<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.0 20120330//EN" "JATS-archivearticle1.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>The Compound Stimuli Visual Information (CSVI) Task Revisited: Presentation Time, Probability Distributions, and Attentional Capacity Limits</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Corso A. Podestà</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Genova</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Italy</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Lorenzo Muscella</institution>
        </aff>
      </contrib-group>
      <fpage>744</fpage>
      <lpage>749</lpage>
      <abstract>
        <p>This study examined the validity of the Bose-Einstein (B-E) model of the Compound Stimuli Visual Information (CSVI) task, and its assumptions. Two experiments compared adults' performance on the CSVI task in standard (5 sec) presentation condition and with shorter presentation times. Individual participants' performance was analyzed with the B-E model, with different assumptions on the number of attending acts in each condition. Both experiments found that the capacity limit estimates found in both conditions were highly correlated with each other, and their means did not differ. The goodness of fit of B-E distributions to the data was also tested. It is concluded that the B-E model provides a valid estimate of attentional capacity limits in the CSVI.</p>
      </abstract>
      <kwd-group>
        <kwd>limited capacity</kwd>
        <kwd>attention</kwd>
        <kwd>working memory</kwd>
        <kwd>Bose-Einstein distribution</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>Introduction</title>
      <p>
        There is currently widespread agreement that (i) working
memory capacity is a major predictor of intelligence,
reasoning ability, and other complex skills, (ii) the
development of working memory capacity has an important
role in many aspects of cognitive development, and (iii)
domain-general, capacity-limited attentional resources are a
core component of working memory and a basic
determinant of its capacity
        <xref ref-type="bibr" rid="ref1 ref11 ref11 ref17 ref19 ref2 ref5 ref6 ref7 ref9">(e.g., Anderson &amp; Lebiere, 1998;
Barrouillet, Bernardin &amp; Camos, 2004; Cowan, 2002, 2005;
Engle, Kane &amp; Tuholski, 1999; Gathercole &amp; Alloway,
2007; Halford, Wilson &amp; Phillips, 1998; Oberauer, 2002;
Vergauwe, Devaele, Langerock &amp; Barrouillet, 2012)</xref>
        .
      </p>
      <p>
        A pioneering article by
        <xref ref-type="bibr" rid="ref18">Pascual-Leone (1970)</xref>
        anticipated
long ago these three statements, arguing that the motor of
cognitive development throughout Piagetian stages and
substages is the maturational increase of a general-purpose
resource (called “central computing space” in that article).
        <xref ref-type="bibr" rid="ref18">Pascual-Leone (1970)</xref>
        also suggested that the nature of that
resource is a limited amount of attentional energy (or
“mental energy”, from which the term “M capacity” to
indicate the amount of this attentional resource), and that M
capacity is at the core of Spearman’s general intelligence.
Increase of M capacity with age would enable children to
activate a larger number of Piagetian “schemes” and,
therefore, to construct increasingly complex cognitive
structures.
      </p>
      <p>
        To test his model of M capacity and cognitive
development,
        <xref ref-type="bibr" rid="ref18">Pascual-Leone (1970)</xref>
        created the Compound
Stimuli Visual Information (CSVI) task and administered it
to groups of children of different age. In a training phase of
the task, participants acquired a repertoire of specific
schemes, by learning to respond to different features of
figures (e.g., square, red, dashed contour…) with different
gestures (e.g., nod head, raise arm, stand up). When a child
had fully learned these S-R pairs or “artificial schemes”, the
testing phase started; figures with different numbers of
relevant features were presented for 5 sec each, and the
child’s task was to respond appropriately to each feature she
could detect. Pascual-Leone assumed that children allocate
attention to the task features according to a probabilistic
model. Participants were assumed to have a limited capacity
k (where k is an integer, increasing with age) that can be
used to simultaneously activate no more than k schemes.
