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Speed of information processing in the hick paradigm and response latencies in a psychometric intelligence test
Ol91-8869:90 53.00 + 0.00 Copyright C 1990 Pergamon Pxss plc
Person. indiud. Difi Vol. II. No. 2. pp. 147-152, 1990 Printed in Great Britain. All rightsreserved
SPEED OF INFORMATION PROCESSING IN THE HICK PARADIGM AND RESPONSE LATENCIES IN A PSYCHOMETRIC INTELLIGENCE TEST ALJOSCHA
Institute of Psychology, Karl-Franzens-University
C.
NEUBAUER
Graz, Schubertstrahe
6a, A-8010 Graz, Austria
(Received 17 April 1989)
Summary-The relationship between speed of information processing in the Hick paradigm and response latencies to items of Raven’s Advanced Progressive Matrices was determined for 60 university students. For the Hick paradigm shortcomings of previous studies (order effects, visual attention effects, response bias effects. reaction time/movement time strategies) were avoided. Raven’s Advanced Progressive Matrices were administered involving measurement of item response latencies. As predicted, Hick RTs were negatively correlated with intelligence. Correlating the average response latency to Raven’s items with intelligence did not show a relationship, but a moderating effect of item difficulty could be observed: response latencies to relatively simple items were negatively correlated with intelligence, the opposite was true for difficult items. No relationships were observed between Hick RTs and item response latencies, therefore it was concluded that information processing speed in the Hick paradigm and response times in intelligence tests seem to reflect different processes.
INTRODUCTION
One area of increasing interest in psychology deals with the relationship between intelligence and the speed of information processing. Based on early notions by Galton (1883) and Cattell (1890), that reaction time (RT) measures might provide a good index of mental ability, a growing body of evidence demonstrated relationships of RTs on elementary cognitive tasks with intelligence as measured by psychometric tests, e.g. Raven’s Progressive Matrices, the Wechsler Adult Intelligence Scale and others (for a review see Vernon, 1987). Although there is considerable evidence for a negative relationship of intelligence with RTs on relatively simple tasks, such as the Hick paradigm, short-term and long-term memory processing speed and others (Vernon, 1987), some questions still remain unanswered. One of these questions deals with the size and direction of the relationship between intelligence and the speed of responding to more complex tasks, e.g. intelligence test items. Jensen (1982) assumes intelligence to be negatively correlated with RTs on several cognitive tasks, but-on the basis of earlier studies-concluded that this relationship only holds for relatively simple tasks (with RTs below 1000 msec). Response latencies to more complex tasks, e.g. items of an intelligence test, should not correlate with intelligence. He calls this phenomenon the ‘test-speed paradcx’. Recent studies by Larson, Merritt & Williams (1988) and Roberts, Beh & Stankov (1988) emphasized the significance of task complexity in determining the size of (negative) speed-IQ correlations; higher correlations can be observed for more complex tasks. These studies, however, used tasks that are more complex than the Hick paradigm for example, but they do not reach the complexity of intelligence test items. Only few studies deal with the relationship between intelligence and the speed of responses to intelligence test items. Naehrer (1986) found evidence for an independence of speed and ability in intelligence tests. On the contrary MacLennan, Jackson & Bellantino (1988) observed a negative relationship between response latencies to verbal ability test items and overall verbal intelligence scores. For items of intelligence tests involving considerable complexity such as those of Raven’s Advanced Progressive Matrices no such study is known to me. Therefore the first goal of this study was to assess the relationship of speed and intelligence for this test. From this subject another unanswered question can be deduced: if RTs on relatively simple tasks are correlated with intelligence (as assumed, see above) and if the nature of speed-IQ correlations in psychometric intelligence tests is unclear, how, then, is speed of information processing (as measured by the Hick paradigm) related to the speed with which a S performs on the items of a psychometric intelligence test? With respect to this question I know of only a single study. PAlD LIZ-D
147
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ALJOSCHAC. NELBAUER
Nettelbeck, Edwards & Vreugdenhil(l986) used the ‘inspection time paradigm’ (IT), gave their Ss the Wechsler Adult Intelligence Scale to assess their IQ and measured solution times to individual items of Raven’s Progressive Matrices. Although IT, IQ and solution times were all significantly intercorrelated, “IT and solution times appeared to be substantially reflecting different processes” (p. 633). Until now no study has been conducted which involves assessment of RTs in the Hick paradigm and response latencies to items of an intelligence test, therefore this is the first attempt to examine this relationship. As the elementary cognitive task for the measurement of information processing speed in the present study, the Hick paradigm (Roth, 1964; Jensen, 1982) was chosen. While early investigations found consistent negative correlations of Hick RTs with intelligence, Jensen (1987) reviewed 26 independent studies and has shown that subsequent research on this paradigm has shown equivocal results, with RT-IQ correlations ranging from significantly negative to significantly positive. The heterogeneous results appear to be produced by several inadequacies of the Hick paradigm. Amongst other criticisms, Longstreth (1984) has pointed out several sources of artifact, e.g. the confounding of order effects and retinal displacement effects with bits of information, response bias and response practice effects, as well as reaction time/movement time strategies. As this study was part of a larger research project dealing with the relationship between intelligence and Hick RTs for a modified Hick paradigm which avoids these shortcomings, the results of this modified Hick apparatus and procedure are used here. The detailed description of the shortcomings of the classical Hick paradigm and of the modifications of the Hick paradigm used here, as well as a comparison of the results obtained with both tests will be part of another manuscript (Neubauer, 1990) and therefore will not be dealt with here. METHOD Subjects Ss were 60 students (22 males and 38 females) who ranged in age between 18 and 25 yr. Intelligence test The Advanced Progressive Matrices (Raven, 1958) were given to the Ss in single sessions using the following materials: Ss were given the test book and a response-apparatus, which consists of 8 pushbuttons for the 8 response alternatives, an additional pushbutton for cancellation of the last response (‘clear’) and a 2-digit LED display indicating the respective item to be solved. This apparatus was used to allow the measurement of response latencies to individual test items. The procedure for an item was as follows: the S tries to solve an item in the test book, on solution of this item he presses down the pushbutton corresponding to the response alternative, thereby extinguishing the LED display for 1 sec. Subsequently the S turns to the next item, which is now indicated on the display. In case of an error the S can return to the previous item by pressing down the ‘clear’-button. The response apparatus was linked to a Personal Computer, which provided the assessment of response latencies and evaluation of the responses. Ss were first given Set I to accustom them to the test and then allowed 40min to work on Set II. Reaction time apparatus For the present study a modified reaction time/movement time (RT/MT) apparatus was used. It consists of a home button and four response buttons arranged in a quadrant 3 cm around the home button. This-compared to the Jensen apparatus-smaller arrangement was chosen to minimize retinal displacement effects. Adjacent to each response button a green LED was mounted. Reaction time test The Ss had to complete three experimental conditions for (0 bits of information), 2-choice RT (1 bit) and 4-choice RT employed because the slope of the RT-function with increasing to be linear (Hick, 1952). To avoid the confounding of order
the modified Hick task: simple RT (2 bits). A 3-bit condition was not bits of information can be assumed effects with bits of information the
Processing
responselatenciesand IQ
speed,
149
order of conditions was completely randomized. The problem of response bias effects and response practice effects was dealt with in the following manner: for each trial the computer randomly selects l/2/4 (for conditions 0, 1 and 2 bits respectively) critical response keys and indicates them by illumination of the stimulus lights. After a foreperiod of 3 set a warning tone (500 Hz, 100 msec durationj is given and after another foreperiod (varying randomly between 0.7 and 2 set) the reaction stimulus is given by turning off the illuminated stimulus light (0 bits), or one of the illuminated stimulus lights (1 and 2 bits). On presentation of the reaction stimulus the S has to react by releasing the home-button and pressing the correct response-button. For the three conditions (0, 1 and 2 bits) 16, 32 and 64 trials respectively were given. In order to avoid the possibility of RT/MT strategies, stimulus information (i.e. the LEDs that remained illuminated in the 1 and 2 bits conditions) was masked out on release of the home key. Four or more practice trials were given before each condition. Following each condition there was a 2 min break. Procedure
The APM and the modified Hick task were given in different sessions and in part by different experimerters. Statistical analysis
Data of single trials were passed through and MTs < 10 or > 300 msec and replacing analysis program provided median RT and the RT and MT for each condition and for calculated against bits of information.
a validity check, excluding RTs < 120 or > 1000 msec them by the median RT(MT) for that condition. The MT, as well as the SD (intraindividual variability) of each S. The slope of the regression of median RT was
RESULTS
Raven’s scores (Set II) varied from 11 to 33, with a mean of 22.95 and an SD of 5.14. These results are similar to the norms given in the manual of the test. Correlation of Hick-RTs
with inteiligence
Table 1 presents means and standard deviations as well as split-half (odd-even) reliabilities for all RT and MT parameters. The mean of median RTs shows an approximnately linear increase with increasing amount of information but mean MTs remain nearly consistent throughout all conditions. A similar result can be seen for intraindividual variabilities (SD). SDS of RTs show an increase from 0 to 2 bits of information, SDS of MTs do not. The results of correlational analyses (see Table 1) show low to moderate but mainly significant relationships in the predicted direction (negative signs) of intelligence (APM-score) with RT and SD(RT) parameters, with the highest coefficient (r = -0.458) for the 2 bits SD of RT. The correlation of the slope of RT with IQ is in the hypothesized direction, but does not reach
Table I. Means, standard deviations and split-half (odd-even) reliabilities of Hick RT and MT variables and their correlations with Raven’s score (APM)
0 Bits RT I Bit RT 2 Bits RT Slope (RT) O-2 0 Bits SD(RT) I Bit SD(RT) 2 Bits SD(RT) 0 Bits MT I Bit MT 2 Bits MT 0 Bits SD(MT) I Bit SD(MT) 2 Bits SD(MT)
M
SD
Rel.
corn. with APM
267.63 313.77 344.51 38.46 43.23 55.08 80.10 154.75 151.18 156.60 24.10 25.12 27.25
45.51 41.45 50.75 19.12 22.76 21.39 34.66 51.94 38.62 43.58 II.40 II.89 13.24
0.931 0.942 0.951 0.756 0.879 0.778 0.833 0.988 0.990 0.995 0.763 0.830 0.928
-0.259’ -0.306** -0.3W’ -0.203 -0.237. -0.135 -0.458** -0.203 -0.134 -0.074 0.014 0.097 -0.074
lP < 0.05; l*P < 0.01 (l-tailed).
