Category Archives: Quantified Self

Stress Free State Through Lens of HRV

I have been collecting data on my Heart Rate Variability (HRV) to determine how to improve my personal performance and reduce stress. In doing so I have come to doubt the usefulness of “stress measurements” touted by some wearable devices and apps. They claim to be able to measure your physiology and indicate when you are stressed. My experience has been that the readings and the physiology is much more complex than a simple “red, amber, green” meter can indicate. It came to a head recently when I had a nicely engaged conversation with a colleague and my physiology readings indicated signs of stress. In fact, the experience had not been stressful at all. We were working hard on solving a problem and I felt engaged.

So I wanted to get a baseline by looking at data where I knew there was no stress at all. I had an HRV reading from an eight minute meditation session where I was completely relaxed and had nothing to distract me. The graph below shows how my heart rate stayed in the range from sixty-four to seventy-four beats per minute, a normal relaxed range for me.

Slide1

The heart rate is negatively correlated to HRV, meaning that the body when in a state of arousal or stress will elevate the heart rate and naturally lower the amount of variability between beats. The variability in heart rate is examined by looking at the intervals between beats. Here is a graphic showing the “R-R Intervals” and how they vary. You can see in this example how the heart speeds up and slows down beat by beat.

interbeat intervals

For the eight minute session my RR interval steadily rose and fell. In this graphic I display the distance between beats for the entire period. You can see the distance increase and decrease regularly.

Slide2

What you see here is my heart rate following my inhalations and exhalations. When we breathe in our heart accelerates and variability decreases. When we breathe out our heart decelerates and variability increases. During this exercise I used a breath pacer set for one breath every ten seconds. This graphic shows one ten second interval where I took one full breath in and out. You can see how the intervals drop on inhalation, rise on exhalation.

Slide3

When all of the interval changes are put side by side you can see that in a stress free state the changes are regular. The following graph shows the changes both positive and negative in the differences in intervals during the entire eight minute session. Notice that on the outward breath, when the variability is positive, that the scale of the changes are larger than on the inward breath when the variability is negative.This means that the breathing outward has a powerful effect on the overall measured averages of HRV readings.

Slide4

The averages of the readings, expressed best in the time based measure rMSSD, show for this session a settling at at rMSSD of 55 which is relaxed as any average reading for me indicates no stress when above 48.

Slide5

But this reading does not give a good picture of what is actually happening physiologically. It looks like in a relaxed state the rMSSD stays above 48 and implies that if I drop below 48 I will be stressed. So a natural reaction would be to try and devise methods that would keep the rMSSD above 48. But this is an average and obscures how variable the underlying heart beats are.

Looking at those successive heart beats where the interval change is “close” or under 25 milliseconds. These beats would indicate a stress response for a lot of the session, but we know this period was stress free. In this graph you see the blue lines are those intervals under 25 milliseconds, and the white spaces are those intervals over 25.

Slide6

My conclusion is this. In the drive to make wearable devices and apps more palatable to a mass audience the natural tendency is to bring the simplest interface to indicate stress or relaxation, be it a color or a score. This is useful for directing relaxation activity for beginners, and as the quantified self enthusiast digs into the underlying data much more nuanced picture emerges. What the last graph shows is the in the most relaxed and meditative state possible my beat to beat “score” was low for 41% of the beats. The fact is that these low score beats are part of healthy variability that goes both high and low. It isn’t right to see it as “59% good and 41% bad” –  it more correct to understand the mix of highs and lows, short term variability and the incredible speed with which the body becomes aroused and can calm itself. Sadly, the Apple Watch will not solve your stress problem. Data collection, repeated measurements and comparison with experience is the only way forward.

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How Meetings Go – Physiologically

I have been measuring my rMSSD during work meetings to see what factors impact my performance when engaging face to face with others. I recently had two meetings with the same group of people on the same topic about a week apart. Before each meeting I also took my blood sugar to see if there was any information to be gleaned there. Here are the charts:

Slide1My blood sugar was abnormally high at 135 before the first meeting. I can’t account for it as I had salmon for lunch two hours before. My average rMSSD was much lower at 28.7 which is a reading of high stress. My stress point for rMSSD is 48, when I am below that reading it is an indicator that I am in stress. My experience during the meeting was of being overly excited and I breathed regularly during the meeting.

