Inspection time and intelligence: further attempts to eliminate the apparent movement strategy.
Inspection time (IT) refers to the minimum amount of stimulus exposure time required for subjects to make decisions of a specified level of accuracy about a simple sensory stimulus: typically the presentation of two lines of differing lengths followed by a mask (Vickers, Nettelbeck, & Willson, 1972). The time between the onset of the stimulus and the onset of the mask (stimulus onset asynchrony or SOA) can be manipulated so that the amount of time necessary for a subject to discriminate two line lengths at a given level of accuracy can be objectively measured. This critical SOA is widely regarded as a measure of perceptual speed. The IT task received little interest until Nettelbeck and Lally (1976) related performance on the IT paradigm to individual differences in psychometric intelligence. Since then, several investigators have shown IT to correlate with a wide variety of IQ tests on subjects differing in ages, abilities, and cultures. Nettelbeck (1987), in his review of the state of IT-IQ research some 10 years after the first paper linking IT with IQ, concluded that IT accounts for approximately 25% of the IQ variance. Although some have disagreed with this estimate, subsequent studies have generally supported this view (see Howe, 1989; Nettelbeck & Rabbitt, 1992; Nettelbeck & Young, 1991; Stough & Nettelbeck, 1989). Most recently, Deary and Stough (1996) concluded that IT is the most reliable and strongest information-processing correlate of IQ.
The magnitude of the reported correlation between IT and IQ led some to postulate that individual differences in the speed of sensory registration may be an important and even necessary determinant of intelligence (e.g., Brand & Deary, 1982), on the assumption that IT may be a relatively pure measure of perceptual speed. The potential benefits of reducing apparent motion strategies in the IT paradigm have been discussed by Chaiken and Young (1993) who suggest that IT could become an important tool for assessing cognitive performance independently of motor involvement.
However, whether or not IT is indeed a relatively pure measure of perceptual speed (presumably indexing physiological processes) is still to be resolved. IT-IQ correlations, as such, may not represent a perceptual speed-IQ association but rather other mediating processes central to both tasks (e.g., strategy use, attention, and concentration; Mackintosh, 1981, 1986). Mackenzie and Bingham (1985) reported that apparent motion influenced the association between IT and Wechsler IQ. In a group of subjects who perceived apparent motion, a non-significant association between IT and IQ was reported, whereas in subjects who were unable to use apparent motion cues, the relationship between IT and IQ was negative and significant (-0.71 with Full-Scale IQ). A lower mean IT was reported for the apparent motion group, but the two groups did not differ significantly on IQ scores, suggesting that for subjects who were able to employ apparent motion cues, their ITs were not a true indication of their mental speed per se. Mackenzie and Bingham also found that use of apparent motion cues could not be effectively learned by subjects who did not spontaneously use them, suggesting that the ability to detect and use apparent motion cues may not be a cognitive strategy so much as a distinct perceptual ability. Mackenzie and Cumming (1986) replicated this finding with the Raven's Advanced Progressive Matrices (APM) test. Together, these two studies indicate that strategy use may invalidate some IT tasks as indices of perceptual speed, specifically in those subjects who are able to employ apparent motion cues, are able to artificially shorten their ITs. The lack of effect of training on the IT task upon the IT-IQ task has recently been confirmed by Simpson and Deary (1997). These authors, while not recording IQ scores, explored a high-quality "forest" mask presented tachistoscopically, and revealed that feedback neither encouraged nor aided performance on IT. As in the present paper, these authors investigated strategy use and found that, contrary to Ceci's (1990) prediction, strategy users did not record superior IT performance. While this result strongly suggests that strategies cannot play causal role in the correlation between IT and IQ, IQ was, as mentioned, not tested by Simpson and Deary.
Brebner and Cooper (1986) reported that the ability to use IT-shortening strategies was related to personality. Specifically, they found that extraverts were more likely to use cues than were introverts. This finding has been a concern to a number of theorists who have suggested that the IT-IQ correlations may be merely a consequence of dispositional factors such as personality or temperament (Howe, 1989; White, 1993). Two studies have specifically examined this possibility. Egan (1994) confirmed that strategy users did record shorter ITs, but found that this was unrelated to personality. In both strategy users and nonstrategy users, IT and IQ were significantly correlated, suggesting that strategy use does not cause the IT-IQ correlation. Subsequently, Stough et al. (1996) examined the effects of both personality and temperament employing both the Eysenck Personality Questionnaire and the Strelau Temperament Inventory and reported that IT and IQ were significantly correlated independently of either personality or temperament.
Deary, Caryl, Egan and Wight (1989) reported significant negative correlations between both auditory IT (AIT) and visual IT (VIT) and a wide range of IQ tests, and suggested that it is unlikely that any specific strategy can be used across different modalities (i.e., both auditory and visual). The possibility remains, however, that subjects who perceive apparent motion in VIT studies may also be able to adopt other strategies that may artificially decrease AIT. Also, Deary et al. reported a weak correlation between VIT and AIT, suggesting that these tasks may be more different than similar. Their results may imply that AIT and VIT correlations with IQ may both be significant, but index different underlying processes. Indeed, it is yet to be established satisfactorily that current AIT tasks are measuring the same process as VIT tasks, and whether AIT and VIT share the same part of the IQ variance. It appears quite critical what temporal discrimination is used in AIT: with loudness (rather than pitch) being the auditory dimension most sensitive to processing speed and best associated with IQ (Olsson, Bjorkman, Haag, & Juslin, 1998). Egan (1986) suggested that apparent motion effects occur in all backward masking methodologies and that the influence of strategies has not been systematically documented, and argued that further research is required before it would be sensible to suggest that strategy use may confound IT as a measure of perceptual speed. This position is supported by the work of Sharp (1984) who reported a significant negative relationship (-0.49) between Critical Stimulus Duration (CSD) and Raven's Progressive Matrices scores. CSD is similar to IT, except that no mask follows the presentation of the stimulus. Sharp's result indicates then that the backward mask may not be an essential component of the IT paradigm. However, if CSD and IT measure the same process, then this begs the question of how strategy use could be a relevant mediator of the IT-IQ correlation. Whether the IT parameter is an accurate and pure measure of perceptual speed or not, therefore, is an empirical question still to be resolved. In this regard, there have been very few studies that have experimentally examined apparent motion as a phenomenon in the IT paradigm (Evans & Nettelbeck, 1993; Knibb, 1992).
Evans and Nettelbeck (1993) attempted to reduce apparent motion use in the IT paradigm, suggesting that apparent motion use may be eliminated if two features of the masking procedure could be changed. Firstly, if both lines of the mask were to be extended downwards, then both the long and short lines of the stimulus may appear to move, thus not allowing subjects to use downwards apparent motion to identify the shortest line. Secondly, if an apparent flash was seen in the middle of the display, then this might interfere in some way with the flash reported by some subjects when the masking lines covered the shortest line of the stimulus. They designed a "flash" or lightning bolt mask to implement these conceptual advances, and this mask is used in the present experiment.
Knibb (1992) has also proposed a new mask for the IT paradigm, utilising a dynamic approach. In this procedure, the subject is swamped by rapidly changing (dynamic) masks (with frame durations ranging from 20 to 60 ms) after the presentation of the stimulus, creating additional irrelevant apparent motion, peripheral to the target area. Knibb concludes that the employment of these rapidly changing dynamic frames produces apparent motion that is uninterpretable for stimulus discrimination. This procedure has been criticized by Evans and Nettelbeck (1993) because Knibb employed a methodology in which the stimulus parameters were changed, i.e., the two target lines were physically more similar at shorter SOAs than when the traditional mask was used. Obviously then, it is difficult to evaluate whether the dynamic mask is a better mask than the traditional mask if the discrimination has also changed. Longer ITs reported by Knibb may merely indicate that the discrimination has increased in difficulty. On this point, evidence reported by Frank (1992) suggests that the dynamic mask produces shorter, rather than longer, ITs when compared to the traditional mask if the level of discrimination is held constant across the two masking conditions.
Knibb (1992) and Evans and Nettelbeck (1993) have both reported significant negative correlations between their new masks and psychometric intelligence in small samples (N = 22 and N = 17, respectively). Because of the small sample sizes employed in these studies, it is difficult to make comparisons about the magnitude of the correlation between IT and IQ in subjects who employ and do not employ apparent motion strategies.
The present study addresses three issues. Firstly, it investigates the Mackenzie and Bingham (1985) and Mackenzie and Cumming (1986) finding that reported apparent motion influences IT scores and the association between IT and IQ. Secondly, it attempts to replicate the Brebner and Cooper (1986) finding that personality may be related to subjects' ability to use apparent motion cues or strategies. Thirdly, it tests the effectiveness of the standard IT mask against two other masks (flash and lines) developed to reduce apparent motion cues, and compares the correlation between IT and IQ for each mask condition in a larger sample of subjects. The main aim of this experiment was to develop and test the effectiveness of backward masks without changing the physical parameters of the target stimulus. Chaiken and Young (1993) have recently employed a meta-contrast mask consisting of unfilled outline rectangles closely surrounding the areas where each of the vertical lines of the test stimulus had previously been exposed. The design of this mask followed experimental work linking meta-contrast masks with the apparent motion phenomenon (Banta & Breitmeyer, 1985; Fisicaro, Bernstein, & Narkiewicz, 1977). Unfortunately, their mask did not reduce apparent motion use in their sample, suggesting that meta-contrast masks may not operate at the same level as the apparent motion process, nor, perhaps, at the level which is critical to the IT-IQ correlation. Stimuli masked by meta-contrast continue to affect RTs even when d' is reduced to zero (Ansorge, Klotz, & Neumann, 1998). In keeping with this finding, meta-contrast is highly sensitive to attention and can be greatly reduced by strategic attention to contextual features (Ramachandran & Cobb, 1995). These features suggest that meta-contrast will be a poor candidate for improved IT tasks. Therefore, the present study did not further explore the meta-contrast mask, but rather employed two novel backward pattern masks: a flash mask (first reported by Evans & Nettelbeck, 1993) and a lines pattern mask designed after extensive experimentation with a wide variety of masks in our own laboratory.
Fifty psychology students (42 females and eight males), with ages ranging from 19 to 34 ([bar]X = 21.8; S.D. = 3.3), participated as part of a third-year psychology laboratory session. The Alice Heim 5 (AH5) and APM tests were administered to all 50 subjects. Due to time constraints, a short version of the APM was used which included the practice set but omitted the first 12 questions from set II. Subjects were given a Revised Eysenck Personality Questionnaire (EPQ-R) to return after the testing session had been completed. In total, 30 subjects (60%) of the total sample returned the EPQ-R.
A Macintosh computer set to a frame rate of 16.6 ms was used to present the stimuli to subjects. The testing session consisted of an IT assessment followed by the IQ test. Within the IT session, three masks (flash, lines, and traditional) were used in a balanced order across subjects. In the IQ session, the AH5 and a shortened version of the Raven's APM were administered in a balanced order.
The three masks used in this experiment are shown in Fig. 1. For each mask, each subject completed 20 practice trials (10 at 166 ms and 10 at 149.4 ms) followed by 10 trials at each of the following SOAs; 16.6, 33.2, 49.8, 66.4, and 83 ms. In total, therefore, all subjects completed 210 IT trials across three different masks. The total correct trials were recorded and later used to estimate the SOA at 75% responding accuracy (fitting the data with a third-order polynomial function). Subjects were also required at the completion of the IT session to rank the three masks in terms of difficulty and to describe the process used to discriminate the two stimulus alternatives.
The IT procedure used was similar to that described by Nettelbeck (1987). Briefly, a cue was presented for 500 ms prior to each stimulus which consisted of two parallel vertical lines 24 mm and 34 mm long separated by 10 mm (see Fig. 1). Subjects were required to respond by pressing a left key if the shortest line was on the left side, and a right key if the shortest line was on the right side. The shortest line had equal probability of occurrence on the left or right side of the long line. The two lines were joined by a horizontal bar across the top of each line. Following the SOA, a mask was presented for 500 ms. The inter-stimulus interval was controlled by the subject with the next trial being initiated by depressing the space bar. Subjects were instructed to respond as accurately as possible, taking as long as they required to complete the task accurately.
