Recently, our lab members have started using Trello boards for project management. For collaborative projects, we have been using something like this for a while. Recent improvements in Trello make it easier to use than alternatives.
In the course of using Trello, I noticed several typical patterns in micro failures. These failures tend to happen when the members:
1) do not estimate of when to finish an item in “To Do”,
2) overestimate what they can do at a given period, and/or
3) do not plan for the time or possibility of failure.
1) is something I have noticed myself over the years. A certain percentage of items in my “to do” list remain there forever! This has put me under some stress. These accumulated items make me feel that I can’t achieve many things. But I’m not an exception. Psychological studies seem to have shown that ~40% of the items in the To-Do list are never completed!
These studies also point out several disadvantages of the ToDo list. One obvious disadvantage is that all items on the ToDo list look similar in priority.
A pretty effective way to deal with this is to put a self-estimated “time to complete” for every item. This also allows one to realize how much one can do a given task at a given time, which relates to the 2nd problem.
We are pretty bad at estimating how long any project will take. This is known as Planning Fallacy.
Combined with 1 and 2, 3 makes things worse. Many ambitious members have estimated the time to complete as if they are a perfect person. No failures are planned. This makes the planned date to be never achieved.
There are two ways to reduce your planning fallacy. One way is to ask other people to give the estimate. Other people’s judgement seems more registrant against this fallacy.
Another way is for you to estimate the time as if your colleagues were to do it.
There are two great practices that I’ve tried, which worked very well for me!
I) Know thyself. Record as many activities for at least 2 weeks. Get an estimate of how fast you can do a given task per unit time. I recommend a combination of “the Pomodoro technique”.
II) Include ~15% of CHEAT time / day for your schedule. Planning roughly 1-2 hours per day and 1 day per 2 week as a CHEAT day. This seems to improve the success rate of timely achievement of the project.
From a student’s (=your) and a PI’s (=my) viewpoints.
From a short-term and a long-term timescales.
1) A student’s short-term viewpoint
Including myself ~20 years ago, we tend to be very short-sighted when young. Students who are finishing the final year of university are, in particular.
In Australia, 4th year undergraduates do a Master-equivalent Honours thesis project. It tends to be very intense. Usually, it is the first time for students to spend >6 months on one thing and write >10,000 words. While doing this, they don’t have the luxury to think about the future.
At completion of Honours, they tend to think “when to start PhD” by looking at other students, who haven’t thought about it properly too….
If you are at that stage, I recommend you to “take off” at least for several months.
Travel. Read books. Do anything (e.g., volunteer) that you wanted to do. Join a company as an intern and see how the world (outside academia) works. Try to become a Youtuber. Test yourself and gain experience to make you unique.
All these activities will help you identify if it’s worth doing a PhD or not.
Currently, life expectancy is quite long. And you will retire 5 to 10 years later than your parents do / did!
There is no rush to decide when to start your PhD. Delaying it for a year or more doesn’t matter for you in your life. A hasty decision will cost you 3-5 years (depending on programs).
In fact, in my experience, the more mature students tend to do well for PhD. (There may be some stats on this, but I’m not aware of it).
Having said that, it’s important to secure the money. If you don’t have economic security, you might want to work for a while to save the money. Actually, you can gain experience from work, too. If you want to join my lab, working in some company that allows you to learn programming will be a huge plus.
If you have already decided to do a PhD 100%, then try to find well-funded research labs, which interests you the most. They might have TA or RA positions for you. Gaining experience in that domain will be useful for you in a long-term (See #3).
2) PI’s short-term viewpoint
By PI, I’m going to talk about myself. (I recommend that you talk with your future PI.)
I want to make sure you have great TIPS. Techniques, Intelligence, Personality and Speed.
In short, Techniques include various knowledge and skills (e.g., programming). Intelligence includes communications skills and mindsets (e.g., growth-mind). I want to work with someone who fits with the existing members, including myself! Speed includes adaptability upon feedback.
The best way for us to find these out is to work on some projects. It can be several months. It can be a Honours project. etc.
I need to make sure TIPS because I want to have a happy supervision experience. 3.5 years is short-term for me, but a long investment from my viewpoint!
What projects you want to pursue matters. But that’s something we can also figure out better if you do some projects with us.
3) A student’s long-term viewpoint
Students tend to have vague long-term plans, especially after PhD. Roughly, those who want to do PhD with me have 3 plans.
a) academic jobs, including research and teaching
b) jobs outside academia (e.g., clinical or industry)
As I don’t know the reality for option b), I can only give some realistic advice on the pathway a).
The most important thing that students don’t know is that the day you get a PhD will matter in the future. The date of PhD conferral matters. Not your age. This is true for almost all scholarships, fellowships, awards, grants, and promotion.
(In Japan, I know some fellowships/scholarships that use “age” as one of the criteria. But that won’t continue given the current worldview. It’s age based discrimination…)
Your CV at the time of PhD conferral will constrain where you can go next (e.g., postdoc, scholarship, etc). The better your CV is at that time, the better options you have.