Participants can attend repeatedly to the stimulus (having k
units of capacity available on each attending act); in
particular,
        <xref ref-type="bibr" rid="ref18">Pascual-Leone (1970)</xref>
        assumed that after each
attending act a participant evaluates whether she observed
the stimulus well enough, and after k attending acts the
participant feels attentional saturation and stops exploring
the figure.1 Thus, with long stimulus presentation and
unlimited response time, a participant will attend to each
stimulus figure k times, each time with a capacity of k units,
for a total of k2 available units of capacity.
        <xref ref-type="bibr" rid="ref18">Pascual-Leone
(1970)</xref>
        also assumed that the probability distribution of the
number x of correct responses to items with n relevant
features is a Bose-Einstein (B-E) distribution.
      </p>
      <p>
        The probability mass function of this distribution
        <xref ref-type="bibr" rid="ref8">(see also
Feller, 1968)</xref>
        is:
      </p>
      <p> n   r - 1  n + r - 1
p (x) =  n - x   x - 1 /  r 
and, as a more intuitive metaphor, one can think of r
undistinguishable balls thrown to a set of n distinguishable
1 The assumption that the participant attends to a stimulus k
times is clearly a simplification; a capacity of k does not logically
imply that also the number of attending acts is k. However, it
seems at least plausible that, having in episodic short-term memory
(on average) k evaluations of a stimulus, a participant may feel
that, with that stimulus, all of the job is done.
boxes, with the random variable x representing the number
of boxes that turn out to be occupied by at least one ball.
Referring to the CSVI, n is the number of (distinct) relevant
features in a stimulus, r is the number of (undistinguishable)
units of attentional capacity allocated to the stimulus, which
takes the value of k2 for the reasons given above, and x is
the random variable that expresses the number of features
detected and responded to.</p>
      <p>
        The developmental theory proposed by Pascual-Leone to
account for Piagetian stages claimed that k = 2 in typical
five-year-olds, and k would increase on average by 1 unit
every second year, until a capacity of 7 units
        <xref ref-type="bibr" rid="ref13">(reminiscent of
the “magical number seven” of Miller, 1956)</xref>
        is reached
during adolescence.
      </p>
      <p>
        The results of that pioneering study were broadly in
agreement with these hypotheses, and a number of other
studies, with different methods, also supported this theory of
capacity development in childhood and adolescence
        <xref ref-type="bibr" rid="ref15">(see
Morra, Gobbo, Marini &amp; Sheese, 2008, for a review)</xref>
        .
Moreover, subsequent studies, using either the original
CSVI task or a computerized version with a special
keyboard for responses, yielded results consistent with the
B-E model
        <xref ref-type="bibr" rid="ref10 ref12">(e.g., Globerson, 1983; Johnson, Im-Bolter &amp;
Pascual-Leone, 2003)</xref>
        ; in our lab we obtained a mean
estimate of k = 6.21 from an adult sample
        <xref ref-type="bibr" rid="ref14">(Morra, 2015)</xref>
        .
      </p>
      <p>
        Nevertheless, some aspects of the CSVI and its B-E
model could be questionable. First, a long exposure of
stimuli could afford chunking or rehearsal strategies, which
in turn would yield invalid (over-)estimates of capacity
        <xref ref-type="bibr" rid="ref4">(Cowan, 2001)</xref>
        . Second, one could wonder how plausible
the assumption that, with long stimulus presentation,
participants actually attend k times to the stimulus. Finally,
great progress has been made in the last decades in the field
of methods to assess the goodness of fit of expected to
observed distributions; it would be desirable to assess the fit
of the B-E distributions with more refined methods than
those used at the time of the original study.
      </p>
      <p>This paper aims to assess the validity of the B-E model
and its assumptions. In particular, we assess whether brief
presentation of the stimuli, followed by a mask, so that the
participant can attend to the stimulus only once, yields
capacity estimates equivalent to those obtained with the
original 5 sec presentation under the assumption of k
attending acts. Moreover, we shall evaluate the goodness of
fit of B-E distributions to the distribution of correct
responses observed in our participants.</p>
    </sec>
    <sec id="sec-2">
      <title>Experiment 1</title>
      <p>In this experiment we compared two conditions of the
CSVI, one with stimuli presented for 5 sec as usual for this
paradigm, and the other with a brief presentation of 80 msec
followed by a mask. It was obviously expected that more
features would be detected with long than brief presentation.
The main hypotheses, however, were the following.</p>
      <p>First, a parameter k representing the participant’s limited
attentional capacity (i.e., the number of units available on
each attending act) can be estimated in both conditions,
assuming that the stimulus is attended to k times in the long
presentation condition, for a total of k2 available units. In the
short presentation condition, however, we assume that only
one attending act is possible, so that the number of available
units is equal to k. Although more features can be detected
in the long presentation condition, because k2 units are
available in this condition and only k are available with
short presentation, we hypothesize that the mean estimate of
k is the same in the two conditions.</p>
      <p>Second, if the estimate of k obtained from the CSVI is a
valid measure of participants’ capacity, then the individual
participants’ k measures obtained in both conditions should
be highly correlated.</p>
    </sec>
    <sec id="sec-3">
      <title>Method</title>
      <p>Participants A total of 20 adults (18 women and 2 men), all
with university education, with a mean age of 22.1 years
(s.d. = 2.7) volunteered for this experiment.</p>
      <p>Materials and Procedure The CSVI requires participants
to respond to multiple features of a visual stimulus by
pressing different keys on a special response box. The
stimuli were presented on a 15-inch CRT monitor; the
participant was comfortably sitting at a viewing distance of
approximately 70 cm. The relevant features were square
shape, red color, large size, dashed contour, presence of a
frame around the figure, presence of an X in the centre,
presence of an O in the centre, presence of a bar under the
figure, and purple background. The response box had 12
keys, clearly distinguishable by shape and color. Nine keys
were associated each to one of the 9 relevant features, two
more keys were dummy fillers, and a larger red key was an
“enter” key to be pressed by the participant to signal that
s/he had finished responding to a trial.</p>
      <p>The training stimuli were 72 figures, used to train
participants on each of the 9 features; in each set of 8
figures, the intended feature was present in 4 and absent in
the other 4. For each feature, the experimenter told the
participant which key was associated to it and required the
participant to respond. The practice stimuli were 50 figures,
including 45 with one relevant feature (5 per each feature)
and 5 with no relevant feature. They were used to allow the
participant to practice correct responses to each feature,
until a criterion of perfect performance was reached.
Finally, there were 2 more practice stimuli with 3 features
each; one was presented for 5 sec and the other for 80 msec
for the participant’s response.</p>
      <p>There were 56 test stimuli, i.e., 8 trials for each level from
2 to 8, where a “level” is defined as the number of relevant
features present in a stimulus. The stimuli were arranged in
a pseudo-random fixed sequence, the same for all
participants. This sequence was divided into four blocks,
each of which included two trials of each level. Each
stimulus in the first and third block was presented for 80
msec, followed by a mask, and those in the second and
fourth block for 5 sec each; the sets of stimuli presented in
each condition were counterbalanced over participants.
