Psychology
2013. Vol.4, No.10, 760-770
Published Online October 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.410108
760
A Better Look at Learning: How Does the Brain Express
the Mind?
Frederic Perez-Alvarez1,2,3*, Alexandra Perez-Serra4, Carme Timoneda-Gallart2,3
1Neuropediatric Unit, Hospital Universitari ICS Dr J Trueta, Girona, Spain
2JP Das Developmental Centre, Alberta, Canada
3Quality of Life Research Institute and Foundation Carme Vidal Educational Psychology, University of Girona,
Girona, Spain
4Departmnet of Biology, Girona University, Girona, Spain
Received June 30th, 2013; revised August 5th, 2013; accepted September 11th, 2013
Copyright © 2013 F. Perez-Alvarez et al. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
Learning problems in the light of PASS assessment and intervention were studied. Data for 248 subjects
with specific learning impairment (SLI), dyslexia, dyscalculia, and non-defined learning difficulty were
studied. Hierarchical cluster analysis of PASS scores at baseline was performed. PASS re-assessment was
carried out at 6 and 12 months after 6-month period of intervention. Four statistically different cluster
groups were identified. All groups, except one, showed cognitive weakness. Planning weakness, associ-
ated with other weakness, appears involved in all groups except two where isolated planning and succes-
sive weaknesses were identified, respectively. SLI, dyslexia, and dyscalculia are not homogenous entities.
A kind of dyslexia is clearly linked to isolated successive weakness. SLI-expressive (SLIe) and a minority
of both dyslexia and dyscalculia appear linked to successive weakness although associated with planning
and additionally with attention in the case of SLIe. SLI-expressive-receptive (SLIe-r) and Dyscalculia
appear linked to simultaneous weakness, although associated with planning weakness. Other kind of
SLIe-r appears linked to isolated planning weakness. Other types of SLIe-r and Dyscalculia appear liked
to combined planning + successive + attention weakness. Isolated dysfunctional attention does not appear
in any case. After 6 months of intervention, planning improves statistically in all cases. Attention im-
proves in few cases. Successive and simultaneous do not improve. The best result is in dyslexia, SLIe and
a minority of Dyscalculia. The worst result is in those without cognitive deficiency. The effect of inter-
vention at 6 months remains with minor changes at 12 months after 6 months without intervention.
Keywords: Cognition; Learning; Dyslexia; Dyscalculia; PASS
Introduction
For years and years learning difficulties have been challeng-
ing for teachers, school psychologists, doctors, and other pro-
fessionals in the field. Over years, multiple approaches have
been carried out. Although many classifications have been re-
ported, remediation continues to be a challenging point. Data
regarding the etiology, academic outcome, and utility of inter-
ventions are either scarce or lacking. There are indications that
adequacy in learning skills during childhood has an impact on
future professional achievement.
Usually classifications (APS, 2000; Bishop, 1994; Bishop &
Leonard, 2001; Bravo, 1979; Maggiolo & Pavez, 2000; Matute,
Roselli, Ardila, & Ostrosky-Solis, 2005; Rapin, 1998; Rapin &
Allen, 1983; Roselli-Cock et al., 2004; WHO, 1993) are based
on descriptive categories delineating syndromic entities. In fact,
we are dealing with a heterogeneous group of disorders mani-
fested by significant difficulties in the acquisition and use of
listening, speaking, reading, writing, reasoning or mathematical
abilities. These disorders result from impairments in one or
more psychological processes related to learning in combina-
tion with otherwise average abilities essential for thinking and
reasoning. Usually, they are specific, not global, impairments
and as such are distinct from intellectual disabilities. The term
“psychological processes” describes an evolving list of cogni-
tive functions. However, the underlying processes that account
for these deficits are less clear. To date, research has focused on
functions such as: phonological processing, memory and atten-
tion, processing speed, language processing, perceptual-motor
and visual-spatial processing, executive functions (e.g., plan-
ning, monitoring, and meta-cognitive abilities), and so on.
These disorders are intrinsic to the individual and presumed
to be due to central nervous system dysfunction. Even though a
learning disability may occur concomitantly with other handi-
capping conditions (e.g., sensory impairment, mental retarda-
tion, social and emotional disturbance) or environmental influ-
ences (e.g., cultural differences, insufficient/inappropriate in-
struction, psychogenic factors), it is not the direct result of those
conditions or influences. These entities, so classified, have
proved to be useful for prognosis, but not so much for interven-
tion because we are far from knowing the central neurological
*Corresponding author.

F. PEREZ-ALVAREZ ET AL.
pathogenesis. Knowing this central mechanism will allow us to
intervene in the genesis of the problem and not in the conse-
quence of the problem, that is, the external sign or learning
behavior that descriptive categories are based on.
There are children with global learning problem, with very
low scores in all tests of IQ test. Other children have specific
learning difficulties. If such children are given a IQ test, they
score at least average intelligence although still have problem
with learning. If a battery of tests were given to them they
would score average to high on some of them and low on others.
In summary, they have a severe delay in classroom achieve-
ment, and a significant discrepancy between intellectual ability
and academic achievement, and a processing deficit that is
linked to the delay in classroom achievement and significant
discrepancy. They hear and see normally, but they have trouble
with what they see and hear. These are pupils that need to be
taught differently from the norm. Attention-deficit hyperactiv-
ity disorder (ADHD) is often studied in connection with learn-
ing disabilities, but it is not actually included in the standard
definitions of learning disabilities (Perez-Alvarez, Serra-Amaya,
& Timoneda-Gallart, 2009). Deficits in any area of information
processing can manifest in a variety of specific learning dis-
abilities. It is possible for an individual to have more than one
of these difficulties. This is referred to as comorbidity or
co-occurrence of learning disabilities.
Many studies have shown that children with learning diffi-
culties have poorer particular skills as compared to other skills.
To date, most neuropsychological studies have focused on a
limited number of cognitive variables. Specifically, many stud-
ies have been limited to evaluating overall level of intellectual
functioning and focused on one area of cognitive development.
Conclusions from prior studies are limited by multiple factors
including small sample sizes, use of inadequate cognitive
measures, the lack of control groups to assess the influence of
practice effects, and the lack of assessment of both short- and
long-term outcome. Whereas some patients may show early
improvements in functioning, improvements are sometimes not
noted until one year after the intervention. The reflections on
these limitations serve as a basis for establishing directions for
our research.
Since 1997 we count on the DN:CAS (Naglieri & Das, 1997)
to assess the PASS cognitive processing of information (Das &
Kendrick, 1997; Das, Naglieri, & Kirby, 1994; Das, Kar, &
Parrilla, 1996; Das, Garrido, Gonzalez, Timoneda, & Perez-
Alvarez, 1999) . This is considered a useful measure for diag-
nostic testing in Pediatric Neurology (Swaiman, Ashwal, &
Ferriero, 2006). PASS is the acronym for planning, attention,
successive, and simultaneous processes. Planning may be con-
sidered equivalent to executive function. Attention is equivalent
to selective attention. Successive and simultaneous have to do
with serial and relationship processing. The PASS states that
the same clinical manifestation can be the result of different
central processing (software) and different clinical manifesta-
tions can be the consequence of the same central processing.
This principle is essential for understanding what we are refer-
ring to when we are dealing with cognitive assessment. Other-
wise, we are mixing central processing with output and vice
versa. In particular, attention as central processing must be
differentiated from attention as external clinical manifestation
or behavioral expression in the sense of being attentive to. In
other words, not being attentive to the teacher (external behav-
ioral pattern) is compatible with being attentive to (internal,
central processing) what we keep in mind, for instance, the last
interesting film. Therefore, a true attention test must be assess-
ing internal central processing, but not only external behavioral
attention. Likewise, inattentive behavior may be related with
other dysfunctional cognitive processing different from atten-
tion. This way, a behavioral phenotype based on external
manifestations is not exactly the same as a cognitive phenotype
and we must assume that behavior is the consequence of mental
processing but not vice versa.
The DN:CAS battery for assessing PASS processing is based
on cerebral lesion studies (Das, Naglieri, & Kirby, 1994; Das,
Kar, & Parrilla, 1996; Das, Garrido, Gonzalez, Timoneda, &
Perez-Alvarez, 1999; McCrea, 2009). That is, first neurological
lesions were analyzed, then a theoretical framework was de-
duced, the principle of which is the central processing is inde-
pendent of intake and output of information, and then the proc-
ess of test creation was carried out. A relevant consequence is
that every test is specific for a particular processing, and there-
fore no test assesses more than a cognitive function.
The four PASS processes are assessed using the Cognitive
Assessment System (CAS) which was specifically built ac-
cording to the PASS theory (Naglieri & Das, 1997). There is a
strong empirical base to support both the theory and its opera-
tionalization in the CAS. It was standardized on a sample of
2200 children aged 5 through 17 years who were representative
of the USA population on a number of important demographic
variables. The CAS full scale has a high internal reliability
ranging from .95 to .97 for the different age groups. The aver-
age reliability coefficients for the scales are .88 for plan-
ning, .88 for attention, .93 for simultaneous, and .93 for succes-
sive (Naglieri & Das, 1997; Naglieri & Das, 1995).
The purpose and overall objective of this study were to in-
vestigate the learning difficulties in children at primary school
(6 to 11 years old) in the light of PASS processes and to deter-
mine the usefulness of PASS intervention within the one year
follow-up period. This study is oriented to support the idea that
a PASS diagnosis may be very useful to intervene any learning
problem independently of the category we are dealing with. The
direct way to assess the effects of treatments on functioning is
through pre- and post-treatment assessment. It was predicted
(hypothesis) that some PASS cognitive functions will be treat-
ment sensitive while others will be relatively treatment inde-
pendent after intervention. It was also intended to verify long-
term effect of intervention on cognitive function in terms of the
PASS processes.
Methodology
Subjects
Subjects were recruited from the pediatric neurology practice
by direct contact. If the subject or his/her family was interested
in participation, the principal investigator conducted a face-to-
face interview to explain all aspects of the research as docu-
mented on the consent form. Signed parental consent form was
required. Consent was obtained by the principal investigator,
witnessed by a member of the staff. A copy of this form was
then given to the participant, and the original was kept with the
patient’s data file. Ethical approval was granted.
Each child had been clinically referred because of learning
difficulties, therefore with low academic achievement. Teachers
were asked to provide information on achieving, reading, writ-
ing, mathematics (Bravo, 1979). Among the children, there

F. PEREZ-ALVAREZ ET AL.
were cases of difficulties in expressive and receptive speech as
well as in reading and writing o math. Children experiencing
learning/reading difficulties due to emotional, behavioral,
and/or medical condition as indicated by the school record were
not included in the study.
