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Jozsef Fiser, Gergo Orban, Mate Lengyel; Linking implicit chunk learning and the capacity of working memory. Journal of Vision 2008;8(6):213. doi: https://doi.org/10.1167/8.6.213.
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© ARVO (1962-2015); The Authors (2016-present)
Classical studies of the capacity of working memory have posited a fix limit for the maximum number of items human can store temporarily in their memory, such as 7±2 or 4±1. More recent results showed that when the stored items are viewed as complex multi-dimensional objects capacity can be increased and conversely, when distinctiveness of these items is minimized capacity is reduced. These results suggest a strong link between working memory and the nature of the representation of information based on the observer's long-term memory. To test this conjecture, we formalized the information content of a set of stimulus by its description length, which relates the “cost”, the number of bits assigned to a particular stimulus, to its appearance likelihood given the representation the observer has. This formalism highlights that a high-complexity but familiar stimuli need less resource to encode and recall correctly than novel stimuli with lower complexity. Using this formalism, we developed a novel two-stage test to investigate the above conjecture. First, participants were trained in an unsupervised visual statistical learning task using multi-element scenes in which they are known to develop implicitly a chunked representation of the scenes. Next, they performed a change detection task using novel scenes that were composed from the same elements either with or without the chunk arrangements of the training session. Change detection results were significantly better with scenes that were composed of elements that retained the chunk arrangement. Thus the capacity of working memory determined by how easily the stimulus can be mapped onto the internal representation of the observer, and integrated object-based coding is a special case of this mapping.
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