, 14% [10%-19%]), study protocols (0/188, 0% [0%-1%]), raw data (4/188, 2% [1%-4%]), and analysis scripts (1/188, 1% [0%-1%]) was rare. Pre-registration was also uncommon (5/188, 3% [1%-5%]). Many articles included a funding disclosure statement (142/228, 62% [56%-69%]), but conflict of interest statements were less common (88/228, 39% [32%-45%]). Replication studies were rare (10/188, 5% [3%-8%]) and few studies were included in systematic reviews (21/183, 11% [8%-16%]) or meta-analyses (12/183, 7% [4%-10%]). Overall, the results suggest that transparency and reproducibility-related research practices were far from routine. These findings establish a baseline which can be used to assess future progress towards increasing the credibility and utility of psychology research.

Psychological scientists have become increasingly concerned with issues related to methodology and replicability, and infancy researchers in particular face specific challenges related to replicability: For example, high-powered studies are difficult to conduct, testing conditions vary across labs, and different labs have access to different infant populations. Addressing these concerns, we report on a large-scale, multisite study aimed at (a) assessing the overall replicability of a single theoretically important phenomenon and (b) examining methodological, cultural, and developmental moderators. We focus on infants’ preference for infant-directed speech (IDS) over adult-directed speech (ADS). Stimuli of mothers speaking to their infants and to an adult in North American English were created using seminaturalistic laboratory-based audio recordings. Infants’ relative preference for IDS and ADS was assessed across 67 laboratories in North America, Europe, Australia, and Asia using the three common methods for measuring infants’ discrimination (head-turn preference, central fixation, and eye tracking). The overall meta-analytic effect size (Cohen’s d) was 0.35, 95% confidence interval = [0.29, 0.42], which was reliably above zero but smaller than the meta-analytic mean computed from previous literature (0.67). The IDS preference was significantly stronger in older children, in those children for whom the stimuli matched their native language and dialect, and in data from labs using the head-turn preference procedure. Together, these findings replicate the IDS preference but suggest that its magnitude is modulated by development, native-language experience, and testing procedure.

Events—the experiences we think we are having and recall having had—are constructed; they are not what actually occurs. What occurs is ongoing dynamic, multidimensional, sensory flow, which is somehow transformed via psychological processes into structured, describable, memorable units of experience. But what is the nature of the redescription processes that fluently render dynamic sensory streams as event representations? How do such processes cope with the ubiquitous novelty and variability that characterize sensory experience? How are event‐rendering skills acquired and how do event representations change with development? This review considers emerging answers to these questions, beginning with evidence that an implicit tendency to monitor predictability structure via statistical learning is key to event rendering. That is, one way that the experience of bounded events (e.g., actions within behavior, words within speech) arises is with the detection of “troughs” in sensory predictability. Interestingly, such troughs in predictability are often predictable; these regions of predictable‐unpredictability provide articulation points to demarcate one event from another in representations derived from the actual streaming information. In our information‐optimization account, a fluent event‐processor predicts such troughs and selectively attends to them—while suppressing attention to other regions—as sensory streams unfold. In this way, usage of attentional resources is optimized for efficient sampling of the most relevant, information‐rich portions of the unfolding flow of sensation. Such findings point to the development of event‐processing fluency—whether in action, language, or other domains—depending crucially on rapid and continual cognitive reorganization. As knowledge of predictability grows, attention is adaptively redeployed. Accordingly, event experiences undergo continuous alteration.

The field of infancy research faces a difficult challenge: Some questions require samples that are simply too large for any 1 lab to recruit and test. ManyBabies aims to address this problem by forming large-scale collaborations on key theoretical questions in developmental science, while promoting the uptake of Open Science practices. Here, we look back on the first project completed under the ManyBabies umbrella-ManyBabies 1-which tested the development of infant-directed speech preference. Our goal is to share the lessons learned over the course of the project and to articulate our vision for the role of large-scale collaborations in the field. First, we consider the decisions made in scaling up experimental research for a collaboration involving 100ϩ researchers and 70ϩ labs. Next, we discuss

Everyday experience consists of rapidly unfolding sensory information that humans redescribe as discrete events. Quick and efficient redescription facilitates remembering, responding to, and learning from the ongoing sensory flux. Segmentation seems key to successful redescription: the extent to which viewers can identify boundaries between event units within continuously unfolding activities predicts both memory and action performance. However, what happens to processing when boundary content is missing? Events occurring in naturalistic situations seldom receive continuous undivided attention. As a consequence, information, including boundary content, is likely sometimes missed. In this research, we systematically explored the influence of missing information by asking participants to advance at their own pace through a series of slideshows. Some slideshows, while otherwise matched in content, contained just half of the slides present in other slideshows. Missing content sometimes occurred at boundaries. As it turned out, patterns of attention during slideshow viewing were strikingly similar across matched slideshows despite missing content, even when missing content occurred at boundaries. Moreover, to the extent that viewers compensated with increased attention, missing content did not significantly undercut event recall. These findings seem to further confirm an information optimization account of event processing: event boundaries receive heightened attention because they forecast unpredictability and thus, optimize the uptake of new information. Missing boundary content sparks little change in patterns of attentional modulation, presumably because the underlying predictability parameters of the unfolding activity itself are unchanged by missing content. Optimizing information, thus, enables event processing and recall to be impressively resilient to missing content.

Preregistration has been lauded as one of the solutions to the so-called ‘crisis of confidence’ in the social sciences and has therefore gained popularity in recent years. However, the current guidelines for preregistration have been developed primarily for studies where new data will be collected. Yet, preregistering secondary data analyses--- where new analyses are proposed for existing data---is just as important, given that researchers’ hypotheses and analyses may be biased by their prior knowledge of the data. The need for proper guidance in this area is especially desirable now that data is increasingly shared publicly. In this tutorial, we present a template specifically designed for the preregistration of secondary data analyses and provide comments and a worked example that may help with using the template effectively. Through this illustration, we show that completing such a template is feasible, helps limit researcher degrees of freedom, and may make researchers more deliberate in their data selection and analysis efforts.

Preregistration has been lauded as one of the solutions to the so-called ‘crisis of confidence’ in the social sciences and has therefore gained popularity in recent years. However, the current guidelines for preregistration have been developed primarily for studies where new data will be collected. Yet, preregistering secondary data analyses---where new analyses are proposed for existing data---is just as important, given that researchers’ hypotheses and analyses may be biased by their prior knowledge of the data. The need for proper guidance in this area is especially desirable now that data is increasingly shared publicly. In this tutorial, we present a template specifically designed for the preregistration of secondary data analyses and provide comments and a worked example that may help with using the template effectively. Through this illustration, we show that completing such a template is feasible, helps limit researcher degrees of freedom, and may make researchers more deliberate in their data selection and analysis efforts.