150
ALJOSCHA C. NEUBALTR Table 2. Means and standard deviations of average responx later&s to APM items for the whole test and for three levels of item difficulty and their correlations with Raven’s score (APM) Response latencies
Whole test Item difficulty LOW MeaIl High
Number of items
”
36
60
73.25
15.76
0.05
I2 12 12
60 60 59
34.72 69.5 I 133.08
11.88 25.90 52.02
-0.44. -0.10 0.22
M
APM ,
SD
lP c 0.01.
significance. On the other hand, MTs and their intraindividual significantly with intelligence.
variabilities
do not correlate
Correlation of item response latencies with intelligence
An average response latency score for Set II of Raven’s APM was obtained for each S by dividing the totals of their item response latencies by the number of items attempted during testing. Descriptive statistics for this variable are given in Table 2. Correlating the average response latency score with intelligence (Raven’s scores) gives an r of 0.05. Therefore no relationship between intelligence and the speed of responding to intelligence test items can be observed for the test as a whole. from recent evidence showing that the task complexity exerts an influence on latency-IQ correlations (e.g. Larson et al., 1988; Roberts et al., 1988) one could assume response latency-IQ correlations to be varying with the level of difficulty of intelligence test items. In order to examine this assumption, average response latency scores were computed for three levels of item difficulty: for 12 easy items (with P 2 0.85), for 12 items of medium difficulty (with 0.48 < P c 0.85) and for 12 items of high difficulty (P c 0.48). Table 2 shows means and SDS of these variables and their correlations with intelligence. The average response latency for items of low difficulty shows a highly significant negative relationship with intelligence (r = -0.44, P < 0.01, 2-tailed), for items of mean difficulty the correlation is low and nonsignificant, whereas for highly difficult items a weak positive relationship can be observed. Ss who scored higher on the APM thus took less time to respond to easy test items, but tended to have longer response latencies for difficult items. Correlation 01~item response latencies with Hick RTs
The average response latency score for Set II of Raven’s APM (see above) was correlated with the Hick RT parameters. The correlations for median RTs and intraindividual variabilities are shown in Table 3 (first column). MT and slope parameters were omitted because these did not show any significant relationships. For none of the Hick RT parameters can a significant correlation with the average response latency to Raven’s items be observed. Correlating average response latency scores separately for the three levels of item difficulty (described above) with Hick parameters produced only one significant correlation (Table 3): the SD of the 2 bit RT shows a significant positive relationship with average response latencies to items of low difficulty (r = 0.31, P < 0.05, 2-tailed). The other correlations did not reach significance, but a tendency of positive correlations of Hick parameters with response latencies to easy items can be observed. On the other hand, average response latencies to items of mean and high difficulty do not show any relationships with Table 3. Correlations of response latencies to APM items with Hick RT variables for the whole test and for three levels of item difficulty Response latencies Item difficulty Whole
Low
Mean
High
60 0.05
60 0.16
60 0.08
-0.08 59
-0.03 -0.12 0.08 -0.12 0.01
0.23 0.10 0.25 0.00 0.31’
0.02 -0.05 0.05 -0.12 0.10
-0.14 -0.18 -0.01 -0.11 -0.16
test i Bits RT I Bit RT 2 Bits RT 0 Bits SD(RT) I Bit SD(RT) 2 Bits SD(RT) lP < 0.05.
Processing speed, response latencies and IQ
I51
Hick RTs. If these findings can be interpreted at all, one could say that Ss with faster (median RT) and more stable (SD of RT) information processing in the Hick paradigm respond faster to items of low difficulty whereas no relationship with response latencies for more difficult items can be observed. DISCUSSION In the present study the hypothesized negative relationship between speed of information processing in the Hick paradigm and intelligence was observed, a finding which is in conformity with a large body of previous research (see Jensen, 1987, for a review). The main purpose of the study was to investigate, firstly, if the speed-IQ relationship also holds for more complex tasks with RTs above 1000 msec, e.g. items of an intelligence test and, secondly, if speed on simple tasks (such as the Hick paradigm) correlates with speed on more complex tasks (intelligence test items). At a first glance the zero-correlation of item response latencies with intelligence would support Jensen’s hypothesis (1982) of the test-speed paradox: the time a S takes for solution of intelligence test items shows no relationship with his intellectual ability and there is no relationship between speed in the Hick paradigm and speed in solution of intelligence test items. A separate evaluation of response times to test items of different levels of difficulty and their correlations with intelligence leads us to a more complex finding. A negative speed-IQ correlation was observed for the items of low difficulty, whereas for more difficult items a tendency in the opposite direction was found. In other words, brighter individuals take less time to respond to the easier intelligence test items than the less intelligent Ss. The opposite is true for response times to difficult items in the APM: more intelligent Ss take more time to respond than the less intelligent. As a result brighter Ss answer more of the difficult items correctly and therefore reach higher scores on the total test. The relevant variable in determining speed-IQ relationships seems, therefore, to be the task difficulty or complexity. Jensen (1982) assumed the negative speed-IQ relationship to be true only for tasks with RTs below 1000 msec. On the other hand MacLennan et al. (1988) found such a negative speed-IQ correlation even for items of a verbal ability test with average response latencies around 12 set, and in the present study evidence for a negative speed-IQ correlation was found even for low-difficulty items of the APM, which yielded response latencies of around 35 sec. Beyond this point (i.e. for more difficult items with response latencies of 70 set or above) the speed-IQ correlation approaches unity and for very difficult items (with a mean response latency of 133 set) shows a tendency in the opposite direction. From these results I would argue that the speed-IQ relationship also holds for tasks with RTs above 1000 msec but at a certain level of task complexity it approaches unity. Future studies involving tasks of a wide range of complexity should clarify this issue. An alternative explanation for the correlation between speed and intelligence is often put forward. It is argued that this relationship is due simply to whether the intelligence tests used were timed or untimed. Vernon, Nador & Kantor (1985) and Vernon & Kantor (1986) presented evidence that runs counter to such an explanation. In their studies, correlations of RTs with IQ were approximately equal for timed and untimed conditions of intelligence testing. Vernon et al. (1985) offer the following explanation for this finding: Ss of higher intelligence will solve relatively simple items more quickly. As more intelligent Ss progress through the test to more difficult items “these questions will slow them down but their faster processing-speed will increase the probability that they will be able tc solve them correctly. When the time limit is reached, all Ss are probably at a point in the test where the items appear quite difficult to them. However, the more intelligent Ss are further along” (p. 372). The results of the present study confirm Vernon’s first assumption (that brighter individuals are faster solving relatively simple items), but they run counter to the argument that more intelligent Ss are further along when the time limit is reached. A comparison (by means of a t-test) of the number of items of Set II tried by less vs more intelligent Ss during the 40 min period did not reveal a significant difference, t(58) = - 1.46. From the results of the present study I would conclude that more intelligent Ss are further along after having tried the easier items and this advantage in time is used by taking more time for the solution of more difficult items. The less intelligent Ss, I suppose, have less time for the more difficult items and therefore developed the strategy of skipping
IS?
ALJOSCHAC. NEUBAUER
difficult items relatively early, hoping that their response might be correct by chance and/or that they will encounter an item which they find easier. The present study cannot explain how less intelligent Ss might profit from having more time available for the solution of difficult items. To answer this question, studies involving untimed intelligence testing and at the same time measuring response latencies would be necessary. The second question of this study was concerned with the relationship between speed of information processing in the Hick paradigm and speed in responding to intelligence test items. Although the speed of responding to APM-items of low difficulty was slightly correlated with information processing speed and efficiency in the Hick paradigm, correlations were mainly low and nonsignificant. Therefore, in my opinion, processing speed in the Hick paradigm and response times in the intelligence test reflect different processes, a finding which is in line with the study by Nettelbeck et al. (1986) mentioned above. In summary the results of the present study suggest that processing speed in the relatively simple Hick paradigm is related to intelligence. There is only weak evidence for an association of speed in intelligence tests with intellectual ability on the one hand, and with speed in the Hick paradigm on the other. Acknowledgement-This paper was presented at the “3 I. Tagung experimentell arbeitender Psychologen” (TeaP), Bamberg, F.R.G.. 20-23 March 1989.
REFERENCES Cattell, J. MC. (1890). Mental tests and measurements. Mind, 15, 373-380. Galton, F. (1883). Inquiries into human Jbcufty and its development. London: Macmillan. Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimertuf Psycho/ogy, 4, I l-26. Jensen, A. R. (1982). Reaction time and psychometric g. A modelfor intelligence (Edited by Eysenck, H. J.). New York: Springer. Jensen, A. R. (1987). Individual differences in the Hick paradigm. Speed ofinformation processing and intelligence (Ediied by Vernon, P. A.). Norwood, N.J.: Ablex. Larson, G. E., Merritt, C. R. & Williams, S. E. (1988). Information processing and intelligence: Some implications of task complexity. InteNigence, 12, 131-148. Longstreth, L. E. (1984). Jensen’s reaction time investigations of intelligence: A critique. Intelligence, 8, 139-160. Maclennan, R. N., Jackson, D. N. & Bellantino, N. (1988). Response latencies and the computerized assessment of intel!igence. Personality and Individual Differences, 9, 8 1l-8 16. Naehrer, W. (1986). Geschwindigkeit und guete als dimensionen kognitiver leistungen. Berlin: Springer. Nettelbeck, T., Edwards, C. & Vreugdenhil, A. (1986). Inspection time and IQ: Evidence for a mental speed-ability association. Personality and Individual D$erences, 7, 633-641. Neubauer, A. (1990). Intelligence and RT: A modified Hick paradigm and a new RT paradigm. Manuscript submitted for publication. Raven, J. C. (1958). Advanced progressive matrices. London: Lewis. Roberts, R. D., Beh, H. C. & Stankov, L. (1988). Hick’s law, competing-task performance, and intelligence. Intelligence, 12, I1 I-130. Roth, E. (1964). Die geschwindigkeit der verarbeitung von information und ihr zusammenhang mit intelligenz. Zeitschrift fur experimentelle angewandte Psychologie, 11, 6 l-22. Vernon, P. A. (1987). Speed of information processing and intelligence. Norwood, N.J.: Ablex. Vernon, P. A. & Kantor, L. (1986). Reaction time correlations with intelligence test scores obtained under either timed or uniimed conditions. Intelligence, 10, 3 1S-330. Vernon, P. A., Nador, S. & Kantor, L. (1985). Reaction times and speed of information processing: Their relationship to timed and untimed measures of intelligence. Intelligence, 9, 357-374.
