What cats, dogs, and windows can tell you about learning

How playing action video games might be helping you learn to learn

Issue #15 of LFG: Learning from Games is about learning in honour of Back-to-School season. It’s a little different from using games to learn and more about how seemingly unrelated skills gained playing games can benefit us in the real world.

Hello JOMT Reader!

It’s Back-to-School week for many of us — another year of preparing lunches, helping with homework, and learning about all the different ways our kids (and us) take in and process information. For me, it’s equal parts excitement and dread, wondering what we will all get curious and excited about, while at the same time figuring out how to navigate the quantity of information coming at us.

Making that navigation even more challenging is the different ways that we prefer to intake and process information. Many different categories of learning styles have been developed over the years, but one of them — the Solomon-Felder model of learning styles — has tried to incorporate them all into the Unified Categorization of Learning Styles™.¹ It’s important to note that the scale isn’t an either/or classification and can change based on the subject matter, over time, and lived experiences.

In short, the model classifies learners along four different axes:

1. Active/Reflective: how do you process information?
   Active learners like doing something with the information they get; reflective learners like to think on it and toss it around in their heads before they act.

2. Sensing/Intuitive: how do you take in information?
   Sensing learners like concrete and practical information (data and numbers); intuitive learners like abstract, original, and theoretical information.

3. Visual/Verbal: how do you prefer information to be presented?
   Visual learners like diagrams, charts, graphs, and pictures; verbal learners like the written and/or spoken word.

4. Sequential/Global: how do you organize information?
   Sequential learners organize information linearly; global learners organize information holistically, sometimes in a random manner without connecting them together.

As you probably guessed by now, video games are a great way to address the different combinations of learning styles above. A single game can present learners with each of the learning style axes without breaking game immersion. It’s part of the reason why games are such powerful tools for learning.

But there’s one giant issue that hasn’t been resolved.

It’s something called skills transference, or the ability to apply skills learned in one context (i.e., games) to a different one (i.e., real life). Most studies agree that it’s really difficult to teach someone something in one context and ask them to apply that knowledge to something completely different.

But recent studies have started to show otherwise, especially among video game players. Skills like attention,² memory,³ perception,⁴ and reasoning,⁵ have all been shown to be enhanced by playing action video games. But if you’ve ever played an action video game, you’ll know that these real-life skills aren’t specifically targeted for training. It just sort of happens.

That “it just sort of happens” is skills transference. But how does it happen? Can we reliably harness that power so that other skills can “just happen” by playing a seemingly unrelated video game?

Researchers at Shanghai Jiao Tong University studied action video game players to see how they learn to learn. Let’s take a look at what they did.

Learning to learn theory

There are two theories as to how skills transference happens. The first is called immediate generalization, which is the ability of a person to recognize commonalities between what they learned and new situations. According to this theory, only the non-common parts of the new situation need to be learned.

There’s a newer model, called the learning to learn theory, that refers to the ability of a person to break apart a new situation to help them learn it. According to this theory, this ability to break things apart comes with a more accurate understanding of how difficult a task is, which affects how quickly a new situation can be learned. It’s a slight but significant variation of the immediate generalization model, which the researchers wanted to examine.

How learning to learn was tested

Two groups of participants were tested: action video game players (defined as having played more than 5 h of action games [action, sports, driving, real-time strategy, or massively online battle arena games] per week) and non-video game players (defined as having played less than 1 h of action games per week, and less than 3 h per week of any other type of game in the past year).

Sports/driving and RTS games were included in the action category because the definition of an action game included the following characteristics:

1. decision-making under time constraints

2. maintaining divided attention

3. needing to switch between focused and divided attention

The test to see if playing action video games improved skills in unrelated tasks involves cats, dogs, and windows and is a little convoluted but I’m going to try my best to keep it simple.

1. Participants were shown either a yellow or blue window on a computer screen

2. Next, they were asked to guess whether a dog or a cat would be shown by pressing the corresponding letter on the keyboard

3. The correct answer was shown to participants

4. A blank screen concluded the test

This sequence of events counted as 1 trial. Each participant ran through this sequence 320 times.

But there was a twist to the dog/cat/window combination.

