The Representation Of Geometric Figures In Visual Working Memory

Visual Working Memory (VWM) is a popular research question in cognitive psychology. Most of the current work concerning VWM focuses on its capacity and the approaches of storage; however, few studies have attended to the form of object representations in VWM, which is a fundamental question in the field. In cognitive science, there are two main forms of object representations:raster representation and vector representation. The raster representations are built up from pixel-like information of objects, by which a bigger object occupies more storage resources compared to smaller one. In contrast, the vector representations are composed of abstract primitives describing object properties, and the consuming storage resources are insensitive to the size of objects. To clarify the way how objects are represented in VWM can not only help to enhance the understanding of the inherent mechanism of VWM, but also is instructive to the design of cognitive system of intelligent robots.Aimed to explore whether the representations of objects in VWM is vector or raster, this study tested whether size of geometrical figures influences the storage of VWM. Four experiments were conducted. Adopting subjective adjusting paradigm, Experiment 1 compared memory precision of circles with different sizes in order to examine whether the size of object would affect its representation. Experiment 2 surveyed whether the size of circles would affect memory performance, by taking Contralateral Delay Activity (CDA) as an index to reflect the representation maintenance in VWM. The change detection paradigm was employed. In addition, Experiment 3 applied irregular polygons as stimuli intending to rule out the possible irrelevant effect caused by the particularity of using circles as stimuli. Finally in Experiment 4, whether the results could be generalized to varied task difficulties was overviewed by asking the observers to memorize both color and size of irregular polygons in the change detection task.The main findings are as follows:(1) The memory performances in VWM are not affected by the size of objects, no matter simple or complex. (2) Neither the corresponding CDA amplitude is influenced by the size. (3) The memory precision decreases when set size increases and the decline is indifferent for bigger or smaller objects.In conclusion, the findings are supporting the perspective of vector representation of geometric figures.