Shortened complex span tasks can reliably measure working memory capacity.

Measures of working memory capacity (WMC), such as complex span tasks (e.g., operation span), have become some of the most frequently used tasks in cognitive psychology. However, due to the length of time it takes to complete these tasks many researchers trying to draw conclusions about WMC forgo properly administering multiple tasks. But can the complex span tasks be shortened to take less administration time? We address this question by splitting the tasks into three blocks of trials, and analyzing each block's contribution to measuring WMC and predicting fluid intelligence (Gf). We found that all three blocks of trials contributed similarly to the tasks' ability to measure WMC and Gf, and the tasks can therefore be substantially shortened without changing what they measure. In addition, we found that cutting the number of trials by 67 % in a battery of these tasks still accounted for 90 % of the variance in their measurement of Gf. We discuss our findings in light of administering the complex span tasks in a method that can maximize their accuracy in measuring WMC, while minimizing the time taken to administer.

Working memory training may increase working memory capacity but not fluid intelligence.

Working memory is a critical element of complex cognition, particularly under conditions of distraction and interference. Measures of working memory capacity correlate positively with many measures of real-world cognition, including fluid intelligence. There have been numerous attempts to use training procedures to increase working memory capacity and thereby performance on the real-world tasks that rely on working memory capacity. In the study reported here, we demonstrated that training on complex working memory span tasks leads to improvement on similar tasks with different materials but that such training does not generalize to measures of fluid intelligence.

The scope and control of attention as separate aspects of working memory.

The present study examines two varieties of working memory (WM) capacity task: visual arrays (i.e., a measure of the amount of information that can be maintained in working memory) and complex span (i.e., a task that taps WM-related attentional control). Using previously collected data sets we employ confirmatory factor analysis to demonstrate that visual arrays and complex span tasks load on separate, but correlated, factors. A subsequent series of structural equation models and regression analyses demonstrate that these factors contribute both common and unique variance to the prediction of general fluid intelligence (Gf). However, while visual arrays does contribute uniquely to higher cognition, its overall correlation to Gf is largely mediated by variance associated with the complex span factor. Thus we argue that visual arrays performance is not strictly driven by a limited-capacity storage system (e.g., the focus of attention; Cowan, 2001), but may also rely on control processes such as selective attention and controlled memory search.

Do the effects of working memory training depend on baseline ability level?

There is a debate about the ability to improve cognitive abilities such as fluid intelligence through training on tasks of working memory capacity. The question addressed in the research presented here is who benefits the most from training: people with low cognitive ability or people with high cognitive ability? Subjects with high and low working memory capacity completed a 23-session study that included 3 assessment sessions, and 20 sessions of training on 1 of 3 training regiments: complex span training, running span training, or an active-control task. Consistent with other research, the authors found that training on 1 executive function did not transfer to ability on a different cognitive ability. High working memory subjects showed the largest gains on the training tasks themselves relative to the low working memory subjects-a finding that suggests high spans benefit more than low spans from training with executive function tasks. (PsycINFO Database Record

Combining Reaction Time and Accuracy: The Relationship Between Working Memory Capacity and Task Switching as a Case Example.

It is generally agreed upon that the mechanisms underlying task switching heavily depend on working memory, yet numerous studies have failed to show a strong relationship between working memory capacity (WMC) and task-switching ability. We argue that this relationship does indeed exist but that the dependent variable used to measure task switching is problematic. To support our claim, we reanalyzed data from two studies with a new scoring procedure that combines reaction time (RT) and accuracy into a single score. The reanalysis revealed a strong relationship between task switching and WMC that was not present when RT-based switch costs were used as the dependent variable. We discuss the theoretical implications of this finding along with the potential uses and limitations of the scoring procedure we used. More broadly, we emphasize the importance of using measures that incorporate speed and accuracy in other areas of research, particularly in comparisons of subjects differing in cognitive and developmental levels.

Cognitive predictors of a common multitasking ability: Contributions from working memory, attention control, and fluid intelligence.

Previous research has identified several cognitive abilities that are important for multitasking, but few studies have attempted to measure a general multitasking ability using a diverse set of multitasks. In the final dataset, 534 young adult subjects completed measures of working memory (WM), attention control, fluid intelligence, and multitasking. Correlations, hierarchical regression analyses, confirmatory factor analyses, structural equation models, and relative weight analyses revealed several key findings. First, although the complex tasks used to assess multitasking differed greatly in their task characteristics and demands, a coherent construct specific to multitasking ability was identified. Second, the cognitive ability predictors accounted for substantial variance in the general multitasking construct, with WM and fluid intelligence accounting for the most multitasking variance compared to attention control. Third, the magnitude of the relationships among the cognitive abilities and multitasking varied as a function of the complexity and structure of the various multitasks assessed. Finally, structural equation models based on a multifaceted model of WM indicated that attention control and capacity fully mediated the WM and multitasking relationship. (PsycINFO Database Record

No evidence of intelligence improvement after working memory training: a randomized, placebo-controlled study.

Numerous recent studies seem to provide evidence for the general intellectual benefits of working memory training. In reviews of the training literature, Shipstead, Redick, and Engle (2010, 2012) argued that the field should treat recent results with a critical eye. Many published working memory training studies suffer from design limitations (no-contact control groups, single measures of cognitive constructs), mixed results (transfer of training gains to some tasks but not others, inconsistent transfer to the same tasks across studies), and lack of theoretical grounding (identifying the mechanisms responsible for observed transfer). The current study compared young adults who received 20 sessions of practice on an adaptive dual n-back program (working memory training group) or an adaptive visual search program (active placebo-control group) with a no-contact control group that received no practice. In addition, all subjects completed pretest, midtest, and posttest sessions comprising multiple measures of fluid intelligence, multitasking, working memory capacity, crystallized intelligence, and perceptual speed. Despite improvements on both the dual n-back and visual search tasks with practice, and despite a high level of statistical power, there was no positive transfer to any of the cognitive ability tests. We discuss these results in the context of previous working memory training research and address issues for future working memory training studies.