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Strategy use and feedback in inspection time
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Person. individ. Diff. Vol. 23, No. 5, pp. 787-797, 1997 © 1997 Elsevier Science Ltd. All rights reserved Printed in Great Britain PII: S0191-8869(97)00105-0 0191-8869/97 $17.00+0.00
Pergamon
STRATEGY USE A N D FEEDBACK IN INSPECTION TIME Craig
R.Simpson and Ian J. Deary*
Department of Psychology, University of Edinburgh, Edinburgh, Scotland
(Received 5 February 1997) Summary--There is a consensus that psychometric intelligence correlates significantly with inspection time (IT), a putative measure of the efficiency of the early stages of visual information processing. Controversy exists as to whether IT is a cause or a consequence of IQ differences. It has been suggested that individuals with a high IQ form macrolevel strategies which undermine the microlevel processing assumptions of IT testing, and that feedback given in the early stages of IT testing facilitates strategy formation. One such strategy involves detecting apparent movement in IT stimulus-mask displays. We manipulated feedback during IT testing and found: (1) no evidence that feedback encouraged the formation of strategies; and (2) that feedback did not aid performance on IT. Strategy users did not have superior IT performance. The overall level of strategy reporting was low, presumably because an effective backward mask was employed. These results do not support a causal role for strategies in the correlation between IT and psychometric intelligence; however, they are congruent with the notion that strategy reporting in the IT task is a verbal epiphenomenon. With appropriate stimulus presentation devices and an effective backward mask many of the stimulus-mask artefact problems that lead to strategies in the IT task may be avoided. © 1997 Elsevier Science Ltd
INTRODUCTION
In 1970, Vickers (1970) proposed an accumulator model of two-choice visual discrimination whereby observations of the sensory input are sampled quantally on a background of neural noise at a rate that is characteristic of an individual. The results of the samplings of stimulus information were said to be stored in memory buffers until a critical amount of evidence was accumulated favouring one response choice over the other. The efficiency of the early stages of visual information processing thus described--i.e, the size of the quanta or inspections--represents an individual's perceptual speed or inspection time (IT; Vickers, Nettelbeck & Willson, 1972).
Inspection and psychometric intelligence An individual's inspection time is typically derived from the minimum stimulus duration required to discriminate reliably which of two lines of markedly different length is the longer (Fig. 1). The stimulus is preceded by a fixation point and is followed immediately by a backward mask to prevent
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Fig. 1. The traditional inspection time (a) pi stimulus and (b) mask.
*To whom all correspondence should be addressed at: The Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, Scotland. Telephone: 131 650 3452, Facsimile: 131 650 3461, e-mail: [email protected] 787
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any further stimulus processing after stimulus offset. Nettelbeck and Lally (1976) were the first to report an association between inspection time and measures of psychometric intelligence: brighter individuals required shorter stimulus durations to make correct discriminations. Since then, many studies have confirmed that a significant association exists between IT and measured cognitive abilities. The meta-analysis by Kranzler and Jensen (1989) and the semi-quantitative review by Nettelbeck (1987) suggested that the true correlation between IT and IQ is in the region of 0.5. In addition, they suggest that the link between IT and ability is stronger for performance-and fluid-IQ type measures than for verbal IQ, a result confirmed in a study of IT and WAIS-R scores (Deary, 1993). Although it is recognised that the IT-IQ association is robust and moderately strong in effect size, the explanation for the association is the source of controversy (Deary & Stough, 1996). This debate pits those who view IT as a basic process underlying intelligence differences (e.g. Brand & Deary, 1982; Brand, 1984; Vickers & Smith, 1986; Raz, Willerman & Yama, 1987) against those who see IT as a result of high level strategies that are employed by bright individuals (e.g. Ceci, 1990; Howe, 1988; Mackintosh, 1986). The former group believes that the IT task tests microlevel processing efficiency whereas, as we shall see, the latter group has argued that IT may be measuring aspects of macrolevel processing.
Inspection time and cognitive strategies The componential subtheory of Sternberg's triarchic model of intelligence (Sternberg, 1984; Sternberg, 1985) implies that, in performing a mental task, a system of metacomponents actively selects a strategy for combining lower order components which are then sequenced in such a way as to facilitate task performance. A number of studies has examined the possibility that some Ss are able to employ higher-order, cognitive strategies in order to make correct discriminations in the inspection time task (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986; Alexander & Mackenzie, 1992; Chaiken & Young, 1993; Egan & Deary, 1993). In addition, some studies have found that the successful use of such strategies in the IT task can perhaps artificially reduce a S's inspection time (Nettelbeck, 1982; Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986). Penetration of the IT task by higher level strategies, if true, poses a threat to the theoretical importance of the IT-IQ correlation, because the task may be merely an index of the efficiency with which individuals can apply macrolevel cognitive strategies. In other words, rather than being a basic process underlying intelligence, strategic interpretations of IT suggest that IT is just another macrolevel cognitive task--akin to IQ test items themselves--that bright people perform well (Ceci, 1990). There is a number of strategies which Ss have reported using during the IT task, each of which has been documented by introspective self-report. One of the most commonly reported strategies is that of apparent motion (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986). In this perceptual effect, the stimulus line on the shorter of the two sides of the IT stimulus is seen to move as it is overlaid by the longer masking line. This effect is sometimes reported as a movement, a flicker or an increased level of relative brightness. The S then has merely to choose the side of the stimulus contralateral to that of the perceived motion to state that that is the position of the longer line and thereby make the correctdiscriminativejudgement. Associated with the apparent motion effect is the 'flash-brightness' effect (Alexander & Mackenzie, 1992) which is sometimes reported as a flash occurring between the ends of the stimulus figure and the edge of the display. Here, the shorter of the two lines gives rise to the brighter flash, and once again the S need only choose the contralateral side to make a correct judgement. Alexander and Mackenzie (1992) also measured self reports relating to an 'ends-stand-out' effect where, following the onset of the mask a 'small black gap' appears at the end of the stimulus figure. The size of this gap allows the S to determine which side of the stimulus is longer. Finally, some Ss report using an 'after-image' strategy where the longer of the two lines provides a longer retinal after-image if the S closes his/her eyes immediately after the onset of the backward mask. However, the successful use of this strategy can be countered by employing an effective pattern mask to disrupt post-stimulus processing.
