The lab investigates high-level cognition, both at the behavioral and the neural level. More specifically, we are interested in the following questions:
1. What is consciousness, how does it come about, and what are its functions? The latter entails two additional questions; first, how deep does unconscious processing run and how it differs from conscious processing. Second, how is consciousness involved not only in information processing, but also in voluntary action.
2. How does cognition affect perception? We are interested in the way our expectations and semantic knowledge affect perceptual processing and our interpretation of the world.
You can find more information about these questions, and additional ones, on 'Research projects' page. You can also find all our papers on 'Publications' page. It's highly recommend that you go over them, to get a better idea of the type of questions we pursue and the way we do so. Most critical are the following: on consciousness, Mudrik, Faivre & Koch, 2014; Biderman & Mudrik, 2017; Gelbard-Sagiv et al., 2018. On contextual effects on perception: Mudrik, Lamy & Deouell, 2010
All of our lab working protocols, as well as useful "How To" guides (such as EEG section and code conventions), could be found in our Lab Handbook, which is public on the OSF platform. You are welcome to visit and use the content.
The lab includes postdoctoral fellows, PhD students, MA students, rotation students (Sagol MA/PhD students who spend 3 months in the lab), research workshop students (psychology/Sagol BA students who join the lab for a year, expected to contribute at least 2 hours per week to the lab), Sagol project students (BA students who work on a predefined project), research assistants, programmers and the lab manager. So far, we had lab members from different fields; mostly from psychology and neuroscience, but also students who gained additional training in philosophy, computer science and linguistics. On the lab website, in ‘people’ you can find postdoctoral fellows, PhD students, MA students, programmers and lab manager. If you just joined the lab in one of these positions, please send the lab manager a picture and a short description of you (you can find examples there), so we could have you officially on board.
First and foremost, to think. This means that you are ALWAYS welcome to ask questions, suggest new ideas, and doubt the things I (or anyone else) am saying. Thinking is always facilitated by reading and getting exposed to new ideas, so being in the lab also means that you should be reading papers (and sending interesting ones to the group) and keeping up with the literature. I also encourage you to devote the first 2-3 months to pure thinking – read a lot of papers and try to find the question/project that interests you most. Also take the time to go over the tutorials below. While reading papers, always ask “what do I think about these claims?” and “what is still unknown?”. This will help you find your way. I recommend Uri Alon’s paper “How to choose a good scientific paper” (2009, Molecular Cell).
Second, to always be honest. If you find a bug in your code, if you don’t understand something, if you realize something is wrong – NEVER hide it. It is crucial that we all understand everyone makes mistakes, which is completely fine. What is utterly unacceptable is hiding your mistake. This has bearing not only on you, but on the integrity of the entire lab. It is much better to find our own mistakes than to have someone else find them. Honesty is a key value of the lab, and goes hand in hand with our commitment to good scientific practices. This is why we preregister all our studies (i.e., upload a file describing the hypotheses, methods (including sample size) and analyses online. We typically use the OSF platform to do so), share our data and results, and do our best to keep everything transparent.
Third, to be nice. In my opinion, science is always better when conducted through collaboration and mutual help. So be a team player – help your other lab members (in coding, piloting experiments, or whatever else they ask [as long as it’s reasonable…]). Participating in lab meetings and giving feedback to others’ work and ideas is crucial. In addition, we always share our work with others – we have a “Common Resources” folder in which all our analysis codes are stored, together with explanatory files which describe the code and how to use it – for all current and future lab members. I expect everyone who develops a new method/code to save a copy there, together with the explanatory file. Aside from the actual findings we obtain, for me having a strong and collaborative group is a goal by its own right. And so we have social events as well as scientific ones, which are also important. Having a good lab atmosphere is incredibly important to ensure the quality of our scientific work (and to ensure we are also having fun in the process).
Fourth, to be dedicated to your research. I won’t be nudging you to see it through – I completely trust every member of this lab to be responsible for their work and their data. Don’t give up when things don’t work – this is very typical in science, and I can tell you long stories about my own failures. Perseverance is highly important here. Dedication also means that you should be responsible for all lab equipment. Some of our systems are very expensive, or required a lot of care. Respect them. Make sure you know how to properly use the equipment. If you need guidance with equipment or software – don’t ever hesitate to ask for help from one of the senior lab-members who have the experience with the specific equipment. Be extra-careful with the EEG, eye tracker, VR and AR devices etc., but also with the lab’s computers. Don’t download illegal software etc. We trust you.
Fifth, document. I can’t stress this enough – the more you write, the happier you will be in the future. Summarize our meetings, document your decisions, whatever you observe when you run the experiment, each analysis you do. Document also the motivation for your decisions – so that when you read the document a year from now, you know not only what you did but why you did it. I’ve never met anyone who regretted over-documenting. I’ve met many who regretted under-documenting. We currently use Trello to document everything we do – so it is better not to send me emails, and instead upload everything to Trello.
