Abstract
Working memory capacity (WMC) has been measured with a plethora of cognitive tasks. Several preeminent automated batteries of working memory (WM) tasks have been developed recently. However, despite all their advantages, most batteries were programmed in paid platforms and/or only included a single WM paradigm. To address these issues, we developed the OpenWMB, an automated battery comprising seven tasks from three distinct paradigms (complex spans, updating tasks, and binding tasks) that tap into several functional aspects of WM (simultaneous storage and processing, updating, and binding). The battery runs on open-source software (OpenSesame) and is freely available online in a ready-to-download format. The OpenWMB possesses flexible features and includes a data processing script (that converts data into a format ready for statistical analysis). The instrument is available in Portuguese and English. However, we only assessed the psychometric properties of the former version. The Portuguese version presented good internal consistency and considerable internal and predictive validity: all tasks loaded into a single factor. Additionally, the WMC estimate was strongly correlated with a fluid intelligence factor. This study also tried to contribute to the ongoing debate regarding the best method to assess WMC. We computed a permutation analysis to compare the amount of variance shared between a fluid intelligence factor and (1) each WM task, (2) homogenous WMC factors (based on multiple tasks from the same paradigm), and (3) heterogeneous WMC factors (derived from triplets of tasks from different paradigms). Our results suggested that heterogeneous factors provided the best estimates of WMC.