Inspection time, cognitive strategies and feedback Criticism has been directed at those offering a macrolevel, strategy-based account of IT performance for not providing testable hypotheses (Brand, 1987). However, a promising start may be
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found in Sternberg (1984)'s suggestion that external feedback provides a useful source of information for monitoring, adjusting and improving one's skill at a given task. Task-related skill may be continually refined until it becomes almost automatic, requiring little in the way of conscious thought. The inspection time task is suited to conceptualisation in terms of refinement of a skill as it offers the necessary precondition of task consistency which requires that the task does not change over time (Fisk & Schneider, 1983). Furthermore, if, as strategy theorists suggest, some Ss are able to recognise and attend to salient information available in an IT trial additional to that which was intended by the experimenter, they can devote their information processing capacities to such artefacts and hence penetrate the task by encoding information other than that explicitly available from the brief stimulus display (Egan, 1991). Thus Brand (1987) suggested that Ss using strategies to solve IT trials should be especially influenced by feedback, because strategy formation depends not only on the availability of unintended stimulus-relevant information but also on the provision of discrete feedback about performance. If IT task performance by strategy users were to prove amenable to influence in this way, this would call into question the validity of the IT task as a basic, microlevel measure of perceptual speed. In turn, this would threaten the theoretical underpinning of the IT-IQ relationship by suggesting that it might be an artefact of Ss' differential penetration of the IT task through strategy use. Egan and Deary (1993) tested Brand's hypothesis by experimentally manipulating the feedback that Ss received during performance of an IT task. In order to do this they employed the so-called ascending durations inspection time (ADIT) task, a procedure whereby the IT stimuli are presented at durations beginning well below the S's psychophysical threshold, such that the S could not be certain of the accuracy of their responses. Egan and Deary (1993) theorised that if the IT task were to be given from below as opposed to above the psychophysical threshold, the S would rely even more strongly on feedback, as one can never otherwise be sure whether any artefacts/cues detected in the stimulus-mask array have led to a correct response. In addition they included a condition whereby some Ss received false feedback in an attempt to disrupt strategy formation and use. Egan and Deary (1993) reasoned that, if IT judgements were made purely on the basis of explicitly available stimulus information as intended by the researcher, without recourse to strategy use and stimulus-mask artefact detection, feedback should not affect performance. They found that a relatively weak manipulation of feedback did have an effect on performance between the two groups. Those receiving false feedback did not perform as well on the ADIT task as those receiving truthful feedback. However, the false feedback group performed significantly better on a standard IT task which was included as an estimate of baseline IT performance. Egan and Deary (1993) concluded that "false feedback can disrupt effective performance on IT." However, their study was hampered by a number of methodological and design limitations. In the present study we aimed to clarify the effect of feedback on performance and strategy use in the IT task by making a number of methodological improvements on the study by Egan and Deary (1993). First, the stimuli in the present study were presented tachistoscopically in order to eliminate the technical limitations of the presentation of IT stimuli on a computer monitor (Barrett & Kranzler, 1994). Tachistoscopic presentation allows one to employ a psychophysical step size of as little as 1 ms, and affords the use of more appropriate pattern masking techniques than the computer offers. Second, Egan and Deary (1993) provided only weak true and false feedback after every 10 trials, and the nature of this feedback was ambiguous. In their true feedback condition, if the S scored eight or more correct responses in a batch of ten trials, the computer displayed the words 'Well done', whereas if the S scored less than eight, the message 'Please pay attention' was displayed. These messages do not offer clear feedback to help strategy formation on a trial to trial basis. In the present study, Ss in the feedback-receiving condition were informed by the experimenter after every trial whether their response was 'Correct' or 'Wrong'. This is intended to provide an explicit record of the S's performance, and to maximise any effect of feedback on IT performance. Third, with respect to the experimental manipulation of feedback, it is more appropriate to test strategy theory using conditions with true or no feedback in the A D I T task. If some individuals can make use of artefactual cues to score well on stimulus durations below which they can properly identify the longer of the two lines in an IT task then the provision of either true or false feedback is informational (potentially leading to significantly above and below-chance responding, respectively), whereas the withholding of feedback effectively fails to link any artefactual information to the
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correct discriminatory response, and is the preferred comparator of true feedback in this study. In Egan and Deary (1993)'s study it is possible that those Ss receiving false feedback on the A D I T task were apparently performing worse because they were correctly associating their (false) feedback information with particular (but wrong) discriminatory responses. Given that emphasis is generally placed on the use of information about the IT stimulus and its interaction with the backward mask in order to study strategy formation, it was decided to test Ss who had never before seen the IT task. Therefore, we recruited Ss who had never participated in an inspection time study previously. This was a strict prerequisite dictated by the experimental design, because those who had experienced the task before may have had an opportunity to develop strategies on a previous occasion. This principle was reflected in the experimental design and procedure, whereby conditions involving the manipulation of feedback took place before any measurement of a S's conventional visual inspection time ('baseline IT') was made. Therefore, by ensuring that the task was totally novel to the S at the start of the experimental session, and thereby assuming that all Ss were IT-strategy novices, the manipulation of feedback and its potential influence over strategy formation and use and task performance was maximised. Finally, two forms of self-reported strategy use were employed during the experimental session. The first was a free-response, written self report, as employed by Egan and Deary (1993), which provided an indication of any conscious strategy use following the A D I T task. The second report was recorded in a structured questionnaire format as employed by Alexander and Mackenzie (1992). Here, the S was required to rate five given forms of strategy use on a five-point scale. These two methods of data collection were intended to encourage Ss to reflect on their strategy use as much as possible while ensuring that they were oblivious to the experimental manipulation. Therefore, the aims of the study were: to examine the effect of feedback on performance in the ADIT condition, on subsequent 'baseline IT', and on the formation and use of strategies on the IT task. The design also allowed us to examine the associations between reported strategy use and IT performance. METHOD
Subjects Forty undergraduates (20 men and 20 women; mean age 21.5 yr) acted as participants. None of them had participated in an inspection time experiment before. All participants had 6/6 vision as tested by a Snellen-type chart.
Design A between Ss design was employed, with one group of 20 Ss (10 men and 10 women) allocated to the feedback condition and the other group receiving the control condition with no feedback. The dependent variables were the ADIT inspection times and the Ss' later baseline inspection times, and the self-reports of strategy use.
Inspection time A three field tachistoscope connected to a Ralph Gerbrands Company 300 series millisecond timer was used to present the stimuli. Data were recorded on a BBC Model B Microcomputer. The longer of the two lines of the inspection time stimulus was 42.9 mm in length and the shorter was 28.6 mm. This difference between the two lines corresponded to an angle of 0.8 ° visual acuity at a distance of 760 mm. The two lines were joined at the top by a line 14.3 mm in length to form a pishaped stimulus (Fig. 2a). Prior to stimulus presentation, a fixation point in the form of a cross was presented for 500 ms in the centre of the tachistoscope display. An interval of 500 ms then followed before the stimulus was presented at the predetermined duration. Stimulus presentation was followed immediately by a backward mask thereby preventing any additional processing beyond the duration between target onset and mask onset (Vickers et al., 1972). The mask used was designed to minimise apparent motion and after-image cues, often associated with the traditional solid black IT mask. To ensure its effectiveness, the mask was tested in a number of pilot sessions and re-designed according to the
Strategy use and feedback in inspection time dl,4,
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Fig. 2. The inspection time (a) stimulus and (b) forest mask used in this study.
perceptual effects observed. The mask that was found to be the most effective at eliminating any artefactual perceptual effects is illustrated in Fig. 2b. All IT stimuli were presented in the three field tachistoscope, with the S required to state the position of the longer of the two lines in the IT stimulus. Responses were made at leisure with Ss encouraged to respond slowly and with maximum accuracy. Responses were made verbally. The experiment was designed such that the manipulation of feedback occurred during the Ss' first ever experience of the inspection time task. Before the experiment began, the Ss were briefed verbally as to the order of presentation of each trial and were given 4 practice trials at 300 ms to familiarise them with the types of stimulus materials used in the task, whilst not allowing them any significant amount of practice in which to develop any strategy.
Ascending durations inspection time ( ADIT) The feedback and control conditions were administered using the A D I T procedure (Egan & Deary, 1993) which began at a duration of 5ms (which is well below the IT of any tested S) and then increased by 2 ms for every three errors made per block of 15 trials. This process continued until the S had completed 15 trials at a given duration with fewer than three errors (corresponding to 86.67% accuracy). Following each stimulus presentation, the S registered their response verbally, indicating whether they thought the left or right line of the stimulus was longer. In the feedback condition, the experimenter informed the S whether or not they were correct after each trial by stating 'Correct' or 'Wrong'. In the control condition, no feedback was given. Baseline I T Conventional inspection time (baseline IT) was measured using the method of constant stimuli (MCS), which presented a fixed number of trials at each of a number of predetermined target exposure durations (SOA's) in a random block. These were selected to provide a representative sweep of the ogival IT response curve and to include the expected level of the sample's mean IT. A pilot experiment suggested that a range of stimulus durations between 10 ms and 100 ms would be appropriate. Within these limits 10 different durations were employed, each increasing in 10ms intervals. Each stimulus duration was presented 10 times in a randomised order and an equal number (50 each per block of 100 trials) of stimuli had the right or left lines longer. Procedure Each S was first presented with either the feedback or control condition. They were then asked to complete a written report regarding their use of strategies during the task (Brebner & Cooper, 1986). The strategy record form asked Ss to describe how they would explain to a friend the most effective way to distinguish the difference between the lengths of the two lines. These self reports were processed by content analysis. The reports were classified by two independent raters who identified whether the S was a strategy user and which strategy they were using. Ss were rated as to whether they used one of the five given strategies (Alexander & Mackenzie, 1992) and a 90% level of agreement was met between the two raters. The free-response, self report strategy description phase was followed by a training session which
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Craig R. Simpson and Ian J. Deary Table 1. Descriptions of strategy types which were rated by Ss following the baseline IT task
1. Apparent motion 2.
Flash-brightness
3.
Ends-stand-out
4.
After-image
5.
