Making ERP research more transparent: Guidelines for preregistration

A combination of confirmation bias, hindsight bias, and pressure to publish may prompt the (unconscious) exploration of various methodological options and reporting only the ones that lead to a (statistically) significant outcome. This undisclosed analytic flexibility is particularly relevant in EEG research, where a myriad of preprocessing and analysis pipelines can be used to extract information from complex multidimensional data. One solution to limit confirmation and hindsight bias by disclosing analytic choices is preregistration: researchers write a time-stamped, publicly accessible research plan with hypotheses, data collection plan, and the intended preprocessing and statistical analyses before the start of a research project. In this manuscript, we present an overview of the problems associated with undisclosed analytic flexibility, discuss why and how EEG researchers would benefit from adopting preregistration, provide guidelines and examples on how to preregister data preprocessing and analysis steps in typical ERP studies, and conclude by discussing possibilities and limitations of this open science practice

The sustainability argument for open science

Ever-increasing anthropogenic greenhouse gas emissions narrow the timeframe for humanity to mitigate the climate crisis. Scientific research activities are resource demanding and, consequently, contribute to climate change; at the same time, scientists have a central role in advancing knowledge, also on climate-related topics. In this opinion piece, we discuss (1) how open science – adopted on an individual as well as on a systemic level – can contribute to making research more environmentally friendly, and (2) how open science practices can make research activities more efficient and thereby foster scientific progress and solutions to the climate crisis. While many building blocks are already at hand, systemic changes are necessary in order to create academic environments that support open science practices and encourage scientists from all fields to become more carbon-conscious, ultimately contributing to a sustainable future

Reflexivity in quantitative research: A rationale and beginner's guide

Reflexivity is the act of examining one's own assumptions, beliefs, and judgments, and thinking carefully and critically about how these influence the research process. The practice of reflexivity confronts and questions who we are as researchers and how this guides our research. It is central in debates on objectivity, subjectivity, and the very foundations of social science research and generated knowledge. Incorporating reflexivity in the research process is traditionally recognized as one of the most notable differences between qualitative and quantitative methodologies. Qualitative research centres and celebrates participants’ lived experience, and qualitative researchers are readily encouraged to consider how their own positionalities inform the research process, forming an important part of qualitative research training. Quantitative methodologies in social and personality psychology, on the other hand, have remained seemingly detached from this level of reflexivity and general reflective practises. In this commentary, we, three quantitative researchers who have grappled with the compatibility of reflexivity with our research, argue that reflexivity has much to offer quantitative methodologists, in social and personality psychology and beyond. The act of reflexivity prompts researchers to acknowledge and centre their positionalities, encourages a more thoughtful engagement with every step of the research process, and thus, as we argue, contributes to the ongoing reappraisal of openness and transparency in psychology. In this paper, we make the case for integrating reflexivity across all research approaches, before providing a ‘beginner’s guide’ for quantitative researchers wishing to engage reflexivity within their work, providing concrete recommendations, worked examples, and reflexivity prompts

EEG ERP preregistration template

This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space

A community-sourced glossary of open scholarship terms

Supplementary Information: This list of terms represents the ‘Open Scholarship Glossary 1.0’ (available at: https://forrt.org/glossary/. Glossary available under a CC BY NC SA 4.0 license at: https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-021-01269-4/MediaObjects/41562_2021_1269_MOESM1_ESM.pdf).https://static-content.springer.com/esm/art%3A10.1038%2Fs41562-021-01269-4/MediaObjects/41562_2021_1269_MOESM1_ESM.pd

Voice-speech interaction in infant phoneme acquisition: PhD proposal

Phonemes are the building blocks of words. Acquiring phonemes is a difficult task, because infants are faced with the problem of invariance: speakers differ widely in how they produce their phonemes, yet in order to distill meaning, listeners need to ignore this variation. This project tests whether speaker-distinguishing mechanisms help infants to reduce this variability, thereby facilitating phoneme acquisition

Theoretical accounts on speech perception

In this presentation, I will give an overview of some of the models used to account for speech perception under the condition of speaker variability. I will discuss their benefits and difficulties, and compare their aims. I will furthermore compare how these models have been used in the literature on adults versus infant speech processing

Preregistration: Increasing transparency in electrophysiological research

In this talk, we will discuss how preregistration can be used to increase transparency in electrophysiological research. We will start by discussing how confirmation bias (looking for information that supports prior beliefs), hindsight bias (overestimating in how far past events predicted a current outcome), and pressure to publish can lead to (unconscious) data exploration after which only (statistically) significant results are reported. We will highlight some of the problems associated with this undisclosed analytic flexibility, focusing on EEG research, in which complex multidimensional data can be preprocessed and analyzed in many possible ways. We argue that transparently disclosing analytic choices can mitigate confirmation and hindsight bias and make EEG research more verifiable. One possible tool for transparent reporting is preregistration: providing a time-stamped, publicly accessible research plan with hypotheses, a data collection plan, and the intended pre-processing and statistical analyses, written before the data were accessed. We will provide examples on how to create preregistrations for EEG studies that are specific, precise and exhaustive, focusing on data pre-processing and analysis steps. Finally, we will highlight the benefits and critically discuss the limitations of adopting preregistration for EEG researchers