------------------------------
Discussion
The present study revealed an important new result: Controlling
crystallized intelligence did not have an impact on the explained
variance betweenWMand reasoning. Moreover, the study replicated that
WM and sustained attention together account for about 83% of reasoning
variance. Also consistent with previous findings, we confirmed that
storage in the context of processing and coordination were significant
predictors of reasoning. As hypothesized, we could show that
coordination and sustained attention were highly correlated. Finally,
the correlations between all factors can be explained by a speed and g
factor. All tasks showed significant loadings on the speed factor,
while only storage in the context of processing, coordination, and
intelligence tasks loaded significantly on g. If we control for g, we
can explain the remaining correlations by speed. Supervision predicted
speed and storage in the context of processing. The first goal of the
present study was to confirm that the relationship between WM and
reasoning does not decrease if we control for crystallized
intelligence.As pointed out by Oberauer et al. (2005), there is a
mismatch between WM and intelligence constructs. It was assumed that
intelligence relies on conceptual structures while WM does not.
As a consequence, the relationship between WM and reasoning should not
decrease if crystallized intelligence is controlled. This could be
shown within the present study. The explained variance of WM and
sustained attention factors predicting reasoning was 83% and thus
lower than that
obtained by Buehner et al. (2005). But it did not change when
crystallized intelligence was controlled for. In addition,
the central findings by Buehner et al. could be replicated: The
factors coordination and storage in the context of processing were
significant predictors of reasoning. However, the importance of the
predictors was slightly reversed: Coordination seemed to be a more
important predictor of reasoning than storage in the context of
processing. This might be the result of sample fluctuations. All in
all, two factors of WM serve as significant predictors of reasoning:
coordination and storage in the context of processing. Since construct
reliability of WM turned out to be low, we applied an additional path
analysis to predict reasoning using aggregated z-scores.
Unfortunately, aggregates in this special case have the undesirable
property that content variance is mixed up with variance from
functional facets (positively correlated error variance). This leads
to systematically increased variances of the aggregates. Despite this,
and despite a higher reliability of aggregates, the variance between
WM and reasoning was only about 50% (see also Buehner et al., 2005;
Wittmann & Suess, 1999). This is exactly the amount assumed by Kane et
al. (2005). However, they probably based their assumptions on
correlations between latent factors and did not consider coordination
in their calculation. In contrast to this, Ackermann et al. (2005)
claimed that the common variance between intelligence and WM is much
lower than suggested by Kane et al.. This cannot be confirmed within
the present study: using a carefully selected set of tasks for two
central WM components (coordination and storage in the context of
processing) led to more common variance between WM factors and
reasoning than reported by Ackermann et al.. Taking into account that
the sample in the present study was homogeneous and, thus, restricted
in range, the common variance between WM and reasoning might even be
underestimated. All in all, it is reasonable to assume that
the explained variance of reasoning predicted by WM obtained in
multiple regression analyses probably represents a lower bound. One
might argue that the results of confirmatory factor analyses in the
present study suggest that WM and reasoning were almost identical when
controlling for measurement error: WM and reasoning shared about 83%
of variance. This result is clearly in line with findings obtained by
Buehner et al. (2005), Colom et al. (2004), and Colom, Abad, Rebollo,
and Shih (2005). Again, considering that our sample was restricted in
range, one might suppose that both constructs are indeed identical or
isomorphic constructs. But we do not believe that: As pointed out, SEM
results lead to an overestimation of the common variance between
reasoning and WM when the construct reliability (of the factors) is
low. This might have occurred since the communality estimates in SEM
were dramatically lower than the reliability estimates calculated by
Cronbach's α.
Things get even more complicated since Cronbach's α represents an
upward-biased estimate of reliability when correlated errors occur as
it is the case in our models. If measures are tau-congeneric,
Cronbach's α is biased downward. This is also the case in the present
study. The two biases might cancel each other. Regarding construct
validity, the reliability estimates were considerably higher than
communalities, indicating that the true reliability might be
considerably higher than communality estimates in SEM but lower than
Cronbach's α. Thus, SEM results can lead
to an overestimation of correlations between latent variables. This
was the case in the present study and probably holds true for several
other studies as well (see Buehner et al., 2005). Moreover, if a
correlated trait correlated uniqueness (CTCU) model is applied, the
trait factor loadings are likely to be larger than for correlated
trait correlated method models (CTCM; Lance, Noble & Scullen, 2002).