During the second meeting I was careful about my food choices for the day and had a good blood glucose level of 105. My average rMSSD for the meeting was higher at 38.8. I felt more in the zone in the second meeting physiologically. We were digging into more details in the second meeting and I felt challenged at a few points, and you can see where the reading drops to a very low rMSSD at a few points.

If asked I would have said the first meeting was more successful based on the discussion. And I would have said the second meeting was more challenging. However physiologically the second meeting was far less stressful than the first. When less stressed I imagine my actual performance was better. So my perception of the outcome was very different than the physiological reality.

Next step is to measure outcomes and see if the results correlate with the physiological state occurring during the meetings. I can’t rely on my perception of the situation so further readings will determine outcomes with physiology.

My Quantified Self Gear 2014

I have steered clear of reviewing products because I think simply buying products has very little to do with Quantified Self. And I thought it good for me to review what I used and how useful some of it was. My premise for my QS work in 2014 was to use technology to train myself to be happier.  I had used several Garmin products to successfully train for a half Ironman. Why couldn’t I train myself to be happier?

I pulled everything out of my wearables storage drawer and took this photo of everything I bought in 2014:

QSGear

The items:

I started with a Pebble smartwatch that my wife had given to me as a birthday gift. $99 from the original Kickstarter campaign. I love it and still use it daily with one app called Motiv8 that tracks activity.

Google Glass. What can I say. I fancied myself as an Explorer with $1500 burning a hole in my pocket. I once looked up the population of the state of New Jersey on it and sent my son an email saying “Hi this is my talking to my Google Glass.” That about sums it up. It has not been charged up for about 8 months now. It was so deep in the drawer it did not make the picture above and I just now remembered having it. Enough said.

Zensorium’s Tinke. Billing itself as a stress and fitness measurement device, I purchased one at the Quantified Self Europe conference in Amsterdam for over $100. Its readings made no sense to me and it went into the drawer pretty quickly.

Heartmath’s emWave2 & emWave Pro. This was over $400 worth of gear and if you follow this blog or my QS speeches at all I did get a lot of use out of both products. I conducted multiple experiments and accrued 183,843 “coherence points” – which is quite a few hours of cardiac coherence. In the end I grew out of it as coherence was not my ultimate goal. I think this product is way overpriced and was useful.

Neurosky Mindwave & Mindwave Mobile. Over $200 in cost, I could never get either headset to work consistently. I took some readings but any attempt to get the devices to reliably produce output was frustrated by bluetooth connectivity issues  of some sort. A big disappointment from Neurosky.

Emfit sleep monitor. I met the Emfit team at the QS EU conference and they helpfully offered me a free trial of their product. A combination of wireless connectivity issues and my move from London to San Francisco resulted in my never getting it working.

Mio heart rate band. Very slick implementation and a comfortable wrist band that uses pulse oximetry. I loved the idea, and it was not useful for heart rate variability experiments. The accuracy was not good enough so into the drawer it went. I paid over $100 for it.

After visiting with a friend who worked at Basis I dutifully bought the first version of the watch for around $150. I liked a lot of the ideas but did not really take to the interface or the gamification element of the online account. By the time I bought it I had eliminated pulse oximetry as reliable source of heart rate data. I gave it to a friend and he likes it.

Fitbit flex. I ended up buying two for $99 each because the first one gave out and stopped charging. The second one was spotty on charging as well. I used the product for 10 months and got a lot of value from it. In the end, the inability to charge it and a policy change that eliminated active minutes as a goal had me put it in the drawer. I replaced it with the $79 Garmin Vivofit because I do like to monitor my daily activity. So far that seems to be working out.

Sweetbeatlife & the VitalConnect Patch. Sweetbeat Life is an app that takes heart rate data from either a belt or the VitalConnect Patch. The patch seemed novel as it was convenient and comfortable. And it did not stay adhered on my chest for more than a few sessions. It was a breathtaking $199 for a set of 10 patches. I did not understand the real cost until the first patch fell off after the second use. Really cool and really expensive. I went back to the old reliable Polar H7 heart rate belt for a nice price of $80.  And one belt will last the whole year.

One thing that is not clearly stated is that you need top end smartphones to use apps associate with all this hardware. Neurosky, Fitbit, VitalConnect Patch and even my much loved Pebble need a phone with Bluetooth LE. I had an older version Android phone without Bluetooth LE so I needed to buy an iPod5 for iOS only apps and devices $199. And for Android I had to buy another device with LE so I bought a Nexus 7 tablet for $245.

So a quick add up gives me approximately $3,500 worth of gear of which 42% of that is the Google Glass. What did that expenditure do for me? It taught me through brute force that picking an area of Quantified Self to study and focussing there is 90% wikipedia work and networking with other people who have knowledge. 10% is hardware. And ultimately the majority of value came from about $500 worth of the gear I bought (Heartmath Pro, Polar H7, iPod5). The rest helped me understand some things but were not good value for money. For the Quantified Self, as in life, money cannot buy you happiness.

My Quantified Self 2014 in Review

I had a good Quantified Self year this year. As a long time logger and casual athlete I have always logged my personal data in some form. This year with the support of the Quantified Self community I was able to explore two specific areas. First, I moved stress tracking from self reporting to the use of wearable devices. Though I bought a few more devices than I would have liked I found that heart rate variability measurements using $65 worth of equipment was sufficient to track stress. Second, I was able to pull out insights about consciousness and heart rate variability that set the stage for future studies.

I explored 20 ideas this year that I organized into five umbrella studies. I started looking at the data I had collected through self reporting of “Upset Events.” I followed that up with a look at Upset intensity given different situations. After seeing the limits of self reporting I started using different devices to measure stress, settling on Heartmath used during working session. Using the device I discovered Freakback can have an effect on results. After learning how to work through that I completed a first study on how I recovered from Upsets.

As I was conducting these studies I had an emerging idea that emotion is navigation. The regularity of emotional shifts seemed like “sighting” as I worked through different ideas. As I worked on this idea I found that Heartmath was too limited in what it measures. Heart Rate Variability has a more direct measurement in rMSSD. I dropped Heartmath and started using Sweetwater HRV’s SweetbeatLife to monitor rMSSD. Using this tool I started measuring stressful events like getting a tooth drilled and firing a shotgun. I played with machine learning and straight statistical regression and determined my “stress point” when read by rMSSD. This provides me a tool to study a variety of situations going forward.

Along the way I gave five Quantied Self meetup talks, 2 in London, 1 in Amsterdam and 2 in the Bay Area. In London and Amsterdam I did my talk We Never Fight on Wednesdays, and in London my followup Don’t Just Stand There. In the Bay Area I presented my talk Every Other Minute where I talked about the navigation impulse. And finally my Bay Area presentation on heart rate variability and Flow. These talks went well and I am set up to give a presentation at the QS Global conference (QS15) in June.

Some of the 20 ideas did not pan out. My work on 800 numbers went nowhere. Ideas about reading my heart rate while doing The Work by Byron Katie did not have sufficient detail to be interesting. Several other ideas blew up on the launchpad. However, I’m pleased with the progress this year. In my next post I will talk about the lessons I have learned during this work.

Glucose & Heart Rate Variability

My sister-in-law is a doctor and follows my Heart Rate Variability (HRV) adventures. She suggested that I look at my glucose levels and see how it effects HRV. My first step was to buy a glucose monitor and I was somewhat uncomfortable with the idea of drawing blood daily. A trip to Target to buy an Onsync glucose meter was pretty easy and the blood drawing process is far less uncomfortable than I thought it was.

So I started pulling my glucose each morning when I got up. Immediately afterward I took my HRV focussing on rMSSD using the Polar H7 and Heart Rate Variability Logger. I took a reading for 1:30 with three 30 second readings which I averaged out for the session. While taking the reading I used the Paced Breathing Android App. After I was completed I entered the glucose reading and rMSSD in an excel spreadsheet.

Here is what I found.  Glucose levels have a strong negative correlation (Pearson value of -0.4) with HRV. That means higher blood glucose had a strong relationship with lowered HRV.  That means eat food that jacks your blood sugar and your are less responsive to your environment. Eat candy and be dumber when talking to your boss.

Here is a graph of eight readings:

GlucoseReading

Looking at the figures you can see that generally my glucose level averaged about 104. When I fasted (12 hours of no food) it dropped below 100. The rMSSD was between 55 and 78, all well above the stress line. So my morning readings showed no stress and normal blood sugars. What it also showed was a strong correlation. So measuring before and after daily events will give more information to see if I really am dumber talking to the boss after eating candy.

V1bes on Indiegogo

I met the founder of V1bes, Gustaf Krank, at a wearables gathering in Helsinki last year. He gave a dynamic presentation to the conference and afterward walked me through the technology with a personal demo. The approach is like no other in that is aims to pull together multiple electromagnetic signals from brain, heart and the environment through a ring.

V1bes has launched an idiegogo campaign. I am going to to get one to see how its measurements correlate with Heart Rate Variability (HRV). The idea of electromagnetic “smog” as an influencer of HRV is something worth looking at. Unlike HRV Gustaf’s invention does not have the large number of medical studies with which to compare but that is part of the fun.

Dual-3-Back & HRV Update

I have continued the experiment I reported earlier on playing Dual-n-back game while monitoring my progression Heart Rate Variability (HRV). I have manually set the game to Dual-3-back, meaning I have to remember a location and letter that is three iterations in the past. For a full explanation of how much cognitive load that adds to the situation you can read Gwern’s FAQ on Dual-n-back here. I can tell you from experience that 3 back is a lot harder than 2 back. The data shows the difference in scores:

Slide1

 

You can see I was reliably getting percent scores in the 70’s and 80’s playing 2 back. When I increased the difficulty to 3 back my scores dropped to the 30’s. An you can see a progression where the most recent plays are moving toward 50%. How has this effected my HRV? Here is my rMSSD for the last 8 sessions of 2 back and first 19 sessions of 3 back.

Slide2

 

As I have reported before an rMSSD above 48 for a 30 second reading occurs when I am relaxed and feeling stress free. Each of these games are 4.5 minutes long, so that is 9 consecutive 30 second readings. You can see the 2 back games toward the end were averaging above 50 so I was feeling stress free during those sessions which makes sense because I my average score for those sessions was 77%. I had the feeling of having mastered that level.

When I started with 3 back  the rMSSD dropped to an average of 43.3 for the first ten sessions. 3 back was definitely harder and I was seeing very slow progression in the scores. I recall feeling a bit negative about the process and unsure if I could get better at the task. I did not really try any strategies, I just tried to improve through repetition.

The last four 3 back sessions are interesting. At session fifteen I was thinking about how to move the score I decided to try focussing on only the location and “wing it” for remembering the audio cue. To my surprise BOTH measures went up. I saw better results and got interested about pushing this strategy. For the following sessions you can see both my scores and my rMSSD going up. My rMSSD average for those sessions is 54.25. I was enjoying the process because I saw there was a path to improvement.

What is intriguing is that my rMSSD (and stress) changed not as a result of the scores, but at the specific point I felt I had discovered a way to improve the process. My perception alone drove the change in HRV. My story about improvement and efficacy moved my stress level, not the performance of the game at all. So growth and learning is gradual, but our story about how the progression of the learning can be more dramatic.

Upsets Are Smaller Than They Appear

I had a good opportunity to unpack an Upset in real time. I was with another person and they did something that caught me off guard in a negative way. I was wearing my Polar H7 heart rate belt and was measuring my heart rate variability using Heart Rate Variability Logger app by Marco Altini. Here is a graph showing 30 second snapshots of my rMSSD, a measure of the variability of my heart rate.

Slide1

I know from previous readings that a reading above 48 of my rMSSD is a reading that indicates low stress, an rMSSD below 48 indicates stress. The event that caught me off guard happened at the “13” time mark on the chart. As it unfolded I chose not to react to the situation and take stock. When it happened my rMSSD was at 51, a comfortable stress free reading. As I sat quietly and processed the event you can see my rMSSD drop to 47, not too bad, then pop back up to 58 which is quite relaxed. When I decided on some level to show I was displeased, shown in the chart at time hack “22,” my rMSSD dropped to 27. This is a reading comparable to being in the dentist chair or firing a shotgun at a range.

So my decision to react and show my displeasure was the stress inducing action, not the original event itself. My decision to be visibly confrontational created the deep negative rMSSD reaction. I think this event has measured my fight/flight response. The response occurred a full four minutes after the original event. So I chose to enter that state.

Once I walked away I maintained my state of Upset. On being clear of the situation you can see the opening reading is still in a somewhat stressful state, but 30 seconds after that I was back in a state of no stress.

Slide2

After I was clear I was still mentally considering my reaction and the event, but I was not physiologically in a confrontational state as measured by my heart rate variability.

So a few insights come from this event. First, the actual event was not the physiological trigger. My interpretation, arrived at a full four minutes after the fact was the trigger. Once into it, the time I was in fight/flight was quite short. Though I felt I was still in an Upset state I had emerged from it a full minute or two earlier.

Interpretation is a choice, Upset results from interpretation. Conflict creates fight/flight, removing oneself from conflict seems to reduce fight/flight reaction. Regardless of the mental rehearsals before and after, an Upset unfolding in real time is quite a bit smaller than it appears when you look to the data.

Dual N-Back, Learning and Heart Rate Variability

Following my Heart Rate Variability, Learning and Flow study I took the recommendations from fellow quantified selfers and ran another round of sessions using a Dual N-Back game produced by the Brain Workshop.

What I did:

  • played a Dual 2-Back brain game on my Mac once or twice sitting at the same location in the morning each day for 8 days,
  • set the game for 90 trials that lasted anywhere from 273 to 282 seconds per session resulting in a measured success score that was expressed in terms of % correct,
  • measured the total heart rate coherence accumulated points score as given by Heartmath Pro over the total session,
  • divided the total accumulated points per session by the number of minutes to derive a “points per minute” score per session,
  • after entering that data into excel graphed the resulting points per session over the learning cycle.

How I did it:

Everything for this study was executed on the Mac. Heartmath Pro, the DualNBack game and excel were all windows on the Mac screen. Because I had set a disciplined approach to the first brain game I simply rotated the method in here.

As before I started the Heartmath Pro and waited until 30 seconds had elapsed so I was getting an HRV based cardiac coherence reading. After the coherence reading began I would start the Dual 2-Back game. The game was approximately 4.5 minutes to play. Here is a short YouTube video showing the mechanic of a Dual N-Back game.

Playing Dual 2-Back meant I was remembering the letter and location shown two iterations prior to the one being show on the screen currently.

What I Learned:

My correct answer percentage per session increased linearly the more I played. When I started I played using instinct then begin using a simple strategy to track the different elements around session 9. From there the learning continued but it was still linear. The strategy alone did not take the scores immediately higher, but it did enable continued improvement.

Slide1My Heart Rate Variability (HRV) also steadily increased the more I played. The points/minute proxy for HRV started at a low of 10 when I started playing and was in the 25 to 30 range at the 15 through 17th session. My subjective experience was of stress and concentration in the early session then more relaxed in the later sessions, mostly because I had worked out a strategy and attributed different scores to my having implemented the strategy well or not. Specifically, my mindset went from “I don’t know how to improve” in sessions 1 through 8 to “I need to implement a known strategy better” in 9 through 17. Here are the points/minute results over the sessions:

Slide2

Dual N-back demands constant attention, unlike the nearly autonomous reactions the category brain game allowed after repeated play. My experience with Dual N-back was consistent low level stress where the category game was more like “zoning out” and watching my fingers hit the screen. In comparison to the range of 10 to 30 points/minute in the Dual N-back game the category game points/minute were routinely between 30 and 50 as shown here:

Slide3

One insight I gleaned was that there is no “know it” or “don’t know it” binary state with respect to mastering a task. Knowledge and capability steadily increase as reflected in the quality of the output. Physiological stress decreases steadily as reflected in the increase in HRV. So my own tendency to think of mastery as binary is incorrect. Mastery at the capability and physiological level is a continuum and we move linearly along it. And movement along that continuum is as much about belief in the ability to improve as it is about underlying capability.

Heart Rate Variability, Learning & Flow

I looked at how my Heart Rate Variability (HRV) changed through the process of learning a simple task from introduction of the activity through to a point where the task was nearly pre-conscious and automatic. I compared the change in HRV to Csikszentmihalyi’s concept of moving from anxiety through Flow to boredom.

What I did:

  • played an Android based “brain game” twice through for approximately 4.5 minutes per session for twelve sessions,
  • measured the total heart rate coherence accumulated points score as given by Heartmath Pro over the total session,
  • divided the total accumulated points per session by the number of minutes to derive a “points per minute” score per session,
  • after entering that data into excel graphed the resulting points per session over the learning cycle.

How I did it:

In each session I would start Heartmath Pro and wait until 30 seconds had elapsed so I was getting an HRV based cardiac coherence reading. As soon as software started getting this reading started I would begin to play “Mind Games” on a Nexus 7 tablet. The game I played was 120 seconds long and included choosing one of four categories appropriate for a shown picture. The choices were cards, dice, cars or people and this example screenshot shows an image for a car with the four buttons below:

Screenshot_2014-10-09-10-15-10

 

I played two repetitions of the 120 second game per HRV reading session for an approximate total session length of 4.5 minutes and two sessions a day for six days.

Heartmath Pro awards points per five second interval based on a “coherence score.” The score is based on the ratio of Low Frequency cardiac output to High Frequency, where cardiac coherence is considered to be achieved when all frequencies group in the Low Frequency band around .1Hz. Every five seconds the software awards achievement points based on the coherence score, meaning if your coherence score was 3.5 for that five seconds the software adds 3.5 points to your points total. The final outcome of a session looks like the this:

Slide1

This scoring is a proxy for how variable your heart rate was during a session and thus how relaxed your physiology was in the period. Comparing the points total per minute to other measures like LF/HF and rMSSD show that higher points per minute and more “stress free” LF/HF and rMSSD correlate.

Once the sessions were completed I entered the session length and score in an excel spreadsheet which then calculated the points/minute for that session. In the session shown above 138 achievement points divided by 4.52 minutes gave an outcome of 40.9 points/minute. Scores playing the game over the twelve sessions ranged from 21.0 to 52.3 points/minute.

What I learned:

My initial idea was to avoid creating stress with the mind game by not paying attention to the mind game score, which was a measure of correctly categorized images. I was trying to only engage my attention and take a reading of HRV. I wanted to compare my HRV during engaged attention to a baseline where I let my mind wander and to when I was working on a computer. It was in session seven where I entered a very relaxed state and both the points/session and the mind game scores were going noticeably upward that I started looking at this learning curve in isolation.

In that seventh session I entered a relaxed state and no longer had to think about the answer as the image flashed on the screen. My fingers just moved. By sessions eight through eleven I was watching my fingers move without really thinking at all. And time, while it did not disappear, was no longer in my attention. I thought I may have entered a state described by Csikszentmihalyi’s Flow theory where  challenge and skills balance, as shown in this graph:

Flow Channel Image

When undertaking a task and the challenges match the skills and feedback is immediate one enters a relaxed and enjoyable state where time seems to disappear and the action just emerges. In the seventh and eighth sessions I realized this might be happening. Here is how that change in state was reflected in my HRV readings:

Slide1

As higher HRV is associated with a relaxed state, what we see here is my physiological reaction to a challenge of a uniform difficulty was becoming more relaxed with repetition.

My subjective experience in sessions one through five was that of feeling “alert in the head” meaning I was calculating the answers as the images came up. And I was keenly aware of the time during each session and recall saying “only 20 seconds to go” or “only one more session.” This was a state of low level stress and anxiety.

Session six still felt like that but the HRV points/minute score was starting upward. By session seven the subjective experience started to change. I was relaxed during those sessions and time, while not completely gone, seemed to fade. My HRV points/minute continued upward. During session eleven and twelve the scores seemed to drop. I was doing the brain game automatically and not feeling stress of doing the game but my mind was starting to wander to other topics. Boredom had begun.

Using the Flow chart this is what I think the HRV chart shows:

Slide2

In the first five sessions my HRV reading was an average of 25 points/minute. As subjectively I began entering the Flow state from session six through ten my average HRV reading was 43 points/minute. During session 11 and 12 as my mind started to wander a bit the average on those sessions was 35 points per minute. Not stress, but not as relaxed and engaged.

Issues and Next Steps:

The issue here is I am writing this up before gathering sufficient data to see the full curve. For example, if I keep playing the game to absolute stultifying boredom where does the HRV points/minute level out? And how do I add challenge that is compatible with the “learned skill” of choosing the four categories?

The next steps are to see if HRV can be an indicator of location on the Flow continuum with respect to a learning task. If so, the challenge inherent in that task can be adjusted if the HRV readings indicatate that the learner is either 1) not emerging from early stages of learning anxiety or 2) the learner is dropping into a state of boredom because the task is mastered.