Chunking is a flexible way of learning. Karl Lashley, in his classic paper on serial order (Lashley, 1951), argued that the sequential responses that appear to be organized in linear and flat fashion concealed an underlying hierarchical structure. This was demonstrated in motor control by Rosenbaum et al. (1983). Thus sequences can consist of sub-sequences and these can in turn consist of sub-sub-sequences. Hierarchical representations of sequences have an edge over linear representations. They combine efficient local action at low hierarchical levels while maintaining the guidance of an overall structure. While the representation of a linear sequence is simple from storage point of view, there can be potential problems during retrieval. For instance, if there is a break in the sequence chain, subsequent elements will become inaccessible. On the other hand, a hierarchical representation would have multiple levels of representation. A break in the link between lower level nodes does not render any part of the sequence inaccessible, since the control nodes (chunk nodes) at the higher level would still be able to facilitate access to the lower level nodes.
Chunks in motor learning are identified by pauses between successive actions (Terrace, 2001). He also suggested that during the sequence performance stage (after learning), subjects download list items as chunks during pauses. Terrace also argued for an operational definition of chunks suggesting a distinction between the notions of input and output chunks from the ideas of short-term and long-term memory. Input chunks reflect the limitation of working memory during the encoding of new information, i.e., how new information is stored in long-term memory, and how it is retrieved during subsequent recall. Output chunks reflect the organization of over-learned motor programs that are generated on-line in working memory. Sakai et al. (2003) showed that subjects spontaneously organize a sequence into a number of chunks across few sets, and that these chunks were distinct among subjects tested on the same sequence. Sakai et al. (2003) showed that 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.
Memory training systems
The phenomenon of chunking as a memory mechanism can be observed in the way we group numbers and information in our day-to-day life. For example, when recalling a number such as 14101946, if we group the numbers as 14, 10 and 1946, we are creating a mnemonic for this number as a day, month and year. An illustration of the limited capacity of working memory as suggested by Miller can be seen from the following example: While recalling a mobile phone number such as 9849523450, we might break this into 98 495 234 50. Thus, instead of remembering 10 separate digits that is beyond the “seven plus-or-minus two“, we are remembering 4 groups of numbers.
Various kinds of memory training systems and mnemonics include training and drill in specially-designed recoding or chunking schemes. Such systems existed before Miller’s paper, but there was no convenient term to describe the general strategy. The term “chunking” is now often used in reference to these systems.