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Mathematical Social Sciences 38 (1999) 343–359

Prior expectations in cross-modality matching

*Donald LamingDepartment of Experimental Psychology, University of Cambridge, Downing Street,

Cambridge, CB2 3EB UK

Abstract

A mathematical model is proposed to accommodate both the ‘regression’ effect in cross-modality matching and the central tendency of judgment. The basic idea is that the subject has twosources of data, (a) the stimulus to be judged and (b) previous stimuli in the experiment whichafford some idea what magnitude of stimulus to expect. The minimum variance estimate ofstimulus magnitude is a weighted average of these two. That weighted average biases judgmentstowards the prior expected value; at the same time, the variability of the judgments is reducedbelow the value which would attach to judgments based on datum (a) alone.

This basic idea is packaged in a process model which purports to describe the minutiae involvedin making a cross-modality match. The process model adds this one substantive assertion to themathematical idea: that prior expectations cannot be disregarded (notwithstanding that they areirrelevant to the judgment) because the prior experiences from which they are derived are anintegral component of the subject making the judgment. 1999 Elsevier Science B.V. All rightsreserved.

Keywords: Cross-modality matching; Judgment; Process model; Prior expectations

1. Introduction

The model which follows addresses both the ‘regression’ effect (Stevens andGreenbaum, 1966) and the central tendency of judgment (Hollingworth, 1910).

1.1. The ‘regression’ effect

Stevens and his colleagues have shown many times over that subjects can assign

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344 D. Laming / Mathematical Social Sciences 38 (1999) 343 –359

numbers to stimulus magnitudes in such a way that the geometric average increasesapproximately according to the power law relation

bN 5 aX , (1)

where N is the number uttered by the subject and X is the physical stimulus magnitude.Subjects can also adjust the magnitude of a stimulus to match a given number. Reynoldsand Stevens’ (1960) subjects first judged the loudness of seven different levels ofbroadband white noise. Then they adjusted the level of the noise to match six differentnumbers, numbers chosen to lie within the range used in the first (estimation) phase ofthe experiment. Both estimations and productions conformed reasonably to a power law(see Stevens and Greenbaum, 1966, Fig. 5); but the production data required a higherexponent (b ) in Eq. (1) than did the estimates. A similar discrepancy is found betweenthe two values of the exponent when subjects adjust one stimulus to match another.Stevens and Greenbaum (1966) asked 10 observers to match the duration of a light tothe duration of noise; then the same 10 observers matched the duration of the noise tothat of the light. Their geometric mean matches are shown in Fig. 1.

The dotted line of gradient 1 in Fig. 1 shows how the data would lie if matching wasveridical. It is clear that both sets of matches are biased. The power law exponent,

Fig. 1. Geometric mean matches of the duration of a light to that of a noise and of the duration of the noise tothat of the light. Data from Stevens and Greenbaum (1966, Table 1).

D. Laming / Mathematical Social Sciences 38 (1999) 343 –359 345

expressing the regression of log duration of the light on the log duration of the noise is0.87 when the light is matched to the noise, but 1.30 when the noise is matched to thelight. These and other similar findings have been succinctly summarised by Stevens(1975).

The effect is systematic. The difference in gradient is often small in data aggregatedfrom a small cohort of observers, but always lies in the same direction. ‘The observertends to shorten the range of whichever variable he controls’ (Stevens, 1971, p. 426).Magnitude productions always have a steeper gradient than estimates. The difference ingradient can, however, be large for individual subjects. Stevens and Greenbaum (1966,Fig. 10) exhibit two examples from Stevens and Guirao (1962) for the magnitudeestimation and production of levels of noise.

This systematic difference between the estimated gradients for magnitude estimationand production was named the ‘regression’ effect by Stevens and Greenbaum (1966).But that is misleading. It invokes an analogy with linear regression when there are errorsin both variables (Kendall and Stuart, 1967, Ch. 29). In that case (errors in bothvariables; call them x and y) maximum likelihood analysis must correctly partition thevariability between the two. Assigning all the variability to one variable, as in linearregression, gives a biased result; the regression of x on y has a different gradient to theregression of y on x. The gradients of the two regression lines always differ according tothe pattern of Fig. 1, whence Stevens and Greenbaum’s (1966) label. But in the presentdata one of the variables, either the stimulus magnitude to be estimated or the number tobe matched, is a mathematical variable known (to the experimenter) without error. Thismeans that regression of the response variable on the stimulus value assigns thevariability correctly and is unbiased as a method of analysis. There is no statisticalartefact (Cross, 1973) and we are looking at a real phenomenon of judgment.

