Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Identification and location tasks rely on different mental processes: a diffusion model account of validity effects in spatial cueing paradigms with emotional stimuli

<p>Spatial cueing paradigms are popular tools to assess human attention to emotional stimuli, but different variants of these paradigms differ in what participants’ primary task is. In one variant, participants indicate the location of the target (location task), whereas in the other they indicate the shape of the target (identification task). In the present paper we test the idea that although these two variants produce seemingly comparable cue validity effects on response times, they rest on different underlying processes. Across four studies (total <i>N</i> = 397; two in the supplement) using both variants and manipulating the motivational relevance of cue content, diffusion model analyses revealed that cue validity effects in location tasks are primarily driven by response biases, whereas the same effect rests on delay due to attention to the cue in identification tasks. Based on this, we predict and empirically support that a symmetrical distribution of valid and invalid cues would reduce cue validity effects in location tasks to a greater extent than in identification tasks. Across all variants of the task, we fail to replicate the effect of greater cue validity effects for arousing (vs. neutral) stimuli. We discuss the implications of these findings for best practice in spatial cueing research.</p

Crowdsourcing hypothesis tests: making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim