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James Hewitt
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What can space travel teach us about measuring cognitive work?

How do you measure cognitive work, and how can you ensure that you direct your mental efforts to maximise efficiency and effectiveness? Athletes use various measures, such as heart rate and power, to manage and monitor their physical work. It helps them to pace their efforts and maximise performance. Applying knowledge and using our skills is effortful, but we rarely consider the intensity and distribution of cognitive work through the day. Measuring cognitive work, in terms of cognitive load, and weighing how we ‘pace’ knowledge work in terms of effort and recovery, can help us to understand and improve this distribution.

There are multiple definitions of cognitive load, depending on whether you are coming from the perspective of cognitive psychology, instructional design, or neurobiology. Still, the most commonly used definition describes cognitive load as the amount of working memory that is required to carry out a cognitive task 1,2

Mission to Mars

Many knowledge workers would like to upgrade their cognitive performance, perhaps to differentiate themselves from their peers, or simply to make their workloads more manageable and improve the quality of their life. But, if you’re a knowledge worker flying through space at 27 km/second, on a collision course with Mars, your cognitive performance may have more pressing consequences 3.

I first wrote about the Mars500 mission in 2016, in the book ‘Exponential’ 4. It’s a fascinating story and an interesting experiment.

On November 4th, 2011, a team of six astronauts emerged from a five-module isolation facility, following a simulated mission to Mars. The ‘Mars500’ mission was a psychosocial experiment to prepare for a future human-crewed spaceflight to the Red Planet. The volunteers lived and worked for 520 days in a mock-up spacecraft, situated at the Institute of Biomedical Problems (IBMP) in Moscow, Russia. The experiment was designed to study the effects of long-term close-quarters isolation5. The five modules consisted of a living area, utility module, medical centre, main spacecraft, Martian-lander and simulated Martian surface. The combined volume of the facility was 550 m3; not much larger than an average two-story home6.

How to measure cognitive load in space

In space flight, impaired cognitive performance can have devastating consequences. Researchers were interested in how the subject’s cognitive performance could be negatively affected by emotional responses to events and the conditions of their extreme working environment during a mission7. Specifically, they wanted to measure the cognitive demands of mission tasks and how performance was impacted by emotions, task complexity, the speed at which they performed the work and the number of times they switched tasks. It would not be practical to measure pupil dilation throughout the entire mission – the equipment is too bulky. They instead used a model based on the Cognitive Task Load (CTL) approach. The researchers used this model to create a subjective, yet reliable, method for assessing the interaction between cognitive load, performance and emotion. In a great example of scientific understatement, researchers concluded that isolating yourself with five other people for 520 days on a simulated Mars mission “inflicts unique stressors such as social isolation, incidents and boredom”7. They also noted that long-term missions should consider cognitive task load and emotional state in task scheduling.

Measuring cognitive work in knowledge workers

We use our cognitive abilities to carry out cognitive work. These abilities include our executive functions, such as decision-making, organisation and planning and our cognitive control, including our attention, working memory and goal management. The CTL tool summarises how we apply and distribute these capabilities, and how this impacts our cognitive workload, using three dimensions:

Dimension 1: Time pressure

The amount of time we spend working on an activity, relative to the time available, influences cognitive load. If we feel that we are under time pressure, resulting in us focussing attention and working without pauses, cognitive load increases.

Dimension 2: Switching

During an activity, switching between tasks increases cognitive load. The more we switch, the more the cognitive load increases.

Dimension 3: Complexity

Activities that require effortful thinking, rather than relying on automated or routine knowledge, increase cognitive load. The more complex the task, the higher the cognitive load.

Defining the cognitive load of an activity

Each dimension in the CTL method features a question to help you assess the cognitive load of a task. For example, imagine that you have a 10-minute break and decide to use your smartphone. You’re not under too much time pressure; however, you begin your smartphone session by browsing through your e-mail, then switch to Twitter. You see a link you are interested in, so you click on it, which opens your browser. You read the first paragraph on the page, but get bored, so you open a social-media app: you’re switching tasks, a lot. Finally, consider complexity. The interface feels intuitive, which may trick you into thinking that complexity is low. Still, you are making several fast and conscious decisions: reading text, deciding on search terms, thinking about what to look for next.

We rarely consider our cognitive load and, as a result, we can miss opportunities to produce great work in periods of focussed attention, and also compromise our recovery by engaging in relatively demanding tasks, when we could be resting.

Considering your CTL

Think about a typical hour at work and answer the questions below.

Question 1: Time pressure

How much of the available time are you focused on your work? Have you allocated enough time for specific tasks?

Question 2: Switching

How often are you switching from one process or task, to the other?

Question 3: Complexity

How complex, or routine, are the tasks that you’re engaged in?

Cognitive gears

As I’ve described previously, we can use these three dimensions in a heuristic rule-of-thumb, where we consider how these three dimensions aggregate to represent three ‘gears’, representing the overall cognitive load for an activity. For example:

  • Low-gear tasks represent relaxation and recovery (low complexity, low switching) E.g. Unfocussed creativity, relaxed interaction & restoration.
  • Medium-gear tasks represent routine and quick tasks (medium complexity, higher switching) E.g. High environmental attention & switching
  • High-gear tasks represent focused and demanding tasks (high complexity, low switching) E.g. Unbroken concentration & focussed attention.

You can use the cognitive gears framework as a quick and straightforward way to plan and reflect on how we are allocating our cognitive resources and distributing cognitive work through the day. You can read more about how to use cognitive gears concerning your circadian rhythm, and ‘peak periods’, here.

References

1. Paas, F. & Tuovinen, J.E. (2003) Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist. 38 p. 63 – 71

2. Ophir, E., Nass, C.I. & Wagner, A.D. (2009) Cognitive control in media multitaskers. Articles from Proceedings of the National Academy of Sciences of the USA. 106 p. 15583–15587

3. Budnik, F. (Retrieved 07/10/2016). The speed with which we get to mars; http://blogs.esa.int/rocketscience/2016/03/18/the-speed-with-which-we-get-to-mars/

4. Hewitt, J. & Hintsa, A. (2016). Exponential: Better Life Better Performance, from Formula 1 to Fortune 500. Hintsa Performance. Helsinki.

5. European Space Agency. (Retrieved 07/10/2016) ESA’s participation in Mars500; http://www.esa.int/Our_Activities/Human_Spaceflight/Mars500

6. European Space Agency. (Retrieved 07/10/2016) ESA’s participation in Mars500; http://www.esa.int/Our_Activities/Human_Spaceflight/Mars500

7. Cohen, I. et al. (2016) Work content influences on cognitive task load, emotional state and performance during a simulated 520-days’ Mars mission. Computers in Human Behavior 55 p. 642–65

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