Writing the acknowledgment section of my PhD thesis felt like a reward at the end of a long journey: taking the time to highlight everyone who contributed, and appreciating the importance of humanity in science. While there is no such thing as a postdoc thesis, it feels just as significant to wrap up the last 2.5 years of my life and career. Since I’m a sucker for end-of-year lists and reflections, these December days of 2020 (what a year it’s been) seems like as good an opportunity as any to reflect on the many people who shaped my postdoc years. Here goes.
A long-ish Twitter thread on the dangers of conflating credit and responsibility assignment in scientific authorship.
The photos below show me, with the brain hat I made during my studies at ENS Paris. I’ve asked students who attended my lecture on neuroanatomy to send me theirs, and I’ll update this page as responses (hopefully) come in.
Since Tweets tend to get lost/unfindable, I’m putting the links for self-organized NMA material study groups here.
I’d be happy to hear back (comment on this post) if you’ve found a pod. How are your experiences going through the materials?
How would you generate a sequence of random numbers, if you didn’t have a computer or calculator? Each time you typ rng default or random.randint, numbers get drawn from precise observations of some natural process or special algorithms to produce sequences of numbers with certain properties of randomness. But what if your laptop died, your phone had no reception, or you’d suddenly find yourself transported 50 years back in time? How could you approximate random sampling from different distributions just using pen, paper, and whatever you could find in your house?
I thought of three categories (to start with): A. human-made randomization gadgets, B. measurement, C. just you in an empty room.
Rules: Please share your best guesses and intuitions and limitations of each method. If you know what exact distribution can be approximated with each process, please let me know – I’ll update the post as more ideas come in. Do not Google (or be honest if you did). Let’s play!
Update: see the Twitter thread for a bunch of interesting responses and suggestions – I’ve copied some of those into the list of suggestions below (no guarantees).
The last few weeks in the US have been a political and emotional tornado. As protests spread around the country and beyond, I compulsively read the news, went to a local protest and watched Ava DuVernay’s chilling documentary 13th. I also thought a lot about the different ways in which societal and systemic racism manifests itself in the US vs. in Europe. I’m most familiar with the situation in The Netherlands, where (after some initial squabbling about social distancing during a large Amsterdam protest) there has been a more serious conversation about structural obstacles facing people of color and those without traditional Dutch last names (see e.g. here and here).
After a week or so, the conversation among academic on Twitter rightfully shifted. People went from focusing their outrage at police brutality, to examining the many problems with racism that take place in our own professional spheres. On Twitter, #BlackInTheIvoryTower launched a much-needed, painfully honest conversation about the many ways racism pervades academic culture.
I’m here sharing my thoughts, personal commitments to fighting racism in science, and resources.
Climate change is the most urgent problem currently facing humanity – including a subset who call themselves (neuro)scientists. While many academics still consider (political) activism far outside their comfort zone, the broader scientific community is slowly waking up to the urgency of the situation and the role we can play as a community of evidence-minded individuals. I believe there are few excuses for not engaging with this issue, and there are many ways to productively do so.
Since many scientists seem reluctant to speak up or unsure where to start, I’m collecting a short list of concrete things you can do (in approximate order of effort/difficulty). Pick one, get started and join the global effort necessary to tackle this problem; after all, aren’t we problem-solvers?
Note [December 2020]: this blogpost led to an SfN petition, and then to an opinion paper that lays out in more detail what neuroscientists can do against climate catastrophe. If you prefer something with a doi, that one’s for you.
This post describes my latest paper: Urai AE, de Gee JW, Tsetsos K, Donner TH. (2019) Choice history biases subsequent evidence accumulation. eLife, 8:e46331, and was reposted from the DonnerLab website.
To study the mechanisms of decision-making, researchers often treat individual decisions as isolated events. However, as we go around the world, our decisions can be strongly influenced by previous experiences. Even in cases where it may be best to focus only on the current situation, our choices can be hard to separate from contextual factors. For example, studies of perceptual decision-making use well-controlled, but artificial, stimuli about which observers have no prior knowledge. Still, it has long been known that the choice an observer made seconds ago influences what she will report next.
Although we’d all like academia to be a true meritocracy, ample research shows that implicit biases create significant hurdles to achieving diversity in our communities.
Here is an overview of the data (showing both the extent to which gender biases cause problems in science, and the different factors that may be significant contributors) and possible solutions. Different iterations of slides I presented on this topic are here and here.