Research reveals that four-year-olds can devise efficient strategies for complex challenges, such as independently creating sorting methods akin to those used by computer scientists. The researchers assert that these abilities appear much earlier than once thought, warranting a reevaluation of developmental psychology.

Past experiments led by Swiss psychologist Jean Piaget popular in the 1960s, required children to physically arrange sticks by length. His findings indicated that structured strategies didn’t emerge until around age seven, as children tended to experiment haphazardly through trial and error.

Contrarily, recent work by Huiwen Alex Yang and his team at the University of California, Berkeley, shows that a notable fraction of four-year-olds can create algorithmic solutions for the same task, with more than a quarter exhibiting these skills by age five.

“Perhaps we haven’t given our children enough credit,” Yang states. “We must delve deeper into their reasoning capabilities.”

In a study involving 123 children aged 4-9, researchers asked them to sort digital images of bunnies by height. Initially, they could view groups of bunnies and directly compare their heights, allowing all children to sort them aptly using straightforward methods.

However, once the heights were obscured, the children had to compare only two bunnies at a time while being informed whether their order was correct. This approach necessitated the development of new strategies, as they couldn’t see the entire group simultaneously.

The researchers examined the children’s application of these new strategies, looking for evidence of known solutions and demonstrated instances where children utilized established algorithms. It was found that overall, children frequently outperformed random chance. Remarkably, they independently identified at least two efficient sorting algorithms recognized in computer science: Selection Sort and Shaker Sort.

In 34% of trials, children employed various comparisons, signaling their use of known sorting algorithms for a portion of the time. Out of a total of 667 tests run, the children utilized selection and shaker sorting in 141 instances, with some employing combinations of both strategies. Notably, 67 out of 123 children demonstrated at least one recognizable algorithm, and 30 children used both at different stages in the experiment.

Nonetheless, the age of the children directly influenced how many used algorithms. Only 2.9% of four-year-olds applied identifiable methods, while this rose to 25.5% among five-year-olds and 30.7% for six-year-olds. By age nine, over 54% were using identifiable algorithms.

“This has long been a challenge to Piaget,” remarks Andrew Bremner from the University of Birmingham, UK. He acknowledges Piaget’s groundbreaking contributions to developmental psychology in setting stages for learning but emphasizes that Piaget often designed experiments without proper controls. “Critics have been eager to illustrate that children can achieve more than Piaget claimed.

Essentially, while Piaget initially had a correct understanding of child development, his assessments of the ages at which children achieve certain milestones were overly pessimistic. This latest study strengthens the evidence supporting earlier development stages. Interestingly, it revolves around sorting. Bremner indicates this as the last bastion of Piaget’s work, proving applicable to younger children than once believed.

“Children can successfully navigate this particular problem much sooner than we anticipated,” states Bremner. “They do not approach the world as mere blank slates, but rather implement strategic techniques in problem-solving.”

Sam Wass from the University of East London points out that Piaget contended that children needed a comprehensive grasp of complex systems before they could devise strategies to engage with them, a notion he is finding increasingly unnecessary.

“This research signifies a significant trend in psychology that contests the assumption that intricate thoughts and understanding are prerequisites for executing complex behaviors,” notes Wass. “The study illustrates that complex behaviors may emerge from a far simpler array of rules.”