Colom(2016)advances in intelligence

The Spanish Journal of Psychology (2016), 19, e92, 1–8.
© Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid
doi:10.1017/sjp.2016.87
Making general predictions about the future is a
risky business. Science is no exception. This mono-
graph includes several articles, produced by an impres-
sive panel of experts, covering a wide set of topics
ranging from the measurement of intelligence to the
research of the genetic underpinnings of this latent
psychological trait of paramount relevance for under-
standing human behavior (Cooper, 2015; Detterman,
2016; Hunt, 2011a; Sternberg & Kaufman, 2011). Before
describing the results of the interchange between the
experts participating on the international seminar
and myself (the discussant here), I will present some
thoughts in three sections: unsolved problems, APA
(American Psychological Association) reports on intel-
ligence, and interlude.
Nevertheless, I would like to briefly declare from
the outset my own view about the future of intelli-
gence research. This view has been explained in two
short articles (Colom, 2014a,b) and one long keynote
address (Colom, 2015). In essence, I predict remark-
able advances relying on technical developments
devoted to the exhaustive analysis of brain features
supporting intelligent behavior. I strongly think that
a deep understanding of what is going on in our
brains when the genotype and the environment are
integrated –The Brain Connection (TBC)—will set
up the framework for a deep understanding of every-
thing else relevant for intelligent behavior.
In 2013, the Journal of Intelligence invited a group of
researchers to answer the next question: ‘Intelligence,
Where to Look, Where to Go?’ In some way, this resem-
bles the goal pursued by the International Seminar we
are discussing here. I contributed to the published vol-
ume and my general perspective was summarized by
the title of one of my comments: ‘All We Need Is Brain
(and Technology)’. After acknowledging the complex-
ities of the human brain, I suggested that the scien-
tific community interested on the intelligence construct
should concentrate its resources on the brain for
answering how it produces intelligence. For stimulating
this process our involvement in large-scale research pro-
grams, such as the BRAIN Initiative and the Human
Brain Project, would be required (Colom, 2014b).
The unsolved problems of…
Speaking of the brain, Ralph Adolphs published in
2015 one short and thought-provoking article address-
ing the unsolved problems of neuroscience. He identi-
fied three meta-questions:
a. What counts as understanding the brain?
b. How can a brain be built?
c. What are the different ways of understanding the
brain?
He highlighted that the most important unsolved
problem is “how the brain generates the mind”. This can
Advances in Intelligence Research: What Should be
Expected in the XXI Century (Questions Answers)
Roberto Colom
Universidad Autónoma de Madrid (Spain)
Abstract. Here I briefly delineate my view about the main question of this International Seminar, namely, what
should we expecting from the XXI Century regarding the advancements in intelligence research. This view can be
summarized as ‘The Brain Connection’ (TBC), meaning that neuroscience will be of paramount relevance for increasing
our current knowledge related to the key question: why are some people smarter than others? We need answers to
the issue of what happens in our brains when the genotype and the environment are integrated. The scientific com-
munity has devoted great research efforts, ranging from observable behavior to hidden genetics, but we are still far
from having a clear general picture of what it means to be more or less intelligent. After the discussion held with
the panel of experts participating in the seminar, it is concluded that advancements will be more solid and safe
increasing the collaboration of scientists with shared research interests worldwide. Paralleling current sophisti-
cated analyses of how the brain computes, nowadays science may embrace a network approach.
Received 16 May 2016; Revised 13 October 2016; Accepted 20 October 2016
Keywords: cognitive ability, individual differences, intelligence, neuroscience.
Correspondence concerning this article should be addressed to
Roberto Colom. Universidad Autónoma de Madrid. 28049. Madrid
(Spain).
E-mail: roberto.colom@uam.es
I thank Ma Ángeles Quiroga and Richard J. Haier for their comments
to a draft of this article.
http://dx.doi.org/10.1017/sjp.2016.87
Downloaded from http:/www.cambridge.org/core. New York University Libraries, on 15 Dec 2016 at 16:33:35, subject to the Cambridge Core terms of use, available at http:/www.cambridge.org/core/terms.

2 R. Colom
be translated into The Brain Connection (TBC) view
(Colom, 2015): the biggest unsolved problem is how
the brain generates intelligence from the genotype and
from the environmental relevant ingredients.
