Working memory and intelligence

Working memory and intelligence§

Roberto Coloma,*, Carmen Flores-Mendozab, Irene Rebolloa

aFacultad de Psicologıa, Universidad Autonoma de Madrid, 28049 Madrid, SpainbDepartamento de Psicologıa, Universidade Federal de Minas Gerais—UFMG, Brazil

Received 17 August 2001; received in revised form 29 November 2001; accepted 3 January 2002

Abstract

The correlation perspective shows that working memory (WM) is strongly related to psychometricintelligence. Although there are numerous psychometric abilities, there is a powerful single source of var-iance, namely, g. g Is evidenced by the positive correlation between all psychometric cognitive abilities. Theconstruct of WM distinguishes contents (verbal, numerical, spatial) and operations (storage and proces-sing). However, some studies found a high correlation between several diverse WM tasks, which supportsthe construct validity of the concept of WM as one general cognitive resource. This study explores thestructure of WM drawing on the methodology of intelligence structure research. Then, WM is related tointelligence. One hundred and eighty-seven participants took part in the study. WM was assessed througheight computerized tasks, while intelligence was assessed through the Raven Matrices or the PMA-R. Theresults show that WM can be considered as one general cognitive resource and that this resource is stronglyrelated with intelligence (r=+0.7). The statement that there is something underlying WM and intelligenceis discussed. # 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Working memory; Storage; Processing; Intelligence; Raven Progressive Matrices; PMA-R

1. Introduction

Working memory (WM) has become a central cognitive construct for theories of psychometricintelligence (Colom, Palacios, Kyllonen, & Juan-Espinosa, submitted for publication; Jensen,1998; Kyllonen & Christal, 1990; Oberauer, Suss, Schulze, Wilhelm, & Wittmann, 2000; Stauffer,Ree, & Carretta, 1996). Carpenter, Just and colleagues studied individual differences in complexmental activities, like language comprehension (Just & Carpenter, 1992) or performance on the

0191-8869/02/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved.

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Personality and Individual Differences 34 (2003) 33–39

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§ The research referred to in this article was supported by a grant funded by the Spanish ‘‘Ministerio de Educacion yCultura’’ (Grant No. PB98-0066).* Corresponding author. Tel.: +34-91-397-41-14; fax: +34-91-397-52-15.

E-mail address: [email protected] (R. Colom).

mailto:[email protected]

Raven Progressive Matrices (Carpenter, Just & Shell, 1990). The concept of WM has a strongappeal to these researchers (Just, Carpenter & Keller, 1996).Kyllonen and Christal (1990) reported an influential factor-analytic study claiming that rea-

soning, measured through psychometric ability tests, is closely related to WM capacity. Con-firmatory factor analyses yielded high estimates of the correlation between WM and reasoningability. Stauffer et al. (1996) found a correlation of +0.995 between a factor representing generalintelligence (g) and a factor representing WM. Colom et al. (submitted for publication) foundthat a higher-order factor representing g strongly predicts an endogenous first-order factorrepresenting WM. These authors show that WM is not distinguishable from g after the resultsobserved in several confirmatory factor analyses. Thus, the correlation perspective says that theconstruct of WM is strongly related to some central abilities.However, although g is the single most powerful source of variance evidenced by psychometric

tests, there are several doubts about the structure of WM. Is there a powerful single source ofvariance underlying the variety of WM tasks employed in laboratory research? There is a seriousobstacle to answer this question: WM is usually operationalized through a single task, althoughthere are some attempts to operationalize WM drawing on the methodology of intelligencestructure research. Thus, for example Oberauer et al. (2000) found that simultaneous storage andtransformation of information on WM were inseparable. Furthermore, tasks that involve notransformation of the information measured the same as tasks for simultaneous storage andtransformation. The addition of a processing component was not crucial for defining WM.However, they found a separation between spatial and verbal–numerical WM tasks.Some relevant questions are: (1) is there a general WM capacity? (2) Are there several specific

WM systems distinguished by the function facets or the content domains? (3) Has WM a hier-archical structure with a general resource underlying several specific facets? Intelligence structureresearch postulates a general factor (g) located at the apex within a hierarchy of several diversecognitive abilities (Carroll, 1993; Jensen, 1998). Can WM be characterized in the same way?According to Oberauer et al. (2000) ‘‘the same picture now emerges for the construct of WMcapacity. The high positive inter-correlation of all WM tasks used in this study strongly supportsthe construct validity of the concept of WM as one general cognitive resource’’ (p. 1041, italicsadded).The present research explores the structure of WM. The correlation between WM and intelli-

gence is also analyzed. WM is operationalized through several diverse computerized tasks, whileintelligence is measured through a typical marker.

2. Method

2.1. Participants

One hundred and eighty-seven participants took part in the study. Seventy-one were tested inBrazil and 116 were tested in Spain. The Brazilian sample comprised high-school students, whilethe Spanish sample comprised mostly university undergraduates, but also non-university people.The mean age was 18.19 (S.D.=3.49, age range=14–47). Sixty-one were males (meanage=17.93, S.D.=4.66) and 121 females (mean age=18.26, S.D.=2.77).

