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A'r : An estimate of area under isosensitivity curves
A'r : An estimate of area under isosensitivity curves
A'r : An estimate of area under isosensitivity curves
A'r : An estimate of area under isosensitivity curves
A'r : An estimate of area under isosensitivity curves
A'r : An estimate of area under isosensitivity curves
A'r : An estimate of area under isosensitivity curves
A'r : An estimate of area under isosensitivity curves
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A'r : An estimate of area under isosensitivity curves

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  • 1. Behavior Research Methods, Instruments, & Computers1996,28 (4),590-597 Ar : An estimate of area under isosensitivity curves WAYNE DONALDSON and COLETTE GOOD University ofNew Brunswick, Fredericton, New Brunswick, Canada A was identified by Pollack and Norman 0964, p. 126) as "the average of the areas subtended by the upper, and by the lower, bounds ~ of the isosensitivity curve. That estimate of the area under the isosensitivity curve is used when only one point on the curve is available, as with yes/no data. All es- timates of area when more than one point is available, such as with confidence rating data, have set- tled for using the value of the lower bound. A general procedure, and an example of the calculations, is presented for calculating Air, the average of the minimum and maximum areas subtended by mul- tipoint data. Data from a recognition-memory experiment are analyzed to show the extent to which the use of Air improves the accuracy and reduces the uncertainty surrounding the estimate of area under the isosensitivity curve. Over 30 years ago (Green, 1964; Norman, 1964), the used rating-scale data and estimated area by use of thearea under the isosensitivity curve was introduced as a trapezoidal rule. Green and Moses (1966) did the sameviable estimate of unbiased performance in correspon- with their rating data. Pollack and Hsieh (1969) exam-dence experiments (Macmillan & Creelman, 1991) such ined the variability of an estimate ofarea called Ag, whichas recognition memory. Since then, the developments have was obtained by adding the areas under histograms. Theincluded: the introduction ofthe statistic A as an estimate value of Ag in any set of data is the same as that producedof the area under the isosensitivity curve when one has a by application of the trapezoidal rule. Both Grier (197 I)single point on the curve (Pollack & Norman, 1964); the and Simpson and Fitter (1973) pointed out that the trape-demonstration that the area under the isosensitivity curve zoidal rule underestimated area, and indeed the valueequals the percent correct in a two-alternative forced produced by the trapezoidal rule is the minimum possi-choice (2AFC) task (Green & Moses, 1966); the deriva- ble area under the isosensitivity curve. Pollack and Nor-tion of computational formulas for A and response bias man (1964, p. 126) identified A as "the average of the(Grier, 1971); the presentation of the A formula for areas subtended by the upper, and by the lower, bounds"below-chance performance (Aaronson & Watts, 1987); the of the isosensitivity curve, whereas analyses of ratingcomparison of the memory and bias measures, which fared data settled for an estimate of area that was, in fact, onlypoorly, with other measures (Snodgrass & Corwin, 1988); the lower bound of that area.and the revision and reevaluation of the measures (Don- Brown (1974, 1976) introduced a measure he called R,aldson, 1992, 1993) with favorable outcomes. which conceptually transforms yes/no and confidence- However, there is a major gap in the literature. No non- rating data into 2AFC data. It does so by conceptuallyparametric procedure exists for estimating the area under pairing every target in a yes/no task with every distractoran isosensitivity curve on which one has more than just to produce a large number of hypothetical 2AFC tests. Aa single point, such as occurs with confidence-rating data. yes/no task with 20 targets and 20 distractors producesSuch data have been considered, but no measure compar- 400 different target-distractor pairs. The item selected asable toA has been developed. Norman (1964) pointed the the old one in each of those pairs would be whichever ofway in his original paper, graphing the critical areas when the two had been more confidently selected as old in thethe unit square contained two data points rather than just yes/no task. Choosing between tied items is done byone, and noting that "the addition of the second point ... chance, with probability .5. The R value is the percent cor-reduces the ambiguous region in the unit square consid- rect on those hypothetical 400 pairs.erably" (p. 245). Pollack, Norman, and Galanter (1964) Brown (1974) pointed out that the value of R was an underestimation of the expected 2AFC performance. In fact, it turns out that the value of R in any data set is iden- I would particularly like to thank Roman Mureika, Department ofMathematics and Statistics, University of New Brunswick, for his el- tical to Ag and to that calculated by the trapezoidal rule.egant mathematical contribution to this paper. I would also like to All three produce a number that is the minimum possiblethank Doris Aaronson, Doug Creelman, and Michael Hautus for com- area under an isosensitivity curve passing through thements on various versions of this work. An initial presentation of the data points. Brown also pointed out that when the R valueAr statistic was made at the 1995 Psychonornic Society meeting inLos Angeles. Correspondence should be addressed to W. Donaldson, was calculated on two-category, yes/no data, the amountDepartment of Psychology, University of New Brunswick, Fredericton, of underestimation was "higher if the single boundaryNB, Canada E3B 6E4 (e-mail: donaldsn@unb.ca). does not lie midway between the means" (p. 2 I). A checkCopyright 1996 Psychonomic Society, Inc. 590
  • 2. Edited by Foxit Reader Copyright(C) by Foxit Corporation,2005-2009 Ar 591 For Evaluation Only.of the unit square shows that to the extent yes/no perfor- probably" or "yes-sure" if the item is thought to be old.mance is biased, the size of the ambiguous areas in- In other words, a 3-point scale is used to produce the twocreases and the size of the minimum area decreases. Pro- data points. Point (xI,Y,) then represents the confidentcedures that calculate the minimum area from such data false-alarm rate (proportion "yes-sure" responses pro-will increasingly underestimate the full area. duced when the item was new, 0.10 in this case) and the Thus, all attempts to estimate the area under the iso- confident hit rate (proportion "yes-sure" responses pro-sensitivity curve from confidence rating data are under- duced when the item was old, 0.60) respectively. Pointestimates because they all use the minimum possible value. (x2Y2) at (0.30,0.80) represents the cumulative values,Nobody has followed up on Normans (1964) conceptu- combining "sure" and "probable" responses, the 0.30 be-alization of the problem to develop a generalized Ar (r ing the overall false-alarm rate (proportion of "yes" re-for rating) that will apply whether there is only one point sponses when the item was new) and 0.80, the overall hitin the unit square or more than one. Richardson (1972) rate (proportion of "yes" responses when the item wassuggested that "computation for more than one or two data old). Figure I also includes the point (xo,Yo) at (0,0) andpoints would be very arduous" (p. 430). The purpose the point (x3Y3) at (I, I). In the general case, the cornerhere is to show that the task is not that difficult. (0,0) will always be identified as (xo,Yo) and the corner The paper will work through a two-step process, pre- (1,1) will be (xn+"Yn+I)senting the general formulas to analyze a unit square Following Norman (1964), the space in the unit squarewith n data points. At the same time, each step will be il- is divided into a region labeled I (for Inferior), since anylustrated using the 2-point data shown in Figure I, which data point falling in this area represents performance thatfollows Norman (1964). The plotted data will be identi- is inferior to that represented by the initial two points. Itfied as (xk,Yk) where the values of k go from I to n, I rep- is the area subtended by the straight lines connecting theresenting the most confident "yes" response. A rating adjacent data points, including (0,0) and (I, I). That areascale of n + I points will produce the n points in the unit can be calculated by the trapezoidal rule, Ag, or Brownssquare. The xk values are the proportion of responses R. The general formula for the area I iswith confidence rating k, or lower, made to the new items(distractors), and the Yk values are the proportion of re- 1= 1/2L(Yk+, + Yk)(xk+1 - xk)sponses with rating k, or lower, made to the old items L from k = 0 to n. (I)(targets). The data plotted in Figure I show (x"y,) at(0.10,0.60) and (x2Y2) at (0.30,0.80). These data might In the 2-point data of Figure I, the value of I is 0.80.have come from a recognition task where a person says Thus, we know that the area under an isosensitivity curve"no" if an item is thought to be new and says either "yes- passing through those two data points has a minimum value of 0.80. The region labeled S in the upper left rep- resents superior performance, since any point in that area represents better performance than do the two initial l.G0 r----,----::;;;;r--"T"-.----..",... points. Any isosensitivity curve that passes through the two data points will pass through the three triangles that remain. They are labeled A, since the exact location of the isosensitivity curve through those areas is unknown, .75 and it is ambiguous whether any particular point in those areas represents superior or inferior performance as compared with the original data. Ar, the estimate of the area under the isosensitivity curve, like A, is area I plus one half of the ambiguous areas. The analysis requires calculating the slopes and intercepts of the lines in Fig- ure I and the locations of the interconnecting points (xk,Yk) .15 The first step in the analysis produces the parameters of the lines that connect and extend beyond adjacent data points. In the general case, the number of lines always (xo,Yo).{x· -1 ,y. -1 ) equals the number of points, including (0,0) and (I, I). .08 ......:..:.::.--=..:.._....L:.-----_--- ----- -- Thus, a rating scale with n + I categories generates n data .00 .Z5 .50 .75 1." points plus the two corners and n + 2 lines. In the ex- xk ample, the two points generate four lines. The first line Figure 1. The unit square containing two data points is shown. (lineo) connects (0,0) to (Xl ,y,) and extends beyondThe data points divide the square into an area below any isosen- (xl>Y,) until it intersects linej. The second line (line.)sitivity curve that might pass through the two points (Area I), an connects (x, ,y,) to (x2Y2) extends below (Xl YI) until itarea above any such isosensitivity curve (Area S), and three am-biguous areas (A). The data (xk,Yd, lines connecting the data intersects the ordinate, and extends above (X2Y2) until itpoints (linek) and the critical intersections (x"j"yl) of those lines intersects the top line where Y equals 1.0. The third lineare shown. See text for further details. (linej ) connects and extends beyond (xbY2) and (I, I),
  • 3. 592 DONALDSON AND GOOD Table 1 General Format and Worked Example for Calculating Ar k -I 0 I n n+1 n+Z xd FA) (0.00) x, xn (1.00) Yk (H) (0.00) Y, Yn (1.00) mk (linej ) mo ml mn (0.00) bk (linej ) (0.00) bl bn (1.00) x*k (0.00) (0.00) x"j x*n (1.00) ( 1.00) Yk (0.00) yO y"j (1.00) (1.00) (1.00) Area (linej ) Aline., Aline, Aline, Alinen+1 Ar = l/ZI Areatlinej ) Ak AI A n k -I 0 I Z 3 4 xk(FA) (0.00) 0.0 0.30 ( 1.00) Yk(H) (0.00) 0.60 0.80 ( 1.00) mk (linej ) 6.0000 1.0000 0.Z857 (0.0000) bk (Iinej ) (0.0000) 0.5000 0.7143 (1.0000) xk (0.0000) (0.0000) 0.IZ50 0.5000 ( 1.0000) ( 1.0000) Yk (0.0000) 0.5000 0.7500 (1.0000) ( 1.0000) ( 1.0000) Area (Imej ) 0.0469 0.3750 0.7656 0.5000 Ar = 0.8438 Ak 0.8472 0.8348and the fourth line (linej) connects the point where line , Having established that the slopes of successive lines dointersects Y = I to (I, I). not increase, row 5 calculates the intercepts (b k) of each Table 1 shows a format that is useful for doing the re- of the four lines with the ordinate. For line., in any dataquired calculations. The upper part ofthe table is a gen- set, bowill always be 0 and can be entered without calcu-eral representation showing which values have to be calcu- lation. For line. by Formula 3, b l = YI - (ml)(xI) =lated and which do not. Entries in brackets, such as (0.00), 0.60 - (1.00)(0.10) = 0.5000. This is the Y value whererequire no calculations, and the number, always 0 or 1, line. meets X = 0, the ordinate. For linez, bz = 0.7143, andcan be entered in the table. All other entries require cal- for linej, b3 = 1.0000. The last line, in the general caseculations. The lower part of the table fills in the calcu- line n + 1 and in this case linej, will always have an inter-lated values for the sample data. The first row, labeled k, cept of I and can be entered without calculation.identifies the subscripts such as the I in XI and the 2 in The critical points in the calculations become thoseIinej, The next two rows record the actual data. Row 2 points where lines intersect nonadjacent lines. Thus, con-records the response rates to distractors (xk) usually sider line. in Figure I. Line. intersects line., at (xI,Yl)identified as false alarms (FA), and row 3 records the re- and line, at (xz,yz). Those values we know. We need tosponse rates to targets (Yk) usually called hits (H). Rows know the intersection point of line. with a line below4 and 5 are the slopes (mk) and intercepts (b k) of the four (xIYI) That point happens to be on the ordinate at alines (line., through line-). The general formulas are: point to be called (x~,y~). Similarly, line. intersects the Y = I line at (x1,Y1) Line., intersects line, above (xt>YI) mk = (Yk+ I - Yk)/(xk+ I - xk) (2) at the point (xt,yt), and its lower intersection is below bk = Yk - (mk)(xk) (3) (xo,Yo) at a point labeled (x~ 1,Y~ I) Since one cannot go below (xo,Yo), the value of (x~ t>Y~ I) is in fact (0,0) but These values need to be calculated for each ofthe n + 2 labeling it as conceptually below (xo,Yo) simplifies thelines except when Table I indicates fixed values. In the analysis. Similarly, line , goes from (xt,yt) to (x!,yn.example, there are four lines. Looking at slopes first, by Line, goes from (X1,Y1) to (x~,y:). Both (x!,yn andFormula 2, the slope of lineo, mo, is (YI - YO)/(xI - xo) = (x:,y:) fall beyond (x3Y3) but, since that is the limit of(0.60 - 0.00)/(0.1 0 - 0.00) = 6.0000; the slope ofline I the unit square, both are in fact at (I, I). Those values canis ml = (0.80 - 0.60)/(0.30 - 0.10) = 1.0000; the slope be entered in the table without calculations. Rows 6 andof line-, m2 = (1.00 - 0.80)/(1.00 - 0.30) = 0.2857; and 7 in Table I present the values of xt and yt, respectively.the slope of linej, m3 = 0.0000. In any data set of size n, The general formulas are:the slope of line n + I will be zero and can be so entered inthe table without calculation. xt = (bk+1 - bk-I)/(mk-I - mk+l) (4) This first step of calculating the slopes is very impor-tant since each slope must be less than or equal to the one yt = (mk+l)(xX) + bk+I (5)before it. Besides being required for this analysis, the re- By Formula 4, the value for xt = (0.7143 - 0.00)/quirement follows Normans (1964) that the operating (6.00 - 0.2857) = 0.1250 and x1 = (1.00 - 0.50)/characteristic be monotonic from (0,0) to (I, I) with non- (1.00 - 0.00) = 0.5000. By Formula 5, the value ofincreasing slope. In the current example, the slopes go Y~ = (1.00)(0.00) + 0.50 = 0.5000 and yt = (0.2857)from 6, to I, to less than I, to O. (0.1250) + 0.7143 = 0.7500.
