Cognitive Abilities Of Humans Peak At The Age Of 35, Shows A Chess Study

chess cognitive ability

A group of researchers used data from over 24,000 chess games played between 1890 and 2014 to find the right age at which the cognitive abilities of a person peaks. The data used for the study was based on all the games played by world champions and their respective opponents throughout their entire lives. 

The study, involving the analysis of 1.6 million move-by-move observations, made two major conclusions — firstly, humans reach their cognitive peak at the age of 35, which begins to decline after the age of 45; secondly, the cognitive abilities of humans today are superior to our ancestors.

Published in the Proceedings of the National Academy of Sciences, the study observed that performance reveals a hump-shaped pattern over the life cycle. Individual performance increases sharply until the early 20s and then reaches a plateau, with a peak around 35 years and a sustained decline at higher ages.”

The Study Findings

For the study, an empirical strategy was formed to estimate the age profile of the performance in chess, a cognitively demanding task. This strategy is based on the analysis of data from professional chess tournaments involving world champions and their opponents. Considering chess performance data to gauge cognitive abilities have the following features that make them ideal for measuring age-performance profiles:

  • Chess is a paradigmatic cognitive task that combines processes related to perception, memory, and problem-solving. The quality of a particular chess move can reveal performance capability in demanding tasks. This is gaining importance in the labour market.
  • The chess data is of exceptionally high quality, and it helps in measuring individual performance with extreme accuracy.
  • The move-level performance is comparable across individuals and environments. This means that the same benchmarks can be applied to each configuration which will not change over time.
  • The analysis of performance observed continuously for the same individual allows for decomposing age patterns across different cohorts and over time.
  • The performance estimates based on professional chess players constitute an upper bound of cognitive abilities over the life cycle.

The resultant hump shape pattern in cognitive performance reveals an increase in performance with age for younger chess players below the age of 35 years. However, this same performance decreases above the age of 45 years, although the decline is not statistically significant (the graph is given below).

A similar acceleration is found for performance patterns against calendar years. It was found that the performance increased steadily over the 20th century with the steepening of performance increase during the 1990s. Coincidently, this period aligns with the phase where powerful and affordable chess engines on home computers chess-specific knowledge are highly accessible, positively affecting the preparation possibilities for participants (graph given below).

Interesting Observations

While the study considers a wide range of samples for its analysis, there are still a few limitations and loopholes that cannot be ignored.

Firstly, since the study considers world champions and their respective opponents, this study presents a potential problem of positive selection based on playing strengths and skill levels. Hence the study suggested that the results must be seen as an upper bound of cognitive performance patterns in the overall population. However, the implications of unobservable selections were not very clear.

Secondly, the systematic variation in the length of games may affect the performance through fatigue. However, it was found that among recent games, the younger players could somewhat play longer games even though the effects were not very pronounced. This could also be seen as an advantage. Additionally, it was found that the performance increases with the number of moves played per game. This factor is seemingly unaffected by fatigue.

Thirdly, it has been observed that the complexity of the game has decreased across cohorts and over time. This could possibly explain the increased performance levels in recent birth cohorts and variations in the age pattern.

Finally, it was observed that the younger cohorts are more experienced against a given age, and this experience is higher in the recent period. This could explain the observed increase in performance at younger ages.

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Shraddha Goled
I am a technology journalist with AIM. I write stories focused on the AI landscape in India and around the world with a special interest in analysing its long term impact on individuals and societies. Reach out to me at

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