In a test of short-term memory recall, two subjects attempted to recall various lists. For unpracticed subjects, the time it took to read the list is a better predictor of immediate recall than the number of items on the list. For practiced subjects, the two predictors do about equally well. If the items that must be recalled are unfamiliar, it is advantageous to keep the items short to pronounce. On the other hand, if the same items will be encountered over and over again, it is advantageous to make them distinctive, even at the cost of adding to the number of syllables

Hayt, Cathrin

Schweickert, Richard

English

NASA Technical Reports Server

N88-23386
SHORT-TERM MEMORY LOAD AND PRONUNCIATION RATE
JI
Richard Schwelckert
and
Cathrin Hayt
Purdue University
West Lafayette, IN 47907
f .
t
One of the major components determining mental _rkload is the amount of
material that must be maintained in short term memory. Some tasks, such as
air traffic control, involve coordination between people, and the main
communication is verbal. Critical parts of the communlcatlon require memory
not only for the gist or meaning of the material, but for verbatum recall
(ref. I). Even tasks which do not involve communication between people often
have a verbal component. Communication between humans and computers often
requires the human to remember certain information verbatim which has
disappeared from the screen (ref 2). Everyone has had the experience of
looking up the call number of a book in a library, and rehearsing it while
trying to find the shelf.
The capacity of short term memory was described in a classic paper by
Miller (ref. 3) as 7 plus or minus 2 chunks, where a chunk is a meaningful
unit of materisl. This gives a good rule of thumb, but it has at least two
problems. First, the number seven is an estimate of the memory span, that is,
the number of items that can be immediately recalled correctly half the time.
But there is nothing special about probability one-half. In most practical
situations, we would llke to be able to predict probability of correct recall
over a range of probabilities, or at least be able to estimate the length of a
llst that can be recalled with a high probability, say, .99. The second
problem is that the probability of correct recall depends on the type of
material. The memory span is greater for color names, such as red and orange,
than it is for shape names, such as circle and square. Although one can
define the capacity of the short term memory to be 7 chunks, this leads to the
curious notion that there are more chunks in the name of s shape than in the
name of a color.
Another approach is to assume the short term memory is limited in the
time for which it can hold items. The support for this has waxed and waned
over the years, but the decay hypothesis has enjoyed renewed interest
recently. This is because Mackworth, Baddeley, and others have found that the
memory span for a type of material can be predicted quite well from the amount
of material that can be pronounced in about 1.5 seconds (refs. 4, 5, 6). For
example, the memory span for digits is 7.98 and that for four-letter concrete
nouns is 5.76 (ref. 7). It turns out that these are the number of digits and
nouns, respectively, that a typical subject can pronounce in 1.5 seconds.
This result can be summarized by saying
Si = 1.5 sac X rl, (I)
where S I is the memory span for items of type i and ri is the rate of
pronunciation of items of type i, in items/see.
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The explanation is straightforward. Suppose when a subject is presented
with material for immediate recall, he forms a verbal trace, and the trace
begins to decay. If the subject can emit the items before the trace has
deteriorated, recall will be correct, otherwise it will be incorrect.
Evidently, on the average, the trace decays after 1.5 seconds, which
determines the span.
Equation I resolves the second problem, accounting for differences in
memory span for different types of material in terms of differences in their
pronunciation rates. Schwelckert and Boruff (ref. 6) proposed a resolution to
the first problem by saying the probability of correct recall is simply the
probability that the duration of recall is less than the duration of the
verbal memory trace,
P : Prob [Tr < Tv] , C2)
where P is the probability of correct recall, Tr is the time the subject
requires to recall the list, and Tv is the duration of the memory trace. In
an experiment, subjects were presented with 6 list lengths of 6 types of
material. A good account of the data was given by Equation 2. Normal
distributions were assumed for T_ and T v. The mean and variance of the trace
duration were estimated to be I.§8 see and .187 see 2, respectively.
An equally good, but more easily calculated, estimate of the probability
of correct recall was found, based on linear, regression,
z = -2.02 Tr + 3.87. (3)
Here z is the standard normal deviate of the probability of correct recall of
a list, and Tr is the average amount of time required to read the list aloud.
The correlation between the z-score for correct recall and pronunciation
time was .977, so 95% of the variance is accounted for by pronunciation time.
In contrast, the analogous linear regression equation using the number of
items in the list as the predictor yielded a correlation of .849, so only 72%
of the variance is accounted for by list length.
It is of interest to note that Equations 2 and 3 underestimated the
probability of correct recall for digits, the material subjects had most
experience with in daily life, and overestimated the probability of correct
recall for nonsense syllables, the material least familiar to the subjects.
The subjects in the experiment were not particularly practiced. They came for
three one hour sessions, and learned only 60 lists of each material type. The
nonsense syllables are hardly chunks, in the usual sense. The following
experiment was done to investigate memory in highly practiced subjects.
Method
Subjects. Two subjects completed 4 practice sessions followed by 30 test
sessions. They were paid by the hour. Each session lasted about an hour and
a half.
M_M__is. Five types of material were used: consonants, color names,
prepositions, shape names, and three letter concrete nouns. To make the
232
probability of correct guessing low, each set contained 20 items. This
