Barriers and solutions for early career researchers in tackling the reproducibility crisis in cognitive neuroscience

Neural correlates of linguistic collocations during continuous speech perception

Language is fundamentally predictable, both on a higher schematic level as well as low-level lexical items. Regarding predictability on a lexical level, collocations are frequent co-occurrences of words that are often characterized by high strength of association. So far, psycho- and neurolinguistic studies have mostly employed highly artificial experimental paradigms in the investigation of collocations by focusing on the processing of single words or isolated sentences. In contrast, here we analyze EEG brain responses recorded during stimulation with continuous speech, i.e., audio books. We find that the N400 response to collocations is significantly different from that of non-collocations, whereas the effect varies with respect to cortical region (anterior/posterior) and laterality (left/right). Our results are in line with studies using continuous speech, and they mostly contradict those using artificial paradigms and stimuli. To the best of our knowledge, this is the first neurolinguistic study on collocations using continuous speech stimulation.

Credibility Beyond Replicability: Improving the Four Validities in Psychological Science

Psychological science’s “credibility revolution” has produced an explosion of metascientific work on improving research practices. Although much attention has been paid to replicability (reducing false positives), improving credibility depends on addressing a wide range of problems afflicting psychological science, beyond simply making psychology research more replicable. Here we focus on the “four validities” and highlight recent developments—many of which have been led by early-career researchers—aimed at improving these four validities in psychology research. We propose that the credibility revolution in psychology, which has its roots in replicability, can be harnessed to improve psychology’s validity more broadly.

The past, present and future of Registered Reports

Registered Reports are a form of empirical publication in which study proposals are peer reviewed and pre-accepted before research is undertaken. By deciding which articles are published based on the question, theory and methods, Registered Reports offer a remedy for a range of reporting and publication biases. Here, we reflect on the history, progress and future prospects of the Registered Reports initiative and offer practical guidance for authors, reviewers and editors. We review early evidence that Registered Reports are working as intended, while at the same time acknowledging that they are not a universal solution for irreproducibility. We also consider how the policies and practices surrounding Registered Reports are changing, or must change in the future, to address limitations and adapt to new challenges. We conclude that Registered Reports are promoting reproducibility, transparency and self-correction across disciplines and may help reshape how society evaluates research and researchers.

NeuroKit2: A Python toolbox for neurophysiological signal processing

NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.

Open science challenges, benefits and tips in early career and beyond

The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also significant challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs). Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research. The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research. We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider.