Okay, so this is the Low/High Index of Pupillary Activity. I'll try to do this in three minutes.
It's work with myself Krzysztof Krejtz, Nina Gehrer in Germany, Tanya Bafna and Per Baekgaard in Denmark.
We're supported by the NSF in the US, Bevica Fonden in Denmark,
and Polskie Ministerstwo Nauki i Szkolnictwa Wyższego in Poland.
So in essence what this is about is the low frequency/high frequency ratio of the pupil diameter oscillation to estimate cognitive load.
And this is in part based on Peysakhovich's result, where he did it a Toulouse n-back task and could differentiate between task difficulty.
And also, in part it's based on our 2018 CHI paper on the IPA,
the Index of Pupillary Activity where we use wavelets,
the wavelet coefficient here gives us the detail signal and the scaling function gives us a multi resolution analysis of the signal.
Basically just a simple convolution using
wavelet filters or averaging filters.
The Haar filter, for example, in Table 4 on the right or Daubechies-4; we ended up using symlet-16.
The other thing that we do is find edges in the signal and that is done via modulus maxima detection.
So the value in the center has to be greater than or equal to both neighbors and has to be strictly greater than one of them.
The code for the IPA was available in the CHI paper of 2018 and we also made it available for 2020.
Essentially what we do is we just now take inside the wavelet transform, we take the low to high frequency ratio. And then we compute that aspect of it
to give us the L-H-I-P-A or LHIPA signal. And so here we've got three experiments that we did.
One that we replicated, or actually re-analyzed again the data from 2018,
which was a simple fixed gaze experiment following Siegenthaler et al.
And so those results for the 2018 paper showing that it was difficult to count backwards by 17 or easy to count forward by two,
and LHIPA on the right shows us good differentiation between difficult task and the control easy, just like the IPA.
Second experiment was no longer fixed-gaze, we followed the Tuebingen group's design for an n-back task and we use the 1-back and 2-back
with a limited vocabulary and this is the setup for the experiment.
Results also showed that LHIPA can distinguish between the baseline and the n-back tasks; the IPA could not however.
Finally, our third experiment is an eye-typing task also easy and difficult based on the LIX readability score.
Here's the setup where somebody's typing a phrase, they had to remember previously.
And again, LHIPA shows good results differentiating between easy and difficult tasks, but the IPA does not.
One thing to remember is that LHIPA is inversely proportional to task difficulty and that, in essence, is it.
So essentially the LHIPA looks like a pretty robust way to measure cognitive load using pupil diameter oscillation,
whereas the 2018 IPA seems to have faltered when gaze started moving instead of being a fixed-gaze task.
I welcome any questions, if there are any. Thank you very much.