Nevertheless, perhaps the most suggestive issue dis-
cussed by Adolphs relates to the distinction among
different types of problems: (four) problems that are
solved (or soon will be), (eight) problems that we
should be able to solve in the next fifty years, (five)
problems that we should be able to solve but who
knows when, and (three) problems we may never
solve (see his Box 2).
We already know how single neurons compute
and what is the connectome of small nervous systems,
but we still lack reliable knowledge regarding how
neuronal circuits compute or how brains produce
decisions. Consistent with TBC view, Adolphs thinks
we should be able to solve two key problems:
a. What is the complete connectome of the human
brain?
b. How could we make everybody’s brain function
best?
In this respect, on his monumental book about
intelligence research, Hunt (2011a) admits the cru-
cial role of the brain for understanding human intel-
ligence. Indeed, everything related with intelligent
behavior is supported by actions taking place in our
brains: “if we knew the nature of every connection between
the approx. five billion neurons in a person’s brain, and
if we knew the algorithms the brain uses to activate
and alter these connections, we would know everything
there is to know about that person’s cognition” (Hunt,
2011a).
On his recent inspiring book about the neurosci-
ence of intelligence, Richard J. Haier acknowledges
that, at the end of the day, scientific research is ori-
ented towards the key goal of finding efficient ways
for enhancing the intelligence level of the popula-
tion (Haier, 2016). But for achieving this ultimate
(and desirable) purpose, we must understand how
our brains produce our intelligence (TBC again).
Let me close this section noting that Ralph Adolphs
considers that the ‘mind’ includes cognition, compu-
tation, information processing, thinking, and reasoning.
Interestingly, all these features are comprised by the
shared definition of intelligence: “a very general mental
capability that, among other things, involves the ability to
reason, plan, solve problems, think abstractly, comprehend
complex ideas, learn quickly, and learn from experience”
(Gottfredson, 1997). Adolphs himself goes further by
assuming that all these features are somehow related.
Intelligence researchers know very well what this fact
involves. I will return to this key issue below.
Intelligence according to the American Psychological
Association (1996–2012)
The APAreports on intelligence published fifteen years
apart are useful here because they identified ‘knowns
and unknowns’ within the intelligence research area.
These were the main points noted by Neisser et al.
(1996) after their exhaustive revision of the available
evidence:
- Research has identified correlations of cognitive
(information-processing) and brain measures with
psychometric intelligence. However, we do not
know why these factors are related.
- Genetic differences contribute to intelligence differ-
ences. However, we do not know the mechanics
linking genes and behavior.
- The impact of genetic differences increases across
the life span. However, we do not know why this
happens.
- We know the environment is important, but we
still lack solid knowledge regarding the specific
factors contributing to intelligence differences.
- Intelligence test scores have meaningfully increased
in the last decades (Flynn effect). However, scientists
still argue about the causes behind this secular trend.
- Identified average population differences (sex, race)
do not result from biases related with test construction
or test administration.
Nisbett et al.’s (2012) updated this report and five
were the most relevant points:
- There is a strong correlation between intelligence
and working memory capacity, but we still don’t
know why.
- Secular gains in intelligence (Flynn effect) are envi-
ronmentally caused and this fact is consistent with
the strong genetic influence over intelligence differ-
ences within generations.
- The general factor of intelligence (g) is unitary at
the psychometric level, but there are models sug-
gesting that it is not unitary at the cognitive and
brain levels. The ‘Process Overlap Theory’ proposed
by Kovacs and Conway (2016) is a recent example
(see Colom, Chuderski, Santarnecchi, 2016; for a
critical comment).
- Presumed non-cognitive psychological factors such
as ‘self-control’ might be relevant for understanding
intellectual performance differences.
- Finally, ‘stress’ may have some impact over the
observed group differences in intelligence.
Both reports acknowledge how research based on
psychometric methods greatly contributed to our

Advances in Intelligence Research 3
impressive current knowledge, and they also invite to
increase shared and sustained efforts for going one
step further avoiding crude political assertions. The
XXI Century must move beyond old-fashioned and
outdated arguments regarding this psychological factor.
Thus, for instance, we already know test scores do
reflect intelligence and saying that these scores are not
socially important ignores the available scientific facts.
Furthermore, these facts dramatically fail to support the
popular perspective that intelligence can be described
by a set of independent cognitive abilities. We know
these abilities are related to a substantial extent (Hunt,
2011a). Revisiting these core issues again and again is
not really smart or wise.
