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Intelligence, attention, motivation and speed-accuracy trade-off in the hick paradigm
Person. individ. 01% Vol. 13, No. 12, pp. 13251332, Printed in Great Britain. All rights reserved
1992
0191-8869/92
$5.00 + 0.00
Copyright 0 1992 Pergamon Press Ltd
INTELLIGENCE, ATTENTION, MOTIVATION AND SPEED-ACCURACY TRADE-OFF IN THE HICK PARADIGM* ALJOSCHA Institute
of Psychology,
C.
NEUBAUER,
CHRISTA
Karl-Franzens-University (Received
BAUER Graz,
10 November
and
GERALD
SchubertstraSe
HULLER
6a, 8010 Graz,
Austria
1991)
Summary-The role of attention and motivation in the frequently observed negative relationship between intelligence and reaction times (RT) in the Hick paradigm (HICK) was investigated in two studies. The subjects (1 l- to 15-year-old children) completed the HICK, Raven’s Standard Progressive Matrices, and the d2 (attention measure). Different motivation was introduced by varying RT feedback in the HICK. Correlations of HICK parameters with intelligence and attention depended on an interaction of gender and feedback, which was found to be due to group differences in speed-accuracy trade-OR RTs and SDS (intraindividual variabilities) correlated with intelligence only in groups emphasizing both speed and accuracy. In groups with a strong set either for speed or accuracy RTs and SDS only correlated with attention, but in these subjects the number of errors in the HICK was associated with intelligence. Only the use of an information measure (average information rate), which combines speed and accuracy, revealed a significant association of the HICK with intelligence and attention independent of speedaccuracy trade-off. These findings question the universal validity of RT and SD as processing speed indices and provide a strong argument for the use of information measures in research on RTs and intelligence.
The relationship between reaction times (RTs) and intelligence has been studied intensively during the last 10 years. In most of the studies negative correlations of low to moderate size between the performance on so-called elementary cognitive tasks (like inspection time or choice reaction time, the so-called Hick paradigm) and psychometric intelligence have been reported (see Vernon, 1987; and Juhel, 1991; for reviews). Jensen, Eysenck and other proponents of this line of research explain the relationship by ‘bottom-up’ processing, i.e. they assume a neural basis like speed or efficiency of neural transmission in the brain that should affect both performance on these elementary cognitive tasks and on intelligence tests (Jensen, 1980; Eysenck, 1982). This theory, however, is subjected to severe criticism. The critics consider the relationship to be mediated by ‘top-down’ processing (Longstreth, 1984, 1986) or metacomponential processes (Marr & Sternberg, 1987). They assume that other variables, like motivation, attention, or certain personality traits lead to an overestimation of the RT-intelligence (or RT-IQ) relationship (Detterman, 1987). Regarding the influence of attention it is assumed that low-IQ Ss are not paying as much attention in RT experiments as high-IQ Ss, perhaps because “they do not recognize the importance of paying attention in order to minimize reaction time” (Longstreth, 1986, p. 187) and this would have the effect of inflating the correlations between RT and IQ. Only two studies deal with the influence of attention on the RT-IQ relationship: Carlson, Jensen and Widaman (1983) and Carlson and Widaman (1987) reported evidence for an association of high attention with fast and consistent RTs; they also found a significant association of RTs with intelligence. From these results they conclude that “attention deployment appears to play a significant role in the generally observed relationship between reaction time and intellectual ability” (1983, p. 3420. Detterman (1987), as well as Marr and Sternberg (1987) consider individual differences in motivation to perform quickly in tests of mental speed as a confounding variable. Again, it is assumed that high-IQ Ss are more highly motivated in RT experiments than Ss of lower intelligence, thereby inflating the RT-IQ relationship. There is, however, no empirical study dealing with the influence of motivation on the RT-IQ relationship. *This paper is based on diploma’s theses of the second and third author, conducted under the supervision of the first author. Portions of this paper were presented at the First European Conference on Psychological Assessmenr (ECPA) in Barcelona, Spain, 23-24 September 199 1. 1325
ALJOSCHA C. NEUBALJERet al.
1326
The following two studies will be reported. The purpose of the first study (Holler, 1990) was to assess the role of attention in the relationship between RT and intelligence. The second study (Bauer, 1991) also dealt with this aspect and was, therefore, also an attempt to replicate the first study; but, additionally, motivation was manipulated by varying RT feedback in the Hick paradigm (following a suggestion by Detterman, 1987). STUDY
1
Method Subjects. The Ss were 81 children (45 males, 36 females; aged 11 to 15 years, mean age = 12.95, SD = 0.81) chosen randomly from 5th to 7th grades of an elementary school. Measures. All children were given the Raven’s Standard Progressive Matrices @PM) Raven (1960) as a measure of general intelligence as well as the d2 (‘Aufmerksamkeits-Belastungs-Test’ by Brickenkamp, 1978) for the measurement of attention deployment. The task in the d2 is to cross out as fast as possible all d’s with two commas out of a number of d’s and p’s with one, two, three, or four commas. The resulting score is the number of items processed correctly within a given time-limit. Finally, Ss were tested on the Hick paradigm, a simple and choice RT task. To avoid the shortcomings of Jensen’s RT task (Jensen & Munro, 1979) pointed out by Longstreth (1984) as order effects, response bias effects and RT/movement time strategies, a modified Hick paradigm was used: stimuli were squares and plus-signs presented on the video screen of a personal computer. In the 2-bit condition (4-choice) four squares (arranged in a row) were presented on the display. After 1.5 set a warning tone was presented and after a foreperiod varying randomly between 0.7 to 2 set a plus-sign was presented as reaction stimulus in one of the squares (see Fig, 1). The Ss had to press the corresponding response-button on a ‘fingers-on-keys’ apparatus with four response-buttons, arranged to fit comfortably under the middle and index fingers of each hand (following a suggestion by Smith, 1989). The use of this apparatus was preferred to one with homeand response-buttons, because of the problems associated with RT/movement time strategies (see Smith, 1989). In the l-bit condition (Zchoice RT) for each new trial two squares were selected randomly out of the four squares (from the 2-bit condition) and were displayed on the screen. After the foreperiod the reaction stimulus was presented in one of the squares and again had to be responded to by pressing the corresponding response-button. Through this procedure an equal number of specific responses (with middle and index fingers of each hand) was attained, thereby avoiding response bias effects (Neubauer, 1991). In simple RT only one square was shown (again
Fig.
1. Example
of modified
Hick paradigm
with 4-choice
task.
Intelligence,
attention
and the Hick paradigm
1327
randomly selected), in which-after the foreperiod-the reaction stimulus appeared that had to be answered in the same way as in the other conditions. The order of conditions was randomized between Ss. Thirty-two trials were presented during each condition. To increase motivation RT was fed back to the Ss after each trial by displaying the RT in milliseconds. Data of single trials were checked for outliers eliminating RTs < 100 msec and > 2000 msec and by subsequent “winsorizing”, i.e. eliminating all RTs that fall more than 3 SDS above the Ss own mean RT. The following measures were obtained from the RT trials: median RT and SD (intraindividual variability) for 0 to 2 bits were computed. Median RTs fitted very well to Hick’s (1952) law (mean r* = 0.94). There were only minor differences in correlational patterns of the bit-conditions with intelligence and attention, therefore means of median RTs and of SDS were computed by averaging over conditions. The results for the slope of RT will not be reported here, because it mainly yielded low and non-significant correlations with the other variables. Additionally, the percentage of errors was used for further computations. Procedure. The SPM and the d2 were applied as a group test in a first session, and some days later the Hick paradigm was given in individual sessions. Results SPM raw scores varied from 9 to 52 (M = 37.47, SD = S.Ol), d2 scores from 171 to 495 (M = 332.68, SD = 60.81). Because of large sex differences in correlational patterns (which will be shown later) the results of the Hick parameters are also displayed separately for males and females (see Table 1). Males respond somewhat faster but are more variable and error-prone. Testing for differences between the sexes by means of t-tests yields only a significant difference in the percentage of errors in the Hick paradigm [t(77.61) = 3.21, P < 0.011; in addition, the variance of errors is larger in the males’ sample (F = 2.07; P < 0.05). Moreover, females perform better in the d2 than males [352.94 vs 316.47, respectively; t(78.02) = -2.90, P < 0.011. Males display larger variance than females in the d2 (66.06 vs 46.98, respectively; F = 1.98, P < 0.05) and in the SPM (9.22 vs 6.15, respectively; F = 2.25; P < 0.05). There were no significant sex differences in mean or SD of age. To tackle the problem of different speed-accuracy trade-offs of males and females, an information analysis was performed for the Hick paradigm. This allows the combination of speed (RT) and accuracy (errors) in a single score. According to a procedure described by Fitts (1966; see also Mittenecker & Raab, 1973) the average information transmitted per response, H,, was computed. Average information rate (H,/sec or bits/set) per S was then estimated by dividing average H, by median RT (see Table 1 for descriptive statistics of H,/sec). There is no significant sex difference in mean and variance of HJsec. Table 2 shows correlations of Hick parameters with intelligence (SPM) and attention (d2) for the total sample and for males and females. The hypothesized negative correlation of RTs and SDS with intelligence can be observed only in females, not in males. In males, however RTs and variabilities are correlated only with attention. In males, solely the percentage of errors shows a significant negative relationship with intelligence. The information measure H,/sec, however, shows that the relationship between processing speed in the Hick paradigm and intelligence is about the same for the sexes. On the other hand, it is
Table I. Descriptive statistm for Hick parameters of Study 1(SDS are given in parentheses below the means) RT SD Error% H,/SE
Total
Males
Females
395.84 (78.59) 93.20 (42.37) 6.08 (4.94) 4.33 (0.77)
387.59 (83.85) 96.89 (47.53) 7.52 (5.35) 4.28 (0.80)
406.16 (71.29) 8X.59 (34.99) 4.28 (3.71) 4.38 (0.74)
RTs and SDS are given in milliseconds, ermrs as a percentage value and H,/sec is scaled in bits/xc.
ALJOSCHA C. NEUBAUER et al.
1328
Table 2. Correlations of Hick parameters with intelligence (SPM) and attention (d2) for males and females (Study 1)
RT x SPM SD x SPM Error% x SPM H,/sec x SPM RTxd2 SDxd2 Error% x d2 H,/sec x d2
Total
Males
Females
-0.14 -0.25’ -0.30” 0.29** -0.34** -0.42*** -0.10 0.47***
-0.02 -0.19 -0.34’ 0.28* -0.55’” -0.58*** 0.02 0.59”’
- 0.46** -0.40” -0.10 0.30’ -0.09 - 0.03 -0.04 0.26
*P < 0.05; l*P < 0.01; ***P
< 0.001 (one-tailed).
surprising that the relationship of H,/sec with attention is larger for males than for females; the difference between these coefficients, however, is not significant. To find out if the observed relationship between processing speed in the Hick paradigm and intelligence is due to individual differences in attention, a first-order partial correlation coefficient between H,/sec and intelligence controlling for attention was computed (the other Hick parameters were not used, because they depended strongly on the individuals’ set for speed versus accuracy). A partial r of 0.24 (P < 0.05, one-tailed) was found for the total sample, which is only somewhat lower than the zero-order correlation of 0.29 (see Table 2). This is not surprising, because the relationship of intelligence with attention is rather weak (r = 0.18, P > 0.05). An explanation for the surprising sex differences in correlational patterns might be found in the tendency of boys to react more quickly but be significantly more error-prone (see Table 1). In terms of speed-accuracy trade-off males seem to sacrifice speed for accuracy as compared with females, who react slower and are more accurate. This seems to have the effect of invalidating RTs and variabilities of males as indicators of information processing that, therefore, cannot correlate with intelligence. In males, however, the percentage of errors seems to reflect information processing, as indicated by its significant correlation with intelligence. Only the information measure H,/sec showed that performance in the Hick paradigm is equally related to psychometric intelligence in both sexes. It can be argued that the sex difference in validity of the conventional Hick parameters (RT and SD) might be at least partially due to the RT feedback given in the Hick paradigm. In boys of the age range investigated here the feedback might have a stronger motivating effect to trade speed for accuracy. To examine this hypothesis, RT feedback versus no feedback was introduced as a between Ss variable in the second study. STUDY
2
Method Subjects. The Ss were 125 children (64 males, 61 females) of the same age range as in Study 1 (M = 13.11, SD = 1.02), who were again chosen randomly from 5th to 7th grades of two elementary schools. Measures and procedure. The same measures as in Study 1 were used; with the only difference being that in the Hick paradigm 64 Ss (33 males and 31 females) again received RT feedback (as in Study l), whereas 61 Ss (31 males and 30 females) were not informed about their RTs. Results
The raw scores on Raven’s SPM varied from 13 to 55 (M = 42.24, SD = 7.28), those on the d2 varied from 196 to 539 (M = 337.86, SD = 66.15). Descriptive statistics for the Hick parameters are shown in Table 3. In two-way ANOVAs with gender and experimental conditions (feedback vs no feedback) as between Ss variables significant main effects of gender and of experimental conditions on Hick parameters were found. Males display shorter RTs [F( 1,121) = 16.21, P < O.OOl] and more errors [F(1,121) = 5.30, P < 0.051 than females. In the feedback condition RTs are shorter [F(1,121) = 20.18, P < O.OOl] and less variable [F(1,121) = 24.10, P < O.OOl] as compared with the no-feedback condition, but more errors are committed in the feedback condition [F(1,121) = 6.59, P < 0.051. The average information transmitted in the Hick paradigm (H,/sec) is higher for males than for females [F(1,121) = 15.65, P < O.OOl]and with feedback than without
Intelligence,
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and the Hick paradigm
1329
Table 3. Descriptive statistics for Hick parameters of Study 2 (SDS are eiven in oarentheses below the means1 Feedback
RT SD Error% H&c
No-feedback
Total
Males
Females
Males
Females
326.04 (76.28) 83.57 (26.50) 2.40 (2.36) 5.94 (1.27)
284.16 (61.58) 73.31 (21.26) 3.54 (3.14) 6.65 (1.39)
315.52 (66.13) 73.31 (23.54) 2.26 (2.30) 6.04 (I .05)
321.14 (61.26) 86.69 (21.52) 2.15 (1.62) 6.00 (1.10)
387.39 (79.46) 102.17 (29.18) 1.56 (1.57) 5.00 (0.93)
RTs and SDS are given in milliseconds, and H,/sec is scaled in bits/set.
errors as a percentage
value
feedback [F(1,121) = 17.32, P < O.OOl].The tendency for a better fit to Hick’s law in the feedback condition than without feedback just fails to reach significance (mean r2 = 0.96 vs 0.91, respectively). None of the interactions reached significance. There are no significant effects for the SPM and the d2 and-more important when comparing correlation coefficients between groups-the paper-pencil tests and the age variable have equivalent variances as indicated by non-significant Bartlett-Box tests for homogeneity. Homogeneous variances can also be found for RT, SD and H,/sec, only the number of errors differs significantly in homogeneity [F(3,26283) = 6.62, P < O.OOl]. As in Study 1, there were no group differences in mean or SD of age. Subsequently, the Hick parameters were correlated with intelligence and attention for the total sample as well as separately for males and females with and without feedback (see Table 4). For the total sample all Hick parameters correlate significantly with both, intelligence and attention. In the second and third column of Table 4, the correlations of males and females in the feedback condition (which corresponds to Study 1) are shown. The results of this condition partially replicate the findings of Study 1. Again intelligence correlates with median RTs in females but not in males; and again RTs and SDS are correlated with attention in males. If no-feedback is given in the Hick paradigm, the correlational patterns are different; now median RTs also correlate with intelligence in males and this correlation is somewhat higher than in females. Correlations of SDS with intelligence show a similar pattern as in the feedback condition. The most interesting finding, however, is that all Hick parameters now correlate more highly with attention in females. Summarizing, in females with feedback and males without feedback the RTs are correlated more highly with intelligence whereas in the other groups (males with feedback and females without feedback) the error percentage correlates with intelligence and the RT and SD parameters are associated more highly with attention. On inspection of Table 3 an interesting result can be observed, i.e. that those groups with higher RT-intelligence correlations (males without and females with feedback) are those who display non-extreme mean RTs and error percentages, whereas the other two groups (males with and females without feedback) are more extreme in their speed-accuracy trade-off. However, using the information measure H,/sec, it can be shown that the average information rate in the Hick paradigm displays more similar correlations with psychometric intelligence in all groups, although the tendencies described above are still apparent. The same conclusion can be drawn for the Table 4. Correlations
of Hick parameters with intelligence (SPM) and attention and females with and without feedback (Study 2) Feedback
RT x SPM SD x SPM Error% x SPM H,lsec x SPM RTxd2 SDxd2 Error% x d2 H,/sec x d2
(d2) for males
No-feedback
Total
Males
Females
Males
Females
-0.38*** -0.29*” -0.19’ 0.36*** -0.25** -0.27** -0.17* 0.29”.
-0.19 -0.41” -0.46” 0.31. -0.41” -0.46** -0.13 0.45.’
-0.58*** -0.09 -0.20 0.46.’ -0.38’ -0.05 -0.06 0.39’
-0.48” -0.33. 0.10 0.45’1 -0.18 -0.36’ -0.13 0.24
-0.33’ -0.28 -0.38* 0.28 -0.41’ -0.45** -0.37* 0.50’.
lP < 0.05; l*P < 0.01; ***P
d 0.001
(one-tailed).
1330
ALJOSCHAC.NEUBALER
et al.
relationship of the information measure with attention, the differences in correlations between groups being smaller but still apparent. Comparing feedback and no-feedback conditions (disregarding sex differences) it can be shown that H,/sec correlates somewhat higher with intelligence in the feedback condition (P = 0.37, P < 0.01, one-tailed) than in the no-feedback condition (r = 0.28, P < 0.05, one-tailed), but the difference between these coefficients is not significant. The same non-significant tendency emerges for the relationship of H,/sec with attention (r = 0.39, P < 0.01 and r = 0.23, P < 0.05 for feedback vs no-feedback, respectively). Finally, a first-order partial correlation coefficient between H,/sec and intelligence controlling for attention was computed. Similar to Study 1, the partial r of 0.30 (P < 0.001, one-tailed) for the total sample is only somewhat lower than the zero-order correlation of 0.36 (see Table 4) although the correlation between intelligence and attention (r = 0.33, P < 0.001) is higher than in Study 1. DISCUSSION Usually, median RT and intraindividual variability (SD) in the Hick paradigm are interpreted as indices for speed and efficiency of information processing, which is assumed to underlie performance in psychometric intelligence tests. The results of the two studies reported, however, show that this interpretation is problematic, The validity of RT and SD as measures of information processing turned out to depend on a rather complex interaction of Ss characteristics (gender) with procedural properties in the Hick paradigm (differential motivation introduced by feedback vs no-feedback). An explanation of this finding might be found in the groups’ different speed-accuracy trade-off. Males reacted faster but were more error-prone than females, a finding that is supported by previous studies (Larson & Saccuzzo, 1986; Welford, 1980). Providing feedback shortens RTs but increases the number of errors. These main effects led to the finding that females with feedback and males without feedback displayed similar RTs and error percentage values. For these groups (which do not show a clear preference for speed or accuracy) the hypothesized negative relationship between median RT and intelligence was found. In those groups with a clear set for speed or accuracy (i.e. fast and error-prone, like males with feedback or slow and accurate, like females without feedback), however, RT and SD are determined mainly by attention deployment. In these groups the number of errors in the Hick paradigm seems to reflect information processing and, therefore, is associated with psychometric intelligence. Performing outside the optimum range of approximately equal emphasis of speed and accuracy appears to have the effect of making RTs invalid measures of information processing, which, for this reason, do not correlate with intelligence. A presumably hasty conclusion from these findings could be: in order to obtain valid RT and SD measures (as indices of information processing) females should be given feedback in the Hick paradigm whereas males should not be informed about their RTs. However, before jumping to such a conclusion some qualifying remarks should be made: first, the use of a rather different apparatus and procedure compared with Jensen’s RT paradigm (Jensen & Munro, 1979) might be partially responsible for large individual differences in speed-accuracy trade-off. For the apparatus employed here, stimulus-response compatibility is probably lower than in Jensen’s RT paradigm and a “fingers-on-keys” apparatus instead of one with a “home-/response-buttons design” was used. These differences resulted in higher error rates (as well as longer RTs and higher SDS) as compared with Jensen’s RT paradigm (Jensen, 1987; see also Larson & Saccuzzo, 1986) and this makes the emergence of individual differences in speed-accuracy trade-off more likely. In addition, it should be mentioned that personality might be a variable determining speed-accuracy trade-off in the Hick paradigm: Larson and Saccuzzo (1986) found a significant negative association of errors with neuroticism. Second, it should be kept in mind that children between 5th and 7th grade were investigated. Extending the study of RTs and intelligence to other age groups should show if developmental differences between the sexes at this age might account for the findings reported. Third, it is not clear whether the present results might be due to individual differences in familiarity with equipment (Detterman, 1987). Because male children usually have more experience with
Intelligence,
attention
and the Hick paradigm
1331
computers and video-games in Western societies, one could argue that this might be at least partially responsible for the sex differences found. The problem of a differential validity of speed and accuracy parameters of the Hick paradigm depending on the groups’ set for speed vs accuracy could be tackled only by computing an information measure (average information rate) that allows the combination of speed (RT) and accuracy (errors) in a single score. This measure displayed more similar correlations with psychometric intelligence in all groups, although group differences were still apparent. The question for the role of attention in the RT-IQ relationship can, therefore, be answered only for the average information rate (H,/sec). In both studies this measure was correlated significantly with both intelligence and attention: in Study 1 the relationship with attention was stronger, in Study 2 the relationship with intelligence was somewhat stronger (in both studies the difference between correlations with intelligence and attention did not reach significance). In line with 1987) it can be concluded, that previous findings (Carlson et al., 1983; Carlson & Widaman, performance in the Hick paradigm relates to both intelligence and attention deployment. In both studies, controlling for attention led to a rather slight reduction of the correlation between processing speed in the Hick paradigm and intelligence. Attention deployment plays a significant role in RT performance, but it does not seem to be largely responsible for the relationship between intelligence and speed of information processing in the Hick paradigm. Different motivation (introduced by varying feedback in the Hick paradigm) had only small effects: providing feedback yielded a slightly better fit to Hick’s law and slightly higher correlations of Hick performance with intelligence and attention as compared to the condition without feedback. Finally, a remark on the rather large age differences (11 to 15 years) in our sample must be made. The reader might suspect these age differences to be at least partially responsible for the findings reported. To show that this is not the case it must be mentioned that the groups compared in both studies (two groups in Study 1, four in Study 2) did not differ significantly either in mean or in homogeneity of age. In addition, there are no findings indicating that the age differences in our sample might be responsible for the correlational patterns found. Computing first-order partial correlations controlling for age produced only minor changes in coefficients (mainly changes in the second decimal place). Only one of the coefficients changed from a significant to a non-significant value (Table 4: SD x d2 in males without feedback dropped to -0.26). No correlation changed from a non-significant to a significant value. Summarizing, it can be said the validity of conventional Hick parameters (RT and SD) as information processing indices depends heavily on an interaction of gender and feedback conditions. This interaction was probably due to groups’ differences in speed-accuracy trade-off. More similar relationships of the Hick paradigm with intelligence for all groups were found only for an information measure combining speed and accuracy. This finding provides a strong argument for the use of information measures. Therefore, in future studies on relations between elementary cognitive tasks and intelligence, these measures should always be used when large individual differences in speed-accuracy trade-off are observed. Detterman (1987) raised doubts about Jensen’s assumption that parameters derived from the Hick paradigm are pure measures of unitary processes. Detterman asked “Is choice reaction time a simple task?” (1987, p. 192). Regarding our results, the answer must be “No”. REFERENCES Bauer, C. (1991). Geschlechtsspezifische Motivationsunterschiede und ihr Einflulj auf die Beziehung zwischen Intelligenz, Konzentration und Informationsverabeitungsgeschwindigkeit. Unpublished diploma’s thesis, Karl-Franzens-University, Graz, Austria. Brickenkamp, R. (1978). Test d2. Aufmerksnmkeirs-Belasfungstesl. Gottingen: Hogrefe. Carlson, J. S. & Widaman. K. F. (1987). Elementary cognitive correlates of g: Progress and prospects. In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Carlson, J. S., Jensen, C. M. & Widaman, K. (1983). Reaction time, intelligence and attention. Infeliigence, 7, 329-344. Detterman, D. K. (1987). What does reaction time tell us about intelligence? In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Eysenck, H. J. (1982). A model for intelligence. Heidelberg: Springer. Fitts, P. (1966). Cognitive aspects of information processing: III. Set for speed versus accuracy. Journal of Experimental Psychology, 71, 849-857.
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Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4, 11-26. Holler, G. (1990). Informationsverarbeitungsgeschwindigkeit und ihr Zusammenhang mit Intelligenz unter Beriicksichtigung der Konzentrationsfahigkeit. Unpublished diploma’s thesis, Karl-Franzens-University, Graz, Austria. Jensen, A. R. (1980). Bias in mental testing. New York: The Free Press. Jensen, A. R. (1987). Individual differences in the Hick paradigm. In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Jensen, A. R. & Munro, E. (1979). Reaction time, movement time, and intelligence. Intelligence, 3, 121-126. Juhel, J. (1991). Relationships between psychometric intelligence and information-processing speed indexes. Cahiers de psychologie cognitive [European Bulletin of Cognitive Psychology] 11, 73-106. Larson, G. E. & Saccuuo, D. P. (1986). Gender, neuroticism and speed-accuracy tradeoffs on a choice reaction-time task. Personality and Individual Differences, 7, 919-992. Longstreth, L. E. (1984). Jensen’s reaction time investigations of intelligence: A critique. Intelligence, 8, 139-160. Longstreth, L. E. (1986). The real and the unreal: A reply to Jensen and Vernon. Intelligence, 10, 181-191. Marr, D. B. & Sternberg, R. J. (1987). The role of mental speed in intelligence: A triarchic perspective. In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Mittenecker, E. & Raab, E. (1973). Informationstheorie fir Psychologen. Gottingen: Hogrefe. Neubauer, A. C. (1991). Intelligence and RT: A modified Hick paradigm and a new RT paradigm. Intelligence, 1.5, 175-192. Raven, J. C. (1960). Standard Progressive Matrices. London: Lewis. Smith, G. A. (1989). Strategies and procedures affecting the accuracy of reaction time parameters and their correlations with intelligence. Personality and Individual Differences, IO, 829-836. Vernon, P. A. (1987). Speed of information processing and intelligence. Norwood, NJ: Ablex. Welford, A. T. (1980). Reaction times. New York: Academic Press.
1992
0191-8869/92
$5.00 + 0.00
Copyright 0 1992 Pergamon Press Ltd
INTELLIGENCE, ATTENTION, MOTIVATION AND SPEED-ACCURACY TRADE-OFF IN THE HICK PARADIGM* ALJOSCHA Institute
of Psychology,
C.
NEUBAUER,
CHRISTA
Karl-Franzens-University (Received
BAUER Graz,
10 November
and
GERALD
SchubertstraSe
HULLER
6a, 8010 Graz,
Austria
1991)
Summary-The role of attention and motivation in the frequently observed negative relationship between intelligence and reaction times (RT) in the Hick paradigm (HICK) was investigated in two studies. The subjects (1 l- to 15-year-old children) completed the HICK, Raven’s Standard Progressive Matrices, and the d2 (attention measure). Different motivation was introduced by varying RT feedback in the HICK. Correlations of HICK parameters with intelligence and attention depended on an interaction of gender and feedback, which was found to be due to group differences in speed-accuracy trade-OR RTs and SDS (intraindividual variabilities) correlated with intelligence only in groups emphasizing both speed and accuracy. In groups with a strong set either for speed or accuracy RTs and SDS only correlated with attention, but in these subjects the number of errors in the HICK was associated with intelligence. Only the use of an information measure (average information rate), which combines speed and accuracy, revealed a significant association of the HICK with intelligence and attention independent of speedaccuracy trade-off. These findings question the universal validity of RT and SD as processing speed indices and provide a strong argument for the use of information measures in research on RTs and intelligence.
The relationship between reaction times (RTs) and intelligence has been studied intensively during the last 10 years. In most of the studies negative correlations of low to moderate size between the performance on so-called elementary cognitive tasks (like inspection time or choice reaction time, the so-called Hick paradigm) and psychometric intelligence have been reported (see Vernon, 1987; and Juhel, 1991; for reviews). Jensen, Eysenck and other proponents of this line of research explain the relationship by ‘bottom-up’ processing, i.e. they assume a neural basis like speed or efficiency of neural transmission in the brain that should affect both performance on these elementary cognitive tasks and on intelligence tests (Jensen, 1980; Eysenck, 1982). This theory, however, is subjected to severe criticism. The critics consider the relationship to be mediated by ‘top-down’ processing (Longstreth, 1984, 1986) or metacomponential processes (Marr & Sternberg, 1987). They assume that other variables, like motivation, attention, or certain personality traits lead to an overestimation of the RT-intelligence (or RT-IQ) relationship (Detterman, 1987). Regarding the influence of attention it is assumed that low-IQ Ss are not paying as much attention in RT experiments as high-IQ Ss, perhaps because “they do not recognize the importance of paying attention in order to minimize reaction time” (Longstreth, 1986, p. 187) and this would have the effect of inflating the correlations between RT and IQ. Only two studies deal with the influence of attention on the RT-IQ relationship: Carlson, Jensen and Widaman (1983) and Carlson and Widaman (1987) reported evidence for an association of high attention with fast and consistent RTs; they also found a significant association of RTs with intelligence. From these results they conclude that “attention deployment appears to play a significant role in the generally observed relationship between reaction time and intellectual ability” (1983, p. 3420. Detterman (1987), as well as Marr and Sternberg (1987) consider individual differences in motivation to perform quickly in tests of mental speed as a confounding variable. Again, it is assumed that high-IQ Ss are more highly motivated in RT experiments than Ss of lower intelligence, thereby inflating the RT-IQ relationship. There is, however, no empirical study dealing with the influence of motivation on the RT-IQ relationship. *This paper is based on diploma’s theses of the second and third author, conducted under the supervision of the first author. Portions of this paper were presented at the First European Conference on Psychological Assessmenr (ECPA) in Barcelona, Spain, 23-24 September 199 1. 1325
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The following two studies will be reported. The purpose of the first study (Holler, 1990) was to assess the role of attention in the relationship between RT and intelligence. The second study (Bauer, 1991) also dealt with this aspect and was, therefore, also an attempt to replicate the first study; but, additionally, motivation was manipulated by varying RT feedback in the Hick paradigm (following a suggestion by Detterman, 1987). STUDY
1
Method Subjects. The Ss were 81 children (45 males, 36 females; aged 11 to 15 years, mean age = 12.95, SD = 0.81) chosen randomly from 5th to 7th grades of an elementary school. Measures. All children were given the Raven’s Standard Progressive Matrices @PM) Raven (1960) as a measure of general intelligence as well as the d2 (‘Aufmerksamkeits-Belastungs-Test’ by Brickenkamp, 1978) for the measurement of attention deployment. The task in the d2 is to cross out as fast as possible all d’s with two commas out of a number of d’s and p’s with one, two, three, or four commas. The resulting score is the number of items processed correctly within a given time-limit. Finally, Ss were tested on the Hick paradigm, a simple and choice RT task. To avoid the shortcomings of Jensen’s RT task (Jensen & Munro, 1979) pointed out by Longstreth (1984) as order effects, response bias effects and RT/movement time strategies, a modified Hick paradigm was used: stimuli were squares and plus-signs presented on the video screen of a personal computer. In the 2-bit condition (4-choice) four squares (arranged in a row) were presented on the display. After 1.5 set a warning tone was presented and after a foreperiod varying randomly between 0.7 to 2 set a plus-sign was presented as reaction stimulus in one of the squares (see Fig, 1). The Ss had to press the corresponding response-button on a ‘fingers-on-keys’ apparatus with four response-buttons, arranged to fit comfortably under the middle and index fingers of each hand (following a suggestion by Smith, 1989). The use of this apparatus was preferred to one with homeand response-buttons, because of the problems associated with RT/movement time strategies (see Smith, 1989). In the l-bit condition (Zchoice RT) for each new trial two squares were selected randomly out of the four squares (from the 2-bit condition) and were displayed on the screen. After the foreperiod the reaction stimulus was presented in one of the squares and again had to be responded to by pressing the corresponding response-button. Through this procedure an equal number of specific responses (with middle and index fingers of each hand) was attained, thereby avoiding response bias effects (Neubauer, 1991). In simple RT only one square was shown (again
Fig.
1. Example
of modified
Hick paradigm
with 4-choice
task.
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randomly selected), in which-after the foreperiod-the reaction stimulus appeared that had to be answered in the same way as in the other conditions. The order of conditions was randomized between Ss. Thirty-two trials were presented during each condition. To increase motivation RT was fed back to the Ss after each trial by displaying the RT in milliseconds. Data of single trials were checked for outliers eliminating RTs < 100 msec and > 2000 msec and by subsequent “winsorizing”, i.e. eliminating all RTs that fall more than 3 SDS above the Ss own mean RT. The following measures were obtained from the RT trials: median RT and SD (intraindividual variability) for 0 to 2 bits were computed. Median RTs fitted very well to Hick’s (1952) law (mean r* = 0.94). There were only minor differences in correlational patterns of the bit-conditions with intelligence and attention, therefore means of median RTs and of SDS were computed by averaging over conditions. The results for the slope of RT will not be reported here, because it mainly yielded low and non-significant correlations with the other variables. Additionally, the percentage of errors was used for further computations. Procedure. The SPM and the d2 were applied as a group test in a first session, and some days later the Hick paradigm was given in individual sessions. Results SPM raw scores varied from 9 to 52 (M = 37.47, SD = S.Ol), d2 scores from 171 to 495 (M = 332.68, SD = 60.81). Because of large sex differences in correlational patterns (which will be shown later) the results of the Hick parameters are also displayed separately for males and females (see Table 1). Males respond somewhat faster but are more variable and error-prone. Testing for differences between the sexes by means of t-tests yields only a significant difference in the percentage of errors in the Hick paradigm [t(77.61) = 3.21, P < 0.011; in addition, the variance of errors is larger in the males’ sample (F = 2.07; P < 0.05). Moreover, females perform better in the d2 than males [352.94 vs 316.47, respectively; t(78.02) = -2.90, P < 0.011. Males display larger variance than females in the d2 (66.06 vs 46.98, respectively; F = 1.98, P < 0.05) and in the SPM (9.22 vs 6.15, respectively; F = 2.25; P < 0.05). There were no significant sex differences in mean or SD of age. To tackle the problem of different speed-accuracy trade-offs of males and females, an information analysis was performed for the Hick paradigm. This allows the combination of speed (RT) and accuracy (errors) in a single score. According to a procedure described by Fitts (1966; see also Mittenecker & Raab, 1973) the average information transmitted per response, H,, was computed. Average information rate (H,/sec or bits/set) per S was then estimated by dividing average H, by median RT (see Table 1 for descriptive statistics of H,/sec). There is no significant sex difference in mean and variance of HJsec. Table 2 shows correlations of Hick parameters with intelligence (SPM) and attention (d2) for the total sample and for males and females. The hypothesized negative correlation of RTs and SDS with intelligence can be observed only in females, not in males. In males, however RTs and variabilities are correlated only with attention. In males, solely the percentage of errors shows a significant negative relationship with intelligence. The information measure H,/sec, however, shows that the relationship between processing speed in the Hick paradigm and intelligence is about the same for the sexes. On the other hand, it is
Table I. Descriptive statistm for Hick parameters of Study 1(SDS are given in parentheses below the means) RT SD Error% H,/SE
Total
Males
Females
395.84 (78.59) 93.20 (42.37) 6.08 (4.94) 4.33 (0.77)
387.59 (83.85) 96.89 (47.53) 7.52 (5.35) 4.28 (0.80)
406.16 (71.29) 8X.59 (34.99) 4.28 (3.71) 4.38 (0.74)
RTs and SDS are given in milliseconds, ermrs as a percentage value and H,/sec is scaled in bits/xc.
ALJOSCHA C. NEUBAUER et al.
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Table 2. Correlations of Hick parameters with intelligence (SPM) and attention (d2) for males and females (Study 1)
RT x SPM SD x SPM Error% x SPM H,/sec x SPM RTxd2 SDxd2 Error% x d2 H,/sec x d2
Total
Males
Females
-0.14 -0.25’ -0.30” 0.29** -0.34** -0.42*** -0.10 0.47***
-0.02 -0.19 -0.34’ 0.28* -0.55’” -0.58*** 0.02 0.59”’
- 0.46** -0.40” -0.10 0.30’ -0.09 - 0.03 -0.04 0.26
*P < 0.05; l*P < 0.01; ***P
< 0.001 (one-tailed).
surprising that the relationship of H,/sec with attention is larger for males than for females; the difference between these coefficients, however, is not significant. To find out if the observed relationship between processing speed in the Hick paradigm and intelligence is due to individual differences in attention, a first-order partial correlation coefficient between H,/sec and intelligence controlling for attention was computed (the other Hick parameters were not used, because they depended strongly on the individuals’ set for speed versus accuracy). A partial r of 0.24 (P < 0.05, one-tailed) was found for the total sample, which is only somewhat lower than the zero-order correlation of 0.29 (see Table 2). This is not surprising, because the relationship of intelligence with attention is rather weak (r = 0.18, P > 0.05). An explanation for the surprising sex differences in correlational patterns might be found in the tendency of boys to react more quickly but be significantly more error-prone (see Table 1). In terms of speed-accuracy trade-off males seem to sacrifice speed for accuracy as compared with females, who react slower and are more accurate. This seems to have the effect of invalidating RTs and variabilities of males as indicators of information processing that, therefore, cannot correlate with intelligence. In males, however, the percentage of errors seems to reflect information processing, as indicated by its significant correlation with intelligence. Only the information measure H,/sec showed that performance in the Hick paradigm is equally related to psychometric intelligence in both sexes. It can be argued that the sex difference in validity of the conventional Hick parameters (RT and SD) might be at least partially due to the RT feedback given in the Hick paradigm. In boys of the age range investigated here the feedback might have a stronger motivating effect to trade speed for accuracy. To examine this hypothesis, RT feedback versus no feedback was introduced as a between Ss variable in the second study. STUDY
2
Method Subjects. The Ss were 125 children (64 males, 61 females) of the same age range as in Study 1 (M = 13.11, SD = 1.02), who were again chosen randomly from 5th to 7th grades of two elementary schools. Measures and procedure. The same measures as in Study 1 were used; with the only difference being that in the Hick paradigm 64 Ss (33 males and 31 females) again received RT feedback (as in Study l), whereas 61 Ss (31 males and 30 females) were not informed about their RTs. Results
The raw scores on Raven’s SPM varied from 13 to 55 (M = 42.24, SD = 7.28), those on the d2 varied from 196 to 539 (M = 337.86, SD = 66.15). Descriptive statistics for the Hick parameters are shown in Table 3. In two-way ANOVAs with gender and experimental conditions (feedback vs no feedback) as between Ss variables significant main effects of gender and of experimental conditions on Hick parameters were found. Males display shorter RTs [F( 1,121) = 16.21, P < O.OOl] and more errors [F(1,121) = 5.30, P < 0.051 than females. In the feedback condition RTs are shorter [F(1,121) = 20.18, P < O.OOl] and less variable [F(1,121) = 24.10, P < O.OOl] as compared with the no-feedback condition, but more errors are committed in the feedback condition [F(1,121) = 6.59, P < 0.051. The average information transmitted in the Hick paradigm (H,/sec) is higher for males than for females [F(1,121) = 15.65, P < O.OOl]and with feedback than without
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Table 3. Descriptive statistics for Hick parameters of Study 2 (SDS are eiven in oarentheses below the means1 Feedback
RT SD Error% H&c
No-feedback
Total
Males
Females
Males
Females
326.04 (76.28) 83.57 (26.50) 2.40 (2.36) 5.94 (1.27)
284.16 (61.58) 73.31 (21.26) 3.54 (3.14) 6.65 (1.39)
315.52 (66.13) 73.31 (23.54) 2.26 (2.30) 6.04 (I .05)
321.14 (61.26) 86.69 (21.52) 2.15 (1.62) 6.00 (1.10)
387.39 (79.46) 102.17 (29.18) 1.56 (1.57) 5.00 (0.93)
RTs and SDS are given in milliseconds, and H,/sec is scaled in bits/set.