Results and discussion Each trial was scored for number of
correct responses, i.e., number of correctly detected
features. Each participant’s total number of correct
responses (maximum possible = 140) was computed in both
short and long presentation conditions.</p>
      <p>The participants’ total number of correct responses ranged
from 52 to 115 (mean = 85.10, s.d. = 17.56) with short
presentation, and from 114 to 138 (mean = 125.40, s.d. =
6.50) with long presentation. The effect of presentation time
was significant, t(19) = 13.59, p &lt; .001. This outcome was
expected as quite obvious, and this analysis was carried out
merely as a manipulation check, to ensure that short
presentation actually reduced participants’ ability to detect
the relevant features.</p>
      <p>The point of actual interest was the comparison of the
capacity estimates obtained in both conditions. A
participant’s vector of mean number of correct responses on
levels 2 to 8 was compared with all vectors of expected
means generated by the Bose-Einstein model for each value
of k from 2 to 9. In the B-E distributions, n was the number
of features in each level, x was the number of correct
responses (1 ≤ x ≤ n), and r was set as k in the short
condition and k2 in the long condition. The k value that
yielded the smallest chi-square was selected as the best
fitting estimate, in that condition, of the measure k of the
participant’s capacity.</p>
      <p>As explained above, we assumed k attending acts with
long presentation, but only 1 with short presentation. Under
these assumptions, the mean estimated value of k was 6.55
(s.d. = 1.50) with long presentation, and 6.90 (s.d. = 2.25)
with short presentation. The difference between these means
was nonsignificant, t(19) = -1.02, p &gt; .32.</p>
      <p>The implication of this finding seems clear; although
fewer relevant features were detected correctly with short
presentation, the B-E estimates of attentional capacity in the
two conditions were equivalent, provided that adequate
assumptions were made on the number of attending acts in
each condition. In other words, the different number of
correct responses in the two presentation conditions was due
to the different number of attending acts in each condition,
but the participants’ capacity limit remained the same across
conditions.</p>
      <p>The correlation between the estimates of k obtained with
long and short presentation was highly significant, r(18) =
.73, p &lt; .001. This high correlation, together with the
nonsignificant difference between the means, strongly
suggests that the k estimates obtained in the two conditions
measure the same construct. 2</p>
    </sec>
    <sec id="sec-4">
      <title>Experiment 2</title>
      <p>This experiment was identical to the previous, with only one
change. We replaced the short presentation (80 msec +
mask) condition with a condition in which the participant
would see the stimulus for three times,3 each of them with a
presentation of 80 msec, followed each time by a different
mask. We assumed that, in this condition, the participant
would attend to each stimulus exactly three times.
Therefore, the long condition (in which we assume,
according to Pascual-Leone’s task analysis, k attending acts
for a total of k2 units of attentional capacity) can be
compared with a triple-short condition, in which we assume
3 attending acts for a total of 3k available units.</p>
    </sec>
    <sec id="sec-5">
      <title>Method</title>
      <p>Participants A total of 20 adults (11 women and 9 men), all
with university education, with a mean age of 21.7 years
(s.d. = 2.4) volunteered for this experiment.</p>
      <p>Materials and Procedure Everything was identical to the
previous experiment, except that the short presentation
condition was replaced by a triple-short condition, in which
the stimulus was presented for three times in a row, each
time for 80 msec, and each time followed by a different
mask.</p>
      <p>Results and discussion The way of scoring and analyzing
the data was the same as in the previous experiment, except
that, to estimate the amount k of the participant’s capacity,
in the B-E distributions r was set as k2 in the long condition
and 3k in the triple-short condition.</p>
      <p>The participants’ total number of correct responses ranged
from 87 to 129 (mean = 108.55, s.d. = 11.86) with short
presentation, and from 115 to 137 (mean = 123.80, s.d. =
5.96) with long presentation. The effect of presentation time
was significant, t(19) = 6.69, p &lt; .001. This shows that,
compared with long presentation, also the triple-short
presentation actually reduced participants’ ability to detect
the relevant features.</p>
      <p>Assuming k attending acts with long presentation and 3
with short presentation, the mean estimated value of k was
6.25 (s.d. = 1.59) with long presentation, and 6.30 (s.d. =
2.23) with triple-short presentation. The difference between
these means was nonsignificant, t(19) = -.12, p &gt; .90.</p>
      <p>The correlation between the estimates of k obtained with
long and short presentation was significant, r(18) = .53, p &lt;
.02. Once again, the significant correlation between the two
estimates of k, together with the nonsignificant difference
between their means, indicates that the k estimates obtained
in the two conditions measure the same construct.4 The
2 The estimate of a participant’s k is based on the distribution of
correct responses in the whole task. As a proxy to reliability of
measurement, we computed the correlation between the number of
correct responses in the first and second half of the task, which was
r = .88 for short presentation and r = .68 for long presentation.
3 The authors are very grateful to Nelson Cowan for suggesting
this experiment.</p>
      <p>4 In this experiment, the correlation between the number of
correct responses in the first and second half of the task was r = .73
for short presentation and r = .59 for long presentation.
findings of the first experiment can be generalized to the
comparison between the two conditions of the current one.</p>
    </sec>
    <sec id="sec-6">
      <title>Fit of Probability Distributions</title>
      <p>Both experiments reported above included conditions in
which the manipulation of presentation times ensured that
participants could attend to the stimulus only once or three
times, respectively. The estimates of k obtained with long
presentation were equivalent to those obtained, respectively,
with short or triple-short presentation; therefore, we can
conclude that the assumption of k attending acts in the long
condition was supported. It can also be concluded that the
capacity estimates obtained from the CSVI task with short
or long presentation have a similar degree of validity.</p>
      <p>
        However, it remains to examine whether the distribution
of correct responses in the CSVI task actually approximates
the B-E distribution. In the short and triple-short conditions
we only have the data of 20 participants per condition – too
few for assessing the form of their distributions. In the long
condition, however, we can use the data from 60 people,
i.e., 20 from each of the experiments reported above, and 20
more from another similar experiment
        <xref ref-type="bibr" rid="ref16">(Morra &amp; Patella,
2012, Exp.1)</xref>
        . Because each participant performed 4 trials
per level, with 60 participants we can rely on 240 data
points for each of the distributions from level 2 to 8 in the
long condition.
      </p>
      <p>The participants were classified according to their k-value
estimated in the long presentation condition. B-E
distributions were generated for all values of n from 2 to 8
and all values of k from 4 to 9 (i.e., for the complete range
of values found in the participants). Then, expected
distributions for the total sample were obtained, for each n,
as a weighted average of the distributions for each k value,
with weights proportional to the number of participants who
obtained that k value. The goodness of fit of these
distributions to the data was assessed by mean of chi-square
tests. For these tests, whenever the expected frequency of a
value of x (i.e., for a certain number of correct responses)
was &lt; 1, both the expected and the observed frequencies for
that x were collapsed with the following value of x.</p>
      <p>The observed distributions and the distributions predicted
from the B-E model are shown in Figure 1. Table 1 presents
the goodness of fit of the B-E model (i.e., the chi-squares
for the comparisons between observed and expected
distributions, along with their probabilities) for each of
these distributions.</p>
      <p>As an alternative model, to be contrasted with the B-E
model, we devised a binomial model. In this binomial
model we assumed that, when a stimulus was presented, at
least one feature would be detected, and the other features
would be detected with a certain probability p, to be
estimated from the data. The estimated value of p was .866.
This alternative model has some face plausibility, because it
makes simple assumptions on dichotomous events (each
feature can either be detected or not), but it does not assume
limited attentional capacity or indistinguishable units of
attentional resources. The goodness of fit of the
distributions predicted by this binomial model was tested in
the same way as for those predicted by the B-E model.</p>
      <p>The distributions predicted from the binomial model are
also shown in Figure 1. Table 2 presents the goodness of fit
of the B-E model for each of these distributions.
(Note: * p&lt;.05, ** p&lt;.01, *** p&lt;.001 for the discrepancy
between an observed and an expected distribution)</p>
      <p>Both Figure 1 and Table 1 indicate that the Bose-Einstein
distributions fit the data reasonably well. Four out of seven
distributions showed a good fit (p &gt; .1) and in two other
cases (n=2 and n=4) the discrepancies between expected and
observed distributions, although significant, were actually
very small. Only for n=8 there is some notable difference
between observed and observed distributions, the observed
scores being slightly lower than predicted by the model.</p>
      <p>Figure 1 and Table 2 show that the binomial model,
instead, did not fit well the data. Only two of the seven
distributions fit the data well, and in the other five cases the
discrepancies between observed and expected distributions
were much larger. Also in the case (n=8) where the fit of the
B-E model was least satisfactory, still the B-E model was
much closer to the observed data than the binomial model
was.</p>
      <p>One could still wonder whether the good fit of the B-E
model to the data was not an artifact, due to the calculation
of a weighted average of six B-E distributions (for the six
estimated values of k found in different participants). To
check for this possibility we computed, in the same way as
above, the goodness of fit of 42 B-E distributions (i.e., 7
values of n times 6 values of k), in order to detect any
possible bias or interaction between k values and the fit of
the distributions. We do not report here the details of this
analysis, but we only mention that, out of 42 tests, only 4
showed a significant (p&lt;.05) discrepancy between the
observed and expected distributions. In particular, the
participants with k=5 performed better than predicted on
level 2 stimuli, and with smaller variance than predicted on
level 7 stimuli; the participants with k=7 performed better
than predicted on level 4 stimuli; and the participants with
k=9 performed better than predicted on level 7 stimuli. No
systematic bias or effect for different values of k could be
detected, and therefore we can rule out the possibility that
there was any artifact due to averaging B-E distributions for
different groups of participants.</p>
    </sec>
    <sec id="sec-7">
      <title>Conclusions</title>
      <p>A detailed comparison between the predicted and observed
distributions supported the validity of the B-E model, with
parameters n and k2, for the number of features that
participants can detect in stimuli presented for 5 sec. The
results of both experiments 1 and 2 showed that the
estimates of k obtained for each participant from stimuli
presented for 5 sec do not differ from, and correlate highly
with, the estimates obtained in conditions of shorter
stimulus presentation.</p>
      <p>Therefore, all of the results of this study support the view
that the B-E model, with the assumption of k attending acts
to stimuli presented for 5 sec, with k units of attentional
capacity available on each attending act, offers a valid and
reliable estimate of the participants’ attentional capacity.</p>
      <p>The estimated capacity of the participants in both
experiments, averaged across experiments and conditions,
was 6.5.</p>
      <p>
        This study differed from the original
        <xref ref-type="bibr" rid="ref18">(Pascual-Leone,
1970)</xref>
        because the participants were adults instead of
children, the responses were given pressing different keys
on a special keyboard, the stimuli were presented for either
5 sec or shorter times, and the testing technology and the
statistical tools were more refined than they could be when
the original study5 was carried out. Despite all these
differences and, in some cases, methodological refinements,
all of the results supported the original B-E model. We can
conclude that the CSVI task, analyzed according to the B-E
model, can be used reliably to estimate the limits of
attentional capacity.
      </p>
      <p>
        It would be useful to compare the estimates of k derived
from the CSVI with the capacity estimates obtained with
other procedures, such as complex span or change detection.
Complex span tasks
        <xref ref-type="bibr" rid="ref3">(e.g., the counting span; Case, 1985)</xref>
        are often used as working memory measures; a comparison
would require modelling the encoding and retrieval
operations of the memory task, as well as the capacity
demands of the interpolated task. Other researchers
        <xref ref-type="bibr" rid="ref4">(e.g.
Cowan, 2001)</xref>
        used a visual array task to derive capacity
estimates that are generally lower than the ones proposed by
Pascual-Leone, and obtained here.
        <xref ref-type="bibr" rid="ref16">Morra and Patella (2012)</xref>
        suggested that this discrepancy could be explained, noting
that Cowan’s estimate only considers the declarative
information involved in the visual array task, but if one also
takes into account the attentional capacity allocated to the
procedural information, then the two estimates come closer.
Space limitations prevent extensive discussion here of this
problem, which will be the topic of a subsequent paper.
5
        <xref ref-type="bibr" rid="ref18">Pascual-Leone (1970)</xref>
        averaged the distributions of correct
responses across levels (i.e., values of n) into a single distribution,
and evaluated visually the correspondence between the expected
and observed distributions. In this study, we used current
techniques to evaluate the goodness of fit of the expected
distribution for each value of n.
      </p>
    </sec>
  </body>
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