Since we used CTCU models to ensure model identification, it is
reasonable to assume that loadings are biased upward. Thus, the
explained variance of reasoning might be overestimated (see also
Lance, Lambert, Gewin, Lievens, & Conway, 2004). We believe that, for
these reasons, WM and reasoning are not identical. All in all, we
believe that Oberauer et al. (2005) made a reasonable estimate of how
strongly WM and reasoning overlap, namely, about 70%. Another goal was
to clarify the relationship between coordination and sustained
attention. Through SEM and path analysis it could be confirmed that
sustained attention and coordination are highly correlated. The
correlation might even be larger for the following reasons: First of
all, in our study the coordination tasks differ in several ways from
the applied sustained attention tests. Three sources of method
variance might have reduced the correlation between coordination and
sustained attention: The d2 was administered as a paper-and-pencil
test, the factor sustained attention contained only figural content,
and the factor coordination consisted of four tasks, while sustained
attention of only two (lack of symmetry). Consequently, future studies
applying balanced sets of coordination and sustained attention tasks
should reveal an even higher correlation. If that were the case, how
should we interpret this similarity of concepts? Do sustained
attention tests and coordination tasks simply assess an integration
function? Or are coordination and sustained attention linked by speed?
The present study reveals that the variance shared by coordination and
sustained attention results from speed and not from an integration
function. If we control for speed, only a small correlated error
variance between both factors remained which failed to reach
significance. This might serve as explanation for results found by
Schweizer and Moosbrugger (2004), whose study revealed an incremental
validity of the FACT in predicting reasoning above and beyond storage
in the context of processing. Our results suggest that this finding
might have occurred because of speed. We also assumed that speed might
be responsible for the correlation between storage in the context of
processing and coordination. This was confirmed within the present
study. The variance not attributed to g can be attributed to speed.
All other cognitive task aggregates also have significant loadings on
speed. Speed in turn can be predicted by supervision, which might
enhance the application of speed. As mentioned in the Introduction,
the application of speed improves the performance in speeded cognitive
tasks. This also holds true for storage in the context of processing.
However, there also exists a direct path from supervision to storage
in the context of processing. This is in line with Kane and colleagues
and can be explained with attentional control. Unfortunately, the WM
model applied in the present study does not include inhibition. On the
basis of the proposals by Kane et al. (2005), it is reasonable to
implement inhibition into this model. Concerning the last model
presented, it is important to remember that g is a very illusive
construct and is measured differently in every study. If we disregard
this aspect, we might conclude that WM is g plus speed. However, they
only explain between 50% and 60% of WM variance. Looking at the
reliability ofWMaggregates, there is either much specificity left or
much more to be explained. Colom et al. (2005) showed that one source
of variance neglected in this study is short-term memory (STM).
Thus, WM might be decomposed in variance from reasoning or g, variance
from speed, variance from STM, content variance, and functional
variance (the latter two according to Oberauer et al., 2003). This
directs the way of future research: Only a model that includes all
possible sources of variance could enhance our understanding of
cognitive abilities and their interplay.
On Sep 22, 11:23 pm, genvirO <carsthatdr...@hotmail.com> wrote:
> This is interesting stuff! If anyone wants any of the articles just
> let me know.
> -------------------------------------------------------------------------------------------------------
> I haven't checked the reference section but I think this may be a
> follow up study...
>
> Title: (2006) Cognitive Abilities and Their Interplay: Reasoning,
> Crystallized Intelligence, Working Memory Components, and Sustained
> Attention
>
> Journal of Individual Differences
> Volume 27, Issue 2, June 2006, Pages 57-72
>
> Link -http://www.sciencedirect.com.ezproxy.lib.swin.edu.au/science/article/...
>
> Abstract
> The aim of this study was to confirm that coordination and storage in
> the context of processing are significant predictors of reasoning even
> if crystallized intelligence is controlled for. It was also expected
> that sustained attention and coordination would be highly correlated.