errors as a percentage
value
feedback [F(1,121) = 17.32, P < O.OOl].The tendency for a better fit to Hick’s law in the feedback condition than without feedback just fails to reach significance (mean r2 = 0.96 vs 0.91, respectively). None of the interactions reached significance. There are no significant effects for the SPM and the d2 and-more important when comparing correlation coefficients between groups-the paper-pencil tests and the age variable have equivalent variances as indicated by non-significant Bartlett-Box tests for homogeneity. Homogeneous variances can also be found for RT, SD and H,/sec, only the number of errors differs significantly in homogeneity [F(3,26283) = 6.62, P < O.OOl]. As in Study 1, there were no group differences in mean or SD of age. Subsequently, the Hick parameters were correlated with intelligence and attention for the total sample as well as separately for males and females with and without feedback (see Table 4). For the total sample all Hick parameters correlate significantly with both, intelligence and attention. In the second and third column of Table 4, the correlations of males and females in the feedback condition (which corresponds to Study 1) are shown. The results of this condition partially replicate the findings of Study 1. Again intelligence correlates with median RTs in females but not in males; and again RTs and SDS are correlated with attention in males. If no-feedback is given in the Hick paradigm, the correlational patterns are different; now median RTs also correlate with intelligence in males and this correlation is somewhat higher than in females. Correlations of SDS with intelligence show a similar pattern as in the feedback condition. The most interesting finding, however, is that all Hick parameters now correlate more highly with attention in females. Summarizing, in females with feedback and males without feedback the RTs are correlated more highly with intelligence whereas in the other groups (males with feedback and females without feedback) the error percentage correlates with intelligence and the RT and SD parameters are associated more highly with attention. On inspection of Table 3 an interesting result can be observed, i.e. that those groups with higher RT-intelligence correlations (males without and females with feedback) are those who display non-extreme mean RTs and error percentages, whereas the other two groups (males with and females without feedback) are more extreme in their speed-accuracy trade-off. However, using the information measure H,/sec, it can be shown that the average information rate in the Hick paradigm displays more similar correlations with psychometric intelligence in all groups, although the tendencies described above are still apparent. The same conclusion can be drawn for the Table 4. Correlations
of Hick parameters with intelligence (SPM) and attention and females with and without feedback (Study 2) Feedback
RT x SPM SD x SPM Error% x SPM H,lsec x SPM RTxd2 SDxd2 Error% x d2 H,/sec x d2
(d2) for males
No-feedback
Total
Males
Females
Males
Females
-0.38*** -0.29*” -0.19’ 0.36*** -0.25** -0.27** -0.17* 0.29”.
-0.19 -0.41” -0.46” 0.31. -0.41” -0.46** -0.13 0.45.’
-0.58*** -0.09 -0.20 0.46.’ -0.38’ -0.05 -0.06 0.39’
-0.48” -0.33. 0.10 0.45’1 -0.18 -0.36’ -0.13 0.24
-0.33’ -0.28 -0.38* 0.28 -0.41’ -0.45** -0.37* 0.50’.
lP < 0.05; l*P < 0.01; ***P
d 0.001
(one-tailed).
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relationship of the information measure with attention, the differences in correlations between groups being smaller but still apparent. Comparing feedback and no-feedback conditions (disregarding sex differences) it can be shown that H,/sec correlates somewhat higher with intelligence in the feedback condition (P = 0.37, P < 0.01, one-tailed) than in the no-feedback condition (r = 0.28, P < 0.05, one-tailed), but the difference between these coefficients is not significant. The same non-significant tendency emerges for the relationship of H,/sec with attention (r = 0.39, P < 0.01 and r = 0.23, P < 0.05 for feedback vs no-feedback, respectively). Finally, a first-order partial correlation coefficient between H,/sec and intelligence controlling for attention was computed. Similar to Study 1, the partial r of 0.30 (P < 0.001, one-tailed) for the total sample is only somewhat lower than the zero-order correlation of 0.36 (see Table 4) although the correlation between intelligence and attention (r = 0.33, P < 0.001) is higher than in Study 1. DISCUSSION Usually, median RT and intraindividual variability (SD) in the Hick paradigm are interpreted as indices for speed and efficiency of information processing, which is assumed to underlie performance in psychometric intelligence tests. The results of the two studies reported, however, show that this interpretation is problematic, The validity of RT and SD as measures of information processing turned out to depend on a rather complex interaction of Ss characteristics (gender) with procedural properties in the Hick paradigm (differential motivation introduced by feedback vs no-feedback). An explanation of this finding might be found in the groups’ different speed-accuracy trade-off. Males reacted faster but were more error-prone than females, a finding that is supported by previous studies (Larson & Saccuzzo, 1986; Welford, 1980). Providing feedback shortens RTs but increases the number of errors. These main effects led to the finding that females with feedback and males without feedback displayed similar RTs and error percentage values. For these groups (which do not show a clear preference for speed or accuracy) the hypothesized negative relationship between median RT and intelligence was found. In those groups with a clear set for speed or accuracy (i.e. fast and error-prone, like males with feedback or slow and accurate, like females without feedback), however, RT and SD are determined mainly by attention deployment. In these groups the number of errors in the Hick paradigm seems to reflect information processing and, therefore, is associated with psychometric intelligence. Performing outside the optimum range of approximately equal emphasis of speed and accuracy appears to have the effect of making RTs invalid measures of information processing, which, for this reason, do not correlate with intelligence. A presumably hasty conclusion from these findings could be: in order to obtain valid RT and SD measures (as indices of information processing) females should be given feedback in the Hick paradigm whereas males should not be informed about their RTs. However, before jumping to such a conclusion some qualifying remarks should be made: first, the use of a rather different apparatus and procedure compared with Jensen’s RT paradigm (Jensen & Munro, 1979) might be partially responsible for large individual differences in speed-accuracy trade-off. For the apparatus employed here, stimulus-response compatibility is probably lower than in Jensen’s RT paradigm and a “fingers-on-keys” apparatus instead of one with a “home-/response-buttons design” was used. These differences resulted in higher error rates (as well as longer RTs and higher SDS) as compared with Jensen’s RT paradigm (Jensen, 1987; see also Larson & Saccuzzo, 1986) and this makes the emergence of individual differences in speed-accuracy trade-off more likely. In addition, it should be mentioned that personality might be a variable determining speed-accuracy trade-off in the Hick paradigm: Larson and Saccuzzo (1986) found a significant negative association of errors with neuroticism. Second, it should be kept in mind that children between 5th and 7th grade were investigated. Extending the study of RTs and intelligence to other age groups should show if developmental differences between the sexes at this age might account for the findings reported. Third, it is not clear whether the present results might be due to individual differences in familiarity with equipment (Detterman, 1987). Because male children usually have more experience with