Finally, in order to validate our measure of apparent motion (subject report), 12 subjects were recalled to perform an apparent-motion rating task. In this task, each subject viewed the computer monitor from a distance of 50 cm as two 1-cm-diameter black circles were presented in alternating order, one displaced horizontally by 75 mm from the other. The time between stimulus offset and the onset of the next stimulus was varied across 0, 50, 100, 250, and 500 ms and subjects were asked to rate the perceived apparent motion on a Lickert scale from 0 to 7. Subject's ratings of apparent motion correlated positively (r = .97, P [is less than] .001) with the gap between stimulus onset, a variable known to predict the occurrence of apparent motion (Korte, 1915; Stevens, 1951). When asked, all subjects stated that this form of apparent motion was similar to the effect they had noted to vary among the three IT mask conditions. Thus, subjects appeared to be sensitive to the apparent motion dimension, and subjectively related this dimension to the difficulty level of the traditional IT task.
As noted in the Methods section, the subject reports of apparent motion were validated against the Kourte and Stevens task. Additionally, as noted, the IT measure was calculated at 75% accuracy. The mean ITs (SOAs) in milliseconds and S.D. values in parentheses required for 75% accuracy for each mask were: Flash 49.2 (13.9), Lines 53.2 (12.2), and Traditional 33.5 (16.3).
There was a significant main effect of mask type with the lines and flash mask conditions requiring significantly longer presentation durations than in the traditional mask condition ([F.sub.(2, 49)] = 40.8, P [is less than] .001). ITs obtained from the flash and lines masks were both significantly above those obtained in the traditional mask ([t.sub.(49)] = 5.5, P [is less than] .01 and [t.sub.(49)] = 7.5, P [is less than] .01, respectively). ITs for the flash and lines masks did not differ significantly ([t.sub.(49)] = 1, NS). The percentages of subjects reporting having used an apparent motion cue to discriminate between the stimuli were; traditional 35.6%, flash 20.4%, and lines 10.2%, values which differed significantly from one another ([chi square] (df = 2) = 20.05, P [is less than] .0001). Inspection of the post-hoc cell contributions revealed that the significant chi-square resulted from greater apparent motion use in the traditional condition (4.14), with less than expected from the lines mask (-3.55) and the flash mask intermediate (-0.591). Thus, the traditional mask allowed more subjects to use apparent motion cues to shorten their ITs than did the other masks.
The mean ITs for subjects reporting and not reporting the use of apparent motion cues in the three mask conditions are reported in Table 1. Subjects reporting use of apparent motion cues had significantly shorter ITs than did non-users, but only in the traditional IT mask condition ([F.sub.(1, 46)] = 6.7, P = .01). Although reported use of apparent motion cues is necessarily a subjective phenomenon, it is important to note that there is a high degree of convergence between the reporting of the ability to use apparent motion cues and shorter ITs, indicating that the subjective reporting of the use of apparent motion cues is a good predictor of observable differences in IT.
Table 1 IT means and S.D. (in parentheses) for apparent motion and non-apparent motion users Apparent motion No apparent motion Flash 45.9 (14.7) 54.6 (11.5) Lines 57.5 (5.9) 53.9 (12.1) Traditional 23.4 (12.6) 36.8 (16.9)
The results demonstrate that apparent motion was significantly reduced in the lines and flash mask conditions. Subjects also rated the lines mask the hardest mask (73% vs. 27% for the flash condition and 0% for the traditional condition). These results suggest that use of apparent motion cues to shorten ITs is specific to the mask and presentation technique. This result is further supported by Simpson and Deary's (1997) report of no apparent motion effects with their tachistoscopic forest mask.