34 R. Colom et al. / Personality and Individual Differences 34 (2003) 33–39

2.2. Measures and procedures

Eight WM tasks were considered. They were computer administered. Raven Matrices (SPM)was the intelligence measure for the Brazilian sample, while PMA-R was the intelligence measurefor the Spanish sample (see Appendix for task description).The participants took the WM tasks in a single session lasting 2 h and 30 min. However, a rest

of 20 min was introduced between a block of tasks and the other. Half of the participants took ablock of tasks before the rest period, while the other half took the same block of tasks after thatperiod. The participants were required to take the intelligence test another day.

2.3. Analyses

The correlation matrix was submitted to a factor analysis. A MINRES analysis was performed.Then, factor scores were obtained through the Bartlett method. Finally, factor scores were cor-related with the scores obtained on the intelligence tests.

3. Results

The descriptive statistics, the correlation matrix, and the factor matrix are shown in Table 1.All the correlations are significant at P<0.001 (the residual matrix is shown below the diagonal

in Table 1). The inspection of the residual matrix is favorable to the thesis that WM is unitary,because the values are very small. Note that span tasks correlate as high between them as with theremaining tasks. The same can be said for verbal, numerical and figurative WM tasks. The factoranalysis extracted one single powerful factor explaining almost 70% of the common variance.The solution converged in five iterations.

Table 1Descriptive statistics, correlation matrix (the residual matrix is shown below the diagonal) and factor matrix

Descriptives Correlation matrix Factor matrix

Mean S.D. 1 2 3 4 5 6 7 8

1 Matrix span 60.07 15.42 0.653 0.715 0.589 0.617 0.610 0.632 0.548 0.7482 Letter span 4.17 1.13 0.04 0.826 0.542 0.680 0.707 0.753 0.679 0.8383 Digit span 5.54 1.44 0.08 0.09 0.527 0.709 0.734 0.726 0.695 0.872

4 Matrix scan 94.67 5.48 0.07 0.02 0.04 0.611 0.597 0.559 0.600 0.6745 ABC numerical 67.94 24.09 0.03 0.04 0.03 0.01 0.786 0.789 0.795 0.8646 ABCD Gram 77.7 21.36 0.04 0.03 0.02 0.006 0.02 0.795 0.798 0.884

7 Alphabet 61.93 27.95 0.02 0.01 0.02 0.03 0.02 0.01 0.774 0.8768 Digit ordering 23.60 6.59 0.08 0.04 0.03 0.02 0.05 0.05 0.02 0.856PMA-R 21.82 3.64Raven 37.36 9.45

R. Colom et al. / Personality and Individual Differences 34 (2003) 33–39 35

Factor scores were obtained after the solution shown in Table 1. These factor scores were cor-related with the intelligence tests’ scores. For the Brazilian sample, the Pearson correlation was+0.693 (P<0.001, N=62). For the Spanish sample, the Pearson correlation was +0.710(P<0.001, N=55).Therefore, the result suggests that a factor representing ‘‘general WM’’ strongly correlates with

a standard measure of intelligence.

4. Discussion

This study shows that WM can be considered as one general cognitive resource: all the cor-relations are high, between +0.6 and +0.8 (Table 1) irrespective of the content domain or thedifferentiation between span and storage+processing WM tasks. This general cognitiveresource is strongly related to intelligence: the factor score corresponding to a statisticalrepresentation of general WM is highly correlated with intelligence. Note that the value of thecorrelation is the same for the Spanish and the Brazilian samples (around +0.7), which increasesone’s confidence in the result. Therefore, there is a large source of variance common to both WMand intelligence.Intelligence is strongly related with the mental operations claimed in typical WM tasks. The

best measures of intelligence involve complex cognitive operations (inductive and deductive rea-soning, as well as abstraction). Furthermore, laboratory tasks correlate with psychometric cog-nitive abilities. Although different laboratory tasks can be devised to elicit different elements ofthe information processing system, individual differences remain invariant across tasks (Jensen,1998; Stauffer et al., 1996). These facts could help to explain the high correlation found in thisstudy between a composite measure of WM and measures of intelligence.The high correlation could result from the recruitment of similar mental resources. Functions

like monitoring the contents of WM, switching between tasks requiring WM, and so forth, havesome component in common. The component could be something that monitors operations per-formed on WM or something responsible for the maintenance of the goal structure needed toguide processing in any cognitive task.Prabhakaran, Smith, Desmond, Glover, and Gabrieli (1997) discovered strong links between

WM and performance on the Raven Matrices. They proposed that the link occurs because bothWM tasks and the Raven Matrices involve common neural systems. Raven performance acti-vated not only areas associated with rehearsing and storing domain-specific information, but alsoareas associated with the executive WM systems. Duncan et al. (2000) compared spatial, verbal,and perceptuo-motor tasks with high-g involvement and matched low-g control tasks. High-gtasks did not show diffuse recruitment of multiple brain regions. Despite very different task con-tent in several high-g and low-g cognitive tasks, lateral frontal recruitment was similar. Theirconclusion was that g derives from a specific frontal system important in the control of cognitiveactions.In summary, the mental resources claimed for WM tasks and intelligence tests could be

related to functions of the frontal cortex. These functions could help to explain the high cor-relation found in this study. However, future research is needed to find out the germanefunctions.


