Thursday, 12 October 2017

Being an academic

This blog post was prompted by a long twitter conversation on science careers, number vs quality of publications, and the inevitable inclusion of the term ‘glam mags’. Although twitter is great for an immediate exchange of ideas, it isn’t good for nuance. Here are some of my thoughts on a career in academia.

First, if you want to be an academic in the long-term, you probably can be. It may not be the academic career you dreamt of, or at the university you wanted to be at. You may not be teaching exactly what you want. You may not have much time for research at all. However, if you decide to be an academic, you work hard, and you aren’t precious about your definition of ‘success’, then it’s a career like any other. There are plenty of people out there without glittering CVs who are grafting and getting by, but ultimately are fully fledged academics who do amazing jobs day-after-day for little praise or reward.

Much of the conversation on twitter relates to academic ‘success’ rather than simply what it takes to be an academic long-term. People obviously have high expectations, and want to be successful. I get that. I want to do amazing science with clever supportive colleagues, teach happy engaged students, and have a vibrant well-funded lab. Where I think there might be an issue is the expectation that this is what it means to be an ‘academic’. To me, this is what it means to be a high-flying successful academic. None of us have a right to expect such a career. I am grateful that I have a good solid job in a university I like. If I have success over-and-above this, it is a massive bonus. I will of course be under pressure from those above me to be that high-flying academic, and I will work towards that as best as I can, but I can’t expect it and it certainly isn’t a right.


So what is my advice to those more junior than myself? During your PhD, do the best possible science you can and try to ensure you publish at least one good solid paper. This is clear evidence that you can push a project from start to finish, and that you have developed a set of experimental/methodological skills. If you feel you have time, apply for fellowships that might propel your career into the stratosphere, but don’t bank on it. Concentrate on finding a postdoc in a lab where you feel you would fit in, you can further your skills set, and you can do good science. Join twitter and read conversations about careers, glam mags, etc. but don’t let it get under your skin. Understand the system you are trying to navigate, but appreciate that there is a huge amount of inherent noise such that there is no one-size-fits-all recipe for career success. Most importantly, enjoy yourself as much as possible. If you do commit to academia and don’t get that dream job, you will probably look back on your PhD and postdoc positions rather fondly. If you do stay in academia though, well done you. Regardless of what your CV looks like, you’re a success.

Friday, 16 December 2016

My first year as a lecturer (actually 11 months)

A few months ago I blogged about my first 6 months as a lecturer. This is the (almost) one year since starting follow up. Last time I confessed to feelings of loneliness as I was tasked with writing grants and setting up a lab before the teaching and administrative load started to ramp up. Well, the latter has now happened. I have been given a relatively substantial administrative position (though certainly not the most arduous) and although I am yet to deliver lectures, this term I have dedicated a significant amount of time to activities related to teaching. This means I have had virtually zero time to do research, but all is not lost on this front (see good news below).

Administratively I am now “strand-leader” of the neurosciences strand of the Natural Sciences programme at York. This does take up a reasonable amount of time, but certain aspects of the job are actually quite fun. I have spent several afternoons interviewing incredibly bright A-level students (most are expected to get straight A/A*s), asking them about their scientific interests and trying to get them to think laterally about topics/questions they may not have encountered before. The sheer enthusiasm of many of these students is tonic for the soul. On the other hand, I had to sit through a 3 hour board of studies meeting the other day.

Teaching this term has consisted of supervising “literature surveys” – talking to final year students in small groups (~7) about their topic of interest, helping them frame their specific question/topic, and providing feedback on their plans. I have also started to supervise 3rd year empirical projects, though the bulk of that occurs next term. Finally, as I am teaching two modules next term, I have spent a considerable amount of time reading around certain topics, and preparing lectures. This can be rewarding as it forces you to go back to basics in a particular area, but it is hugely costly in terms of time and is relatively open-ended. As a new lecturer how do I know when I have read “enough” to teach the topic to 2nd or 3rd year BSc students? My conclusion was if I care enough to worry about it, I’m conscientious enough to do a good job, but time will tell.