For the first 80 trials, there was a 75% chance that the correct answer would be repeated. So, if the correct answer was blue window → dog, then there was a 75% chance that the next time a blue window was shown, the answer would be dog. Of course, that means 25% of the time it could be a cat instead. The important thing is that this % chance didn’t change for the first 80 trials.

For trials 81–160, that % chance changed. Instead of a constant 75%, it switched from being 20% or 80%. Now, the participants had a much harder time guessing what was going to come after the blue window.

The unchanging 75% chance was used again for trials 161–240. Finally, for the final 80 trials (trials 241–320), the % chance fluctuated again between 20% or 80%.

A lot of complicated maths later, the researchers used this information to see whether participants could learn and predict these fluctuating percentages of either a dog or cat appearing after one of the coloured windows.

Action video game players learned faster

Although it’s hard to tell, action video game players (red line in the figure below) learned significantly faster than non-video game players (blue line in the figure below), as they adapted to the changing % chances of cats or dogs appearing after a coloured window.

Figure taken from original study, published here. The red line shows action video game players, who had an overall higher learning rate compared to non-video game players (blue line).

The graph above shows that per trial, action video game players were better at predicting whether a dog or a cat would be shown after one of the coloured windows.

But there’s actually two levels of learning that is happening: one at the level of the individual trials and another at the “higher” level of the collection of trials. On the one hand, participants had to realize that each trial was associated with some probability for a cat/dog/window combination (“lower” level thinking). On the other hand, participants also had to recognize that these changes were taking place as a collection of trials, where the % chance for the correct answer kept changing for the blocks of 80 trials (“higher” level thinking).

Figure taken from original study, published here. The red line shows action video game players, who were better able to predict the changing percentage chance that a dog or cat would be shown after a coloured window, compared to non-video game players (blue line).

At both these levels, action video game players learned and adapted faster than non-video game players. The figure above shows how well each group learned to predict the changing percentage chance of a dog or cat appearing after a coloured window for the blocks of 80 trials.

It’s amazing because neither of these skills (lower and higher level thinking) are specifically associated with action video games. But what is it about action video games that bestows its players with these kinds of superpowers?

The researchers think that the fast-paced nature of these games and the fact that players have to switch between different levels of tasks is one of the reasons why. In RTS and FPS games, the tactical decisions you make (which units to send where, who to shoot at for how long) are just as important as the strategic decisions (what resources to defend, enemies to attack first). Often, these decisions have to be made at the same time and continuously updated as game conditions change. These tactical/strategic decisions could correspond to the lower and higher level thinking required of the dog/cat/window task.

Second, there’s usually a lot happening on screen at the same time in action video games, which makes players really good at being sensitive to small changes in visual cues. This ability could have helped action video game players react to changes in the trial conditions much faster than non-video game players.

Limitations

I say this for almost every study I write about but the study population for this study was very small: data from only 67 total participants were analyzed, which is hardly enough to draw generalizable conclusions. There is always a cost to recruiting a larger number of participants for a study, but I would love to see some creative ways for researchers to include a large number of participants in their study.

The researchers also point out that their small sample may be biased for video game players who are naturally gifted learners. Because action games reward fast learners with results and achievements, there’s a chance that fast learners are drawn to action video games, skewing the results. Nonetheless, a larger sample set should help offset these limitations.

Final remarks

These are the kinds of studies why I’m convinced that gaming can be used for more than entertainment. Even if someone is vehemently opposed to using games for anything other than entertainment, there is a very large chance that they are benefitting in some way. My goal is to shed light on what those benefits are and to find ways to harness it so that we have the option to use what we learn in a more targeted manner.

If you want to read more about the study, you can access it here for free: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365284/

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1. https://uwaterloo.ca/centre-for-teaching-excellence/catalogs/tip-sheets/understanding-your-learning-style
This site provides an overview of the learning styles that I wrote about, from a Centre of Teaching Excellence at a prestigious university in Canada.

2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2680769/
This article is about action video game players — and how the studied population all had better attentional skills.

3. https://pubmed.ncbi.nlm.nih.gov/25068696/
This article is about how playing action video games can train our memory of things that we see.

4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4250112/
This article is about how playing action video games can help us to develop better perception.

5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2956114/
This article talks about how action video games can make you better at predicting uncertainties based on reason.