All correlations between reported ability to use apparent motion cues and EPQ personality dimensions were small ([is less than] 0.18) and statistically non-significant (P [is greater than] .05). Subject's ability to use apparent motion cues in the traditional mask was significantly related to AH5 figural scores ([F.sub.(1, 49)] = 6.46, P = .01) and to AH5 total scores ([F.sub.(1, 49)] = 4.63, P [is less than] .05). AH5 verbal and APM scores were not significantly related to subjects' ability to use apparent motion cues. It should be noted that the AH5 taps mental imagery, rotation, and other perceptual processes. Subjects who reported apparent motion in the traditional masking condition recorded significantly higher ([t.sub.(49)] = 3.953, P [is less than] .001) AH5 figural scores ([bar]x = 20.1) than subjects who did not report the use of apparent motion cues ([bar]x = 16.7). Subjects reporting apparent motion cues in the traditional mask were also significantly ([t.sub.(47)] = 3.743, P [is less than] .001) more likely to report the use of apparent motion cues in the flash mask condition than in the lines mask condition (r = .746, P [is less than] .01 vs. r= .352, P [is less than] .05). Possibly, some common faculty was tapped by this ability.
ITs derived from the three mask conditions correlated negatively and significantly with performance on the IQ tests (see Table 2). The magnitude of the correlations reported is likely to underestimate the true magnitude of the correlation between IT and IQ because of restriction in range of IQ scores. It should be noted, though, that the design of the study is experimental rather than correlational, so that the presentation of the different masks was balanced across subjects. Accordingly, correlations between IT and IQ based on this design are less than optimal because of the confounding of practice effects. Although this design should affect all masks and therefore correlations based on each mask equally, there is a residual increase in error associated with each correlation.
Table 2 Pearson correlations between IT and IQ uncorrected for restriction in range or test reliability AH5 Verbal AH5 Figural AH5 Total APM Flash -0.33(**) -0.34(**) -0.39(**) -0.47(**) Lines -0.38(**) -0.24(*) -0.34(**) -0.24(*) Traditional -0.31(*) -0.26(*) -0.28(*) -0.20 (*) P<.05. (**) P<.01.
In an attempt to test Mackenzie and Bingham's (1985) finding that the inclusion of subjects who use apparent motion cues significantly decreases IT-IQ correlations, we calculated Pearson correlations between IT and IQ for apparent motion and non-apparent motion groups in the traditional mask condition (see Table 3).
Table 3 Pearson correlations between IT and IQ scores uncorrected for restriction in range and test reliability for apparent motion and non-apparent motion groups in the traditional mask condition AH5 Verbal AH5 Figural AH5 Total APM Apparent motion -0.07 -0.17 -0.14 -0.09 No apparent motion -0.46(**) -0.28 -0.37 -0.31(*) (*) P<.05. (**) P<.01.
Our results provided some support for the Mackenzie and Bingham (1985) and Mackenzie and Cumming (1986) finding that, in the traditional mask condition, the correlation between IT and IQ was greater in the group of subjects who did not use apparent motion cues than in the group who reported using apparent motion cues. More importantly, the correlations between IQ and IT were significant only in the group not reporting apparent motion. This analysis was not computed for the other two masking conditions because of the small number of subjects reporting the use of apparent motion cues.
AH5 figural and AH5 total scores also both correlated significantly with subjects' report that they had or had not used apparent motion cues (r=.4 and .33, respectively, P [is less than] .05), suggesting that the ability to perceive apparent motion is positively related to performance on intelligence tests.
If IT has previously been regarded as a relatively pure measure of perceptual speed, then these results would suggest that for some masks in computer-generated presentations and for subjects who report the use of apparent motion cues, this may not be a valid assumption. The two new mask conditions (flash and lines) reduced reported use of apparent motion cues with relatively few subjects reporting using apparent motion cues. Only 10% of the subjects in the lines mask condition reported observing apparent motion, suggesting that in terms of providing a measure of perceptual speed that is relatively free from apparent motion effect, the lines masks is superior to the traditional mask. These results suggest that, although the use of apparent motion cues in the traditional mask condition weakens the correlation between IT and IQ, strategy use cannot be responsible for the underlying correlation between IT and IQ. Significant negative correlations were reported in both the lines and flash mask conditions (in which little apparent motion use was reported), indicating that IT and IQ are significantly and negatively correlated.
Interestingly, in the lines mask condition, mean IT for the apparent motion group (N = 5) was greater than in the non-apparent motion group, indicating that perhaps in this condition, an ability to perceive apparent motion hindered performance. In the flash mask condition, the ability to use apparent motion cues was not significantly related to smaller ITs, although this effect approached significance. Once again, only 10 (of 50) subjects reported apparent motion cues and so comparison is difficult.
