Instead of finding evidence of decline, researchers led by Dr Michael Ramscar of Tubingen University, discovered that most standard cognitive measures, which date back to the early twentieth century, are flawed.
"The human brain works slower in old age but only because we have stored more information over time," said Ramscar.
Computers were trained, like humans, to read a certain amount each day, and to learn new things. When the researchers let a computer "read" only so much, its performance on cognitive tests resembled that of a young adult.
Often it was slower, but not because its processing capacity had declined. Rather, increased "experience" had caused the computer's database to grow, giving it more data to process - which takes time, researchers said.
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Researchers found that standardised vocabulary tests, which are used to take account of the growth of knowledge in studies of ageing, massively underestimate the size of adult vocabularies.
It takes computers longer to search databases of words as their sizes grow, which is hardly surprising but may have important implications for our understanding of age-related slowdowns.
Take "paired-associate learning," a commonly used cognitive test that involves learning to connect words like "up" to "down" or "necktie" to "cracker" in memory.
Using Big Data sets to quantify how often different words appear together in English, the team showed that younger adults do better when asked to learn to pair "up" with "down" than "necktie" and "cracker" because "up" and "down" appear in close proximity to one another more frequently.
When the researchers examined performance on this test across a range of word pairs that go together more and less in English, they found older adult's scores to be far more closely attuned to the actual information in hundreds of millions of words of English than their younger counterparts.