Researching at last

July has been a refreshing DProf month for researching and reading. I have now completed two sets of pilot interviews. The first set were people known to my supervisor. They weren’t totally in my area but they were really valuable for learning about the interview process. Apart from valuable research information, they also gave feedback on the process and logistical aspects. The second set of pilots were with two neuroscience Professors who responded to my invitation email – 2 from 35 invitations isn’t bad.

My learning so far: The embedded link to Skype-for-Business isn’t as user friendly as I thought but I need to use it as the recording facility is reliable. With 3 out of 4 interviewees struggling with it initially I’ll need to put in some simple guidelines on connecting to it. Actually, I could do a side piece of research on the profile of who can and can’t easily use the link in the invitation! I reckon you can all guess which stereotype finds it easiest as it’s the one I think we’d all guess it to be when it comes to being tech-savvy.

I also refined opening the interview by reiterating the invitation email and situating my research in the coaching of change-hesitant coachees. The latter helped me focus the conversation much better so I am glad I was forced to get my head around it. In fact, both the Professors referred to it which helped to guide and anchor the discussion.

Everyone was very generous with their time. Having asked for just 20 minutes, all gave around 40-45. I was very grateful for that as 20 minutes on this topic only just gets people warmed up. I am going to regret it when I type up the transcripts though. I have found that initially the conversation is quite conceptual and I have had to push it down in to ‘So how does that actually happen in the brain?’. I then noticed a little pause – almost of surprise – and then they give me what I am really looking for and talk about a variety of mechanisms and caveats. I am really pleased that I read that Neuroscience textbook last year as it made the conversations much easier.

There are two things I have really loved about having these conversations so far: Firstly, it is just a conversation where ideas are given, explored and questions are proffered and answered. As someone commented, it must be nice not to have to talk in words of just one syllable – so right (lol). Secondly, they are very down to earth people and are clear about the constraints of their research and how animal research is difficult to use for hypothesizing about human aspects. A refreshing change from all the neuro-hype.

On the down-to-earth and refreshing reading side, my supervisor recommended ‘Neuro’ by Rose and Abi-Rached. A very different book as it is about the history of neuroscience blended with a critical review of some of its emerging themes, directions and assertions. It picks up on some of my favourite themes – medical hypes that have little foundation, neuroscience as court evidence, lab settings affecting experiments that deal with the brain and the blurring of the use of the words ‘the self’. Although I need to be careful here otherwise I might undermine the very research that I am conducting but it does bring home that neuroscience is a very interpretivistic science at the moment. (A useful reference for my Chapter 3 claims on epistemology and methodology.) One of the sadder facts is where it says that most research aimed at helping with mental illness has in fact not generated many new medical practices. Thus we are still using drugs from many years ago as they are the best we have.

Another book, which will provoke outcries, is called ‘How emotions are made’ (Lisa Feldman-Barrett) although I enjoyed it. Basically, she is differentiating between us labelling something as ‘fear’ verses it being a collection of responses due to a stimulus. It is the same as LeDoux where he splits apart the feeling of fear from the threat response. Part of their thinking is that a mouse, for example, has a threat response but we don’t know if it feels fear as we can’t ask it. We tend to ascribe fear to the mouse through our interpretation of what we see it doing but that is us ‘humanising’ things. What was fascinating is that she talks about more recent research verses older research on archetypal emotional faces. Effectively it appears that the older research which concluded that there were universal emotional faces which everyone recognised isn’t entirely true, well not true at all if you concur with Lisa. Here it links back to the ‘Neuro’ book and Lisa talks about how the research wasn’t actually as ‘clean’ as it espoused.

She believes that we learn during childhood that a certain collection of responses are labelled as ‘fear’ or ‘joy’ or ‘sadness’ and that the actual reactions for a feeling are quite diverse: Think of different joy responses such as a big smile or wide-eyed and open-mouthed. She feels that the standard faces are unusable just as the average family having 2.4 children is fairly meaningless. She also covers how feelings affect the decisions we make (don’t get a court appearance just before lunch/ make sure the interviewer holds a warm drink) and our behaviours which is worth a read. She also discusses how much the brain uses concepts to group things together such as colours in a rainbow. Most of us see it as 6 distinct bands when really it is a gradient. Russians view it as 7 distinct bands as culturally they view light blue as a different colour to dark blue, as green and blue are viewed differently. This she suggests makes colour a cultural thing not a reality.

This links nicely to an article discussing how children beat computers on some tasks and how far computers have yet to go.

Movement, oscillations and emerging patterns

(Hot off the press: Today I had my Project Proposal resubmission approved. So, I have finally completed Part 1. I am now an official doctoral candidate – exciting and scary.)

