That is one confusing title! The point is this: When light reaches your eyes, you’re not immediately aware of that. It takes some time for your visual system to process the light, and to translate it into something the rest of your brain can work with. When that’s done, you consciously ‘see’. In a new paper, we show that the process of becoming aware of what you see, is affected by how large an object is. With an oversimplified example: If light bounces of a puppy, into your eyes, it takes a fraction of a second for you to become aware of the puppy. And it takes a fraction of a second longer if it’s a fat puppy.
This morning, the EyeTribe announced via an email to their customers that they would stop the development of their products. The particular reason is rather vague (“we’ve decided to go in a different direction with our technology“), and researchers across the board are not happy. The EyeTribe was the only real option for cheap eye tracking: It was great for demonstrations, for pupillometry and fixation control, it had a very elegant API, and the hardware was great for how much you paid for it. Best of all: It didn’t come with the restrictive licenses that almost all of the EyeTribe’s competitors use to milk their customers for more money. I, for one, am sad about the loss of this great company.
This doesn’t need any clarification: cancer really sucks. It’s mentally and physically exhausting, even for people who catch it as a young grad student. My experience until now (described here) was very positive given the circumstances, but support for serious illnesses can be lacking at other funding institutions and universities. Students should be better protected, both during and after their treatment.
Open Science (#openscience) is great! It entails sharing data and code between scientists, so that we can all benefit from each other’s efforts. However, there is a downside to sharing your stuff: You become a helpdesk for people who would like to use it, and sharing distracts from a core part of the job: publishing papers! Because research positions are offered to those who publish a lot, distracting yourself from doing so might put you out of a job in the long run. To solve this problem, publishing open data and software should be valued as much as publishing papers.
Although it sounds like a lot of effort, creating a Twitter bot is actually really easy! This tutorial, along with some simple tools, can help you create Twitter bots that respond when they see certain phrases, or that periodically post a tweet. These bots work with Markov chains, which can generate text that looks superficially good, but is actually quite nonsensical. You can make the bots read your favourite texts, and they will produce new random text in the same style!
Sigmund Freud is back! He returned in the form of a Twitter bot that replies when someone uses the hashtag #askFreud in their tweets. Not unlike the real Freud, Sigbot produces nonsensical, but real-looking text that is produced using a Markov chain. The bot can recognise and respond to specific keywords, and it can speak both German and English.
The Dutch Psychonomic Society’s biennial Winter Conference is upon us! Here, Dutch and international members of the Society meet to discuss cutting edge research. I’ll be there to listen to all of the amazing speakers, and to present a poster on our work in speed skating. Read this post for some additional info, and for a digital copy of the poster.
Threatening elements (think spiders) in your surroundings tend to grasp your attention more strongly than non-threatening things (think puppies). Some scientists believe that your brain is wired to notice threatening stimuli quicker, via a special sub-cortical route. In a new experiment, we show that task-irrelevant threatening stimuli are prioritised over non-threatening stimuli, but that they are not processed any quicker.
Two weeks ago, we published a Perspective article on how the starting procedure in racing sports could bias competitions. Some speed skating enthusiasts suggested we analyse the 100-meter times from the races we reported on. So we did! The results are very similar to our earlier results: Longer ready-start intervals lead to slower 100-meter times in Olympic speed skating.
Yesterday, we reported that random variability in the starting procedure of racing sports can bias competitions, even at Olympic events. Not everyone agreed. In this post we address all questions and criticisms, and provide an extra analysis that looks at within-athlete effects of changes in the ready-start interval on changes in race times. This analysis is robust to differences between skaters’ individual qualities, and has causal power. Our results indicate that there still is evidence that random differences in ready-start intervals might bias competitions. At the very least, this calls for future research into the starting procedure of racing sports. Which is exactly what we intended to provoke with yesterday’s publication.