First of all, WISC-R was given and an IQ equal o superior to
80 was considered to be acceptable. Then, the children were
identified by using a 2-stage screening process. In the first stage,
the teachers of the children were interviewed by two blinded-
researchers and each of the subjects was assessed with respect
to the following questions. Concerning language impairment,
whether expressive or mixed receptive-expressive, language
comprehension, disfluency of speech, short and laboriously
produced utterances, impaired phonology as omissions, substi-
tutions and distortions of speech sounds, distortions of conso-
nants and consonants clusters in all word positions, production
of unpredictable and unrecognizable sounds making speech
impossible to understand, atypical grammar, not merely de-
layed, telegraphic speech. Concerning dyslexia (Maggiolo &
Pavez, 2000), problems in speaking like mispronunciation of
long or complicated words, non-fluent speech, use of imprecise
language, problems in reading like very slow progress in ac-
quiring reading skills, trouble reading unknown or unfamiliar
words that must be sounded out, inability to read small “func-
tion” words, choppy and labored oral reading, disastrous spell-
ing, very slow and tiring reading, extreme difficulty learning a
foreign language. In particular, the following phonological
tasks were carried out: rhyme oddity, syllable completion, ini-
tial phoneme identification, onset oddity, single phoneme onset
oddity, and phoneme elision. Concerning dyscalculia, accord-
ing to age, difficulties involving early math skills, the meaning
of numbers, learning to count or matching them with amounts,
difficulties sorting objects by shape size or color, slower at
developing math, problems with basic math skills as adding,
subtracting, multiplying and dividing, trouble telling time,
chronology, sequencing events, remembering schedules or fol-
lowing directions, difficulty with his/her sense of direction,
rendering him/her disoriented, how he/she handles the concept
of money, he/she can’t grasp abstract concepts as coins, bills,
credit, budgeting or financial planning, difficulty with games
that require strategy or keeping score, difficulty with math
abilities as estimating quantities, figuring out change or count-
ing days to an event.
If person being evaluated fulfill half or more of these ques-
tions, he/she was formally recruited to be formally tested. An
interobserver agreement of 80% was required as inclusion cri-
terion. Those selected underwent phase 2 evaluation according
to the following tests. All tests were administered with the per-
mission given by the parents. Oral language subtests, assessing
either expressive or receptive (comprehension), of the validated
Evaluación Neuropsicológica Infantil battery (Matute et al.,
2005; Roselli et al., 2004); those scoring in the lowest 20% on
these tests were classified as SLI. Translated but not standard-
ized Word Attack and Word Identification of the Woodcock
Word Reading task; reading scores at or below the 25th percen-
tile on both tests were required for dyslexia category. Trans-
lated normalized arithmetic battery that includes questions on
number comprehension, production, and calculation (McCloskey
et al., 1985; Shalev et al., 1998) was used for dyscalculia; those
scoring in the lowest 5th percentile of the normative group were
identified as having dyscalculia. All of them were administered
SNAP-IV and the translated, back-translated and validated
Strengths and Difficulties Questionnaire (SDQ) in order to rule
out ADHD and comorbidity.
After the screening process, 71 were classified as mixed Ex-
pressive and Receptive Specific Language Impairment (SLIe-r),
30 as Expressive Specific Language Impairment (SLIe), 66 as
Dyslexia (Dysl), and 59 as Dyscalculia (Dysc). 22 were classi-
fied as Nonspecific Category because they did not meet criteria
for any of the categories above (Table 1). Those identified as
Language Impairment correspond to the former dysphasia
category. The distinction between developmental dysphasia on
the one hand and dyslexia on the other is perhaps the hardest in
terms of phonological processing, but we must remark our sub-
jects were recruited such that each category did not meet crite-
ria of the other ones.
The sample included 248 children, boy/girl ratio 4:1, aged 6
to 11 years (primary-elementary school), 41 subjects for each
age group except two strata with 42, 90% of them were right-
handed, 10% left-handed or ambidextrous. The study popula-
tion reflects the demographics of the pediatric population at the
region and it is in accordance with learning disabilities affect-
ing an estimated 5% to 15% of children in the normal school
population. All of them were with native language proficiency.
The children came from middle class families. The sample was
WISC performance IQ = 90, SD = 18.2 and verbal IQ = 81.4,
SD = 16.1. This sample was recruited from clinical setting. The
subject population was readily available from the very large
pediatric neurology practice at our institution. Those with any
neurological disorder, psychiatric disorder, sensory disorder
like hearing impairment, any known syndrome like Angelman
syndrome and others were excluded from the study to reduce
any confounding factors. All children were screened for vision,
and hearing. If necessary, additional medical examination was
ordered and investigations by audiologists, neurologists, psy-
chologists were carried out as needed. Any previous medication
or therapy was also ruled out.