Interlude
In this interlude, I want to highlight, before moving
ahead, four key issues related with the presentations of
this international seminar.
First, as said before, the psychometric approach has
been very useful for expanding our understanding
of the intelligence construct. Furthermore, millions of
intelligence tests are administered annually world-
wide because they are appreciated as reliable and valid
assessment devices (Detterman, 2014). These tests mea-
sure cognitive abilities unique to the testing situation
(testing skills), cognitive abilities relevant for our
society (reasoning), and cognitive abilities common
to humankind (spatial-visual reasoning and memory)
(Hunt, 2011a). Detterman’s presentation discussed
evidence leading to the unappreciated finding that
students’ cognitive abilities measured by these stan-
dardized intelligence tests largely surpass traditionally
underscored factors, such as teachers and schools, for
predicting learning outcomes.
Classic standardized testing will be with us for
a while, but we must move beyond these classic
approaches for monitoring behavior outside standard-
ized testing. We must invest our resources for moving
from conventional testing procedures to the rigorous
measurement of intelligent behavior in settings as close
as possible to everyday life (Hunt, 2011b). In this regard,
Quiroga’s proposal for measuring intelligence using
video games seems highly relevant.
Second, general intelligence (g) is mainly related with
two information-processing cognitive abilities, namely,
working memory capacity (WMC) and speed of infor-
mation processing. Colom et al. (2016) revised this
issue at length, concluding that the strong relationship
between g and WMC can be explained by a common
capacity (abstract working memory) or the ability to
construct and maintain arbitrary bindings (relational
integration). Basic executive control processes, such as
attention, interference resolution, and inhibition, are
much less relevant in this regard. Furthermore, process-
ing speed seems to play some role only when children
or older adults are considered (Tourva, Spanoudis,
Demetriou, 2016).
Third, more than four centuries ago, Huarte de
San Juan (1575) acknowledged that intelligence must
have one strong biological substrate. We now know
this is the case, but we are also still looking for answers.
Nevertheless, we have learned many interesting
things since Huarte’s times. Stuart Ritchie, Emiliano
Santarnecchi, Adam Chuderski, and Norbert Jausovec
discussed several of these things on their presenta-
tions, mainly focused on functional data.
The available neuroscience evidence is generally
consistent with the parieto-frontal integration theory
(P-FIT) of intelligence (Jung Haier, 2007) meaning
that individual differences in frontal and parietal
regions, along with their communication, are espe-
cially relevant for supporting intelligence differences
(Barbey et al., 2012; Colom et al., 2009; Gläscher et al.,
2010; Pineda-Pardo, Martínez, Román, Colom, 2016).
However, available findings also show that different
brain networks might be relevant for different individ-
uals (Martínez et al., 2015). In this regard, the recent
meta-analysis by Basten, Hilger, and Fiebach (2015)
showed that structural and functional features of the
brain do not overlap for accounting for intelligence
differences.
Interestingly, after analyzing the relationships between
functional connectomes and almost 300 behavioral/
demographic measures in more than 450 individ-
uals, Smith et al. (2015) discovered one single mode
of population covariation. These researchers made
one straightforward parallelism between the identi-
fied population covariation and the general factor of
intelligence (g) discovered more than a century ago
by Charles Spearman. However, they underscore
the fact that the covariation goes beyond intelligent
behavior and includes further factors such as years
of education, income, or life satisfaction. The general
mode of positive (or negative) function results from
the coordinated interactions among brain networks.
This is definitely good news for those who assume
that simple explanations are better than complex ones.
In passing, note this key finding is highly consistent
with TBC view.
Finally, the goal of ‘hunting’ specific genes respon-
sible for the acknowledged genetic contribution to
individual differences in intelligence has been elusive
for more than twenty years. The recent study by Spain
et al. (2015) is a key example. They compared 1409
individuals with IQs 170 and 3253 non-overlapping
controls, failing to find significant differences at the
genetic level. This failure highlighted “the complex
genetic architecture of intelligence”. However, somewhat

4 R. Colom
surprisingly, using the UK Biobank (N = 112151)
Davies et al. (2016) have reported one successful
attempt after applying genome-wide complex trait
analysis (GCTA-GREML). They found genome-wide
significant SNP-based associations in twenty genomic
regions along with significant gene-based findings
in forty-six regions. Nevertheless, as underscored in
Danielle Posthuma presentation, novel approaches,
based on uncovering the specific paths going from the
genome to behavior, are strongly required.
Questions Answers
I made two questions to each participant on this
International Seminar. Of course, these are questions
that interested me after listening to their talks and
maybe they do not capture further issues that might
be also relevant to the field. I will divide this section
into short subsections devoted to each presentation.
Afterwards, I will describe their answer to a common
question.
Ma Ángeles Quiroga
Taking into account that she registered video game
performance across several years, observing that vol-
unteers required progressively less time for completing
the same video game, my first question was: is there
some sort of Flynn effect also affecting video game per-
formance? The answer was straightforward (and pre-
dictable): not sure. More data are required for providing
reliable responses.
The second question was based on the demonstrated
fact that playing video games requires intelligence (in
the classic sense), as shown in the thought-provoking
report by Quiroga et al. (2015): is it possible to enhance
intelligence (the general factor, to be more specific) just
playing a lot on a regular basis? The answer was ‘prob-
ably not’: maybe in the long run, playing video games
of diverse kinds may promote the abilities and skills
belonging to the intelligence realm, but the available
evidence does not support this expectation.
Douglas K. Detterman
The main thesis of his presentation was this: teachers
account for 1% –7% of the total variance in educa-
tion, whereas the students’ intellectual level accounts
for much of the 90% of variance associated with
learning outcomes. Therefore, problems encountered
for improving education might be explained by the
(wrong) focus on schools and teachers instead on the
(really) important variable (the student).
However, if this is the case, it is difficult to under-
stand why different countries with the same average
intelligence level show large performance differences
on international assessments of school skills, such as
PISA. Finland and Spain are good examples. Both
countries show exactly the same average IQ level (97)
(Lynn Vanhanen, 2012), but the first shows a PISA
score of 523 while Spain shows a PISA score of 477 (the
OCDE average score is 500).
The school system may have a great impact on devel-
oping countries, as demonstrated by the excellent
study by Flores-Mendoza et al. (2015): the socioeco-
nomic status (SES) of schools exerted a large impact on
PISA scores. The effect of SES over school outcomes
was higher for schools than for students. However,
these sharp differences hardly apply to developed
countries such as Finland and Spain. Detterman did
not provide satisfactory answers to the core question
because he thought there might be hidden relevant
information.
My second question was straightforward: can we
replace teachers by computerized courses and, there-
fore, save a big piece of public money? Now the answer
was quick: yes, we can. No matter how you teach,
because the relevant variable is the student. We must
find novel ways for dealing with this reality.
Stuart Ritchie
His presentation focused on cognitive and physical
decline in old age using a short-term longitudinal
dataset from the Lothian Birth Cohorts. An interesting
result was this: there is no a generalized decline. Some
individuals do show cognitive but not physical decline
(and vice versa). This is certainly shocking because it
suggests some sort of dissociation between body and
mind.
Nevertheless, my first question was: why the classic
distinction between fluid and crystallized intelligence
is neglected here? It is widely recognized that these
two cognitive abilities show distinguishable trajec-
tories across the life span (Hunt, 2011a). Talking about
cognitive decline may hide different paths for these
abilities, in the same sense as the mind-body dissocia-
tion noted above. This was Ritchie answer: only a short
period of time was considered and, therefore, the evi-
dence is clearly insufficient to detect those probable
different paths for these cognitive abilities.
The second issue was also highly relevant: it is not
possible to predict change based on the pretest mea-
sures (baseline). This was the rationale for my next
question: are random effects responsible for this pre-
diction failure? Jensen (1997) and Pinker (2002) inspired
this question. Both seem to converge on the sugges-
tion that non-shared variance can be attributed to con-
tinuously changing random effects. Ritchie accepted
this possibility: you cannot extract relevant signals
from noise.

Danielle Posthuma
As said above, the hunt for genes accounting for the
heritability estimates of intelligence has been disap-
pointing. Posthuma acknowledged the fact, providing
interesting examples from his own research group.
My first question was related with TBC view: relating
the genome with behavior skipping the brain sounds
like an inefficient research strategy, to say the least.
Therefore, endophenotypes such as the brain must be
incorporated into the big picture for a proper under-
standing of the genes-behavior relationships. Posthuma
agreed with this view.
