Processing Speed, Working Memory, and Fluid Intelligence ...

PSYCHOLOGICAL SCIENCE

Research Report PROCESSING SPEED, WORKING MEMORY, AND FLUID INTELLIGENCE: Evidence for a Developmental Cascade

Astrid F. Fry and Sandra Hale WashingtonUniversity - Processing speed, working memory capacity, and Abstract fluid intelligencewere assessed in a large sample (N = 214) of children,adolescents, and young adults (ages 7 to 19 years). Results of path analyses revealed that almost half of the agerelatedincrease in fluid intelligencewas mediatedby developmentalchanges in processing speed and workingmemory,and nearly three fourths of the improvementin workingmemory was mediatedby developmentalchanges in processing speed. Moreover,even whenage-relateddifferencesin speed, working memory,andfluid intelligencewere statisticallycontrolled,individualdifferences in speed had a direct effect on working memorycapacity, which, in turn, was a direct determinantof individualdifferencesin fluid intelligence. As children mature, they can process information faster (Cerella & Hale, 1994; Kail, 1991), hold more items in working memory (Dempster, 1981; Gathercole & Baddeley, 1993), and perform better on tests of fluid intelligence (Court & Raven, 1982). The co-occurrence of these changes raises the question of the nature of the relationship among them. Carpenter, Just, and Shell (1990) have argued that working memory is a critical determinant of performance on tests of fluid intelligence such as the Standard Raven's Progressive Matrices. Their theory of analytical intelligence suggests that age-related increases in working memory might contribute to the improvement in children's fluid intelligence as they mature. Recently, Kail (1992; Kail & Park, 1994) has argued that faster information processing underlies the well-documented increases in memory span with age. Thus, much of cognitive development may represent a cascade wherein age-related changes in processing speed lead to changes in working memory that, in turn, lead to changes in performance on tests of fluid intelligence (Kail & Salthouse, 1994). Recent evidence concerning the general nature of age differences in processing speed appears to strengthen the likelihood that a cascade model describes cognitive development. Given that a test like the Standard Raven's Progressive Matrices is believed to measure a very general ability (Carpenter et al., 1990; Snow, Kyllonen, & Marshalek, 1984), test performance should be significantly affected by increases in processing speed only if speed also represents a very general ability. That is, an improvement in speed on a single information processing task would have only minimal consequences for an untimed test of general fluid intelligence unless improvement on this task were highly correlated with improvements on a wide variety of Address correspondence to Astrid F. Fry, Department of Psychology, Washington University, Campus Box 1125, One Brookings Dr., St. Louis, MO 63130; e-mail: [email protected]. VOL. 7, NO. 4, JULY 1996

other processing tasks. Recent findings from multitask experiments suggest that performances on many different speeded tasks improve in concert during childhood (Hale, 1990; Hale, Fry, & Jessie, 1993; Kail & Park, 1992). Moreover, the speed of information processing on different tasks remains highly correlated in young adults (Hale & Jansen, 1994; Vernon, 1983). These results are consistent with a global trend in the development of processing speed. Because of the relationship between processing speed and working memory, this global trend might influence the development of working memory for many different kinds of information, thereby affecting performance on fluid intelligence tests. Alternatively, it is possible that the causal relation is reversed from that just described. That is, age-related changes in fluid abilities other than processing speed, such as the ability to see patterns and relationships, might be responsible for faster performance on information processing tasks (Anderson, 1992). Thus, developmental changes in speed could be either the cause or the consequence of changes in fluid intelligence. Although previous studies have examined the causal relation between age-related changes in processing speed and memory (Kail, 1991; Kail & Park, 1994), no previous developmental study has examined the causal relations among all four variables of current interest, that is, age, processing speed, working memory, and fluid intelligence. The present study was designed specifically to determine whether there is a developmental cascade in which children's information processing becomes faster, leading to improvements in working memory, and improved working memory, in turn, leads to increases in fluid intelligence. In addition, we tested the alternative hypothesis that changes in fluid intelligence drive changes in speed. These hypotheses were evaluated using path analytic methods that also provided information on the determinants of individual differences in cognitive performance when age-related differences were statistically controlled.