Person. indiud. Difi Vol. II. No. 2. pp. 147-152, 1990 Printed in Great Britain. All rightsreserved
SPEED OF INFORMATION PROCESSING IN THE HICK PARADIGM AND RESPONSE LATENCIES IN A PSYCHOMETRIC INTELLIGENCE TEST ALJOSCHA
Institute of Psychology, Karl-Franzens-University
C.
NEUBAUER
Graz, Schubertstrahe
6a, A-8010 Graz, Austria
(Received 17 April 1989)
Summary-The relationship between speed of information processing in the Hick paradigm and response latencies to items of Raven’s Advanced Progressive Matrices was determined for 60 university students. For the Hick paradigm shortcomings of previous studies (order effects, visual attention effects, response bias effects. reaction time/movement time strategies) were avoided. Raven’s Advanced Progressive Matrices were administered involving measurement of item response latencies. As predicted, Hick RTs were negatively correlated with intelligence. Correlating the average response latency to Raven’s items with intelligence did not show a relationship, but a moderating effect of item difficulty could be observed: response latencies to relatively simple items were negatively correlated with intelligence, the opposite was true for difficult items. No relationships were observed between Hick RTs and item response latencies, therefore it was concluded that information processing speed in the Hick paradigm and response times in intelligence tests seem to reflect different processes.
INTRODUCTION
One area of increasing interest in psychology deals with the relationship between intelligence and the speed of information processing. Based on early notions by Galton (1883) and Cattell (1890), that reaction time (RT) measures might provide a good index of mental ability, a growing body of evidence demonstrated relationships of RTs on elementary cognitive tasks with intelligence as measured by psychometric tests, e.g. Raven’s Progressive Matrices, the Wechsler Adult Intelligence Scale and others (for a review see Vernon, 1987). Although there is considerable evidence for a negative relationship of intelligence with RTs on relatively simple tasks, such as the Hick paradigm, short-term and long-term memory processing speed and others (Vernon, 1987), some questions still remain unanswered. One of these questions deals with the size and direction of the relationship between intelligence and the speed of responding to more complex tasks, e.g. intelligence test items. Jensen (1982) assumes intelligence to be negatively correlated with RTs on several cognitive tasks, but-on the basis of earlier studies-concluded that this relationship only holds for relatively simple tasks (with RTs below 1000 msec). Response latencies to more complex tasks, e.g. items of an intelligence test, should not correlate with intelligence. He calls this phenomenon the ‘test-speed paradcx’. Recent studies by Larson, Merritt & Williams (1988) and Roberts, Beh & Stankov (1988) emphasized the significance of task complexity in determining the size of (negative) speed-IQ correlations; higher correlations can be observed for more complex tasks. These studies, however, used tasks that are more complex than the Hick paradigm for example, but they do not reach the complexity of intelligence test items. Only few studies deal with the relationship between intelligence and the speed of responses to intelligence test items. Naehrer (1986) found evidence for an independence of speed and ability in intelligence tests. On the contrary MacLennan, Jackson & Bellantino (1988) observed a negative relationship between response latencies to verbal ability test items and overall verbal intelligence scores. For items of intelligence tests involving considerable complexity such as those of Raven’s Advanced Progressive Matrices no such study is known to me. Therefore the first goal of this study was to assess the relationship of speed and intelligence for this test. From this subject another unanswered question can be deduced: if RTs on relatively simple tasks are correlated with intelligence (as assumed, see above) and if the nature of speed-IQ correlations in psychometric intelligence tests is unclear, how, then, is speed of information processing (as measured by the Hick paradigm) related to the speed with which a S performs on the items of a psychometric intelligence test? With respect to this question I know of only a single study. PAlD LIZ-D
147
I48
ALJOSCHAC. NELBAUER
Nettelbeck, Edwards & Vreugdenhil(l986) used the ‘inspection time paradigm’ (IT), gave their Ss the Wechsler Adult Intelligence Scale to assess their IQ and measured solution times to individual items of Raven’s Progressive Matrices. Although IT, IQ and solution times were all significantly intercorrelated, “IT and solution times appeared to be substantially reflecting different processes” (p. 633). Until now no study has been conducted which involves assessment of RTs in the Hick paradigm and response latencies to items of an intelligence test, therefore this is the first attempt to examine this relationship. As the elementary cognitive task for the measurement of information processing speed in the present study, the Hick paradigm (Roth, 1964; Jensen, 1982) was chosen. While early investigations found consistent negative correlations of Hick RTs with intelligence, Jensen (1987) reviewed 26 independent studies and has shown that subsequent research on this paradigm has shown equivocal results, with RT-IQ correlations ranging from significantly negative to significantly positive. The heterogeneous results appear to be produced by several inadequacies of the Hick paradigm. Amongst other criticisms, Longstreth (1984) has pointed out several sources of artifact, e.g. the confounding of order effects and retinal displacement effects with bits of information, response bias and response practice effects, as well as reaction time/movement time strategies. As this study was part of a larger research project dealing with the relationship