Finally, although I currently have very little time for research, I am fortunate in that I managed to secure a (smallish) grant to employ a postdoc over the next 2 years. That, alongside a PhD student who started in October, means data will be accruing whilst I am teaching next term. It could be a lot worse, and to be honest it couldn’t get much better.

The contrast between the first and second 6 months has been stark. The increased administrative and teaching load has been a shock to the system, and for a 3-4 week period I really felt like I was drowning. I am just as busy now, but I seem to be learning to deal with it a bit better. I am getting better at trusting that I can get things done relatively last minute if necessary (this goes against my innate nature, so has been difficult to learn). The upside to this increased stress has been a sense of increased belonging. The negative way of putting this would be “siege mentality”, but I feel these is a sense of camaraderie with colleagues that I haven’t felt before. Alongside this is an increased awareness of the “big picture” - understanding how the university and department function and how the nitty-gritty of day-to-day teaching/research/administration works. This helps in generating distance from the minor setbacks one receives. As with last time, a few possible words of advice:
  1. If you have time before teaching starts, get grants in ASAP. Smaller ones in particular that have a faster turnaround time and potentially have a higher chance of success. Concentrate on getting money for personnel – having individuals to collect data will reap rewards when you’re teaching; an expensive bit of kit that you have no time to use won't.
  2. As I suggested last time, talk to colleagues as much as possible. Get their advice when you’re struggling. Ask questions about the department and how it functions. Turn up to meetings and talks. Become part of the academic community.
  3. Realise that your first lecture isn’t going to be perfect. Cover a sensible amount of material as clearly as possible. Don’t worry too much about whether the students walk away thinking you’re amazing. Make sure they have learnt something in the 1-2 hours you have with them.
  4. Accept that you won’t have control of everything at all times. Allow some things to slip if you have to. Prioritise your time as effectively as possible. It may feel like you’re constantly putting out fires (that’s certainly how I feel), but as long as nothing develops into an inferno then I count that as a success. 

Thursday, 7 July 2016

My first (almost) six months

In February I became a lecturer, with all that it entails. I have a permanent contract, I have responsibilities, I have my own office, and I have undergraduates to teach. In short, things have changed. Given I am approaching the six month mark in my new job, I thought I would write a post reflecting on what I have done and what I have learnt. The short answer to both being: (subjectively) not very much.

First, what have I done? Or perhaps the more informative question should be: what should I have done? I am in the fortunate position that I have minimal teaching responsibilities until January 2017. I realise I am lucky in this regard. As such, I have been given the opportunity to set up my lab and get my research up and running unencumbered by the responsibilities associated with teaching.

I think my job at present broadly falls into three categories: (1) finish up postdoc work, (2) get new projects up and running and (3) apply for grants for future projects. As such, I have to balance demands from the past, present and future. Which one is more important? The simple answer is none. I have to try to make progress on all fronts in the long-run, but concentrate on one of these aspects in the short term to actually make some form of progress. I have tried to not make too many long-term “deadlines”, instead I simply try to come in everyday and get something meaningful done. If I’m feeling less inspired, I tackle easier jobs but still make sure I tackle them. If I’m feeling more inspired, I tackle harder jobs. It’s amazing how much you can achieve by simply getting stuck in. This approach has potentially worked. I have managed to resubmit a postdoc paper (now fully published), write and submit a short grant proposal, and collect some preliminary data on a more short-term research project. My hope is I can continue with this policy until the postdoc work tapers off over the next year.

One difficulty I found initially was actually getting started on a job. This was largely driven by the inevitable feeling of being alone relative to when I was a postdoc. I was accustomed to sharing an office with other postdocs and constantly discussing science. I was accustomed to having regular discussions with my PI about what I had done and what I was going to do. Despite the fact that I had relative freedom in my postdoc, the continual everyday input from other scientists shaped what I did on a day-to-day basis. I didn’t fully realise this at the time. Although my PI never directly told me what to do, I did not appreciate how much he steered me in the appropriate direction. I now have very supportive colleagues who I speak to regularly, but the onus is definitely on me to do what I think is best. Essentially I now have to fully rely on my frontal lobes to makes day-to-day decisions.

Although I am yet to fully immerse myself, the other stark contrast is the amount of administration involved in a faculty position. Again, as a postdoc I was relatively sheltered from the bureaucratic side of academia. Now, the small jobs, and associated paper work, are already starting to affect my day-to-day work schedule. No longer can I rely on my brain to remember all the small administrative jobs I am required to do and when I need to do them. This is before I have even been given a ‘proper’ administrative role in the department, such as contributing to a departmental committee. At present it feels a bit like the calm before the storm. I have the ominous feeling that things will only get worse. As such, I am trying to be much more organised, using Google Calendar to dictate what I need to do and when.

I sum, it’s been fascinating, overwhelming, scary, fun, boring, lonely, engaging, and many other adjectives. A bit like any other day in the life of an academic. Would I do anything differently? Probably not. It’s too early to tell whether I’ve made the most of my first 6 months, or whether I should have done things differently. Here’s a few thoughts that might prove useful to some though:

  1. Get stuff done. As academics we are prone to thinking things over and questioning ourselves. Don’t let this get in the way of doing something. Start a small experiment, analyse some old data. Just do something.
  2. Talk to others. Starting a faculty position can be lonely. Talk to as many colleagues as you can. Go for lunch, go for coffee, ask for feedback on a grant, discuss new experimental ideas. They went through the same process once, and know how difficult it can be. Ultimately, they want you to succeed just as much as you do.
  3. Act in the short-term but plan for the long-term. Think about big projects and grants. Mull over how different experimental ideas might fit into a larger question. Push ideas further than you have before. Thank bigger and longer-term than you did as a postdoc. But don’t wait around for grant money to start these projects. Don’t let (3) get in the way of (1).
  4. Don’t listen to me, I’ve only been in the job for less than six months.

Thursday, 22 October 2015

Computational modelling courses

Recently on twitter I asked for advice on computational modelling and/or computational neuroscience courses, in particular summer schools for early career researchers. I received quite a few suggestions so thought I would create a list for anyone else who is interested. Note, I only know about these courses through recommendations and/or the information on their website. For some, the link is for a previous years course, so I can't guarantee they are definitely still running. Still, I hope the list proves useful for some.

Computational modelling of cognition with applications to society

http://escop.eu/news/conferences-news/third-european-summer-school-on-computational-modeling-of-cognition-with-applications-to-society-/

Advanced course in computational neuroscience

http://www.accn.pt/venue/lisbon-portugal/previous-course-editions

Computational psychiatry course (Zurich)

http://translationalneuromodeling.org/cpcourse/

Computational psychiatry course (London)

https://sites.google.com/site/comppsychcourse/2015schedule

Summer school in computational sensory-motor neuroscience (CoSMo)

http://www.compneurosci.com/CoSMo/

Model-based neuroscience summer school

http://www.modelbasedneuroscience.com/

OIST computational neuroscience course

https://groups.oist.jp/ocnc

Brains, minds and machines

http://www.mbl.edu/education/special-topics-courses/brains-minds-and-machines/

Computational neuroscience and the hybrid brain

http://www.nncn.de/en/news/events/computational-neuroscience-hybrid-brain

ACT-R spring school and master class

http://act-r.psy.cmu.edu/?post_type=announcement&p=15589

If anyone else has further suggestions, I'm happy to continue to update this list. Just leave a comment with the link and name of the course.

Thursday, 2 July 2015

Research briefing: Evidence for holistic episodic recollection via hippocampal pattern completion


Horner, A.J., Bisby, J., Bush, D., Lin, W-J., & Burgess, N. (2015) Evidence for holisitic episodic recollection via hippocampal pattern completion, Nature Communications, 6:7462 doi: 10.1038/ncomms8462


Think back to your last birthday. Perhaps you were at home, eating good food. Perhaps you were in a pub, drinking good beer. Perhaps you were in a club, dancing to terrible music.

When we recall events like these from our past we are able to re-immerse ourselves in the experience, as if we were there once again. You might remember being in your dining room, eating birthday cake, whilst your friends sing happy birthday. You might even remember incidental details, like what you were thinking at the time or the music playing in the background. How do we remember and re-experience these complex events?

A long-standing theory, originally proposed by Marr but developed by many others, suggests that the individual elements of a complex event are represented in distinct neocortical regions. For example, the faces of our friends might be represented in visual regions in the ventral temporal lobe whilst the background music might be represented in auditory regions in the lateral temporal lobe. These distinct elements are thought to be bound in a single coherent memory – what Tulving referred to as an ‘event engram’. It is the hippocampus, receiving input from multiple neocortical regions (acting as a ‘convergence zone’ in the words of Damasio), that is thought to form these event engrams when we first experience an event.

What happens when remembering this event at a later date? Perhaps you meet a friend who attended your birthday party. This friend acts as a ‘cue’ to retrieve the previous event. Importantly, with a single cue we are able to retrieve the entire event. In this case, we see our friend and that enables us to remember the room we were in, our birthday cake, the background music etc. This retrieval of a complete memory from a partial cue is known as ‘pattern completion’ and is thought to be a key function of the hippocampus (and particularly subfield CA3 of the hippocampus). Following this pattern completion process in the hippocampus, all the retrieved elements are thought to be ‘reinstated’ in the neocortex. In other words, the same representations that were active when we first experienced an event become active at retrieval. It is this hippocampal pattern completion process, followed by reinstatement of all event elements in the neocortex, that is thought to underpin ‘recollection’ – our ability to subjectively re-experience a previous life event.

Despite a wealth of evidence for the involvement of the human hippocampus in episodic memory, and recollection in particular, evidence has not been presented for this pattern completion process in relation to the retrieval of complex events.

Participants learnt pairwise associations of locations (e.g., kitchen), famous people (e.g., Barack Obama), objects (e.g., hammer) or animals (e.g., dog). Importantly, each pairwise association overlapped with other associations, forming complex ‘associative structures’ (see Figure 1). For example, you might learn ‘Kitchen-Obama’ on one trial, ‘Obama-hammer’ on a second trial and ‘hammer-kitchen’ on a third trial. As such, we build relationships between multiple elements across separate encoding trials. This is an example of a ‘closed-loop’ structure, where each element is paired with each other element (forming a triangle of associations). This closed-loop condition is compared to ‘open-loop’ structures, where a chain of three associations is formed between four elements (see Figure 1). Importantly, both conditions are formed from three pairwise associations across three encoding trials. Participants are asked to vividly imagine the two elements for each association ‘interacting in a meaningful way’.



At retrieval we tested each pairwise association. For example, we cued with ‘Obama’ and participants were required to retrieve ‘kitchen’. They were shown six elements of the same type (locations in this example) and asked to select the element (kitchen) originally paired with the cue (Obama). The retrieval trials were identical for both the closed-loop and open-loop condition.

How does this allow us to look for pattern completion? If pattern completion is present then when retrieving a single element, all other elements should also be retrieved. In our example, when cued with ‘Obama’ and retrieving ‘kitchen’ the object associated with these two elements (‘hammer’) should also be retrieved. This is despite ‘hammer’ being task-irrelevant during this trial.

This retrieval should have behavioural consequences – retrieval accuracy for any two elements within an event should be related (called ‘behavioural dependency’). If you successfully retrieve ‘kitchen’ when cued with ‘Obama’, you should be more likely to retrieve ‘hammer’ when cued with ‘Obama’. This is because your retrieval success for one element is based on the strengths of all the associations for a single event. We provide evidence for this ‘behavioural dependency’ in our closed-loop, but not open-loop, condition (see Figure 2). This suggests, despite their similarity at both encoding and retrieval, that pattern completion is present in the closed-loop but not the open-loop condition.

