Chunking research paper
Chunking learning theory
We do not argue that retrieval from an offline state must always be slower than accessing information in WM, a generalization that fails in many different scenarios e. To examine the underlying processes involved in effortful chunking, one must consider methods that permit greater exploration of internal cognitive processes. Fig 7. 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. The GLV model is a non-linear dynamical system that is attractive for its mathematical simplicity: the existence of a SHC can be proven rigorously [ 44 ], and in the three-dimensional case its bifurcations have been extensively investigated [ 43 ]. 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. This assumption was originally used by Tulving and Patkau in their adopted chunk method. In the "fbicbsibmirs" example, one will only remember four acronyms, FBI, CBS, IBM, and IRS, as content-free labels, but the composition of each acronym is not represented on-line and is retrieved from an offline state when it is needed. At the start of each trial, a small white fixation cross was presented in the center of the display for ms, and then the stimuli were presented for ms and then disappeared.
We reasoned that this retrieval process should take time, thereby delaying responses that are guided by those individuated representations.
Consider an observer with a memory capacity of three chunks.
If the additional information available in the patterned condition has to be retrieved from an offline state, then the regularity-based advantage should be abolished when brief response deadlines preclude successful retrieval.
Method of limiting short-term capacity 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.
Memory capacity significantly increased in patterned blocks than control blocks, confirming the previous finding.
If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Sample stimuli displays of Experiments 1 and 2 are respectively shown in Fig.
One second following the disappearance of the stimuli, the test display was presented marking 1 of the 8 positions and showing two choices e. In each trial of control blocks, the 8 colors were randomly arranged in the 8 possible positions. Here we propose an alternative hypothesis that does not entail any increase in the number of individuated representations stored online. Access to information in working memory: Exploring the focus of attention. Thus, the chess masters do not have a greater number of chunks than novices in their memory. For the sample display illustrated in Fig. This test display remained on the display until the observers responded. Our model self-organizes to learn and recall sequences in a robust manner. First, the memory for low-probability pairs was also better when they were stored with high probability pairs, while a strategy of guessing based on the value of one item in a pair predicts worse performance for low probability pairs. The program was written in Microsoft Visual Basic 6. We did not attempt to exclude RT outliers in Experiments because the RTs were already limited by the response deadlines.
We propose a model that builds on [ 21 ] by introducing a synaptic weight update rule that accommodates the unsupervised learning of the chunking process. Storage of the Content of Chunks.
based on 108 review