Instrument
All subjects (n = 248) were administered translated and vali-
dated for local population DN:CAS (Das Naglieri Cognitive
Assessment System) battery at baseline, and at months 6 and 12
of follow-up period. Intervention was over 6 months. No inter-
vention between 6 and 12 months. The 6 mo/12 mo follow-up
allows us to rules out the potential “practice effect” of two
Table 1.
Distribution of learning problems according to cluster analysis.
Cluster 12 3 4 Total
SLIe-r 38 20 71
SLIe-r + Dysc 13 + 12
Dysc 25 17 59
Dysc + SLIe + Dysl 5 + 30 + 5
SLIe 30
Dysl 61 66
Non-specific 2222
Total 248
Note: SLIe-r: expressive-receptive SLI; SLIe: expressive SLI; Dysc: dyscalculia;
Dysl: dyslexia.
762

F. PEREZ-ALVAREZ ET AL.
closely spaced psychological tests. Subjects were run individu-
ally over five sessions. Sessions lasted on average 30 minutes,
though subjects were allowed to take breaks or discontinue the
sessions whenever they desired. The test administration was
individual, over a three-month period for baseline.
The battery (Naglieri & Das, 1997) assesses PASS process-
ing, namely, planning, attention, successive and simultaneous.
Tests of planning are: Matching Numbers, Planned Codes, and
Planned Connections. Those of attention are: Expressive Atten-
tion, Number Detection, and Receptive Attention. Simultaneous
tests are: Nonverbal Matrices, Verbal-Spatial Relations, and
Figure Memory. Successive ones are: Word Series, Sentence
Repetition, Sentence Question (from 8 to 17 years) and Succes-
sive Speech Rate (from ages 5 to 7 years). Each of the four
PASS scales yields a standard score with a normative mean of
100 and a standard deviation (SD) of 15. For three subtests in
each of the four scales, the mean is 10 and the SD is 3.
Matching Numbers requires children devise a strategy to find
and underline two numbers that are the same in a row. The
numbers increase in length form one digit to seven digits.
Planned Codes show distinct set of codes and arrangements of
rows and columns. A legend at the top of each page shows how
letters correspond to simple codes (e.g. A,B,C,D correspond to
OX, XX, OO, XO, respectively). Children must fill in the ap-
propriate codes in empty boxes beneath each letter in any effi-
cient manner (plan). Planned Connections requires children to
efficiently connect numbers in sequence or numbers and letters
in alternating orders. Expressive Attention demands children to
name the color ink the words, Blue, Yellow, Green, and Red
are printed in according to Stroop phenomenon. Number Detec-
tion consists of pages of numbers in different formats. Children
are required to find, for instance, numbers 1, 2, and 3 on a page
containing many distractors (e.g. the same number printed in
different font). The child's performance is timed and it takes
into account accuracy (correct minus false detections). Recep-
tive Attention demands the child identify letters' pairs that meet
specified criteria among many letters pairs that do not. Non-
verbal Matrices shows shapes and geometric designs that are
interrelated through spatial or logical organization. Verbal-
Spatial Relations shows drawings and a printed question; for
instance, “Which picture shows a circle to the left of a cross
under a triangle above a square?” Figure Memory requires the
child identify a geometric design when it is embedded in a
complex figure. Word Series demands the child repeat words in
the same order as stated by the examiner. Sentence Repetition
requires the child repeat sentences, such as “The blue is yel-
lowing” that are read aloud by the examiner. Sentence Ques-
tions (for those in age from 8 to 17 years) uses the same previ-
ous sentences, but in different manner. Children are read a sen-
tence and then asked a question about the sentence. For exam-
ple, the sentence: “The blue is yellowing”. The question: “Who
is yellowing?” The answer: “The blue.” Successive Speech Rate
requires the child to repeat a series de words in particular linear
order.