Given the restricted IQ S.D. values (see Table 2) after correction for restriction in range, the correlation between IT and IQ is likely to be of the order of -0.35 to -0.70. Correlations between IT and IQ across all masking conditions were all negative in sign (11/12 were significantly different from zero at the .05 level of probability). The pattern of results, therefore, suggests that IT and IQ are negatively correlated across conditions which do and do not allow the use of apparent motion cues or strategies. Indeed, the strongest correlations were reported in the flash and lines mask conditions which were the two conditions allowing the least use of apparent motion cues. Thus, it appears reasonable to conclude that the use of apparent motion strategies does not determine the negative correlation between IT and IQ, but that the availability of motion artifacts may decrease the magnitude of this correlation. There was suggestion that provision of misleading artifacts (as in the lines mask) may increase the ITs of subjects using these cues without feedback.
The present study also replicated the Mackenzie and Bingham (1985) finding that the use of apparent motion cues may interfere with the negative association between IT and IQ in the traditional mask condition, with the correlation between IT and IQ being greatest in subjects who did not report the use of apparent motion cues. In this sense, the use of apparent motion cues may be regarded as adding error to the measurement of IT. In the traditional condition, ITs of subjects who report the use of apparent motion cues may not be an accurate reflection of their mental speed, but instead an estimate artificially shortened by their ability to use apparent motion cues.
In this experiment, higher-IQ subjects were more likely to report the use of apparent motion cues than were lower-IQ subjects. However, these variables were only weakly correlated and the ability to use apparent motion cues certainly cannot be entirely attributable to high IQ. We hypothesize that this ability (to use apparent motion cues) taps some of the same processes that involve perceptual-figural visual manipulations of IQ items. Our data are not conclusive on this point, and a definitive relationship must await further studies investigating apparent motion and performance on a wide battery of tests that measure a perceptual-figural ability.
Apparent motion is most likely to be observed in the traditional IT mask that has been predominantly used in past IT-IQ studies. It is plausible to suggest that past inconsistencies in studies reporting correlations between IT and IQ, of the type advanced by Howe (1989), may be due to the fact that different experiments have different proportion of subjects who utilized apparent motion cues, and therefore artificially shortened their ITs. Our results suggest that ITs derived from the traditional masking condition provide a relatively poor measure of perceptual speed and that future studies should pay careful attention to the use of backward masks that do not allow the perception of apparent motion to bias ITs. In the present study, in the lines mask condition, only 10% of the sample reported using apparent motion cues. Future experiments should not employ masks that enable many subjects to use apparent motion strategies (e.g., the traditional mask), but should now adopt different masks.
This experiment investigated the effectiveness of different backward visual masks in the IT paradigm. The ability of subjects to use apparent motion was investigated in three backward masking conditions (traditional IT mask and two new masks). Apparent motion was most frequently reported by subjects under the traditional masking condition. The results generally support the conclusion that the significant negative correlation between IT and IQ, which has been reported in previous studies employing the traditional mask, may not be due to the ability of high-IQ subjects to use apparent motion cues. There was no difference between the magnitude of the correlations between IT and IQ under mask conditions which differed in the extent to which subjects reported using apparent motion cues. This result is supportive of the view of Egan (1994) who regards the ability to use apparent motion cues as an epiphenomenon of short ITs. There are a number of issues that future research may wish to address. Firstly, the further refinement of better backward masks for the IT paradigm is required. The authors are not of the opinion that the stimuli or the task itself should be changed in order to make the task less prone to apparent motion or other strategy use (e.g., Knibb, 1992). The interest in IT is that, theoretically, it measures the minimum amount of time to accurately