Evidence through modified tasks for the robustness of dynamic visual noise as a selective visuo-spatial interference technique
Dissertation - Andrew Cruickshank.rtf (614.6Kb)
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Visual mental imagery has been a topic of some controversy since the onset of the imagery debate between Pylyshyn (1973) and Kosslyn (1994). The Working Memory model created by Baddeley & Hitch (1974) has allowed for great strides to be made in the understanding and characterisation of process involved in image generation and maintenance through the discovery and refinement of techniques which selectively interfere with the processing is the visuo-spatial sketchpad (VSSP). One such technique utilised for disrupting performance of a visual imagery memory task but which has no effects on verbal processing is dynamic visual noise (DVN). This effect appears to be a reliable in the literature with dual-task paradigms which involve the peg word mnemonic (Quinn & McConnell, 1996b; Andrade et al, 2002; Quinn & McConnell, 2006), however little evidence exists which applies this visual interference technique to other visual imagery tasks. The current paper therefore attempts to provide evidence for the robustness of DVN by applying a modified version, based on Quinn & McConnell’s (1996b) original conception, which effectively measures the attention being paid to the task and the adapted method of loci (AMOL) implemented. Results show that the DVN selectively interfered with performance of the AMOL task but not so for the verbal memory task (p=.006) as predicted. The level of attention paid to the secondary task was found to be equal in terms of correct and erroneous decisions of whether an image was embedded within the display and further analysis revealed that the decrement in performance in the AMOL with DVN conditions was not simply a result of the build up of interference over trials. The findings are discussed with relevance to demand characteristics and the possibility that the modified DVN and adapted AMOL could be profitable avenues for further characterising the VSSP.