I have got to ‘Movement’ in my textbook and whilst I am fascinated in how the body coordinates itself, I am definitely feeling uncomfortable reading about the animal experiments that have led to this knowledge. ‘Ablate’ is a fancy word for ‘destroying’ or ‘removing’ a function and I am frequently reading that word in this chapter. Also, cats are being used a lot which is unpleasant. My sons will laugh at this as I regularly moan about cats and chase them away from the birdfeeders in the garden.

On the movement side what is very interesting, if I understand it correctly, is that when your muscles contract, basically each cell within that muscle contracts. The basis of muscle contraction is about overlapping thin F-actin and thicker Myosin protein filaments, which are laid out in this pattern:

____________                ______________  F-actin

                   ̻̻  ̻̻  ̻̻__________  ̻̻  ̻̻  ̻̻                                      Myosin

____________                ______________

                   ̻̻  ̻̻  ̻̻__________  ̻̻  ̻̻  ̻̻

____________                ______________


The thicker Myosin filaments have little bundles at each end. These have an extended ‘finger’ which, when excited, ‘ratchet’ the overlapping layers inwards towards each other, bit by bit, thus contracting the cell.

I have also managed to read “The Oscillating Brain” by Timothy Sheehan which is well written and was an easy read overviewing brain oscillations. This topic seems to be causing excitement in the neuroscience world so I thought I’d better get a heads up on it. It also covers a good overview of complex systems theory, which is easier to conceptually understand than I thought.

The oscillations, seem to be the pattern caused by all the neurons that are firing. A bit like a Mexican wave at a sports stadium which can vary the speed with which it goes around and around. Although formed of individual people there is a ripple effect that can be seen from afar rather than the individual people.

There seems to be a regular resting baseline of Alpha oscillations (8-12Hz) which increase to Beta oscillations (13-30Hz) when we get sensory inputs. Once consciousness kicks in we get Gamma Oscillations at 30-70Hz which means that the neurons are firing more rapidly. I read another technical paper on neural oscillations that talks about how the oscillation networks coupled with the electrical circuit characteristics of neurons, enable filters to be created which amplify certain aspects and suppress others. (More third year electrical engineering degree material) One example it gives is how the cortical and thalamus oscillations progressively decrease neural responsiveness to external factors thus creating deeper sleep. Inhibitory neurons feature a lot.

In the brain, there appear to be a lot of inhibitory neurons which help to sharpen the difference between what’s on and off. (Used a lot in vision). Therefore, once one set of neurons are excited they inhibit other neurons so it is even less likely they will fire (it takes a bigger input signal to activate the inhibited neurons) thus sharpening the definition between them. A good understanding of decision maths or logic circuits is useful as the brain seems to use these a lot.

I must admit I had been putting off reading about Complex Systems Theory as it sounded a bit heavy going. Also, I feel that there are two phrases within complex system theory that people seem to love throwing around: complex system and emergent properties. The good news is that complex system theory as overviewed by Timothy Sheehan is easier to follow than I thought.

The ‘complex’ part refers to the system rather than the theory (I am sure that will follow) and in essence the theory attempts to make complex systems simpler. A good example of a complex system is the weather. There is a lot going on and there are a lot of things that affect it such that you cannot really predict the weather patterns off just one variable. Also, the patterns of interaction between the variables are hard to predict in a precise manner. That definitely sounds like the weather forecast.

The term emergent property seems to be about the most predominant trend(s) or pattern(s). That is, if you overlay all the individual aspects what is the most predominate trend(s) that appears? An example of this is if you take Euston Railway Station concourse: During the day people seemingly walk across all parts of it but if you superimposed all their paths you’d probably find that most people use just 4 or 5 main routes through it. Across the day these trends might vary (rush hour verses off-peak) but still stay within a limited number of options. I think a ‘Wordle’ is another example as it pictorially simplifies the information about how often a word is used and makes it easy to see the trends.

A complex system also has some themes. Firstly, repetitive activity underlies the stability of the system along with feedback loops. Also, it follows a ‘S’ shaped curve. They mainly operate on the more vertical part of the ‘S’ which gives them their steady-states. For example, our weather is predominately a temperate climate with our winters generally colder than our summers. However sometimes the system can move towards either end of the curve. At one end, it becomes ‘weak’ and easily affected by other factors more than it usually would be. At the other end, the system can become overloaded and break down, giving rise to unexpected behaviour. Thus, sometimes complex systems do things which are very unlike them.

This also characterises people. Over time, we form assumptions of people’s behaviour but in times of stress they do ‘out of character’ things, like becoming unusually forceful, rigid or argumentative. When they are feeling ‘fragile’ they seem more easily overcome by daily occurrences. So, a complex system is simply something which has lots of variables and is not easy to predict precisely, although it has a steady state trend(s) and occasionally does something unexpected. Like our weather or us. Although I am not sure if this helps us understand people or is just another analogy.