Zach Shipstead

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 capacity and the scope and control of attention

Complex span and visual arrays are two common measures of working memory capacity that are respectively treated as measures of attention control and storage capacity. A recent analysis of these tasks concluded that (1) complex span performance has a relatively stronger relationship to fluid intelligence and (2) this is due to the requirement that people engage control processes while performing this task. The present study examines the validity of these conclusions by examining two large data sets that include a more diverse set of visual arrays tasks and several measures of attention control. We conclude that complex span and visual arrays account for similar amounts of variance in fluid intelligence. The disparity relative to the earlier analysis is attributed to the present study involving a more complete measure of the latent ability underlying the performance of visual arrays. Moreover, we find that both types of working memory task have strong relationships to attention control. This indicates that the ability to engage attention in a controlled manner is a critical aspect of working memory capacity, regardless of the type of task that is used to measure this construct.

Why is working memory capacity related to matrix reasoning tasks

One of the reasons why working memory capacity is so widely researched is its substantial relationship with fluid intelligence. Although this relationship has been found in numerous studies, researchers have been unable to provide a conclusive answer as to why the two constructs are related. In a recent study, researchers examined which attributes of Raven’s Progressive Matrices were most strongly linked with working memory capacity (Wiley, Jarosz, Cushen, & Colflesh, Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 256–263, 2011). In that study, Raven’s problems that required a novel combination of rules to solve were more strongly correlated with working memory capacity than were problems that did not. In the present study, we wanted to conceptually replicate the Wiley et al. results while controlling for a few potential confounds. Thus, we experimentally manipulated whether a problem required a novel combination of rules and found that repeated-rule-combination problems were more strongly related to working memory capacity than were novel-rule-combination problems. The relationship to other measures of fluid intelligence did not change based on whether the problem required a novel rule combination.

Working Memory Capacity and Fluid Intelligence Maintenance and Disengagement

Working memory capacity and fluid intelligence have been demonstrated to be strongly correlated traits. Typically, high working memory capacity is believed to facilitate reasoning through accurate maintenance of relevant information. In this article, we present a proposal reframing this issue, such that tests of working memory capacity and fluid intelligence are seen as measuring complementary processes that facilitate complex cognition. Respectively, these are the ability to maintain access to critical information and the ability to disengage from or block outdated information. In the realm of problem solving, high working memory capacity allows a person to represent and maintain a problem accurately and stably, so that hypothesis testing can be conducted. However, as hypotheses are disproven or become untenable, disengaging from outdated problem solving attempts becomes important so that new hypotheses can be generated and tested. From this perspective, the strong correlation between working memory capacity and fluid intelligence is due not to one ability having a causal influence on the other but to separate attention-demanding mental functions that can be contrary to one another but are organized around top-down processing goals.

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

Low cognitive load strengthens distractor interference while high load attenuates when cognitive load and distractor possess similar visual characteristics

Studies on visual cognitive load have reported inconsistent effects of distractor interference when distractors have visual characteristic that are similar to the cognitive load. Some studies have shown that the cognitive load enhances distractor interference, while others reported an attenuating effect. We attribute these inconsistencies to the amount of cognitive load that a person is required to maintain. Lower amounts of cognitive load increase distractor interference by orienting attention toward visually similar distractors. Higher amounts of cognitive load attenuate distractor interference by depleting attentional resources needed to process distractors. In the present study, cognitive load consisted of faces (Experiments 1–3) or scenes (Experiment 2). Participants performed a selective attention task in which they ignored face distractors while judging a color of a target dot presented nearby, under differing amounts of load. Across these experiments distractor interference was greater in the low-load condition and smaller in the high-load condition when the content of the cognitive load had similar visual characteristic to the distractors. We also found that when a series of judgments needed to be made, the effect was apparent for the first trial but not for the second. We further tested an involvement of working memory capacity (WMC) in the load effect (Experiment 3). Interestingly, both high and low WMC groups received an equivalent effect of the cognitive load in the first distractor, suggesting these effects are fairly automatic.