Gleaning the mood of the nation
Chris - So first of all, kick off and tell us how have social scientists collected data in the past and what are the problems with using those techniques?
Jason - Well, most research in psychology collects data in laboratory settings. So just about any psychology department at any university, you'll have a space designated for psychologists to recruit university students and then to subject these students to all sorts of interesting, often mundane, tasks. Essentially, what ends up happening is that these university students will complete a variety of different questionnaires, they may be presented with various stimuli and asked to respond to these stimulus and may be looking at reaction time, how quickly people hit space bars, and occasionally, there may be hidden cameras present in these rooms, and the researchers will then watch the videos of these participants' behaviour and code them on various indices.
Chris - This is not however, a natural environment for those individuals, so there's a real danger they're going to tell you what you want to hear or you're basically changing the outcome by the way you're measuring the data.
Jason - Well yes - that's exactly right. In theory, psychology and in particular, social psychology, the area that I work in is, interested in understanding how people behave in the natural environment and a lot of psychologists will go to great lengths to create a seemingly natural ambience or atmosphere within these laboratory settings to simulate these real world experiences. Of course, the big advantage of working in a laboratory is that you have a lot of control over extraneous variables and that's one of the main reasons why researchers will do their work in labs as opposed to the real world.
Chris - So given the constraints then, what are you trying to do to get around this problem?
Jason - Well essentially, we're trying to use new technologies to understand how people behave in their everyday lives. I was approached a few years ago to begin collaborating with some computer scientists Cecilia Mascolo who's at the computer laboratory at Cambridge, and together with other colleagues, we've started using the sensors in Smartphones and really, any of the typical Smartphones that are on the market now are capable of measuring a wide variety of different sorts of behaviour. So for example, Smartphones have accelerometers, light sensors, obviously have microphones, GPS, and by extracting data from these different sensors, we have the potential to observe how people behave in their natural environment.
Chris - Because they're more comfortable using the phone because they're well acquainted with using it, it's not an artificial situation for them. It's incorporated into their everyday lives. It doesn't change their behaviour, its involvement, and therefore, you get a more accurate representation of what's real for that person.
Jason - That's absolutely correct. I mean, I don't know the most recent statistics but millions of people carry around a Smartphone with them every day and by writing applications that will essentially extract information from these sensors and then by doing more controlled research, we can begin looking at how behavioural profiles, as measured by these sensors, may represent certain dispositions, or people who are in certain locations engaged in certain tasks which can be extremely informative from a social scientific perspective.
Chris - So what sorts of problems are you going to be focusing on? What can you use this to address?
Jason - Well currently, the work that we've been doing has looked at emotional expression and essentially, what we've done is designed an application whereby, participants will carry around their phone that's running this application as a kind of motion sense. The phone will register when the user is speaking and there is a predefined set of emotion categories in this application, and essentially, the user's speech is analysed in real time, and it looks at certain speech parameters and compares them to this predefined dictionary. Essentially, it will make inferences about the likelihood that the person may be experiencing happiness, sadness, fear, anger, or being in a neutral state. And so, this is one way in which we can look at emotional expression and experiences in a real world and then pair that information with location base information as well as whether or not the user is in a social interaction and perhaps with whom they're interacting.
Chris - That's passive acquisition of information. What about flipping the coin over so that the phone then does something back to the person? Is that something that could be done in the same sort of way?
Jason - Yes, that's another direction that we're going in collaboration with some psychologists and computer scientists at Southampton, we've recently secured funding to essentially design behavioural interventions that we can deliver using these mobile phones. Behavioural interventions getting at, for example, smoking cessation or weight management.
Chris - So you could take say, the behaviours that you know tend to be displayed by someone who might be about to relapse and have a cigarette when they're trying to quit because you know what those sorts of behaviours based on their profile would be and then you could trigger the phone to engage some kind of helping strategy or distraction or phone a friend to dissuade you from lighting up.
Jason - That's exactly right. There have been studies that have used text messaging to help people quit smoking and in these types of studies essentially, users or participants will explain and provide information about their reasons for wanting to quit, their motives, and they will provide information about the triggers that make it difficult for them to quit. By providing text messages without any other information, you may be sending messages whenever they're not really needed, but using the sensors in these phones, we might learn from the participants that on Friday nights, whenever they go to a particular pub or when they're with a certain group of individuals, their urge to smoke is strongest. So we can essentially programme this information into the user's phone and then trigger messages to them whenever they're in these vulnerable situations.
Chris - There is still a slight snake with this, that being that it relies on the fact that it assumes that everyone's got a Smartphone and there are many people - actually, I don't own one (there's a confession) so there are many people who wouldn't have one and they would lie outside the scope of the acquisition of your data. Do you think this is a problem that will be solved by time anyway?
Jason - I suspect so. I think adoption rates are quite high for these Smartphones and you're absolutely right, that there are constraints. However, even other phones have capabilities that would still allow for delivering such interventions. They just may not have all the full functionality as on the Smartphones.
Chris - But what I'm getting at is that a lot of the behavioural monitoring you're talking about doing, is there not a risk that the kind of person who will have a phone and the kind of person who will sign up to let you eavesdrop on their behaviour may not be representative of the population at large so you've still got a problem?
Jason - Yes, that's absolutely right. I mean, not everyone as you said has a Smartphone and not everyone would be comfortable using such technology even if they did have a Smartphone, and that certainly will be a limitation in the research that we're doing, we are using other methodologies. Some of our collaborators have been very successful at delivering these interventions online - so for desktop computers or laptops for example - and will be doing other studies in more controlled settings to essentially not only see whether or not there are user differences depending on which form of the intervention they choose but also, whether certain interventions may be more effective than others.
Chris - Jason, thank you. That's Jason Rentfrow from Cambridge University.