This means, the later you start PhD, the better. In fact, the best strategy I usually recommend is to publish a paper before you start PhD. If you can do that, that will increase the chance of your success in the long-term.
4) PI’s long-term viewpoint
From my viewpoint, choosing which PhD candidates to work with for the next 3.5 years is quite important in the long run. Having great PhD students shapes the lab in a sustainable manner.
I, as a PI in Australia, generally want to take PhD students. If you, the student, can get a scholarship, I don’t have to pay you. This is important for my long-term viewpoint.
If I were in the US, I would generally have to cover your stipend in some way. If I were in Japan, I would like to take students who can get a JSPS fellowship so that they can focus on research. I may write on these issues later. Specific details differ for each country.
Roughly, mid-sized labs like mine tend to consist of PI, postdocs, PhD candidates, RAs. Some other short-term students (like Honours, interns, etc) also join in and out.
Hiring postdocs is pretty expensive in Australia. Generally, one grant can hire one postdoc for 3 years in my field. So, relying on a postdoc is quite stressful (as the grant success rate is not that high ~10-25%) and not stable.
So, I would be very happy to work with PhD students, who can contribute to the lab in various ways. That’s why I pay attention to TIPS. In particular P: personality.
5) Conclusion & Summary
I hope that what I wrote here has some capacity to generalise to other fields and PIs, but I’m not sure.
At least, if you want to join my lab as a PhD, this is pretty much what I will ask you to think…
1) Take off and think about long-term plans.
2) Delay the start of PhD to maximize your chance in the future.
3) Think a bit from the PI’s perspective as well.
I have also posted some videos answering related questions from a student who is thinking of applying for US/Australian PhD (English), a high school student thinking of going to US University (Japanese) and a student thinking of US grad school (Japanese).
The goal of our research is to understand the physical substrate of consciousness. How is our conscious experience supported by our brain? To address this question, we need to make various breakthroughs. We strive to establish new paradigms and techniques based on our creative ideas. We will develop new and clever experimental or analytical developments. They will allow us to observe and manipulate consciousness for rigorous scientific investigation.
We have contributed to this in several domains (See Research focus so far). We are also building novel empirical methodologies (See Future and ongoing research).
Building on our basic research, we also work on research translation. In particular, into clinical and industry domains. These researches aim to improve the accuracy in measurement of consciousness. It also aims to improve conscious creativity while reducing mind wandering. It also aims to improve (collective) intelligence research informed by consciousness research.
Research focus so far
We have used various techniques. The range of techniques include but not limited to: psychophysics, neuroimaging, and modeling. We collaborate with researchers who work on the brains of humans and animals. Interdisciplinary collaboration has involved philosophers, clinicians, engineers, neurophysiologists, mathematicians and physicists.
1) Understanding the boundary between conscious and nonconscious processing.
One of the most effective ways to study consciousness is to contrast
conscious and unconscious states of the brain. When conscious, people or animals are awake. Unconscious states can result from brain injury, general anesthesia, or deep sleep.
Another most effective method is to contrast conscious and nonconscious processing. When we are conscious, we perceive certain things but not others at a moment. That is the contrast in contents of consciousness, or qulia for short.
We invented a technique, known as Continuous Flash Suppression (CFS; Tsuchiya & Koch 2005).
CFS is a technique in visual psychophysics. With CFS, researchers can suppress a salient visual stimulus. CFS can suppress almost any image for a long duration in a controlled manner. CFS helps uncover the neural processing that does not give rise to consciousness. We used CFS to study nonconscious emotional processing in the lesion patients (Tsuchiya et al 2007). Cognitive neuroscientists worldwide use CFS in combination with neuroimaging. Our work continues to clarify the boundary between the conscious and the nonconscious.
2) Clarifying the relationships between consciousness and associated psychological processes
What is the relationship between consciousness and other psychological processes? Attention, memory, access and report are all important parts of our life. How are they related to consciousness?
Philosophers and psychologists have long discussed the relationship between attention and consciousness. And this discussion is now extending to AI and other fields.
Based on empirical evidence, we have been proposing to distinguish the two (Koch & Tsuchiya 2007). The two can work together but often dissociate. Sometimes working towards the opposite directions. We demonstrated it using psychophysics (van Boxtel, Tsuchiya, Koch 2010) and EEG (Davidson et al 2020). Key is to independently manipulate consciousness and attention. We have various paradigms at hands to do this (Matthews et al 2018).
No-report paradigms are also necessary to truly understand the neural basis of consciousness (Tsuchiya et al 2015). They allow us to distinguish the effects of the act or intention to report on the contents of consciousness.
3) Testing the quantitative theories of consciousness
We have been working on Integrated Information Theory (IIT) of consciousness. IIT is currently one of the most promising quantitative theories of consciousness. Its explanatory and predictive power on consciousness is quite attractive. Yet, IIT has been criticized as being impossible to be tested.
