Gazepoint is a relatively small player on the eye-tracking market. They sell two devices: the 60 Hz GP3 at a price of $695, and the 150 Hz GP3 HD at $1995 (both of those prices exclude VAT and shipping). Because of its relatively low price, the basic GP3 is an appealing model for researchers on a budget. As of today, PyGaze supports Gazepoint’s trackers through their OpenGaze API. Download the new code from GitHub, and have fun!
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 PyGaze website was down for a few days, because it exceeded its monthly bandwidth allowance. This has been a problem for a few months now, so we’ve decided to upgrade. Our apologies for any inconvenience, and thanks for using PyGaze!