Authorship: Who should be on the paper? Where? And who not?

Today, I talked with Rick Betzel ( about authorship.

Authorship is the most important academic currency. It is the main way of performance evaluation and rankings for positions. There are many differences between disciplines, but generally, the first (usually the person who did the work) and last (usually the person who advised and oversaw the project from start to end) author position are the most important. The middle authors should have made significant intellectual contributions to the project. Everybody else goes in the acknowledgement section.

But of course it is not always that easy. Watch our discussion for many different cases with conflict potential:

The most important thing is to discuss authorship with the other people involved, ideally already when distributing the work and responsibilities. Never take for granted that other people agree with you on this.

Further readings:

General recommendations:

Interesting point system of the Kosslyn lab:


How to land a Postdoc position.

Doing a postdoc can be a fantastic experience. In the last session of ACL, I talked with Sho Tsuji from Ecole Normale Supérieure de Paris who – just as me – is a very happy postdoc.

The most important thing is to find  lab in which you can grow and have a PI that will be a great mentor not only for now, but for the rest of your career. Do a careful screening of whom you want to work with and try to get to know them and people who worked with them (or still do!). Be open-minded and use your network to find out about labs, job search specifics or grant opportunities in individual countries, and personal recommendations.

You will get the most out of your postdoc if you know what you want to get out of it. Make this guide you to what kind of project or lab you want to work in and whether you want to work on your own grant or for somebody else.

In order to find the jobs you want, you need to know your market. Finding a postdoc in the US can be very different from in the EU or Japan or somewhere else. Talk to people who know the market and generally, let people in your network know that you are looking. Your colleagues, supervisors, friends, or conference acquaintances can be invaluable sources of information. Start looking early (at least 1 year before you graduate), identify your goals and potential starting points and then start reaching out. Don’t be shy to email people you are interested in.

Now you need to be competitive. Try to have at least 1 publication in a peer-reviewed journal. It doesn’t matter that much how high impact the journal but you must show that you can get a project from start to publication. In some countries, doing a PhD takes more than 6 years and people typically come out of it with several publications. PIs know that countries are different in that respect, but you must show that you have potential. Talking about potential: make sure your skills stand out on your CV and that you sell yourself as competent in your discipline AND motivated to learn specific new skills. The other thing that is important is your visibility. If you don’t have a google scholar profile yet, make one now. A personal website where you can present yourself as an individual rather than being part of a lab is also strongly recommended (I regret not having done mine earlier). Use the social media outlets you are comfortable with to create your online presence in your discipline. And of course, go to as many academic meetings, conferences, or symposia as possible or even better help organizing one!

Now, if you want to hear more about our personal experiences, watch the video:

How to use twitter for academia

What is twitter useful for?

  1. Stay up to date with new research
  2. Find jobs, grant opportunities, academic meet-ups
  3. Network and become (more) visible
  4. Attend conferences virtually (check out the all-twitter brain conference  and of course in Twitter)
  5. Get access to paywalled papers (e.g. #canihaspdf)
  6. Find help & resources
  7. Get input from other scientists on data, stats, writing, etc.
  8. Teach the public, and learn from other twitter users

To learn more, watch the video from the live session where Nikola (, Tommi  ( and me talked about our personal experiences with academic twitter:

Here are some more practical tips on how to get the most out of twitter (what we couldn’t cover yesterday during the live session): 

    • Whom to follow?
      • Other scientists (and labs), journalists, funding bodies, science enthusiasts and communicators
      • See who your followers are following in turn, or whose content they retweet
      • Use public lists that others have created for specific topics. (Here’s a good one for CogNeuro people:
    • Keep your newsfeed manageable
      • Use services like TweetDeck ( – this allows you to split up your main feed into “sub-feeds” which are more manageable, based on topic or any other criterion you want.
      • Websites such as Buffer ( and Hootsuite ( will let you schedule tweets – you can make a queue of tweets which will then be posted automatically at (ir)regular intervals. This saves your time, and also benefits your followers who live in different time zones.
      • Turn off most if not all notifications! Social networks are built to be addictive – having your phone buzz every time someone tweets or replies to you will become very distracting once you start following more people. Making sure that you use Twitter at times that suit *you* is key.
      • Remember to weed your following list periodically – unfollow people you are no longer interested in, or add new people who you just met at conferences, etc.
    • Your public persona
      • It is generally a good idea to keep your personal and professional social media accounts separate, especially if you are an early career researcher.
      • If you want to keep your account professional, make a separate private account. Then you’ll have the flexibility to post about anything else you want, while not angering all the people who followed you for your science content.
    • How to tweet and content ideas
      • Sharing links – share why a news story, publication, video, or image captured your interest
      • Post short updates on your research; make these posts longer by using threads (almost like a blog post)
      • Start a dialogue or conversation by tagging other users, or using hashtags. Some good ones are: #phdchat #postdoc #ECRchat #scicomm #openscience
      • Do an online journal club – Twitter can be great to talk about new papers you’ve read, ask questions, etc.
      • Public AMAs (ask-me-anything sessions) – answer questions from the public about your work. One good example of this is @IAmSciComm, which has a new scientist host their account each week, and talk to thousands of science interested followers!
      • Live-tweet talks at conferences – you don’t need to take it too seriously, even a couple of bullet points per talk will contribute, and if more than one person does it it will create a nice public record of a conference. It is very common to get thank you messages from people for sharing info from conferences they couldn’t attend.

