HUMAN
FACTORS.1977.19(6),601-606
Sequential Expectancy in Visual Search TIMOTHY H. MONK,· Department of Psychology, University of Nottingham, England
An experiment was designed to determine whether there was a sequential expectancy effect in visual search by which subjects carried over an expectation of the duration of the search from one trial to the next and produced a shorter search time on the nih trial if the (n -I)1h trial had had a search time of similar magnitude. Search time was controlled by the surreptitious insertion of lags between the onset of the background (i.e., start of search) and the actual appearance ofthe target. Post-target search time (total search time minus lag) was then used as the dependent variable. Three lags (a, 7.5, and 15 s) were used in a random order. Two effects emerged. Post-target search time was found to be reduced if the previous trial had used the same lag as the given trial; and post-target search time was found to increase with lag. Both effects were explained by the construction of a sequential expectancy model.
INTRODUCTION
The visual search tasks that are typically found in industry and defense (such as product inspection or sonar detection) do not usually occur as a single search trial in isolation but as part of a sequence of trials, each having a slightly different display and/or target specification. Such a sequence is likely to induce "carry-over" effects whereby search performance on a particular trial is influenced by the trial that came immediately before it. In a visual search task involving the discrimination between one target and 199 non-target elements, Monk (l974a) has demonstrated the presence of a target repetition effect. Anyone of four possible targets could appear in a given triaL and the search time for a given target was significantly shorter if the immediately preceding trial had also used that particular target. Although other factors, such as physical similarity and interfixation I Requests for reprints should be sent to Timothy H. Monk, MRC Perceptual and Cognitive Performance Unit, Laboratory of Experimental Psychology, University of Sussex, Brighton BNI 9QG. England.
distance, would probably have aided search for repeated targets, sequential expectancies might also have contributed towards the repetition effect. The aim of the present study was to isolate the sequential expectancy factor to see whether subjects can have an expectancy that a given search trial will last about as long as its predecessor. The concept of expectancy has long been used to describe changes over time in the readiness of a subject to accept a signal in a human performance task. The term gained its initial popularity in studies of refractoriness (e.g .. Poulton, J 950) and since then it has been used extensively in reaction time and vigilance studies (e.g., Bertelson, 1967; Buckner and McGrath, 1963; Deese, 1955). No study seems to have been made. however, of the temporal expectancies built up by a subject from one trial to the next in a visual search situation. To eliminate the confounding effects produced by situations of target uncertainty it was necessary to produce a task in which, though the physical characteristics of the
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target and non-targets were the same throughout, search time could be varied in a systematic way and latency used as the dependent variable. The problem was solved by devising a task in which delays (or "lags") were surreptitiously inserted between the onset of the background (i.e., the start of the search) and the appearance of the target itself. Post-target search time (i.e., total search time minus lag) could then be used as the dependent variable. and the same target used throughout. Since subjects remained unaware of the presence of lags. the subject was. for the duration of the lag. searching (unwillingly) for a target that had not yet appeared. Search times could thus be lengthened at will without any reason for the change being apparent to the subject. If a sequential expectanc~' effect was to operate in such a situation. this would be revealed by post-target search times being shorter when both present and previous trials had the same lag, and longer when they had different lags. The feasibility of such an approach was tested by a pilot experiment, using the hardest target condition of the Monk (1974a) study. Three subjects each experienced 200 trials using the same target and non-targe!:> as were to be used in the main experiment; ten lags were used (I through 10 s). In visual search tasks involving confusing non-target elements, the target seems to "pop out" when it is found, and this effect seemed to disgUIse the presence of the lags. Careful questioning of the subject!> revealed that none had "caught on" to the fact that the target was not always there. As is usual with search data, there was a wide range of post-target search times, and a lag of at least 7 s was found to be necessary for a separation of total search times to emerge. The pilot experiment also re\'ealed that post-target search time seemed to increase with lag. This result was surprising in that a
FACTORS
new configuration of the 199 non-targets was used on each trial. amI unt.' would expect that the longer lags would t.'nable the subject to dt.·termine the most cffkient scan pattern for that particular configuration. Kreuger (1970) has shU\\ n search time to be shorter when the non-target material IS more familiar to the subject. The result could be explained in terms of fatigue or stress, however. since it would seem reasonable to assume that the subject might lose efficiency when forced by the presence of a lag to search for a long time. Boynton (1960) found that fixation rate tended to decrease aftn 12 s of search, because the subjech felt like giving up. In fact (sec discussion scl:tiun) the decrease in performance was later found to be a natural consequence of the sequential expectancy effect. METHOD Appara/ll.~
Displays were presented on a cathode ray oscilloscope !Hewlett-Packard 1208 with t\ pc P3 J. green phosphor). Viewing distance \\ as held at 620 nun b\ a head-rest; a Fresnel !t.·m (focal length 125 mm) placed 10 mm from the screen, produced a magnified image of tht' di!>plav, subtending 7°30' at the subjt'ct's eve. Nun-targets were 199 bright dots (of luminance 10.6 cd/m!) distributed randomly in an imaginar. 20 x 20 matrix of possible positiun!>; the configuration of non-targets Was different fur each trial. One of the remaining 20 I po!>it ions was randoml~' chusen to be the target po~itiun fur the gi\'en trial. For the duration uf the lag (see below) tht.· target position cuntaim'd a dot of non-target brightness. but as !>oon 3!> the lag had t.'lapsed its brightness was increased tu 15.8 cd/m!, and that dot thw. became the target. All luminances Were measured with an S.E.I. photumeter using "Maxwellian View." The subject viewed the display from within a sound-allenuating cubicle. Two poten.
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tiometer knobs and a response key were situated within easy reach. Procedure
The experiment was run under the control of a computer program which generated the displays and output them to the oscilloscope. timed subjects' responses. and decided whether or not they were correct. A tone sounded to signify the start of each trial. The subject pressed on the response key. keeping it pressed. and the display was presented to the oscilloscope screen. As soon as the subject had found the target dot he released the key and the display disappeared leaving a single cursor dot remaining on the screen. Using the two potentiometer knobs. the subject placed the cursor dot to indicate the position of the target. within a predetermined tolerance (Monk. 1974b). He then pressed the key again. and a message was displayed telling him whether or not he was correct. Unknown to the subject. one of three possible lags (0. 7.5. or 15 s) had to elapse after the onset of the display before the target dot actually appeared as such. If the subject terminated his search before the lag had elapsed. the trial was marked as a "false alarm" and the response "wrong" was automatically given upon the key press following the positioning of the cursor dot. Other incorrect trials were merely marked as "misses." Incorrect trials did not count towards the required number of trials per block. Each subject was given 16 blocks with 45 correct trials in each. The lag to be used on a given trial was determined by a pseudorandom process. Each lag was equiprobable throughout. but the probability of the same lag being used on two adjacent trials (the probability of lag repetition) was artificially raised to even out the number of repeats and transitions. Each subject experienced one or two blocks per daily session. resting between blocks. and
after the 25 th correct trial in each block. Rest periods were at kast 2 min long and were lengthened if the subject felt fatigued. Sessions usually lasted about one hour. Before the experiment began. each subject was given 90 correct practice trials administered in 6 blocks of 15 correct trials each. Subjects were urged to be as fast as was possible without resorting to errors; they were reminded that wrong trials did not count towards the required number of trials per block. and that it was thus in their own interest to be accurate. All subjects were entirely naive as to the purpose of the experiment and the presence of lags. Careful questioning during the experiment. and detailed interrogation after it, ascertained that none of the subjects had "caught on" that the targets were not always appearing at the same time as the nontargets. Three subjects were used in the experiment, two of them female. All were students or employees of the University of Nottingham with 20/20 vision (corrected in two cases) and a mean age of 22 years. They were each paid a fixed sum for their services. RESULTS Error Analysis
Summing over the three subjects, a total of 61 incorrect trials occurred. producing an average error rate of 2.75%. Twenty of these errors were false alarms (i.e .• negative posttarget search times), the remainder "misses." There was an approximately equal split of incorrect trials between the three lag conditions (36.1% for the zero lag. 26.2% for the 7.5-s lag. and 37.7% for the 15-s lag). There were. of course. no false alarms in the zero-lag condition. and as expected there were more false alarms in the 15-s lag condition (15) than there were in the 7.5-s lag condition (5), (p < 0.05. sign test. 2-tailed). There were insufficient data to make a
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more detailed analysis. Incorrect trials were excluded from alI subsequent analvses. Frequency Distributions As found by most authors, the frequency distribution of the search times was highlv skewed (Bloomfield, 1972; Krendel and Wodinsky, 1960). Each item of data was thus logarithmicaIly transformed to the natural base, and an acceptable normal distribution was produced (Kirk, 1968). As a measure of average search time the geometric mean XJl/n was used throughout; this is the ext I ponential of the mean of the logarithmically n transformed data (exp [(lIn) ,~ I log, Xl I I, and has been used in other visual search experiments (Green and Anderson, 1956; Kreuger, 1970; Monk and Brown, 1975). Geometric mean search time would seem to give a better estimate of central tendency than the (more usual) arithmetic mean. Although the arithmetic mean of the logarithmically transformed data is the statistically best estimate of central tendency, exponentiation of it to give the geometric mean enables the data to be expressed in recognized units.
(fi ~
The Multisequential Analysis Edge effects, causing targets appearing on the outer half of the display to have longer search times than those nearer to the center, have bet>n weIl documented in the search literature (Baker, Morris, and Steedman, 1960, Enoch, 1959; Ford, White, and Lichtenstein, 1959). Monk (1974a) confirmed this effect in displays similar to those used in the present study and also discovered a sequential edge effect whereby targets appearing in the inner half of the display had reduced search times if the target on the immediately preceding trial had also appeared on the inner, rather than the outer, half of the display. Since such ef· fects often tend to mask the major effects, the present analysis extracted both the edge effect and the sequential edge effect as factors.
FACTORS
A computer program read through the data, indexing each correct post-target search time b~' (I) the lag used, (2) the lag used on the immediately preceding trial, (3) the target area group ("inner" or "outer"), and (4) the target an:a group of the immediately preceding trial. Trials following rest periods and errors were excluded from the analysis. Cell sizes were not equal. and the data were arranged in a form suitablt.' for the application of a 5-wav analysis of variance on unweighted means. The five factors considered were: subjects (3 levels), target area group (2 levels), area repetition or transition (2 levels), lag on given trial (3 lewIs), and lag repetition versus transition (2 levels). The main effects of lag, F(2,4) = 17.0, P < 0.05. lag repetition versus transition, FO.2) = 30.8. P < 0.05, and target area group, FO,2) = 149.5, P < 0.01, all achieVed significance. No other significant effects or interactions emerged (P -, 0.10 in all cases). The geometric mean post-target search times are given in Table I. Post-target search time was seen to increase with lag, and post-target search times for repeats were shorter than those for transitions. Parenthetically, the edge effect was in the expected direction; the geometric mean post-target search times for targets on the inner and outer halves of the display Were 3.44 sand 4.81 s, respectively. DISCUSSION The two main hypotheses to be tested by this experiment were that repeated lag trials would have shorter post-target search tirnes than those representing a lag transition. and that post-target search time would increase with lag. Both hypotheses were supported by the data. Construction of a model to describe how expectancy might operate in such a situation seemed to be the best way to explain the observed data. The model relied upon a division of the search time into a series of discrete fix-
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TABLE 1 Geometric Mean Post-Target Search Times (s) for Each Subject Showing the Effects of Lag and Lag Repetition and Results of the Computer Simulation Subject Lag Used (s)
Computer
S1
S2
S3
Overall
Simulation
0 7.5 15.0
3.74 4.04 4.27
5.05 5.64 5.79
2.62 3.35 3.37
3.69 4.23 4.35
3.97 4.23 4.33
Lag Repeats Lag Transitions
3.76 4.41
5.23 5.71
2.90 3.31
3.83 4.40
3.73 4.42
ations or glimpses. as in Cowan (1968). and followed the systems of Miller and Ludvigh (1960) and Krendel and Wodinsky (1960) in assigning a probability of success (P) to each glimpse and assuming (as suggested by Volkman. 1962) that little useful information is received during a saccade. The model also utilized the "random sampling with replacement" assumptions used by Krendel and Wodinsky (1960) and Howarth and Bloomfield (1969) in that it asserted that unless expectancy was acting (or a lag was in force). the value of P was held to be a constant (p).
For the purposes of the model. the time dimension was divided into four discrete "time intervals" (0-7.5 s, 7.5-15 s. 15-22.5 s. and 22.5 s and over). and expectancy was held to operate as a uniform step function. Expectancy was assumed to operate in the time interval that followed the interval defined by the lag that had been used in the preceding trial. and was hypothesized to operate by incrementing P from p to p + e (where e is a small positive constant) for the duration of that interval. For the duration of a lag. of course. the value of P remained at zero and could not be incremented. Thus. for example. whereas the four values of P for a 7.5 slag trial preceded by a 15 s lag trial would be O. p. p + e. and p, those of a 7.5 s lag trial preceded by a zero lag trial would be O. p. p. and p.
A "sequential condition" was defined by the lags used on two temporally adjacent trials. Each sequential condition held a particular sequence of four values of P corresponding to the four time intervals. Using the rule defined above. a sequence of values was generated for each sequential condition (Table 2). Simple inspection of Table 2 reveals that not only the lag repetition effect. but also the lag effect itself, is explained by the expectancy model. The six lag transition sequential conditions contain only three incremented (p + e) intervals among them. while the three lag repeat sequential conditions all contain one incremented interval each. Since repetitions thus have. on average, twice as many incremented intervals as transitions, their search times would be significantly shorter than those of the latter. Similarly. the sequential conditions with O. 7.5. and 15 slags on the present trial have. in total. 3. 2. and I incremented intervals. respectively. and the lag effect itself would thus also be predicted. As a mere illustration of the similarity between the predictions of the model and the observed data. a Monte Carlo computer simulation was undertaken. A fixation rate of 3 per second as suggested by the data of Enoch (1959) and Ford. White, and Lichtenstein (1959) was adopted. and saccade time was assumed to be zero.
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FACTORS
TABLE 2 Probability of Success of a Given Fixation in Each of the Four Time Intervals for the Nine Sequential Conditions (The information in this table forms the basis of the lag simulation model.) Lag Used on Previous Trial (s)
0 7.5 15.0 0 7.5 15.0 0 7.5 15.0
Lag Used on Present Trial (s)
0 0 0 7.5 7.5 7.5 15.0 15.0 15.0
TIme Interval (5)
0-7.5
7.5-15
15-22.5
p+e
p
P P 0 0 0 0 0 0
p+e
P
P P
P
Since the computer simulation was designed to be descriptive rather than predictive,the required values ofp and e were found entirely by simple trial-and-error. An arbitrary one second was added to the simulated results. to take into account factors such as the edge effect and reaction time. which had not been considered by the model. Suitable values were found to be 0.06 for p and 0.02 for e. Using these values, 300 trials were run under each of the nine sequential conditions, and the resulting geometric mean post-target search times calculated. Inspection of the final column of Table I re\'eals that the similarity between simulated and actual search times was surprisingly good, particularly when one remembers all the assumptions and simplifications made by the model. It would thus seem highly plausible that both the lag effect and the lag repetition effect were manifestations of the simple sequential expectancy effect that is described by the model. For convenience, this model has been described in terms of an increment in P over the "expectant" interval. An alternative argument, based on the hypothesis that subjects experience a decrement in performance outside this interval instead. would seem to be
p p+e
p+e
p p+e
P 0 0 0
P P
p+e
over 22.5 P p p P
p
p P P p
equally valid. Since both p and e are arbitrar_ ily defined parameters, however. such an ex~ planation would remain completely in agreement with the predictions of the model and the results of the experiment. The inter-trial interval (III) used in the experiment was not constant. varying between 5 sand 20 s, depending upon the speed with which the subject positioned the cursor dot. Clearly, the III is of great importance in any study of sequential effects. but the necessity for feedback from the subject makes standard_ ization of the ITI practically impossible in this situation. The main purpose of this study has been to show that the large amount of variability in search times from one trial to the next may cause sequential expectancy ef. fects in visual search, and that search trials should no longer be considered as single, isolated events. CONCLUSIONS Observers who are engaged in repeated trials of a visual search task can build up expectancies about search time from one trial to the next. In some cases such expectancies can significantly disrupt performance, It is thus recommended that, wherever possible, the task should be rescheduled so that within a
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block of trials, all search times will tend to be roughly the same. ACKNOWLEDGMENTS Grateful thanks are due to Dr. R.B. Henry for his guidance and encouragement and to Professor C.1. Howarth for his helpful advice and criticism. This work was supported by a Medical Research Council studentship. and contributed towards work undertaken for a Doctor of Philosophy degree at the University of Nottingham.
REFERENCES Baker. C A, Morris, D. F., and Steedman, W. C. Target recognition on complex displays. Human Factors. 1960,2, 51-61. Bertelson, P. The time course of preparation. Quarterlv Journal of Experrmental Psychology, 1967,/9,272-279. Bloomfield, J. R. Visual search in complex fields: Size dif· ferences between target disc and surrounding discs, Human Factors, 1972,14, 139-148, Boynton, R. M. Concluding remarks in Visual search techniques, Washington, D.C.: National Academy of Science, National Research Council, Publication 712, 1960,231-239. Buckner, D. N. and McGrath, J. J. (Eds.) Vigilance: A symposium. New York: McGraw-Hili, 1963. Cowan, T. M. An observing response analysis of visual search. Psychological Review, 1968, 75, 265-270 Deese, J, Some problems in the theory of vigilance. PsychologIcal Review, 1955,62. 359·368 Enoch, J. M. Effect of the size of a complex display upon visual search. Journal of the OptIcal SocIety of Amerrca, 1959,49, 280-286, Ford, A., White, C. T" and Lichtenstein, M. Analysis of eye
movements during free search. Journal of the Optical Society of America, 1959,49,287-292. Green, B. F, and Anderson, L. K. Color coding in a visual search task. Journal of Experimental Psychology. 1956, 51,19-24. Howarth, C. I. and Bloomfield, J. R. A rational equation for predicting search times in simple inspection tasks. Psychonomlc SCIence, 1969,17,225-226. Kirk, R. E. Experimental design: Procedures for the behavioral sciences. Belmont, California: Brooks-Cole, 1968. Krendel, E Sand Wodinsky, J. Search in an unstructured visual field. Journal of the Optical Society of America. 1960, 50. 562-568. Kreuger, L. E. Effect of frequency of display on speed of visual search. Journal of Experimental Psychology, 1970, 84, 495-498. Miller, J W. and Ludvig, E. Time required for detection of stationary and moving objects as a function of size in homogenous and partially structured fields. In Visual Search Techniques. Washington, D.C.: National Academy of Science, National Research Council, Publication 712, 1960, 170-180. Monk, T. H. Sequential effects in visual search. Acta Psychologica, 1974,38, 315-321. (al Monk, T. H. Sequential and spatial effects in visual search. Unpublished doctoral dissertation, University of Not· tingham, 1974. (bl Monk, T, H, and Brown, B. The effect of target surround density on visual search performance. Human Factors. 1975, 17. 356-360. Poulton, E. C. Perceptual anticipation and reaction time. Quarterly Journal of Experimental Psychology. 1950,2, 99-113. Volkman, F. C. Vision during voluntary saccadic eye movements. Journal of the Optical Society of America, 1962,52.571-578.
Downloaded from hfs.sagepub.com at UNIVERSITE LAVAL on April 30, 2016
FACTORS.1977.19(6),601-606
Sequential Expectancy in Visual Search TIMOTHY H. MONK,· Department of Psychology, University of Nottingham, England
An experiment was designed to determine whether there was a sequential expectancy effect in visual search by which subjects carried over an expectation of the duration of the search from one trial to the next and produced a shorter search time on the nih trial if the (n -I)1h trial had had a search time of similar magnitude. Search time was controlled by the surreptitious insertion of lags between the onset of the background (i.e., start of search) and the actual appearance ofthe target. Post-target search time (total search time minus lag) was then used as the dependent variable. Three lags (a, 7.5, and 15 s) were used in a random order. Two effects emerged. Post-target search time was found to be reduced if the previous trial had used the same lag as the given trial; and post-target search time was found to increase with lag. Both effects were explained by the construction of a sequential expectancy model.
INTRODUCTION
The visual search tasks that are typically found in industry and defense (such as product inspection or sonar detection) do not usually occur as a single search trial in isolation but as part of a sequence of trials, each having a slightly different display and/or target specification. Such a sequence is likely to induce "carry-over" effects whereby search performance on a particular trial is influenced by the trial that came immediately before it. In a visual search task involving the discrimination between one target and 199 non-target elements, Monk (l974a) has demonstrated the presence of a target repetition effect. Anyone of four possible targets could appear in a given triaL and the search time for a given target was significantly shorter if the immediately preceding trial had also used that particular target. Although other factors, such as physical similarity and interfixation I Requests for reprints should be sent to Timothy H. Monk, MRC Perceptual and Cognitive Performance Unit, Laboratory of Experimental Psychology, University of Sussex, Brighton BNI 9QG. England.
distance, would probably have aided search for repeated targets, sequential expectancies might also have contributed towards the repetition effect. The aim of the present study was to isolate the sequential expectancy factor to see whether subjects can have an expectancy that a given search trial will last about as long as its predecessor. The concept of expectancy has long been used to describe changes over time in the readiness of a subject to accept a signal in a human performance task. The term gained its initial popularity in studies of refractoriness (e.g .. Poulton, J 950) and since then it has been used extensively in reaction time and vigilance studies (e.g., Bertelson, 1967; Buckner and McGrath, 1963; Deese, 1955). No study seems to have been made. however, of the temporal expectancies built up by a subject from one trial to the next in a visual search situation. To eliminate the confounding effects produced by situations of target uncertainty it was necessary to produce a task in which, though the physical characteristics of the
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target and non-targets were the same throughout, search time could be varied in a systematic way and latency used as the dependent variable. The problem was solved by devising a task in which delays (or "lags") were surreptitiously inserted between the onset of the background (i.e., the start of the search) and the appearance of the target itself. Post-target search time (i.e., total search time minus lag) could then be used as the dependent variable. and the same target used throughout. Since subjects remained unaware of the presence of lags. the subject was. for the duration of the lag. searching (unwillingly) for a target that had not yet appeared. Search times could thus be lengthened at will without any reason for the change being apparent to the subject. If a sequential expectanc~' effect was to operate in such a situation. this would be revealed by post-target search times being shorter when both present and previous trials had the same lag, and longer when they had different lags. The feasibility of such an approach was tested by a pilot experiment, using the hardest target condition of the Monk (1974a) study. Three subjects each experienced 200 trials using the same target and non-targe!:> as were to be used in the main experiment; ten lags were used (I through 10 s). In visual search tasks involving confusing non-target elements, the target seems to "pop out" when it is found, and this effect seemed to disgUIse the presence of the lags. Careful questioning of the subject!> revealed that none had "caught on" to the fact that the target was not always there. As is usual with search data, there was a wide range of post-target search times, and a lag of at least 7 s was found to be necessary for a separation of total search times to emerge. The pilot experiment also re\'ealed that post-target search time seemed to increase with lag. This result was surprising in that a
FACTORS
new configuration of the 199 non-targets was used on each trial. amI unt.' would expect that the longer lags would t.'nable the subject to dt.·termine the most cffkient scan pattern for that particular configuration. Kreuger (1970) has shU\\ n search time to be shorter when the non-target material IS more familiar to the subject. The result could be explained in terms of fatigue or stress, however. since it would seem reasonable to assume that the subject might lose efficiency when forced by the presence of a lag to search for a long time. Boynton (1960) found that fixation rate tended to decrease aftn 12 s of search, because the subjech felt like giving up. In fact (sec discussion scl:tiun) the decrease in performance was later found to be a natural consequence of the sequential expectancy effect. METHOD Appara/ll.~
Displays were presented on a cathode ray oscilloscope !Hewlett-Packard 1208 with t\ pc P3 J. green phosphor). Viewing distance \\ as held at 620 nun b\ a head-rest; a Fresnel !t.·m (focal length 125 mm) placed 10 mm from the screen, produced a magnified image of tht' di!>plav, subtending 7°30' at the subjt'ct's eve. Nun-targets were 199 bright dots (of luminance 10.6 cd/m!) distributed randomly in an imaginar. 20 x 20 matrix of possible positiun!>; the configuration of non-targets Was different fur each trial. One of the remaining 20 I po!>it ions was randoml~' chusen to be the target po~itiun fur the gi\'en trial. For the duration uf the lag (see below) tht.· target position cuntaim'd a dot of non-target brightness. but as !>oon 3!> the lag had t.'lapsed its brightness was increased tu 15.8 cd/m!, and that dot thw. became the target. All luminances Were measured with an S.E.I. photumeter using "Maxwellian View." The subject viewed the display from within a sound-allenuating cubicle. Two poten.
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tiometer knobs and a response key were situated within easy reach. Procedure
The experiment was run under the control of a computer program which generated the displays and output them to the oscilloscope. timed subjects' responses. and decided whether or not they were correct. A tone sounded to signify the start of each trial. The subject pressed on the response key. keeping it pressed. and the display was presented to the oscilloscope screen. As soon as the subject had found the target dot he released the key and the display disappeared leaving a single cursor dot remaining on the screen. Using the two potentiometer knobs. the subject placed the cursor dot to indicate the position of the target. within a predetermined tolerance (Monk. 1974b). He then pressed the key again. and a message was displayed telling him whether or not he was correct. Unknown to the subject. one of three possible lags (0. 7.5. or 15 s) had to elapse after the onset of the display before the target dot actually appeared as such. If the subject terminated his search before the lag had elapsed. the trial was marked as a "false alarm" and the response "wrong" was automatically given upon the key press following the positioning of the cursor dot. Other incorrect trials were merely marked as "misses." Incorrect trials did not count towards the required number of trials per block. Each subject was given 16 blocks with 45 correct trials in each. The lag to be used on a given trial was determined by a pseudorandom process. Each lag was equiprobable throughout. but the probability of the same lag being used on two adjacent trials (the probability of lag repetition) was artificially raised to even out the number of repeats and transitions. Each subject experienced one or two blocks per daily session. resting between blocks. and
after the 25 th correct trial in each block. Rest periods were at kast 2 min long and were lengthened if the subject felt fatigued. Sessions usually lasted about one hour. Before the experiment began. each subject was given 90 correct practice trials administered in 6 blocks of 15 correct trials each. Subjects were urged to be as fast as was possible without resorting to errors; they were reminded that wrong trials did not count towards the required number of trials per block. and that it was thus in their own interest to be accurate. All subjects were entirely naive as to the purpose of the experiment and the presence of lags. Careful questioning during the experiment. and detailed interrogation after it, ascertained that none of the subjects had "caught on" that the targets were not always appearing at the same time as the nontargets. Three subjects were used in the experiment, two of them female. All were students or employees of the University of Nottingham with 20/20 vision (corrected in two cases) and a mean age of 22 years. They were each paid a fixed sum for their services. RESULTS Error Analysis
Summing over the three subjects, a total of 61 incorrect trials occurred. producing an average error rate of 2.75%. Twenty of these errors were false alarms (i.e .• negative posttarget search times), the remainder "misses." There was an approximately equal split of incorrect trials between the three lag conditions (36.1% for the zero lag. 26.2% for the 7.5-s lag. and 37.7% for the 15-s lag). There were. of course. no false alarms in the zero-lag condition. and as expected there were more false alarms in the 15-s lag condition (15) than there were in the 7.5-s lag condition (5), (p < 0.05. sign test. 2-tailed). There were insufficient data to make a
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more detailed analysis. Incorrect trials were excluded from alI subsequent analvses. Frequency Distributions As found by most authors, the frequency distribution of the search times was highlv skewed (Bloomfield, 1972; Krendel and Wodinsky, 1960). Each item of data was thus logarithmicaIly transformed to the natural base, and an acceptable normal distribution was produced (Kirk, 1968). As a measure of average search time the geometric mean XJl/n was used throughout; this is the ext I ponential of the mean of the logarithmically n transformed data (exp [(lIn) ,~ I log, Xl I I, and has been used in other visual search experiments (Green and Anderson, 1956; Kreuger, 1970; Monk and Brown, 1975). Geometric mean search time would seem to give a better estimate of central tendency than the (more usual) arithmetic mean. Although the arithmetic mean of the logarithmically transformed data is the statistically best estimate of central tendency, exponentiation of it to give the geometric mean enables the data to be expressed in recognized units.
(fi ~
The Multisequential Analysis Edge effects, causing targets appearing on the outer half of the display to have longer search times than those nearer to the center, have bet>n weIl documented in the search literature (Baker, Morris, and Steedman, 1960, Enoch, 1959; Ford, White, and Lichtenstein, 1959). Monk (1974a) confirmed this effect in displays similar to those used in the present study and also discovered a sequential edge effect whereby targets appearing in the inner half of the display had reduced search times if the target on the immediately preceding trial had also appeared on the inner, rather than the outer, half of the display. Since such ef· fects often tend to mask the major effects, the present analysis extracted both the edge effect and the sequential edge effect as factors.
FACTORS
A computer program read through the data, indexing each correct post-target search time b~' (I) the lag used, (2) the lag used on the immediately preceding trial, (3) the target area group ("inner" or "outer"), and (4) the target an:a group of the immediately preceding trial. Trials following rest periods and errors were excluded from the analysis. Cell sizes were not equal. and the data were arranged in a form suitablt.' for the application of a 5-wav analysis of variance on unweighted means. The five factors considered were: subjects (3 levels), target area group (2 levels), area repetition or transition (2 levels), lag on given trial (3 lewIs), and lag repetition versus transition (2 levels). The main effects of lag, F(2,4) = 17.0, P < 0.05. lag repetition versus transition, FO.2) = 30.8. P < 0.05, and target area group, FO,2) = 149.5, P < 0.01, all achieVed significance. No other significant effects or interactions emerged (P -, 0.10 in all cases). The geometric mean post-target search times are given in Table I. Post-target search time was seen to increase with lag, and post-target search times for repeats were shorter than those for transitions. Parenthetically, the edge effect was in the expected direction; the geometric mean post-target search times for targets on the inner and outer halves of the display Were 3.44 sand 4.81 s, respectively. DISCUSSION The two main hypotheses to be tested by this experiment were that repeated lag trials would have shorter post-target search tirnes than those representing a lag transition. and that post-target search time would increase with lag. Both hypotheses were supported by the data. Construction of a model to describe how expectancy might operate in such a situation seemed to be the best way to explain the observed data. The model relied upon a division of the search time into a series of discrete fix-
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TABLE 1 Geometric Mean Post-Target Search Times (s) for Each Subject Showing the Effects of Lag and Lag Repetition and Results of the Computer Simulation Subject Lag Used (s)
Computer
S1
S2
S3
Overall
Simulation
0 7.5 15.0
3.74 4.04 4.27
5.05 5.64 5.79
2.62 3.35 3.37
3.69 4.23 4.35
3.97 4.23 4.33
Lag Repeats Lag Transitions
3.76 4.41
5.23 5.71
2.90 3.31
3.83 4.40
3.73 4.42
ations or glimpses. as in Cowan (1968). and followed the systems of Miller and Ludvigh (1960) and Krendel and Wodinsky (1960) in assigning a probability of success (P) to each glimpse and assuming (as suggested by Volkman. 1962) that little useful information is received during a saccade. The model also utilized the "random sampling with replacement" assumptions used by Krendel and Wodinsky (1960) and Howarth and Bloomfield (1969) in that it asserted that unless expectancy was acting (or a lag was in force). the value of P was held to be a constant (p).
For the purposes of the model. the time dimension was divided into four discrete "time intervals" (0-7.5 s, 7.5-15 s. 15-22.5 s. and 22.5 s and over). and expectancy was held to operate as a uniform step function. Expectancy was assumed to operate in the time interval that followed the interval defined by the lag that had been used in the preceding trial. and was hypothesized to operate by incrementing P from p to p + e (where e is a small positive constant) for the duration of that interval. For the duration of a lag. of course. the value of P remained at zero and could not be incremented. Thus. for example. whereas the four values of P for a 7.5 slag trial preceded by a 15 s lag trial would be O. p. p + e. and p, those of a 7.5 s lag trial preceded by a zero lag trial would be O. p. p. and p.
A "sequential condition" was defined by the lags used on two temporally adjacent trials. Each sequential condition held a particular sequence of four values of P corresponding to the four time intervals. Using the rule defined above. a sequence of values was generated for each sequential condition (Table 2). Simple inspection of Table 2 reveals that not only the lag repetition effect. but also the lag effect itself, is explained by the expectancy model. The six lag transition sequential conditions contain only three incremented (p + e) intervals among them. while the three lag repeat sequential conditions all contain one incremented interval each. Since repetitions thus have. on average, twice as many incremented intervals as transitions, their search times would be significantly shorter than those of the latter. Similarly. the sequential conditions with O. 7.5. and 15 slags on the present trial have. in total. 3. 2. and I incremented intervals. respectively. and the lag effect itself would thus also be predicted. As a mere illustration of the similarity between the predictions of the model and the observed data. a Monte Carlo computer simulation was undertaken. A fixation rate of 3 per second as suggested by the data of Enoch (1959) and Ford. White, and Lichtenstein (1959) was adopted. and saccade time was assumed to be zero.
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FACTORS
TABLE 2 Probability of Success of a Given Fixation in Each of the Four Time Intervals for the Nine Sequential Conditions (The information in this table forms the basis of the lag simulation model.) Lag Used on Previous Trial (s)
0 7.5 15.0 0 7.5 15.0 0 7.5 15.0
Lag Used on Present Trial (s)
0 0 0 7.5 7.5 7.5 15.0 15.0 15.0
TIme Interval (5)
0-7.5
7.5-15
15-22.5
p+e
p
P P 0 0 0 0 0 0
p+e
P
P P
P
Since the computer simulation was designed to be descriptive rather than predictive,the required values ofp and e were found entirely by simple trial-and-error. An arbitrary one second was added to the simulated results. to take into account factors such as the edge effect and reaction time. which had not been considered by the model. Suitable values were found to be 0.06 for p and 0.02 for e. Using these values, 300 trials were run under each of the nine sequential conditions, and the resulting geometric mean post-target search times calculated. Inspection of the final column of Table I re\'eals that the similarity between simulated and actual search times was surprisingly good, particularly when one remembers all the assumptions and simplifications made by the model. It would thus seem highly plausible that both the lag effect and the lag repetition effect were manifestations of the simple sequential expectancy effect that is described by the model. For convenience, this model has been described in terms of an increment in P over the "expectant" interval. An alternative argument, based on the hypothesis that subjects experience a decrement in performance outside this interval instead. would seem to be
p p+e
p+e
p p+e
P 0 0 0
P P
p+e
over 22.5 P p p P
p
p P P p
equally valid. Since both p and e are arbitrar_ ily defined parameters, however. such an ex~ planation would remain completely in agreement with the predictions of the model and the results of the experiment. The inter-trial interval (III) used in the experiment was not constant. varying between 5 sand 20 s, depending upon the speed with which the subject positioned the cursor dot. Clearly, the III is of great importance in any study of sequential effects. but the necessity for feedback from the subject makes standard_ ization of the ITI practically impossible in this situation. The main purpose of this study has been to show that the large amount of variability in search times from one trial to the next may cause sequential expectancy ef. fects in visual search, and that search trials should no longer be considered as single, isolated events. CONCLUSIONS Observers who are engaged in repeated trials of a visual search task can build up expectancies about search time from one trial to the next. In some cases such expectancies can significantly disrupt performance, It is thus recommended that, wherever possible, the task should be rescheduled so that within a
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TIMOTHY H. MONK
block of trials, all search times will tend to be roughly the same. ACKNOWLEDGMENTS Grateful thanks are due to Dr. R.B. Henry for his guidance and encouragement and to Professor C.1. Howarth for his helpful advice and criticism. This work was supported by a Medical Research Council studentship. and contributed towards work undertaken for a Doctor of Philosophy degree at the University of Nottingham.
REFERENCES Baker. C A, Morris, D. F., and Steedman, W. C. Target recognition on complex displays. Human Factors. 1960,2, 51-61. Bertelson, P. The time course of preparation. Quarterlv Journal of Experrmental Psychology, 1967,/9,272-279. Bloomfield, J. R. Visual search in complex fields: Size dif· ferences between target disc and surrounding discs, Human Factors, 1972,14, 139-148, Boynton, R. M. Concluding remarks in Visual search techniques, Washington, D.C.: National Academy of Science, National Research Council, Publication 712, 1960,231-239. Buckner, D. N. and McGrath, J. J. (Eds.) Vigilance: A symposium. New York: McGraw-Hili, 1963. Cowan, T. M. An observing response analysis of visual search. Psychological Review, 1968, 75, 265-270 Deese, J, Some problems in the theory of vigilance. PsychologIcal Review, 1955,62. 359·368 Enoch, J. M. Effect of the size of a complex display upon visual search. Journal of the OptIcal SocIety of Amerrca, 1959,49, 280-286, Ford, A., White, C. T" and Lichtenstein, M. Analysis of eye
movements during free search. Journal of the Optical Society of America, 1959,49,287-292. Green, B. F, and Anderson, L. K. Color coding in a visual search task. Journal of Experimental Psychology. 1956, 51,19-24. Howarth, C. I. and Bloomfield, J. R. A rational equation for predicting search times in simple inspection tasks. Psychonomlc SCIence, 1969,17,225-226. Kirk, R. E. Experimental design: Procedures for the behavioral sciences. Belmont, California: Brooks-Cole, 1968. Krendel, E Sand Wodinsky, J. Search in an unstructured visual field. Journal of the Optical Society of America. 1960, 50. 562-568. Kreuger, L. E. Effect of frequency of display on speed of visual search. Journal of Experimental Psychology, 1970, 84, 495-498. Miller, J W. and Ludvig, E. Time required for detection of stationary and moving objects as a function of size in homogenous and partially structured fields. In Visual Search Techniques. Washington, D.C.: National Academy of Science, National Research Council, Publication 712, 1960, 170-180. Monk, T. H. Sequential effects in visual search. Acta Psychologica, 1974,38, 315-321. (al Monk, T. H. Sequential and spatial effects in visual search. Unpublished doctoral dissertation, University of Not· tingham, 1974. (bl Monk, T, H, and Brown, B. The effect of target surround density on visual search performance. Human Factors. 1975, 17. 356-360. Poulton, E. C. Perceptual anticipation and reaction time. Quarterly Journal of Experimental Psychology. 1950,2, 99-113. Volkman, F. C. Vision during voluntary saccadic eye movements. Journal of the Optical Society of America, 1962,52.571-578.
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