Stevens’ (1971, p. 426) assertion that observers tend to compress the range ofwhichever variable they control does no more than describe what is observed to happen.This is equally true of Poulton (1989) who categorises the ‘regression’ effect as astimulus contraction bias. Baird (1997) has proposed a sensory interpretation. Envisagethat each stimulus engages a population of neurons and that the magnitude perceiveddepends on the aggregate discharge. During the time between presentation of thestimulus and production of the response, ‘the memory representation (trace) of thestimulus drifts in that the traces of the slow-firing neurons drop out of the aggregate’(Baird, 1997, pp. 112–113). If, now, stimulus and response continua are interchanged,as in cross-modality matching, this adaptation of the neural response acts in the contrarydirection. But no reason is given why the drift of the neural response should compressthe range of the stimuli as perceived by the subject, and not simply reduce all perceivedmagnitudes by the same proportion.

This article offers an explanation of the ‘regression’ effect in terms of priorexpectations.

1.2. The central tendency of judgment

An experiment by Hollingworth (1909) on the reproduction of guided movementspoints the way to an understanding of the ‘regression’ effect. There was a narrow slit cut


in a strip of cardboard which was then pasted onto a similar strip to form a guide. Thisguide could be readily traced with a pencil held by a blindfolded subject. Two secondsafter tracing the guide, the subject attempted to produce a movement of the same lengthunguided, on a sheet of plain paper, and 2 s later a second reproduction. Different targetlengths were presented in random order, each followed by two reproductions, until atotal of 50 reproductions of each target had been recorded. Fig. 2 shows the meanreproductions of each target length.

In different experimental sessions different sets of target lengths were presented forreproduction: Series A ranged from 10 to 70 mm, Series B from 30 to 150 mm, andSeries C from 70 to 250 mm. Within each series, the shorter movements wereoverproduced while the longer movements were underproduced, and log mean reproduc-tion is approximately a linear function of log target length. Mean reproductions arebiased towards a fixed point internal to each stimulus series; but different series arebiased towards different fixed points, showing that the bias depends on the particularlengths the subjects have recently traced. But if one target length is presented repeatedly,by itself, in a separate session (filled circles in Fig. 2), the bias is negligible.Hollingworth (1910) dubbed this phenomenon the ‘central tendency of judgment’.

In respect of its formal design, this experiment has recently been replicated by Marks

Fig. 2. Mean reproductions of target lengths of guided movement from Hollingworth (1909). Series A, B, andC are from different sessions in which different ranges of lengths were presented for reproduction. The filledpoints display the results of three further sessions in which one length only was presented repeatedly. (Figurefrom The Measurement of Sensation by Laming (1997, p. 22).)


(1993, Exp. 1), using magnitude estimation of 500 Hz tones instead of blindfoldreproduction of lines drawn under guidance. Like Hollingworth (1909), Marks (1993)used sets of seven stimuli drawn from the ranges 25–55, 40–70, and 55–85 dB SPL;but, unlike Hollingworth, the stimulus sequences drawn from these ranges were allpresented within the one session, in blocks, without any demarcation or other indicationto the subject. Nevertheless the configuration of the geometric mean estimates in Fig. 3is very similar to that of the open symbols in Fig. 2.

The faint dotted line is an aggregate relation for all three sets of stimuli takentogether. It is a power law between judged magnitude and stimulus intensity fitted to thegeometric mean estimates from each whole set. Mean estimates for individual stimuliwithin each set are biased towards a fixed point internal to the set; with different setsbiased towards different fixed points, just as in Fig. 2.

Baird (1997, p. 192) presents a simulation of Marks’ (1993) data. The essential idea(I deliberately do not go into detail) is that the judgment on trial n assimilates to theprevious judgment on trial n21, the strength of assimilation increasing with thesimilarity between the successive stimuli. This means that there is greater assimilationbetween stimuli within each range than there is between ranges. This is equivalent to anassimilation of the perceived stimulus magnitudes which Poulton (1989) wouldcategorise as a stimulus contraction bias. The idea I suggest below is related; it is,

Fig. 3. Geometric mean magnitude estimates of the loudness of 500 Hz tones from Marks (1993, Exp. 1). Thelow, medium, and high sets of stimuli were presented in blocks within one continuous series. Data from Marks(1993).


approximately, the net effect of such an assimilation, taking expectations over asequence of stimuli drawn at random from each range.

1.3. Other experimental paradigms . . .

An article on sequential effects in psychophysical judgment inevitably invokesquestions of the form ‘What about this experiment?’ In the interests of simplicity Iexclude any consideration of category judgment. In such an experiment the fixed rangeof the available responses introduces an additional complication that I can well dowithout. For example, Johnson (1955, p. 354) writes:

. . . this phenomenon of central tendency is one manifestation of the regressioneffect that always occurs when the series of data are not perfectly correlated. (italicsoriginal)

1. Johnson (1955, p. 354) is commenting on the category ratings of colour differencesfrom Philip (1947) in which errors of assignment of the extreme stimuli mustnecessarily put them in a category closer to the centre of the response range. There isno such constraint on the cross-modal matching of one stimulus by another, or on itsreproduction, or on magnitude estimation.

2. Johnson’s implicit explanation is another version of Stevens and Greenbaum’s (1966)‘regression’ idea. It does not apply to the data shown in Figs. 1 and 2 because therewas no constraint in those experiments from a small fixed response range.

3. The data set out in Figs. 1–3 are (geometric) means of the original observations.There is no reason why that kind of calculation should be appropriate for categoryjudgments which, to my mind, require a quite different kind of analysis (cf. Braidaand Durlach, 1972).

1.4. . . . and other sequential effects

An article on sequential effects in psychophysical judgment also invokes questions ofthe form ‘What about this effect?’ Again, for reasons of simplicity, I confine myattention to the phenomena displayed in Figs. 1–3. There is, for example, a remarkableautocorrelation of log numerical estimates (e.g. Baird et al., 1980) which is related toother effects in both magnitude estimation and category judgment (see Laming, 1984,1997). That also is excluded from consideration. Envisage that there are several intrinsicconstraints operating trial-to-trial in psychophysical judgment tasks. I intuit — I may bewrong, but I intuit — that the data in Figs. 1–3 (and other similar data) exhibit one ofthose elementary trial-to-trial processes relatively uncontaminated by the others. Let’s goafter that one.

I am concerned in what follows with an approximately linear relation of log response(matching stimulus magnitude or reproduction or numerical estimate) to log stimulusmagnitude that systematically shows the response range to be compressed in a mannerspecific to the set of stimuli presented for judgment. This particular ‘regression towardsthe mean’ is not a statistical artefact. It requires a psychological explanation.


2. Two ideas

Hollingworth’s (1909) experiment shows that the length of a reproduced movement isa compromise between the actual length of the guided movement on that particular trialand a length which the subject has come to expect on the basis of previous trials,recently experienced. So the psychological idea is that the observer uses his experienceof previous stimuli in the experiment to provide some prior idea of the likely length orduration of the next stimulus. To that psychological intuition I add the mathematicalidea that the influence of previous stimuli on perception of the present one can bemodelled as the optimal combination of data from two independent sources.

Envisage two experiments providing independent data to test the same hypothesis. Inthe present context one experiment is the presentation of a stimulus S of magnitude X .i i

That experiment provides a datum j , wherei

2 21 / 2 2 21]f(j uS ) 5 (2ps ) exph 2 (j 2 log X ) /s j. (2)i i i i2

The second experiment subsumes the presentation of all previous stimuli and is assumed,for present purposes, to be equivalent to the presentation of a notional prior stimulus Sp

of magnitude X , giving a datum j , wherep p

2 21 / 2 2 21]f(j uS ) 5 (2ps ) exph 2 (j 2 log X ) /s j. (3)p p p p p p2

Notwithstanding that j results from the observation of some other stimulus, it isp

interpreted (for the purposes of the model) as though it provided information about S .iStatistical theory now tells us that the minimum variance estimate of (log) stimulus

1 22 22 22 22magnitude is (s j 1 s j ) /(s 1 s ).p p p22The inverse variance s is Fisher’s (1922) measure of the amount of information per

sample from f(j uS ) about the value of log X . Denote this by I and likewise I for thei i i p

amount of information per sample from f(j uS ). The minimum variance estimate canp p

then be written (Ij 1 I j ) /(I 1 I ), and it can be seen that the two sample values j andp p p

j are weighted exactly in proportion to the amount of information which each carries.p

This minimum variance estimate has

mean 5 (I log X 1 I log X ) /(I 1 I ), (4)i p p p

21variance 5 (I 1 I ) , (5)p

and thereby generates a gradient of the cross-modal matching relation equal to I /(I 1 I )p

which is always less than 1.