More practical tips can be found in a recent Cogtales post.

The problem with humanities and sciences not talking to each other

This post is written by me (F) and Charles Prusik (C):

F: In the latest session, I talked with Philosopher Dr Charles Prusik from Villanova University about the communication problem between humanities and the (social) sciences that results in critical knowledge gaps. While historically being close siblings, in the current race for publications and funding the humanities seem to think of the sciences and social sciences as evil step sisters, while the other two think of the humanities as having lost touch with reality. If you ever made even the smallest attempt to work interdisciplinary, you know what I am talking about.

The problem is that everybody is loosing in this game. We need each other and have to find a way to reconcile. The hard part about this is that academics (or really everybody) gets taught this disciplinary divide as a nature’s given. We already separate children in school depending on whether they are talented in STEM subjects, social subjects, or arts and languages. As this continues in higher and academic education, we end up with highly trained specialist that struggle to talk to people outside their discipline. They speak different languages and have fundamentally different views of the world.

Psychology and cognitive (neuro)science as a result suffer from lack of terminological clarity and well defined concepts. Moreover, we struggle to develop theories of cognition whose consequences are relevant outside our small disciplinary subdivision. 

C: Within the humanities, scholars tend to be rewarded by virtue of their contributions to increasingly narrow areas of specialization. This specialization, which is reinforced by the emphasis on publication records, results in humanities scholars being forced to retreat from interdisciplinary study. A substantive, historical, and empirical knowledge-base of the sciences has become a remote possibility for many scholars in philosophy, literature, and even the social sciences. As a response to this dynamic (at least in part),  humanities scholars have resorted to writing in highly specialized jargons and technical vocabularies, often without any attempt to clarify or explain the stakes of their claims for external disciplines.

The inability to communicate across disciplinary boundaries is reinforced, in my view, by the empirical social sciences as well, insofar as these disciplines have largely banished abstract, speculative, or conceptual forms of argumentation from their methodologies. This has resulted in a deeper chasm between the humanities and social sciences. A first step towards bridging the gap between the sciences and humanities would require scholars to recognize the constructed nature of the academic division of labor—disciplinary boundaries are not real, but they become realities through their institutional codification and reproduction. In addition to making contributions in their own specialized areas, scholars in the sciences and humanities should also make the effort to translate their findings and arguments into a discourse that is more directly accessible to non-specialists—even if the subject matter is simplified for purposes of clarity. Moreover, scholars from all disciplines should spend time creating networks and forums (e.g., digital humanities, social network platforms, open science), where interdisciplinary dialogue and research can occur.

Further readings:

How to deal with gender bias in academia?

My honest first answer before this session was ‘I don’t know’. Similar as Atsuko put it in this live session, I am not living in my home country for many years and it is hard to tell what is about me being a women and what is a cultural thing I don’t understand. But there is this almost painful awareness of having to justify my existence all the time. This constant feeling of having to proof that I deserve to be here.

The most important thing I learned in this session was that this will never stop. And accepting that this is something which will always be part of my professional life also brings some kind of relief with it. I am not alone and I am just as much part of the solution as everybody else.

I am very grateful for the insightful and genuine discussion with my colleagues and friends Asli Ozyurek, Yoed Kenett, Emily Coderre, and Atsuko Takashima. I learned a lot and I recommend everybody to watch the discussion. Instead of trying to summarize the contents I give you the content overview with minutes and topics we discussed so you can listen to what is most relevant to you.

3:39 Different career stages, different problems
13:45 Role models and leadership styles
22:19 Coaching women how to survive in a male dominant culture
33:55 Networking strategies
46:30 The struggles of being a parent in acdemia
1:03:20 Realistic applications and making a change for the individual as well as for the system

Feel free to reach out to me if you want to talk about more!

How to write a letter to the editor when submitting a manuscript for publication.

First of all, check the submission guidelines for the journal that you want to submit to. If there is any information regarding cover letters to editors for manuscript submission, this will overwrite everything I say here.

Second, disciplines vary widely in their conventions regarding cover letters and in some it is in fact considered bad taste to write one. If you don’t know what the standard in your field and there is no information on the journal page, ask senior colleagues or email the journal or editorial office if you should submit one or not. (Be ready to receive an arrogant reply because people are often ignorant of the fact that cultures and conventions can differ substantially but by no means take it personal.)