discriminate a simple, non-verbal stimulus. It is therefore preferable to design more appropriate backward masks than to change the stimulus conditions. The results of this experiment and that by Evans and Nettelbeck (1993) demonstrate that changing the stimulus parameters from an easy visual discrimination to a more complex visual discrimination is not necessary to demonstrate significant negative correlations between psychometric intelligence and IT, and a reduction in the number of subjects who can employ apparent motion strategies. We believe that the past body of research linking IT with IQ using this simple methodology is valuable and enables comparisons to be made across groups of different ages, sexes, intellect, and cultures. This is an especially important point if IT is to be used as an adjunct or as a separate measure of intelligence (Brand & Deary, 1982; Nettelbeck & Lally, 1976). Following the work of Flynn (1987), who raises doubts over the long-term validity of IQ as a measure of intelligence, it has been suggested that new measures (IT, Reaction Time, and Averaged Evoked Potentials) may be examined as measures of intelligence (Flynn, 1987; Matarazzo, 1992; Stough, Nettelbeck, & Cooper, 1992). Whether this observation is true or not will await further empirical evidence. In terms of future research with IT, there are a number of key issues that need to be addressed. Most importantly, further experimental research is urgently required to define the optimal stimuli, masks, and other experimental procedures for this task. Although it has recently been concluded that IT remains the most reliable and valid information-processing correlate of IQ (Deary & Stough, 1996, 1997), further research is required to improve current methodologies, to place the IT task within the context of visual masking theory, and to encourage the adoption of common procedures in different laboratories. Accomplishing these tasks will firmly underpin further theoretical and, ultimately, practical advances.
Ansorge, U., Klotz, W., & Neumann, O. (1998). Manual and verbal responses to completely masked (unreportable) stimuli: exploring some conditions for the meta-contrast dissociation. Perception, 27, 1177-1189.
Banta, A. R., & Breitmeyer, B. G. (1985). Stationary patterns suppress the stroboscopic motion. Vision Research, 25, 1501-1505.
Brand, C., & Deary, I. J. (1982). Intelligence and "inspection time". In: H. J. Eysenck (Ed.), A model for intelligence (pp. 133-148). Berlin: Springer.
Brebner, J., & Cooper, C. J. (1986). Personality factors and inspection time. Personality and Individual Differences, 7, 709-714.
Ceci, S. J. (1990). On the relation between microlevel processing efficiency and macrolevel measures of intelligence: some arguments against current reductionism. Intelligence, 14, 141-150.
Chaiken, S. R., & Young, R. K. (1993). Inspection time and intelligence: attempts to eliminate the apparent movement strategy. American Journal of Psychology, 106, 191-210.
Deary, I. J., Caryl, P. G., Egan, V., & Wight, D. (1989). Visual and auditory inspection time: their relationship and correlations with IQ in high-ability subjects. Personality and Individual Differences, 10, 525-533.
Deary, I. J., & Stough, C. (1996). Intelligence and inspection time: achievements, prospects and problems. American Psychologist, 51, 599-608.
Deary, I. J., & Stough, C. (1997). Looking down on intelligence. American Psychologist, 52, 881-882.
Egan, V. (1986). Intelligence and inspection time: do high-IQ subjects use cognitive strategies? Personality and Individual Differences, 7, 695-700.
Egan, V. (1994). Intelligence, inspection time and cognitive strategies. British Journal of Psychology, 85, 305-316.
Evans, G., & Nettelbeck, T. (1993). Inspection time: a flash mask to reduce apparent movement effects. Personality and Individual Differences, 15, 91-94.
Fisicaro, S. A., Bernstein, I. H., & Narkiewicz, P. (1977). Apparent movement and meta-contrast suppression: a decisional analysis. Perception and Psychophysics, 22, 517-525.
Flynn, J. R. (1987). The ontology of intelligence. In: H. Forge (Ed.), Measurement, realism and objectivity (pp. 1-40). Dordrecht, the Netherlands: Reidel.
Frank, N. K. (1992). Unpublished honours thesis, University of Adelaide, South Australia.
Howe, M. J. A. (1989). Letter -- assumptions on intelligence. The Psychologist: Bulletin of The British Psychological Society, 2, 244.
Knibb, K. (1992). A dynamic mask for inspection time. Personality and Individual Differences, 13, 237-248.
Korte, A. (1915). Kinematoskopische Untersuchungen. Z Psychol, 72, 193-296.
Mackenzie, B., & Bingham, E. (1985). IQ, inspection time and response strategies in a university sample. Australian Journal of Psychology, 37, 257-268.