On a more day-to-day note, we have a weekly lab meeting – be there. I have weekly personal meetings with MA, PhD and postdocs – make sure to schedule one with me. Other than that, I expect you to attend the school’s colloquium. I know it looks, to some, like a waste of time, but a lot of excellent ideas were actually born from hearing seemingly unrelated talks. So please make sure to be there. I very much encourage people to work in the lab – this promotes collaborations. It is not obligatory, but it is highly recommended. This is not a work place – no one is counting your hours, but I do expect you to be there, and to let me know in advance when you are going abroad (feel free to send pictures when you’re there ). It’s important for the long-term planning of the lab. We have a lab calendar – please also write it there, so we won’t bother you when you are away. Generally speaking, I want people in the lab to be happy and satisfied – and for that you need (among other things) to make sure you take care of yourself (do things that make you happy, sleep well, etc.), and to feel good about your work in the lab. These two sometimes seem to collide, and that’s OK – I trust you to find the right balance, and I am happy to consult if needed.
I am here to help you conduct your research, under the themes of the lab (otherwise, I won’t be able to help – as I am no expert on other issues). This means that you are MOST WELCOME to consult with me with every aspect of your research. Needless to say, if one of the lab members (or google, a dear friend to us all), can help you solve the problem on your own, go ahead and solve it. But if not, I’m always here. I am happy to discuss theoretical issues, methodological ones and analysis-related questions. Don’t be embarrassed to ask anything. That’s what I’m here for. Mentorship is one of the most important aspects of my job, and the one that I value most. It’s a privilege and an honor to cultivate bright minds (and if you are in the lab, I think you’re bright) – I received excellent mentoring as a student and a postdoc, and I’m happy to have the opportunity to do the same for others.
You can also expect me to help you form connections with other researchers, write you recommendation letters for scholarships, awards, or future positions. I will gladly do so, and promise to write the best letter I can (but I will always be honest – so please take that into account). Don’t hesitate to ask me for such letters (generally speaking, I recommend being proactive in getting awards and research opportunities – besides the short-term benefit, they will look great on your CV).
I do my best to reply as soon as possible and not delay your research. Due to my schedule, I’m not always successful to the degree I would like to be, but I try. If for some reason I didn’t reply in due time, don’t be shy – ask again. On the other hand, don’t assume that I can go over your draft in two days; if you know you must submit your thesis (or a paper) by a certain date, make sure you send it to me enough time in advance so I could properly respond. If it is a thesis, at least two months before submission, so we can reiterate over and over. The best way to communicate with me is via email (firstname.lastname@example.org) or Trello. If anything urgent comes up, you can also text/call me, but I trust you to keep that for urgent matters. And I am here throughout the week (on Wednesday I usually work from home), usually from very early morning (~6:00) to early afternoon (~15:00; see below for home-work life balance).
You can expect me to give feedback in a respectful and constructive manner. If I fail to do so (which I hope won’t happen), tell me. I will never be upset by any type of (constructive) criticism. I am also happy to give advice in other fields, or talk about important issues like balancing home-work life, but you should always remember that this is only my personal opinion…
Aside from the ongoing reading and your familiarity with the literature (which is built over time… don’t be alarmed!), I highly recommend programming. We work mainly with matlab (with psychtoolbox) and R, and there are various online tutorials which can help you get started. Here are a few recommendations:
Matlab coursera class (though we have a class in Psychology as well, which might be moresuited to our goals)
Python class (though we mostly use Matlab, Python might also come handy)
Here are some useful R resources for you to begin with: R coursera class; R tutorial 1; R tutorial 2 (both can help). This link discusses mixed linear models, which we sometimes use.
A very basic and easy to follow introduction to statistics (which you most likely know, but it can’t hurt to refresh your memory can be found here.
We tend to complement our analyses with Bayesian statistics. Here too, there is a lot of recommended reading. I would start here:
Bayesian statistics, coursera class
Bayesian hierarchical modeling (pretty heavy, don’t start there…)
EEG processing can be done with Analyzer, or with EEGLAB/FieldTrip. Your choice, but make sure you follow the protocol very closely.
Our lab manager (email@example.com) will bring you up to speed with all the procedures; getting a key, being included in the mailing list and calendars, how to recruit subjects and reserve the experimental rooms etc. We have an organized system for subject payment – you don’t need to use your own money. The same goes for buying materials, printing posters etc. – we will cover your expenses. As for traveling to conferences, this depends on the availability of funds I have. Always try to also obtain your own funds – it will allow me to allocate more funds to others. But I am happy to cover your expenses if I can.