among participants tested on the same sequence. They also demonstrated that the performance of a shuffled sequence was poorer when the chunk patterns were disrupted than when the chunk patterns were preserved. Chunking patterns also seem to depend on the effectors used. Perlman found in his series of experiments that tasks that are larger in size and broken down into smaller sections had faster respondents than the task as a large whole. The study suggests that chunking a larger task into a smaller more manageable task can produce a better outcome. The research also found that completing the task in a coherent order rather than swapping from one task to another can also produce a better outcome. Chunking in seven-month-old infants Previous research shows that the mechanism of chunking is available in seven-month-old infants. This means that chunking can occur even before the working memory capacity has completely developed. Knowing that the working memory has a very limited capacity, it can be beneficial to utilize chunking. In infants, whose working memory capacity is not completely developed, it can be even more helpful to chunk memories. These studies were done using the violation-of-expectation method and recording the amount of time the infants watched the objects in front of them. Although the experiment showed that infants can use chunking, researchers also concluded that an infant's ability to chunk memories will continue to develop over the next year of their lives. Chunking as the learning of long-term memory structures This usage derives from Miller's (1956) idea of chunking as grouping, but the emphasis is now on long-term memory rather than only on short-term memory. A chunk can then be defined as "a collection of elements having strong associations with one another, but weak associations with elements within other chunks". Chase and Simon in 1973 and later Gobet, Retschitzki, and de Voogt in 2004 showed that chunking could explain several phenomena linked to expertise in chess. Following a brief exposure to pieces on a chessboard, skilled chess players were able to encode and recall much larger chunks than novice chess players. However, this effect is mediated by specific knowledge of the rules of chess; when pieces were distributed randomly (including scenarios that were not common or allowed in real games), the difference in chunk size between skilled and novice chess players was significantly reduced. Several successful computational models of learning and expertise have been