No strategy
"When the second pair of lines comes on, the ends of the first pair look like they are moving. The first lines grow or stretch to meet the ends of the second lines. The longer line is the one that doesn't move as much." "You can see a kind of flash when the second set of tines comes on (i.e. the mask). The flash is between the end of the first pair of lines and the edge of the display. The shorter line has the brightest flash." "When the second covering lines come on, there is a small black gap that you can see just at the end of the first set of lines. This shows where each line ended, so you can tell from that which one was longer." " I f you close your eyes just after the second pair of lines come on, you can see a kind of after image for a second or so, like when you look at a bright light. The longer line gives you a longer after image." "There wasn't any particular trick to it. I just watched as carefully as I could and saw which one was longer."
consisted of 20 trials at a duration time given by the formula (ADIT + ADIT/2) ms. The training session was intended to equate both groups' experience of feedback on the task by presenting stimuli at a constant duration where the S would he able to see the stimulus with relative ease, allowing them to keep track of their accuracy, and to spot any associations between stimulus-mask cues and correctness. Ss then completed the conventional or baseline IT task followed by a second self-report in the form of a structured questionnaire as used by Alexander and Mackenzie (1992). This questionnaire required Ss to rate their use of five strategies (one of which is the use of no strategy) on a five point scale [1 (never) to 5 (all the time)]. Table 1 outlines the strategy descriptions to be rated. Therefore each experimental session followed the sequence: (1) A D I T estimation, (2) Freeresponse, strategy use reporting, (3) IT training, (4) Baseline IT estimation, (5) Strategy questionnaire completion. RESULTS
Does feedback improve inspection times? The feedback group performed slightly, but not significantly (unpaired t test) better on the A D I T task (mean A D I T = 7 0 . 2 m s ) than those in the control condition (mean A D I T = 7 4 . 0 5 m s ) . In addition, mean total scores from the baseline IT condition revealed no significant differences between the two groups. Figure 3 illustrates the performance of the two groups on the baseline task. A more rigorous test of any difference between feedback and control conditions is to compare the groups after controlling for baseline IT scores. Therefore, an analysis of covariance (ANCOVA) was employed: condition, with two levels (control and feedback), was a between Ss factor and baseline IT was entered as a covariate. The dependent variable was the ADIT score. The effect of condition remained non-significant, and baseline IT was a significant covariate of ADIT ( F = 61.23, df= 1.37, p < 0.001). This confirms that feedback had no significant effect on performance, even when baseline
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Duration (msecs) Fig. 3. C o m p a r i s o n o f f e e d b a c k a n d c o n t r o l g r o u p s on baseline inspection time p e r f o r m a n c e ( m e a n a n d s t a n d a r d errors). S q u a r e s with c o n t i n u o u s line represents c o n t r o l Ss a n d d i a m o n d s with d o t t e d line represents f e e d b a c k g r o u p .
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Fig. 4. Mean and standard errors for use of the fivestrategy types for the feedback (black bars) and control (white bars) groups. For details of strategy type see Table 1 (note: Type 5 represents 'No Strategy').
IT was held constant, but that baseline IT and A D I T measures were very closely related (r = - 0 . 7 9 2 , p<0.001).
Does feedback increase strategy use? Strategy questionnaire. The mean levels of strategy use for four strategy types and 'no strategy' for the feedback and control groups are shown in Fig. 4. The profiles of strategy use for the two groups are very similar, with most Ss in both groups using no strategy (strategy type 5) most of the time. Between group comparisons using Mann-Whitney U tests revealed no significant differences between groups for any strategy type. Free response strategy reports. According to the free response self-reports of strategy use, 'apparent motion' was the most popular strategy (rater 1, n = 8; rater 2, n = 9) followed by 'flash brightness' (rater 1, n =4; rater 2, n = 4 ) and 'retinal after image' (rater 1, n = 3; rater 2, n = 5). There was no significant effect of condition (feedback or control) on rated strategy use (phi was used to express the strength of the relationship). This contradicts Brand's hypothesis that feedback would encourage strategy use. IS S T R A T E G Y USE A S S O C I A T E D W I T H B R I E F E R I N S P E C T I O N T I M E S ?
Strategy questionnaire The relationship between IT performance, on both the A D I T and baseline IT, and amount of use of any of the four strategies and 'no strategy' was tested using Spearman's rank correlation. There were no significant associations (n = 40): all except one of the correlations was below 0.2, and most of these very small correlations fell in a direction which would indicate that strategy use, if anything, was associated with poorer IT performance.
Free response strategy reports An unpaired t-test indicated that there was no significant effect of rated strategy use (as determined on the basis of free response self-reports) on the A D I T task as rated by rater 1, but there was a tendency for strategy use to affect baseline performance adversely (p < 0.10), with non users scoring a higher mean number of correct responses. This tends to contradict the notion that strategy use facilitates performance in the IT task. There was no significant effect of strategy use (as rated by rater 2) on IT performance in either the A D I T or baseline IT task. CONGRUENCE
OF R A T E D A N D S E L F R E P O R T E D
S T R A T E G Y USE
Rated strategy use (from the free response self-reports) was correlated with reported strategy use (from the strategy questionnaire) in order to measure the congruence of the two types of strategy assessment. There were significant positive correlations (Kendall's tau, corrected for ties) between
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rater l's classification of strategy use and reported use of strategy 1 (apparent motion: r=0.33, p < 0.01) and strategy 2 (flash brightness) (z = 0.23, p < 0.05). Reported use of strategy 5 (no strategy) correlated negatively with rated strategy use (z = -0.43, p < 0.001) confirming that those who were rated as non-users reported infrequent strategy use. The other strategy types contained too few Ss to justify correlational analyses. Only the correlations between apparent motion (z = 0.28, p < 0.01) and no strategy use (z = - 22, p < 0.05) were significant as classified by rater 2.
DISCUSSION Feedback had no effect on performance in an inspection time task which began from below the psychophysical threshold, nor on a conventional IT task performed later in the experimental session which employed the method of constant stimuli in a random block presentation procedure. Feedback did not encourage strategy use, and Ss who reported using strategies did not differ significantly in IT performance from the control group. If anything, those who reported using strategies tended to perform slightly less well on the IT tasks. The correlation between task performance in the two IT tasks (ADIT and baseline IT) confirmed that they were comparable and reliable measures of inspection time. In addition, analyses of self reported strategy use confirmed that Ss were fairly consistent in their responses to both the free-response self report and the structured strategy use questionnaire. The results here confirm Egan and Deary (1993)'s finding that feedback had no effect on the formation and reporting of strategy use. However, there are other results of theirs which are not confirmed. In the present study accurate, trial by trial feedback had no significant effect on ascending durations inspection time (ADIT) performance where the use of strategies might be particularly useful in obtaining a brief IT estimate. The similarity in performance between the feedback and nofeedback groups remained when baseline IT differences were controlled. It is likely that these differences from the results of Egan and Deary (1993) arise from two sources. First, as we argued in the introduction, the feedback manipulation used in the present study was more appropriate; it is quite possible that Ss in Egan and Deary's study were using false feedback accurately to make wrong discriminations and thereby appear worse than those receiving accurate feedback. However, one must ask why, with a stronger feedback manipulation, we did not find that Ss receiving feedback performed better on ADIT. A probable reason is simply that there were fewer, if any, informative stimulus-mask artefacts in our IT task. After all, we posited that feedback might especially aid those Ss who were attempting to link the appearance of some stimulus-mask artefact with the correct discriminative response. If none existed, Ss had recourse only to the information provided in the brief stimulus display. Egan and Deary (1993), then, might well be correct: if the IT stimuli and mask are of poor quality and ridden with perceptual artefacts then feedback might help to link otherwise ambiguous information with a particular response. However, if the IT stimulus-mask complex is well designed, perceptual artefacts are rarer and much less helpful in finding a way round the task.
Computer-screen IT displays A major message to IT testers, then, must be that the use of computerised displays using conventional screens is potentially problematic. Computerisation of the IT stimulus presentation is experimentally attractive and convenient in that it allows Ss to pace themselves, and allows the experimenter to employ convenient, widely available screens for stimulus displays without the need for interfacing specialised equipment such as tachistoscopes or light emitting diode displays. Not surprisingly, therefore, computerised versions of the IT task have been employed in several studies (Irwin, 1984; Garnett, 1985; Deary, Caryl, Egan & Wight, 1989; Anderson, 1989; Knibb, 1992). However, computerisation with screen presentations introduces two potential sources of error (Barrett & Kranzler, 1994). First, the stimulus presentation time is limited by the refresh rate of the computer monitor. This rate varies between 50-60 Hz depending on the mains frequency, which limits the speed of presentation and offers only a crude range of SOAs. It also makes the task prone to artefactual after image effects. Tachistoscopic presentation provides finer duration intervals, and the use of a more effective masking procedure. Timing introduces the second source of error,
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particularly where the timer is generated using a high-level language. When this form of timing is used (e.g. Zhang, 1991; Colet, Piera & Pueyo, 1993) the accuracy of the test will be between 10 and 54.94 ms units; not accurate enough for timing the IT task. The mode of presentation was not the only consideration in this study. In the light of recent research, an improved backward mask was employed. The pitfalls of the traditional IT mask are well documented, In virtually all of our studies, some Ss have appeared able to make use of other sources of information other than the briefly exposed stimulus figure, such as the subtle post-masking cues associated with apparent movement, and small changes in brightness. (Nettelbeck, 1982). Current research favours the use of a pattern mask which floods the post stimulus period with multiple, overlapping lines (e.g. Knibb, 1992; Evans & Nettelbeck, 1993). One of the most effective masks in achieving this end appears to be a mask called the 'forest mask' characterised by a series of vertical lines of varying length. A variation on this design was developed for this study. This was found to eliminate the retinal after-image more successfully than the pi-shaped mask, and also appeared to reduce artefactual perceptual effects such as apparent motion (Stough, Bates, Mangan, Colrain & Pellet, in press).
