Academic journal article Memory & Cognition

The Relational Integration Task Explains Fluid Reasoning above and beyond Other Working Memory Tasks

Academic journal article Memory & Cognition

The Relational Integration Task Explains Fluid Reasoning above and beyond Other Working Memory Tasks

Article excerpt

Published online: 25 September 2013

© The Author(s) 2013. This article is published with open access at Springerlink.com

Abstract This study aimed to evaluate how well fluid reasoning can be predicted by a task that involves the monitoring of patterns of stimuli. This task is believed to measure the effectiveness of relational integration-the process that binds mental representations into more complex relational structures. In Experiments 1 and 2, the task was indeed validated as a proper measure of relational integration, since participants' performance depended on the number of bindings that had to be constructed in the diverse conditions of the task, whereas neither the number of objects to be bound nor the amount of elicited interference could affect this performance. In Experiment3, by means of structural equation modeling and variance partitioning, the relation integration task was found to be the strongest predictor of fluid reasoning, explaining variance above and beyond the amounts accounted for by four other kinds of well-established working memory tasks.

Keywords Relational integration · Workingmemory · Fluid reasoning

Fluid reasoning (fluid intelligence, Gf), most often assessed with matrix problems or visual analogies (Snow, Kyllonen, andMarshalek 1984), has been assumed to be the core component ability in most of the influential models of human intelligence (seeMcGrew 2009). Because of the fact that measures of working memory capacity (WMC) appear to most strongly predict Gf, for the last 20 years most researchers' views (e.g., Cowan 2001; Kane et al. 2007a; Oberauer et al. 2007) have converged on the idea that Gf primarily relies on working memory (WM)-a mechanism for active maintenance and transformation of a limited amount of information crucial for the current task (Cowan 2001).

However, several different methods have been developed to tap WMC. A classic method, which was derived from the research on short-term memory (STM), involves tasks that require memorizing a set of several stimuli, and then either recalling that set (i.e., recall/span tasks) or deciding whether a subsequent stimulus was or was not drawn from it (e.g., the Sternberg task or the change detection paradigm). Despite early skepticism regarding the plausibility of STM tasks as bothWMC measures and Gf predictors (e.g., Engle, Tuholski, Laughlin, and Conway 1999), more recent studies have suggested that proper versions of these tasks (i.e., excluding mnemotechniques like chunking and phonological rehearsal) can be very useful tools in WM and Gf research. It has been found that the number of items that people can successfully maintain in WM predicts a substantial part of variance in Gf (e.g., Colom, Abad, Quiroga, Shih, and Flores-Mendoza 2008; Unsworth and Engle 2007b).

Another class of paradigmatic WM tests, called complex span tasks, combine the maintenance of several stimuli for later recall with a number of simple manual decisions, and appear especially popular in psychometric research (see Conway et al. 2005; Unsworth and Engle 2007b). Because these tasks also predict various measures of executive control, like error rates in the antisaccade task (Unsworth, Shrock, and Engle 2004) and lapses of attention in the psychomotor vigilance task (Unsworth, Redick, Lakey, and Young 2010), some investigators (e.g., Burgess, Gray, Conway, and Braver 2011; Kane et al. 2007a) have proposed that the performance in both complex span tasks and Gf tests depends primarily on the effectiveness of domaingeneral control over attention. In consequence, tasks that do not require any memorization, but instead impose a strong load on executive processes, like the antisaccade task, have also been used with some success as predictors of Gf (Unsworth et al. 2010; Unsworth and Spillers 2010; Unsworth, Spillers, and Brewer 2009).

In cognitive neuroscience, the so called n-back task has gained much popularity as a WMC measure (see Owen, McMillan, Laird, and Bullmore 2005). …

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