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by Jamaal Ryan

Rensselaer Polytechnic Institute’s Department of Cognitive Science is trying to find ways in which we can get better at video games. With Ph.D student John Lindstedt as the research project’s lead (under Principle Investigator Wayne Gray), the research team is using an in-house coded version of Tetris to measure players’ skill level and patterns in efforts to develop a teaching method that would increase a person’s skill in the games they play.

The research event I participated in at Genericon XXVIII was disguised as a Tetris tournament. Players registered to compete for the highest score, in which the top three players earned cash prizes. It sounded intimidating, but the overly welcoming nature of the research students made it anything but.

“We used the tournament as a way to attract skilled players while in reality, we want to gather players of all skill levels” says Lindstedt. “That's why we try to make the atmosphere generally relaxed and as fun as we can, so that when we say ‘Hey! It's free, and it's for science!’ We get people who think, ‘Ah, what the heck’, rather than ‘Oh, no, I couldn't, I'm terrible, I'll mess up your study.’”

And I can full attest to the notion that they succeeded. Play sessions were individualized where each participant was paired with a desktop and a technician, and were all given two playthroughs to compensate for any bad runs. But given that the nature of the term ‘tournament’, and the opportunity to win cash prizes, would almost inevitably attract more skilled players, the research team also holds sessions throughout the school year to observe enrolled students as well. All of this has been part of their efforts in gathering data over the past three years.

The department’s coded version of Tetris, properly known as Meta-T, is specifically designed to do just that: every key stroke, every block rotation, and in some cases, even eye movement (not studied in the tournament) is tracked and compiled into data with millisecond accuracy. Meta-T provides a platform for various experimental options such as narrowing the field of vision with the use of eye tracking, and altering or removing the Tetrominoes’ fall rate completely. Meta-T allows for comparative data collection between skilled and novice players which make it helpful to identify areas in which less skilled players can improve.

One of the most fascinating aspects of Meta-T, however, is its support for computational cognitive modeling. Lindstedt illustrated computational cognitive modeling as an artificial intelligence that’s “still written in code, but its purpose is to make the computer behave in some of the same ways as humans”.

Computational cognitive modeling – or to break down those eleven syllables, CCM – scales back from the computational processing that typical AI use to come to a solution. Instead coming up with an answer instantaneously, like humans, the process that a CCM takes in reaching a solution occurs in stages.

Here, Lindstedt concretely parses the difference between AIS and CCMs:

“An AI can churn through a billion numbers per second, simulating 50 moves ahead, all within a millisecond of the block appearing on the screen. A cognitive model, does things differently— it has to shift its attention to the block (a couple hundred milliseconds), it has to focus on it for a moment to comprehend it (a few hundred more), it has to then look around to see how that block could fit into the existing pile (a few more), then it has to press its first key (150 milliseconds minimum) … All still happening in under a second, a snail’s pace compared to its AI cousin, and yet a much more familiar process.”

The research purposes of developing such models that closely mimic human cognition is to allow researchers to tangibly study the human thought process by way of altering the system’s parts to test various results. It would be impossible to do so any other way outside of some sort of psychic puppetry one could imagine in science fiction.

Interestingly enough however, the team has also incorporated a system of AIs that sound similar to these CCMs. For the purpose of highlighting areas of improvement in player skill, the team has also developed AI systems that operate as automated coaches. While they aren’t able to maneuver Tetris at the same level as highly skilled players, they certainly can teach less experienced players a thing or two.

“So, the A.I. systems we’re developing are actually being used right now in a study examining different automated methods of coaching (i.e., using suggestions from the AI to give guidance to players)” Lindstedt explains in an email exchange. “The systems are super simple (just using simple addition and multiplication to rate “good” vs “bad” options in the game), so no Skynet to be found here!”

But in efforts to further develop a proper framework in which will be used to coach and hopefully increase player skill, the subject’s cognitive capabilities need to be taken into consideration. When I asked if the research team has considered the classic trifecta of learning methods: visual, auditory, and tactile, Lindstedt said something that I, a former psychology student that admittedly hasn’t studied the subject in years, wasn’t expecting:

“Those three individualized methods of learning have since been discredited over the years.”

“Really?” I ask.

“There really isn’t such a thing as learners who strictly learn through one or the other. It would be inefficient for a teacher to develop three separate ways to teach a classroom full of students.” He continues, “If I can find an effective way to teach something visually, everyone in the classroom should benefit from that. You can’t really put anyone in a box.”

Of course while Lindstedt states that such discrimination in learning types don’t necessarily exist in the categorized manner we were all once taught, he is aware of the various factors that can influence one’s ability to learn:

“I guess a limitation of our experiment is not factoring in various physiological, psychological, and biological factors that can impact player measured performance in our research. The real question, however, is that have those differences predisposed them to playing video games, or have playing video games helped them develop these differences and skill sets?”

Essentially, he’s asking which came first: the chicken or the egg. Over the years, seemingly in ways to shake up the conventional “wisdom” about video games having a violent impact on children, there have been several research studies and experiments correlating the act of playing video games with activities such as eye tracking, rapid decision making, laparoscopic surgery, and more. Many of these have analyzed the direct changes in efficiency right after playing games. But one might ask whether or not if the physiological make-up of some of these participants, or the chance that some might be closeted gamers, had an impact on the results of each study.

With such an ambitious undertaking - seeking to find the most effective teaching method to improve player skill, it warrants the question everyone wants answered, “Can you train me to perform at E-Sports level?”

“It’s an impossible question to answer at this point. When folks talk about getting better at games, the idea is to train, and train, and train. But for some people, that’s not enough. Folks may eventually hit a plateau, but we want to move past that. We want to meet them at their perceived limit and then find ways to grind them even further.”

We’ve all experienced the stark contrast between competing against ourselves and friends, and competing against global leaderboards, online matches, and other players at video game conventions - regardless how much we practice. It’s almost as if there’s a dense cognitive barricade that’s preventing us to do what many skilled players make look so easy. So when I hear that researchers are actively trying to develop methods which could improve player performance, especially in a market where couch and online gaming are becoming more and more commonplace, it sparks more than intrigue, it sparks excitement.

"Video games are an excellent emerging domain for this sort of research, especially with respect to the immense cognitive skills these players often display. As a lifelong gamer myself, it’s great to be involved in connecting these fields, and we’re particularly excited to be studying a topic that has such a great community behind it. There’s near limitless enthusiasm for Tetris, both from its players and from fellow researchers hearing about its relevance to cognitive science for the first time— a researcher couldn’t ask for better."

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