Strategy use, IT and IQ IT strategy studies have traditionally found that strategy use artificially improves performance in the IT task and, if they have any effect, the presence of strategies tends to reduce the IT-IQ correlation (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986; Alexander & Mackenzie, 1992; Chaiken & Young, 1993). However, contrary to these empirical findings, most 'common sense' explanations assume that the IT-IQ correlation is spuriously created by the presence of strategy use in some Ss, because they posit that higher IQ Ss are more likely to employ strategies as they have more cognitive resources to apply in the task than their lower IQ counterparts. However, Alexander and Mackenzie (1992) reported that low IQ Ss exhibit a similar frequency distribution of strategy use as average and high IQ Ss. Research in this field has consistently found no relation between strategy use and IQ (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986; Alexander & Mackenzie, 1992; Egan & Deary, 1993). Similarly, intelligence does not appear to have any effect on the type of strategies that Ss employ.
Can strategies be learned? The present study aimed to test the hypothesis that feedback would help strategy formation in the IT task. However, there is evidence to suggest that there are individual differences in tendencies toward strategy use, and we may not assume that all Ss will respond equally to any strategy-relevant feedback. If one attempts to teach a non-strategy user to use a strategy, performance is impeded rather than enhanced (Mackenzie & Bingham, 1985). It might be that the use of strategies is more trait-like than learned; that is, strategy use might be a stable predisposition rather than a learned skill. If strategy users in our sample were predisposed to using strategies, then around half of them can be considered strategic to some degree. This is a lower figure than found in most previous research in this area, although this discrepancy appears largely due to the success of our masking procedure. Further evidence for a predisposition to strategy use emerges if we consider that feedback exerted no influence over strategy use in the present study.
Strategy use and metacognition Feedback allows the S to keep track of performance in relation to a criterion such that they are able to act on their knowledge of their performance in order to minimise the response-target discrepancy (Egan & Deary, 1993). Marr and Sternberg (1986)'s metacognitive model of strategy use and intelligence would classify strategy use in the IT task as a product of knowledge acquisition components applying specialised knowledge to perform the task. This then allows Ss to attend to any salient information presented in the task and allow them to devote their cognitive capacities to processing information less basic than the task itself. Given that Ss in this study were presented with a consistent task and one half of them had this coupled with explicit external feedback, one would expect the faster automatisation of skilled
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responses in the feedback group. This would be reflected in improved task performance and an increased frequency of strategy use reporting. However, no such relationship between feedback and performance was found. This does not necessarily contradict Ceci (1990)'s suggestion that high level knowledge may mediate between microlevel processing speed and macrolevel measures of intelligence. His 'top-down' model interprets differential patterns of tachistoscopic recognition performance as determined, not by information processing speed, but according to individual differences in how the target stimulus is enmeshed in macrolevel knowledge structures or schemata. Ceci bases his argument on the tachistoscopic recognition of alphanumeric characters. This model has found some empirical support. Roth (1983) demonstrated that the more elaborately a stimulus is represented, the faster the speed of recognition, suggesting that knowledge base effects exist. Furthermore, Roberts (1992) found metacognitive effects in a variation on the IT task. Russianand English-alphabet letters were presented in a tachistoscope. English speakers with familiarity of Russian performed better on the tachistoscopic recognition of Russian characters than English speakers who had no knowledge of Russian characters. However, a clear interpretation of this result was obviated by non-equality of the groups on a baseline task involving tachistoscopic recognition of English characters and a complex order effect. However, Ceci (1990)'s macrolevel processing argument is convincing only until it is applied to the traditional IT task. The task stimuli are simple, abstract and subject to limited knowledge representation effects, unlike letters, words, numbers and images. In addition, strategy use in the IT task is not affected by previous experience of fast perceptual tasks e.g. video games (Mackenzie & Cumming, 1986). Therefore, it is yet to be demonstrated that the macrolevel processing model may be applied to conventional IT procedures. Owing to the limitations of the metacognitive model of strategy use in the conventional IT task, it is pertinent to consider the level of consciousness at which information processing occurs in the IT task. Egan and Deary (1992) provided the PASAT, a difficult, fixed-pace, continuous mental arithmetic task, concurrently with the standard IT task in a dual-task experiment. Strategy use was hypothesised to require more attentional resources due to its demand on working memory and therefore require higher level processing. An interference effect was found between the IT task and the PASAT suggesting that the two tasks drew on a common cognitive resource. However, strategy users obtained faster IT's than nonusers, and the IT-IQ correlation was higher amongst this group of Ss. Egan and Deary (1992) interpreted IT performance, even in those using apparent movement artefacts, as the result of microlevel processing. Although strategy use may operate at a microlevel, information concerning it is accessible to introspection. This is manifest in Ss' ability to explicitly describe their strategy use. Therefore strategy use distribution may represent individual differences in Ss' ability to access information concerning it. However, strategy use, as construed above, loses much of its theoretical grip because it is not assumed to affect performance; rather it is seen as a more or less trustworthy retrospective verbalisation of the operation of a microlevel process in operation. In this interpretation strategy use becomes an epiphenomenon, as suggested by Egan and Deary (1993), and its appearance subject to the vagaries of individuals' ability to verbalise subtle information processing phenomena. In the setting of an IT task that uses abstract stimuli and has few stimulus-mask artefacts, verbally recounted strategies, on the whole, might be little more than symptomatic of a S who is good at putting their microlevel cognitive processing into words, or of one who invents a story for that to which they have no conscious access.
REFERENCES Alexander, J. R. M. & Mackenzie,B. D. (1992).Variations of the 2-line inspection time stimulus. Personality and Individual Differences, 13, 1201 1211. Anderson, M. (1989). The effect of attention on developmentaldifferencesin inspection time. Personality and Individual Differences, 10, 559-563. Barrett, P. T. & Kranzler, J. H. (1994). The use of personal computer hardware and software for the measurement of inspection time. Personality and Individual Differences, 16, 347 348. Brand, C. R. (1984). Intelligenceand inspection time: An ontogeneticrelationship?In C. J. Turner & H. B. Miles (Eds.), The biology of human intelligence. Humberside,England: Nafferton. Brand, C.R. (1987). A rejoinderto Pellegrino.In S. Modgil & C. Modgil (Eds.), Arthur Jensen: Consensus and controversy. Brighton, England: Falmer.
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Brand, C. R. & Deary, I. J. (1982). Intelligence and 'inspection time'. In H. J. Eysenck (Ed.), A model for intelligence. Berlin: Springer. Brebner, J. & Cooper, C. (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 motion strategy. American Journal of Psychology, 106, 191-210. Colet, A. V., Piera, P. L. & Pueyo, A. A. (1993). Initial stages of information processing and inspection time: Electrophysiological correlates. Personality and Individual Differences, 14, 733-738. Deary, I. J. (1993). Inspection time and WAIS-R IQ subtypes: A confirmatory factor analysis study. Intelligence, 17, 223236. Deary, I. J., Caryl, P. G., Egan, V. & Wight, D. (1989). Visual and auditory inspection time: Their interrelationship 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. Egan, V. (1991). Intelligence, inspection time and cognitive strategies. Unpublished PhD Thesis, University of Edinburgh. Egan, V. & Deary, I. J. (1992). Are specific inspection time strategies prevented by concurrent tasks?. Intelligence, 16, 151167. Egan, V. & Deary, I. J. (1993). Does perceptual intake speed reflect intelligent use of feedback in an inspection time task? The effect of restricted feedback. Journal of General Psychology, 120, 123-137. Evans, G. & Nettelbeck, T. (1993). Inspection time: A flash mask to reduce apparent movement effects. Personality and Individual Differences, 15, 91-94. Fisk, A. D. & Schneider, W. (1983). Category and word search: generalising search principles to complex processing. Journal of Experimental Psychology: Learning, Memory and Cognition, 10, 181-197. Garnett, P. D. (1985). Intelligence measurement by computerised information processing using inspection time. Journal of General Psychology, 112, 325-335. Howe, M. J. A. (1988). Intelligence as an explanation. British Journal of Psychology, 79, 349-360. Irwin, R. J. (1984). Inspection time and its relation to intelligence. Intelligence, 8, 47-65. Knibb, K. (1992). A dynamic mask for inspection time. Personality and Individual Differences, 13, 237-248. Kranzler, J. H. & Jensen, A. R. (1989). Inspection time and intelligence: A meta-analysis. Intelligence, 13, 329-347. Mackenzie, B. & Bingham, E. (1985). IQ, inspection time and response strategies in a university population. 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. (1986). The biology of intelligence? British Journal of Psychology, 77, 1-18. Marr, D. B. & Sternberg, R. J. (1986). Analogical reasoning with novel concepts: differential attention of intellectually gifted and non-gifted students to contextually relevant and irrelevant, novel information. Cognitive Developmengt, 1, 53-72. Nettelbeck, T. (1982). Inspection time: An index for intelligence?. Quarterly Journal of Experimental Psychology, 349, 299312. Nettelbeck, T. (1987). Intelligence and inspection time. In P. A. Vernon (Ed.), Speed of information processing and intelligence. Norwood, New Jersey: Ablex. Nettelbeck, T. & Lally, M. (1976). Inspection time and measured intelligence. British Journal of Psychology, 67, 17-22. Raz, N., Willerman, L. & Yama, M. (1987). On sense and senses: Intelligence and auditory information processing. Personality and Individual Differences, 8, 201-210. Roberts, A. (1992). What does inspection time measure? Men tal speed vs. breadth of association. MA (Hons) Thesis, University of Edinburgh. Roth, C. (1983). Factors affecting developmental changes in the speed of processing. Journal of Experimental Child Psychology, 35, 509-528. Sternberg, R. J. (1984). Toward a triarchic theory of human intelligence. The Behavioural and Brain Sciences, 7, 269-315. Sternberg, R. J. (1985). Beyond IQ: A Triarchic theory of human intelligence. Cambridge: Cambridge University Press. Stough, C., Bates, T., Mangan, G. L., Colrain, I. & Pellet, O. (In Press). Inspection time and intelligence: Further attempts at reducing the apparent motion strategy. Intelligence. Vickers, D. (1970). Evidence for an accumulator model of psychophysical discrimination. Ergonomics, 13, 37-58. 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. Vickers, D. & Smith, P. L. (1986). The rationale for the inspection time index. Personality andlndividual Differences, 7, 609623. Zhang, Y. (1991). Inspection time correlation with IQ in Chinese students. Personality and Individual Differences, 12, 217219.
Person. individ. Diff. Vol. 23, No. 5, pp. 787-797, 1997 © 1997 Elsevier Science Ltd. All rights reserved Printed in Great Britain PII: S0191-8869(97)00105-0 0191-8869/97 $17.00+0.00
Pergamon
STRATEGY USE A N D FEEDBACK IN INSPECTION TIME Craig
R.Simpson and Ian J. Deary*
Department of Psychology, University of Edinburgh, Edinburgh, Scotland
(Received 5 February 1997) Summary--There is a consensus that psychometric intelligence correlates significantly with inspection time (IT), a putative measure of the efficiency of the early stages of visual information processing. Controversy exists as to whether IT is a cause or a consequence of IQ differences. It has been suggested that individuals with a high IQ form macrolevel strategies which undermine the microlevel processing assumptions of IT testing, and that feedback given in the early stages of IT testing facilitates strategy formation. One such strategy involves detecting apparent movement in IT stimulus-mask displays. We manipulated feedback during IT testing and found: (1) no evidence that feedback encouraged the formation of strategies; and (2) that feedback did not aid performance on IT. Strategy users did not have superior IT performance. The overall level of strategy reporting was low, presumably because an effective backward mask was employed. These results do not support a causal role for strategies in the correlation between IT and psychometric intelligence; however, they are congruent with the notion that strategy reporting in the IT task is a verbal epiphenomenon. With appropriate stimulus presentation devices and an effective backward mask many of the stimulus-mask artefact problems that lead to strategies in the IT task may be avoided. © 1997 Elsevier Science Ltd
INTRODUCTION
In 1970, Vickers (1970) proposed an accumulator model of two-choice visual discrimination whereby observations of the sensory input are sampled quantally on a background of neural noise at a rate that is characteristic of an individual. The results of the samplings of stimulus information were said to be stored in memory buffers until a critical amount of evidence was accumulated favouring one response choice over the other. The efficiency of the early stages of visual information processing thus described--i.e, the size of the quanta or inspections--represents an individual's perceptual speed or inspection time (IT; Vickers, Nettelbeck & Willson, 1972).
Inspection and psychometric intelligence An individual's inspection time is typically derived from the minimum stimulus duration required to discriminate reliably which of two lines of markedly different length is the longer (Fig. 1). The stimulus is preceded by a fixation point and is followed immediately by a backward mask to prevent
(a)
(b)
Fig. 1. The traditional inspection time (a) pi stimulus and (b) mask.
*To whom all correspondence should be addressed at: The Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, Scotland. Telephone: 131 650 3452, Facsimile: 131 650 3461, e-mail: [email protected] 787
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any further stimulus processing after stimulus offset. Nettelbeck and Lally (1976) were the first to report an association between inspection time and measures of psychometric intelligence: brighter individuals required shorter stimulus durations to make correct discriminations. Since then, many studies have confirmed that a significant association exists between IT and measured cognitive abilities. The meta-analysis by Kranzler and Jensen (1989) and the semi-quantitative review by Nettelbeck (1987) suggested that the true correlation between IT and IQ is in the region of 0.5. In addition, they suggest that the link between IT and ability is stronger for performance-and fluid-IQ type measures than for verbal IQ, a result confirmed in a study of IT and WAIS-R scores (Deary, 1993). Although it is recognised that the IT-IQ association is robust and moderately strong in effect size, the explanation for the association is the source of controversy (Deary & Stough, 1996). This debate pits those who view IT as a basic process underlying intelligence differences (e.g. Brand & Deary, 1982; Brand, 1984; Vickers & Smith, 1986; Raz, Willerman & Yama, 1987) against those who see IT as a result of high level strategies that are employed by bright individuals (e.g. Ceci, 1990; Howe, 1988; Mackintosh, 1986). The former group believes that the IT task tests microlevel processing efficiency whereas, as we shall see, the latter group has argued that IT may be measuring aspects of macrolevel processing.
Inspection time and cognitive strategies The componential subtheory of Sternberg's triarchic model of intelligence (Sternberg, 1984; Sternberg, 1985) implies that, in performing a mental task, a system of metacomponents actively selects a strategy for combining lower order components which are then sequenced in such a way as to facilitate task performance. A number of studies has examined the possibility that some Ss are able to employ higher-order, cognitive strategies in order to make correct discriminations in the inspection time task (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986; Alexander & Mackenzie, 1992; Chaiken & Young, 1993; Egan & Deary, 1993). In addition, some studies have found that the successful use of such strategies in the IT task can perhaps artificially reduce a S's inspection time (Nettelbeck, 1982; Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986). Penetration of the IT task by higher level strategies, if true, poses a threat to the theoretical importance of the IT-IQ correlation, because the task may be merely an index of the efficiency with which individuals can apply macrolevel cognitive strategies. In other words, rather than being a basic process underlying intelligence, strategic interpretations of IT suggest that IT is just another macrolevel cognitive task--akin to IQ test items themselves--that bright people perform well (Ceci, 1990). There is a number of strategies which Ss have reported using during the IT task, each of which has been documented by introspective self-report. One of the most commonly reported strategies is that of apparent motion (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986). In this perceptual effect, the stimulus line on the shorter of the two sides of the IT stimulus is seen to move as it is overlaid by the longer masking line. This effect is sometimes reported as a movement, a flicker or an increased level of relative brightness. The S then has merely to choose the side of the stimulus contralateral to that of the perceived motion to state that that is the position of the longer line and thereby make the correctdiscriminativejudgement. Associated with the apparent motion effect is the 'flash-brightness' effect (Alexander & Mackenzie, 1992) which is sometimes reported as a flash occurring between the ends of the stimulus figure and the edge of the display. Here, the shorter of the two lines gives rise to the brighter flash, and once again the S need only choose the contralateral side to make a correct judgement. Alexander and Mackenzie (1992) also measured self reports relating to an 'ends-stand-out' effect where, following the onset of the mask a 'small black gap' appears at the end of the stimulus figure. The size of this gap allows the S to determine which side of the stimulus is longer. Finally, some Ss report using an 'after-image' strategy where the longer of the two lines provides a longer retinal after-image if the S closes his/her eyes immediately after the onset of the backward mask. However, the successful use of this strategy can be countered by employing an effective pattern mask to disrupt post-stimulus processing.
Inspection time, cognitive strategies and feedback Criticism has been directed at those offering a macrolevel, strategy-based account of IT performance for not providing testable hypotheses (Brand, 1987). However, a promising start may be
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found in Sternberg (1984)'s suggestion that external feedback provides a useful source of information for monitoring, adjusting and improving one's skill at a given task. Task-related skill may be continually refined until it becomes almost automatic, requiring little in the way of conscious thought. The inspection time task is suited to conceptualisation in terms of refinement of a skill as it offers the necessary precondition of task consistency which requires that the task does not change over time (Fisk & Schneider, 1983). Furthermore, if, as strategy theorists suggest, some Ss are able to recognise and attend to salient information available in an IT trial additional to that which was intended by the experimenter, they can devote their information processing capacities to such artefacts and hence penetrate the task by encoding information other than that explicitly available from the brief stimulus display (Egan, 1991). Thus Brand (1987) suggested that Ss using strategies to solve IT trials should be especially influenced by feedback, because strategy formation depends not only on the availability of unintended stimulus-relevant information but also on the provision of discrete feedback about performance. If IT task performance by strategy users were to prove amenable to influence in this way, this would call into question the validity of the IT task as a basic, microlevel measure of perceptual speed. In turn, this would threaten the theoretical underpinning of the IT-IQ relationship by suggesting that it might be an artefact of Ss' differential penetration of the IT task through strategy use. Egan and Deary (1993) tested Brand's hypothesis by experimentally manipulating the feedback that Ss received during performance of an IT task. In order to do this they employed the so-called ascending durations inspection time (ADIT) task, a procedure whereby the IT stimuli are presented at durations beginning well below the S's psychophysical threshold, such that the S could not be certain of the accuracy of their responses. Egan and Deary (1993) theorised that if the IT task were to be given from below as opposed to above the psychophysical threshold, the S would rely even more strongly on feedback, as one can never otherwise be sure whether any artefacts/cues detected in the stimulus-mask array have led to a correct response. In addition they included a condition whereby some Ss received false feedback in an attempt to disrupt strategy formation and use. Egan and Deary (1993) reasoned that, if IT judgements were made purely on the basis of explicitly available stimulus information as intended by the researcher, without recourse to strategy use and stimulus-mask artefact detection, feedback should not affect performance. They found that a relatively weak manipulation of feedback did have an effect on performance between the two groups. Those receiving false feedback did not perform as well on the ADIT task as those receiving truthful feedback. However, the false feedback group performed significantly better on a standard IT task which was included as an estimate of baseline IT performance. Egan and Deary (1993) concluded that "false feedback can disrupt effective performance on IT." However, their study was hampered by a number of methodological and design limitations. In the present study we aimed to clarify the effect of feedback on performance and strategy use in the IT task by making a number of methodological improvements on the study by Egan and Deary (1993). First, the stimuli in the present study were presented tachistoscopically in order to eliminate the technical limitations of the presentation of IT stimuli on a computer monitor (Barrett & Kranzler, 1994). Tachistoscopic presentation allows one to employ a psychophysical step size of as little as 1 ms, and affords the use of more appropriate pattern masking techniques than the computer offers. Second, Egan and Deary (1993) provided only weak true and false feedback after every 10 trials, and the nature of this feedback was ambiguous. In their true feedback condition, if the S scored eight or more correct responses in a batch of ten trials, the computer displayed the words 'Well done', whereas if the S scored less than eight, the message 'Please pay attention' was displayed. These messages do not offer clear feedback to help strategy formation on a trial to trial basis. In the present study, Ss in the feedback-receiving condition were informed by the experimenter after every trial whether their response was 'Correct' or 'Wrong'. This is intended to provide an explicit record of the S's performance, and to maximise any effect of feedback on IT performance. Third, with respect to the experimental manipulation of feedback, it is more appropriate to test strategy theory using conditions with true or no feedback in the A D I T task. If some individuals can make use of artefactual cues to score well on stimulus durations below which they can properly identify the longer of the two lines in an IT task then the provision of either true or false feedback is informational (potentially leading to significantly above and below-chance responding, respectively), whereas the withholding of feedback effectively fails to link any artefactual information to the
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correct discriminatory response, and is the preferred comparator of true feedback in this study. In Egan and Deary (1993)'s study it is possible that those Ss receiving false feedback on the A D I T task were apparently performing worse because they were correctly associating their (false) feedback information with particular (but wrong) discriminatory responses. Given that emphasis is generally placed on the use of information about the IT stimulus and its interaction with the backward mask in order to study strategy formation, it was decided to test Ss who had never before seen the IT task. Therefore, we recruited Ss who had never participated in an inspection time study previously. This was a strict prerequisite dictated by the experimental design, because those who had experienced the task before may have had an opportunity to develop strategies on a previous occasion. This principle was reflected in the experimental design and procedure, whereby conditions involving the manipulation of feedback took place before any measurement of a S's conventional visual inspection time ('baseline IT') was made. Therefore, by ensuring that the task was totally novel to the S at the start of the experimental session, and thereby assuming that all Ss were IT-strategy novices, the manipulation of feedback and its potential influence over strategy formation and use and task performance was maximised. Finally, two forms of self-reported strategy use were employed during the experimental session. The first was a free-response, written self report, as employed by Egan and Deary (1993), which provided an indication of any conscious strategy use following the A D I T task. The second report was recorded in a structured questionnaire format as employed by Alexander and Mackenzie (1992). Here, the S was required to rate five given forms of strategy use on a five-point scale. These two methods of data collection were intended to encourage Ss to reflect on their strategy use as much as possible while ensuring that they were oblivious to the experimental manipulation. Therefore, the aims of the study were: to examine the effect of feedback on performance in the ADIT condition, on subsequent 'baseline IT', and on the formation and use of strategies on the IT task. The design also allowed us to examine the associations between reported strategy use and IT performance. METHOD
Subjects Forty undergraduates (20 men and 20 women; mean age 21.5 yr) acted as participants. None of them had participated in an inspection time experiment before. All participants had 6/6 vision as tested by a Snellen-type chart.
Design A between Ss design was employed, with one group of 20 Ss (10 men and 10 women) allocated to the feedback condition and the other group receiving the control condition with no feedback. The dependent variables were the ADIT inspection times and the Ss' later baseline inspection times, and the self-reports of strategy use.
Inspection time A three field tachistoscope connected to a Ralph Gerbrands Company 300 series millisecond timer was used to present the stimuli. Data were recorded on a BBC Model B Microcomputer. The longer of the two lines of the inspection time stimulus was 42.9 mm in length and the shorter was 28.6 mm. This difference between the two lines corresponded to an angle of 0.8 ° visual acuity at a distance of 760 mm. The two lines were joined at the top by a line 14.3 mm in length to form a pishaped stimulus (Fig. 2a). Prior to stimulus presentation, a fixation point in the form of a cross was presented for 500 ms in the centre of the tachistoscope display. An interval of 500 ms then followed before the stimulus was presented at the predetermined duration. Stimulus presentation was followed immediately by a backward mask thereby preventing any additional processing beyond the duration between target onset and mask onset (Vickers et al., 1972). The mask used was designed to minimise apparent motion and after-image cues, often associated with the traditional solid black IT mask. To ensure its effectiveness, the mask was tested in a number of pilot sessions and re-designed according to the
Strategy use and feedback in inspection time dl,4,
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m
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Fig. 2. The inspection time (a) stimulus and (b) forest mask used in this study.
perceptual effects observed. The mask that was found to be the most effective at eliminating any artefactual perceptual effects is illustrated in Fig. 2b. All IT stimuli were presented in the three field tachistoscope, with the S required to state the position of the longer of the two lines in the IT stimulus. Responses were made at leisure with Ss encouraged to respond slowly and with maximum accuracy. Responses were made verbally. The experiment was designed such that the manipulation of feedback occurred during the Ss' first ever experience of the inspection time task. Before the experiment began, the Ss were briefed verbally as to the order of presentation of each trial and were given 4 practice trials at 300 ms to familiarise them with the types of stimulus materials used in the task, whilst not allowing them any significant amount of practice in which to develop any strategy.