The second question was not a real question.
Researchers acknowledge the fact that the effect of
individual genes must be minuscule. This may help
to explain the repeated failure underscored before.
But perhaps there is also some sort of Butterfly effect:
meager genetic differences from the outset (from a
developmental perspective) can make a huge differ-
ence at latter developmental stages. This perspective
fits the Dickens-Flynn model, which underscores
that minor genetic advantages at the individual level
might allow capturing relevant environmental factors
for intelligence development in the long run (Flynn,
2007). This possibility must be investigated using a
developmental perspective, as recently suggested by
Johnson (2013).
Emiliano Santarnecchi
His presentation was devoted to the neuroscience of
intelligence, showing findings derived from functional
data mainly obtained at rest. The brain shows sponta-
neous activity and there are large individual differences
in how regions across the cortex are connected. Thus,
for instance, high IQ individuals are more resilient to
random and targeted attacks to their networks than low
IQ individuals (Santarnecchi, Rossi, Rossi, 2015).
He also presented evidence related with the problem of
how can we enhance brain power using transcranial
magnetic stimulation. There is still a lot of work
remaining, but available results are encouraging.
My two questions were somewhat related. I firstly
asked how the presented functional evidence could be
integrated with the available structural results. Based
on the meta-analysis described above (Basten et al.,
2015) that revealed non-overlapping findings for struc-
tural and functional correlates of intelligence differ-
ences, I asked for probable ways of combining and
making sense of both lines of evidence. However, the
response was far from straightforward. Again, more
research is required.
The second question derived from the observation
that connectivity at rest shows greater reliability than
task-evoked connectivity. This suggests that it may be
more likely to find commonalities between the former
connectivity and structural connections computed after
extracting signals from diffusion data (Pineda-Pardo
et al., 2016). Santarnecchi agreed with this perspective,
but he also recognized that the suggested multimodal
approach would be highly complex. We already know
this is the case, but we are also making progress in
the right direction (Chamberland, Bernier, Fortin,
Whittingstall, Descoteaux, 2015).
Adam Chuderski
Chuderski’s presentation was devoted to the problem
of how individual differences in brain waves, espe-
cially their coupling, may account for observed intelli-
gence differences (Chuderski Andrelczyk, 2015). The
analysis of Theta/Gamma couplings provides findings
supporting the view that larger numbers of gamma
cycles within a given theta cycle is related with higher
intelligence levels. My first question asked about the
possibility of increasing (undesirable) noise within the
system at higher levels of gamma activity. However,
the answer rejected the issue relying on the available
computer simulations. Nevertheless, it remains to be
seen if these simulations properly capture the realities
of the human brain.
The second question was related with what we
know about complex systems functioning. We may be
tempted to experimentally modify the way brain waves
are coupled in, say, low IQ individuals showing less
gamma cycles within a given theta cycle. Modifying
gamma activity is possible using neurofeedback (Keizer,
Verschoor, Verment, Hommel, 2010), and, therefore,
the goal seems doable. However, as noted before, the
brain is a complex system and, therefore, it is far from
clear how modifying one single factor may stimulate a
chain reaction with unclear consequences. No straight-
forward response was provided.
Norbert Jausovec
In this final presentation of the seminar, Jausovec
described three studies devoted to the exciting pos-
sibility of increasing intelligence performance using
cognitive and brain stimulation. My two questions
had a methodological flavor. Firstly, I focused on the
sex variable, asking about the relevance of control-
ling for the acknowledged average sex difference in
brain volume when studying brain connectivity. There
are reports showing sex differences in brain connec-
tivity (Ingalhalikar et al., 2014), but the observation
vanishes when individual differences in brain volumes
are controlled (Hanggi, Fovenyi, Liem, Meyer, Jancke,
2014). Therefore, we must be very careful when
studying men and women from a neuroscience per-
spective (Escorial et al., 2015).

6 R. Colom
The second question derived from the discussion
related with the source of the remarkable individual
differences in working memory capacity that may sup-
port intelligence differences. As discussed by Bays
(2015) available evidence fits with the perspective that
working memory performance is influenced in a con-
tinuous way by increased memory load. Noise in neural
activity degrades the stability/reliability of internal
representations for further processing (Colom, Abad,
Quiroga, Shih, Flores-Mendoza, 2008; Martínez et al.,
2011), which contrasts with the view that working
memory capacity is limited by a fixed number of slots
(Luck Vogel, 2013). Jausovec was silent regarding
this debate.