between intelligence and Hick RTs for a modified Hick paradigm which avoids these shortcomings, the results of this modified Hick apparatus and procedure are used here. The detailed description of the shortcomings of the classical Hick paradigm and of the modifications of the Hick paradigm used here, as well as a comparison of the results obtained with both tests will be part of another manuscript (Neubauer, 1990) and therefore will not be dealt with here. METHOD Subjects Ss were 60 students (22 males and 38 females) who ranged in age between 18 and 25 yr. Intelligence test The Advanced Progressive Matrices (Raven, 1958) were given to the Ss in single sessions using the following materials: Ss were given the test book and a response-apparatus, which consists of 8 pushbuttons for the 8 response alternatives, an additional pushbutton for cancellation of the last response (‘clear’) and a 2-digit LED display indicating the respective item to be solved. This apparatus was used to allow the measurement of response latencies to individual test items. The procedure for an item was as follows: the S tries to solve an item in the test book, on solution of this item he presses down the pushbutton corresponding to the response alternative, thereby extinguishing the LED display for 1 sec. Subsequently the S turns to the next item, which is now indicated on the display. In case of an error the S can return to the previous item by pressing down the ‘clear’-button. The response apparatus was linked to a Personal Computer, which provided the assessment of response latencies and evaluation of the responses. Ss were first given Set I to accustom them to the test and then allowed 40min to work on Set II. Reaction time apparatus For the present study a modified reaction time/movement time (RT/MT) apparatus was used. It consists of a home button and four response buttons arranged in a quadrant 3 cm around the home button. This-compared to the Jensen apparatus-smaller arrangement was chosen to minimize retinal displacement effects. Adjacent to each response button a green LED was mounted. Reaction time test The Ss had to complete three experimental conditions for (0 bits of information), 2-choice RT (1 bit) and 4-choice RT employed because the slope of the RT-function with increasing to be linear (Hick, 1952). To avoid the confounding of order
the modified Hick task: simple RT (2 bits). A 3-bit condition was not bits of information can be assumed effects with bits of information the
Processing
responselatenciesand IQ
speed,
149
order of conditions was completely randomized. The problem of response bias effects and response practice effects was dealt with in the following manner: for each trial the computer randomly selects l/2/4 (for conditions 0, 1 and 2 bits respectively) critical response keys and indicates them by illumination of the stimulus lights. After a foreperiod of 3 set a warning tone (500 Hz, 100 msec durationj is given and after another foreperiod (varying randomly between 0.7 and 2 set) the reaction stimulus is given by turning off the illuminated stimulus light (0 bits), or one of the illuminated stimulus lights (1 and 2 bits). On presentation of the reaction stimulus the S has to react by releasing the home-button and pressing the correct response-button. For the three conditions (0, 1 and 2 bits) 16, 32 and 64 trials respectively were given. In order to avoid the possibility of RT/MT strategies, stimulus information (i.e. the LEDs that remained illuminated in the 1 and 2 bits conditions) was masked out on release of the home key. Four or more practice trials were given before each condition. Following each condition there was a 2 min break. Procedure
The APM and the modified Hick task were given in different sessions and in part by different experimerters. Statistical analysis
Data of single trials were passed through and MTs < 10 or > 300 msec and replacing analysis program provided median RT and the RT and MT for each condition and for calculated against bits of information.
a validity check, excluding RTs < 120 or > 1000 msec them by the median RT(MT) for that condition. The MT, as well as the SD (intraindividual variability) of each S. The slope of the regression of median RT was
RESULTS
Raven’s scores (Set II) varied from 11 to 33, with a mean of 22.95 and an SD of 5.14. These results are similar to the norms given in the manual of the test. Correlation of Hick-RTs
with inteiligence
Table 1 presents means and standard deviations as well as split-half (odd-even) reliabilities for all RT and MT parameters. The mean of median RTs shows an approximnately linear increase with increasing amount of information but mean MTs remain nearly consistent throughout all conditions. A similar result can be seen for intraindividual variabilities (SD). SDS of RTs show an increase from 0 to 2 bits of information, SDS of MTs do not. The results of correlational analyses (see Table 1) show low to moderate but mainly significant relationships in the predicted direction (negative signs) of intelligence (APM-score) with RT and SD(RT) parameters, with the highest coefficient (r = -0.458) for the 2 bits SD of RT. The correlation of the slope of RT with IQ is in the hypothesized direction, but does not reach
Table I. Means, standard deviations and split-half (odd-even) reliabilities of Hick RT and MT variables and their correlations with Raven’s score (APM)
0 Bits RT I Bit RT 2 Bits RT Slope (RT) O-2 0 Bits SD(RT) I Bit SD(RT) 2 Bits SD(RT) 0 Bits MT I Bit MT 2 Bits MT 0 Bits SD(MT) I Bit SD(MT) 2 Bits SD(MT)
M
SD
Rel.
corn. with APM
267.63 313.77 344.51 38.46 43.23 55.08 80.10 154.75 151.18 156.60 24.10 25.12 27.25
45.51 41.45 50.75 19.12 22.76 21.39 34.66 51.94 38.62 43.58 II.40 II.89 13.24
0.931 0.942 0.951 0.756 0.879 0.778 0.833 0.988 0.990 0.995 0.763 0.830 0.928
-0.259’ -0.306** -0.3W’ -0.203 -0.237. -0.135 -0.458** -0.203 -0.134 -0.074 0.014 0.097 -0.074
lP < 0.05; l*P < 0.01 (l-tailed).