Ascending durations inspection time ( ADIT) The feedback and control conditions were administered using the A D I T procedure (Egan & Deary, 1993) which began at a duration of 5ms (which is well below the IT of any tested S) and then increased by 2 ms for every three errors made per block of 15 trials. This process continued until the S had completed 15 trials at a given duration with fewer than three errors (corresponding to 86.67% accuracy). Following each stimulus presentation, the S registered their response verbally, indicating whether they thought the left or right line of the stimulus was longer. In the feedback condition, the experimenter informed the S whether or not they were correct after each trial by stating 'Correct' or 'Wrong'. In the control condition, no feedback was given. Baseline I T Conventional inspection time (baseline IT) was measured using the method of constant stimuli (MCS), which presented a fixed number of trials at each of a number of predetermined target exposure durations (SOA's) in a random block. These were selected to provide a representative sweep of the ogival IT response curve and to include the expected level of the sample's mean IT. A pilot experiment suggested that a range of stimulus durations between 10 ms and 100 ms would be appropriate. Within these limits 10 different durations were employed, each increasing in 10ms intervals. Each stimulus duration was presented 10 times in a randomised order and an equal number (50 each per block of 100 trials) of stimuli had the right or left lines longer. Procedure Each S was first presented with either the feedback or control condition. They were then asked to complete a written report regarding their use of strategies during the task (Brebner & Cooper, 1986). The strategy record form asked Ss to describe how they would explain to a friend the most effective way to distinguish the difference between the lengths of the two lines. These self reports were processed by content analysis. The reports were classified by two independent raters who identified whether the S was a strategy user and which strategy they were using. Ss were rated as to whether they used one of the five given strategies (Alexander & Mackenzie, 1992) and a 90% level of agreement was met between the two raters. The free-response, self report strategy description phase was followed by a training session which
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Craig R. Simpson and Ian J. Deary Table 1. Descriptions of strategy types which were rated by Ss following the baseline IT task
1. Apparent motion 2.
Flash-brightness
3.
Ends-stand-out
4.
After-image
5.
No strategy
"When the second pair of lines comes on, the ends of the first pair look like they are moving. The first lines grow or stretch to meet the ends of the second lines. The longer line is the one that doesn't move as much." "You can see a kind of flash when the second set of tines comes on (i.e. the mask). The flash is between the end of the first pair of lines and the edge of the display. The shorter line has the brightest flash." "When the second covering lines come on, there is a small black gap that you can see just at the end of the first set of lines. This shows where each line ended, so you can tell from that which one was longer." " I f you close your eyes just after the second pair of lines come on, you can see a kind of after image for a second or so, like when you look at a bright light. The longer line gives you a longer after image." "There wasn't any particular trick to it. I just watched as carefully as I could and saw which one was longer."
consisted of 20 trials at a duration time given by the formula (ADIT + ADIT/2) ms. The training session was intended to equate both groups' experience of feedback on the task by presenting stimuli at a constant duration where the S would he able to see the stimulus with relative ease, allowing them to keep track of their accuracy, and to spot any associations between stimulus-mask cues and correctness. Ss then completed the conventional or baseline IT task followed by a second self-report in the form of a structured questionnaire as used by Alexander and Mackenzie (1992). This questionnaire required Ss to rate their use of five strategies (one of which is the use of no strategy) on a five point scale [1 (never) to 5 (all the time)]. Table 1 outlines the strategy descriptions to be rated. Therefore each experimental session followed the sequence: (1) A D I T estimation, (2) Freeresponse, strategy use reporting, (3) IT training, (4) Baseline IT estimation, (5) Strategy questionnaire completion. RESULTS
Does feedback improve inspection times? The feedback group performed slightly, but not significantly (unpaired t test) better on the A D I T task (mean A D I T = 7 0 . 2 m s ) than those in the control condition (mean A D I T = 7 4 . 0 5 m s ) . In addition, mean total scores from the baseline IT condition revealed no significant differences between the two groups. Figure 3 illustrates the performance of the two groups on the baseline task. A more rigorous test of any difference between feedback and control conditions is to compare the groups after controlling for baseline IT scores. Therefore, an analysis of covariance (ANCOVA) was employed: condition, with two levels (control and feedback), was a between Ss factor and baseline IT was entered as a covariate. The dependent variable was the ADIT score. The effect of condition remained non-significant, and baseline IT was a significant covariate of ADIT ( F = 61.23, df= 1.37, p < 0.001). This confirms that feedback had no significant effect on performance, even when baseline
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Duration (msecs) Fig. 3. C o m p a r i s o n o f f e e d b a c k a n d c o n t r o l g r o u p s on baseline inspection time p e r f o r m a n c e ( m e a n a n d s t a n d a r d errors). S q u a r e s with c o n t i n u o u s line represents c o n t r o l Ss a n d d i a m o n d s with d o t t e d line represents f e e d b a c k g r o u p .
Strategy use and feedback in inspection time •
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e 4 w
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Fig. 4. Mean and standard errors for use of the fivestrategy types for the feedback (black bars) and control (white bars) groups. For details of strategy type see Table 1 (note: Type 5 represents 'No Strategy').
IT was held constant, but that baseline IT and A D I T measures were very closely related (r = - 0 . 7 9 2 , p<0.001).
Does feedback increase strategy use? Strategy questionnaire. The mean levels of strategy use for four strategy types and 'no strategy' for the feedback and control groups are shown in Fig. 4. The profiles of strategy use for the two groups are very similar, with most Ss in both groups using no strategy (strategy type 5) most of the time. Between group comparisons using Mann-Whitney U tests revealed no significant differences between groups for any strategy type. Free response strategy reports. According to the free response self-reports of strategy use, 'apparent motion' was the most popular strategy (rater 1, n = 8; rater 2, n = 9) followed by 'flash brightness' (rater 1, n =4; rater 2, n = 4 ) and 'retinal after image' (rater 1, n = 3; rater 2, n = 5). There was no significant effect of condition (feedback or control) on rated strategy use (phi was used to express the strength of the relationship). This contradicts Brand's hypothesis that feedback would encourage strategy use. IS S T R A T E G Y USE A S S O C I A T E D W I T H B R I E F E R I N S P E C T I O N T I M E S ?
Strategy questionnaire The relationship between IT performance, on both the A D I T and baseline IT, and amount of use of any of the four strategies and 'no strategy' was tested using Spearman's rank correlation. There were no significant associations (n = 40): all except one of the correlations was below 0.2, and most of these very small correlations fell in a direction which would indicate that strategy use, if anything, was associated with poorer IT performance.
Free response strategy reports An unpaired t-test indicated that there was no significant effect of rated strategy use (as determined on the basis of free response self-reports) on the A D I T task as rated by rater 1, but there was a tendency for strategy use to affect baseline performance adversely (p < 0.10), with non users scoring a higher mean number of correct responses. This tends to contradict the notion that strategy use facilitates performance in the IT task. There was no significant effect of strategy use (as rated by rater 2) on IT performance in either the A D I T or baseline IT task. CONGRUENCE
OF R A T E D A N D S E L F R E P O R T E D
S T R A T E G Y USE
Rated strategy use (from the free response self-reports) was correlated with reported strategy use (from the strategy questionnaire) in order to measure the congruence of the two types of strategy assessment. There were significant positive correlations (Kendall's tau, corrected for ties) between
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rater l's classification of strategy use and reported use of strategy 1 (apparent motion: r=0.33, p < 0.01) and strategy 2 (flash brightness) (z = 0.23, p < 0.05). Reported use of strategy 5 (no strategy) correlated negatively with rated strategy use (z = -0.43, p < 0.001) confirming that those who were rated as non-users reported infrequent strategy use. The other strategy types contained too few Ss to justify correlational analyses. Only the correlations between apparent motion (z = 0.28, p < 0.01) and no strategy use (z = - 22, p < 0.05) were significant as classified by rater 2.
DISCUSSION Feedback had no effect on performance in an inspection time task which began from below the psychophysical threshold, nor on a conventional IT task performed later in the experimental session which employed the method of constant stimuli in a random block presentation procedure. Feedback did not encourage strategy use, and Ss who reported using strategies did not differ significantly in IT performance from the control group. If anything, those who reported using strategies tended to perform slightly less well on the IT tasks. The correlation between task performance in the two IT tasks (ADIT and baseline IT) confirmed that they were comparable and reliable measures of inspection time. In addition, analyses of self reported strategy use confirmed that Ss were fairly consistent in their responses to both the free-response self report and the structured strategy use questionnaire. The results here confirm Egan and Deary (1993)'s finding that feedback had no effect on the formation and reporting of strategy use. However, there are other results of theirs which are not confirmed. In the present study accurate, trial by trial feedback had no significant effect on ascending durations inspection time (ADIT) performance where the use of strategies might be particularly useful in obtaining a brief IT estimate. The similarity in performance between the feedback and nofeedback groups remained when baseline IT differences were controlled. It is likely that these differences from the results of Egan and Deary (1993) arise from two sources. First, as we argued in the introduction, the feedback manipulation used in the present study was more appropriate; it is quite possible that Ss in Egan and Deary's study were using false feedback accurately to make wrong discriminations and thereby appear worse than those receiving accurate feedback. However, one must ask why, with a stronger feedback manipulation, we did not find that Ss receiving feedback performed better on ADIT. A probable reason is simply that there were fewer, if any, informative stimulus-mask artefacts in our IT task. After all, we posited that feedback might especially aid those Ss who were attempting to link the appearance of some stimulus-mask artefact with the correct discriminative response. If none existed, Ss had recourse only to the information provided in the brief stimulus display. Egan and Deary (1993), then, might well be correct: if the IT stimuli and mask are of poor quality and ridden with perceptual artefacts then feedback might help to link otherwise ambiguous information with a particular response. However, if the IT stimulus-mask complex is well designed, perceptual artefacts are rarer and much less helpful in finding a way round the task.