METHOD Participants were students at local private schools in the St. Louis metropolitan area and undergraduates at Washington University in St. Louis. The sample of 214 participants (96 females and 118 males, ages 7 to 19 years) consisted of 20 second graders, 20 third graders, 40 fourth graders, 36 fifth graders, 32 sixth graders, 17 seventh graders, and 49 young adults (high school seniors and 1st- and 2nd-year college students). Processing speed, working memory, and fluid intelligence were assessed using computerized tasks. All software was written in Turbo Pascal by the authors and included timing and display routines provided by PCX Toolkit, by Genus. Copyright © 1996 American Psychological Society

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Speed and Memory Mediate Cognitive Development Processing speed was measured using four quite different information processing tasks that were selected from those used in a previous study (Hale & Jansen, 1994). That study demonstrated that an individual's speed on these tasks is relatively independent of which task is considered; that is, a given individual is approximately the same percentage faster (or slower) than average on all of the tasks. Thus, the four tasks provide separate assessments of the global processing-speed construct. The four speeded tasks were (a) disjunctive reaction time (two vertical arrows pointed in the same or different directions), requiring a same/different judgment (two conditions); (b) shape classification (two simple geometric forms whose shapes were either the same or different and whose sizes were either the same or different), requiring a same/different shape judgment (four conditions based on crossing shape with size); (c) visual search (green squares and red circles served as distractors and half the trials included a red square target; total number of items was either 9 or 25), requiring a target present/absent judgment (four conditions based on crossing set size with target presence); and (d) abstract matching-to-sample (three patterns that could vary along four dimensions - shape, color, number, and orientation), requiring a judgment as to which of two upper patterns best matched the lower pattern (four conditions based on the number of irrelevant dimensions held constant across all three patterns). For each participant, a single speed index was calculated by first taking the individual's response time (RT) in each of the 14 conditions and dividing it by the mean RT for the young-adult group in that condition, and then averaging the 14 ratios to obtain a mean RT ratio. Working memory was assessed using four different tasks that tested memory for two different types of items (digits and spatial locations), each with two levels of concurrent processing requirements. For the digit memory tasks, participants viewed a series of digits, then recalled the digits aloud in the order of presentation to the best of their ability. For the location memory tasks, participants saw a series of grids, each with an X in a different location, then indicated the locations they recalled by marking directly on the screen with a felt- tip pen. In the digit and location memory tasks with minimal concurrent processing requirements, participants simply had to maintain old items in storage while encoding new items. In the tasks with increased concurrent processing requirements, participants also had to report the colors of items while maintaining their identities (digits) or locations (Xs) in working memory. Previous research in our laboratory has shown that, depending on the nature of the report response, the processing required to report colors can interfere selectively with verbal or spatial memory (Hale, Myerson, Rhee, Weiss, & Abrams, 1996). For the digit memory task, participants named the color of each digit as it appeared in the series. For the location memory task, participants indicated the color of the X by pointing to a matching color in a palette presented to the right of the grid. For each participant, the maximum number of items that could be maintained in working memory was determined for each of the four tasks, and a general working memory index was calculated as the mean of the four span measures. Fluid intelligence was assessed using an untimed test, the Standard Raven's Progressive Matrices (Court & Ravens, 1982). In order to maintain the video-game-like quality of the 238

overall procedure, we presented test items on the video monitor. For each participant, the raw score (number correct) was used as the measure of fluid intelligence. (Note that the scores were not converted to percentiles because such a conversion, although useful in estimating IQ, would mask the impact of development on fluid intelligence.)