If there are no clear guidelines against a cover letter and you have not encountered horrified faces/email replies when asking around, just write it. Even if it is not obligatory, just do it. The goal of this letter is to make the editor’s job easier (remember these are overworked academics who do this as a side job) and this ultimately can have a positive effect on how and how fast your submission will be processed. Here are tips how I do it (template and mini-summary below):

Here  is my template for a cover letter. Feel free to use this alternative template (sorry I forgot where I got it from) or another template from one of these pages with more helpful tips:

What I need you to take home from this:

  • Know and communicate what article type you are submitting and make sure it fits ALL requirements in the guidelines for authors!!!!
  • be concise and professional, jokes are strictly prohibited (even if you are best friends with the editor!)
  • do not oversell your research
  • avoid jargon and name dropping
  • clearly communicate why your article should be published in this journal
  • make sure you include all legally required statements (Guide for authors!)

How to review a manuscript for a journal.

Good reviews are supportive, constructive, thoughtful and fair. They identify both strengths and weaknesses alike and offer concrete suggestions for improvement. Good reviewers acknowledge their own biases and knowledge limitations and justify their conclusions.

Bad reviews are superficial, petty, and arrogant. Bad reviewers are very opinionated but typically don’t justify their biases. Their reports focus on weaknesses only but don’t offer solutions or other form of helpful feedback.

In today’s session, I walked you through the review process and told you how I write review reports:


Here you can find a template for the review report.

Additional ressources: offers a detailed step by step guide. offer additional advice and concrete examples of how to express criticism diplomatically. features a lot of personal strategies and experiences which are often different from what I do.

Where I stole the summary from (almost word by word):

How to stay on top of trends and findings in your field.

Keeping up with the literature and current issues is challenging. But thanks to different tools you can make this an easier task. The best tool in my experience is Google Scholar ( If you don’t have a profile yet, make one today. You can use it to follow colleagues’ publications, track citations of seminal papers, and get recommendations based on your usage or core papers in your field.

If you are interested in the output of a certain lab and they are not active on google scholar, you can use tools like to track their publications. This obviously only works for labs that have a frequently updated website.

Twitter is an amazing tool to stay up to date with current discussions and topics in your field. It takes a while until you figure out whom to follow, but it’s worth the investment.

Another useful tool to find older but still important papers is Mendeley. Technically this is a citation manager and library tool, but you get recommendations based on the articles in your library. Most of the time I find them very useful.

Journal updates are useful but should be limited to a few journals. The maximum of what I find manageable is 3. I follow Brain and Language, Behavioural and Brain Sciences, and Neuroscience and Biobehavioural Reviews. I only scan the headlines when the articles come in and decide if and what i will read in detail.

Researchgate can be useful if a lab is very active, but most of the time it is only selectively helpful to keep in touch/utd with a certain group. These ~10people I interact with on Researchgate only are the single reason why I still have my profile there.

Search alerts for journal databases like PubMed ( or Web of Science ( can be great tools if you figure out good search  terms and restrictions. I have not succeeded with this, but I found this blog with seemingly useful recommendations: There are also tools like Pubcrawler or Pub-Chase (which I will certainly try out next because it looks great!).

The most important thing is to get 2 types of reading integrated into you academic life:

  1. Quick scanning of the newest output and deciding what  to read (should be done weekly at least).
  2. Extensive reading and broader researches for articles.

More details and tips in the video:

A practical guide to linear mixed effect models in Rstudio

In this episode of the Academic Crisis Line, Stacey Humphries and I gave a practical introduction to linear mixed-effects models. We talked about the background and key concepts about LMEMs, focused around 5 key questions that people often have when starting to encounter LMEMs for the first time.

  1. Why is a LMEM better than an ANOVA?
  2. What are fixed- and random-effects?
  3. What is the difference between a random intercept and a random slope?
  4. Should I include random slopes in my model?
  5. How can I tell if my model predictors significantly affect my dependent variable?

For answers to all these questions and more, check out the video! We also briefly walked through some practical aspects of running these analyses in R, but unfortunately the stream died at this point. We tried to re-record the parts that were lost, so this is annoyingly split over 3 videos. We hope it is useful nonetheless.

Video links:

Part 1:


Part 2:

Part 3:


We mentioned a lot of different papers and links in the video which are listed here for your convenience, along with a few others you might find interesting:

You can find the visualisations of random intercepts and random slopes that we walked through here:

Bodo Winter put together some fantastic tutorial for LMEMs:

Clark (1973) The language-as-fixed-effect-fallacy:

Baayen et al. (2008) Mixed-effects modeling with crossed random effects for subjects and items

Barr et al. (2013) Random effects structure for confirmatory hypothesis testing: Keep it maximal

Bates et al. (2015) Parsimonious mixed models

Matuschek et al. (2017) Balancing Type I error and power in linear mixed models

Baayen et al. (2017) The cave of shadows: Addressing the human factor with generalized additive mixed models

Luke (2017) Evaluating significance in linear mixed-effects models in R

Summary of Luke (2017) by Richard Morey:

Brysbaert (2018) Power analysis and effect size in mixed effects models: A tutorial




If you have any questions that we didn’t cover, please feel free to tweet us (@_SHumphries, @FranHartung, @Ph_Dial), or email us (hstace[AT], fhartung[AT]