Rate of forgetting and intelligence

RATE OF FORGETTING AND INTELLIGENCE GERALD E. LARSON NAVY PERSONNEL RESEARCH AND DEVELOPMENTCENTER,

ABSTRACT: established panied

Research relationship

by a comparable

on

mentally

between relationship

retarded learning between

subjects rate

and

indicates intelligence

forgetting

rate and

SAN DIEGO

that is not

the

well

accom-

intelligence.

To

date,

however, almost nothing is known about the link between intelligence and forgetting when subjects are exclusively drawn from the normal ability ranges. In the present study, one hundred and sixteen normal young men were asked to recall problem solutions after performing a distractor task consisting of one, two, or three speeded math items. The results indicate that longrr distractor intervals result in diminished recall, but, more importantly, that high and low ability subjects forget at equal rates.

Although there is much evidence linking intelligence with learning ability (Campione, Brown, & Bryant 1985; Estes 1982; Jensen 1989; Zeaman & House 1967), few studies have investigated the related question of whether intelligence covaries with individual differences in forgetting once the learning task has been mastered. Moreover, there is no justification for simply ass~~~zi~z~ that a predictor of learning (i.e., intelligence) might also predict forgetting, particularly since the latter ‘can involve unique psychological processes such as reorganization and elaboration of knowledge with the passage of time, and errors (such as substitution) upon recall. The matter is further clouded by the fact nearly all studies on the present topic involve mentally retarded subjects, which could put the robustness of the studies’ conclusions in doubt. It is, nevertheless, valuable to note one seemingly reliable conclusion from work with retardates; when rehearsal is prevented, groups of differing intelligence have similiar forgetting curves (e.g., Borys & Spitz 1976; Brown 1974; Ellis, McCartney, Ferretti, & Cavalier 1977; Ferretti 1982: But see Ellis & Meador 1985). For example, Ferretti (1982) required normal and retarded subjects to recall sequences of consonants after performing an interpolated task (tonal detection) for various amounts of time. Ferretti found that the forgetting curves of retarded Directall correspondenceto: Gerald

E Larson, Aptitude Research Diwsion, Navy Personnel Research and Development Center

San Dlego. CA 92152 Learning and Individual Differences, Volume 5. Number 3, 1993, pages 187-197. All rights Iof reproduction In any form reserved

CopyrIght (Q 1993 by JAI Press, Inc ISSN: 1041-6080

188

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and nonretarded subjects were parallel. This would indicate that the well established relationship between intelligence and Icar~zirr~~ mte is not accompanied by significant correlations of intelligence with for#ti?z~ rate. Again, however, it is not clear that this conclusion would hold true for normal subjects. For example, Kyllonen and Tirre (1988) studied normal adults and concluded that there were, contrary to the findings just cited, strong correlations between forgetting and intellectual abilities such as general knowledge and reasoning. Kyllonen and Tirre’s methodology, however, while extremely sophisticated, was focused on global relationships between learning and forgetting, rather than on individual differences in the time course of forgetting per se. Thus the question of whether intelligence correlates with forgetting rate in normal adults remains unanswered. In the current article, intelligence scores from a sample of non-retarded young men are correlated with recall of learned material (i.e., problem solutions) folowing various amounts of interpolated cognitive processing. The interpolated task was designed to prevent rehearsal, since it is widely known that rehearsal (or strategy) differences can complicate the interpretation of memory scores (e.g., see Campione, Brown, & Bryant 1985). The main question to be addressed is whether intelligence and retention interval have an interactive effect on recall performance.

METHOD SUBJECTS Subjects were 116 male Navy recruits (mean age 18.6 years, SD = 1.48 years) selected at random from groups undergoing in-processing at the Recruit Training Command, San Diego. The sample was 85 percent Caucasian, 9 percent Black, and 6 percent “other.” INSTRUMENTS

AND PROCEDURES

All subjects were first tested on two paper and pencil reasoning measures (Dominoes and Raven’s Advanced Progressive Matrices), after which they were presented with a computer-based task involving alternating math (“Arithmetic Tracking”) and memory problems. The computer tests were group-administered on Hewlett-Packard Integral microcomputers using a simplified keyboard. The keyboard was modified by using a plastic mask that revealed only the designated response keys along with a key labeled HELP that could be pressed during testing to suspend the program and request assistance. The S, F, H, K, and ; keys were relabeled as: A, B, C, D, and E. The space bar was relabled ENTER. The numeric keypad keys retained their meanings. The computers operate un-

