Is technology tweaking our brains?

Published in the journal e Life, Prof. Alon Chen and colleagues at the Weizmann Institute, Israel, present a new method for analysing how groups of mice interact with each other.

This new system has taken five years to develop and may save hours of scientists time on consuming data analysis. It works by painting the fur of individual mice with different coloured UV paints, illuminating them with a UV lamp and then recording the movements of the nocturnal mice during the night. A computer system then takes the video'd movements, generates 10, 0000 rows of data per hour of recorded footage, and translates this into predictive behaviours such as grooming, sniffing, feeding together, exploring or simply avoiding the other mice!

By providing a system for analysing complex social interactions between large groups of mice, this could pave the way to a greater understanding of disorders associated with altered social interaction, such as autism.

Researchers can tweak the genes or environment of mice and then relatively quickly see how it affects their social behaviour. The scientists have firstly applied this technique to find out how the environment affects how mice interact with each other. Their finding? Mice that have been raised in a stimulus-rich environment have less complexity in their social interactions than those growing up in more Spartan conditions. In essence: mice that had grown up in the complex environment were more territorial and worked as pairs rather than as a larger group. The scientists comment that this finding in mice ties in with the  common belief for humans that our modern, stimulation-filled environment encourages individualistic behaviour while simpler surroundings give rise to a more developed community life.

The authors  plan to continue collecting data from this system, asking questions such as "How do mutations in various genes affect social behaviour? What about the behaviour of mice that overproduce such hormones as oxytocin (the love hormone) or testosterone? Do mice with the behaviour patterns of autism or schizophrenia function better in certain environments? How do groups of mice learn in a group?"

Chris -  Scientists have discovered how to create a thinking cap.  I love this!  It helps people to become much more creative.  The work is based on observations that sometimes damage to the front part of the brain's left temporal lobe can disclose extraordinary artistic and musical talents that a patient never knew they had.

Now, Richard Chi and Allan Snyder from the Centre for the Mind at the University of Sydney have used a non-invasive technique called transcranial direct current stimulation to harmlessly reduce the activity in this front part of the brain which boosted the problem solving abilities of a large group of healthy volunteers.  Allan Snyder...

Allan -   Well, the big picture is inspired by the quote from William Blake, "If the doors of perception were cleansed everything would appear to man as it is, infinite."  So we were confronting the challenging problem of how to artificially induce a less-filtered view of world, one less constrained by preconceptions.

Chris -   In other words, the world that we see is one tinted by past experience.  You learn something and that informs the way that you interpret the world henceforth.

Allan -   Precisely.  Our perceptions, our memory, our decisions are based on filtered information.  We view the world, in a sense, "top down" through concepts, through mental templates, which are built up from our past experience.  Of course, these concepts are crucially important for our survival.  They enable us to make rapid predictions about what is most likely, based on only partial information.  But the strategy leaves us susceptible to certain kinds of perceptual and cognitive errors.  Visual illusions, false memories, prejudice, and it makes us inclined to connect the dots in ways that are familiar rather than to explore novel interpretations.

Chris -   Which makes it much harder to think outside the box if you're trying to solve a problem and you're trying to solve a hard problem that other people have grappled with.  There's probably going to be an original solution.  Going down the same wrong road they have is the wrong approach.  You need to think in a new way and if we can find a way to do that, we'd be better off.

Allan -   Yeah.  I mean, it's not the wrong approach.  It's the good approach, but it's not going to work if it doesn't apply.  In other words, our observations of the world and the problems we are talking about are strongly shaped by our preconceptions from previous problems where that didn't work.

Chris -   So how have you tried to get an angle on what the brain is doing and how to get around that problem then?

Allan -   What if we could temporarily inhibit this top down processing and thereby access a level of perception normally hidden from conscious awareness?  Might we be able to have a world which is less preconceived?  Of course, we'd only want to do this temporarily.  We need our conceptual make-up, we don't want to be like an infant, but that's the kind of rationale that's behind our work.

Chris -   So how did you actually tackle this problem?  What did you do?

Allan -   We used safe, non-invasive, transcranial direct current stimulation to inhibit the left anterior temporal lobe - that's an area associated with conceptual processing, labels and categories.  In addition, we simultaneously excited the right anterior temporal lobe - an area associated with insight and novel meaning.  The objective was to temporarily induce a less filtered, less assumption-driven cognitive style.

Chris -   And what did you ask people to whom you were doing this to do, in order to see if they were thinking in a new way or thinking more originally?

Allan -   Well we took a standard problem of insight, a match stick arithmetic visual problem, and we showed them how to do one class of those problems and then asked them to do a much harder problem that required a novel turn, a novel twist, and the people who received direct current stimulation, three times as many as them solved the problem than those in the control group.

Chris -   The argument would be that because you had to think about the problem in a novel way, this suppression of the left side of the brain which normally forces you to think in this hypothesis-led familiar or way informed by familiarity, that having been turned off, they began to think in a novel way and that's what gave them this insight to solve the problem in a new way.

Allan -   Yeah.  That's the way we look at it.