Procedure
All tests were administered by trained technical staff who
were blinded to conditions of treatment. All tests proposed for
use in this study are reliable and valid measures of cognitive
functioning and are commonly used in practice and research.
There are no known risks associated with the cognitive testing
procedures except fatigue and frustration. In order to minimize
this, where age appropriate, frequent breaks in the testing or
separate serial testing sessions were be planned. The risks to the
subjects in undergoing cognitive tests are very minimal, and
may not exist at all, other than the potential for fatigue and the
time lost in undergoing testing. Instead, potential benefits to
individual subjects include the identification of cognitive defi-
cits that might help in maximizing school interventions.
The intervention sessions (Das & Kendrick, 1997) were ap-
plied to individual. Each child received 15 sessions of 45 min-
utes over a period of 6 months. After the intervention they were
tested again. 6 months later, without further intervention, they
were tested again. The PREP is founded on the premise that the
transfer of principles can be facilitated through inductive rather
than deductive inference. The program is structured so that
tacitly acquired strategies are likely to be used in appropriate
ways. Children are encouraged to engage in discussions, both
during and following their performance. Each task is designed
to develop strategies. Thus children develop their ability to use
these strategies through experience with the task. Children are
encouraged to become aware of the use of strategies through
verbalization. Both “near transfer” and “far transfer” take place
over the course of remediation.
The program consists of ten tasks. Each task involves both a
non-reading global training component and a curriculum-re-
lated bridging component. Both of them require the application
of simultaneous or successive strategies, providing children
with the opportunity to internalize strategies in their own way,
thus facilitating transfer. The global tasks begin with content
that is familiar and non-threatening. Complexity is introduced
gradually. The global and bridging components are further
divided into three levels of difficulty. A system of prompts is
integrated into each global and bridging component. Thus the
tasks are completed with a minimal amount of assistance and a
maximal amount of success. A criterion of 80% correct re-
sponses is required before a child can proceed to the next level
of difficulty. To summarize briefly, PREP is a program that
aims at improving the information processing whatever the
specific task involved.
The PASS scores were subjected to a hierarchical clustering
method (SPSS v. 13.0) in order to see whether the sample was
homogeneous or heterogeneous in regard to cognitive process-
ing (Aldenderfer & Blashfield, 1984). Baseline PASS cognitive
scores were compared to 6 and 12 months follow-up scores.
Analysis of variance (MANOVA), Scheffe test, and paired
Student t-test with effect size statistic (Cohen’s δ) was applied
where appropriate.
Results
Four clusters described those groups in which the degree of
association is high between the members of the same group and
low between members of different groups. Tables 1 and 2 show
the four group resolution. With groups of this size, we have
statistical power to detect a difference between groups. The
four clusters differed significantly from each other in
MANOVA (F(246) = 615; p < .01 ).
As Tables 1-3 show, 4 clusters are identified and they differ
from each other. The cluster 4 differs from the rest in that there
is no PASS dysfunction. The rest of the clusters show PASS
dysfunction. They have in common planning dysfunction ex-
ept in sub-cluster 3.2 corresponding to Dysl where there is c

F. PEREZ-ALVAREZ ET AL.
764
Table 2.
Distribution of cases and PASS profiles, according to cluster analysis.