RATE @FORGETTING

AND /NTELL/GENCE

189

der UNIX(TM) and the tests were written in standard C. Finally, scores for the 10 tests of the Armed Services Vocational Aptitude Battery were gathered from the personnel records of the examinees. Further details of the various measures are provided below. Dolrzinoes-The Dominoes test of reasoning skills (Gough & Domino 1963) consists of 44 items and requires individuals to determine what domino would complete a series; twenty-five minutes are allowed and the test has a maximum score of 88 (top and bottom half of the completing domino must be specified and are scored separately). Rar~rz’s Adumced Puu,grcssiveMatrices (APM) test--A half length (18 item) version of the APM (Raven, Court, & Raven 1986) was administered with a 20 minute time limit. The eighteen items were selected on statistical grounds (based on previous data from over 2,000 subjects tested under standardized conditions) to provide the best possible estimate of total score on the full length version. ASVAB-The Armed Services Vocational Aptitude Battery (ASVAB), which consists of the ten tests shown in Table 1, is used for selection and classification of military applicants. ASVAB scores were obtained from military personnel records.

Arithmetic Tracking Pretest. The Arithmetic Tracking (Christal 1986) pretest consisted of 20 problems measuring speed of carrying out elementary arithmetic operations. For example, a typical item might consist of the following sequence of ten screens: 8 -3 +5 12 x3 -3 +4 -7 -4 ZZ After mentally performing the operation indicated on each screen, the examinee presses the key to go on to the next screen until the I‘=“ screen appears, whereupon the examinee enters a one-digit answer using the numeric keypad. (All problems involved one division operation, one multiplication, a combination of six additions and subtractions, and a single digit answer). Latency was measured as the time from the first screen to the “=I’ screen. (Time to input the response was not recorded because it reflects visual keyboard search time as well as math processing time.) Response accuracy was also recorded on each trial, and subjects were asked to rework items that they had answered incorrectly. Only the latency associated with the correct response for each item

LEARNING AND /ND/V/DUALDIFFERENCES

190

TABLE 1 Tests, in ASVAB Forms 11, 12,

VOLUME

5 NUMBER

3 1993

and 13

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was recorded. The mean and standard deviation of the twenty latencies were calculated for each subject, and these values later formed the basis for warning subjects about working too slowly when similiar math items were used as distractors in a subsequent part of the study (described below).

Delayed Recall Task. Each item in the delayed recall task had three parts. First, subjects

read the following

Now you will have

to guess

few tries as possible. will be different

instructions

each

the order

The numbers time.

Each

on the computer of 6 numbers.

will always number

The goal is to guess

be 1,2,3,4,5,

is only

monitor:

used

and 6. Their

in as order

ONCE.

Next, the computer program guided subjects through several practice problems. Each problem began with a row of 6 dashes in the middle of the computer screen. The dashes served as place holders for the to-be-guessed digits. The subject’s initial task was to discover the first digit by individually entering the numbers 1 through 6 (order of entry was determined by subjects). When an incorrect digit-guess was entered, the word “NO” was momentarily shown