  • 4. Edited by Foxit Reader Copyright(C) by Foxit Corporation,2005-2009 Ar 593 For Evaluation Only. Knowing the values of (XkY!> allows the calculation 0.8348. The minimum value with only that one point inof areas in a variety of different ways. The easiest is to cal- the square is 0.7500 for a range between minimum andculate the area under each of the four critical lines from maximum of 0.1696. Having two data points rather thanthe points where each line intersects the nonadjacent lines. just one cuts the ambiguity almost in half. The amount ofThe general formula for the area under line., is: reduction in the ambiguous areas will depend on where the data points are located in the unit square. Brown (1974) Aline; = 1/2 (Yk+1 + Y*k-l)(xk+1 - xk-I) (6) and Bamber (1975) both point out that the accuracy ofThe calculated area under line. goes from (xb,Yb) to the estimates depends on such things as the spacing be-(x~,y~), and by Formula 6, that area = 1/2(y~ + Yb) tween data points.(x~ - Xb) = 1/2(1.0000 + 0.5000)(.5000 - 0.0000) =0.3750. The important difference between the current DATAand previous applications of the trapezoidal rule is the useof the Xk and Yk values rather than the Xk and Yk values. A simple memory experiment was conducted to pro-The area under each line, is calculated from (Xk-I Yk~ I) vide data for a preliminary exploration ofAr. Twenty uni-to (Xk+ I ,Yk+ I) The sum of those areas gives a number versity students were individually tested. Each was giventhat includes the I area twice and each of the ambiguous a recognition test on a list of 512 words after having seenareas once. Dividing that sum by 2 then gives an Ar an initial list of256 words. The test list was organized invalue consisting of area I plus one half of the ambiguous eight blocks of 64 words each, although subjects wereareas. In general, not aware of the blocks. Each block of 64 words included 32 high-frequency and 32 low-frequency words, the sub- Ar = 1/4 L(Yk+1 + Yk-l)(xk+1 - xk-I) ject having previously seen half of each. In other words, L from k = 0 to n + I (7) 16 words of each frequency were old words. The 256 words on the initially presented list were also organizedIn the present example the four individual areas are shown in eight blocks, with 32 words in each block. The first 32in row 8 labeled Area (linej ). The sum of those values words presented, 16 high and 1610w frequency, were thehalved is shown in row 9 as Ar. For comparison, Row 10 32 old words in the first test block, albeit in a new ran-shows the traditional A values calculated on each of the dom order from their initial presentation, and randomlytwo separate data points. The formula for A is mixed with 32 new words, half high and half low fre- AI. = 0.5 + [(Yk - xk)(I + Yk - xk)]/[4Yk(l - xk)] quency. Two different presentation lists were used so that (8) the test words that were new for half the subjects were old for the other half. In the limiting case, when one has only one data point, The initial list of words was presented by a computerthat is, n = I, Formula 7 reduces to at the rate of 1.5 sec per word with a 0.5-sec blank inter- Ar = 0.5 + 1/4(1 + Yb - X*l) (9) val between words. The subjects were instructed simply to read each of the words out loud and to try to remem-which in turn reduces to Formula 8. Just as the A For- ber them. They were told that the list was very long andmula 8 cannot be used when data points fall below the that they were not to worry when they felt they were notchance diagonal, so Formula 9 for Ar will yield bizarrevalues for below-chance data. The procedure seems complicated, so perhaps Richard- Table 2son (1972) was right. But setting up a table such as Table I Sample Raw Data (Subject 85)on a computer spread sheet and putting the formulas into Confidence Ratingthe cells where computations are required makes the 3 Z -I -2 -3process very simple. The result is a single estimate of the High frequencyarea under the isosensitivity curve that uses all the data New 13 16 32 17 31 19from the rating scale. Like A itself, the value of Ar can Old 59 23 20 9 12 5be calculated on individual subject data as long as those Low frequency New 9 4 8 12 16 79data satisfy the constraint that the points in the unit square Old 83 5 9 8 12 IIare concave, that is, the successive slopes never increase. As an example of the extent to which "the addition of Cumulative Datathe second point reduces the ambiguous region in the k=1 k=2 k=3 k=4 k=5unit square" (Norman, 1964, p. 245), the data in the ex- High frequencyample yield an Ar of 0.8438. As previously noted, the New 13 29 61 78 109minimum area from Formula I is 0.80. Thus, the ambig- Old 59 82 102 III 123uous areas in Figure I have a total area of 0.0876 (twice Low frequency New 9 13 21 33 49the difference between Ar and the minimum possible Old 83 88 97 105 117value). In other words, the area under the isosensitivity Note-s-The entries in each cell are the number of responses (out ofcurve is 0.8438 :±: 0.0438. In contrast, had one used only 128) in each rating category to old and new high- and low-frequencythe yes/no data, (xz ,Yz) = (0.30,0.80), the A value is words. The lower part of the table shows the numbers cumulated.
  • 5. 594 DONALDSON AND GOODremembering. The test was given after only a short break The low-frequency data for Subject B5, shown in theand was self-paced. A 6-point rating scale, ranging from middle of Table 3, were nonmonotonic. The slope de- -3 (sure new) to +3 (sure old) appeared on the bottom creased from ma through m3 but then increased at m4of the screen and remained throughout the test. As each Note that the other calculations are all completed in theof the 512 words appeared, the subject read it aloud and table but are clearly not meaningful in light of the slopeverbally reported a number representing memory (plus increase. Eliminating the k 4 data point and rerunning theor minus) and confidence (l, 2, or 3), which was re- calculation produces the satisfactory 4-point solutioncorded by the experimenter. After each response, the sub- shown in the bottom of Table 3. Since successive dataject touched the space bar for the next word to appear. points are cumulative,the k4 data have not been eliminated; For the initial analyses, each subjects data were orga- instead, they have been collapsed into the k 5 data point.nized as in Table 2, which shows the data for Subject B5. Each of the 20 subjects provided two data sets, one forThey were then cumulated from the most conservative high- and one for low-frequency words. Of the 40 data( + 3) category on down, as shown in the lower part of sets, 14 provided complete 5-point estimates ofAr. Nine-Table 2. Each set of data was then entered into the gen- teen provided 4-point estimates and 7 had to be reducederal formula format of Table 1, with results as shown in to 3-point isosensitivity curves.Table 3. For Subject B5, the high-frequency data were An obvious question concerns how to transform asatisfactorily fit on all five data points, as shown by the nonmonotonic 5-point isosensitivity curve to an accept-consistently declining slopes, mk (Iinej ), The final Ar able 4- or 3-point curve. The first rule would seem to bevalue was .7629, as shown. to eliminate any zero entries. Thus, if a person never gave Table 3 The Analysis of Subject 85s Data in the General Format Shown in Table 1* High Frequency k -I 0 I 2 3 4 5 6 7 xk(FA) 0.0000 0.1016 0.2266 0.4766 0.6094 0.8516 1.0000 Yk(H) 0.0000 0.4609 0.6406 0.7969 0.8672 0.9609 1.0000 mk (linej ) 4.5385 1.4375 0.6250 0.5294 0.3871 0.2632 0.0000 bk (Iinej ) 0.0000 0.3149 0.4990 0.5446 0.6313 0.7368 1.0000 xt 0.0000 0.0000 0.1275 0.2529 0.5560 0.7221 0.9525 1.0000 1.0000 yt 0.0000 0.3149 0.5787 0.6785 0.8465 0.9269 1.0000 1.0000 1.0000 Area k 0.0369 0.1256 0.3054 0.3766 0.3660 0.2677 0.0475 Ar 0.7629 Ak 0.7949 0.7954 0.7535 0.7393 0.7127 Ir 0.7494 0.0234 0.0688 0.1797 0.1105 0.2214 0.1455 Low Frequency k -I 0 I 2 3 4 5 67 xk(FA) 0.0000 0.0703 0.1016 0.1641 0.2578 0.3828 1.0000 Yk(H) 0.0000 0.6484 0.6875 0.7578 0.8203 0.9141 1.0000 mk (linej ) 9.2222 1.2500 1.1250 0.6667 0.7500 0.1392 0.0000 bk (Iinej ) 0.0000 0.5605 0.5732 0.6484 0.6270 0.8608 1.0000 xt 0.0000 0.0000 0.0708 0.1507 0.1432 0.4026 0.4974 1.0000 1.0000 yt 0.0000 0.5605 0.6529 0.7489 0.7344 0.9168 1.0000 1.0000 1.0000 Area k 0.0231 0.0986 0.0502 0.2098 0.3071 0.5726 0.5026 Ar 0.8821 Ak 0.8784 0.8761 0.8734 0.8609 0.8605 Ir 0.8619 0.0228 0.0209 0.0452 0.0740 0.1084 0.5907 Low Frequency (adjusted to 4 points) k -I 0 I 2 3 4 5 6 xk(FA) 0.0000 0.0703 0.1016 0.1641 0.3828 1.0000 yd H) 0.0000 0.6484 0.6875 0.7578 0.9141 1.0000 mk (linej ) 9.2222 1.2500 1.1250 0.7143 0.1392 0.0000 bk (Iinej ) 0.0000 0.5605 0.5732 0.6406 0.8608 1.0000 xt 0.0000 0.0000 0.0708 0.1495 0.2917 0.5031 1.0000 1.0000 yt 0.0000 0.5605 0.6529 0.7474 0.9014 1.0000 1.0000 1.0000 Area k 0.0231 0.0978 0.1716 0.3090 0.6734 0.4969 Ar 0.8859 Ak 0.8784 0.8761 0.8734 0.8605 Ir 0.8624 0.0228 0.0209 0.0452 0.1829 0.5907 *See text for details.