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computers and video-games in Western societies, one could argue that this might be at least partially responsible for the sex differences found. The problem of a differential validity of speed and accuracy parameters of the Hick paradigm depending on the groups’ set for speed vs accuracy could be tackled only by computing an information measure (average information rate) that allows the combination of speed (RT) and accuracy (errors) in a single score. This measure displayed more similar correlations with psychometric intelligence in all groups, although group differences were still apparent. The question for the role of attention in the RT-IQ relationship can, therefore, be answered only for the average information rate (H,/sec). In both studies this measure was correlated significantly with both intelligence and attention: in Study 1 the relationship with attention was stronger, in Study 2 the relationship with intelligence was somewhat stronger (in both studies the difference between correlations with intelligence and attention did not reach significance). In line with 1987) it can be concluded, that previous findings (Carlson et al., 1983; Carlson & Widaman, performance in the Hick paradigm relates to both intelligence and attention deployment. In both studies, controlling for attention led to a rather slight reduction of the correlation between processing speed in the Hick paradigm and intelligence. Attention deployment plays a significant role in RT performance, but it does not seem to be largely responsible for the relationship between intelligence and speed of information processing in the Hick paradigm. Different motivation (introduced by varying feedback in the Hick paradigm) had only small effects: providing feedback yielded a slightly better fit to Hick’s law and slightly higher correlations of Hick performance with intelligence and attention as compared to the condition without feedback. Finally, a remark on the rather large age differences (11 to 15 years) in our sample must be made. The reader might suspect these age differences to be at least partially responsible for the findings reported. To show that this is not the case it must be mentioned that the groups compared in both studies (two groups in Study 1, four in Study 2) did not differ significantly either in mean or in homogeneity of age. In addition, there are no findings indicating that the age differences in our sample might be responsible for the correlational patterns found. Computing first-order partial correlations controlling for age produced only minor changes in coefficients (mainly changes in the second decimal place). Only one of the coefficients changed from a significant to a non-significant value (Table 4: SD x d2 in males without feedback dropped to -0.26). No correlation changed from a non-significant to a significant value. Summarizing, it can be said the validity of conventional Hick parameters (RT and SD) as information processing indices depends heavily on an interaction of gender and feedback conditions. This interaction was probably due to groups’ differences in speed-accuracy trade-off. More similar relationships of the Hick paradigm with intelligence for all groups were found only for an information measure combining speed and accuracy. This finding provides a strong argument for the use of information measures. Therefore, in future studies on relations between elementary cognitive tasks and intelligence, these measures should always be used when large individual differences in speed-accuracy trade-off are observed. Detterman (1987) raised doubts about Jensen’s assumption that parameters derived from the Hick paradigm are pure measures of unitary processes. Detterman asked “Is choice reaction time a simple task?” (1987, p. 192). Regarding our results, the answer must be “No”. REFERENCES Bauer, C. (1991). Geschlechtsspezifische Motivationsunterschiede und ihr Einflulj auf die Beziehung zwischen Intelligenz, Konzentration und Informationsverabeitungsgeschwindigkeit. Unpublished diploma’s thesis, Karl-Franzens-University, Graz, Austria. Brickenkamp, R. (1978). Test d2. Aufmerksnmkeirs-Belasfungstesl. Gottingen: Hogrefe. Carlson, J. S. & Widaman. K. F. (1987). Elementary cognitive correlates of g: Progress and prospects. In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Carlson, J. S., Jensen, C. M. & Widaman, K. (1983). Reaction time, intelligence and attention. Infeliigence, 7, 329-344. Detterman, D. K. (1987). What does reaction time tell us about intelligence? In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Eysenck, H. J. (1982). A model for intelligence. Heidelberg: Springer. Fitts, P. (1966). Cognitive aspects of information processing: III. Set for speed versus accuracy. Journal of Experimental Psychology, 71, 849-857.
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Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4, 11-26. Holler, G. (1990). Informationsverarbeitungsgeschwindigkeit und ihr Zusammenhang mit Intelligenz unter Beriicksichtigung der Konzentrationsfahigkeit. Unpublished diploma’s thesis, Karl-Franzens-University, Graz, Austria. Jensen, A. R. (1980). Bias in mental testing. New York: The Free Press. Jensen, A. R. (1987). Individual differences in the Hick paradigm. In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Jensen, A. R. & Munro, E. (1979). Reaction time, movement time, and intelligence. Intelligence, 3, 121-126. Juhel, J. (1991). Relationships between psychometric intelligence and information-processing speed indexes. Cahiers de psychologie cognitive [European Bulletin of Cognitive Psychology] 11, 73-106. Larson, G. E. & Saccuuo, D. P. (1986). Gender, neuroticism and speed-accuracy tradeoffs on a choice reaction-time task. Personality and Individual Differences, 7, 919-992. Longstreth, L. E. (1984). Jensen’s reaction time investigations of intelligence: A critique. Intelligence, 8, 139-160. Longstreth, L. E. (1986). The real and the unreal: A reply to Jensen and Vernon. Intelligence, 10, 181-191. Marr, D. B. & Sternberg, R. J. (1987). The role of mental speed in intelligence: A triarchic perspective. In Vernon, P. A. (Ed.), Speed of information processing and intelligence. Norwood, NJ: Ablex. Mittenecker, E. & Raab, E. (1973). Informationstheorie fir Psychologen. Gottingen: Hogrefe. Neubauer, A. C. (1991). Intelligence and RT: A modified Hick paradigm and a new RT paradigm. Intelligence, 1.5, 175-192. Raven, J. C. (1960). Standard Progressive Matrices. London: Lewis. Smith, G. A. (1989). Strategies and procedures affecting the accuracy of reaction time parameters and their correlations with intelligence. Personality and Individual Differences, IO, 829-836. Vernon, P. A. (1987). Speed of information processing and intelligence. Norwood, NJ: Ablex. Welford, A. T. (1980). Reaction times. New York: Academic Press.