RATE OFFORGE77/NG AND /NTELL/GENCE

191

above the first dash, then the screen went blank and the 6 dashes reappeared. When a correct guess was made the word “YES“ appeared above the first dash. The subject was then required to try and guess the second digit from the 5 numbers remaining in the set 1 . 6. If the first attempt at guessing the second digit in the sequence was correct, then the word “YES” appeared above the second dash and the task shifted to guessing the third digit. If the guess was incorrect then the word “NO” appeared above the second dash, followed by a blank screen and the reappearance of the 6 dashes. Thus, the subject had to go back to the beginning of the problem whenever a mistake was made. But in this example the subject already knows the first digit and is expected to immediately re-enter the correct value. Next, a new guess at the second digit must be made. The variable of interest was the number of key presses required to correctly indicate all 6 digits. Digit sequences used in the task were randomly generated with the constraint that each of the digits 1 through 6 should appear four times in each of the positions 1 through 6. There was thus a total of 24 problems. After the subject had correctly guessed all 6 digits, the program branched to a set of from one to three Arithmetic Tracking problems, corresponding to three levels of retention interval for the recall task. The purpose of the math problems was t’o prevent rehearsal by displacing the digit sequence from short-term memory. The problems were presented in the same format as in the pre-test, except that subjects were warned that they were working too slowly if their item latenties were more than 1.5 standard deviations above the mean established in the pre-test. This was to ensure that subjects devoted complete attention to solving the math problems, as opposed to rehearsing the digit sequence. Feedback on accur,acy was also given for every trial. Accuracy and latency were saved from each trial for later analysis. Following the Arithmetic Tracking problem(s) the recall (or “reconstruction”) portion of the digit sequence problem was presented. The six dashes were again shown on the screen and subjects were told to repeat the digit sequence that they entered prior to the math problem(s). The format of the recall portion of the task was the same as that used in the guessing portion. In summary, each item had three parts: (a) correctly solve a digit sequence problem, (b) solve from one to three Arithmetic Tracking problems, and (c) recall/reconstruct the digit sequence. There were 24 items; eight each with one, two, and three interpolated Tracking problems. The test was preceeded by three practice items. After the twelfth test item subjects were given an (approximately) five minute break, in the form of video game in which the task was to “launch missiles” in order to intercept a “UFO” traveling at varying speeds and paths across the video screen.

RESULTS Descriptive statistics for the psychometric ability scores and for the variables from the delayed recall test are shown in Table 2. The ASVAB scores (GS to EI)

192

LEARNING

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P.yhomce%

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TABLE 2 Statistics for Variables in the Study

A/C/if> 9.22

3.04

Domino

60.42

I0.04

GS

53.41 53.01

R&Jell

AR WK PC NO cs AS MK MC El A,_ih?rr~i~~

s2.77 53.02 54.41 53. I6 53.50 53.60 54.72 SO.89

6.61 6.43 4.77 4.59 6.43 6.72 7.x0 1.34 7.15 7.30

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indicate that the sample is slightly above average in psychometric ability, given that the test is standardized to a mean of 50 and standard deviation of 10 in the 19-23 year-old American youth population. Results for the math task (Arithmetic Tracking) indicate that subjects processed the mathematical operations significantly faster (t [115] = 9.46, p < .Ol) and less accurately (t [115] = 3.89, p < .Ol) when the task was used as an interpolated distractor than during the baseline pre-test. This, of course, suggests that a speed/accuracy trade-off might have occurred during testing. Data at the individual level, however, reveals a significant negative correlation (- .31, p < .Ol) between speed and accuracy of Arithmetic Tracking during the distractor condition, meaning that subjects with shorter latencies had a lz~~yl~ev proportion correct than did subjects with long latencies. Thus, there is no evidence for a speed/accuracy trade-off at the individual level. Moreover, an accuracy of .81 on a distractor task of nontrivial difficulty (i.e., Arithmetic Tracking) indicates that subjects were indeed doing math rather than rehearsing the digit sequences. Subjects were thus behaving in accordance with the experimental design. Table 2 also shows overall results for the solution of digit sequences under the guessing and recall conditions. As can be seen there was a pronounced reduction in the mean number of key presses needed in the recall condition as com-

RATE OF FORGETTING AND INTELLIGENCE

193

pared with the guessing condition (t [115] = 41.0, p < .Ol), indicating substantial memory for the digit sequences despite negligible rehearsal opportunity. Table 3 shows the correlations between the psychometric ability scores (grouped by aptitude construct) and performance on the computer task. Inspection of the table reveals that the RT and accuracy measures from Arithmetic Tracking were correlated with the majority of psychometric ability dimensions, although the correlations tended to be highest with the psychometric math and clerical speed scores. This latter finding is highly logical given that Arithmetic Tracking is itself a speeded math task. For the digit sequences task, performance was correlated with the fluid, math, verbal, and mechanical ability dimensions, but not with clerical speed or specific technical knowledge. This broad, nonspecific pattern of relationships suggests that the most parsimonious analysis would be to relate digit memory performance to general intellectual ability rather than to specific abilities. As an initial step, two summary psychometric ability scores were calculated. The first was a fluid ability composite calculated as the average of the standardized scores for the Raven APM and Dominoes tests. The second psycho-