Acknowledgements

We thank Arthur Jensen for reviewing a first draft of this article. We also thank Sybil Eysenckfor editing the manuscript.

A1. Appendix. Task description

A1.1. Working memory tasks

Matrix span (adapted from Detterman, Mayer, Caruso, Legree, Conners, & e Taylor, 1992):five empty squares are displayed on the middle of the computer screen and one empty square isdisplayed at the top of the computer screen. After a warning signal, the five empty squares aresequentially filled by a 4�4 matrix (each square for 1 s). When the next square is filled, the pre-vious display disappears. The inter-stimulus interval was 500 ms. To avoid learning, the sequenceof stimuli was presented at random from 15 possible matrices. The participant’s task is toremember the appearance of each of the five squares in order to decide where the display thatappears at the top of the computer screen was located within the five middle squares. The per-centage correct after 75 trials was used as the participant’s score.Letter span. Several letters are sequentially displayed on the computer screen. The task begins

with three letters, increasing their number until the participant cannot accurately reproduce thesequence using the keyboard (three errors of five attempts). There are two conditions: reproduc-tion in a direct order and reproduction in a reverse order. The mean number of accuratelyreproduced letters in the direct and the reverse condition is obtained as the participant’s score.Digit span. Several single digits are sequentially displayed on the computer screen. The task

begins with three digits, increasing their number until the participant cannot accurately reproducethe sequence using the keyboard (three errors of five attempts). There are two conditions: repro-duction in a direct order and reproduction in a reverse order. The mean number of accuratelyreproduced digits in the direct and the reverse condition is obtained as the participant’s score.Matrix scan (adapted from Detterman et al., 1992): a warning signal appears for 500 ms, and

then two empty squares appear on the middle of the computer screen. One, two or three filled4�4 squares (each square for 1.5 s) appear at the top of the computer screen. After this pre-sentation, the square located at the left of the middle of the computer screen is filled by a patternmatrix of black and white squares; the square located at the right of the middle of the computerscreen remains unfilled. The participant’s task is to decide if the filled square was positive (match)or negative (not match) with some of the filled squares sequentially presented at the top of thecomputer screen. If this is the case, then the filled square must be selected. The percentage correctafter 75 trials (37 trials matching and 38 not matching) was obtained as the participant’s score.ABC numerical (adapted from Kyllonen & Christal, 1990): there are two types of items within

this task. In the first one, two equations are sequentially displayed (for 1.5 s each). For instance: ‘‘A=B+1:B=3::A=?’’ The participant is asked to retain the first equation while waiting for thesecond equation. Then the B value must be mentally replaced in the first equation to compute thevalue corresponding to A. In the second type of item, three equations are sequentially displayed.For instance: ‘‘A=B+2:B=C+4:C=1::A=? B=?’’ Only when the third equation is presented


can the participant compute the value of B and then the value of A. Each equation was displayedfor 3 s, with a delay of 750 ms. The percentage correct was obtained after 45 trials as the parti-cipant’s score.ABCD Gram (adapted from Kyllonen & Christal, 1990). Three sentences are sequentially dis-

played (for 4 s each). The first two sentences refer to the relative position of two single letters,while the third sentence refers to the relative position of the letters considered in the first (pair Aand B) and second sentences (pair C and D). For instance: ‘‘D before C:B not before A::Sentence1 not before sentence 200. The participant must decide which of four alternatives represents theposition of the four letters. For instance: ABCD:DCAB:BADC:CDAB. The percentage correctwas obtained after 32 trials as the participant’s score.Alphabet (Adapted from Craik, 1986). Several words are sequentially displayed on the compu-

ter screen (for 1.5 s each, delay of 750 ms). The participant must retain the first letter of eachword. After a delay of 2 s, she must reproduce through the keyboard the retained letters, butaccording to their position within the alphabet. The percentage correct after 30 trials wasobtained as the participant’s score.Digit ordering. Pairs of single digits are sequentially presented on the computer screen (2.5 s

each pair, delay of 300 ms). For instance ‘‘ 1 4:2 7:4 2:7 100. The participant’s task is to mentallyadd each pair of digits retaining the result of the summation. Then, she must reproduce throughthe keyboard the numbers in ascending order. In the example, the reproduced sequence must be:‘‘ 5 6 8 900. The percentage correct after 30 trials was obtained as the participant’s score.

A1.2. Intelligence tests

PMA-R. This is the letter series test from the Primary Mental Abilities Test (Thurstone, 1938).The participant is asked to select a letter taking into account the inductive relationships estab-lished between several letters serving as the item domain. The test includes 30 items and has atime limit of 6 min.Standard Progressive Matrices. This is the well-known test designed by J. Raven (CEPA, 1993).

The SPM includes 60 items. It was administered without a time limit.

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Working memory and intelligence