RESULTS The intercorrelations between individual participants' mean RTs for the four processing-speed tasks ranged from .73 to .88, and were highest between the two tasks that produced the shortest RTs (i.e., disjunctive choice reaction time and shape classification, r = .88) and between the two tasks that produced RTs of intermediate length (i.e., shape classification and visual search, r = .88). The intercorrelations between individual participants' four memory span measures ranged from .44 to .61, and were highest between the two measures of working memory for the same type of item (i.e., memory for digits with and without interference, r = .61, and memory for locations with and without interference, r = .60). The developmental trend in processing speed was very similar to that reported by Kail (1991), having the form of an exponential decay in the ratio of child to young-adult RTs (see Fig. 1; an age window of 8 months was used so as to maximize the number of data points while maintaining a minimum of 7 individuals per age group). The speed index is comparable to the slope of the relation between child and adult RTs used by Kail (1991) and us (Hale, 1990; Hale et al., 1993) as both indicate how many times longer a child's RTs are than those of the average young adult. In subsequent analyses, however, the logarithm of the speed index was used in order to decrease skew and heterogeneity of variance between age groups. With the transformed speed index, strong correlations were observed

Fig. 1. Mean response time (RT) ratio plotted as a function of age. The open circles represent the means calculated for 13 groups created by subdividing the sample on the basis of age into bins of 8 months each. The solid line is the best-fitting exponential decay function (Equation 6 in Kail, 1991). VOL. 7, NO. 4, JULY 1996

PSYCHOLOGICALSCIENCE

Astrid F. Fry and Sandra Hale amongspeed, memory, and fluid intelligence, and all three of these measureswere stronglycorrelatedwith age (absolutevalues of all rs > .60; Table 1). We conducted path analyses using Bentler's Structural EquationsProgram(Bentler, 1989)in orderto examinepossible causal relations among age, speed, memory, and fluid intelligence. The first path analysis tested two hypotheses: (a) that age-relatedimprovementsin performanceon the Raven's are mediatedby changes in workingmemorycapacity and (b) that when age-relateddifferencesin workingmemoryand Raven's performanceare statisticallycontrolled, working memory capacityis still a significantdeterminantof fluid intelligence.The resultsof this analysis are presentedin the top panel of Figure 2; the numbernext to each path from a causal variable to a dependentvariableis the standardizedpathcoefficientand representsthe change in the dependentvariable,in standarddeviation units, expected to result from a change of one standard deviationin the causal variable,if other possible determinants specifiedin the model were held constant. The solid lines represent statisticallysignificantpaths, and the dotted lines represent paths that failed to pass the Wald test for inclusionin the model being tested. Althoughthere is a significantdirect path between age and Raven's performance,the developmentalincrease in workingmemorycapacity mediatesmuch of the relationshipbetween age and fluid intelligence.That is, decomposition revealedthat 41%of the total age-relatedeffect on fluid intelligenceis mediatedby age-relateddifferences in working memory.Notably, even with age-relateddifferencesin working memoryandfluidintelligencestatisticallycontrolled,there was still a significanteffect of workingmemoryon Raven's performance. The second path analysis tested three hypotheses: (a) that age-relatedimprovementsin workingmemory mediate the relationshipbetween age differences in speed and fluid intelligence; (b) that when age-relateddifferencesin speed, working memory,and fluid intelligenceare statisticallycontrolled,individualdifferencesin workingmemorymediatethe relationship between speed and Raven's performance;and (c) that when age-relateddifferences are statistically controlled, individual differencesin speed are a significantdeterminantof working memorycapacity. The results of this analysis are shown in the middlepanel of Figure 2. Although age has significantdirect effects on both workingmemoryand Raven's performance,decompositionrevealedthat 71%of the total age-relatedeffect on working memory capacity is mediated by age differences in processing speed, and 45% of the total age-relatedeffect on fluidintelligenceis mediatedby age-relateddifferencesin speed

