Adaptive Processing of Visual Motion - Brandeis...

Journal of Experimental Psychology:Human Perception and Performance1981, Vol. 7, No. 4, 780-794

Copyright 1981 by the American Psychological Association, Inc.0096-1523/81 /0704-0780S00.75

Adaptive Processing of Visual Motion

Karlene BallNorthwestern University

Robert SekulerDepartments of Psychology and Ophthalmology

Northwestern University

Three studies relating perception of motion to stimulus uncertainty are reported.Generally, detectability declines when the observer is uncertain about the di-rection in which a target will move, but the visibility loss associated with directionuncertainty can be attenuated if the observer has adequate practice. This atten-uation seems to depend upon the observer's ability to switch among directionallyselective visual mechanisms in an adaptive fashion. The implications of thesefindings for models of motion detection are discussed.

Although a great deal is known about howvisual motion is detected in the laboratory,there are impediments to applying that in-formation outside the laboratory. Inside thelaboratory, observers are typically well in-formed about the stimuli they will have todetect; outside the laboratory, most visualstimuli are not so predictable. This unpre-dictability, termed stimulus uncertainty,strongly affects visibility of motion. Our con-cern in this article is with stimulus uncer-tainty and with the ways in which observerscope with it. More particularly, we are con-cerned with one source of uncertainty, thatassociated with the direction in which a tar-get will move.

There are two main reasons for examiningstimulus uncertainty in the context of motionperception. First, it is already known thatstimulus uncertainty produces quite sub-stantial performance changes with movingtargets (Ball & Sekuler, 1980; Sekuler &Ball, 1977). Second, there already exists agood model of the likely basis of motion per-ception (Sekuler, Pantle, & Levinson, 1978).As we shall see, this model helps us interpret

This research was sponsored by the Air Force Officeof Scientific Research, under Grant AFOSR 79-0064.We thank James Zacks, Gordon Shulman, and StephenPalmer for their constructive comments on the workreported here. We also thank William Marshak for al-lowing us to use the results of one of his Monte Carlosimulations.

Requests for reprints should be sent to Robert Sek-uler, Cresap Neuroscience Laboratory, Department ofPsychology, Northwestern University, Evanston, Illinois60201.

whatever performance changes might beproduced by stimulus uncertainty.

Let us begin by summarizing the modelof motion perception that we are using.Motion perception seems to depend upondirectionally selective mechanisms, filtersthat attenuate some input signals (directionsof movement) more strongly than others(Ball & Sekuler, 1979). Each mechanismcan be characterized by the direction towhich it is most sensitive and the rate atwhich its response changes as inputs deviatefrom its optimal direction. When observersare instructed to detect a particular directionof motion, they presumably can use the in-formation from those mechanisms that wouldhave the greatest sensitivity to that direction.Of greater interest, though, is how thesemechanisms are used when the observer isuncertain about the direction of motion thatmight occur. We already know that an ob-server's uncertainty about the direction inwhich a target might move reduces the vis-ibility of that target (Sekuler & Ball, 1977).We do not know, however, what, if anything,observers can do to minimize the deleteriouseffects of uncertainty.

From time to time in this article, we com-pare the performance of our observers withthat predicted by two models originally usedto explain observers' behavior when facedwith frequency uncertainty in audition. Thesemodels described subjects as either single-band (Patterson, 1976; Tanner, Swets, &Green, Note 1) or multiple-band (Green,1958) listeners. Our adaptations of these

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ADAPTIVE PROCESSING AND MOTION 781

models to motion perception assume that theobserver uses sensory information eitherfrom a single, directionally selective mech-anism or from an aggregation of such mech-anisms.

A single-band model assumes that at anyone moment observers monitor sensory in-formation from just one directionally selec-tive mechanism. It further assumes that ob-servers can select the mechanism from whichthey will derive this information (Patterson,1976; Tanner, Swets, & Green, Note 1). Theselection may be determined by variablessuch as the relative probabilities with whichvarious directions of motion occur, as wellas by the costs and values of detecting orfailing to detect each of these possible di-rections. We must note that very little workhas been done previously on this last ques-tion.

A multiple-band model assumes that ob-servers can monitor activity in a number ofdirectionally selective mechanisms, eachmaximally sensitive to one of the possibletarget directions (Green, 1958). As in thesingle-band approach, the multiple-bandmodel assumes that observers have somecontrol over which mechanisms they willmonitor, that is, they can choose whichmechanisms will be included in the set onwhich their responses will be based.

In auditory research, attempts to choosebetween single- and multiple-band listenermodels have produced contradictory andconfusing results. In the same study, somelisteners conform to one model, while othersconform to the alternative (Swets, 1964).The fact is that listeners are likely to exerciseactive control over their own listening strat-egies. Sorkin, Pastore, and Gilliom (1968)found that subjects could narrow the effec-tive listening band when changing probabil-ities of signal occurrence made it advanta-geous to do so. Penner (1972) was able toinfluence detection performance by present-ing cues and varying payoffs. She inter-preted her results as reflecting changes inlistening strategy. Note, however, that notall studies have demonstrated a change indetection mediated by biases in attention(e.g., Larkin & Greenberg, 1970). This dis-crepancy may arise from the fact that theutility of different strategies varies with the

demands of the experiment. Moreover, suchutility may be differently evaluated by eachof several observers.

Although these two models have been usedmost frequently to explain stimulus uncer-tainty effects, we would like to mention aparticular, single-band model that we usedelsewhere to account for a limited set of un-certainty effects. The midway model (Ball& Sekuler, 1980) was proposed to accountfor direction uncertainty and was tested un-der conditions in which only two equallylikely directions of motion could occur. Thismodel is a modified single-band scheme inwhich the observer monitors the direction-selective mechanism most sensitive to thedirection midway between the two directionsthat might occur. For this strategy to beuseful, this midway mechanism must havenontrivial sensitivity to each of the two po-tential directions of motion. Although we donot believe that the midway model providesa complete description of how observers copewith uncertainty in all situations, it does of-fer a first step toward defining how muchcontrol observers have over their own view-ing strategies. Previous work with the mid-way model was restricted to simple forms ofuncertainty, such as exists when there aretwo possible directions of motion and the twofall within the same quadrant. In the presentresearch, we extend this work to more com-plicated cases, including more than two pos-sible directions and directions that differwidely from one another. As indicated pre-viously (Ball & Sekuler, 1980), the midwaymodel was not intended to cover these con-ditions.

Although our previous experiments dem-onstrated uncertainty effects for motion per-ception (Sekuler & Ball, 1977), this findingseems to be far from inevitable. Shiffrin andhis colleagues, for example, have studiedvisual localization (Shiffrin & Gardner,1972), detection of dots (Shiffrin, Gardner,& Allmeyer, 1973), localization on the skin(Shiffrin, Craig, & Cohen, 1973), and rec-ognition of speechlike syllables (Shiffrin,Pisoni, & Castaneda-Mendez, Note 2). Innone of these experiments did uncertaintyhave any effect: Subjects were able to attendto more than one stimulus at a time.

There are two possible reasons why we

782 KARLENE BALL AND ROBERT SEKULER

found, and Shiffrin et al. failed to find, per-formance losses with target uncertainty.First, it may be that uncertainty about targetdirection and speed produces greater reduc-tions in detectability than is produced withnonmoving stimuli. Alternatively, the effectof target uncertainty may be diminished byextended practice with the detection taskunder conditions of uncertainty. All the ob-servers tested by Shiffrin et al. had consid-erable practice under conditions of uncer-tainty, but ours did not have much. Someof the work we report in this article is de-signed to examine the possible reduction inthe effect of uncertainty with practice.

In our previous work on uncertainty aboutdirection, the stimulus ensemble consistedof just two equi-probable directions of mo-tion (Ball & Sekuler, 1980; Sekuler & Ball,1977). But outside the laboratory, observersordinarily face far more complex forms ofuncertainty about the moving targets thatthey have to detect and to which they mustrespond. In this article, we report how ob-servers cope with direction uncertainty un-der conditions more nearly approximatingthose encountered outside the laboratory:Several directions could occur, and their rel-ative probabilities were allowed to vary.

Experiment 1This experiment measured detection of

motion when any one of four directions ofmotion could occur on each trial. We variedthe relative probabilities with which the fourpossible stimulus directions were presentedand compared the abilities of two kinds ofmodels to predict the resulting changes inperformance. In order to generate predic-tions, the single-band model requires severalassumptions. First, the model assumes thatour four directions are widely enough sep-arated that the directionally selective mech-anism maximally sensitive to any one willhave essentially zero sensitivity to any of theothers. Measurements of the tuning of di-rectionally selective mechanisms make thisassumption quite plausible (Ball & Sekuler,1979). Second, the model has to assume thaton any trial only one mechanism is moni-tored. As we discuss later, there are reasonsto question the validity of this second as-

sumption. Third, the model assumes that themechanism monitored when there are un-equal probabilities has its maximum sensi-tivity to the most likely direction.

To make predictions from the multiple-band viewer model requires the assumptionthat the observer simultaneously monitorsfour directionally selective mechanisms—one maximally sensitive to each of the pos-sible directions. The cost of this effort is anincreased level of noise; the result is a re-duction in sensitivity (</') by a factor equalto the square root of the number of inde-pendent mechanisms being monitored. Notethat this strict form of the multiple-bandmodel assumes that noise is completely un-correlated from one mechanism to the next.Correlated noise would reduce the effect ofstimulus uncertainty by an amount propor-tional to the correlation; perfectly correlatednoise would yield no performance loss dueto stimulus uncertainty.

MethodObservers. Three paid observers participated in this

experiment. All were experienced psychophysical ob-servers with uncorrected 20/20 vision. All were naivewith respect to the hypotheses of interest and had min-imal experience with this particular task.

Apparatus. Stimuli were patterns of isotropic ran-dom dots presented on a cathode-ray-tube (CRT) dis-play under computer control. Isotropic patterns haveFourier spectra with equal power in all directions. Suchpatterns allow us to study responses to motion withoutthe complications of oriented contours that would bepresent with nonisotropic patterns. In addition, isotropicpatterns produce a convenient experimental result: Withsuch patterns, an observer's sensitivity to various direc-tions of motion is constant (Ball & Sekuler, 1979). Inall of our experiments, speed of movement was heldconstant at 4°/sec.

The CRT on which dots were displayed was illumi-nated by a constant veiling light, 1.5 cd/m2. Observerswere seated, with head supported by a chin rest, 57 cmfrom the display. Viewed binocularly, approximately400 dots of the pattern were visible within an 8°-di-ameter circular aperture. In any one display frame (33msec), the computer plotted 512 dots on the CRT.

Procedure. A two-alternative forced-choice (2AFC)procedure was used. Each trial consisted of two 600-msec intervals, separated by 200 msec. A tone, presentedconcurrently with each interval, defined the interval forthe observer. During one of the two intervals, selectedrandomly, a pattern of moving dots was presented onthe CRT. During the other interval, no pattern was pre-sented, and the CRT was illuminated only by the con-stant veiling light. The observer had to indicate whichinterval, first or second, contained moving dots. Feed-


back, in the form of a tone for a correct response, wasprovided after each trial; we hoped thereby to help theobserver maximize performance (Swets, 1964).

Before collecting any data, we identified that lumi-nance that would produce approximately 90% correctidentification of the interval containing movement foreach subject. This luminance was estimated from blocksof trials during which movement was always upward.Motion, in the experiment itself, could be in any of fourdirections: 90° (upward), 180° (leftward), 270° (down-ward), and 360° (rightward).

We tested observers under three conditions that dif-fered in the relative probabilities with which variousdirections could occur. We refer to the conditions ascertainty (only one direction possible per block of trials),unbalanced-uncertainty (four directions possible, withone occurring 70% and the others 10% of the time each),and balanced-uncertainty (four directions equally likely).Every observer was tested in four 50-trial blocks foreach condition; order of testing was independently ran-domized for each observer. The direction used in cer-tainty conditions changed from one block to the next sothat any observer was tested in one block for each ofthe four possible directions. The dominant (70%) direc-tion in the unbalanced-uncertainty condition also changedfrom one block to the next, allowing each observer tobe tested once with each direction the dominant. Beforeeach block, observers were carefully instructed aboutthe upcoming condition, for example, what the exclusiveor dominant direction of motion might be. In addition,they were given several practice trials before each blockto make sure they were well acquainted with the testconditions.

Observers were paid 2 cents for every correct re-sponse; 1 cent was deducted for every incorrect response.Thus, in a block of 50 trials, observers could earn 25cents if they were performing at chance or as much as$ 1.00 if they were performing perfectly. This bonus wasadded to the hourly pay ($3.00) and was used to assurethat all observers would remain highly motivatedthroughout the experiment.

ResultsPercent correct performance was calcu-

lated for each observer and condition; thesevalues were then converted to d' scores (Ta-ble II, Appendix 1 of Swets, 1964). Our sta-tistical analyses used d' rather than percentcorrect scores because d' is linearly relatedto sensitivity. Values of d' for each block of50 trials and for each observer are plottedin Figure 1. Performance is shown separatelyfor the four possible directions of movement.Data for the unbalanced-uncertainty con-dition are further broken down to show per-formance for the dominant direction and themean of the three nondominant directions.

Mean percent correct was 95 for the cer-tainty condition, 88 for the unbalanced-un-certainty condition, and 72 for the balanced-

uncertainty condition. The corresponding rf'swere 2.31, 1.64, and .84. An analysis of vari-ance showed a significant effect of condition,F(2, 33) = 35.59, p < .05.

In the unbalanced-uncertainty condition,percent correct detection of the dominantdirection tended to be slightly better thandetection of the nondominant direction. Theadvantages of dominant over nondominantwere 5.4%, 11.2%, and 1.6% for ObserversS.J., J.C., and D.B., but this difference be-tween performance with dominant and non-dominant directions was not statistically sig-nificant (p > .20). How slight the advantageenjoyed by the dominant direction was canbe appreciated from the fact that in only 3of the 12 possible comparisons did detectionof the dominant direction surpass that ofevery one of the three nondominant direc-tions.

DiscussionLet us first consider the predictions of sin-

gle and multiple-band models for the out-come of this experiment. For the unbal-anced-uncertainty condition, the single-bandmodel predicts performance equivalent tothat of the certainty condition on 70% of thetrials (when the dominant direction occurs)and chance (50%) on the remaining trials.Since the certainty condition yielded 95%correct, the single-band model predicts

Pct( Correct) = .7(95) + .3(50) = 83.5in the unbalanced-uncertainty condition.

For the balanced-uncertainty condition,the model predicts observers will be moni-toring the appropriate mechanisms on 25%of the trials (since they cannot predict whichof the four equally likely directions will oc-cur) and an inappropriate mechanism on75% of the trials. Since they would performat the same level as in the certainty conditionwhen monitoring the appropriate mecha-nism, and at chance when monitoring an in-appropriate one, the model predicts

Pct( Correct) = .25(95) + .75(50) = 61.3in the balanced-uncertainty condition. Assuggested earlier, our estimates of perfor-mance under conditions of stimulus uncer-tainty for the multiple-band model assumes


CERT/UNIFY U NB

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DIRECTION OF MOVEMENT

CERTAINTY UNBALANCED BALANCED

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CERTAINTY UNBALANCED BALANCED

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DIRECTION OF MOVEMENT

Figure 1. d' values for each condition and observer in Experiment 1. (In each of the three conditions,performance is shown for all four possible directions of movement. In the unbalanced-uncertainty con-dition, the data are further broken down such that the unfilled bars represent performance for thedominant direction and the filled bars represent the performance for the three other directions.)

that noise is entirely uncorrelated from onedirectionally selective mechansim to an-other. With four possible directions—suffi-ciently different to make the assumption ofuncorrelated noise at least plausible—per-formance, expressed in the d' metric, shouldbe reduced by the square root of four.

Predictions from the single-band and mul-tiple-band models are presented in Table 1for both uncertainty conditions and for allthree observers. Since predictions from themultiple-band model are most easily calcu-lated in terms of d', all predictions and ob-tained data are expressed in that metric. Forall observers in the unbalanced-uncertainty

condition, the single-band model provideda closer fit than did the multiple-band model.Note, however, that two of the observers(S.J. and D.B.) performed much better thanpredicted. In the balanced-uncertainty con-dition, neither model's predictions fit thedata closely, although the multiple-bandpredictions were slightly closer than the sin-gle-band predictions.

What do these results tell us about thebehavior of observers faced with uncer-tainty in a detection task? Let us considereach of the uncertainty conditions, in turn,taking the unbalanced condition first. Thesingle-band model predicted that perfor-


Table 1Predicted and Obtained A's in Experiment 1

Observer

J.C.S.J.D.B.

Unbalanced uncertainty

Predicted Obtained

Single-band model

1.26 1.251.19 1.891.29 1.80

Balanced uncertainty

Predicted Obtained

.41 .75

.39 .76

.42 1.00

J.C.S.J.D.B.

Multiple-band model

1.17 1.25 1.171.04 1.89 1.041.26 1.80 1.26

.75

.761.00

mance in the unbalanced-uncertainty con-dition would exceed that in the balanced-uncertainty condition. As we have seen, thisprediction was correct, although its numer-ical accuracy was poor. A more detailed ex-amination reveals that the single-band modeldid an inadequate job of predicting the ex-periment's outcome. If observers had be-haved as the model states, we should expectthat detection of the dominant directionwould have been as good in the unbalanced-uncertainty condition as it was in the cer-tainty condition. Mean percent correct per-formance for the certainty condition was 95(SE = .79%). Mean percent correct perfor-mance for the dominant directions in theunbalanced-uncertainty condition was 88(SE = 2%). It is clear that performance waspoorer for the dominant direction than pre-dicted by the single-band model. Moreover,detection of the three nondominant direc-tions was well above the chance levels pre-dicted by the model. Mean percent correctfor the nondominant directions was 82 (SE —3%). Probably the greatest failure for thesingle-band model was the very slight, andstatistically nonsignificant, advantage ob-servers showed in detection of the dominantdirection. Although the single-band modelpredicts performance equivalent to thatachieved under certainty conditions, detec-tion of the dominant direction was justbarely better than detection of the nondom-inant directions.

What about performance in the balanced-uncertainty condition? As Table 1 shows,

the multiple-band model predicted betterperformance than did the single-band model.In fact, our observers performed much betterthan predicted by the single-band model butworse than predicted for the multiple-bandmodel.

We were interested in the possibility thatalthough an observer might monitor only onedirection per block of 50 trials, this directionmight change from one block to the next.Such switching would be consistent with sin-gle-band descriptions of the subject's behav-ior within any block, since it would producemuch better performance with one directionthan with the others. However, this partic-ular approach does lead to one complication:The direction that was detected best wouldvary between blocks, and averaging dataacross blocks would obscure this form of sin-gle-band behavior. To determine whetherthis model gave an adequate description, weanalyzed data in units of 50-trial blocks.Within any block, random fluctuations alonecould cause performance with one directionto be best and performance with some otherdirection to be worst, even if the observerwere not consistently monitoring one direc-tion. As a result, we needed to determine thelikelihood of obtaining various magnitudesof difference in detectability between the"best" and "worst" direction in a block oftrials when, in fact, there was no real dif-ference in sensitivity to the various direc-tions. Since normal inferential statisticaltechniques were inapplicable, we used MonteCarlo procedures.


We simulated many repetitions of our ex-periment under conditions of no real differ-ences in sensitivity to the four directions.Four each observer and actual block of trials,we simulated 1,000 blocks of trials. Eachsimulation assumed binomial variability anda mean percent correct equal to that ob-tained in one block of the actual experimentfor one observer. A random-number gener-ator produced data for each of four possibledirections for 50 trials. The computer thenordered the data for the directions, fromhighest to lowest percent correct and re-corded the difference between highest andlowest. From 1,000 such runs, the computergenerated a table showing the proportion ofruns that produced various size differencesbetween highest and lowest percents correct.This table allowed us to estimate the prob-ability of obtaining a difference betweenhighest and lowest percent correct as largeor larger than the one obtained in each blockof our experiment under the null hypothesis,that is, with no real difference in the pro-cesses generating data for the four direc-tions.

For Observers D.B. and S.J., we werenever able to reject the assumption of no realdifference in sensitivity among the four di-rections in a block. This was not true forObserver J.C., however. Her data showedstatistically significant (p < .05) differencesin the detectabilities of the most and leastdetectable directions in three of the four testblocks. Thus, Observer J.C. behaved like asingle-band observer in the balanced-uncer-tainty condition, and Observers D.B. andS.J. did not.

We should note that there are forms ofsingle-band observer behavior to which ourMonte Carlo procedure would have been in-sensitive. Consider just two examples. First,an observer might monitor just a single di-rection on each trial, but which directionthat was might vary randomly from one trialto the next, rather than from block to block.Alternatively, during any one trial, an ob-server might switch between directions, firstmonitoring one, then another. If the orderin which various directions were monitoredwas random, the data produced would re-semble that of a multiple-band observer. Theperformance loss of such an observer would

be inversely proportional to the amount oftime spent monitoring any one band.

Thus the results of Experiment 1 mightbe consistent with some form of single-bandmodel in which more than one directionallyselective mechanism can be monitored dur-ing a trial. This would explain the increasedsensitivity to the nondominant directions inthe unbalanced-uncertainty condition, andthe higher sensitivity in the balanced-uncer-tainty condition, than predicted by the con-ventional single-band model. The model'sassumption that on a given trial only onemechanism is monitored appears to be inquestion; this requires further testing. Ob-servers may not be attending to only onedirection on a given trial, but they do notappear to be monitoring all four directionssimultaneously, as suggested by the multi-ple-band model.

Note also that for each observer, perfor-mance appears to improve across the fourblocks of the experiment. Mean percentscorrect for the four blocks were 62, 73, 70and 81. An analysis of variance confirmedthe existence of a significant linear trendacross the four blocks, F(l, 2) = 79.15,p < .05. Most modern theories of detection,including the single-band and multiple-bandmodels described here, treat the perceiver asa passive information processor and assumethat perceptual mechanisms are immutable(Neisser, 1976). Furthermore, the modelshave made no distinction between skilled orhighly practiced and unskilled observers. In-deed, most of the studies that led to the for-mulation of these models made use of un-practiced observers (Moray, 1969). Incontrast, the present experiment shows thatquite minimal practice is able to diminishthe debilitiating effects of stimulus uncer-tainty. Experiments 2 and 3 were designedto determine how far practice can go towardcombatting the performance losses associ-ated with direction uncertainty.

Experiment 2

Psychoacoustics has provided much an-ecdotal evidence that practice may producedramatic results on tasks involving stimulusuncertainty effects (Gundy, 1961; Moray,


1969). But these reports have not been fol-lowed up systematically. That practiced ob-servers can learn to do complex perceptualtasks impossible for the beginner (Neisser,1976) is less surprising than the fact thatpractice affects seemingly simple detectiontasks. Why, for example, is there improve-ment in simple visual detection over as muchas 4 months' practice with the same stimuli(Taylor, 1964)?

In one of the best known demonstrationsof practice effects in perception, Neisser,Novick, and Lazar (1963) found that afterseveral weeks' practice, observers couldsearch for any of 10 possible targets as rap-idly as for just 1 previously specified target.In other words, subjects learned to overcomethe harmful effects of stimulus uncertainty.In light of the improvement observed in Ex-periment 1 over four blocks of trials, wewondered whether the effects of directionuncertainty could be totally eliminated witheven more extensive practice. Thus Experi-ment 2 was designed to provide observerswith extensive practice on a task known ini-tially to produce substantial uncertainty ef-fects.

MethodThe observers, apparatus, and stimuli were the same

as in Experiment 1. As before, for each observer we firstfound the dot luminance that produced about 90% cor-rect performance when target motion was exclusivelyupward. Again, a 2AFC procedure was used.