> Therefore, 20 working memory tests, 2 attention tests, and 18
> intelligence subtests were administered to 121 students. We were able
> to replicate results indicating that storage in the context of
> processing and coordination are significant predictors of reasoning.
> Controlling for crystallized intelligence did not decrease the common
> variance between working memory and reasoning. The study also revealed
> that the factors coordination and sustained attention were highly
> correlated. Finally, a model is presented with the latent variables
> speed and g, which can explain almost all of the common variance of
> the applied aggregates. A detailed discussion of the results supports
> the view that working memory and intelligence share about 70% of the
> common variance.
>
> On Sep 22, 7:27 pm, Colin Dickerman <collin.silvern...@gmail.com>
> wrote:
>
>
>
>
>
>
>
> > Chimps have more working memory than humans!?
>
> > I saw a documentary that featured a really smart crow. They have tiny
> > brains, but can understand and use tools. How long did it take us to
> > invent the wheel?
>
> > On Sep 21, 9:52 pm, Mike <mikebk...@gmail.com> wrote:
>
> > > Fascinating. Thanks for posting. It makes sense from a computational point
> > > of view: reasoning requires recursively confronting different pieces of
> > > information, in a kind of trial&error. The wider the trial (storage and
> > > coordination of that storage) the better the result.
> > > What do you think of this?
>
> > > Also, chimps have a better working memory than humans, and are indeed
> > > observed to be extremely inventive in many experiments, often more than the
> > > average human.
> > > What do you think of this second idea? Confirmations and infirmations
> > > welcome.
>
> > > On Thu, Sep 22, 2011 at 12:32 AM, genvirO <carsthatdr...@hotmail.com> wrote:
> > > > So, based on there findings they cited two important WM elements that
> > > > are highly predictive of reasoning ability.
> > > > 1. Storage in the context of processing
> > > > 2. Coordination
>
> > > > What do these two elements relate to? Well, the following info
> > > > describes what is meant here based on the measures used in the study.
>
> > > > 1. Storage in the context of processing tasks
>
> > > > The "storage in the context of processing" component of the working
> > > > memory model was assessed by dual tasks. One processing and one
> > > > storage task were combined for each trial. The procedure was as
> > > > follows: First, the materials to be remembered were presented one
> > > > immediately after another (1-s inter-stimulus interval). Second,
> > > > participants had to perform a series of CRTs described above, which
> > > > were unrelated to the material to be remembered. The CRTs lasted for 5
> > > > s (no matter how many trials the participants had performed within
> > > > this time) to keep the time between learning and recall constant and
> > > > to measure the recall independent of the processing speed. Finally,
> > > > the participants were asked to recall the memory set (see Fig. 1).
>
> > > > The materials to be remembered were either nouns, digits, patterns
> > > > (3×3 matrix, partially filled), or spatial locations of dots. The
> > > > stimuli always had to be recalled in the correct order. For dual tasks
> > > > with verbal material nouns had to be recalled and, in between, CRT
> > > > categories task had to be performed. The number of nouns to be
> > > > remembered increased from 3 to 7. Numerical dual tasks combined CRT
> > > > odd–even tasks and a series of digits to be remembered. Three items
> > > > were administered for each memory load, whereas memory loads varied
> > > > from 4 to 8 digits. Also, two spatial dual tasks were applied. The
> > > > first one combined CRT pattern symmetry with a task where the spatial
> > > > location of dots presented (within a rectangle frame) had to be
> > > > remembered. In the course of the second spatial task, participants had
> > > > to remember several partially filled 3×3 patterns and perform CRT
> > > > arrows up–down tasks. The spatial dual tasks consisted of memory loads
> > > > varying from 2 to 4, each level represented by five items.
>
> > > > Two scores were obtained from these dual tasks: the number of elements
> > > > correctly remembered (memory performance) and the log-transformed
> > > > reaction times for the CRT subtasks. Since the correlations between
> > > > these two subtask scores were low, and since it is common practice to
> > > > evaluate storage and processing tasks according to memory performance
> > > > only (e.g., Daneman & Carpenter, 1980), the analyses were based only
> > > > on the dual tasks' memory scores.
>
> > > > --------------------------------------------------------------------------- -
>
> > > > 2. Coordination tasks
>
> > > > The "coordination" component of the working memory model was measured
> > > > by monitoring tasks. Changing relations between several independently
> > > > changing objects had to be monitored. Participants were instructed to
> > > > detect certain critical relations. In order to compute and to
> > > > continuously update the relations between the objects, simultaneous
> > > > access to them was required.2
>
> > > > The verbal monitoring task consisted of a 3×3 matrix with a word in
> > > > each of the nine cells. One randomly chosen word was replaced every 2
> > > > s. The space bar had to be pressed whenever three rhyming words were
> > > > presented in either the horizontal, vertical, or diagonal line. During
> > > > one trial, 2 to 5 target rows appeared within 10 to 20 replacements.
> > > > In the numerical monitoring task, three-digit numbers were presented
> > > > in each of the 9 cells. Rows with equal last digits had to be
> > > > detected. One randomly chosen number changed every 1.5 s. After each
> > > > trial, feedback about hits, misses, and false alarms was presented.
> > > > Scores were obtained by subtracting false alarms from hits.
>
> > > > "Flight control" was the first spatial monitoring task. A number of
> > > > airplanes (ranging from 5 to 9 during the 15 items) represented by
> > > > triangles moved across the screen in various directions with 4
> > > > different speeds. Mountains (clusters of brown squares) were located
> > > > on the screen. Unpredictably, airplanes appeared on the border of the
> > > > screen. Their flight direction maintained the same until they left the
> > > > screen. The instruction was to monitor that no plane crashed either
> > > > with another plane or a mountain. Plane movement could be stopped by
> > > > pressing the space bar, then one plane had to be chosen by mouse click
> > > > and redirected. Traffic started again after pressing the space bar.
> > > > The participants were told that they started with 100 credit points at
> > > > each trial. Each crash would cost 10 points and each movement stop 3
> > > > points. The goal was to avoid crashes and to stop the planes as seldom
> > > > and as briefly as possible. Duration of movement stops was also
> > > > measured. Without interruption each trial lasted about 12 s. Feedback
> > > > was given after each trial regarding the number of crashes, the
> > > > remaining points, and the cumulative duration of movement stops.
> > > > Scores were obtained by counting the number of crashes (see Fig. 2).
>
> > > > "Finding squares," the second spatial coordination task, consisted of
> > > > 8 to 12 red dots randomly located within a 10×10 matrix. Two randomly
> > > > chosen dots changed their position every 1.5 s. Twenty items were
> > > > presented. Participants had to press the space bar whenever four dots
> > > > formed a square. Position and size of the square were not relevant.
> > > > Scores were obtained by subtracting false alarms from hits.
>
> > > > On Sep 22, 2:19 pm, genvirO <carsthatdr...@hotmail.com> wrote:
> > > > > Pretty interesting article!
> > > > > ----------------------------------
>
> > > > > (2005) Title: Reasoning=working memory=attention
>
> > > > > Date - Available online 2 March 2005
> > > > > Journal - Intelligence 33 (2005) 251–272
>
> > > > > Link -
> > > >http://www.sciencedirect.com.ezproxy.lib.swin.edu.au/science/article/...
>
> > > > > Abstract -
> > > > > The purpose of this study was to clarify the relationship between
> > > > > attention, components of working memory,
> > > > > and reasoning. Therefore, twenty working memory tests, two attention
> > > > > tests, and nine intelligence subtests were
> > > > > administered to 135 students. Using structural equation modeling, we
> > > > > were able to replicate a functional model of
> > > > > working memory proposed by Oberauer, Suess, Wilhelm, and Wittmann
> > > > > (2003) [Oberauer, K., Suess, H.-M.,
> > > > > Wilhelm, O., & Wittmann, W. W. (2003). The multiple faces of working
> > > > > memory: Storage, processing,
> > > > > supervision, and coordination. Intelligence, 31, 167–193]. The study
> > > > > also revealed a weak to moderate relationship
> > > > > between the selectivity aspect of attention and working memory
> > > > > components as well as the finding that
>
> ...
>
> read more »
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