Fig. 2. Schematic representationsof the three models with standardizedpath coefficients. Arrows representthe direction of causality tested; solid and dotted lines representpaths that were significantand not significant,respectively. and workingmemory.The coefficient for the causal path from speed to Raven's performanceis not statistically significant, indicatingthat speed has no directeffect on fluidintelligence.In

Table 1. Intercorrelationsbetween age, speed, memory,andfluid intelligence Age Age Processingspeed Workingmemory Raven's raw score

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-

Processing speed

Working memory

Raven's raw score

-.849 -

+.651 - .706 -

+.633 - .607 + .639 -

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Speed and Memory Mediate Cognitive Development contrast, individualdifferences in speed do have an effect on workingmemory capacity, even when age-relateddifferences are statisticallycontrolled.Moreover,there is still a significant effect of workingmemoryon Raven's performancewhen speed and age are both statisticallycontrolled. The third and final path analysis tested the alternativehypothesis that age-relatedchanges in fluid abilities other than processingspeed account for age-relateddecreases in RTs. As the bottompanel of Figure2 shows, there is a weak but significant direct path from Raven's performance to processing speed. In contrast, the direct relationship between age and speed is very strong, and decomposition revealed that it accounts for more than 91% of the total age-relatedeffect on processingspeed. The finding that the direct relationship between age and speed is minimallyaffected by either individualdifferencesor age-relateddifferences in fluid intelligence was furtherexamined in the followingmanner.Because of the extensive rangein Raven's performancewithin each age group, it was possible to create groups matchedon raw scores. Childrenfrom adjacent gradelevels (second and thirdgraders,fourthand fifth graders, sixthand seventhgraders)were pooled in orderto maximizethe size of the matchedgroups. Then, three groupsof childrenand a group of young adults were selected, with 18 individualsin each group.The participantsselected for these groupswere all closely matchedon the StandardRaven's ProgressiveMatrices (see Table 2). Despite the fact that matching participantsof differentages on their Raven's raw scores necessarily meant that IQ was negatively correlated with age, the children's groups had longer RTs than the young-adultgroup on every task. As can be seen in Figure 3, even under these circumstances, there were clear age differences in processing speed, and these differencesin speed were global, with similareffects on performanceof diverse tasks, both simple and complex. Thatis, for each age group, the ratio of child RTs to adult RTs was approximatelyconstantacross tasks and conditions,as indicatedby the fact that the data were well describedby simple linear functions with intercepts close to zero (see Table 3). Moreover,the slopes of these regression functions decreased systematicallyas the age of the child group increased. DISCUSSION The findings of the present study reveal that age-related changesin processingspeed mediatemost of the developmental increasesin workingmemory capacity. Moreover, age-related increasesin speed and workingmemoryaccountfor nearlyhalf of the total age-relatedeffect on fluid intelligence.The present

Table2. Means, standarddeviations, and ranges of Raven's raw scores for the matched groups Group

Mean

SD

Range

Second and thirdgraders Fourthand fifth graders Sixth and seventh graders Adults

40.61 38.72 38.22 40.11

5.42 4.99 5.59 5.83

31-48 3(M7 30-48 30-48

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Fig. 3. Meanresponsetimes (RTs)for Raven's-matchedgroups of childrenas a functionof a matchedadultgroup'smean RTs. Each data point indicatesthe relationbetween the performance of a specific child group in a particulartask condition and the performanceof the young-adultgroupin thatcondition.Thatis, the y-coordinatefor a data point is the mean RT of the child group, and the jc-coordinateis the mean RT of the young-adult group.The dotted line indicateswhere the datawould fall if the performanceof a child group and the adult group were equivalent. Solid lines representthe best-fittinglinearfunctionsfit for each age group separately. Parametervalues for each of the three functionsand fit statistics are providedin Table 3. results replicate and extend Kail's (1991; Kail & Park, 1994) recent findingsin developmentalstudies concerningspeed and workingmemoryand are consistent with Kail and Salthouse's (1994) previously untested model of the relations among age, speed, workingmemory,and fluid intelligenceduringdevelopment. The present findingsalso provide supportfor the theory of analytical (fluid) intelligence proposed by Carpenter et al. (1990)and extend their findingswith respect to adults to individual differences in fluid intelligence in children and adolescents. Consistent with their theory, there was a direct causal relation between working memory capacity and the performance of individualchildren,adolescents, and young adultson

