Fig. 8: Advantages of QCE-VWM model.

a The comprehensive exploration functions as an enhanced literature review, aiming to merge the precision and evidence-based characteristics of traditional experimental studies with the broad, holistic scope of literature reviews. b The guidance neural network and baseline VP-F-NT model have achieved effectiveness and parsimony, respectively. In contrast, the QCE-VWM model has managed to simultaneously achieve both; it surpasses the guidance neural network in terms of data fit and is also comparatively parsimonious. Four reduced versions of the guidance neural network are indicated by the gray dots. Their performance substantially decreases when the number of parameters is reduced to 628 and 208. This confirms that the neural network cannot remain effective without its complexity, highlighting the distinct advantage of the QCE-VWM model in simultaneously achieving effectiveness and parsimony.