Computer-screen IT displays A major message to IT testers, then, must be that the use of computerised displays using conventional screens is potentially problematic. Computerisation of the IT stimulus presentation is experimentally attractive and convenient in that it allows Ss to pace themselves, and allows the experimenter to employ convenient, widely available screens for stimulus displays without the need for interfacing specialised equipment such as tachistoscopes or light emitting diode displays. Not surprisingly, therefore, computerised versions of the IT task have been employed in several studies (Irwin, 1984; Garnett, 1985; Deary, Caryl, Egan & Wight, 1989; Anderson, 1989; Knibb, 1992). However, computerisation with screen presentations introduces two potential sources of error (Barrett & Kranzler, 1994). First, the stimulus presentation time is limited by the refresh rate of the computer monitor. This rate varies between 50-60 Hz depending on the mains frequency, which limits the speed of presentation and offers only a crude range of SOAs. It also makes the task prone to artefactual after image effects. Tachistoscopic presentation provides finer duration intervals, and the use of a more effective masking procedure. Timing introduces the second source of error,
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particularly where the timer is generated using a high-level language. When this form of timing is used (e.g. Zhang, 1991; Colet, Piera & Pueyo, 1993) the accuracy of the test will be between 10 and 54.94 ms units; not accurate enough for timing the IT task. The mode of presentation was not the only consideration in this study. In the light of recent research, an improved backward mask was employed. The pitfalls of the traditional IT mask are well documented, In virtually all of our studies, some Ss have appeared able to make use of other sources of information other than the briefly exposed stimulus figure, such as the subtle post-masking cues associated with apparent movement, and small changes in brightness. (Nettelbeck, 1982). Current research favours the use of a pattern mask which floods the post stimulus period with multiple, overlapping lines (e.g. Knibb, 1992; Evans & Nettelbeck, 1993). One of the most effective masks in achieving this end appears to be a mask called the 'forest mask' characterised by a series of vertical lines of varying length. A variation on this design was developed for this study. This was found to eliminate the retinal after-image more successfully than the pi-shaped mask, and also appeared to reduce artefactual perceptual effects such as apparent motion (Stough, Bates, Mangan, Colrain & Pellet, in press).
Strategy use, IT and IQ IT strategy studies have traditionally found that strategy use artificially improves performance in the IT task and, if they have any effect, the presence of strategies tends to reduce the IT-IQ correlation (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986; Alexander & Mackenzie, 1992; Chaiken & Young, 1993). However, contrary to these empirical findings, most 'common sense' explanations assume that the IT-IQ correlation is spuriously created by the presence of strategy use in some Ss, because they posit that higher IQ Ss are more likely to employ strategies as they have more cognitive resources to apply in the task than their lower IQ counterparts. However, Alexander and Mackenzie (1992) reported that low IQ Ss exhibit a similar frequency distribution of strategy use as average and high IQ Ss. Research in this field has consistently found no relation between strategy use and IQ (Mackenzie & Bingham, 1985; Mackenzie & Cumming, 1986; Alexander & Mackenzie, 1992; Egan & Deary, 1993). Similarly, intelligence does not appear to have any effect on the type of strategies that Ss employ.
Can strategies be learned? The present study aimed to test the hypothesis that feedback would help strategy formation in the IT task. However, there is evidence to suggest that there are individual differences in tendencies toward strategy use, and we may not assume that all Ss will respond equally to any strategy-relevant feedback. If one attempts to teach a non-strategy user to use a strategy, performance is impeded rather than enhanced (Mackenzie & Bingham, 1985). It might be that the use of strategies is more trait-like than learned; that is, strategy use might be a stable predisposition rather than a learned skill. If strategy users in our sample were predisposed to using strategies, then around half of them can be considered strategic to some degree. This is a lower figure than found in most previous research in this area, although this discrepancy appears largely due to the success of our masking procedure. Further evidence for a predisposition to strategy use emerges if we consider that feedback exerted no influence over strategy use in the present study.
Strategy use and metacognition Feedback allows the S to keep track of performance in relation to a criterion such that they are able to act on their knowledge of their performance in order to minimise the response-target discrepancy (Egan & Deary, 1993). Marr and Sternberg (1986)'s metacognitive model of strategy use and intelligence would classify strategy use in the IT task as a product of knowledge acquisition components applying specialised knowledge to perform the task. This then allows Ss to attend to any salient information presented in the task and allow them to devote their cognitive capacities to processing information less basic than the task itself. Given that Ss in this study were presented with a consistent task and one half of them had this coupled with explicit external feedback, one would expect the faster automatisation of skilled
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responses in the feedback group. This would be reflected in improved task performance and an increased frequency of strategy use reporting. However, no such relationship between feedback and performance was found. This does not necessarily contradict Ceci (1990)'s suggestion that high level knowledge may mediate between microlevel processing speed and macrolevel measures of intelligence. His 'top-down' model interprets differential patterns of tachistoscopic recognition performance as determined, not by information processing speed, but according to individual differences in how the target stimulus is enmeshed in macrolevel knowledge structures or schemata. Ceci bases his argument on the tachistoscopic recognition of alphanumeric characters. This model has found some empirical support. Roth (1983) demonstrated that the more elaborately a stimulus is represented, the faster the speed of recognition, suggesting that knowledge base effects exist. Furthermore, Roberts (1992) found metacognitive effects in a variation on the IT task. Russianand English-alphabet letters were presented in a tachistoscope. English speakers with familiarity of Russian performed better on the tachistoscopic recognition of Russian characters than English speakers who had no knowledge of Russian characters. However, a clear interpretation of this result was obviated by non-equality of the groups on a baseline task involving tachistoscopic recognition of English characters and a complex order effect. However, Ceci (1990)'s macrolevel processing argument is convincing only until it is applied to the traditional IT task. The task stimuli are simple, abstract and subject to limited knowledge representation effects, unlike letters, words, numbers and images. In addition, strategy use in the IT task is not affected by previous experience of fast perceptual tasks e.g. video games (Mackenzie & Cumming, 1986). Therefore, it is yet to be demonstrated that the macrolevel processing model may be applied to conventional IT procedures. Owing to the limitations of the metacognitive model of strategy use in the conventional IT task, it is pertinent to consider the level of consciousness at which information processing occurs in the IT task. Egan and Deary (1992) provided the PASAT, a difficult, fixed-pace, continuous mental arithmetic task, concurrently with the standard IT task in a dual-task experiment. Strategy use was hypothesised to require more attentional resources due to its demand on working memory and therefore require higher level processing. An interference effect was found between the IT task and the PASAT suggesting that the two tasks drew on a common cognitive resource. However, strategy users obtained faster IT's than nonusers, and the IT-IQ correlation was higher amongst this group of Ss. Egan and Deary (1992) interpreted IT performance, even in those using apparent movement artefacts, as the result of microlevel processing. Although strategy use may operate at a microlevel, information concerning it is accessible to introspection. This is manifest in Ss' ability to explicitly describe their strategy use. Therefore strategy use distribution may represent individual differences in Ss' ability to access information concerning it. However, strategy use, as construed above, loses much of its theoretical grip because it is not assumed to affect performance; rather it is seen as a more or less trustworthy retrospective verbalisation of the operation of a microlevel process in operation. In this interpretation strategy use becomes an epiphenomenon, as suggested by Egan and Deary (1993), and its appearance subject to the vagaries of individuals' ability to verbalise subtle information processing phenomena. In the setting of an IT task that uses abstract stimuli and has few stimulus-mask artefacts, verbally recounted strategies, on the whole, might be little more than symptomatic of a S who is good at putting their microlevel cognitive processing into words, or of one who invents a story for that to which they have no conscious access.
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