  • 6. Edited by Foxit Reader Copyright(C) by Foxit Corporation,2005-2009Ar 595 For Evaluation Only.a + 3 confidence rating to a new item, the first point on calculations wiU not work because the slope of the linethe isosensitivity curve would be the +2 new-item re- that would connect (xl,YI) with (x2,Y2) is less than thatsponses plotted against the combined + 3 and + 2 old-item from (x2Y2) to (x3,Y3) Eliminating (x2,Y2) leaves the dataresponses. Four of the 19 4-point estimates were achieved in Figure I. Aaronson suggested that, instead ofthe elim-by coUapsing unused confidence rating categories. The ination of (x2Y2), it might perhaps be replaced by someremaining 15 4-point estimates were obtained as shown point on the line connecting (x"Yl) with (x3Y3) The pointin Subject 85s low-frequency data. The increased slope might be one with the same false-alarm rate (x2) and an(m) at line.,, as compared with linej , points to the col- increased hit rate or perhaps one on the diagonal connect-lapse of the k 4 data. CoUapsing either of the adjacent ing (x2Y2) to the upper left-hand corner. In fact, one couldpoints, either k3 or ks, almost never produced a satisfac- replace (x2,Y2) with any point on the line, and the out-tory 4-point solution. Thus, the second rule to apply is to come would be the same. First, the minimum area, Ag re-eliminate the data point at the lower end ofthe line with the mains the same whether (x2,Y2) is eliminated or replaced,slope increase. FinaUy, of the seven 3-point solutions, two that is, 0.80. Second, no matter what point on the line re-were obtained by applying both of the above rules. An places (x2Y2), the resultant Ar value is 0.8375, slightlyunused category was eliminated and then the point at the lower than the value of 0.8438 that is achieved by elim-lower end of an increasing slope line was eliminated. The inating (x2,Y2) The latter result obtains because the in-remaining five 3-point solutions had to correct 5-point clusion of any point on the line forces the isosensitivityplots that each had two slope increases. In four ofthe five curve to go through the replacement value for (x2Y2) andcases, there was a decrease in slope from linea to line" the points on either side. This eliminates the ambiguousand after that the pattern was increase, decrease, increase, area (A) between (xl,Yl) and (x3Y3) and above the linedecrease. The final case involved a decrease from linea to connecting them. In other words, any point chosen to re-line, and from line. to line- and then an increase from place (x2,Y2) forces the curve to assume the lower boundline- to line, and a further increase from line , to line4 on its path between (Xj,y,) and (x3Y3) That these calculations require that the slopes of suc- Other ways to handle data points that violate the slopecessive lines be monotonicaUy decreasing is not unusual. restrictions need to be explored. Of those examined, how-Monotonicity is also a requirement for parametric as- ever, we prefer dropping the offending data point by col-sessments of isosensitivity curves and is often achieved lapsing it into the one above, because that choice is, in fact,by using best fitting lines on z-transformed axes. The sug- the most conservative one explored to date. It is so be-gestion that monotonicity can be achieved in these cal- cause reducing the number of data points on the isosen-culations by coUapsing the data point at the lower end of sitivity curve increases the size of the ambiguous areas,a line that increases in slope is only one possibility. Doris leaving a wider possible range about the value of Ar.Aaronson, in reviewing this paper, suggested an alterna-tive. Figure 2 shows a fairly typical situation where the DISCUSSION Is the calculation of Ar worth the effort? For two rea- 1.00 ,.-----r-----,----,.....--....".,. sons, we believe it is. The first concerns the variability of Ar compared with that of A. The second concerns the accuracy of the statistic as an estimate of the area under the isosensitivity curve. .75 The question of variability is concerned with the size of the ambiguous areas (A) surrounding a possible iso- sensitivity curve. As one reviewer pointed out, however, the area of ambiguity is related to accuracy of Ar if the obtained data points faU exactly on the isosensitivity curve and "sampling variability guarantees that this wiU not happen." This is, of course, so. But the same situation exists when one calculates A or, for that matter, d from a single data point. Ones data point is simply the best esti- mate one has. Consider a single data point (0.2,0.6). The A value is 0.7917 :::: 0.0917, that is, the ambiguous areas total 0.1834. •00 e.:-(x~o:.::.y..:=O~)_-l... --l. .L_ .J Suppose that the hit rate (0.6) could actuaUy be any- .00 .%5 .75 1.00 where between 0.55 and 0.65. A wiU, of course, be lower with the former value than with the latter, 0.7685 com- pared with 0.8137. The lower bound on the lower estimate Figure 2. The Figure I unit square containing two data pointsis shown with the addition ofa third data point (x 2 Y2) which pro- is 0.6750 and the upper bound on the upper estimate isduces nonmonotonicity and thus fails to provide a three-point so- 0.9024, for a combined ambiguous area of 0.2274. Con-lution. sider what happens when one has a second data point,
  • 7. Edited by Foxit Reader596 DONALDSON AND GOOD Copyright(C) by Foxit Corporation,2005-2009 For Evaluation Only. (0.4,0.8), on the isosensitivity curve. The two points yield ject comparisons of A andAr are less favorable. On high- Ar = 0.7850 ± 0.0450. Again, suppose that sampling frequency words, A was as likely to be higher as it was to variability could put the lower hit rate (0.6) anywhere be- be lower than Ar. Of the 10 cases where A was lower, 1 tween 0.55 and 0.65 and the higher hit rate between 0.75 was below the lower bound of the individuals Ar value. and 0.85. If both hit rates were actually at their highest Four of the 10 cases where A was higher were above the values, the calcutedAr = 0.8150 ± 0.0450, and ifboth upper bound on Ar. With low-frequency words, 16 of the were at the lower end of their range, the calculatedAr = A values were below Ar, while only 4 werehigher. Ten of 0.7537 ± 0.0437. The lower bound on the lower estimate the 16 low values were below the lower bound on Ar and is 0.7100 and the upper bound on the upper estimate is 1 of the 4 higher values was above the upper bound. Thus, 0.8600, for a combined ambiguous area of 0.1500. Even overall, 16 ofthe 40A values were outside the uncertainty under the most extreme variability assumptions, the ad- bounds of their respective Ar values. The tendency was dition ofa second data point reduces the ambiguous areas for A to underestimate Ar (1 I ofl6) and to be more likely from 0.2274 to 0.1500. to provide underestimates at higher values of Ar. One can then compare the area of ambiguity around Finally, perhaps one could more easily handle confi-Ar as a function of the number of data points one has, dence rating data by calculating the separate A values ranging from I (A) on up. As noted in the introduction, for each of the points on the isosensitivity curve and then as the number of points in the isosensitivity curve in- taking an average. Thus, for Subject B5 s high-frequency creases, the size ofthe ambiguous areas (A) will decrease. data, one would calculate five separate A values and av- And the sample data showed that just moving from one erage them for a final value, which turns out to be 0.7592.point to two can cut the ambiguity in half. We can now For that persons low-frequency data, one should useuse the present data to further assess the size of that re- only the four points that enter the final Ar calculation duction in uncertainty. An A value can be calculated on for comparison with Ar, and that value is 0.8721. This each persons yes/no data, as can the size of the inferior was done for the 40 data sets, and the comparisons be-area, I, which represents the minimum possible A value. tween mean A and Ar are similar to those between theOver the 20 subjects, the average A value on high- yes/no A and Ar. With high-frequency words, 7 of the frequency words was 0.7612, and the average minimum mean A values were lower than Ar and 13 were higher.value was .6793. The difference between the two is 0.0819, With low-frequency words, mean A values were lower so the A of 0.7612 has a possible range of ±0.0819, or than Ar in 19 of the 20 cases, and II of those 19 were be-lower and upper bounds of 0.6793 and 0.8431, respec- low the lower bound on Ar. Overall, 18 of the 40 aver-tively. The average values for the low-frequency words age A values were outside the range of their comparisonwere an A of 0.8844, an average minimum area of 0.8125, Ar values. Taking an average of multiple A values is noand thus A has a range of ± 0.0719. These can be com- better than simply calculating a single A on the yes/nopared with the Ar values from each subjects best isosen- data. And, in either case, almost half of the A values fellsitivity curves. For high-frequency words, the average outside the Ar boundaries.Ar was 0.7597, with a range of ±0.0205, and for low- In conclusion, this paper provides a technique for cal-frequency words, the Ar was 0.8955, with a range of culating a statistic called Ar. Ar provides an estimate of ±0.0183. Ar has an average range one quarter the size the area under an isosensitivity curve on which one hasof that associated with A calculated on the yes/no data. any number ofdata points from one on up. The more pointsThese values are averaged over the 5-, 4-, and 3-point iso- one has on the curve, the less the uncertainty about thesensitivity curves. Combined over high and low frequency estimate of the area. Numerous writers (e.g., Macmillanbut separately for the 5-, 4-, and 3-point curves, the am- & Creelman, 1991, p. 85) have encouraged researchersbiguous areas were ±0.0145, ±0.0206, and ±0.0257, re- to collect confidence ratings. Until now it has been un-spectively. Thus, the 3-point fits have almost twice the clear how ratings could be directly integrated into a bet-ambiguity of the 5-point fits. The average size of the am- ter estimate of the area under the isosensitivity curve. Arbiguous areas associated with the single-point data (A) is a better measure.are over five times larger than those with a 5-point solu-tion, ±0.0769 compared to ±0.0145. REFERENCES It must be noted that the average A values for both thehigh- and low-frequency words are within the range of AARONSON, D., & WATTS, B. (1987). Extensions of Griers computa- tional formulas for A and s: to below-chance performance. Psy-uncertainty of their respective Ar values. This suggests chological Bulletin, 102,439-442.that, at least for these data, A is a good estimate ofthe area BAMBER, D. (1975). The area above the ordinal dominance graph andunder the isosensitivity curve, but that becomes a ques- the area below the receiver operating characteristic graph. Journaltion of accuracy to which we now turn. ofMathematical Psychology, 12,387-415. Ar is simply a better estimate of the area under the iso- BROWN, J. (1974). Recognition assessed by rating and ranking. British Journal ofPsychology, 65, 13-22.sensitivity curve than is A. The comparison above of the BROWN, J. (1976). An analysis ofrecognition and recall and of prob-average Ar to the average A values obtained from the lems in their comparison. In 1. Brown (Ed.), Recall and recognitionyes/no data was not unfavorable. However, subject by sub- (pp. 1-36). London: Wiley.
  • 8. Ar 597DONALDSON, W. (1992). Measuring recognition memory. Journal of POLLACK, I., & NORMAN, D. A. (1964). A non-parametric analysis of Experimental Psychology: General, 121,275-277. recognition experiments. Psychonomic Science, 1,125-126.DONALDSON, W. (1993). Accuracy of d and A as estimates of sensi- POLLACK, I., NORMAN, D. A., & GALANTER, E. (1964). An efficient tivity. Bulletin ofthe Psychonomic Society, 31, 271-274. non-parametric analysis of recognition memory. Psychonomic Sci-GREEN, D. M. (1964). General prediction relating yes-no and forced- ence, 1,327-328. choice results. Acoustical Society ofAmerica, 36, 1042. RICHARDSON, J. T. E. (1972). Nonparametric indexes of sensitivity andGREEN, D. M., & MOSES, F. L. (1966). On the equivalence of two recog- response bias. Psychological Bulletin, 78,429-432. nition measures of short-term memory. Psychological Bulletin, 66, SIMPSON, A. J., & FITTER, M. J. (1973). What is the best index of de- 228-234. tectability? Psychological Bulletin, 80, 481-488.GRIER, J. B. (1971). Nonparametric indexes for sensitivity and bias: SNODGRASS, J. G., & CORWIN, J. (1988). Pragmatics of measuring Computing formulas. Psychological Bulletin, 75, 424-429. recognition memory: Applications to dementia and amnesia. Jour-MACMILLAN, N. A., & CREELMAN, C. D. (1991). Detection theory: A nal of Experimental Psychology: General, 117, 34-50. user s guide. Cambridge: Cambridge University Press.NORMAN, D. A. (1964). A comparison of data obtained with different false-alarm rates. Psychological Review, 71, 243-246.POLLACK, I., & HSIEH, R. (1969). Sampling variability of the area (Manuscript received August II, 1995; under the ROC-curve and of d:. Psychological Bulletin, 71, 161-173. revision accepted for publication January 23, 1996.)

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