Our aim is to test IIT in its prediction. Towards this aim, we developed accurate proxies of integrated information (Oizumi et al 2016 x2, Cohen et al 2019). Our tools are available on the web. We have characterized integrated information in relation to other known measures. Based on these developments, we have started testing IIT’s claims with real neural recordings (Haun et al 2017, Leung et al 2021). Our measures of integrated information can be applied to other fields of network science. Physicists, social neuroscientists and others have started using them for their research objectives.
Future and ongoing research
4) Big data analysis on loss of consciousness
We have been trying to distinguish states of wakefulness from loss of consciousness. We try to develop better classification between wake and anesthesia-induced loss of consciousness (Cohen et al 2017, 2018, Leung et al 2021). We are also interested in distinguishing dreamful vs. dreamless NREM sleep (Wong et al 2020). We are extending this direction into conscious vs. unconscious brain injured patients.
Our focus so far has been on electroencephalography (EEG) data. EEG has high temporal resolution. making it easy to test some theoretical measures of consciousness. EEG is available in many hospitals and much cheaper than other devices. Thus any methods to detect consciousness based on EEG is easier to translate to clinics.
To develop novel methods, we have collaborated with physicists to apply complexity measures (Munos et al 2020). We are also applying >7700 analysis methods at the same time on the same data to search for the best . This method is called highly comparative time series analysis (Fulcher 2017). For this end, we are quite keen to collaborate with many scientists to construct a large EEG dataset.
5) Massive report paradigms to characterise the structure of conscious experience.
Traditional psychophysics on consciousness has focused on binary categorical responses. For example, seen vs. unseen responses through button presses. While easy to analyze, they are poor in capturing richness of consciousness. We need a better way to characterize the structure of human conscious experience.
We have been developing various ways to extend reporting methods. We call these as “massive report paradigms” to distinguish it from simpler alternatives. To collect a big enough dataset, we develop various web-based psychophysics experiments.
The challenge of these new experiments is the complexity of the analysis. We have been exploring automatizing the scoring procedure. Here, we see a huge opportunity for collaborating with researchers in other fields. AI, math, linguistic and topological data analysis.
6) Discovering a structural mapping between consciousness and information
What do we need to understand the relationship between the brain and consciousness? We have proposed a three-step approach (Tsuchiya et al 2016). It starts with revealing the structures of consciousness. In parallel, it tries to reveal the structures of information. This information structure has to be relevant to consciousness. Then, it eventually reveals the structural mapping between consciousness and information.
The last step of the project requires collaboration with theoreticians. We are using various concepts from category theory and tools from applied mathematics. It benefits from interdisciplinary collaboration with mathematicians, physicists and artificial intelligence.
Our policy of recruiting new members (prospective PhD candidates and postdocs)
We believe that it’s important to have a “get-to-know” period (like an internship period) before we make decision on whether to work with you.
During the friends period, we ask you to work with us on one project. This can be a summer/winter intern project. It can be a research unit in your university, where I serve as a co-supervisor. Or a new collaborative project with your current supervisor. It may be with / without contract and would last for a couple of months or up to one year, depending on the cases.
From our end, we want to see several aspects in you that we think are critical. We want to see if you fit with us.
TIPS
Techniques: This includes various knowledge and skills. These are the things difficult to glance from your CV. For example, we want to see if you can program in the way that everyone can understand. We want codes that are easy to debug. We don’t need any complex and clever codes for our projects.
Intelligence: We care your intelligence in various domains. This includes communications skills and mindsets. Can you communicate effectively with others using various tools? Can you plan projects taking into account of rest and failure? Can you manage yourself? Do you strive to beat yourself yesterday?
Personality: We want to make sure if your personality fits with the existing members. We want to become friends before collaborators!
Speed: We want to see if you can adapt and grow speedily. When you receive feedback and suggestions, do you react quickly? Can you improve the projects and yourselves?
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During the friends period, you can also test us, and in particular, see if you like with working with me. I know that this is quite unusual kind of laboratory. But I hope this makes sense to both of us. We are striving to make our lab happier and more productive lab!
Procedure:
We are not urgently and actively recruiting postdocs or PhD candidates at the moment. But, if you are interested in working with us in the future, please send your CV. Depending on the project and availability of the lab members, we may offer you the friends period.
As described , we are interested in people who share the goal with us. To address the problem of consciousness. Any background is welcomed. In particular, mathematics (especially category theory), physics (especially quantum physics, quantum information), and linguistics (especially cognitive linguistic – how much of what we experience is influenced by our language?). But these are neither necessary nor sufficient.
Most projects in the friends periods are either online psychophysics or data analysis. But it can be a theoretical project. Data analysis projects require substantial programming skills. We tend to work with Python, Matlab, R or other language.
Nao Tsuchiya's conscious blog 