Table3. Regressionparametersandfit statisticsfor the data shown in Figure 3 Group

Slope

Intercept

r2

Second and thirdgraders Fourthand fifth graders Sixth and seventh graders

2.13 1.63 1.40

- . 132 - .056 - .032

.98 .99 .99

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PSYCHOLOGICAL SCIENCE

Astrid F. Fry and Sandra Hale Standard Raven's Progressive Matrices even when age-related differences in processing speed, working memory, and fluid intelligence were all statistically controlled. This theory suggests two possible interpretations of the direct effect of age on Raven's performance. In addition to individual and age differences in the capacity of working memory, there are differences in the skill with which people manage multiple problem-solving goals in working memory. This skill may be taught (Lawson & Kirby, 1981), and therefore likely improves with age and experience. There are also differences in the ability to induce abstract relations, an ability that Carpenter et al. (1990) suggested is especially important for solving relatively difficult matrix problems. Age and experience (including formal education) may result in greater abstraction ability as well as in better goal management, and either or both may contribute strongly to the direct path from age to Raven's performance observed in the present study. Finally, the present findings reveal a strong connection between individual differences in processing speed and working memory. This connection is consistent with the results of previous studies (Miller & Vernon, 1992) and appears to parallel the observed connection between age-related differences in speed and working memory. However, the nature of the mechanism (or mechanisms) underlying these connections is not yet completely understood, especially with respect to processing and maintaining nonlexical information. With respect to lexical information, Kail (1992; Kail & Park, 1994) has shown that processing-speed differences lead to differences in articulation rate that, in keeping with Baddeley's (1992) model of working memory, are significant determinants of digit and letter span in children and adults. However, overt articulation rate can only be a proxy for the speed of covert rehearsal. This point is underscored by the finding that children with a form of cerebral palsy that is associated with very slow speech rates have memory spans equivalent to those of children in an age-matched control group (White, Craft, Hale, & Park, 1994), suggesting that children can develop normal rates of covert rehearsal even if their overt speech is impaired. The present study combines both method and theory from the areas of experimental and developmental psychology as well as from psychometrics in order to examine the sources of age and individual differences in cognitive function. The results of path analyses provide evidence of a developmental cascade in which increases in processing speed result in improvements in working memory that, in turn, contribute to improvements in fluid intelligence. In addition, statistically controlling for agerelated differences revealed that a similar cascade affects individual differences in cognition: Differences in speed have a direct effect on working memory capacity, and these individual differences in memory are a direct determinant of fluid intelligence. Clearly, both developmental and individual differences in speed and working memory play important roles in higher cognitive abilities; however, a more detailed account of how both lexical and nonlexical information are maintained in (and lost from) working memory will be needed before the way in

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which processing-speed differences initiate these cascades can be understood. Acknowledgments- The data presented here were collected as part of a study conducted in partial fulfillment of the requirements for a doctoral degree by the first author. A preliminary report was presented at the meeting of the Psychonomic Society, November 1994, in St. Louis. We wish to thank Joel Myerson for his helpful comments at all stages of this project and Beth Oberlander, Jamie Michael, Jill Raney, and Karen Topping for their assistance with data collection and stimulus preparation. In addition, we are indebted to the students, teachers, and administrators at the following St. Louis area schools: Bishop DuBourg High School, St. Joan of Arc Elementary School, St. Mary Magdalene Elementary School, and Immacolata Elementary School.

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