Hello and welcome to this video presentation
with the title
"One does not Simply RSVP: Mental Workload to Select Speed Reading Parameters using Electroencephalography".
This work was done at the LMU Munich
and is presented on behalf of my co-authors Albrecht Schmidt, Simon Thanheiser, and Lewis Chuang.
And in this talk, I'm going to talk about how we can read using a paradigm
called Rapid Serial Visual Representation on devices with small screen spaces
and how we can actually find the parameters that optimize the information intake using RSVP.
Looking at how we read media in the past, most of you probably used books or magazines, media that is printed.
While this media allows you to skim large amounts of text more and more people are going to digitize printed media using electronic devices.
This enables users to take a lot of books with them when used in a digital manner and the text is displayed on a display.
However, with shrinking sizes of wearable devices, we see that the text is becoming more and more difficult to read.
So for example, on smartphones, smartwatches, and smart glasses, the textual information is not optimal anymore as you can see on these examples here.
One paradigm to cope with this is RSVP where one word per line is displayed on a specific device.
Here we can see an example of how this looks like.
You can read the text by just focusing on the words on the display which run next to each other.
And this allows you to read on devices with small screen spaces that are capable of displaying one line of text.
Additionally, it is claimed that you can read faster using this paradigm by enhancing the text alignment and by enhancing the speed
you can actually consume text faster.
Related work has looked into how RSVP parameters can be adjusted.
For example, here we have work from Dingler and colleagues were users read on smartphones, smartwatches, or on smart glasses
and they can adjust several RSVP parameters, for example, the speed, by using gestures.
However, for most users, the optimal RSVP reading speed is unknown.
When you start reading for the first time, you have to try out different settings
and maybe after some time, you have to readjust these settings again because, after some time, your cognitive reading capabilities are deteriorating.
Rzayev and colleagues found that you can have a good reading performance while walking and reading with RSVP.
Related work has informed us about two particular parameters that can be manipulated to modify your reading efficiency.
One of them is the presentation speed. The presentation speed can be modified to read faster or slow down your reading speed.
The second parameter is the text alignment, where text alignments modify how many eye movements you include in your reading task.
And the more eye movements you have to include in your reading task, the more time you lose.
It makes it difficult for beginners and people who use RSVP over a long-term time to find their optimal settings regarding presentation speed and text alignment.
I argue that current RSVP applications require users to set the reading alignment and speed manually. However, finding parameters that optimize the reading efficiency are often unbeknownst to the user,
which means that they have to employ a brute-force approach, have to adjust the parameters while reading which puts additional cognitive load on the user.
Therefore, it would be nice to have a method that assesses this automatically.
In this work, we are using physiological sensing, specifically electroencephalography, in short EEG, as a measure for cognitive workload
and working memory to assess different RSVP parameters for future BCI systems that set the optimal presentation speeds and text alignments automatically.
Let's have a look at the different presentation speeds, so we can have a little bit of a notion of how they look like.
First of all, let's have a look at 200 words per minute (WPM), 350 WPM, and 500 WPM.
As you can see, 500 WPM is way faster than 200 WPM and if you are unused to RSVP, it is difficult to follow 500 WPM.
At the same time watching too long with 200 WPMs, you may become bored because it takes a lot of time and cognitive effort to read the actual text.
And of course, you can vary this over a time-span to set a suitable reading speed. But the question is, what are suitable reading speeds you can read over a long term of time.
For the text alignments, we selected three different text alignments for the experiment.
The first one is a centered representation, where we present the eye movements with dotted lines while reading.
For example, you can see that between the words "red" and "brown" and "brown" and "fox" are eye movements, hence you will use more time since you have to move your eyes.
Next, we look into fixed centered characters where we focus, for example, on the second character
and you have very steady eye movements because you are approximating the written text to the left and the right.
And also you can color the fixed centered character to recognize where the eyes should focus to read.
Hence, we conducted a study in which we evaluate "what are optimal RSVP parameters?" and we employed EEG as a physiological measure to measure the working memory and task engagement
that is placed on the user to see if we can use EEG as a predictive metric for the suitability of speed reading parameters.
Looking at the methodology, we conducted a user study with 18 participants and each of the participants conducted ten reading trials.
One reading trial for the baseline where participants read a full-text PDF and nine other reading trials, where participants read with the presented RSVP parameters.
Our dependent variables were the time to read a full-text PDF which is the essential baseline and to see how fast the participants are reading anyway.
We measure subjective workload using the NASA-TLX questionnaire and text comprehension.