Mackenzie, B., & Cumming, S. (1986). How fragile is the relationship between inspection time and intelligence? The effects of apparent motion cues and previous experience. Personality and Individual Differences, 7, 721-729.
Mackintosh, N. J. (1981). A new measure of intelligence? Nature, 289, 529-530.
Mackintosh, N. J. (1986). The biology of intelligence? British Journal of Psychology, 77, 1-18.
Matarazzo, J. D. (1992). Psychological testing and assessment in the 21st century. American Psychologist, 47, 1007-1018.
Nettelbeck, T. (1987). Inspection time and intelligence. In: P. A. Vernon (Ed.), Speed of information processing and intelligence (pp. 295-346). Norwood, NJ: Ablex.
Nettelbeck, T., & Lally, M. (1976). Inspection time and measured intelligence. British Journal of Psychology, 67, 17-22.
Nettelbeck, T., & Rabbitt, P. M. A. (1992). Ageing, cognitive performance, and mental speed. Intelligence, 16, 189-205.
Nettelbeck, T., & Young, R. (1991). Inspection time and intelligence in 7-year-old children. Personality and Individual Differences, 11, 1283-1289.
Olsson, H., Bjorkman, C., Haag, K., & Juslin, P. (1998). Auditory inspection time: on the importance of selecting the appropriate sensory continuum. Personality and Individual Differences, 25, 627-634.
Ramachandran, V. S., & Cobb, S. (1995). Visual attention modulates meta-contrast masking. Nature, 3 73, 66-68.
Sharp, D. M. (1984). Inspection time, decision time and visual masking: an investigation of their relationship to measured intelligence. Unpublished thesis for the MSc degree in Clinical Psychology, University of Aberdeen.
Simpson, C. R., & Deary, I. J. (1997). Strategy use and feedback in inspection time. Personality and Individual Differences, 23, 787-797.
Stevens, S. S. (1951). Handbook of experimental psychology. New York: Wiley.
Stough, C., Brebner, J., Nettelbeck, T., Cooper, C. J., Bates, T. C., & Mangan, G. L. (1996). The relationship between intelligence, personality and inspection time. British Journal of Psychology, 87, 255-268.
Stough, C., & Nettelbeck, T. (1989). Inspection time and IQ. The Psychologist: Bulletin of the British Psychological Society, 2, 341 (Letter).
Stough, C., Nettelbeck, T., & Cooper, C. J. (1992). IT, RT and AEPs as correlates of intelligence. International Journal of Psychology, 27, 338.
Vickers, D., Nettelbeck, T., & Willson, R. J. (1972). Perceptual indices of performance: the measurement of "inspection time" and "noise" in the visual system. Perception, 1, 263-295.
White, M. (1993). The inspection time rationale fails to demonstrate that inspection time is a measure of the speed of post-sensory processing. Personality and Individual Differences, 15, 185-198.
C. Stough(a,b,*), T.C. Bates(c), G.L. Mangan(d), I. Colrain(e)
(a) School of BSEE, Swinburne University of Technology, PO Box 218 Hawthorn, Melbourne, Victoria 3122, Australia
(b) Brain Sciences Institute, Swinburne University, Melbourne, Australia
(c) Department of Psychology, Macquarie University, Sydney, Australia
(d) Department of Psychology, University of Auckland, Auckland, New Zealand
(e) Department of Psychology, University of Melbourne, Melbourne, Australia
Received 1 April 1997; received in revised form 14 December 1999; accepted 31 March 2000
(*) Corresponding author. Tel.: +61-3-9214-8167; fax: +61-3-9214-5230.
E-mail address: email@example.com (C. Stough).
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||effect of inspection time on intelligence test results|
|Author:||Stough, C.; Bates, T.C.; Mangan, G.L.; Colrain, I.|
|Article Type:||Statistical Data Included|
|Date:||Sep 1, 2001|
|Previous Article:||Age differences in the structure of intelligence Influences of information processing speed.|
|Next Article:||Viewing Spearman's hypothesis from the perspective of multigroup PCA A comment on Schonemann's criticism.|