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SummaryData analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed.

For selecting an action, traditional theories suggest a cognitive architecture made of serial processing units. Others suggested that action selection emerges from the parallel implementation of and competition between multiple action plans. To disentangle these 2 hypotheses, we created a reaching task assessing the temporal dynamics of action selection. Crucially, our design did not force action selection processes to operate in parallel, allowing an informative comparison between the hypotheses. We manipulated the probability of congruence between a cue and a delayed reach target to investigate, in an unbiased way, whether congruence probability interacts with reach trajectory. Our results show that reach trajectories are modulated by the probability of congruence. Hence, action selection is temporally spread, continues after movement onset, and emerges from a competition between multiple afforded action plans, in parallel biased by relevant task factors (e.g., probability of reach).

Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks. The neural mechanism of effort investment is thought to be structured hierarchically, with dorsal anterior cingulate cortex (dACC) at the highest level, recruiting task-specific upstream areas. In the current fMRI study, we tested whether dACC is generally active when effort demand is high across tasks with different stimuli, and whether connectivity between dACC and task-specific areas is increased depending on the task requirements and effort level at hand. For that purpose, a perceptual detection task was administered that required male and female human participants to detect either a face or a house in a noisy image. Effort demand was manipulated by adding little (low effort) or much (high effort) noise to the images. Results showed a network of dACC, anterior insula (AI), and intraparietal sulcus (IPS) to be more active when effort demand was high, independent of the performed task (face or house detection). Importantly, effort demand modulated functional connectivity between dACC and face-responsive or house-responsive perceptual areas, depending on the task at hand. This shows that dACC, AI, and IPS constitute a general effort-responsive network and suggests that the neural implementation of cognitive effort involves dACC-initiated sensitization of task-relevant areas.

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