I wrote this post on March 26 as a kind of escapism, under four barrages of ballistic missiles fired at my town by the Iranians. I was sitting in my safe room, which is a concrete-reinforced room that can protect you from a nearby hit, but is far from good-enough to withhold a direct hit. But our defense system worked well that day, and there was not even a nearby hit. It has been quiet here since March, but in our part of the world, it is always temporary.
And now more to the point:
Processing
speed is sometimes considered a simple, technical ability, unrelated to
higher-order thinking, and one whose contribution to general intelligence is
low.
However, it turns out that processing speed
plays an important role in the development of abstract thinking. In a series of studies shown in
the table below, a developmental cascade was found: an age-related increase in
processing speed leads to an increase in working-memory capacity, which in turn
leads to an increase in fluid ability. Because this phenomenon
appears repeatedly in many studies, it is probably very robust.
What causes this cascade? The ability to
think and solve problems requires manipulating information that is held in
working memory. The number of items we can hold in working memory is limited,
and these items fade if we do not rehearse them “in our head,” or they are lost
because of interference. Salthouse
(1996) argues that faster processing improves working memory by allowing a
person to complete the thinking process before the information in working
memory fades. Processing
speed also helps complete the thinking process when environmental conditions
limit the time during which the information is available, for example when
processing spoken language.
Because the amount of information that can be held in working memory
limits thinking ability, higher working-memory capacity is associated with
better thinking — that is, with higher fluid ability.
The cascade model emphasizes that cognitive abilities influence one
another and do not operate separately. Therefore, when a child has several weak
abilities, the combined effect of all the weaknesses is greater than the effect
of each weakness separately. Each of the weak abilities “pulls” the other weak
abilities even further down.
When a child has several weak abilities, it
is useful to think about where the core difficulty lies: which of the weak
abilities is the main one impairing the child’s functioning — in reading,
writing, arithmetic, and/or social and emotional functioning — and how this
ability negatively affects the other abilities.
When the weak abilities are processing speed,
working memory, and fluid ability, the cascade model points to processing speed
as the primary focus of difficulty, which then leads to lower working memory
and lower fluid ability. Thus, reduced processing speed may limit the
development of fluid ability.
Studies that examined the cascade model
|
Study |
What
they did |
What
they found |
|
Fry
and Hale, 1996 |
Children
aged 7–19 completed tests assessing processing speed, working memory, and
fluid ability (Raven’s Matrices). |
Age-related
increases in processing speed led to increases in working-memory capacity,
which led to increases in fluid ability. |
|
Kail,
2007 |
Children
aged 8–13 completed tests assessing processing speed (Woodcock Visual
Matching and rapid picture search), working memory, and fluid ability
(Raven’s Matrices). The children were tested again one year after the first
assessment. |
Age-related
increases in processing speed led to increases in working-memory capacity,
which led to increases in fluid ability. This was found both across the
different age groups and between the first and second measurements. |
|
Nettelbeck
and Burns, 2010 |
Children
aged 8–14 completed tests assessing processing speed, working memory, and
fluid ability. |
Age-related
increases in processing speed led to increases in working-memory capacity,
which led to increases in fluid ability. |
|
De
Alwis et al., 2014 |
Children
aged 6–12 completed tests of processing speed, working memory, learning
efficiency (similar to the Rey Auditory Verbal Learning Test — RAVLT), and
fluid ability (Matrix Reasoning from the WISC-IV and Raven’s Matrices). |
Age-related
increases in processing speed led to greater working-memory capacity, which
led to higher fluid ability. Learning efficiency did not explain unique
variance in fluid ability beyond the variance explained by working memory. |
|
Kail
et al., 2016 |
Children
aged 6–13 completed six testing sessions over two and a half years, in which
processing speed (Woodcock Visual Matching and rapid picture search), working
memory, and fluid ability (Raven’s Matrices) were assessed. The children’s
performance was compared across three time points. |
Processing
speed led directly to improvement in fluid ability. Weaker evidence was found
for an effect of processing speed on fluid ability through working memory.
Working memory at the first time point predicted fluid ability at the second
time point. |
|
Tourva
and Spanoudis, 2020 |
Participants
aged 7–18 completed tests assessing processing speed (reaction time to simple
stimuli, for example deciding as quickly as possible whether an arrow points
right or left), inhibition, working memory, fluid ability (Block Design and
Matrices), and crystallized knowledge (Vocabulary and Similarities). |
Age-related
increases in processing speed led to improvements in inhibition and working
memory. Improvement in working memory led to increases in fluid ability and
crystallized knowledge. |
Can intervention targeting processing speed have a positive effect on working memory and fluid ability? Alison Mackey (Mackey et al., 2011) trained children aged 7–9 using two types of intensive training. One group trained fluid ability through games that emphasized planning and integration. A second group trained processing speed through games that emphasized rapid processing and fast motor responses. Both groups completed tests assessing fluid ability (a matrices test) and processing speed (the Wechsler Coding test and the Woodcock rapid picture-search test) before and after training.
The children in the fluid-ability training
group greatly improved their performance on the matrices test after training,
but did not improve their performance on the processing-speed tests. The
children in the processing-speed training group greatly improved their
performance on the processing-speed tests, but did not improve their
performance on the matrices test. In other words, processing-speed training did not have a positive
effect on fluid ability. Disappointing!
On the other hand, this study shows that processing-speed training can
successfully improve processing speed, and that training fluid ability can
successfully improve fluid ability. Moreover, participants who
received low scores on the matrices test before training improved more on this
test following fluid-ability training than participants who had received high
scores on the matrices test before training. These findings remind us that children’s performance on
tests that assess fluid ability is influenced by exposure and experience with
similar tasks or similar ways of thinking — experience to which children from
less enriching backgrounds are less exposed. It is important to take this into
account when evaluating fluid ability in children from less enriching
backgrounds. When such children are given an opportunity, they have a strong
chance of improving their fluid ability.
De Alwis D, Hale S,
Myerson J. Extended cascade models of age and individual differences in
children’s fluid intelligence. Intelligence. 2014; 46:84–93. doi:
10.1016/j.intell.2014.05.008
Fry, A. F., & Hale,
S. (1996). Processing speed, working memory, and fluid intelligence: Evidence
for a developmental cascade. Psychological Science, 7, 237–241.
Fry, A. F., & Hale,
S. (2000). Relationships among processing speed, working memory and fluid
intelligence in children. Biological Psychology, 54, 1–34.
Kail, R. V. (2007).
Longitudinal evidence that increases in processing speed and working memory
enhance children's reasoning. Psychological Science, 18(4), 312–313. https://doi.org/10.1111/j.1467-9280.2007.01895.x
Kail, R. V., Lervåg, A.,
& Hulme, C. (2016). Longitudinal evidence linking processing speed to the
development of reasoning. Developmental Science, 19(6), 1067–1074.
Mackey AP, Hill SS, Stone
SI, Bunge SA. Differential effects of reasoning and speed training in children.
Developmental Science. 2011; 14:582–590. doi: 10.1111/j.1467-7687.2010.01005.x.
[PubMed: 21477196]
Nettelbeck, T., & Burns,
N. R. (2010). Processing speed, working memory and reasoning ability from
childhood to old age. Personality and Individual Differences, 48(4), 379–384.
Salthouse, T. A. (1996).
The processing-speed theory of adult age differences in cognition.
Psychological Review, 103, 403–428.
Tourva, A., &
Spanoudis, G. (2020). Speed of processing, control of processing, working
memory and crystallized and fluid intelligence: Evidence for a developmental
cascade. Intelligence, 83, 101503.
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