All research requires context in order to apply it practically. However, this particular research article needs more context than most on this blog, mostly because of how easy it would be for gamers to interpret it in a very narrow way.
The title sums up the potential misinterpretation fairly well: reaction times begin declining at age 24 in esports competitors (specifically, in StarCraft 2 players). It would be easy to stop there and thus conclude that individuals under the age of 24 are faster-reacting and hence better, or that players begin an inevitable downhill decline in skill and in-game performance. However, at the risk of spoiling the conclusions of this paper, that conclusion is a bridge too far. While researchers found that reaction time began declining after age 24, there was not a drop-off in performance. Players over 24 developed compensatory strategies–improved processing efficiency, better strategic decision-making, etc.
Esports players have historically placed an outsized emphasis on the importance of reaction time over other executive functions. While this paper shows that reaction time declines after age 24, it also shows that reaction time is not the end-all, be-all of performance.
It’s also useful to identify a number of variables that may have an impact on performance that were not controlled for here, such as the duration of the player’s career (i.e. how early they began training) and non-gaming factors including lifestyle habits.
With those caveats in mind, let’s take a look at the research.
The article explores the common belief that middle age, which is usually thought of as around 45 years of age, is the time when there is a decline in cognitive and motor functioning. However, the author notes that research has shown that this decline may occur earlier and the decline may be limited to laboratory tasks and not applicable to real-world performance. The study of real-world behavior in aging individuals is difficult due to the presence of structural regularities in the task environment that can be used to compensate for cognitive decline. Older individuals can also compensate for age-related declines by developing different approaches to tasks or through their experience and expertise in a specific domain. The study aims to investigate the onset of age-related declines in cognitive and motor speed, as well as dual-task performance, and how domain experience may compensate for the decline. The study uses data collected from players of the video game StarCraft 2, as the game involves real-time decision-making and attentional allocation, making it a real-world task.
Participants provided informed consent and the data was collected using an extensive dataset of game replay files and survey questions. The data was processed using free SC2Gears software and MATLAB scripts were used to extract variables relevant to performance. Participants provided their Battle.net ID to extract league information, which reflects their level of expertise in the game. The age distribution of the sample was 21.7 with a standard deviation of 4.2 and most participants were from the US, Canada, Germany, or the UK. The sample consisted of 3276 males and 29 females.
The primary analyses included looking-doing latency, which was predicted to increase with age, and the number of workers trained, which was predicted to show age-related decline. The exploratory analysis of compensation considered factors such as experience, game performance, unit selection, and use of the mini-map.
The results of the Looking-Doing analysis indicate that there is an age-related slowing of Looking-Doing Latency (LDL), which refers to the time between a player looking at a target and taking action. A linear regression model was used to assess the relationship between age and skill on the LDL and a logarithmic transformation was used to handle heteroskedasticity in the data. The results showed that age is positively related to increased LDL (p<0.01), but the interaction between age and skill was not significant, meaning that level of expertise does not mitigate the decline in LDL.
A piecewise linear model was used to answer the third research question, which was to determine when the decline in LDL begins. The model suggests that the decline begins around 24 years old and that the decline is not influenced by the player’s league. No evidence of a speed-accuracy trade-off was found. The best-fitting piecewise linear model showed that the intercepts vary with league (p<0.05), but the effect of age was not significantly different from zero (p>0.05). The coefficients corresponding to years of age over 24 were found to be significantly non-zero (p<0.05).
The author summarizes the existing evidence and concludes that it starts in the 20s and 30s. The present study, conducted using performance measures from thousands of video game players, provides a more precise estimate: cognitive decline begins around 24.
One of the arguments against taking aging into account in young adulthood is that the declines at that age are small and have no real-world impact. However, the article argues that this is not the case as the increases in looking-doing latency are significant for complex human performance outside of the laboratory. The article provides evidence that looking-doing latency is related to skill and is one of the best predictors of a player’s league, as shown in the independent analysis in Thompson, Blair, Chen, & Henrey [1]. The effect of age on looking-doing latency is substantial, slowing down a typical 39-year-old Bronze player by around 150 milliseconds, which is equivalent to 30 seconds over a typical 15-minute game.
The article argues that looking-doing latency is a more useful measure of cognitive aging than simpler reaction time measures. Looking-doing latency involves several cognitive abilities and may be improved by better memory, task switching capacities, and the ability to coordinate cognitive abilities into complex behavior. The article argues that looking-doing latency is more ecologically valid and relevant to real-world tasks, such as food preparation, than simple reaction time measures.
The article also discusses the issue of whether expertise can attenuate the declines in cognitive aging. The study found no evidence that training can mitigate response time declines, in contrast to some other studies that have shown that expertise can reduce age-related declines. The article suggests that the findings of the present study are more in line with the idea that the possibility of attenuation depends on the task.
The article also touches on the potential neurobiological basis of age-related decline, stating that changes in myelination integrity are not a likely explanation, as these changes peak around 39, which is outside the confidence interval for the declines documented in the study. On the other hand, metabolic changes, such as changes in the ratios of N-acetylaspartate to choline, which begin in the early twenties or sooner, are more likely candidates.
In conclusion, the article provides a precise estimate of when cognitive aging begins, around 24, and argues that the declines in cognitive abilities even in young adulthood can have a significant impact on performance in real-world tasks. The article also provides evidence that looking-doing latency is a useful measure of cognitive aging and raises the issue of whether expertise can reduce age-related declines.
Key Takeaways
Thompson JJ, Blair MR, Henrey AJ. Over the hill at 24: persistent age-related cognitive-motor decline in reaction times in an ecologically valid video game task begins in early adulthood. PLoS One. 2014 Apr 9;9(4):e94215. doi: 10.1371/journal.pone.0094215. PMID: 24718593; PMCID: PMC3981764.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0094215
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