1Assume, for the purposes of choosing an estimator of log stimulus magnitude, that the expectation of j is logp

X. Consider weighted averages of j and j such that the weights sum to 1 (i.e. of the form aj 1 (1 2 a)j );p p

all such estimators have expectation log X and are unbiased. The variance of such an estimator is theexpectation of

2 2 2 2 2 2[a(j 2 log X)] 1 [(1 2 a)(j 2 log X)] 5 a s 1 (1 2 a) s .p p

Differentiation with respect to a and solution of the resulting equation gives2 2 2 22 22 22a 5 s /(s 1 s ) 5 s /(s 1 s ).p p p


The complement of this quantity, I /(I 1 I ), is the relative weight of the priorp p

expectation. Table 1 shows the values of prior expectation and their associated weightsestimated from the experiments in Figs. 1–3.

The gradient of the regression line ‘light adjusted to noise’ in Fig. 1 is 0.87. This isthe weight attaching to the stimulus to be matched and the weight attaching to priorexpectation in Table 1 is its complement. When the level of noise is adjusted to matchthe light, the gradient is 1.30. This has reciprocal 0.772, which is the weight attaching tothe stimulus to be matched, and its complement is 0.229. In this way the optimalcombination of data from two independent sources generates the observed direction ofStevens’ ‘regression’ effect.

A similar calculation gives the weights attaching to prior expectation in Fig. 2because, again, unbiased responses would show a gradient of 1. But the calculation forthe data in Fig. 3 is more complex. The faint dotted line in that figure is an aggregaterelation (assumed unbiased) for all three sets of stimuli taken together. It has equation

log N 5 2 1.424 1 0.0344 log X,

where the stimulus intensity (log X) is expressed in dB. If, for stimulus values selectedfrom the low set, log X is substituted by ha log X 1 (1 2 a) log X j, the gradient withp

respect to stimulus intensity is reduced to 0.0344a which (for the low set) is estimated tobe 0.0293. The weight attaching to the prior expectation [log X ] is (12a) which is thenp

equal to 0.148 (5120.0293/0.0344).When the statistical significance of prior expectation is tested against the variation

about each fitted regression line, it is highly significant; this is apparent even frominspection of the figures. If, more rigorously, significance (in the experiments byHollingworth and Marks) is tested against the variation in weight between experimentalconditions, it is typically significant at the 5% level, sometimes better.

This model has practical application. On the night of 15th October 1987 southernEngland was buffeted by its most severe storm since 1703. The British meteorologicaloffice signally failed to forecast the strength of that storm and one weatherman was on

Table 1Estimated values and weights of prior expectations in the experiments by Stevens and Greenbaum (1966),Hollingworth (1909), and Marks (1993)

Experiment Experimental condition X I /(I 1 I )p p p

Matching of duration Light adjusted to match noise 1.282 (s) 0.129Stevens and Greenbaum (1966) Noise adjusted to match light 0.511 (s) 0.229

Reproduction of guided Series A: 10–70 mm 41.48 (mm) 0.238movement Series B: 30–150 mm 73.38 (mm) 0.228Hollingworth (1909) Series C: 70–250 mm 128.43 (mm) 0.349

Magnitude estimation of Low set: 25–55 dB SPL 34.63 dB 0.148loudness Medium set: 40–70 dB SPL 59.89 dB 0.308Marks (1993) High set: 55–85 dB SPL 67.89 dB 0.366


television earlier that very day saying categorically that there would be no hurricane. Butthere was! Meteorological observations are fragmentary; they have to be extrapolatedforwards sometimes up to 24 h and then interpreted to produce the forecast. In thatprocess the forecast is modified by purely human expectations based on past experienceof weather in southern England in mid-October. Hurricanes do not happen in southernEngland and no meteorologist is going to forecast one.

To this point the interpretation set out above, in terms of the optimal combination ofdata from independent statistical sources, has delivered a weighted mean response of theform of Eq. (4). I comment that, subject only to a reinterpretation of the coefficients, thesame equation could have been derived from other ideas — simple response assimila-tion, for example. That is to say, the ideas put forward here constitute one possibleinterpretation only of the data in Figs. 1–3. If, however, the present statisticalinterpretation be accepted, it generates a specific problem of its own and (via Eq. (5)) ananswer to it.

2.1. Variance of judgments

If prior expectation is actually irrelevant to present judgment, why does it produce thebias that it does? The answer to that question comes in two parts. The first part, which Igive here, links bias in judgment to an increase in repeatability — specifically to adecrease in the variance of a sample of judgments, the shift in the mean beingundetectable by the person making the judgments. The second part of the answer, muchlater, asserts that prior expectation is simply the prior state of the subject so far as thatprior state interacts with the making of the judgment and that any idea of disregardingprior expectation would be a contradiction in terms.

An experiment by Sinha (1952) points the way to the first part of my answer. Sinhaasked his subjects to fixate a rotating spiral. When the spiral was stopped suddenly, thesubjects experienced a strong sensation of contrary movement (expansion because thedirection of rotation made the spiral appear to be continually contracting). Subjects wereasked to measure the duration of the after-effect they experienced with a stopwatch.There were two sessions for each subject 5 or 6 days apart with 10 measurements ineach session. In the first session subjects started the stopwatch as soon as they observedthe contrary movement begin and stopped the watch when the movement ceased, butwere not themselves allowed to look at the watch or to know how long their after-effecthad lasted. Before the second session, however, subjects were told ‘ . . . Incidentally itmay be of interest to you that I have tested about 50 subjects and in the majority ofcases, the average reaction time was found to be . . . [different averages given todifferent subjects] . . . with 30 seconds exposure’ (Sinha, 1952, p. 9). Subjects werethen allowed to read out their measured duration themselves ‘as it was the last day withthem’. Table 2 shows the summary data from two subjects.

Comparing day 1 with day 2, each subject’s mean shifts towards the ‘group’ averageannounced by the experimenter. In addition, the variability of successive judgments isreduced. These two phenomena go hand-in-hand and suggest that prior expectationshave the effect that they do because they reduce the uncertainty of judgment.


Table 2Duration of spiral after-effect (Sinha, 1952)

2Mean Standard Variance (s )duration (s) deviation (s)

Subject 1 1st day 22.5 2.68 7.18(‘Standard’duration 15 s) 2nd day 17.6 1.77 3.13

Subject 2 1st day 14.6 3.41 11.63(‘Standard’

aduration 20 s) 2nd day 20.7 0.94 0.88a Excluding the first measurement from the series of 10.

2Fig. 4 shows the variable errors of the reproductions of movements in Fig. 2 plottedagainst the means, now with a linear abscissa. For the three sessions in which one targetlength only was presented (filled circles) the variable error increases almost inproportion to the mean. That control data may be adequately described by the equation

]]]]]]]]]]2Variable error 5 h1.87 1 0.005(mean reproduction) j (6)œ2where the constant element of variability 1.87 mm is arguably the limiting accuracy to

which a line can be drawn blindfold with the left hand. But the variable errors from theexperimental (mixed) sessions are systematically greater and this requires someexplanation.

Holland and Lockhead (1968) and Ward and Lockhead (1970, 1971) have shown thatin absolute identification tasks the judgment of each stimulus assimilates to itspredecessor in the same way that the reproductions of movements in Fig. 2 assimilate tothe geometric mean of the stimulus set; and Cross (1973) and Ward (1973) havedemonstrated a similar assimilation in magnitude estimation. Such assimilation appearsto be a general phenomenon, and it appears likely that the global bias in Figs. 1–3 issimply the trial-by-trial assimilation of matches, reproductions, and estimations topreceding stimuli averaged over a sequence presented in random order. Certainly theeffect of stimulus frequency on two-alternative choice-reaction times, on both reactiontimes and errors, can be related to sequential effects operating trial-by-trial in this way(Laming, 1968). On this basis the prior stimulus in Hollingworth’s (1909) experimentwill chiefly represent the target length on the preceding trial, and the expected length ofreproduction on trial n will be (rewriting Eq. (6) Fig. 4)

Ehyj 5 (I log X 1 I log X ) /(I 1 I ). (7)n p n21 p

The preceding target length (X ) will, of course, be different for different presentationsn21

2 ¯If y is the length of reproduction of some target length X in Fig. 2, and if y is the mean reproduction of length¯ ¯X, then (y 2 X) is the constant error and ( y 2 y ) is known as the variable error. The variable error therefore

measures the precision of repeated reproductions about their mean, while the constant error measures the bias.¯The data in Fig. 4 are averages of the absolute variable error uy 2 y u (rather than, as one would calculate

today, root mean squares). If the distribution of variable error is normal, the absolute error is equal to 0.80times the standard deviation.


Fig. 4. The variable errors of reproduction from Hollingworth (1909, cf. Fig. 2). As before, Series A, B, and Care from different sessions in which different ranges of amplitudes were presented for reproduction. The filledpoints display the results of three further sessions in which one amplitude only was presented repeatedly; thecontinuous curve is Eq. (6). (Figure from The Measurement of Sensation by Laming (1997, p. 37).)

of a given length X (i.e. X in Eq. (7)) and will contribute an additional element to thei n

variable error.It should be noted that prior expectation applies equally to the single stimulus values

presented in the control series in Fig. 4 so that the coefficient 0.005 in Eq. (6) is an2 21estimate [of 0.8 times] the reduced variance (I 1 I ) ; but when only one target lengthp

is presented, X 5 X on every trial and no bias shows. When, however, successiven21 n

target lengths vary at random, the variance of the aggregated reproductions contains theadditional contribution I Varhlog X j /(I 1 I ) which raises the open data points inp n21 p

Fig. 4 above the control values. This provides a more detailed perspective on themechanism of prior expectations. The idea of a prior stimulus introduced above can nowbe seen to be a convenient mathematical fiction which subsumes the pattern ofsequential interactions operating on a random sequence of stimuli at a trial-to-trial level.

3. Process model

I next set out a process model which purports to describe the minutiae of cross-modalmatching. It provides packaging for the mathematical idea introduced above in the sensethat it shows how Eqs. (2) and (3) might fall out of the operation of selecting a


cross-modal match. I emphasise that this particular model is far from unique; many otherequivalent models could be devised to serve the same purpose. The model does not itselfadd anything of quantitative substance, except to suggest (later) that prior expectationscannot be ignored. As a matter of convenience I envisage a matching task in which thesubject can adjust the matching stimulus ad lib (unlike the example experiment in Fig.1), but in which both stimulus and match are values on the same continuum. Differentmatching tasks will, in fact, require slightly different process models, and the modelwhich follows can easily be modified to suit different experimental tasks.

The hypothesised sequence of operations is set out in Fig. 5.

3.1. Prior state

Each stimulus to be matched is presented to a subject who has been instructed what todo and has accumulated prior experience in the experiment. That prior experience has

Fig. 5. The sequence of operations envisaged in adjusting one stimulus to match another.


already been shown to be material to the choice of match. The initial stage of the processmodel characterises the prior state of the subject by a random variable j . This variablep

takes different values on different trials of the experiment in recognition of the fact thatthe stimuli are presented in random order. In principle j is a multi-dimensional randomp

variable of arbitrary dimensionality. But, in practice, it only has to describe thosematches the subject would make if asked to guess in the absence of any physicalstimulus and can therefore be taken to be a unidimensional variable such as might becontributed by the presentation of a notional prior stimulus S . The prior stimulus Sp p

characterises the subject’s distribution of prior expectations on different trials. I set thedensity function of j to be (as in Eq. (3) above)p

2 21 / 2 2 21]f(j uS ) 5 (2ps ) exph 2 (j 2 log X ) /s j. (3)p p p p p p2

3.2. The stimulus to be matched

The presentation of a stimulus S generates another internal state variable j , againi i

taken to be unidimensional, according to the density function of Eq. (2)

2 21 / 2 2 21]f(j uS ) 5 (2ps ) exph 2 (j 2 log X ) /s j. (2)i i i i2

The variable j encapsulates the subject’s entire knowledge about the magnitude of thei

present stimulus and is the internal value needing to be matched.

3.3. Selection of candidate match

A subject is assumed to proceed by looking at a series of possible matches until one isfound which will do. These candidate matches are not selected entirely at random, butaccording to a prior distribution p(m) which depends, in turn, on the prior state j . Ap

candidate match S (of mean m) is selected in proportion to the probability that, as priorm

stimulus, it would generate the internal state j . Using Bayes’ Theorem,p

`

2 21 / 2 2 21]f(S uj ) 5 f(j uS ) /E (2ps ) exph 2 (j 2 y) /s jdym p p m p p p2 (8)

2`

2 21 / 2 2 21]5 (2ps ) exph 2 (m 2 j ) /s j.p p p2

3.4. Sampling of candidate match

The candidate match, now realised as a physical stimulus, is sampled, generating aninternal state variable j with mean m and density f(j uS ) according to Eq. (2). Them m m

candidate match will be accepted if j is sufficiently close to j . The joint probability ofm i

selecting S and generating the internal state variable j ism m

2 2 2 21 / 2f(j uS )f(S uj ) 5 (4p s s )Gvm m m p p

2 2 2 21]exph 2 [(m 2 j ) /s 1 (j 2 m) /s ]j. (9)p p Gv m2


3.5. Criterion of acceptance

If j is acceptably close to j , the matching process terminates; otherwise a freshm i

candidate match is selected for trial. This termination criterion can be approximated by atest of equality, so I set j 5 j at the termination of the trial.m i

3.6. Posterior distribution of match

This is calculated from Bayes’ Theorem as

f(S uj ,j ) 5 f(j uS )f(S uj )Ym p i i m m p

`2 2( y 2 j ) (j 2 y)1 p i2 2 21 / 2 ] ]]] ]]]E (4ps s ) exp 2 1 dy (10)H F GJp 2 22 s sp2`

When the integral in Eq. (10) is evaluated and divided into the numerator, the match isfound to have

mean 5 (Ij 1 I j ) /(I 1 I ) (11)i p p p

and21variance 5 (I 1 I ) , (12)p

22 22writing I for s and I for s as previously.p p

3.7. Unconditional distribution of matches

Eqs. (11) and (12) give the mean and variance of the match in terms of the internalstate variables j and j which are unobservable. An unconditional distribution ofp i

2matches is required. Since j and j are normal, respectively (log X , s ) and (log X ,p i p p i2

s ), the unconditional distribution will also be normal with

mean 5 (I log X 1 I log X ) /(I 1 I ). (4)i p p p

This, of course, is Eq. (4).Since j and j are random variables, there is an extra element of variability in thep i

21unconditional distribution equal to (I 1 I ) . The unconditional distribution of matchesp

therefore has21variance 5 2(I 1 I ) (13)p

which is exactly twice the value of Eq. (5).These features of the process model are worthy of note.

1. The internal state variables do not appear in the ultimate formulae. So the sameformulae would result from any other assumptions about the internal state of thesubject, provided only that that assumed internal state was sufficient to support the


normal model of Eq. (2). It follows that while the applicability of the normaldistribution is an empirical assumption and admits experimental test, the use of aunidimensional internal state variable does not.

222. If there were no prior expectation, I ( 5 s ) would be zero (prior density of infinitep p

variance), and the variance of the observed distribution of matches 2 /I. Because I isp

positive, 2 /I is an upper limit to the variance specified by Eq. (13). That is, priorexpectations reduce the variability of the judgment. This reduction in variabilitycomes at the expense of the matching bias (the intrusion of the term log X in Eq.p

(4)).2 23. Eq. (13) has the multiplier 2 because the variances s and s enter into the matchingp

process twice over, first in the perception of the stimulus and the prior stimuli andagain in the selection of the candidate match. If the target experiment had been amagnitude estimation /production pair, then the second involvement of the variances

2 2s and s would not have been needed (numbers are mathematical quantities knownp

exactly). I take this opportunity to re-emphasise that an appropriate process modeldepends on the particular experiment, but that in all cases it can be made to deliverformulae of the form of Eqs. (4) and (5).

4. The mean match is (I log X 1 I log X ) /(I 1 I ) which assigns a relative weighti p p p2I /(I 1 I ) to the prior stimulus. This weight is greatest when I is small (s large).p p

That is to say, the effect of prior expectations shows most strongly with the mostdifficult judgments.

3.8. Can prior expectations be disregarded?

I now return to the question: if prior expectations are actually irrelevant to presentjudgments, why do they have the effect that they do? and propose the second part of theanswer. The process model uses j to describe the state of the subject at the point inp

time when the stimulus is presented. Although, mathematically, prior expectation ismodelled as just another source of data which the subject might use or disregard as hepleases, at this point the process model introduces a substantive assumption. Within theterms of that model, prior expectations are (a component part of ) the subject. For thisreason they cannot be disregarded.

The assertion that prior expectations cannot be disregarded is an empirical matter andrequires collateral support. I cite one example from the problems of flying ‘blind’.

When a pilot (I am talking of light aircraft, flown manually) can see the horizon,orientation by sight is satisfactory. In cloud or at night, on the other hand, the pilot mustmust rely on instruments to indicate the attitude of the aircraft. But natural behaviour isto ignore the instruments and rely on postural and vestibular sensations; this is whatpeople do when standing or walking with their eyes closed. Problems arise because theinterpretation of postural sensations is fallible. For example, the vestibular organ issensitive to gravity and to linear acceleration (especially critical when an aircraft isbanking), but not to velocity per se. So illusions occur when bodily orientation can nolonger be monitored visually. A pilot will ‘feel’ the aircraft to be at a different attitude tothat which it really is. One study of judgments of aircraft attitude by experienced, butblindfold, USAF pilots showed 60% errors (Jones et al., 1947).


Learning to fly blind means learning to disregard postural and vestibular sensationsand to rely exclusively on instruments. But, when an inadequately trained pilot flies intocloud, it has repeatedly been found that he is unable to disregard bodily sensations suchas the feel of the seat beneath his bottom, even though he knows (intellectually) thatthose sensations are not to be trusted. This is so, notwithstanding that the pilot’sattention to his postural and vestibular sensations usually leads to loss of control and toloss of life. An inadequately trained pilot in a light aircraft is unable to ignoreinformation that is not only known to be irrelevant, but also fatal.

4. Summary

The global effects of prior stimuli on present judgment can be modelled mathemati-cally as the optimal combination of statistical data from independent sources. Thatmodel accommodates the bias apparent in both Stevens’ ‘regression’ effect (Fig. 1) andHollingworth’s central tendency of judgment (Fig. 2) and also the reduction in thevariability of judgment seen in Table 2 (Sinha, 1952). But the psychological idea thatprior expectation is simply another source of data which the subject can take notice of ordisregard as he pleases needs to be resisted. Prior expectation is the subject making thejudgment.

Acknowledgements

I thank Professor L.E. Marks for kindly providing the numerical data in Fig. 3.

References

Baird, J.C., Green, D.M., Luce, R.D., 1980. Variability and sequential effects in cross-modality matching ofarea and loudness. Journal of Experimental Psychology: Human Perception and Performance 6, 277–289.

Baird, J.C., 1997. Sensation and Judgment: Complementarity Theory of Psychophysics, Lawrence ErlbaumAssociates, Mahwah, NJ.

Braida, L.D., Durlach, N.I., 1972. Intensity perception, II, Resolution in one-interval paradigms. Journal of theAcoustical Society of America 51, 483–502.

Cross, D.V., 1973. Sequential dependencies and regression in psychophysical judgments. Perception &Psychophysics 14, 547–552.

Fisher, R.A., 1922. On the mathematical foundations of theoretical statistics. Philosophical Transactions of theRoyal Society of London, Series A 222, 309–368.

Holland, M.K., Lockhead, G.R., 1968. Sequential effects in absolute judgments of loudness. Perception &Psychophysics 3, 409–414.

Hollingworth, H.L., 1910. The central tendency of judgment. Journal of Philosophy, Psychology, and ScientificMethod 7, 461–469.

Hollingworth, H.L., 1909. The inaccuracy of movement. Archives of Psychology (13).Johnson, D.M., 1955. The Psychology of Thought and Judgment, Harper, New York.Jones, R.E., Milton, J.L., Fitts, P.M., 1947. An investigation of errors made by pilots in judging the attitude of

an aircraft without the aid of vision. AAF memorandum report TSEAA-694-13, Engineering Division, AirMateriel Command, Wright Field, Dayton, OH.


Kendall, M.G., Stuart, A., 1967. Inference and relationship, The Advanced Theory of Statistics, Vol. 2, 2nd.ed., Griffin, London.

Laming, D., 1984. The relativity of ‘absolute’ judgements. The British Journal of Mathematical and StatisticalPsychology 37, 152–183.

Laming, D., 1997. The Measurement of Sensation, Oxford University Press, Oxford.Laming, D., 1968. Information Theory of Choice-reaction Times, Academic Press, London.Marks, L.E., 1993. Contextual processing of multidimensional and unidimensional auditory stimuli. Journal of

Experimental Psychology: Human Perception and Performance 19, 227–249.Philip, B.R., 1947. Generalization and central tendency in the discrimination of a series of stimuli. Canadian

Journal of Psychology 1, 196–204.Poulton, E.C., 1989. Bias in Quantifying Judgments, Lawrence Erlbaum Associates, Hove.Reynolds, G.S., Stevens, S.S., 1960. Binaural summation of loudness. Journal of the Acoustical Society of

America 32, 1337–1344.Sinha, D., 1952. An experimental study of a social factor in perception: the influence of an arbitrary

group-standard. Patna University Journal, 7–16, Jan–Apr.Stevens, S.S., Greenbaum, H.B., 1966. Regression effect in psychophysical judgment. Perception &

Psychophysics 1, 439–446.Stevens, S.S., 1971. Issues in psychophysical measurement. Psychological Review 78, 426–450.Stevens, S.S., Guirao, M., 1962. Loudness, reciprocality, and partition scales. Journal of the Acoustical Society

of America 34, 1466–1471.Stevens, S.S., 1975. In: Stevens, G. (Ed.), Psychophysics: Introduction To Its Perceptual, Neural, and Social

Prospects, Wiley, New York.Ward, L.M., Lockhead, G.R., 1970. Sequential effects and memory in category judgments. Journal of

Experimental Psychology 84, 27–34.Ward, L.M., Lockhead, G.R., 1971. Response system processes in absolute judgment. Perception &

Psychophysics 9, 73–78.Ward, L.M., 1973. Repeated magnitude estimations with a variable standard: sequential effects and other

properties. Perception & Psychophysics 13, 193–200.

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