Cluster gr oups N Cluster Sub-groups (n) PASS (mean under 85 ± SD*
1 63 1.1 (SLIe-r) 38 P < A < Su
(25.40%**) 77 ± 07 79 ± 09 82 ± 10
1.2 (Dysc) 25 P < Su < A
76 ± 11 80 ± 08 84 ± 11
Total 63
2 62 2.1 (SLIe-r) 20 P
(25.0%) 78 ± 13
2.2 Dysc) 17 Si < P
80 ± 11 83 ± 07
2.3 (SSLIe-r + Dysc) 25 P < Si
77 ± 12 82 ± 10
13 (SLIe-r)
12 (Dysc)
Total62
3 101 3.1 (SLIe + Dysc + Dysl) 40
(40.72%) 30 (SLIe) Su < P < A
77 ± 11 79 ± 11 82 ± 09
5 (Dysc) P < Su
75 ± 13 80 ± 12
5 (Dysl) Su < P
78 ± 10 82 ± 14
3.2 (Dysl) 61 Su
79 ± 10
Total101
4 22 No PASS cognitive deficiency
(8.87%)
Total 248 (100%)
Note: *Avarage across subjects; **% of total. PASS profile: P = planning A = attention Su = Successive Si = Simultaneous. SLIe-r: specific
learning impairment, expressive and receptive SLIe: specific learning impairment expressive Dysc: dyscalculia Dysl: dyslexia.
Table 3.
Comparison of cluster groups on PASS processing according to Scheffe test.
PASS Cluster group differences P Scheffe
Planning 1, 2, and 3 lower than 4 <0.001
Attention 1 lower than the other 3 groups <0.01
Successive 3 lower than the other 3 groups <0.01
Simultaneous 2 lower than the other 3 groups <0.001
only successive dysfunction, but they differ from each other in
associated dysfunctions except in cluster 2, sub-cluster 2.1 (n =
20) corresponding to SLIe-r where planning dysfunction is
isolated and non-associated. Thus, we find planning + attention
+ successive dysfunctions in cluster 1 where we can identify
SLIe-r (n = 38), and Dysc (n = 25). In cluster 2 we find isolated
planning corresponding to SLIe-r (n = 20) versus planning +
simultaneous corresponding to Dysc (=17), and SLIe-r + Dysc
(n = 25). Finally, in cluster 3 planning + successive corre-
sponding to Dysc (n = 5) and Dysl (n = 5) versus planning +
successive + attention corresponding to SLIe (n = 30). In other
words, planning is the PASS processing more frequently dys-
functional in learning difficulties whatsoever, although it is
usually associated with other dysfunctions. Only in cluster 2,
sub-cluster 2.1 (n = 20) corresponding to SLIe-r there is iso-
lated planning dysfunction. Instead, only in sub-cluster 3.2,
pertaining to cases of dyslexia (n = 61), planning is not in-
volved (instead, isolated successive is involved).
In summary, the Nonspecific Category of learning difficul-
ties (n = 22) in cluster 4 is clearly defined. We postulate they
do not have cognitive deficiency (maybe, emotional problem)
or instead they have a cognitive deficiency not detected by
PASS. On the other hand, we can see SLI, dyslexia, and dys-
calculia are not homogenous entities in PASS terms. A kind of

F. PEREZ-ALVAREZ ET AL.
dyslexia is clearly liked to isolated successive dysfunction as
we can see in cluster 3, sub-cluster 3.2 (n = 61). In cluster 3,
SLIe (n = 30) and a minority of Dysc (n = 5) appear linked to
successive dysfunction, although associated with planning dys-
function, and attention dysfunction in the case of SLIe (n = 30).
We postulate its common dysfunction has to do with phono-
logical dysfunction. In cluster 2, SLIe-r (n = 13) and Dysc (n =
17 + 12) appear linked to simultaneous dysfunction, although
associated with planning dysfunction. On the other hand in
cluster 2, subcluster 2.1, another SLIe-r (n = 20) appears linked
to isolated planning dysfunction. In cluster 1, other type of
SLIe-r (n = 38) and Dysc (=25) appear liked to combined plan-
ning + successive + attention dysfunction. We must remark
isolated dysfunctional attention does not appear in any case.
Finally, PASS assessment 6 months after intervention, and
assessment at 12 months from baseline after 6 months without
intervention are shown in Tables 4 and 5.
