## 3 Results
### Visual Working Memory Capacity  
In this paper, visual working memory (VWM) capacity was measured as Cowan's K Cowan (1975), as shown in Equation (1) below, which is an estimate of the number of remembered 3D virtual objects:  
\[K=(H-FA)\times N \tag{1}\]  
This equation shows the number of representations remembered in VWM, i.e., the VWM capacity, where \(N\) is the number of stimuli to be memorized; \(H\) is the hit rate, i.e., the probability that a subject will correctly report a probe stimulus when the presented probe stimulus is learnt; and \(FA\) is the false alarm rate, which is the probability that a subject will mistakenly believe a probe stimulus to be learnt when the presented probe stimulus is unlearnt. Figure 6 shows how the mean VWM capacity (Cowan's K) is affected by the total number of 3D virtual objects (set size) and complexity. Table 2 shows the accuracy and Cowan's K (mean and SD) for three levels of complexity of 3D virtual objects in different set size conditions. The main effect of complexity was significant, where \(\Delta\)AIC = \(-\)42, LLR \(\chi^{2}(1)\) = 46.851 and \(p\) < 0.001, such that more low-complexity 3D virtual objects were retained in working memory during MAR interaction. The set size also had a significant main effect, where \(\Delta\)AIC = \(-\)134, LLR \(\chi^{2}(1)\) =135.564, \(p\) < 0.001, which indicated that an increase in the display quantity (set size) results in a reduction in VWM capacity. There was no significant two-way interaction between complexity and quantity for VWM capacity, where \(\Delta\)AIC = \(-\)2, LLR \(\chi^{2}(1)\) = 1.643 and \(p\) = 0.440. These results provided an additional answer to Question 2: participants were less efficient and found it harder to recall the  
Figure 5: Complexity and number of objects versus proportion correct. The number of 3D virtual objects presented is shown on the x-axis, with separate plots for each of the three complexity types. Error bars indicate \(\pm\)1 SEs. highly complex 3D virtual objects. For Question 3, the VWM capacity in MAR interfaces was not fixed and can be reduced by highly complex 3D virtual objects. More importantly, the VWM capacity (Cowan's K) for all three complexity levels showed a decreasing trend from dataset size 4 to 6, but this decreasing trend diminished from dataset size 6 to 8. Specifically, the VWM capacity for low and medium complexity stayed almost constant from set size 6 to 8: the VWM capacity for low complexity was not significantly different between set sizes 6 and 8, where F (1,1238) = 1.587, \(p\) = 0.208 and \(p\) > 0.05, nor for medium complexity between set sizes 6 and 8, where F (1,1238) = 1.663, \(p\) = 0.197 and \(p\) > 0.05. But for the high-complexity condition, the VWM capacity dropped significantly between set sizes 6 and 8, where F (1,1238) = 9.04, \(p\) = 0.0026 and \(p\) < 0.01. These results indicated that the VWM capacity for low- and medium-complex 3D virtual objects seems to be limited when the set size increases from 6 to 8, but the highly complex virtual objects still consume more working memory resources and its VWM capacity continues to decrease. Given that previous studies related to traditional digital screens have shown that a ceiling effect may occur when the set size was 4 [56], the results here for the low-complexity and medium-complexity conditions suggested that the ceiling effect for not quite complex virtual objects in MAR interfaces may occur when the set size is 6. For the high-complexity conditions, one possible explanation for these results from set size 6 to 8 is that those high-complexity 3D virtual objects contain more detailed features and virtual-reality fusion characteristics, which continue to inhibit the efficient allocation of limited attentional resources [56]. Since the cognitive difficulty of these virtual-reality fusion characteristics in 3D virtual objects increases with the level of complexity, we suspect