Correlations

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TABLE 3 Task Performance

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LEARNING AND /ND/V/DUAL DIFFERENCES

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VOLUME

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metric score was a Cy-score based on the ten ASVAB tests. The ASVAB-8 score was derived by performing a hierarchical factor analysis (orthogonalized following Schmid and Leiman 1957) on ASVAB scores from the 1988 fiscal year Navy applicant sample (N = 147,287). The loadings of the 10 ASVAB tests on the hierarchical factor were subsequently used as weights to calculate an ASVAB-g for each individual. Table 4 shows the correlations between the computer tasks and the two sumability mary psychometric ability scores. As can be seen, both psychometric measures were significantly correlated with all of the measures from the experimental task. Though correlations between ability and median number of key presses in the guessing condition are somewhat higher than correlations between ability and the medians for recall presses, these differences are nonsignificant. Of primary interest is the relationship between ability and recall performance following different levels of distraction (i.e., different numbers of arithmetic problems interpolated between initial guessing and recall of the digit sequences). Table 5 shows raw and corrected scores for recall under three levels of distraction. The raw scores shown at the left of the table do not accurately reflect differences in recall difficulty because they are confounded by differences in baseline item difficulty. A common scale is needed before comparisons can be made. To standardize recall performance, z-scores were calculated for each recall item using each item’s baseline mean and standard deviation. The individual item scores were then averaged to create a mean z-score for each item type in the recall condition. These are reported as “corrected” scores in the table. To minimize confusion, the standardized scores were then reflected so that better performance corresponded to a higher score. The corrected scores indicate that recall of digit sequences was equally difficult when one or two Arithmetic Tracking problems were interpolated between guessing and recall. However, when a third problem was presented, recall was significantly lower than for the one item (t [115] = - 2.83, 11< .Ol) and two item (t [ll5] = - 2.58, /J< .05) interpolations. Table 5 also indicates that correlations of psychometric ability with keypresses

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195

RATE QFFQRGE77lNG AND INTELLIGENCE

TABLE 5 Recall Performance

and Correlations with Psychometric Ability Following Three Levels of Distraction (N = 116)

in the recall condition only reached significance at medium to high levels of distraction. In other words, the aptitude-related difference emerged when two or three math problems separated learning and recall, but not when only one math problem was presented. This result may, in accordance with the main question of the study, reflect a genuine time-sensitive link between intelligence and memory, or, alternately, one could argue that the apparent relationship is an artifact of aptitude-related differences in speed of mathematical processing. For example, since high aptitude subjects have faster Arithmetic Tracking RTs (see Table 2), their Tracking performance spans less time and thus creates a shorter “distraction” interval between initial learning of the digit sequences and subsequent recall. However, since the correlations between RTs and key presses shown in Table 4 are all nonsignificant, there is no empirical evidence supporting a speed-based explanation for the present results. Faster RTs were simply not associated with enhanced recall performance. Returning to the question of whether the results in Table 5 suggest a timesensitive link between intelligence and forgetting (in which the “brighter” subjects forget at a slower rate), the issue reduces to whether there is an interaction between the effects of aptitude and retention interval on recall performance. To make this determination, a global psychometric aptitude score was calculated by standardizing the ASVAB-g score and averaging it with the (already standardized) fluid ability score. This global aptitude score was then used as the dependent measure in two separate regression analysis. In the first, recall scores at three levels of delay were entered as predictors. In the second regression analysis, the three recall scores were entered, along with four variables representing all possible products of the recall scores (i.e., 1*2, 1*3, 2*3, 1”2*3). The hypothesis was then tested that the saturated model (with all interactions) significantly improved the prediction of intelligence over the restricted model (no interaction terms) (Draper & Smith, 1981). The hypothesis was rejected (F 4, 112) = ,765, ~7 = .55, indicating that there is no interaction between the effects of aptitude and retention interval on recall performance.