precluded the use of digits, a commonly used material in immediate memory
studies. Lists of a given material were all presented together in a block.
The order of presentation of materials within sessions was governed by six 5
x 5 Latin squares. The lengths of the lists were from 3 to 9 items, inclusive.
List lengths were randomized within the blocks.
_. At the beginning of each trial, a list appeared on a TV
monitor. In pronunciation trials, subjects read the list aloud with no
requirement to remember it. In memory trials, subjects read the list aloud,
and then attempted to recall it by speaking aloud. Voice keys indicated the
onset and offset of their speaking, and the durations of the utterances were
timed with a microcomputer. The pronunciation and recall times are beyond the
scope of this paper.
During recall, the experimenter recorded whether the list was correctly
recalled or not.
Results
The reading time for a list is the time from when the subject started to
read the list until he finished. Reading was followed immediately by recall.
Mean reading times and probability of correct recal I are given in Tables I and
2.
Recall that for the unpracticed subjects in the experiment of Schweickert
and Boruff (ref. 6), reading time was a much better predictor of recall than
the number of items in the list. Here, the number of items is a better
predictor, although only slightly.
For subject I, the correlation between the z-score for correct recall and
the number of items in the llst is -.95, so 90% of the variance in recall is
accounted fop by list length. The correlation between the z-score for correct
recall and reading time is -.90, so 80% of the variance in recall is accounted
for by reading duration.
For subject 2, the results are similar. The correlation using the number
of items in the list was -.95, so 90% of the variance is accounted for by list
length. The correlation using reading time is -.92, so 85% of the variance is
accounted for by reading time. In each case, list length does slightly
better as a predictor than reading time.
The regression equation for predicting the z-score for correct recall is
Z = b0 ÷ b I n,
where n is the number of items. For subject I, the regression coefficients
were b0 = 5.50 and b I = -.83. For subject 2, they were b 0 = 5.40 and b I
= -.80. The coefficients agree remarkably well for the two subjects.
In the calculations, conditions with recall probabilities of 0 or I were
ignored, since the corresponding z-scores are infinite.
Is there an advantage of practice? One way to evaluate this is to note
that the duration of a list recalled half the time was about 2.4 seconds,
233
comparedwith 1.8 seconds for the unpracticed subjects in the previous
experiment.
Increasing the length of the items leads to two competing tendencies.
First, the longer the items, the greater the time required to output the list,
so the greater the chances of trace decay before recall is completed. But,
second, the longer the items, the more distinctive they tend to be, and hence
the greater the chances of guessing an item correctly from a partial trace.
Highly practiced subjects are probably better able to reconstruct the
partially decayed trace of an item to make a correct guess. Them, re familiar
the items are, the better subjects are able to discriminate the fragments
remaining in the traces.
For unpracticed subjects, reading time is a notably better predictor of
immediate recall than the number of items in the list. For practiced
subjects, the two predictors do about as wel i, with a slight advantage for the
number of items. In either case, about 90% of the variance is accounted for,
so for most practical purposes, good estimates of recali probability are
available. If the items that must be recalled are likely to be unfamiliar,
and likely to remain unfamiliar, then it is advantageous to keep the items
short. For example, codes for identifying airplanes or pilots encountered
only once in a while should be short to pronounce. On the other hand, if the
same items will be encountered over and over again, it is advantageous to
concentrate efforts on making them distinctive, even at the cost of adding to
the number of syllables.
1
.
4.
.
.
REFERENCES
Lenorovitz, D. R., & Phillips, M.D.: Human factors requirements
engineering for air traffic control systems. In G. Salvendy (Ed.),
Handbook of human factors. New York: John Wiley, 1986.
Card, S. K., Moran, T. P., & Newell, A. The P_ of human-comDutQr
_. Hillsdale, N. J.: Lawrence Erlbaum, 1983.
Miller, G. A. The magical number seven plus or minus two: Some limits on
our capacity for processing information. Psvcholo=ic_l Review, 6_, 81-
97, 1956.
Mackworth, J. F. The duration of the visual image. Canadian_L_l__lof
_2L_lgK.Y, 17, 62-81, 1963.
Baddeley, A. D., Thomson, N., & Buchanan, M. Word-length and the
structure of short-term memory. Journalof_erball_/£L1_andYerbal
Behavior, 14, 575-589, 1975.
Schweickert, R., & Boruff, B. Short-term memory capacity: Magic number
or magic spell? Journal of _ _: _, __._,
and _gJ_ll__, 12, 419-425, 1986.
234
TABLES
MeanReading Times and Probability of Correct Recall
Table I: Subject I
List Length 3 4 5 6 7 8 9
Colors: Read
Recall
Letters: Read
Recall
Preps: Read
Recall
Shapes: Read
Recall
Words: Read
Recall
• 889 1.273 1.656 2.099 2.499 2.945 3.430
1.000 1.000 .987 .832 .500 .191 .000
•667 .951 1.297 I.659 2.072 2.451 2.896
I .000 1.000 .967 .846 .592 .242 .023
•867 1.212 1.617 2.018 2.428 2.840 3.275
1.000 .993 .940 .805 .415 .113 .020
1.254 1.831 2.399 2.972 3.537 4.037 4.637
1.000 .987 .866 .513 .128 .014 .000
•827 1.195 1.561 1.967 2.380 2.817 3.254
1.000 1.000 .931 .685 .281 .055 .000
Table 2: Subject 2
List Length 3 4 5 6 7 8 9
Colors: Read
Recall
Letters: Read
Recall
Preps: Read
Recall
Shapes: Read
Recall
Words: Read
Recall
.920 1.353 1.764 2.234 2.696 3.151 3.635
1.000 1.000 .967 .839 .476 .148 .020
•677 1.104 1.468 1.959 2.293 2.743 3.130
1.000 .987 .980 .890 .710 .345 .094
.883 1.208 1.647 2.067 2.488 2.897 3.287
I .000 .993 .967 .879 .537 .208 .053
1.621 2.200 2.790 3.356 3.993 4.574 5.039
.993 .953 .800 .547 .157 .013 .000
•873 1.239 1.664 2.145 2.581 3.010 3.433
•993 .993 .927 .627 .366 .088 .007
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Short-term memory load and pronunciation rate

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