VOL. 75, No. 5 SEPTEMBER 1968

PSYCHOLOGICAL REVIEWSTIMULUS INTENSITY AND RESPONSE

EVOCATION1

G. ROBERT GRICE

University of Illinois

A decision model based on that of McGill (1963) relating stimulusintensity to response latency is applied to conditioning and reaction-time data. Points of similarity and identity between this model, Hull-Spence behavior theory, and the theory of signal detection are indi-cated. It is suggested that the concept of a variable, experimentallymanipulable detection criterion or reaction threshold is a principle ofconsiderable potential power in behavior theory. The difference be-tween within-S and between-S stimulus-intensity effects is deducedfrom the model. The effects of motivational, reinforcement, adapta-tion, and practice variables and their relations to stimulus intensityare analyzed.

While stimulus intensity has beenthe classical problem of psychophysics,this variable has played a rather minorrole in theory and research in the areasof learning and response evocation.The one exception to this is in the areaof reaction time (RT), where at leasta moderate literature exists dealingwith the effects of variation in signalintensity. It is interesting that, evenhere, writers have tended to think ofsuch data in the context of sensorypsychophysics rather than in that ofresponse evocation. However, the re-sults of a number of recent experi-ments from the writer's laboratory(Beck, 1963; Grice & Hunter, 1964;Grice, Hunter, Kohfeld, & Masters,1967; Grice, Masters, & Kohfeld, 1966;Kohfeld, 1968; Murray, 1968; Murray

1 Preparation of this paper was supportedby United States Public Health Service Re-search Grant No. MH 08033 from the Na-tional Institute of Mental Health.

& Kohfeld, 1965; Walker, 1960) sug-gest that it is time for behavior theoryto take a new look at intensity vari-ables. The chief reason for this is thediscovery that within-subject manipu-lation of CS intensity or RT signal in-tensity produces substantially enhancedintensity effects when compared withsituations in which the subject (5")receives only a single intensity andcomparisons are between groups. Themere fact that large CS intensity effectshave been obtained in human condi-tioning when earlier studies showedonly small or inconsistent effects is initself reason for renewed interest inthe problem. However, the particu-lar manipulations resulting in theseeffects suggest that there may be aclass of variables of which traditionalbehavior theory has not taken adequatecognizance. This problem certainlylies at the border of learning theory andsensory psychology, and it is possible

3591968 by the American Psychological Association, Inc.

360 G. ROBERT GRICE

that new theoretical efforts and re-search in the area may produce smallsteps toward greater unity in psycho-logical theory.

WlTHIN-5 AND BETWEEN-5"

INTENSITY EFFECTS

Grice and Hunter (1964) originallyproposed that the large within-Ss in-tensity effects could be interpreted interms of adaptation level theory. Ifthe significant intensity variable weredistance of the stimulus from adapta-tion level rather than absolute stim-ulus intensity, they showed that theobtained results would be expected.In further support of this view, itwas subsequently shown in RT ex-periments (Kohfeld, 1968; Murray& Kohfeld, 1965) that preadapting5s to stimuli of particular intensi-ties significantly and persistently af-fected 5s' RTs to a wide range of stim-ulus intensities. However, in spiteof these successes and the obvious needfor concepts of this general type, adap-tation level theory does not contain aprinciple of response evocation and israther difficult to integrate into behav-ior theory when one gets to the detailsof how it might work. Therefore, itis proposed to turn to other conceptual-izations which may ultimately dealwith these mechanisms in more specific

FIG. 1. Graphic representation of latencymodel with variable input.

terms and provide the basis for theanalysis of a wider class of experi-mental variables.

The point of departure of the presentdiscussion is the decision-theory ap-proach of McGill (1961, 1963) in hisstochastic model of latency mecha-nisms. Of immediate concern is hisdiscussion of the relation between sim-ple RT and stimulus intensity. Ac-cording to this view a sensory inputmay be regarded as a series of im-pulses. When the cumulative countreaches some predetermined number,that is, the detection or decision cri-terion, 5" will respond. The time re-quired for the count to reach the cri-terion is the reaction latency. Sincethe impulse rate is probabilistic ratherthan constant, latencies are variablefrom trial to trial. Furthermore, theimpulse rate increases with stimulusintensity, producing the inverse rela-tion between latency and stimulus in-tensity.

One manner of conceptualizing thismodel is indicated in Figure 1, in whichthe number of hypothesized impulsesis plotted as a function of time sincestimulus onset. The two linear func-tions indicate the expected number ofimpulses for two stimulus inputs—for example, tones differing in in-tensity. The constant delay before thestart of the count is the so-called "ir-reducible minimum" RT, or at leastthose components of RT not underthe influence of variations in inten-sity. This delay is regarded to beof the order of 100 milliseconds. Thecriterion is indicated by a horizontalline of fixed ordinate. The expectedmean RTs are at the intersectionsof the two lines with the criterion.Trial-to-trial variability in the num-ber of impulses is indicated by theprobability functions associated witheach input. Since at each point intime, the means of these distributions

STIMULUS INTENSITY AND RESPONSE EVOCATION 361

are on the input line, the distributionserected above the criterion line indi-cate the cumulative probability thatthe count will be above the criterionby time ti. In other words, these arethe predicted cumulative distributionsof reaction latency. The illustrativenormal distributions with equal vari-ance drawn here are not intended torepresent McGill's hypotheses concern-ing their form. However, it is interest-ing to note that the model as presentedhere does predict the precise linear re-lation between mean RT and the stan-dard deviation discovered by Chocholle(1945). The SD of the RT distribu-tion is equal to the SD of the impulsedistribution multiplied by the recipro-cal of the slope of the input function.Mean RT is also linear with respectto the reciprocal of the slope.

In spite of the appeal of this simplemodel, the writer has difficulty takingit seriously in exactly this form as ageneral model for latency mechanisms.The distributional phenomena so ele-gantly deduced by McGill (1963; Mc-Gill & Gibbon, 1965) are dependentupon the criterion's remaining constantfor large blocks of trials under constantexperimental conditions. This seemsunlikely. Various considerations, in-cluding data to be presented here,strongly suggest that the criterion maybe readily and strongly influenced by avariety of experimental and individualdifference variables. It is difficult toconceive of a psychological process ofthis kind which would not be subjectto extensive moment-to-moment fluc-tuations. On the other hand, it isrelatively easy to think of a sensoryinput as a rather stable process, de-termined by the stimulus energy, tem-porally dense and uniform on therather gross time scale in which reac-tion latency displays its variability.In other words, it is being suggestedthat we may alternatively regard all

FIG. 2. Graphic representation of latencymodel with variable criterion.

of the variability as residing in fluctu-ation of the criterion rather than inthe input rate. Parenthetically, it issuggested that this argument also hasmerit when applied to the essentiallyidentical concepts of the criterion of thetheory of signal detection (TSD), thecategory boundary of the law of cate-gorical judgment, and the reactionthreshold of Hull-Spence learningtheory.2

Now it must be admitted that this re-vised model often makes no differencein prediction or methods of data analy-sis. However, it does lead to a some-what different way of thinking, and itmay lead to different research and, pos-sibly, to differential prediction. Fig-ure 2 indicates this scheme, showingthe probability functions associatedwith the location of the criterion.8

Here, it is more natural to speak ofthe probability that the criterion isbelow the input at ti than that thecount is above the criterion. The cri-terion line is shown at its mean loca-

2 Hull at one time utilized the assumptionof an oscillating threshold rather than theconcept of A (Hull, Hovland, Ross, Hall,Perkins, & Fitch, 1940).

3 The ordinate in Figure 2 and in follow-ing similar figures is labeled "Impulses";however, it is not essential to the presentanalysis that this dimension be conceived interms of discrete units.

362 G. ROBERT GRICE

tion. The linear relation between meanRT and the SD also holds for thisarrangement.

A word may be added here concern-ing the relation of this model to Hull'stheory. The input functions presentedhere are essentially similar to the riseof reaction potential with the inputsegment of the stimulus trace. Thecriterion is the same as his reactionthreshold. Thus, latency as a functionof stimulus intensity is deducible fromHull's theory, although he did not actu-ally make this deduction. Had Hullassumed that the threshold was re-sponsive to experimental manipulation,as is suggested here, other deductionswould have followed. The present de-velopment may be viewed either in thecontext of decision theory or withinthe general framework of Hullian be-havior theory. These two approachesare much more alike than is commonlyrecognized.

This model may be applied not onlyto RT responses but to condition-ing as well, at least in situationswhere the CR is of short latency. Here,the usual measure is probability of re-sponse rather than latency. To under-stand how the model applies, oneshould imagine in Figure 1 or 2 anordinate erected at fa corresponding tothe end of the interstimulus interval(ISI) or of the scoring interval. Re-sponses may occur only before thistime. Thus, the predicted probabilityof response is that portion of the cumu-lative distribution above criterion by fa.The model predicts that, for a constantcriterion or a criterion fluctuatingaround a constant mean, probability ofresponse will be an increasing functionof stimulus intensity.

The difference between within-6" andbetween-.? intensity effects can readilybe explained in terms of this theory.First, it is assumed that the value ofthe criterion will be determined to some

FIG. 3. Representation of Grice and Hun-ter (1964) conditioning data in terms ofvariable input model.

degree by the stimuli to which S is ex-posed. At least it is intuitively reason-able, and the Grice and Hunter (1964)data suggest, that 5"s receiving only aweak stimulus will adopt a lower cri-terion than those receiving only astrong stimulus. This will account forthe fact that a 50-decibel difference inCS intensity provided only a slight,and statistically nonsignificant, differ-ence in number of CRs, a finding typ-ical of previous between-.? experiments.The 6"s in the within-.? condition re-ceived both stimuli in an irregular, un-predictable order. Presumably, thevalue of their criterion was also deter-mined by the stimuli received, but itwas necessary for them to respond toboth stimuli with a single criterion,and this turns out to be the crucialpoint. If the value of the criterion isinfluenced by the level of stimulationin the manner suggested above, "̂s re-sponding to stimuli of two or more in-tensities on the basis of a single cri-terion will usually show a greater in-tensity effect than that between groups,each responding to a single stimulus.Figure 3 describes the data of the Griceand Hunter eyelid-conditioning experi-ment in terms of the McGill variableinput model. The distributions asso-ciated with the Loud and Soft inputfunction indicate the trial-to-trial prob-

STIMULUS INTENSITY AND RESPONSE EVOCATION 363

ability density of the number of im-pulses by ti, the end of the I SI. Thehorizontal lines indicate the hypothet-ical location of the criteria for GroupL, receiving only the loud tone; GroupS, receiving only the soft tone; andGroup L-S, receiving both tones.Areas of each distribution above eachof the criteria indicate the appropriateresponse probability. This figure is soconstructed that the areas correspondexactly to the four proportions of CRsin the Grice and Hunter data. It maybe seen that the area of the Loud dis-tribution above the Loud criterion isapproximately the same as the area ofthe Soft distribution above the Softcriterion. However, the area of theLoud distribution above the L-S cri-terion is substantially greater than thearea of the Soft distribution above thissame criterion. Note that this wouldhave been true for any location of theL-S criterion so long as both prob-abilities were not near either zero orone.

At this point it is desirable to pointout additional relationships of thismodel. The vertically erected pair ofdistributions with intersecting criteria isessentially the decision axis of the TSD.The distance between the intersectionsof the input functions with t\ if expressedin SD units is d', or it may be regardedas a Thurstone scale value. Further-more, in Hullian terms, this differencebetween the two input functions at t\,also scaled in SD units, is the differencein reaction potential between the Loudand Soft tones (Ei, — Eg). The supra-threshold reaction potential, as used bySpence (1956), is the normal deviatecorresponding to the area of a distri-bution above one of the criterion, orthreshold, lines. Spence's theory willnot account for the Grice and Hunterfinding, however, since he assumes thethreshold to have a constant location.The remarkable similarity between the

FIG. 4. Representation of Grice and Hun-ter (1964) conditioning data in terms ofvariable criterion model.

Hull-Spence approach and TSD maybe seen especially in Spence's theory ofchoice behavior (Spence, 1956, pp.203-205 and Appendix A, pp. 237-242).

The purpose of Figure 4 is to showhow the variable criterion or oscillatingthreshold model explains or describesthe Grice and Hunter data. The inputfunctions and the criteria have the samelocations as in Figure 1. Now, how-ever, the criterion lines are regarded aslocated at their mean value. The threedistribution functions indicate the hy-pothetical trial-to-trial probability den-sity of the location of the criteria.Horizontal lines are drawn through thefunctions at the ordinates of the inputsat ti. Areas below these lines indicatethe proportions of the times the cri-terion is below the input by ti, thusresulting in response evocation. Thefigure is again so constructed that thefour shaded areas exactly equal the cor-responding percent CRs in the Griceand Hunter data. This model is equiv-alent to the fixed criterion, variableinput theory in describing the data.

Grice and Hunter (1964) also com-pared between- and within-51 intensityeffects for signal intensity in simpleRT. Here, again, a larger intensitydifference was obtained for the withincondition, but 5"s receiving two stimuli

364 G. ROBERT GRICE

200 300TIME (rniec)

FIG. S. Representation of Grice and Hunter(1964) RT data.

were slower in response to both stimulithan were the single stimulus groups tothe corresponding stimuli. What mayhave been going on here, according tothe model, is indicated in Figure 5.Here the irreducible minimum RT istaken as 100 milliseconds. The singlecriterion for the L-S group was as-signed an arbitrary value of 100 units.The RTs for the L-S group to the twostimuli were plotted on this line and theinput functions passed through them.The mean RTs for the L and S groupswere then plotted on these functions.The resulting vertical locations of thesepoints indicate that apparent level oftheir respective criteria. The model inthis way suggests that the S group didhave a somewhat lower criterion thanthe L group, but that the criterion forthe group responding to either was con-siderably elevated. This may have beenthe result of stimulus uncertainty. Thedifference in relative location of theL-S criteria in the two types of experi-ments is possibly due to differences inthe nature of 5"s set. In the RT ex-periment, he has a set to detect thestimuli and respond, while in the con-ditioning experiment his set is thetraditional "neutral" one.

RELATION OF CS INTENSITY TOOTHER VARIABLES

Of course the ultimate utility of thistheory will depend upon the range of

experimental phenomena to which itcan be extended, providing usefulmethods of analysis, accurate predic-tion, and sensible interpretation. Theeyelid-conditioning experiment of Beck(1963), which gave the first informa-tion concerning the large within-5" CSintensity effect, lends itself especiallywell to analysis in these terms. Shemanipulated two presumed motiva-tional variables, UCS intensity and anx-iety (MA scale score), as well as CSintensity. There were two levels ofeach variable, manipulated orthogon-ally, resulting in eight cells. Anxietyand UCS intensity were between-.? ef-fects based on separate groups, whileCS intensity was within 5" with eachstimulus occurring equally often in anirregular order. Beck's data, pre-sented in Figure 6, are based on thetotal number of anticipatory blinks. Inthis figure, the average probability of ablink to the Loud CS is plotted as afunction of the probability of a blink tothe Soft tone for each combination ofthe other two variables. The plot ison normal-normal coordinates. Thisfigure is recognizable as a receiver-operating characteristic (ROC) func-

10 20 30 40 50 60 70 80 90P R/30FT CS

FIG. 6. Between-conditions ROC functionfor Beck's (1963) CS intensity data.

STIMULUS INTENSITY AND RESPONSE EVOCATION 365

tion as used in TSD, although theapplication is somewhat different thanin the detection situation. Here theapplication should be regarded as ascaling operation in which scale separa-tions are to be inferred between tworeadily detectable stimuli rather thanbetween two noise distributions with asignal present and absent. It is appro-priate to think of overall responsivenessin the dimension parallel to the positivediagonal, and the scaled difference inresponse to the loud and soft tones inthe dimension parallel to the negativediagonal. The straight line drawnthrough the points, which obviously fitsquite well, has a slope of unity. Thisis of especial interest because it impliesthat the distance between the loud andsoft tones is approximately constant forall levels of responsiveness. This isquite contrary to Hull's theory that thestimulus intensity dynamism (V) hasa multiplicative relation with motiva-tional variables. According to thatview, the loud-soft distance should in-crease with responsiveness, and theslope should be markedly greater thanone.

Considerably greater understandingof the relation of these data to thepresent model may be gained by aninspection of Figure 7. Actually, thisfigure is fully redundant with the pre-ceding figure, which implies that it canbe drawn. This is the TSD decisionaxis in a different setting. Followingthe variable input model, the two nor-mal distributions represent the prob-ability densities of the loud and softstimulus inputs at the end of the scor-ing interval. Equal variance for thetwo distributions is assumed. Theabscissa is scaled in SD units, and isappropriately labeled "Reaction Poten-tial," which is scaled in the same man-ner. The vertical lines represent thelocations of the four groups' criteria, orreaction thresholds. These were ob-

LOW A-STRONG UCS-y

HIGH A-STRONG UCS 1

,-HIGH A - W E A K UCS

— LOW A-WEAK UCS

REACTION POTENTIAL

FIG. 7. Variable input model forBeck's (1963) data.

tained from Figure 6, by using the"Soft" coordinate of the projection ofeach point on the ROC function. Zerois set at the mean of the "Soft" distri-bution. The distance between themeans of the two functions is EL — Esor d' and was read graphically from the"Soft" intercept of the ROC function.Its value is .57. The areas of the twodistributions which lie above (to theright) of the group criteria indicate thetwo percentages of CRs for each group.Remarkably, these areas simultaneouslydescribe the eight cells of Beck's experi-ment with a maximum error of one per-centage point, In behavior theoryterms, these data are consistent withthe view that the effect of increasingdrive is to lower the reaction thresholdrather than to increase reaction poten-tial in a multiplicative fashion. Thisis not a new view (e.g., see Campbell& Sheffield, 1953), but it has neverbeen thoroughly explored. It is sug-gested that this conception should re-ceive renewed attention.

If one wished to describe the data ofFigure 6 with the single, fixed-thresh-old model of Hull and Spence, it wouldbe necessary to use eight distributions,each with the appropriate area abovethe threshold. If this were done, the

366 G. ROBERT GRICE

-2 'I /,.. > \ «4 LS H W c-w

REACTION POTENTIAL

FIG. 8. Variable criterion model forBeck's (1963) data.

distance between the Loud and Softdistributions for each of the four moti-vational groups would be essentiallyconstant. This would be contrary tothe multiplicative assumption, andwould imply an additive relationshipinstead.

In Figure 8, the oscillating criterionmodel for Beck's data is presented.While not ordinarily discussed in theTSD context, this model is equallyimplied by the ROC function of Figure6. Here, the assumed fixed location ofthe two stimuli are indicated by verticallines separated by the distance d'. Thefour distribution functions located atthe criterion means now represent theprobability densities of criterion values.Areas indicating the criterion below theinputs (to the left) are the predictedresponse percentages. If one is think-ing in these terms, the positive andnegative signs of the z values of Figure6 should be reversed. These eightareas describe the actual values in thesame manner as those of Figure 7.

It is also of interest to examine andcompare the individual 5" data plottedin the same manner as the groupmeans of Figure 6. Such a plotis presented in Figure 9, omitting afew -S"s who made no response to one

or both stimuli. The relation betweentwo such arrays has been previouslydiscussed by the writer (Grice, 1966).The ROC function of Figure 6 is a"between-conditions" regression line.It was pointed out in the previouspaper that such relations are frequentlyboth very high and quite illuminatingbecause the covariation is introducedby known, systematically manipulatedfactors. The linear correlation in thisinstance is .994, a value not unrepre-sentative of other examples previouslygiven. The scatterplot of Figure 9 isa "Total" correlation including bothmanipulated and individual differencesources of correlation. The correlationcoefficient is .82. The best fittingmutual regression line drawn throughthe points has a slope of .985, suggest-ing that the same model applies to theindividual 51 data. In psychometricterms, each point, in either figure, maybe regarded as a two-element vector,each element consisting of five primarycomponents plus possible interactions.(Interactions are disregarded sinceBeck's analysis suggested they were notsignificant.) The components areAnxiety (A), UCS, CS, Individuals(I), and within-5" variability or error.

FIG. 9. Individual 51 scatterplot forBeck's (1963) data.

STIMULUS INTENSITY AND RESPONSE EVOCATION 367

For any given point, the elements differonly in the CS and within-5" error com-ponents. Variation along the Loud-Soft axis consists of the CS effect (d')and within-5 variability. Figures 7and 8 represent alternative ways ofconceptualizing this variability. In thebetween-conditions relation of Figure6, the variability contributes less to thelocation of the points by a factor of1/w, because it is based on means.However, the same underlying with-in-,!? distributions are involved. Theindividual difference component (I),producing variability along the re-sponsivity axis, also contributes lessvariability in Figure 6 for the samereason. The "within-conditions" cor-relation (Grice, 1966) for the fourgroups plotted together in Figure 9 is,as to be expected, somewhat lower, .78.It still indicates a substantial lineartrend within conditions, due to individ-ual differences in responsivity. Theslopes of the within-conditions regres-sion lines are somewhat variable,ranging from .86 to 1.24. Their meanis 1.01.

A different basis for the understand-ing of the present analysis may begained from a consideration of theeffect of a 45° rotation of the axes ofeither Figure 6 or Figure 9. The co-ordinates of each point on the new axesnow become :

1 = 707 OL-.SS)and

It is now obvious that the rotated axesmay appropriately be labeled the In-tensity Effect and the ResponsivenessEffect. The difference between theLoud and Soft coordinates is the in-tensity effect, and their sum is theirjoint responsiveness effect. In Figure10, the data for Figure 6 have beenreplotted on the rotated axes. For con-

S + 0 5

+10 +0.5 0 -05 -1.0

RESPONSIVENESS IZL+ZS>

FIG. 10. Between-conditions ROC func-tion with 45° rotation of axes. (CS in-tensity effect as a function of Responsive-ness.)

venience, after rotation, the data havebeen rescaled by multiplying by 1/.707.The correlation between these dimen-sions is of zero order, and the re-gression line, parallel to the Respon-siveness axis, is of zero slope. Itsintercept is d'. The spacing of thepoints along the Responsiveness axis isproportional to the spacing of the loca-tions of the criteria on the decisionaxis. The relation is simply (,SL +z&)/2. This estimates the locations ofthe criteria from the point midway be-tween the two stimulus distributions.For the variable threshold model, sim-ilar reasoning applies to the location ofthe criterion means. The graphicalmethod of estimation used in theoriginal construction of Figures 7 and8 is a way of obtaining these samevalues.

In general, the picture is one of con-siderable consistency, suggesting thatthe scaled difference in reaction poten-tial does not vary in any systematicway with the level of responsivenessproduced either by the manipulation ofmotivational variables or by individual

368 G. ROBERT GRICE

FIG. 11. Between-conditions ROC func-tion for Beck's (1963) data including stageof conditioning (habit strength) as an addi-tional variable.

differences. Furthermore, the data areconsistent with the model which as-sumes that the effect of drive manipula-tion is to influence 5"s reaction thresh-old or detection criterion. While thisis certainly not the only possible inter-pretation, it is indeed a hypothesis ofconsiderable promise for further theo-retical analysis and for the guidance offuture research.