What should we do next?
As advanced at the beginning of this section, the
final question was common to all presenters:
“What would you propose if Bill Gates offers you a
billion (€ or $)?”
All shared the idea of using the money for financing
top scientists worldwide for finding answers to the
next question:
“Why are some people smarter than others?”
Detterman led this common perspective. In some
sense, the main goal is to replicate previous scientific
efforts such as the Manhattan or the Apollo Projects.
Having the best minds focused on solving a single,
albeit complex, problem seems an efficient research
strategy for a quick advance in our current knowledge
related with the above key question.
I also adhere to this use of the money provided by
Gates. Indeed, in the editorial note published two
years ago (Colom, 2014a) I made a metaphoric com-
parison with the Apollo Program: “we may wonder if we
can make the voyage from the earth to the brain to increase
our understanding of what it means to be more or less intel-
ligent”. The first director of NASA’s Manned Spacecraft
Center (Robert R. Gilruth) acknowledged the fabulous
challenge involved in terms of people and technology,
but he also was strongly prone to pursue the goal all
the way. So I am.
In the farewell editorial note published by
D. K. Detterman after being editor of the journal
‘Intelligence’ for four decades he wrote: “from very early,
I was convinced that intelligence was the most important
thing of all to understand, more important than the origin of
the universe, more important than climate change, more impor-
tant than curing cancer, more important than anything else.
That is because human intelligence is our major adaptive
function and only by optimizing it will we be able to save
ourselves and other living things from ultimate destruction.
It is as simple as that”.
We need shared efforts for enhancing our knowledge.
The XXI Century will see increased numbers of scientists
sharing their resources devoted to key research chal-
lenges. The reconciliation between humans and nature
can only be achieved making a wise use of our main psy-
chological faculty. This may be not an easy target, but
I strongly think it is vital, literally.
References
Adolphs R. (2015). The unsolved problems of neuroscience.
Trends in Cognitive Sciences, 19, 173–175. https://doi.
org/10.1016/j.tics.2015.01.007
Barbey A. K., Colom R., Solomon J., Krueger F., Forbes C.,
Grafman J. (2012). An integrative architecture for general
intelligence and executive function revealed by lesion
mapping. Brain, 135, 1154–1164. https://doi.org/
10.1093/brain/aws021
Basten U., Hilger K., Fiebach C. J. (2015). Where smart
brains are different: A quantitative meta-analysis of
functional and structural brain imaging studies on
intelligence. Intelligence, 51, 10–27. https://doi.
org/10.1016/j.intell.2015.04.009
Bays P. M. (2015). Spikes, not slots: Noise in neural
populations limits working memory. Trends in Cognitive
Sciences, 19, 431–438. https://doi.org/10.1016/j.tics.
2015.06.004
Chamberland M., Bernier M., Fortin D., Whittingstall K.,
Descoteaux M. (2015). 3D interactive trasctography-
informed resting-state fMRI connectivity. Frontiers in
Neuroscience, 9, 275. https://doi.org/10.3389/fnins.
2015.00275
Chuderski A., Andrelczyk K. (2015). From neural
oscillations to reasoning ability: Simulating the effect of
the theta-to-gamma cycle length ratio on individual scores
in a figural analogy test. Cognitive Psychology, 76, 78–102.
https://doi.org/10.1016/j.cogpsych.2015.01.001
Colom R. (2014a). From the earth to the brain. Personality
and Individual Differences, 61/62, 3–6. https://doi.
org/10.1016/j.paid.2013.12.025
Colom R. (2014b). All we need is brain (and technology).
Journal of Intelligence, 2, 26–28. https://doi.org/10.3390/
jintelligence2010026
Colom R. (2015, September). Understanding human
intelligence. The Brain Connection. Invited Keynote Address
at the Annual Conference of the ISIR. New Mexico. USA.
Colom R., Abad F. J., Quiroga M. A., Shih P. C.,
Flores-Mendoza C. (2008). Working memory and
intelligence are highly related constructs, but why?
Intelligence, 36, 584–606. https://doi.org/10.1016/
j.intell.2008.01.002
Colom R., Chuderski A., Santarnecchi E. (2016).
