Like a lot of people, I came to the area of physiological computing via affective computing. The early work I read placed enormous emphasis on how systems may distinguish different categories of emotion, e.g. frustration vs. happiness. This is important for some applications, but most of all I was interested in user states that related to task performance, specifically those states that might precede and predict a breakdown of performance. The latter can take several forms, the quality of performance can collapse because the task is too complex to figure out or you’re too tired or too drunk etc. What really interested me was how performance collapsed when people simply gave up or ‘exhibited insufficient motivation’ as the psychological textbooks would say.
People can give up for all kinds of reasons – they may be insufficiently challenged (i.e. bored), they may be frustrated because the task is too hard, they may simply have something better to do. The prediction of motivation or task engagement seems very important to me for biocybernetic adaptation applications, such as games and educational software. Several psychology research groups have looked at this issue by studying psychophysiological changes accompanying changes in motivation and responses to increased task demand. A group led by Alan Gevins performed a number of studies where they incrementally ramped up task demand; they found that theta activity in the EEG increased in line with task demands. They noted this increase was specific to the frontal-central area of the brain.
We partially replicated one of Gevins’ studies last year and found support for changes in frontal theta. We tried to make the task very difficult so people would give up but were not completely successful (when you pay people to come to your lab, they tend to try really hard). So we did a second study, this time making the ‘impossible’ version of the task really impossible. The idea was to expose people to low, high and extremely high levels of memory load. In order to make the task impossible, we also demanded participants hit a minimum level of performance, which was modest for the low demand condition and insanely high for the extremely high demand task. We also had our participants do each task on two occasions; once with the chance to win cash incentives and once without.
The results for the frontal theta are shown in the graphic below. You can clearly see the frontal-central location of the activity (nb: the more red the area, the more theta activity was present). What’s particularly interesting and especially clear in the incentive condition (top row of graphic) is that our participants reduced theta activity when they thought they didn’t have a chance. As one might suspect, task engagement includes a strong component of volition and brain activity should reflect the decision to give up and disengage from the task. We’ll be following up this work to investigate how we might use the ebb and flow of frontal theta to capture and integrate task engagement into a real-time system.
{ 3 } Comments
Hummm… let me be a reviewer for 5 min :)
From what I read in the literature, engagement and workload are often associated to the ratio of the Beta power to the Theta + Alpha power. This ratio is supposed to increase with increasing workload, which would mean that either Beta energy increases or Theta / Alpha energy decreases which is not what you obtain (even though you did not check Beta energy which might increase more than Theta). Any comments or explanation ?
My EEG knowledge is a bit hazy, so Steve will have to correct me if I’m wrong (which I probably am :)). As you may or may not know theta activity has been found to be associated with different types of mental activity (Schacter 1977) (e.g. increases with lack of attention).
Depending on the location theta is measured from the activity your inferring changes. The beta/(theta+alpha) ratio which was tested by (Pope 1995) in his engagement work does work as you describe. However their recordings are based on a monopolar recordings at CZ (I’m using his videogame work which is basically the same, sorry can’t find my 1995 paper at the moment). The work here uses a different placement and so the inferred mental activity (theta increases with lack of attention) is not applicable.
(Schacter 1977)
EEG THETA WAVES AND PSYCHOLOGICAL PHENOMENA: A REVIEW AND ANALYSIS
(Pope 1995)
Biocybernetic system evaluates indices of operator engagement in automated task
The engagement index was derived from the work of Alan Pope as Kiel points out. These kinds of EEG ratio scores where faster activity is divided by slower activity are a good way of capturing the central idea that slower (theta, alpha) activity is associated with lower levels of brain activation whilst the opposite holds true for faster activity (beta). Studies of alpha activity and fMRI activity (I don’t have the ref to hand) have supported this notion.
However, theta which is fairly slow (3-7Hz) does not seem to fall into the same category. It has been pointed out that the relationship between theta and cortical activity is very sensitive to topography (as first pointed out by Schacter in 1977). Whilst occipital theta at the back of the head has been associated with sleepiness, it seems that frontal theta is increased in the presence of cognitive demand. The guy who did the most work on this is Alan Gevins. His studies demonstrated that: (1) theta at frontal-central site (Fz) increases with working memory load, and (2) this theta activity was probably derived from the anterior cingulate cortex (ACC). Basically we followed up Gevins’ work rather than Pope’s because: (1) I’ve used the engagement index in the past and didn’t find it to be particularly sensitive, and (2) Gevins’ work has been replicated and has a neuroanatomical basis. If you’re really interested, here are some references for his work:
Gevins, A., & Smith, M. E. (2003). Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical Issues in Ergonomic Science, 4(1-2), 113-121.
Gevins, A., Smith, M. E., Leong, H., McEvoy, L., Whitfield, S., Du, R., et al. (1998). Monitoring working memory load during computer-based tasks with EEG pattern recognition models. Human Factors, 40(1), 79-91.
Gevins, A., Smith, M. E., McEvoy, L., & Yu, D. (1997). High resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing and practice. Cerebral Cortex, 7, 374-385.
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