A further assumption of Hull wasthat the stimulus intensity dynamism(V) combined multiplicatively withthe associative factor—habit strength.This implies that the CS intensity ef-fect should increase with training. Inone sense this must be true. At zerohabit strength, no response would beevoked by any stimulus and differencewould be zero. However, Hull's posi-tion implies that intensity differenceshould increase continuously untilasymptote, that is, EL — Es = H (VL— Vg). There are a number of difficultproblems involved in the analysis ofthis problem. These include possiblechanges in the underlying distributionswith practice and the determination of

a meaningful zero for reaction poten-tial. However, without facing up tothese problems, a preliminary, and, atleast illustrative, analysis of this prob-lem has been attempted here. For thispurpose, two blocks of trials at differentstages of training were selected fromBeck's data. The blocks were Trials11-40 and 51-80. Percentages of an-ticipatory responses were then obtainedfor each of the eight conditions for eachtrial block separately. These data arepresented in Figure 11 with PL plottedagainst PS as in Figure 6. The filledcircles are for the late trial block andthe hollow circles are for the early trialblock. Within each block the UCS andA points arrange themselves in thesame order along the responsivenessaxis as in Figure 6. The two squaresare the mean values for the two trialblocks, summed over the other vari-ables. The between-conditions linearcorrelation is .989. The regressionline is arbitrarily drawn with a slopeof one in order to assess the reason-ability of this hypothesis. The bestfitting regression line has a slope of1.08 and would hardly be perceptablydifferent. There is little evidence herethat the scaled intensity distance in-creases systematically with practice.

EFFECT OF STIMULUS PREADAPTATION

Based on the original Grice andHunter (1964) adaptation level inter-pretation of within-S" intensity effects,Murray and Kohfeld (1965) studiedthe effect of brief prior exposure of 5sto auditory stimuli before a series ofRT trials to tones varying in intensity.They found that a group of 5s with 121.5-second presentations of a 40-decibeltone, at the lower end of the series,produced consistently faster RTs to allstimuli than groups without such pre-adaptation, or with similar presenta-tions of a 100-decibel tone, at the upperend of the series. RTs of the 100-

STIMULUS INTENSITY AND RESPONSE EVOCATION 369

decibel group were slower than thoseof the control group. More recently,Kohfeld (1967) has confirmed and ex-tended these findings. In one of hisexperiments, 5"s received a series of 75RT trials to tones of 35-, 50-, 65-, 85-,and 100-decibel sound-pressure levelPrior to the session, separate groupslistened, without responding, to 12presentations of a tone of either 35,65, or 100 decibels. A control groupreceived no preliminary tones. Func-tions were presented showing typicalwithin-51 intensity effects, RT being in-versely related to signal intensity.Functions for the different adaptationgroups were vertically displaced, the100-decibel adaptation group respond-ing considerably slower than the35-decibel group. The other groupswere intermediate. Twenty-four hourslater, 5s received another similar ses-sion, but without additional adaptationpresentations. Somewhat surprisingly,to the writer at least, the effects of the12-tone presentations, 24 hours before,were still consistently and significantlypresent.

Kohfeld discussed these findings interms of adaptation level theory. How-ever, it is a bit difficult to think of anadaptation effect based on such a briefexperience persisting, not only througha series of trials to other intensities, butover a 24-hour delay and through an-other series. On the other hand, the ef-fects are readily understood in terms ofthe present model, if one assumes that5"s detection criterion is determined atleast in part by the intensity of theadapting stimuli. It was suggested inthe discussion of the Grice and Hunterdata that hearing weak stimuli ap-parently leads 51 to adopt lower criteria,while more intense stimuli lead tohigher criteria. This explanation liesin the general domain of set phenomenarather than in that of sensory adapta-tion. The persistence is, perhaps,

100 150 200TIME, mttc

FIG. 12. Latency model applied to Kohfelddata, assuming adaptation conditions influ-ence criterion.

somewhat less surprising in this con-text.

In terms of this model, the differentadaptation groups are assumed to differonly in their criteria, the same stimulusinput functions applying to all groups.Since the stimuli are unpredictable,each group must be regarded as oper-ating with a single criterion, or, in thecase of the oscillating criterion model,a single criterion distribution. In orderto assess the degree to which Kohfeld'sdata fit these assumptions, they havebeen analyzed in a manner suggestedby Figures 1, 2, and 5. This analysisis presented in Figure 12. Because ofsomewhat lower variability, the dataanalyzed are those for the second day.Here, "Impulses" are plotted as a func-tion of time. Since the impulse dimen-sion is hypothetical, only the relativepositions of the criteria can be indi-cated. These relative positions wereinferred from the general means of thefour groups. The criterion for the 100-decibel adaptation group was arbitrar-ily assigned the index of 100. Theother criteria were then located in pro-portion to the ratios of the generalmean RTs minus 100 milliseconds, theassumed irreducible minimum. (Thisanalysis is not very sensitive to theexact value assumed for this.) This

370 G. ROBERT GRICE

FIG. 13. Latency model applied to Kohfelddata for Days 1 and 2, assuming practiceinfluences criterion.

means that an input function startingat 100 milliseconds would pass throughthe four-group general means, its slopebeing determined by the choice of theindex of 100 for the 100-decibel group.The five mean RTs for each of thestimuli for each group were then plot-ted along the four criterion lines. Asexplained earlier, these points are esti-mates of the intersections of the inputfunctions with the criteria. Estimatesof the slopes of the five input functionswere made by least-squares fits ofstraight lines to the four points foreach of the stimulus intensities. Fitswere made with the restriction thateach function originate at 100 milli-seconds. These are the five linesdrawn in Figure 12. The adequacy ofthe model in accounting for the datamay be evaluated by examining the dis-crepancies of the data points from theintersections of the criteria and the in-put functions. While not perfect, it isobvious that the fit is very good. Themodel does, in fact, account for 99.5%of the variance among the 20 datapoints. Since the general means of thegroups were used in locating the cri-teria, the proper basis for a statisticaltest of goodness of fit is "Intensitieswithin Groups." In addition, therewere 5 degrees of freedom used in

estimating the slopes of the input func-tions. Eleven degrees of freedom re-main after fitting the model. A test of"departure from model" is not sig-nificant; F = .53, dj = 11/144. Thisanalysis clearly indicates that the dataare consistent with the interpretationthat the effect of the adaptation condi-tions was to affect 5V detection cri-terion or reaction threshold, and thatthe stimulus inputs were unmodified bythese operations. Methods for esti-mating the relative values of theseparameters are also indicated.

As is typical of RT experiments withunpracticed 5"s, Kohfeld's data show apractice effect from Day 1 to Day 2.The present kind of thinking leads nat-urally to the hypothesis that the effectof practice is a progressive lowering ofthe criterion. An analysis similar tothe preceding one has been made ofthis hypothesis and the result is pre-sented in Figure 13. The data havebeen analyzed for each group sepa-rately. In each case the Day 1 criteriawere assigned a value of 100 and theDay 2 criteria were located on the basisof the relation between the daily gen-eral means. Input functions were fittedin the same manner as above. In eachcase, 3 degrees of freedom remained

FIG. 14. Loudness as a function of slope offitted input functions.

STIMULUS INTENSITY AND RESPONSE EVOCATION 371

after the fit. While tests of goodnessof fit have not been made, it is obviousthe fit here is even better than in theprevious example, the model accountingfor nearly all of the variance among thepoints. It is reasonable to assume thatthe somewhat closer fit is due to thefact that the different criterion levelshere are within-.? differences ratherthan involving separate groups as inthe previous example. In any case, thedata are consistent with the suggestedinterpretation that the level of 5's de-tection or response criterion decreaseswith practice.

In one of his early presentations ofthis type of model, McGill (1961) sug-gested that the impulse rate (slope)should provide the basis for subjectiveloudness. In accord with this sug-gestion, the Stevens (1956) sone trans-formation of the five stimuli has beenplotted as a function of the fitted slopesof Figure 12. This is presented inFigure 14. The curve fitted to thepoints is exponential. The writer has,so far, been unable to arrive at anyrational derivation of this relationship,but the figure is presented for the bene-fit of those who may wish to speculatefurther along these lines.

DISCUSSION

While the approach presented herestemmed most directly from McGill'sdecision model of latency and stimulus-intensity effects, it has large areas ofoverlap with TSD, Hull-Spence learn-ing theory, and the scaling approachesof Thurstone and Torgerson. The ma-jor departure from McGill and Hulland Spence is the intent to view thecriterion or threshold as a more dy-namic concept in the theory, subject toexperimental manipulation * and to

4 In the case of McGill, this is a matter ofemphasis. He has recognized the possibilityof manipulating the criterion, but has notexplored the matter.

variability from individual differencesand from random fluctuation. It issuggested that a number of variableshaving effects of considerable psycho-logical interest may profitably beviewed as influencing the responsethreshold. Thus, this approach hasimplications for learning theory con-siderably wider than the stimulus-in-tensity problem with which this paperis primarily concerned. An extremeview that all of the motivational, train-ing, and set variables which influencethe probability of response work inthis way is even conceivable. The pre-dictive, heuristic, and analytic value ofsuch theory can only be evaluated on along-term basis.

The proposed departure from Mc-Gill's model, attributing random vari-ability to fluctuations of the criterion,is, for the present at least, merely apreference of the writer based onlogical rather than mathematicalgrounds. It seems unrealistic to as-sume that a psychological state, so ap-parently labile in the sense of readymanipulation, could remain literallyconstant for the duration of an experi-mental session. If this reasoning issound, it has the unfortunate implica-tion that McGill's derivation of RTdistributional phenomena is based onan inappropriate assumption. It maybe desirable to seek another approachto the derivation of response distribu-tions based on criterion variability. Itis, of course, recognized that the twoconceptions are not necessarily mu-tually exclusive. If both sourcesexisted, they would be additive. Thepresent preference is expressed for thesake of simplicity, and reflects the be-lief that criterion variability is likely tobe large with respect to sensory vari-ability. In the case of TSD, it is wellto keep in mind, at this point, that thenoise distributions refer to actual noise,physically present in the stimulus. The

372 G. ROBERT GRICE

extension of the model to noise-freestimuli and the treatment of psycho-metric variability as "noise" is a sub-sequent development. Obviously, avariable input model does apply, if thestimulus is, indeed, variable. In thiscontext, the writer prefers to considerpsychometric variability in the cri-terion.

Grice and Hunter (1964) suggestedan interpretation of their intensity ef-fects in terms of adaptation level theory.Decision theory as an alternative de-serves a word of further comment. Itis suggested that there are at least twoqualitatively different classes of adapta-tion phenomena. Sensory adaptationshould be treated as a modification ofsensory processes. Other kinds of adap-tation effects may more properly be re-garded as modifications of judgmentalor response processes. These effectsmay be expected to be more persistent.The present interpretation may be re-garded as a theory of this latter class ofadaptation phenomena. The notion thatadaptation procedures have their effectson the criterion is certainly applicable tojudgmental and psychophysical situ-ations as well as to response evocation.

In this paper, the terms detectioncriterion, decision criterion, and re-action threshold have been used inter-changeably. This has been done toemphasize the mathematical identity ofthese terms and the range of phenom-ena to which the concept may be ap-plied. It is possible that in the futurethere may arise a need for more thanone such concept. So far, this has notbeen necessary. There is one paperwhich superficially seems to imply thatdetection theory will not account forthe intensity effect in conditioning, butupon analysis this implication turns outto be false. In an eyelid-conditioningstudy using weak auditory CSs, Fish-bein and Engel (1966) also had Ssgive verbal reports of stimulus detec-

tion. There was no evidence of con-ditioned eyelid responses on those trialswhen 5" reported no stimulus. How-ever, on those trials when 5 did reporta stimulus, the percentage of CRs stillincreased with intensity. This is read-ily understood in terms of the latencymodel. Their CS was on for 775 milli-seconds, the airpuff beginning after thefirst 525 milliseconds. For detection,defined by the verbal report, the entiretime (775 milliseconds) was availablefor the input to reach the criterion.For a CR to occur, however, detectionwould have had to occur sufficientlybefore 525 milliseconds for the responseto be evoked before that time. Thestronger the CS, the earlier in thisinterval detection would begin, and thegreater would be the number of antic-ipatory CRs. When detection occurredafter the critical interval before 525milliseconds, no CR would occur.While it is also possible that differentcriteria apply to different response sys-tems, this explanation need not be in-voked here.

One area of potentially interestingapplication of this model with its la-tency implications is to ISI phenomenain conditioning. For example, if onecould assume a constant criterion, thereis an implication that CS intensity ef-fects should decrease with increasingISIs. For example, Grice et al. (1967)have found suggestive evidence that thelatency distributions for loud and softstimuli are similar, but that the louddistribution reaches its maximumearlier in the ISI. Walker (I960), ina study complicated by being a be-tween-51 experiment, found that CSintensity significantly affected the fre-quency of blinks early in a .5-secondinterval, but not late in the interval.Perhaps, the notion of an optimum ISIfor association formation is incorrect,and these functions depend upon la-tency phenomena and interactions with

STIMULUS INTENSITY AND RESPONSE EVOCATION 373

other variables, such as stimulus in-tensity. The writer doubts that un-raveling this area will be simple, be-cause it is suspected that the I SI mayinfluence the criterion. Within-5"manipulation of the ISI may be anaid to the solution of this problem.

This analysis of the difference be-tween within-5" and between-.? intensityeffects has a rather interesting method-ological implication not previously men-tioned. In one sense, the early inde-pendent-groups investigations of CSintensity actually represented a meth-odologically inappropriate way to in-vestigate the problem. This is simplybecause one could not assume a com-mon criterion. It is suggested thatthere are many instances, both in psy-chophysical and behavioral research,where, if one wants to assume a com-mon criterion, stimuli should be ad-ministered in an unpredictable orderrather than in blocks or to independentgroups.

In the analyses presented here, thismodel has achieved considerable initialsuccess. The possibilities for furtherapplication and the exploration of addi-tional implications are extensive.

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FISHBEIN, H. D., & ENGEL, S. Human eye-lid conditioning at detection threshold.Psychonomic Science, 1966, 4, 291-292.

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(Received August 7, 1967)