A happy 2019, dear CogTales reader! The time around the change of years is, as is now tradition, a time to look back to 2018, which was an exciting and busy year for your two bloggers, Sho and Christina! (This might also explain the slightly less frequent occurrence of posts, please excuse us, but we’re planning to share what we are learning here, of course).
Two years ago, Team CogTales (Sho and Christina) interviewed Anne Scheel. We were impressed how she stood up to ask a tough question at Germany’s largest Psychology conference (the DGPs Kongress) after a keynote presentation. Two years later, Christina and Anne actually met up at the next installment of the very same conference, and a lot has changed in the short time span. So it seems like a perfect moment to catch up and take stock. Continue reading What happens when you stand up to the big wigs? A follow-up interview with Anne Scheel
French version here
Who hasn’t heard that “Children under two years of age should absolutely not be exposed to screen media, no matter what!” – maybe accompanied by the reasoning that “Screens will hinder the development of children’s intelligence.”
Why does the topic of the effect of screens on young children, especially with regard to their brain development, evoke so much controversy and fear? And should we actually think of all types of screen media as equal? What can scientific research teach us? Continue reading Screen, Baby – Let’s Look at Evidence, not Trends
Sho: I recently gave a talk on data visualization at the International Conference on Infant Studies (you can find my slides, along with the other wonderful talks on power, preregistration, and ethical data peeking here on the OSF). I also played the German Cats and Dogs Scientist in the barbarplots campaign on better data visualization (on the same topic, Article 1 and Article 2 on why bar and line plots hide differences in underlying distributions). In fact, being part of the barbarplots team was my entry point into thinking more about the importance of visualizing your data in a maximally informative and honest way. Informative means finding a good balance between simplifying/summarizing and showing the underlying data structure. Honest means not (accidentally) hiding important aspects of your data. Mahiko is my office mate and – this is something I discovered while preparing the above talk – an enthusiastic data visualizer. That’s why I asked him to put together our (mostly, his) favorite data viz resources.
This is part of (academic) life. The session was short, so I won’t bother to summarize. Just watch the video:
Some important points I had discussed in a previous session also apply here:
Naomi Havron is a postdoc at the Laboratoire de Sciences Cognitives et Psycholinguistique at Ecole Normale Supérieure.
She was recently interviewed for the departmental newsletter on her experiences as a woman and mother in science, and here’s a statement we found particularly on point:
Being a woman in science became difficult once I was a mom. Before, I believed that any inequality could be surmounted by working my ass off. But once I was a mom, I had to leave work early for my children. And I was judged differently from men. If it was me that was leaving early, that was judged as non-professional and proof I wasn’t invested enough in research. If it was a male colleague that was leaving for his children, he was complimented as being a devoted parent. (paraphrased and translated, see original French version here).
Naomi has been involved in organizing two amazing women-in-science events at our department recently, and we’re very excited she’s sharing her experiences and materials here with us.
If you decide to do a postdoc, do everything you can to do it right from the get-go! Watch what Sho (Cogtales) and Franziska (Ph_Dial) have to say ❤
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…
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In our last post, Christina talked about academic networking on social media, specifically Twitter. There’s a reason that was her post, not mine: Even though I’ve been following most of her advice and this has improved my Twitter experience, I still feel awkward and out of place on Twitter, and I can’t get myself to create an account under my own name (instead, I’m tweeting as @cogtalestweet).
So today, I’m talking about my cup of tea: Live, in person networking. Specifically, the focus is on how to initiate conversation.
Within the span of two months, I’ve been asked to give essentially the same talk three times. The topic: how to network on Twitter (and other social media). How did this happen? Well, first a group of Parisian post docs organized a day-long workshop and apparently my tweeting is good enough to warrant inviting me back to my former home. Because I was invited, I took some care to prepare, and I think I did a decent job – decent enough, at least, to get some audience members to tweet about it and putting into practice what I just told them. Continue reading How to use Twitter for networking in academia
Here’s a (maybe not so well-kept) secret: I’ve got a PhD in modeling! No, not the posing kind, I constructed computational models of babies’ minds and behavior to better understand their early language acquisition. I learned a lot about cognition, babies, and data in that time. Next to that and two programming languages (Python and R) I also learned a bit about the modeling world. A key insight came to me after repeatedly trying to network with senior men and that being taken … the very wrong way. I must admit, I don’t know how much not being taken seriously as a modeler by some (no, not all) fellow modelers contributed to the fact that I took a step away from this field and am now an infant and a meta-science researcher most of the time. I am often thinking about what I’d recommend fellow women aspiring to a modeling career. So, at last, here’s the insight: build a support network of women modelers.* For those who watched a recent instalment of Academic Crisis Line, this might not be terribly earth shattering, but you have to realize that this is something that holds for your corner of science. I met a node in this support network soon thereafter, Olivia Guest, with whom I could talk forever about all those “fun” encounters. At some point, the idea to make a list of all fantastic, but probably vastly underappreciated women and nonbinary folks in modeling emerged, as she writes in her blog. There was some back and forth, questions about time investment, criteria, subcategories, so we effectively never got started, but such lists are super useful. For example, I suggested replacement speakers when asked to give a talk recently, and this list would have made my life much easier. So I am glad that Olivia turned to Twitter and simply asked others to make a list. The resulting thread is a goldmine. Continue reading Building a network of women and nonbinary cognitive modelers