The text we used is from a standardized RSVP text set which was accompanied by comprehension tests.
And using these text comprehension tests, we could measure how well participants understood the texts.
Finally, we measure electroencephalography. And this is how our experimental setup looked like.
To measure EEG, you put a cap on the participant's head and put electrodes in it. What you measure is the electrical brain potentials emitted by the human brain.
We have opted in for a layout at the back of the head and the front of the head.
The back of the head (occipital lobe, alpha power) is known to correlate with working memory, a cognitive system which can be briefly described as a cognitive system where you memorize
information for a very short amount of time, storing and modifying them for another output.
At the front of your head (frontal lobe), you can measure the theta power, which is a measure for cognitive workload and task engagement.
Our experiment test if we can measure task engagement and the cognitive effort of our participants while reading with RSVP.
This is interesting because everybody has an individual reading behavior that also changes over time.
And having such a measure, we are able to assess the usability of the RSVP parameters which can then be automatically adjusted by future BCI applications.
Nine out of the ten texts were read with different presentation speeds and text alignments and one PDF was read at the beginning as a regular full-text baseline.
After each of these trials, a text comprehension test was filled to see if the participants were understanding the text and a NASA-TLX questionnaire to receive a self-rated workload measure.
Looking at the results for the alpha power, which is a measure for working memory, we find that for 500 WPMs the least effort on working memory
which was contradictory to our hypothesis that working memory is high when reading with fast WPMs.
At the same time, we find that we have a lower alpha power for 200 WPMs.
Looking at the theta power, where the correlation is inverse, we find that participants had the highest task engagement and the highest workload when reading with 200 WPMs.
The least workload and task engagement were found with 500 WPMs. You can also see that all of the results have significant differences.
This was contradictory to our hypothesis, assuming that the participants were bored with 500 WPMs and more engaged with 200 WPMs.
But when looking at the subjective workload, participants rated the 500 WPMs as cognitively demanding.
And this is something that looks like a mismatch. Participants rate 500 WPMs as really demanding
but at the same time, the EEG data revealed that participants had low workload with 500 WPMs.
Looking at the text comprehension, the results echo the NASA-TLX.
We find that the lowest text comprehension was achieved for 500 WPMs for all text alignments.
It is interesting to note that there are significant differences between the presentation speeds but not between the text alignments for all of our measures.
Asking the participants, why this could be so, that the 500 WPMs were very fast and they were unused to it that they were just not reading anymore.
And this hints into the direction of cognitive overload, so they were not processing the information anymore and there was no task engagement anymore
because they were not immersed in the reading task. This conforms with the findings from the text comprehension tests and the NASA-TLX.
Looking at the predictive regression model, we see that we can find a linear correlation between the alpha power and the theta power.
For this, we took the regular reading time from the full-text PDF as a baseline and we divided the regular reading speed through the RSVP reading speed
to see how much speed the participants gained through RSVP.
A speedgain below one means that participants were reading slower with RSVP compared to regular reading.
A speedgain factor greater one was reading faster using RSVP compared to regular reading.
We correlated this with the frequency powers and plotted this against the speedgain factor.
We find that there is a linear function that enables us to estimate the current reading speed using the alpha power or theta power alone.
If we obtain the alpha and theta power we predict the speedgain factor. Knowing the actual baseline enables us to predict if the participant is reading with a suitable speed relative to the actual reading baseline.
This means that such regression can be used by BCI applications to set the RSVP speeds automatically or analyze reading speeds regarding the user's RSVP reading capabilities.
EEG is a tool to assess the cognitive demand of RSVP reading speeds.
EEG predicts the current reading speed, relative to a "normal" reading baseline.
We find that the presentation speed is a major limiting factor. If you increase the presentation speed
people are cognitively overloaded and are not reading anymore. At the same time, lower reading speeds engage users to read.
Presentation speed is a major factor that should be optimized before the text alignment.
As a potential use case, you can set the presentation speed automatically in future BCI applications.
Also, you can reflect on the reading activity and engagement of text passages that have been read, analyzing which parts of the text have not been read entirely.
A limitation of our work is the selection of reading speeds in our work. However, the overall aim was that we investigate the
the efficiency of a regression that approximates and interpolates our measures between other reading speeds.
We can not disclose that participants had previous knowledge of the text comprehension tests.
Also, we don't know how long-term use of RSVP would influence our results since none of our participants used RSVP for the long-term before.
With this, I want to close this presentation. Thank you very much.
If you have any questions, don't hesitate to contact me via mail. Thank you very much.