150
ALJOSCHA C. NEUBALTR Table 2. Means and standard deviations of average responx later&s to APM items for the whole test and for three levels of item difficulty and their correlations with Raven’s score (APM) Response latencies
Whole test Item difficulty LOW MeaIl High
Number of items
”
36
60
73.25
15.76
0.05
I2 12 12
60 60 59
34.72 69.5 I 133.08
11.88 25.90 52.02
-0.44. -0.10 0.22
M
APM ,
SD
lP c 0.01.
significance. On the other hand, MTs and their intraindividual significantly with intelligence.
variabilities
do not correlate
Correlation of item response latencies with intelligence
An average response latency score for Set II of Raven’s APM was obtained for each S by dividing the totals of their item response latencies by the number of items attempted during testing. Descriptive statistics for this variable are given in Table 2. Correlating the average response latency score with intelligence (Raven’s scores) gives an r of 0.05. Therefore no relationship between intelligence and the speed of responding to intelligence test items can be observed for the test as a whole. from recent evidence showing that the task complexity exerts an influence on latency-IQ correlations (e.g. Larson et al., 1988; Roberts et al., 1988) one could assume response latency-IQ correlations to be varying with the level of difficulty of intelligence test items. In order to examine this assumption, average response latency scores were computed for three levels of item difficulty: for 12 easy items (with P 2 0.85), for 12 items of medium difficulty (with 0.48 < P c 0.85) and for 12 items of high difficulty (P c 0.48). Table 2 shows means and SDS of these variables and their correlations with intelligence. The average response latency for items of low difficulty shows a highly significant negative relationship with intelligence (r = -0.44, P < 0.01, 2-tailed), for items of mean difficulty the correlation is low and nonsignificant, whereas for highly difficult items a weak positive relationship can be observed. Ss who scored higher on the APM thus took less time to respond to easy test items, but tended to have longer response latencies for difficult items. Correlation 01~item response latencies with Hick RTs
The average response latency score for Set II of Raven’s APM (see above) was correlated with the Hick RT parameters. The correlations for median RTs and intraindividual variabilities are shown in Table 3 (first column). MT and slope parameters were omitted because these did not show any significant relationships. For none of the Hick RT parameters can a significant correlation with the average response latency to Raven’s items be observed. Correlating average response latency scores separately for the three levels of item difficulty (described above) with Hick parameters produced only one significant correlation (Table 3): the SD of the 2 bit RT shows a significant positive relationship with average response latencies to items of low difficulty (r = 0.31, P < 0.05, 2-tailed). The other correlations did not reach significance, but a tendency of positive correlations of Hick parameters with response latencies to easy items can be observed. On the other hand, average response latencies to items of mean and high difficulty do not show any relationships with Table 3. Correlations of response latencies to APM items with Hick RT variables for the whole test and for three levels of item difficulty Response latencies Item difficulty Whole
Low
Mean
High
60 0.05
60 0.16
60 0.08
-0.08 59
-0.03 -0.12 0.08 -0.12 0.01
0.23 0.10 0.25 0.00 0.31’
0.02 -0.05 0.05 -0.12 0.10
-0.14 -0.18 -0.01 -0.11 -0.16
test i Bits RT I Bit RT 2 Bits RT 0 Bits SD(RT) I Bit SD(RT) 2 Bits SD(RT) lP < 0.05.
Processing speed, response latencies and IQ
I51
Hick RTs. If these findings can be interpreted at all, one could say that Ss with faster (median RT) and more stable (SD of RT) information processing in the Hick paradigm respond faster to items of low difficulty whereas no relationship with response latencies for more difficult items can be observed. DISCUSSION In the present study the hypothesized negative relationship between speed of information processing in the Hick paradigm and intelligence was observed, a finding which is in conformity with a large body of previous research (see Jensen, 1987, for a review). The main purpose of the study was to investigate, firstly, if the speed-IQ relationship also holds for more complex tasks with RTs above 1000 msec, e.g. items of an intelligence test and, secondly, if speed on simple tasks (such as the Hick paradigm) correlates with speed on more complex tasks (intelligence test items). At a first glance the zero-correlation of item response latencies with intelligence would support Jensen’s hypothesis (1982) of the test-speed paradox: the time a S takes for solution of intelligence test items shows no relationship with his intellectual ability and there is no relationship between speed in the Hick paradigm and speed in solution of intelligence test items. A separate evaluation of response times to test items of different levels of difficulty and their correlations with intelligence leads us to a more complex finding. A negative speed-IQ correlation was observed for the items of low difficulty, whereas for more difficult items a tendency in the opposite direction was found. In other words, brighter individuals take less time to respond to the easier intelligence test items than the less intelligent Ss. The opposite is true for response times to difficult items in the APM: more intelligent Ss take more time to respond than the less intelligent. As a result brighter Ss answer more of the difficult items correctly and therefore reach higher scores on the total test. The relevant variable in determining speed-IQ relationships seems, therefore, to be the task difficulty or complexity. Jensen (1982) assumed the negative speed-IQ relationship to be true only for tasks with RTs below 1000 msec. On the other hand MacLennan et al. (1988) found such a negative speed-IQ correlation even for items of a verbal ability test with average response latencies around 12 set, and in the present study evidence for a negative speed-IQ correlation was found even for low-difficulty items of the APM, which yielded response latencies of around 35 sec. Beyond this point (i.e. for more difficult items with response latencies of 70 set or above) the speed-IQ correlation approaches unity and for very difficult items (with a mean response latency of 133 set) shows a tendency in the opposite direction. From these results I would argue that the speed-IQ relationship also holds for tasks with RTs above 1000 msec but at a certain level of task complexity it approaches unity. Future studies involving tasks of a wide range of complexity should clarify this issue. An alternative explanation for the correlation between speed and intelligence is often put forward. It is argued that this relationship is due simply to whether the intelligence tests used were timed or untimed. Vernon, Nador & Kantor (1985) and Vernon & Kantor (1986) presented evidence that runs counter to such an explanation. In their studies, correlations of RTs with IQ were approximately equal for timed and untimed conditions of intelligence testing. Vernon et al. (1985) offer the following explanation for this finding: Ss of higher intelligence will solve relatively simple items more quickly. As more intelligent Ss progress through the test to more difficult items “these questions will slow them down but their faster processing-speed will increase the probability that they will be able tc solve them correctly. When the time limit is reached, all Ss are probably at a point in the test where the items appear quite difficult to them. However, the more intelligent Ss are further along” (p. 372). The results of the present study confirm Vernon’s first assumption (that brighter individuals are faster solving relatively simple items), but they run counter to the argument that more intelligent Ss are further along when the time limit is reached. A comparison (by means of a t-test) of the number of items of Set II tried by less vs more intelligent Ss during the 40 min period did not reveal a significant difference, t(58) = - 1.46. From the results of the present study I would conclude that more intelligent Ss are further along after having tried the easier items and this advantage in time is used by taking more time for the solution of more difficult items. The less intelligent Ss, I suppose, have less time for the more difficult items and therefore developed the strategy of skipping
IS?
ALJOSCHAC. NEUBAUER
difficult items relatively early, hoping that their response might be correct by chance and/or that they will encounter an item which they find easier. The present study cannot explain how less intelligent Ss might profit from having more time available for the solution of difficult items. To answer this question, studies involving untimed intelligence testing and at the same time measuring response latencies would be necessary. The second question of this study was concerned with the relationship between speed of information processing in the Hick paradigm and speed in responding to intelligence test items. Although the speed of responding to APM-items of low difficulty was slightly correlated with information processing speed and efficiency in the Hick paradigm, correlations were mainly low and nonsignificant. Therefore, in my opinion, processing speed in the Hick paradigm and response times in the intelligence test reflect different processes, a finding which is in line with the study by Nettelbeck et al. (1986) mentioned above. In summary the results of the present study suggest that processing speed in the relatively simple Hick paradigm is related to intelligence. There is only weak evidence for an association of speed in intelligence tests with intellectual ability on the one hand, and with speed in the Hick paradigm on the other. Acknowledgement-This paper was presented at the “3 I. Tagung experimentell arbeitender Psychologen” (TeaP), Bamberg, F.R.G.. 20-23 March 1989.
REFERENCES Cattell, J. MC. (1890). Mental tests and measurements. Mind, 15, 373-380. Galton, F. (1883). Inquiries into human Jbcufty and its development. London: Macmillan. Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimertuf Psycho/ogy, 4, I l-26. Jensen, A. R. (1982). Reaction time and psychometric g. A modelfor intelligence (Edited by Eysenck, H. J.). New York: Springer. Jensen, A. R. (1987). Individual differences in the Hick paradigm. Speed ofinformation processing and intelligence (Ediied by Vernon, P. A.). Norwood, N.J.: Ablex. Larson, G. E., Merritt, C. R. & Williams, S. E. (1988). Information processing and intelligence: Some implications of task complexity. InteNigence, 12, 131-148. Longstreth, L. E. (1984). Jensen’s reaction time investigations of intelligence: A critique. Intelligence, 8, 139-160. Maclennan, R. N., Jackson, D. N. & Bellantino, N. (1988). Response latencies and the computerized assessment of intel!igence. Personality and Individual Differences, 9, 8 1l-8 16. Naehrer, W. (1986). Geschwindigkeit und guete als dimensionen kognitiver leistungen. Berlin: Springer. Nettelbeck, T., Edwards, C. & Vreugdenhil, A. (1986). Inspection time and IQ: Evidence for a mental speed-ability association. Personality and Individual D$erences, 7, 633-641. Neubauer, A. (1990). Intelligence and RT: A modified Hick paradigm and a new RT paradigm. Manuscript submitted for publication. Raven, J. C. (1958). Advanced progressive matrices. London: Lewis. Roberts, R. D., Beh, H. C. & Stankov, L. (1988). Hick’s law, competing-task performance, and intelligence. Intelligence, 12, I1 I-130. Roth, E. (1964). Die geschwindigkeit der verarbeitung von information und ihr zusammenhang mit intelligenz. Zeitschrift fur experimentelle angewandte Psychologie, 11, 6 l-22. Vernon, P. A. (1987). Speed of information processing and intelligence. Norwood, N.J.: Ablex. Vernon, P. A. & Kantor, L. (1986). Reaction time correlations with intelligence test scores obtained under either timed or uniimed conditions. Intelligence, 10, 3 1S-330. Vernon, P. A., Nador, S. & Kantor, L. (1985). Reaction times and speed of information processing: Their relationship to timed and untimed measures of intelligence. Intelligence, 9, 357-374.