196

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DISCUSSION The current results suggest that the well established relationship between intelligence and learning rate is (at least for the present task) not accompanied by significant correlations of intelligence with forgetting rate. Whereas previous research had suggested as much when mentally retarded individuals were used as subjects, there is now reason to extend the conclusion to normal subjects as well. One reason that this finding is of interest is that forgetting rate now joins a select group of cognitive indices that are roof correlated with intelligence. Such exceptions are rare given the near ubiquituous presence of positive manifold among sets of cognitive performance measures. For example, Table 2 indicates that all of the other cognitive performance measures in the current study were correlated with psychometric ability. Thus, it is noteworthy that the only performance parameter from the task that seems unrelated to mental ability is rate of forgetting. The lack of a significant correlation between forgetting rate and intelligence might suggest that a theoretical framework emphasizing the distinction between automatic and effortful processing is applicable to paradigms like the one used here. For example, the learning portion of the experimental task involved conscious, effortful processes related to problem solving and short term memory. In contrast, conscious processing mechanisms were occupied by the distractor task during the retention interval, thus relegating retention itself to automatic processing mechanisms. Since it is thought that intelligence is correlated with effortful processes but not automatic processes (e.g., Ackerman 1986), the pattern of results obtained in the present study is compatible with current theories. Irrespective of any final theoretical interpretation of the present data, however, the lack of a relationship between forgetting rate and intelligence may not be a mere peculiarity of research with retardates, since the same phenomena can hold true for subjects in the “normal” ranges of ability. ACKNOWLEDGMENTS: The opinions expressed in this article are those of the author, are not official, and do not necessarily reflect the views of the Navy Department. The author wishes to thank David Alderton for his many helpful suggestions pertaining to this article.

REFERENCES Ackerman, P.L. (1986). “Individual differences in information processing: An investigation of intellectual abilities and task performance during practice.” Zrrtellig~r~cc~, 20, 101-139. Borys, S.V. & H.H. Spitz. (1976). “Short-term retention in retarded adolescents as a function of load, delay, and interpolated activity.” ]elrrr~nlof Ps~/clro/qy!/, 94, 207-216. Brown, R.M. (1974). “Effects of recall order, cue placement, and retention interval on

RATE OF FORGETTING AND INTELLIGENCE

197

ad Motor Skilk, 39, short-term memory of normal and retarded children.” krccptud 167-178. Campione, J.C., A.L. Brown, & N.R. Bryant. (1985). “Individual differences in learning and memory.” In Hlrrnarr nbilitics: Au ir~formntiw procmiryq nppronch edited by R.J. Sternberg. New York: W. H. Freeman. Christal, R.E. (1986). Experinmtal cornputcrized trstirlg at AFHRL. Presentation to the Navy Personnel Research and Development Center, San Diego, CA. Drape:r, N.R. & H. Smith. (1981). Applied rqrcssim nrdysis (2nd ed.). New York: Wiley. Ellis, N.R. & D.M. Meador. (1985). “Forgetting in retarded and nonretarded persons under conditions of minimal strategy use.” fr~tdlipwr, 9, 87-96. Ellis, N.R., J.R. McCartney, R.P. Ferretti, & A.R. Cavalier. (1977). Recognition memory in mentally retarded persons.” Irrteltipzcc, 2, 310-317. of h~rnrar~ iutd Estes, W.K. (1982). “Learning, memory, and intelligence.” In Har~0mk Iigvrrce, edited by R.J. Sternberg. New York: Cambridge. Ferretti, R.P. (1982). “An analysis of passive memory in normal and mentally retarded persons.” Irztcllipm, 6, 69-87. Cough, H.C. & G. Domino. (1963). “The D 48 test as a measure of general ability among grade school children.” ]ourrznl of Corwrltirlg Ps,~tcl~olo~~y, 27, 344-349. Jensen, A.R. (1989). “The relationship between learning and intelligence.” LrnrrlirlX nrd Imfizdd Dijfmwccs, I, 37-62. Kyllonen, P.C. & W.C. Tirre. (1988). “Individual differences in associative learning and forgetting.” Irlfellipm, 12, 393-421. Raven,. J.C., J.H. Court, & J. Raven. (1986). Mnr~nnl for Rasw~‘s Prqrcssizv Matriccs nmf kcahhy Scnics (Section 4: Advanced Progressive Matrices, Sets I and II). London: Lewis. Zeaman, D. & B.J. House. (1967). “The relation of IQ and learning.” In Lenrrliq ard Irdiz~ihal Diffcrcrms, edited by R.M. Gagne. Columbus, OH: Charles E. Merrill.