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. 2010 Oct;17(5):673-9.
doi: 10.3758/17.5.673.

Quantity, not quality: the relationship between fluid intelligence and working memory capacity

Keisuke Fukuda  1 , Edward Vogel, Ulrich Mayr, Edward Awh
Affiliations

Quantity, not quality: the relationship between fluid intelligence and working memory capacity

Keisuke Fukuda et al. Psychon Bull Rev. 2010 Oct.

Abstract

A key motivation for understanding capacity in working memory (WM) is its relationship with fluid intelligence. Recent evidence has suggested a two-factor model that distinguishes between the number of representations that can be maintained in WM and the resolution of those representations. To determine how these factors relate to fluid intelligence, we conducted an exploratory factor analysis on multiple number-limited and resolution-limited measures of WM ability. The results strongly supported the two-factor model, with fully orthogonal factors accounting for performance in the number-limited and resolution-limited conditions. Furthermore, the reliable relationship between WM capacity and fluid intelligence was exclusively supported by the number factor (r = .66), whereas the resolution factor made no reliable contribution (r = -.05). Thus, the relationship between WM capacity and standard measures of fluid intelligence is mediated by the number of representations that can be simultaneously maintained in WM, rather than by the precision of those representations.

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Figures

Figure 1
Figure 1
A typical example of a fluid intelligence task Note: The correct answer is C.
Figure 2
Figure 2
Illustration of a sample display (set size varied between 4 and 8), and the single-item probe display that appeared after a 1 s retention period.
Figure 3
Figure 3
Results of the factor analysis. Based on the initial exploratory factor analysis, we generated three latent factors for g, working memory slots, and working memory resolution. The g factor was estimated from RAPM and CFT measures. The slots factor was generated from Color k, Big Oval k, and Big Rect k. Lastly, the resolution factor was generated from Small Oval k and Small Rect k. The resulting model above was simultaneously tested. Note: RAPM = Raven’s Advanced Progressive Matrices, CFT = Cattel Culture Fair, Color k = K estimate from color Conditions, Small Oval k = K estimate from within-categorical oval conditions, Small Rect k = K estimate from within-categorical rectangle conditions, Big Oval k = K estimate from big-change oval conditions, Big Rect k = K estimate from big-change rectangle conditions.
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