Judgments regarding replicability are vital to scientific progress. The metaphor of “standing on the shoulders of giants” encapsulates the notion that progress is made when new discoveries build on previous findings. Yet attempts to build on findings that are not replicable could mean a great deal of time, effort, and money wasted. In light of the recent “crisis of confidence” in psychological science, the ability to accurately judge the replicability of findings may be more important than ever. In this Registered Report, we examine the factors that influence psychological scientists’ confidence in the replicability of findings. We recruited corresponding authors of articles published in psychology journals between 2014 and 2018 to complete a brief survey in which they were asked to consider 76 specific study attributes that might bear on the replicability of a finding (e.g., preregistration, sample size, statistical methods). Participants were asked to rate the extent to which information regarding each attribute increased or decreased their confidence in the finding being replicated. We examined the extent to which each research attribute influenced average confidence in replicability. We found evidence for six reasonably distinct underlying factors that influenced these judgments and individual differences in the degree to which people’s judgments were influenced by these factors. The conclusions reveal how certain research practices affect other researchers’ perceptions of robustness. We hope our findings will help encourage the use of practices that promote replicability and, by extension, the cumulative progress of psychological science.

Objectives The present study provides a meta-analytic assessment of how gaze-cued attention – a core social-cognitive process – is influenced by normal adult ageing. Method A multi-level meta-analysis of standardized mean changes was conducted on gaze-cueing effects. Age effects were quantified as standardized mean differences in gaze-cueing effect sizes between young and older adult samples. Results We identified 82 gaze-cueing effects (k = 26, N = 919 participants). Of these, 37 were associated with young adults (k = 12, n = 438) and 45 with older adults (k = 14, n = 481). Relative to younger adults, older adults had a reduced gaze-cueing effect overall, g = -0.59, with this age effect greater when the cues were predictive, g = -3.24, rather than nonpredictive, g = -0.78. Discussion These results provide the clearest evidence to date that adult ageing is associated with a reduction in gaze-cued attention. The results also speak to potential mechanisms of this age effect. In line with cognitive decline models of ageing, it was demonstrated that when gaze cues were predictive, only younger adults seem to benefit, suggesting that older adults exhibit a particularly reduced capacity to use gaze cues volitionally.

The gaze cueing effect is the tendency for people to respond faster to targets appearing at locations gazed at by others compared to locations gazed away from by others. The effect is robust, widely studied, and is an influential finding within social cognition. Formal evidence accumulation models provide the dominant theoretical account of the cognitive processes underlying speeded decision making, but they have rarely been applied to social cognition research. In this study, using a combination of individual-level and hierarchical computational modelling techniques, we applied evidence accumulation models to gaze cueing data (three data sets total, N = 171, 139,001 trials) for the first time to assess the relative capacity that an attentional orienting mechanism and information processing mechanisms have for explaining the gaze cueing effect. We found that most participants were best described by the attentional orienting mechanism, such that response times were slower at gazed away from locations because they had to re-orient to the target before they could process the cue. However, we found evidence for individual differences, whereby the models suggested that some gaze cueing effects were driven by a short allocation of information processing resources to the gazed at location, allowing for a brief period where orienting and processing could occur in parallel. There was exceptionally little evidence to suggest any sustained reallocation of information processing resources neither at the group nor individual level. We discuss how this individual variability might represent credible individual differences in the cognitive mechanisms that subserve behaviourally observed gaze-cueing effects.

The gaze cueing effect is the tendency for people to respond faster to targets appearing at locations gazed-at by others compared to locations gazed away from by others. However, although the gaze cueing effect has been studied extensively and has implications for a variety of theoretical and clinical contexts, much is still unknown about the cognitive mechanisms underlying its emergence. Formal evidence accumulation models provide the dominant account of the cognitive processes underlying speeded decisions, however, they have never been applied to understand the gaze cueing effect. In this study we applied the Diffusion Decision Model (DDM) to gaze and arrow cueing data for the first time in order to 1) better understand the cognitive processes underlying the gaze cueing effect, and 2) see whether these processes could be considered the same as those that underlie arrow cueing effects. To achieve these aims, we applied variants of the DDM to three preexisting gaze cueing data sets and one preexisting arrow cueing data set (n = 171, 139,001 trials total), using a combination of individual-level and hierarchical computational modelling techniques. At the group level, people were best described by an attentional orienting mechanism rather than higher-order decision bias or information processing mechanisms. However, we found evidence for individual differences such that not everyone was best described by an attentional orienting mechanism. Further, the same people who were best described by an attentional orienting mechanism for gaze cues tended not to be best described by that same mechanism for arrow cues, suggesting these cueing effects may induce different responses within the same people – although we interpreted this finding cautiously.