Similarity-based clustering of multifeature objects in visual working memory

This study investigated the similarity-based clustering mechanism of multifeature stimuli, wherein items are separated or grouped based on their similarity in visual working memory (VWM). In particular, we investigated whether clustering occurred at an individual feature level or at an integrated object level when participants encoded objects with multiple features for VWM. To test this, we conducted two experiments in which participants remembered and reconstructed a randomly chosen feature (either color or orientation) from one of five presented stimuli. As a key manipulation, we kept the distributions of the two feature dimensions constant while controlling the conjunction between the two dimensions in two different conditions: congruent conjunction (CC) and incongruent conjunction (IC). With this manipulation, we expected to observe the same number of clusters regardless of the conjunction condition when clustering occurred at the feature level. However, we expected a different number of clusters for CC and IC conditions when clustering occurred at the object level. Across two experiments, we consistently observed evidence that favored feature-level clustering. Nevertheless, we found that the swap error rates increased in the IC condition only when two features had to be encoded in VWM. These results suggest that clustering occurs at the feature level in VWM and that feature-level clustering influences item-level feature binding. Therefore, our study demonstrated the flexibility of representational units in VWM.