## 4 Discussion
### Influence of Complexity and Set Size
In addition, the VWM capacity calculated in our experiment for a set size of 4 (ranging from 3.36 for low-complexity to 3.64 for high-complexity objects) was essentially the same as the thresholds (3.5) for 3D shaded cubes previously estimated by [PERSON] and [PERSON] [31]. There are two possible explanations: one is that the 3D virtual objects have meaning and a high degree of ecological validity, which prompt users to remember them using partial coding strategies [61], thus providing some encoding advantages. The second reason is that MAR interactions have a mitigating effect on cognitive load, with free motion and dynamic viewing helping to reduce the cognitive load on users [34; 62]. For example, it has been shown that AR interaction can clarify similarities between objects, thereby reducing user confusion and cognitive dissonance [63]. Another important finding is that under low- and medium-complexity conditions, the Cowen K value, which initially decreased with the total number of 3D virtual objects, remains almost constant when the set size exceeds 6. These results suggested that there may be a minimum amount (the ceiling effect) of VWM storage for each complexity level. This view has also appeared in other studies [35; 64]. For example, [PERSON] et al. argued that working memory capacity does not change with the number of memorized items when it exceeds a certain range [65]. [PERSON] and [PERSON] [56] indicated that a ceiling effect would occur at set size 4 when performance was quite high. The results here suggested that the ceiling effect for simple virtual 3D objects in MAR interfaces may occur when the display quantity was 6. However, for highly complex virtual 3D objects that contain more virtual-reality fusion characteristics, the limited attentional resources still cannot be allocated effectively even when the display quantity was 8. Our research also challenges the view based on the slot-based theories that the number of items storable in VWM is fixed and equal, independent of the object complexity or the precision of the representations [66; 67]. These studies posit that there is a clear upper limit on visual working memory resources, and the resources available for each storage item are unaffected by the number of features it contains [26; 68]. The VWM capacity formula (Cowan's K) aims to estimate \"how many items were remembered\" in each set and correct the performance difficulties observed at higher set sizes [56]. If participants exhibit a fixed K value across all high set size conditions, with constant reaction times and accuracy unaffected by complexity of 3D virtual objects, this would support the notion of a fixed item limit. However, our experimental results indicated the opposite: all data, including accuracy, reaction time (RT) and VWM capacity (Cowan's K), suggested that the flexible-resource theory is more applicable. The results of our experiment proved that the VWM capacity in MAR interfaces was not fixed, and memory precision along with cognitive efficiency were diminished by the presence of highly complex 3D virtual objects, indicating that the memory resources are flexibly allocated according to the complexity of each virtual AR target. There are two possible explanations for this: first, according to the flexible resource model, the precision of stored items depends on the fraction of total resources allocated to their storage, and the resources are equally distributed among all items. Therefore, users' memory performance decreases as the number of projects increases. Meanwhile, the retrieval and recognition of the in-memory items are incrementally delayed with added variability as the set size increases [26]. Second, most extant studies used one-dimensional objects (e.g., color, shape, orientation) or two-dimensional objects (e.g., cubes, faces) as experimental materials. Compared with AR 3D virtual objects, these objects have fewer features and are easier to remember.