Bridge over troubled water: Commenting on Kovacs and
Conway’s Process Overlap Theory (POT). Psychological
Inquiry, 27, 181–189. https://doi.org/10.1080/1047840X.
2016.1181513

Colom R., Haier R. J., Head K., Álvarez-Linera J.,
Quiroga M. A., Shih P. C., Jung R. E. (2009). Gray
matter correlates of fluid, crystallized, and spatial
intelligence: Testing the P-FIT model. Intelligence, 37,
124–135. https://doi.org/10.1016/j.intell.2008.07.007
Cooper C. (2015). Intelligence and human abilities. London, UK:
Routledge.
Davies G., Marioni R. E., Liewald D. C., Hill W. D.,
Hagenaars S. P., Harris S. E., … Deary I. J. (2016).
Genome-wide association study of cognitive functions
and educational attainment in UK Biobank (N = 112151).
Molecular Psychiatry, 21, 758–767. https://doi.
org/10.1038/mp.2016.45
Detterman D. K. (2014). You should be teaching intelligence!
Intelligence, 42, 148–151. https://doi.org/10.1016/j.intell.
2013.07.021
Detterman D. K. (2016). Was intelligence neccesary?
Intelligence, 55.
Escorial S., Román F. J., Martínez K., Burgaleta M.,
Karama S., Colom R. (2015). Sex differences in neocortical
structure and cognitive performance: A surface-based
morphometry study. NeuroImage, 104, 355–365. https://
doi.org/10.1016/j.neuroimage.2014.09.035
Flores-Mendoza C., Mansur-Alves M., Ardila R., Rosas R. D.,
Guerrero-Leiva M. K., Lucio-Gómez Maqueo M. E., …
Burga León A. (2015). Fluid intelligence and school
performance and its relationship with social variables in
Latin American samples. Intelligence, 49, 66–83. https://doi.
org/10.1016/j.intell.2014.12.005
Flynn J. R. (2007). What is intelligence? New York, NY:
Cambridge University Press.
Gläscher J., Rudrauf D., Colom R., Paul L. K., Tranel D.,
Damasio H., Adolphs R. (2010). The distributed neural
system for general intelligence revealed by lesion
mapping. PNAS, 107, 4705–4709.
Gottfredson L. S. (1997). Mainstream science on
intelligence: An editorial with 52 signatories, history,
and bibliography. Intelligence, 24, 13–23. https://doi.
org/10.1016/S0160-2896(97)90011-8
Haier R. J. (2016). The neuroscience of intelligence. New York, NY:
Hanggi J., Fovenyi L., Liem F., Meyer M., Jancke L.
(2014). The hypothesis of neuronal interconnectivity
as a function of brain size—a general organization
principle of the human connectome. Frontiers in Human
Neuroscience, 8, 915. https://doi.org/10.3389/fnhum.
2014.00915
Huarte de San Juan J. (1575). Examen de ingenios para las
ciencias. Baeza, Spain: Juan Bautista de Montoya.
Hunt E. B. (2011a). Human intelligence. New York, NY:
Hunt E. B. (2011b). Where are we? Where are we going?
Reflections on the current and future state of research on
intelligence. In R. J. Sternberg S. Barry Kaufman (Eds.),
The Cambridge Handbook of Intelligence. (pp. 863–885).
New York, NY: Cambridge University Press.
Ingalhalikar M., Smith A., Parker D., Satterthwaite T. D.,
Elliott M. A., Ruparel K., … Verma R. (2014). Sex
differences in the structural connectome of the human
brain. Proceedings of the National Academy of Sciences of the
United States of America, 111, 823–828. https://doi.
org/10.1073/pnas.1316909110
Jensen A. R. (1997). The puzzle of nongenetic variance. In
R. J. Sternberg E. Grigorenko (Eds.), Intelligence, heredity,
and environment. (pp. 42–88). Cambridge, UK: Cambridge
University Press.
Johnson W. (2013). Whither intelligence research? Journal
of Intelligence, 1, 25–35. https://doi.org/10.3390/
jintelligence1010025
Jung R. E., Haier R. J. (2007). The parieto-frontal
integration theory (P-FIT) of intelligence: Converging
neuroimaging evidence. Behavioral and Brain Sciences, 30,
135–154. https://doi.org/10.1017/S0140525X07001185
Keizer A. W., Verschoor M., Verment R. S., Hommel B.,
2010. The effect of gamma enhancing neurofeedback on
the control of feature bindings and intelligence measures.
