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    SLOTS VS. RESOURCE: AN INVESTIGATION INTO THE EFFECT OF COGNITIVE LOADING OF FEATURE BINDING IN VISUAL WORKING MEMORY.

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    9521.pdf (858.9Kb)
    Date
    2018
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    Abstract
    Feature binding is the most basic approach to the neural binding problem (Allen, Baddeley & Hitch, 2006). Feature binding refers to the binding of characteristics of objects in visual working memory. Research in visual working memory has attempted to understand how unrelated characteristics of an object bind together in order to form a mental representation. However, the slots model proposed by Zhang and Luck (2008) has not been used to investigate feature binding. The current study investigated the way in which features are bound in a slots model of visual working memory. The study also aims to investigate whether cognitive loading effects the way in which information is bound. The study uses a novel paradigm in order to investigate the binding of colour and location, and the effect of high cognitive loading on the binding of information. A three-point isometric Rubik’s cube was presented to participants and they were asked to learn this stimulus; the participants were then shown nine (high cognitive load) or three (low cognitive load) intervening Rubik’s cubes. Participants were then shown a blank Rubik’s cube with a single highlighted tile, and were asked whether the highlighted is either the same colour as the first cube presented to them or a different colour. Participants are asked to press ‘y’ on the keyboard, if the colour was the same, and ‘n’ if different. Bayesian analyses were used to determine whether cognitive loading affected the binding of colour-location information, and whether there was an interaction between the correct responses made and the cognitive loading conditions. Using Bayes Factor Analyses, the findings suggested that the likelihood of cognitive loading impacting feature binding was low, however, a d’ analyses suggested that individuals were less likely to differentiate between the target cube and the intervening cubes; thus suggesting that a resource model may be more apt for understanding visual working memory.
    URI
    https://eresearch.qmu.ac.uk/handle/20.500.12289/9521
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