## 3 Discussion with Conclusions
In our experiments, order was defined as the reasonable degree of symmetry, balance, and regularity of the components. That means that when the organizational relationshipsin each graphical symbol are more ordered, the grouping processing of auto-perception is easier. Therefore, disordered bi-symbol pairs were less efficient to process than those ordered bi-symbol pairs of equal complexity, since asymmetrical and unbalanced graphical symbols were difficult to be grouped quickly. Similarly, in the ordered condition, although complex bi-symbol pairs contained more geometries than simple bi-symbol pairs, the ease of grouping of the two types of stimuli was the same, and thus the performance was unaffected by complexity. Another plausible explanation comes from the chunking theory of [PERSON] [52], [PERSON] [53], and [PERSON] et al. [27]. The chunking theory posits that when information can be encoded or chunked into higher level units, it is easier to process and consumes less WM resources [69; 70]. Recent works by [PERSON] et al. [27] have extended chunking theory and shown that WM capacity depends not only on the number of chunks of information one is attempting to process but also on the strength of those chunks in memory. Our prior study demonstrated that the strength of the chunks reflects the familiarity as one's prior knowledge of the stimuli modulates the effects of complexity [32]. According to the chunking strength theory, we propose that less working memory is required to encode ordered stimuli. That is, the coding advantage in binding ordered bi-symbol pairs was greater since ordered graphical symbol contained more similar parts and these parts were arranged more regularly, representing a greater number of parts that could be more easily chucked into fewer units. Therefore, while four groups of stimulus types (complex and ordered, complex and disordered, simple and ordered, and simple and disordered) expended working memory resources to encode the bi-symbol pair combinations, the processing and binding of ordered stimuli depleted less working memory resources as chunks became stronger. Conversely, disordered graphical symbol were more difficult to encode into bi-symbol pairs and to associate with English words, as the irregular organization led to a weaker chunking strength and only a few parts of disordered constituent elements could be chunked. This view can also be found similarly in other image-related research [33; 40]. For example, [PERSON] [71] manipulated the number and position of blocks on the board and found that the impact of visual complexity is determined by the combination of quantifiable physical attributes and structural factors. Our work builds on theirs by demonstrating that complexity can be moderated by the organizational forms of the elements, i.e., the order degree. Our research also challenges the view that \"order is the opposite pole of complexity and simple equals to ordered\" [11]. Our study proves that objective complexity and visual order are not two irrelevant properties, and order is not the antonym of complexity. The results demonstrate that there is a significant interaction between order and complexity in memory perception. Visual order acts as a reduction in objective complexity. In addition, complexity appears to be a multidimensional property instead of a one-dimensional property, and controlling the effects of objective complexity requires both reducing the number of the constituent elements and improving the visual order. Aside from informing theoretical studies of complexity, the order optimization effects may be potentially useful for improving theories related to visual design. Prior research has shown that an effective way to reduce complexity is to reduce the quantity in such a way as to reduce the working memory and cognitive load during visual cognition, as postulated by [PERSON] and [PERSON] [10]. Several helpful strategies have been proposed to reduce the effects of complexity, such as reducing silhouette points [72] and compression ratios [6].