Chris -   So now you've found this, what's the next step?  Is it to say right, "can we try and apply this to other modalities?"  So that's a problem-solving task, it's part visual, part cognition.  Are you now going to start looking at other things that might be informed by the same strategy?

Allan -   You're right.  Every sensory modality uses the top down process.  So, we indeed have been trying to think about other experiments we could do that would illustrate this concept, and we have a few in mind.

Chris -   Maybe you need to stimulate your brain to suppress it to attune the left antero-temporal lobe to see what comes out.  But practically speaking, could you use this for anything?  Do you think musicians should plug themselves in?  Should mathematicians grappling with tough problems plug themselves in like this, to see if they can free their mind?

Allan -   Richard Chi and I suggested that it might be a "thinking cap".  The concept about a thinking cap, I think many people regard a thinking cap as something that might be a Google retriever, but we don't need that because we have Google.  What we really need in the future is a way to connect seemingly disparate pieces of information into a new synthesis.  In other words, to look at things afresh and that is what I would hope a thinking cap could give us - a creativity enhancer in that sense and yes, this is something that could be used in the future.  It's a very simple device.  It uses a 9-volt battery.  What we need to do is try to optimise the configuration of stimulation on the brain, we need to think about the time interval that we want to expose people to, there are many variables here to optimise this, once you accept the reality or the proof of principle.

Chris -   I think I could use one of those thinking caps.  That was Professor Allan Snyder who is the Director of the Centre for the Mind at the University of Sydney, and he published that work in the journal PLoS One.

Could computers ever have the power to predict our emotions and change them? To find out I caught up with Professor Peter Robinson, Computer scientist at Cambridge University.

Peter -   It's a crucial part of human communications.  It's being studied scientifically since Darwin's time.  He was interested in the way that we use facial expressions to convey these signals.  People who can't do it are at a social disadvantage.  It's people with autism spectrum conditions.  So, in that sense, computers are autistic.  They don't recognise these signals.  So, the computer carries on blithely saying whatever it wants to say or doing whatever it wants to do, and it doesn't look at the expression on your face, or the tone of your voice as you interact with it.  And so, we've been looking at ways that we can give computers some sort of emotional awareness.

Hannah -   Building clever algorithms that can actually read emotion?  That's quite a feat!  It must be very complicated maths that you're plugging in there and it must have been a huge amount of emotion reading that you must have had to sift through in order to build the algorithm in the first place?

Peter -   That's right.  Well, right on both those accounts, we've worked with our friends in the Autism Research Centre, Simon Baron-Cohen and he's interested in the real people who have autism spectrum conditions.  And so, we've joined a lot on his research work into the theory of emotions.  On top of that, we've used all the usual sorts of things that computer scientists use nowadays.  Essentially, this comes down to machine learning rather than actually writing a programme that tabulates exactly how to interpret these social signals that people are giving out.  We write systems that learn from examples.  We've had lots of video clips and audio clips of actors expressing emotions.  We can use those to train our systems.  In this context, we know the probability that you'll particular facial expressions.  If you're feeling a particular emotion, we can calculate those and this allows us to turn it the other way around, so that when we see the facial expressions, we can work out the probability of different mental states.  One of the projects is looking at ways in which we can make computer games that might be able to help children say, with Asperger syndrome who are often very intelligent; they just lack the ability to read these emotions.  They know they have a problem.  They want to do something about it.  We can make computer games that help them learn to read these expressions in other people.

Hannah -   And so, how sensitive or how accurate is this computer at reading and gauging people's emotions?

Peter -   Also, an interesting question.  The sensitivity is fairly good.  Different people have different magnitude with which they express emotion.  Different cultures are more expressive or not.  But the accuracy, well, it's not like the sort of computer system that you're used to - your spreadsheet.  This does not give you a precise, accurate, precise answer.  Emotions are things that different people perceive differently.  There's a consensus view.  But even when we show our video clips to a human audience, we're unlikely to get them all agreeing exactly on what they're seeing.  So, if we give them say, 6 choicesx we get maybe a 70% agreement rate.  But that turns up to be about the same sort of agreement accuracy that you'd get if you were showing it to people rather than the computer.  So, it's quite important when you're using this sort of information from a computer system - the sensors that are reading these social signals to understand that they're a hint rather than an absolute description.  That means that the way that the computers react to this information has got to be rather different and we haven't really had much time to even think about how we use this information, how we change the way the computer is operating in response to the emotions that it's detecting and its uses.  We did some trials particularly just in the problem of car drivers, dealing with busy roads, unfamiliar environments and increasing amounts of technology in the car.  We wanted to see if we could detect when a car driver was upset by the environment and perhaps adapt accordingly.  We set up some simulations for this. 

The thing that we observed first of all is that actually, most of the time in our driving simulator, people are just completely neutral.  They don't have to show any emotions and is rather deceiving.  So, there's then an even more difficult question of what to do in response to the information.  So again, to take the example of a car driver, if you see a car driver who is getting aroused and frustrated, and angry, actually, it's rather a bad idea to have a computer system that patronise and he tells him to calm down actually.  The computer has to mirror the emotion of the person that it's interacting with, but lower intensity - firm but not offensive.

Hannah -   Thanks to you Peter Robinson from Cambridge University.