If pattern completion is present in the closed-loop condition we should see reinstatement of all elements in the neocortex – including the ‘non-target’ element. Using fMRI, we identified neocortical regions associated with the encoding/retrieval of individual elements. Locations were associated with the parahippocampal gyrus, famous people with the medial prefrontal cortex and objects/animals with lateral occipital cortex. We next looked for ‘reinstatement’ of non-target elements. If cuing with a location, and retrieving a person, we should also see reinstatement in the region associated with objects/animals. We found greater activity in non-target regions for closed-loops relative to open-loops, again consistent with pattern completion in the closed-loop but not open-loop condition (see Figure 3). Importantly, we also show this ‘behavioural dependency’ and retrieval of non-target element in a computational model of the hippocampus (an attractor network model), further demonstrating the presence of pattern completion in the closed-loop but not the open-loop condition.

Finally, we correlated this ‘non-target’ reinstatement with the BOLD response across the whole brain to see what other regions correlated with reinstatement. This revealed the hippocampus (see Figure 4). The BOLD response in the hippocampus correlated (across participants) with the amount of neocortical reinstatement for the non-target element. This result supports the idea that the hippocampus is performing pattern completion, retrieving all event elements, allowing for the reinstatement of these elements in the neocortex.

What is critical to our study is that we always compare the closed-loop relative to the open-loop condition. In both conditions participants have learnt a series of overlapping pairwise associations and are successfully performing pairwise associative retrieval. As such, all our results are related to processes over-and-above simple pairwise associative retrieval. It is this careful experimental design that we believe is critical to our ability to infer the presence of pattern completion in our data.

To summarise, we have presented behavioural, computational modelling and fMRI evidence for hippocampal pattern completion and neocortical reinstatement in humans, and related these processes to the retrieval of complex events. We believe this is the first evidence to support a long-standing mechanistic account of recollection – our ability to subjectively re-experience previous life events.     

Monday, 18 May 2015

The academic parent #5 – the aftermath

It has now been about five weeks since I finished full time paternity leave, looking after my daughter when my wife went back to work after nine months. We are now both back at work, and my daughter is going to nursery three days a week. I’m off today to look after her, and she is currently blissfully asleep.

Looking back on my paternity leave, it seems ridiculous that I ever considered not doing it. I took a month of holiday, so it wasn't a long period of time in the grand scheme of things. When I returned to work nothing seemed to have changed. Other people’s projects had moved on slightly, but not to the extent that I now feel I'm underperforming in any significant way. It is clear I won’t be as productive this academic year as I might ordinarily have been, but I just had a child – so yeah.

Apart from actually looking after your baby and spending time with them, the best thing about taking leave is the shared experience. Suddenly all those joys, annoyances, trials and tribulations that my wife would tell me about became real. She would get home from work and we would have the same conversation as we always had, just in reverse. I can’t express how good this is for a relationship rapidly adjusting to a new way of living.

Despite this, the experience isn't the same. My daughter now sleeps through the night, naps regularly during the day, and is predominantly a smiling happy child. Looking after her was hard, but I didn't experience the sheer fear of looking after a helpless newborn alone, whilst suffering chronic sleep deprivation. Equally, although my wife has now shared in the adjustments required when going back to work (see: the academic parent #1), she hasn't experienced dealing with work when chronically sleep deprived. I remember having scientific conversations whilst getting coffee where I had to accept that I just couldn't contribute. My mind wasn't working well enough to have any meaningful discussion. Equally, the guilt of leaving for work knowing full well how bloody hard it's going to be for my wife was hard. I think when my wife went back to work it was more bemusement than anything else. She knew I'd be fine, but wanted to see how I'd initially get basic stuff wrong (I did).

The last day of paternity leave was a mix of emotions. My daughter decided to have one of her rare stroppy days. She was crying and whining for most of the day. That meant I spent much of the day counting down the hours until her bedtime. When it finally arrived I put her in her cot and closed her bedroom door. I sighed with relief, and then wished I could have spent a bit more time with her.

Wednesday, 18 March 2015

Some thoughts on the UCL “Is Science Broken” debate

[Disclaimer: This was written the day after the event, and I took no notes. If I have misrepresented the opinions of any of the panel members then it is a result of my poor attention during, and poor memory following, the debate. My apologies to those involved if this is the case.]

