That is, when first encountering a stimulus set each item may be coded as an individual chunk, but after repeated exposure several items may be coded as one chunk. The chunking hypothesis is therefore a powerful learning mechanism that suggests that we are constantly monitoring patterns in stimuli and in our environment and are coding the patterns as increasingly larger chunks of knowledge.
The benefit of a chunking mechanism is that it mediates the amount of knowledge that one can process at any one time Miller, Information that we use for processing is stored temporarily in short-term memory Baddeley and Hitch, , often perceived as a bottleneck to our learning Crain et al.
If the capacity of short-term memory is limited to a finite number of chunks Miller, ; Cowan, ; Gobet and Clarkson, then when the chunks are small, only a small amount of information can be represented in short-term memory; when the chunks are large, a large amount of information can be represented in short-term memory. Take as an example the learning of new words.
The sounds of our language are represented by phonemes, the smallest unit of speech. For the very young child, only a limited amount of spoken information will be stored in short-term memory.
Relative to older children, very young children have had little exposure to their native language and therefore they will have little opportunity to chunk phoneme sequences into large chunks. Over time, however, children store increasingly larger chunks of phoneme sequences, meaning that over time, children are able to store all of the necessary phonemes for words, phrases, and even whole utterances within their short-term memory, all because they have chunked the constituent phonemes.
Although the chunking hypothesis seems to be able to predict phenomena such as the vocabulary spurt, its use in developmental psychology is not extensive. While it is rare to see any criticism of chunking, it is equally rare for chunking to be mentioned at the forefront of any theoretical literature, including developmental theories.
Obviously no serious researcher would argue against new knowledge being created throughout development. However, while various developmental theories describe different methods of knowledge acquisition e. This paper seeks to address this by not only advocating chunking as a mechanism of development but also illustrating why chunking should be considered as an explanation of age-related changes in performance before any other mechanisms of development.
Outside of knowledge, the two most prominent mechanisms of development are probably short-term memory capacity Case, ; Pascual-Leone, ; Halford, ; Passolunghi and Siegel, and processing speed Kail, , Proponents of short-term memory capacity as a mechanism of development argue that as a child develops, their capacity to temporarily store information increases.
A direct consequence of an increase in capacity is that more information can be held and processed at any one time, leading to improved performance.
A similar argument is made for processing speed. As children develop, it is proposed that their processing speed increases. The result of an increase in processing speed is that information can now be processed more quickly, leading to improved performance.
Chunking and capacity have a long history within developmental psychology focused mainly in the s and early s, when researchers attempted to establish the extent to which each mechanism could explain developmental change.
Proponents of chunking — based on the developmental literature available at the time — concluded that there was insufficient evidence for changes in capacity with age Chi, Subsequent work e. For example, Dempster showed that recall of items was strongly influenced by how well the items could be chunked. Stimuli such as digit sequences — being relatively simple to chunk together — had a greater span than stimuli such as non-words that are relatively difficult to chunk together.
To further complicate matters, Case et al. Further research has also supported processing speed as a mechanism of development e. One might think that arguments for and against different mechanisms of development may have been reconciled in the last three decades.
Unfortunately not — there is still no consensus regarding the extent to which developmental changes in task performance are caused by capacity, processing speed, and changes to knowledge via chunking e. There are arguably two problems with past research examining chunking, capacity, and processing speed.
First, there is sometimes little thought as to how these mechanisms interact with one another. For example, if we can process information more quickly then it stands to reason that we would be able to maintain more information within a limited short-term memory capacity — but how much more, and is it a linear increase or an exponential one?
Second, although some of the studies try and hold one mechanism constant across ages, it is unlikely that this has been accomplished. For example, some studies attempt to ensure that children of all ages have learnt the same chunks on a task by either training all children on particular aspects of the task e.
However, this does not mean that children are equated for the chunks they use when performing a task. Older children are likely to use a broader range of chunks to complete a task than younger children because older children have learnt a greater number of chunks than younger children, owing to their greater real-world experience.
Even if young and old children were matched for chunked knowledge, the use of those chunks is likely to be more efficient for older children e. Computational modeling is an approach whereby all mechanisms have to be fully specified because the aim of a computational model is to simulate human behavior. A computational model of a developmental task may therefore have to specify capacity limitations, how chunks are learnt, and how quickly information is processed.
The beauty of this approach is that some aspects of the model can be held constant in order to investigate how performance changes for those processes that were allowed to vary. I will therefore examine a computational model that incorporates plausible accounts of chunking, processing speed, and capacity in order to examine whether an increase in the number of chunks that are known by the model can account for age-related increases in task performance that are seen in children.
The developmental task that will be used to demonstrate the chunking hypothesis explanation of developmental change is non-word repetition. This task involves accurately repeating a nonsense word non-word after it has been spoken aloud by the experimenter.