International Journal of Psychophysiology, 75, 25–32. https://
doi.org/10.1016/j.ijpsycho.2009.10.011
Kovacs K., Conway A. R. A. (2016). Process overlap
theory: A unified account of the general factor of
intelligence. Psychological Inquiry, 27, 151–177. https://
doi.org/10.1080/1047840X.2016.1153946
Luck S. J., Vogel E. K. (2013). Visual working memory
capacity: From psychophysics and neurobiology to
individual differences. Trends in Cognitive Sciences, 17,
391–400. https://doi.org/10.1016/j.tics.2013.06.006
Lynn R., Vanhanen T. (2012). National IQs: A review
of their educational, cognitive, economic, political,
demographic, sociological, epidemiological, geographic
and climatic correlates. Intelligence, 40, 226–234. https://
doi.org/10.1016/j.intell.2011.11.004
Martínez K., Burgaleta M., Román F. J., Escorial S., Shih P. C.,
Quiroga M. A., Colom R. (2011). Can fluid intelligence
be reduced to ’simple’ short-term storage? Intelligence, 39,
473–480. https://doi.org/10.1016/j.intell.2011.09.001
Martínez K., Madsen S. K., Joshi A. A., Shantanu H. J.,
Román F. J., Villalón-Reina J., … Colom R. (2015).
Reproducibility of brain-cognition relationships using
three cortical surface based protocols: An exhaustive
analysis based on cortical thickness. Human Brain Mapping,
36, 3227–3245. https://doi.org/10.1002/hbm.22843
Neisser U., Boodoo G., Bouchard Jr, T. J., Boykin A. W.,
Brody N., Ceci S. J., … Urbina S. (1996). Intelligence:
Knowns and unknowns. American Psychologist, 51, 77–101.
https://doi.org/10.1037/0003-066X.51.2.77
Nisbett R. E., Aronson J., Blair C., Dickens W., Flynn J.,
Halpern D. F., Turkheimer E. (2012). Intelligence:
New findings and theoretical developments. American
Psychologist, 67, 130–159. https://doi.org/10.1037/
a0026699
Pineda-Pardo J. A., Martínez K., Román F. J., Colom R.
(2016). Structural efficiency within a parieto-frontal
network and cognitive differences. Intelligence, 54, 105–116.
https://doi.org/10.1016/j.intell.2015.12.002
Pinker S. (2002). The blank slate. The modern denial of human
nature. New York, NY: Viking.
Quiroga M. A., Escorial S., Román F. J., Morillo D., Jarabo A.,
Privado J., … Colom R. (2015). Can we reliably measure
the general factor of intelligence (g) through commercial
video-games? Yes, we can! Intelligence, 53, 1–7.

8 R. Colom
Santarnecchi E., Rossi S., Rossi A. (2015). The smarter,
the stronger: Intelligence level correlates with brain
resilience to systematic insults. Cortex, 64, 293–309.
https://doi.org/10.1016/j.cortex.2014.11.005
Smith S. M., Nichols T. E., Vidaurre D., Winkler A. M.,
Behrens T. E. J., Glasser M. F., … Miller K. L. (2015).
A positive-negative mode of population covariation links
brain connectivity, demographics and behavior. Nature
Neuroscience, 18, 1565–1567. https://doi.org/10.1038/
nn.4125
Spain S. L., Pedroso I., Kadeva N., Miller M. B., Iacono W. G.,
McGue M., … Simpson M. A. (2015). A genome-wide
analysis of putative functional and exonic variation
associated with extremely high intelligence. Molecular
Psychiatry, 21, 1145–1151. https://doi.org/10.1038/
mp.2015.108
Sternberg R. J., Kaufman S. B. (2011). The Cambridge
Handbook of Intelligence. Cambridge, UK: Cambridge
University Press.
Tourva A., Spanoudis G., Demetriou A. (2016). Cognitive
correlates of developing intelligence: The contribution
of working memory, processing speed, and attention.
Intelligence, 54, 136–146. https://doi.org/10.1016/
j.intell.2015.12.001

Colom(2016)advances in intelligence

More Related Content

Similar to Colom(2016)advances in intelligence

Recently uploaded

Colom(2016)advances in intelligence