Five stimulus alternatives were equally likely on eachtrial. In all cases, the five stimuli were evenly spacedalong the direction continuum. Moreover, the middlestimulus of the five always consisted of upward (90°)motion. The range of directions covered by the five stim-ulus possibilities constituted the three conditions of theexperiment. The narrowest range covered 40°, with pos-sible directions of 70°, 80°, 90°, 100°, and 110°. Themiddle range covered 80° with possible directions of50°, 70°, 90°, 110°, and 130°. The widest range covered120°, with possible directions 30°, 60°, 90°, 120°, and150°. All directions within a range occurred with equalfrequency but in a random order. As before, the observermerely had to indicate which interval, first or second,contained motion.

To assure a high level of motivation we paid observers2 cents for each correct response; 1 cent was deductedfor each incorrect response. These payoffs were in ad-dition to the hourly pay. A tone sounded after eachcorrect response.

Design, The experiment consisted of five stages: pre-test, practice, Test 1, practice, and Test 2. In any of thetest phases (pretest, Test 1, Test 2), 2AFC measure-

ments were made under each range of uncertainty: nar-rowest (40°), intermediate (80°), and broadest (120°).Order of testing with various ranges was randomizedand consisted of two blocks of 50 trials under each un-certainty range. The practice phases consisted of 15blocks (50 trials each) of 2AFC testing with just the120° uncertainty range. Before any block of trials, theobserver was told what the range would be for that block.

ResultsFrom each block of 50 trials, we calcu-

lated percent correct identification of the in-tervals that contained motion. These percentcorrect values were transformed into the d'values shown in Figure 2. To make inter-pretation less ambiguous, we did separateanalyses of variance on the data from eachof the three phases of the experiment: pre-test, first test, and second test. For mea-surements made in the pretest phase, beforethe subject had received practice, range ofuncertainty was a significant source of vari-ance, F(2, 4) = 7.5, p < .05. As expected,performance was poorer when the possibledirections of movement were spread over awider range.

A separate analysis of variance was per-formed on the d's for Test 1 and Test 2 (i.e.,following 750 and 1,500 practice trials withthe 120° range condition). The analysesshowed that uncertainty range was not a sig-nificant source of variance during either thefirst or the second test phase, F(2, 4) = 2.79,p > .05; F(2, 4) = .68, p > .50, respectively.

It appeared from the data that practicetrials on the 120° range condition improvedperformance (especially for that range).This finding called for a further analysis. Atwo-factor analysis of variance was done inwhich range of uncertainty and performanceon the three test phases were the variables.Significant effects were noted for both therange and test phases, F(2, 4) = 30.69 and8.44, p < .05. More important, however, theinteraction between the range and test vari-ables was significant, F(4, 8) = 3.99, p <.05, which indicates that the difference be-tween the three range conditions in the pre-test phase was eliminated following practice.

DiscussionThis experiment shows that repeated test-

ing reduces detection losses normally asso-


BASELINE

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TEST 240° RANGE 80s RANGE 120° RANGE

rS.J. D.B. J.C. S.J. Dfi. J.C. S.J. DS. J-C.

O B S E R V E R S

Figure 2. d' values for various uncertainty ranges testedin Experiment 2. (Top row: Baseline, pretest perfor-mance in the three conditions prior to any practice trials.Second row: Performance following 750 practice trialsin the 120° range condition. Bottom row: Performancefollowing an additional 750 practice trials in the broad-est range condition.)

elated with direction uncertainty. When pre-test performance was measured, observersdid significantly more poorly with the largerranges of possible directions than with thenarrower range. But by the first test, therewere essentially no differences among thethree range conditions. We should note,however, that the overall effect of uncer-tainty was not entirely eliminated by prac-tice. The average d' values following the fi-nal practice period were 1.66, 1.34, and 1.66for the 40°, 80°, and 120° uncertaintyranges, respectively. Measured at the samepoint in the experiment, d under conditionsof certainty was 2.32. It appears, then, that

observers can learn to process five differentdirections spread over 120° as effectively asthey can the same number of directions cov-ering only 40°. Note, however, that they didnot learn to process the sets of five directionsas efficiently as they could just one fixeddirection.

Several factors might account for this im-provement; for example, observers may havelearned to adjust the width of the direction-selective filters. Several masking studies,from vision as well as audition, suggest thatthe width of sensory filters might actuallybe adjustable. Green (1961), for example,demonstrated that the auditory filter couldchange shape as the bandwidth of the mask-ing noise changed. In vision, DeValois (1977)showed that over time, the threshold eleva-tion produced by a sinusoidal adaptationgrating became more sharply tuned in thespatial frequency domain. Adaptation grat-ings whose spatial frequency was sufficientlyclose to that of the test grating to produceadaptation when testing began were not ableto produce adaptation by the end of thestudy. Although adaptation could still beproduced, only spatial frequencies quiteclose to the test grating's were adequate todo so.

Experiment 2 suggested that with prac-tice, observers learn to monitor five direc-tions more effectively. As already indicated,this practice effect could reflect a change inthe selectivity of direction-selective mecha-nisms. We must also point out that all of ourconclusions so far have been based on sta-tistics that reflect the average performanceover many trials or blocks of trials (Ball &Sekuler, 1980). Such averages might fail tocapture whatever adaptive behavior an ob-server exhibits during a single trial. To in-vestigate what observers are doing within atrial, rather than over a block of trials, weperformed one final experiment.

Experiment 3One adaptive behavior in which an ob-

server might engage is to monitor succes-sively more than one direction-selectivemechanism during the course of a singletrial. If such switching behavior indeed oc-curred, it would likely be limited or facili-


tated by the duration of the observation in-terval. There should be intervals short enoughto permit the observer to monitor just a sin-gle directionally selective mechanism; otherdurations should be sufficiently long to per-mit the observer to monitor more than onemechanism. In Experiment 3, we varied ex-posure duration in four different experimen-tal conditions to determine whether observ-ers would be able to switch amongmechanisms.

MethodThe observers, apparatus, and stimuli remained the

same. We used a luminance that produced about 90%correct in a 2AFC with only upward motion presentedin a 600-msec interval. Another departure from the pre-vious experiments was the introduction of a rating scaleprocedure to assess sensitivity to motion.

A session consisted of 80 trials, each coextensive witha tone informing the observer that a moving random dotpattern might be presented. In fact, a moving patternwas presented on half of the trials, chosen at random;on the remaining trials, the CRT was illuminated onlyby the veiling light.

Four different conditions were run in combinationwith six test durations, 100, 200, 300, 400, SOO.and 600msec. Each combination of duration and condition wastested in a block of 80 trials. In the certainty conditiononly upward movement could occur. In addition to thecertainty condition, we ran three uncertainty conditions,differing in the ranges of possible directions each per-mitted. The 90° range condition permitted 45°, 67.5°,112.5°, and 135° movements to occur equally often butin a random order. The 180° range condition did thesame for 0°, 45°, 135°, and 180°. Finally, the 360°range condition permitted movements of 90°, 180°,270°, and 360°.

Following each trial, the observer responded by push-ing one of six response buttons. The rating scale re-sponses were defined as follows: 1—positively was amoving pattern on the screen; 2—probably was a movingpattern on the screen; 3—possibly was a moving patternon the screen; 4—possibly was no moving pattern on thescreen; 5—probably was no moving pattern on thescreen; and 6—positively was no moving pattern on thescreen. Observers were instructed to try to use all sixresponse categories if at all possible and that a 1 or a6 was no more a "right" answer than 2, 3, 4, or 5.

Results

To describe each observer's performance,we used two different measures, one for sen-sitivity and one for possible variation in cri-teria. P(A) is our nonparametric measure ofdetectability. It is the proportion of the areaof the unit normal square that lies below the

receiver operating characteristics. Largervalues of P(A) signify that the movementwas more easily detected (McNicol, 1972).The criterion-related measure, B, indicatesan observer's bias for or against saying thatthe stimulus had been presented. B is thenumerical category on the rating scale atwhich an individual is equally disposed tosay that the pattern had been presented andto say that it had not. Higher B values in-dicate a tendency to deny that a pattern hadbeen presented. This measure of criterion onthe rating scale is related to the better knownmeasure of criterion in the yes-no signal-detection experiment, beta (McNicol, 1972).

For each observer, condition, and durationof trial, we calculated the two performancemeasures just described, P(A) and B. Al-though P(A) is not linearly related to d', its2 transform is. Therefore in our treatmentof the data, we always used their z trans-forms. We consider the results first with themeasure of sensitivity, z[P(A)]. A MonteCarlo procedure was used to estimate thelowest values of z[P(A)] that would be sig-nificantly greater than chance, z[P(A)] =.0 or P(A) = .50. We simulated 1,000 ratingscale sessions, like our own, under conditionsof no actual difference between signal andnoise. The cumulated probabilities ofz[P(A)]s showed that p < .05 for z[P(A)]equal to or greater than .39 and that p <.01 for z[P(A)] equal to or greater than .66.An analysis of variance revealed that themain effects of condition and duration werestatistically significant, F(3, 6) = 19.4, p <.01; F(5, 10) = 55.0, p<.0l, respectively,as was their interaction, F( 15, 30) = 2.5, p <.025. In general, as the duration of the in-terval increased, the observer's sensitivityrose. As Figure 3 shows, sensitivity increasedfor all observers as the range of possible di-rections of movement constricted. On thebasis of Figure 3, we believe that part of thesignificant interaction resulted from a base-ment effect: With the briefest presentations,observers performed at or near chance in allconditions. In addition, there seems to be aslope difference between the certainty con-dition, on the one hand, and the three rangesof uncertainty, on the other. Performancewith the certainty condition seems to im-prove somewhat more rapidly with increas-


ing duration than does the performance withthe uncertainty conditions.

An analysis of the B values showed nosignificant effects (p > .50). Thus the ob-servers' bias for or against saying that mo-tion had been presented remained constantacross all conditions and durations of theexperiment.

In addition to the analyses just described,we were interested in the sequential depen-dencies that might exist in our data. Moreparticularly, we wondered how performanceon one trial was influenced by the stimulusthat had appeared on the preceding trial. Aswill become clear, such information providesinvaluable clues about the strategy observersuse to cope with direction uncertainty. To

carry this analysis out, we first identified allpairs of trials in which the first trial con-tained moving dots. We then categorizedthese trial pairs on the basis of the differencebetween the directions occurring on the firstand second trials of that pair. Note that ourrating scale procedure requires that movingdots be presented only on half of the trials.This means that only 50% of the second trialsof each pair actually contained motion. Forthose pairs in which the second trial did notcontain motion, the computer randomlychose one direction (from among 0°, 90°,180°, and 270°) as the nominal direction(i.e., the direction that would have oc-curred). This random selection allowed usto calculate the value of z[P(A)] associated

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• - certain• - uncertain 90°a - uncertain 180"A - uncertain 360°

200 300 400 500 600

Duration of Intervalin msec

Figure 3. Values of z[P(A)] as a function of exposure duration. (Separate curves are shown for conditionsof certainty and each of the three ranges of uncertainty (90°, 180°, and 360°.)


with the second trial of all trial pairs ofwhich the first trial contained motion. Thisprocedure distributed those second trialsthat did not contain movement randomlyamong the different successive directions.We must emphasize that the use of the nom-inal direction to characterize second trialsthat contained no actual motion would notbias the obtained values of z[P(A)] unlessthe directions, real and nominal, selected bythe computer for successive trials were cor-related. This was not the case.

Each of the three ranges of uncertaintyhas associated with it its own set of possibledirection differences between Trial n andTrial n + 1. With the narrowest, 90°, un-certainty range, possible differences were 0°(successive trials had the same direction) or22.5°, 45°, 67.5°, or 90°. For the 180° rangecondition, directions could remain the sameor differ by 45°, 90°, 135°, or 180°. Finally,for the 360° range condition, directionscould remain the same or differ by 90° or180°. Data for the various types of pairswere tabulated separately for each of the sixdurations. Because of the similarity amongvalues for the longest durations, on one hand,and for the shortest durations, on the other,data were subsequently collapsed to allowus to obtain a greater number of trials percondition for our analysis. The means of thethree longest (400, 500, and 600 msec) andthe three shortest (100, 200, and 300 msec)durations are shown in Table 2.

Note that within each range of uncer-tainty—90°, 180°, or 360°—performancetends to be best when the same direction waspresented on successive trials, that is, per-formance is best when the difference be-tween directions on Trials n and n + 1 waszero. Next note that within any range ofuncertainty, z[P(A)] decreases as the dif-ference between directions on Trials « andn + 1 grows; for example, with the narrowestrange and the longer durations, z[P(A)]drops from 1.08 to .12 as the difference indirections goes from 0° to 90°.

Discussion

Let us begin with the result that as thedifference between directions presented onTrials n and n + 1 increased, performance

tended to decrease. We have consideredthree possible explanations for this effectand now present each in turn.

First, we considered that some sort ofpriming effect might be operative. This hy-pothesized priming effect links performanceon successive trials by means of an afteref-fect or residue. This residue results frompresentation of a stimulus and is carried overfrom one trial to the next. This residue mightresemble the one postulated by neural quan-tum theory (Stevens, 1972). Stored in thedirectionally selective mechanism stimulatedby the motion on Trial «, the residue wouldaid detection of the same direction of motionon Trial n + 1 by summing with whateveractivity was generated by the stimulus onthat trial. Portrayed in this way, the residuefacilitates detection on Trial n + I only ifthe direction on that trial was either iden-tical or highly similar to the direction pre-sented on Trial n. But this sort of explanationis inappropriate to our results. Our averageintertrial interval, including time for the ob-server to respond, was about 4 sec. This in-terval is sufficiently long that it is unlikelythat the residue of near-threshold stimula-tion could even survive the long delay, letalone produce sequential effects as strong asthose we observed.

A second class of explanation depicts theobserver as using a "win-stay, lose-shift"strategy. If observers detect (or think theydetect) motion on Trial n, this explanationassumes that they continue to monitor thatsame directionally selective mechanism onthe next trial (i.e., they would stay). How-ever, if they fail to detect motion, theychoose among the remaining three possibledirections for the next trial (i.e., they wouldshift). As a result, when directions presentedon successive trials are the same, perfor-mance should be better on the second trialthan had the two directions been different.But, we do not believe that this explanationcan account for all the results of Experiment3. In this scheme, once the observers areunable to detect movement on Trial n, forthe next trial they select from among mech-anisms sensitive to the three remaining di-rections. In conjunction with this win-stay,lose-shift explanation, we considered twodifferent possible rules that may govern the


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way observers switch following a failure todetect. One rule forced observers to chooserandomly from among the three direction-ally selective mechanisms that they had notbeen monitoring on Trial n; the other ruleforced the observers to choose that onemechanism of the three that was tuned toa direction closest to the one monitored onTrial n. Both rules gave the same unsatis-factory prediction: Performance would bethe same for all non-zero differences be-tween directions on Trial n and Trial n + 1.But Table 2 shows a different pattern of re-sults: a systematic decline in performancewith increasing difference in directions onTrial n and Trial n + 1.

The failure of these simple schemes toaccount for our results forced us to considera scheme that assigns to the observer a moredemanding role. In this final scheme, we con-sidered the possibility of a win-stick, lose-shift model in which the shifting occurredduring a trial rather than between trials. Weassume that observers begin each trial mon-itoring the directionally selective mechanismwith which they ended the previous trial. If,after some portion of the trial has elapsed,no motion has been detected, they switch toa mechanism tuned to a similar but differentdirection. Should stimulus duration permit,failure to detect motion within some finitetime causes the observer to shift again. Thisshift, and any subsequent ones, are to mech-anisms tuned to directions different from butsimilar to the one from which the observerbegan.

Thus, the more similar the directions onsuccessive trials, the greater would be theprobability that before the second trial ter-minated, the observer would have located theappropriate mechanism or one sufficientlyclose to afford detection at above chancelevel. Furthermore, with similar directionspresented on successive trials, more timewould remain in the second trial for observ-ers to accrue sensory information from theappropriate mechanism. This additional timetranslates into higher detection rates withlonger durations (see Figure 3).

This scheme would reproduce the resultsshown in Table 2, at least in qualitative fash-ion. It would explain (a) optimal detectionwhen motion on two successive trials was in


the same direction and (b) the steady andsystematic decline in performance with in-creasing divergence between those successivedirections. We should note that this within-trial switching scheme could be elaboratedby treating the observer as an adaptive userof the sequential statistical character of thesensory information accruing during thetrial. The scheme could also accommodatethe possibility that the observer monitorsmore than just two mechanisms over thecourse of a trial. Of course, the number ofmechanisms that could be monitored shouldbe proportional to the trial duration, withsufficiently short trials preventing within-trial switching.

General Discussion

In the introduction we posed the question;What happens when observers are unsure ofthe directions they will be required to detect?The three experiments presented in this ar-ticle show clearly that performance improvesdramatically when an observer has advanceknowledge of the target's direction. Al-though the effect may be minimized by prac-tice (Experiment 2), there seems to be noescape from the fact that performance willdecline when uncertainty is introduced. Letus now give more detailed consideration tothe character of this decline and what it tellsus about motion perception.

As developed originally in audition, thesingle-band and multiple-band models viewthe observer in a passive role. But our resultsindicate that, at least in perceiving motion,the observer is anything but passive. Exper-iment 1, for example, showed that observersdo have some control over the way infor-mation generated by direction selectivemechanisms is to be used. Recall that ob-servers appeared to be monitoring more thanone direction during the 600-msec observa-tion interval. This was demonstrated by anincreased percentage of correct detectionsfor the three low-probability directions in theunbalanced-uncertainty condition. This couldnot have occurred with the single-band ap-proach unless the observer monitored direc-tions other than the most dominant duringthe observation interval. We must also takeaccount of the discrepancy between perfor-

mance in the certainty condition, in whichit was most likely that only one direction-selective mechanism was monitored per trial,and performance with the dominant direc-tion in the unbalanced-uncertainty condi-tion. Detection was much better in the cer-tainty condition than with the dominantdirection. This increases the likelihood thatin the unbalanced-uncertainty condition ei-ther (a) observers monitored one mechanismsteadily but it was not the one appropriatefor best detection of the dominant directionor (b) observers switched, during a trial,from monitoring the dominant to monitoringone or more of the other directionally selec-tive mechanisms. We think the first of thesealternatives is unlikely. In fact, observers didslightly better than what would be expectedif they had merely monitored the dominantdirection steadily. Steady monitoring of thedominant direction would have produced83.5% correct; we obtained 88%. This secondalternative describes the observers as mul-tiplexing or time-sharing their directionallyselective mechanisms, and it has consider-able support from other of our experimentsas well.

Thus the results of Experiment 1 are con-sistent with the notion that observers controltheir viewing strategy, not awaiting inputspassively but actively adapting to the de-mands of the experiment. Neither the single-band nor the multiple-band model, as usu-ally stated, is consistent with these results.In fact, to be consistent with our results, anymodel must permit the observer to adjust toexperimental conditions in an adaptive way.

As described in the introduction, there isextensive evidence for the existence of hu-man visual mechanisms, or filters, tuned todifferent directions of motion. The resultsof Experiment 2 showed that observers wereable to learn to process five different direc-tions in a 120° range so as to reduce thepreviously observed uncertainty effect. Theseresults imply that the observer either is ca-pable of adjusting the width of these direc-tionally selective filters through practice orcan somehow learn to monitor more than onefilter at a time (possibly by switching be-tween them more quickly).

We now know that practice can work inseveral ways to reduce the effect of uncer-


tainty. The results of analyzing pairs of trialsin Experiment 3 suggest that observers canswitch from one directionally selective mech-anism to another during the course of onetrial.

In summary, the present work offers sev-eral important lessons. First, direction un-certainty clearly diminishes detection ofmotion. Second, this diminution of detecta-bility can, within some limits, be overcomeby opportunity for practice. Third, if trialsare sufficiently long to permit it, observersseem to switch between directionally selec-tive mechanisms during a single trial. Fi-nally, models of motion detection, and verylikely models of other visual functions aswell, cannot ignore the possibility that ob-servers will exhibit active, adaptive behaviorwhen the experimental task makes such be-havior appropriate.

Reference Notes1. Tanner, W. P., Jr., Swets, J. A., & Green, D. M.

Some general properties of the hearing mechanism(Tech. Rep. No. 30). Ann Arbor: University of Mich-igan, Electronic Defense Group, 1956.

2. Shiffrin, R. M., Pisoni, D. B., & Castaneda-Mendez,K. 7; attention shared between the ears? (IndianaMathematical Psychology Program Report SeriesNo. 73-3). Bloomington: Indiana University, 1973.

ReferencesBall, K., & Sekuler, R. Masking of motion by broad-

band and filtered directional noise. Perception & Psy-chophysics, 1979, 26, 206-214.

Ball, K., & Sekuler, R. Models of stimulus uncertaintyin motion perception. Psychological Review, 1980,87,435-469.

DeValois, K. Spatial frequency adaptation can enhancecontrast sensitivity. Vision Research, 1977,17, 1057-1065.

Green, D. M. Detection of multiple component signalsin noise. Journal of the Acoustical Society of Amer-ica, 1958,50,904-911.

Green, D. M. Detection of auditory sinusoids of uncer-tain frequency. Journal of the Acoustical Society ofAmerica, 1961, 33, 897-903.

Gundy, R. F. Detection of an unspecified signal: A studyin auditory discrimination learning. Unpublisheddoctoral dissertation, Indiana University, 1961.

Larkin, W., & Greenberg, G. Z. Selective attention inuncertain frequency detection. Perception & Psycho-physics, 1970, 8, 179-184.

McNicol, D. A primer of signal detection theory. Lon-don: George Allen and Unwin Ltd., 1972.

Moray, N. Attention: Selective processes in vision andhearing. London: Hutchinson Educational LTD, 1969.

Neisser, U. Cognition and Reality. San Francisco: Free-man, 1976.

Neisser, U., Novick, R., & Lazar, R. Searching for tentargets simultaneously. Perceptual and Motor Skills,1963, 77,955-961.

Patterson, R. D. Auditory filter shapes derived withnoise stimuli. Journal of the Acoustical Society ofAmerica, 1976, 59, 640-654.

Penner, M. J. The effect of payoffs and cue tones ondetection of sinusoids of uncertain frequency. Percep-tion & Psychophysics, 1972, //, 198-201.

Sekuler, R., & Ball, K. Mental set alters visibility ofmoving targets. Science, 1977, 198, 60-62.

Sekuler, R., Pantle, A., & Levinson, E. Physiologicalbasis of motion perception. In R. Held, H. Leibowitz,& H.-L. Teuber (Eds.), Handbook of sensory phys-iology, Berlin: Springer-Verlag, 1978.

Shiffrin, R. M., Craig, J. C., & Cohen, E. On the degreeof attention and capacity limitations in tactile pro-cessing. Perception & Psychophysics, 1973, 13, 328-336.

Shiffrin, R. M., & Gardner, G. T. Visual processingcapacity and attentional control. Journal of Experi-mental Psychology, 1972, 93, 72-83.

Shiffrin, R. M. Gardner, G. T., & Allmeyer, D. H. Onthe degree of attention and capacity limitations invisual processing. Perception & Psychophysics, 1973,14, 231-236.

Sorkin, R. D., Pastore, R. E., & Gilliom, J. D. Signalprobability and listening band. Perception & Psycho-physics, 1968, 4, 10-12.

Stevens, S. S. A neural quantum in sensory discrimi-nation. Science, 1972, 177, 749-762.

Swets, J. A. (Ed.). Signal detection and recognition byhuman observers. New York: Wiley, 1964.

Taylor, J. H. Practice effects in a simple visual detectiontask. Nature, 1964, 201, 691-629.

Received January 18, 1980 •

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