For those that weren't aware, UCL hosted a talk and debate last night entitled “Is Science Broken? If so, how can we fix it?” Chris Chambers (@chriscd77) gave a talk about the recent introduction of Registered Reports in the journal Cortex. This was followed by a broader panel discussion on the problems facing science (and psychology in particular) and how initiatives, such as pre-registration, might be able to improve things. Alongside Chris, Dorothy Bishop (@deevybee), Sam Schwarzkopf (@sampendu), Neuroskeptic (@Neuro_Skeptic) and Sophie Scott (@sophiescott) took part in the debate, and David Shanks chaired.
First, I found Chris’ talk very informative and measured. Words such as “evangelist” are often bandied about on social media. Personally, I found him to be passionate about pre-registration but very realistic and honest about how pre-registration fits into the broader movement of “improving science”. He spend at least half of his talk answering questions that he has received following similar presentations over the last few months. I would guess about 90% of these questions were essentially logistical – “will I be able to submit elsewhere once I've collected the results?”, “couldn't a reviewer scoop my idea and publish whilst I’m data collecting?” It is obviously incumbent upon Chris, given he has introduced a new journal format, to answer these legitimate logistical questions clearly. I think he did a great job in this regard. I can’t help feeling some of these questions come from individuals who are actually ideologically opposed to the idea, trying to bring about death by a thousand cuts. Often these questions implicitly compare pre-registration to an “ideal” scenario, rather than to the current status quo. As a result, I feel Chris has to point out that their concern applies equally to the current publishing model. I may just be misreading, but if people are ideologically opposed to pre-registration I’d rather they just come out and say it instead of raising a million and one small logistical concerns.
On to the debate. This worked really well. It is rare to get five well-informed individuals on the same stage talking openly about science. There was a lot of common ground. First, everyone agreed there should be more sharing of data between labs (though the specifics of this weren't discussed in detail, so there may have been disagreement on how to go about doing this). Dorothy also raised legitimate ethical concerns about how to anonymise patient data to allow for data sharing. There was also common ground in relation to replication, though Chris and Neuroskeptic both cautioned against only replicating within-lab, and pushed for more between-lab replication efforts, relative to Sophie.
Where I think there was disagreement was in relation to the structures that we put in place to encourage good practice (or discourage bad practice). On several occasions Chris asked how we were going to ensure scientists do what they should be doing (replicating, data sharing, not p-hacking etc.). Essentially it boils down to how much scope we give individual scientists to do what they want to do. Pre-registration binds scientists (once the initial review process has been accepted) to perform an experiment in a very specific way and to perform specific statistics on the data collected. This should (though we need data, as pointed out by both Chris and Sam) decrease the prevalence of certain issues, such as p-hacking or the file drawer problem. You can’t get away from the fact that it is a way of controlling scientists though. I think some people find that uncomfortable, and to a certain extent I can understand why.  However, what is key to pre-registration is that it is the scientists themselves who are binding their own hands. It is masochistic rather than sadistic. Chris isn't telling any individual scientist how to run their experiment, he is simply asking scientists to clearly state what they are going to do before they do it. Given the huge multivariate datasets we collect in cognitive neuroscience, giving individuals a little less wiggle room is probably a good thing.
Sophie pointed out at the beginning of the debate that science isn't measured in individual papers. In one hundred years no-one will remember what we did, let alone that specific paper we published in Nature or Neuron (or Cortex). This is a reasonable point, but I couldn't quite see how it undermined the introduction of formats such as pre-registration. I don’t think anyone would claim a pre-registered paper is “truth”. The success (or failure) of pre-registration will be measured across hundreds of papers. The “unit” of science doesn't change as a result of pre-registration.

Where I found common ground with Sophie was in her emphasis on individual people rather than structures (e.g., specific journal formats). Certainly, we need to get the correct structures in place to ensure we are producing reliable replicable results. However, whilst discussing these structural changes we should never lose sight of the fact that science progresses because of the amazingly talented, enthusiastic, nerdy, focussed, well-intentioned, honest, funny, weird, clever people who design the experiments, collect the data, run the statistics and write the papers. The debate wonderfully underlined this point. We had five individuals (and a great audience) all arguing passionately about science. It is that raw enthusiasm that gives me hope about the future of science more than any change in journal format.