Saturday, August 11, 2018

Joining the editorial board of PLOS ONE

I have joined the Editorial Board of PLOS ONE. There are a few things about PLOS ONE that particularly appeal to me:
  • Broad scope is great for interdisciplinary research. My own research is primarily driven by experimental psychology, neuroscience, and computer science, as well as linguistics and neuropsychology/neurology. Before writing a manuscript, I often have to decide whether I will be submitting it to a cognitive psychology journal or a clinically-oriented (neuropsychology or neurology) journal or a neuroscience journal. This decision is not always easy and it has a major impact on how the manuscript needs to be written and who will review it. Since the scope of PLOS ONE covers the full range of natural and social sciences as well as medical research, I (you) don't need to worry about that. Just clearly describe the motivation, methods, results, and conclusions of the study and trust that Editors like me will find appropriate reviewers.
  • Accepts various article types. In addition to standard research articles, PLOS ONE accepts systematic reviews, methods papers (including descriptions of software, databases, and other tools), qualitative research, and negative results. If your manuscript is reporting original research, then it is a viable submission.
  • Publication decisions based on scientific rigor, not perceived impact (see full Criteria for Publication). It is difficult to try to guess what kind of impact a paper will have on the field and unnecessary because the field can figure that out on its own. As a reviewer, I focus on scientific rigor and whether the methods and results align with the motivation and conclusion. It's nice that PLOS ONE has the same focus. This emphasis on technical and ethical standards also means that PLOS ONE can publish good replication studies and negative results, which is critical for reducing publication bias and moving our field forward.
  • Fast decision times. Editors are expected to make decisions within a few days and reviewers are asked to complete their reviews in 10 days. Of course, this is no guarantee that a manuscript will have a fast decision -- it can take a long time to find reviewers and reviewers do not always meet their deadlines. But I think giving reviewers 10 days instead of 4-6 weeks (typical for psychology journals) and expecting editors to make fast decisions is a step in the right direction.
  • Open access at reasonable cost. This is not the place to discuss the relative merits of the standard reader-pay publication model and the open access author-pay model used by PLOS ONE. Suffice it to say that I like the open access model and I appreciate that PLOS ONE is doing it at a cost ($1595 USD) that is on the low end compared to other established open access journals.

Monday, April 16, 2018

Correcting for multiple comparisons in lesion-symptom mapping

We recently wrote a paper about correcting for multiple comparisons in voxel-based lesion-symptom mapping (Mirman et al., in press). Two methods did not perform very well: (1) setting a minimum cluster size based on permutations produced too much spillover beyond the true region, (2) false discovery rate (FDR) correction produced anti-conservative results for smaller sample sizes (N = 30–60). We developed an alternative solution by generalizing the standard permutation-based family-wise error correction approach, which provides a principled way to balance false positives and false negatives. 

For that paper, we focused on standard "mass univariate" VLSM where the multiple comparisons are a clear problem. The multiple comparisons problem plays out differently in multivariate lesion-symptom mapping methods such as support vector regression LSM (SVR-LSM; Zhang et al., 2014, a slightly updated version is available from our github repo). Multivariate LSM methods consider all voxels simultaneously and there is not a simple relationship between voxel-level test statistics and p-values. In SVR-LSM, the voxel-level statistic is a SVR beta value and the p-values for those betas are calculated by permutation. I've been trying to work out how to deal with multiple comparisons in SVR-LSM.

Friday, March 23, 2018

Growth curve analysis workshop slides

Earlier this month I taught a two-day workshop on growth curve analysis at Georg-Elias-Müller Institute for Psychology in Göttingen, Germany. The purpose of the workshop was to provide a hands-on introduction to using GCA to analyze longitudinal or time course data, with a particular focus on eye-tracking data. All of the materials for the workshop are now available online (, including slides, examples, exercises, and exercise solutions. In addition to standard packages (ggplot2, lme4, etc.), we used my psy811 package for example data sets and helper functions.