Non-word repetition is an ideal task for a chunking hypothesis to simulate because it consistently shows developmental change, with older children reliably out-performing younger children Gathercole and Baddeley, ; Jones et al. This paper will show how the chunking hypothesis not only demonstrates developmental change in non-word repetition, but also how chunking causes perceived changes in short-term memory capacity and perceived changes in processing speed — even though both of these will be fixed within the simulations.
I first describe a computational model of non-word repetition that instantiates the chunking hypothesis. Second, I illustrate how the results of the model show developmental change and how the chunking hypothesis explains other developmental mechanisms such as processing speed.
Finally, I conclude by detailing the implications of the results presented. The computational instantiation of the chunking hypothesis Jones et al. A fuller description of the model can be found in the Appendix. However, the modeling environment is not overly important since the method of chunking is very straightforward, as is the method by which capacity constrains the amount of information that is heard by the model and how new chunks are learnt by the model.
Let me consider each in turn, before I describe how the model performs the non-word repetition test. Input to the model consists of any word or utterance in its phonemic form e.
For any given phonemic input, the model encodes the input in as few chunks as possible, based on its existing knowledge of chunked phoneme sequences. In line with the working memory model Baddeley and Hitch, , a highly influential model of short-term memory, the capacity for verbal information is set at 2, ms.
Information that requires less time than 2, ms can be reliably stored, albeit temporarily, in working memory. Once this capacity is exceeded however, then the temporary storage of auditory information becomes unreliable. The EPAM model of phoneme chunk learning assigns a time to encode each chunk that varies depending on the size of the chunk. The allocation of a time to encode a chunk stems from a reconciliation of the chunking account of short-term memory e.
Based on a series of studies involving span and reading measures for Chinese and English words and characters, Zhang and Simon found that the time to encode a chunk was roughly ms and the time to encode each phoneme in a chunk was roughly 30 ms, excluding the first phoneme 1.
Note that there is no limit to the size of a chunk and any given input is represented by as few chunks as possible. There are other more complex views of chunk learning and chunk matching. For example, Servan-Schreiber and Anderson suggest that chunks compete with one another for representing a given input, with the winning chunk s being selected based on the usage of each chunk and its sub-chunks.
A usage-based account is also used in part by Pothos when explaining how entropy can be used to account for artificial grammar learning. Pothos suggests that bigrams and trigrams i. This previous research could have been used to include a selection process for chunks that are in competition with one another and a parameter could have been set to limit chunk length to a particular number of elements e.
However, my goal is to provide the most parsimonious explanation of the effects seen. I therefore minimize the number of parameters in the model and when I am forced into using a parameter, its value is set based on previous literature rather than any of my own research. The model therefore uses the timing estimates of Zhang and Simon without additional mechanisms relating to the encoding of chunks. Since the capacity for verbal information is 2, ms, the utterance would fail to be reliably encoded.
This provides a simple illustration of how, when capacity is exceeded, the information in short-term memory is compromised. Once an input has been encoded as chunks, the model can learn new chunks. The method for learning a new chunk is very simple: two chunks that are adjacent in the encoded list of chunks, provided both have been reliably encoded, can be chunked together to become one chunk. When an utterance can only be encoded in a time that exceeds the 2, ms capacity limit, then each chunk cannot be encoded reliably.
As shown earlier, when capacity is exceeded, the reliable encoding of chunks becomes probabilistic. Learning will only proceed for adjacent chunks that have been reliably encoded, thus reducing the amount of learning that can take place when short-term memory capacity is compromised. In order to learn chunked phoneme sequences, the model is trained on a linguistic input that mirrors the style of input that children receive.
The inclusion of the sentences gradually increases as more input is presented to the model and replaces mother utterances. The model is presented with 31, lines of input but sentences from books form an increasingly larger proportion of the input over time. The model is able to perform a non-word repetition test early on in its training to compare performance against 2- to 3-year-old children and later on in its training to compare performance against 4- to 5-year-old children.
Figure 1. For each presentation of an input utterance or sentence, the processes described in the above sections are carried out — that is, encoding the input into as few chunks as possible, attempting to reliably store the encoded chunks in short-term memory, and learning new chunks from the stored input. Basic Books, Cowan N.
The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behav Brain Sci. Miller GA. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol Rev. Your Privacy Rights. To change or withdraw your consent choices for VerywellMind. At any time, you can update your settings through the "EU Privacy" link at the bottom of any page.
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Types of Memory and their Functions. What Is Short-Term Memory? How False Memories Are Formed. How Context-Dependent Memory Works. We conclude that a chunk reduces the load on WM via retrieval of a compact chunk representation from long-term memory that replaces the representations of individual elements of the chunk. This frees up capacity for subsequently encoded material.
Abstract Chunking is the recoding of smaller units of information into larger, familiar units. Grant support Swiss National Science Foundation.
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