On the capacity of attention: its estimation and its role in working memory and cognitive aptitudes

Abstract

Working memory (WM) is the set of mental processes holding limited information in a temporarily accessible state in service of cognition. We provide a theoretical framework to understand the relation between WM and aptitude measures. The WM measures that have yielded high correlations with aptitudes include separate storage-and-processing task components, on the assumption that WM involves both storage and processing. We argue that the critical aspect of successful WM measures is that rehearsal and grouping processes are prevented, allowing a clearer estimate of how many separate chunks of information the focus of attention circumscribes at once. Storage-and-processing tasks correlate with aptitudes, according to this view, largely because the processing task prevents rehearsal and grouping of items to be recalled. In a developmental study, we document that several scope-of-attention measures that do not include a separate processing component, but nevertheless prevent efficient rehearsal or grouping, also correlate well with aptitudes and with storage-and-processing measures. So does digit span in children too young to rehearse.

Figure 1

Memory for ignored speech (solid lines) and attended speech (dashed lines) in three age groups (graph parameter) as a function of set size relative to a predetermined span (x axis). Redrawn from Cowan, Nugent, Elliott, Ponomarev, & Saults (1999).

Figure 2

Experiment 1, mean number of items correct at each set size in each task (graph parameter) for third-grade children (top panel), fifth-grade children (middle panel), and adults (bottom panel). Values are included only for set sizes in which all participants produced data.

Figure 3

Experiment 1, prediction of ACT composite score in adults, conjointly by subsets of three types of WM variable. The diagram is based on regressions shown in Table 5. Numbers within the overlapping sections of the circles do not represent collinarity between the variables, but portions of ACT variance that are predicted in common by the WM variables shown as overlapping.

Figure 4

Experiment 1, prediction of high school grades percentile in adults, conjointly by subsets of three types of WM variable. The diagram is based on regressions shown in Table 5. Numbers within the overlapping sections of the circles do not represent collinarity between the variables, but portions of high school grades variance that are predicted in common by the WM variables shown as overlapping.

Figure 5

Experiment 1, prediction of within-age variance in CAT composite score in children, conjointly by subsets of three types of WM variable. The diagram is based on regressions shown in Table 5. Numbers within the overlapping sections of the circles do not represent collinarity between the variables, but portions of CAT variance that are predicted in common by the overlapping WM variables.

Figure 6

Experiment 2, correlations between each WM variable and four aptitude tasks (Peabody Picture Vocabulary Test, Stanford-Binet Vocabulary subtest, Ravens Progressive Matrices, and Stanford-Binet Pattern Analysis subtest), based on attenuation-corrected raw correlations (top panel) and partial correlations, with age partialled out, based on attenuation-corrected scores (bottom panel).

Figure 7

Experiment 2, prediction of g score based on four aptitude tests (Peabody Picture Vocabulary Test, Stanford-Binet Vocabulary subtest, Ravens Progressive Matrices, and Stanford-Binet Pattern Analysis subtest), conjointly by subsets of three types of WM variable. The diagram is based on regressions shown in Table 9. Numbers within the overlapping sections of the circles do not represent collinarity between the variables, but portions of g variance that are predicted in common by the WM variables shown as overlapping.

Figure 8

Experiment 2, prediction of within-age-group variance in g score (with age-group variance removed) based on four aptitude tests (Peabody Picture Vocabulary Test, Stanford-Binet Vocabulary subtest, Ravens Progressive Matrices, and Stanford-Binet Pattern Analysis subtest), conjointly by subsets of three types of WM variable. The diagram is based on regressions shown in Table 9. Numbers within the overlapping sections of the circles do not represent collinarity between the variables, but portions of within-age-group g variance that are predicted in common by the WM variables shown as overlapping.

Figure 9

Structural equation model of performance in Experiment 2. Measures: RS = running span, VA = visual arrays, AS = auditory sequences, CS = counting span, LS = listening span, RA = Ravens Progressive Matrices, PA = Stanford-Binet pattern analysis, VO = Stanford-Binet vocabulary, PV = Peabody Picture Vocabulary Test, NVIQ = nonverbal IQ, VIQ = verbal IQ. Fit indices: GFI = goodness-of-fit index, AGFI = adjusted goodness-of-fit index, TLI = Tucker-Lewis index, CFI = comparative fit index, RMSEA = root mean square error of approximation.