We can see the effect of intervention at 6 months remains at
12 months with minor changes after 6 months without interven-
tion. We must remark that planning, which is dysfunctional in
all cases apart from in sub-cluster 3.2 (Dysl), is the PAS dys-
function more susceptible (sensitive) to intervention. Therefore,
all categories can ameliorate with PASS intervention because
planning amelioration involves academic achievement im-
provement. Particularly, the best result corresponds to cluster 3,
namely, SLIe (n = 30) + Dysc (n = 5) + Dysl (n = 5) [p/Cohen’s
δ, 0.001/0.9] and Dysl (n = 61) [p/Cohen’s δ, 0.001/1.5]. That is,
we can expect the best response to intervention in these catego-
ries. The worst result is in cluster 4, that is, those with no PASS
deficiency at all. Within those with PASS deficiency, the worst
result is in cluster 2, namely, SLIe-r (n = 20) [p/Cohen’s δ,
0.05/0.5], Dysc (n = 17) [p/Cohen’s δ, 0.04/0.4], and SLIe-r +
Dysc (n = 25) [p/Cohen’s δ, 0.04/0.4]. In other words, some
SLIe-r, and some Dysc show the most serious learning difficul-
ties. These SLIe-r and Dysc subjects are different from those
included in the cluster 1. Those in cluster 1, SLIe-r (n = 38)
[p/Cohen’s δ, 0.02/0.6] and Dysc (n = 25) [p/Cohen’s δ,
0.03/0.5] respond better than those previously commented.
However these ones show worse results than those in cluster 3,
as we have said above. In other words, within SLIe-r and Dysc
subjects we identify two types with different degree in difficul-
ties.
Apart from planning, only PASS attention appears sensitive
to intervention, with amelioration particularly better in cluster 1,
and worse in cluster 3. On the contrary, both successive and
simultaneous remain insensitive to intervention.
Discussion
This study was designed to explore learning difficulties, in
particular what we call specific language impairment, dyslexia
and dyscalculia, in the light of PASS assessment and interven-
tion. The key question is which central processing we are as-
sessing with the tests being used in the studies dealing with
learning difficulties. In other words, can the tests be really
measuring different central processes of information? The an-
swer is given by the process involved in the creation of the test,
that is, the process is the product. Therefore, both reliability in
the sense of consistency, and validity in the sense of accuracy
depend on this concept. Furthermore, both sensitivity and spe-
cificity are also influenced by this construct process. We are
used to seeing that a particular test is assumed to measure several
Table 4.
Differential improvement in PASS scores after 6 months of intervention.
Cluster 1 Cluster 2 Cluster 3 Cluster 4
SLIe-r
n = 38
P < A < Su
Dysc
n = 25
P < Su < A
SLIe-r
n = 20
P
Dysc
n = 17
Si < P
Comb
n = 25
P < Si
Comb
n = 40
Su/P/A
Dysl
n = 61
Su*
n = 22
Planning 0.02/03 0.03/0.5 0.05/0.5 0.04/0.4 0.04/0.4 0.001/0.9 0.001/1.5 0.05/0.2**
Attention 0.03/0.3 0.04/0.2 NS NS NS 0.05/0.2 0.05/0.2 NS
Successive NS NS NS NS NS NS NS NS
Simultaneous NS NS NS NS NS NS NS NS
Note: *Cognitive weakness at baseline: an individual PASS (planning, attention, successive, simultaneous) score lower than the child’s mean and below normative standard
score 85. P = planning A = attention Su = successive Si = simultaneous Comb in cluster 2 is SLIe + Dysc Comb in cluster 3 is SLIe +Dysc + Dysl. **p paired Student
t-test/effect size as average across subjects. Statistical effect size according to Cohen’s δ: trivial (<0.1), small (0.1 - 0-3), moderate (0.3 - 0.5), large difference effect (>0.5).
Table 5.
Differential improvement in PASS scores at 12 months assessment, 6 months after intervention.
Cluster 1 Cluster 2 Cluster 3 Cluster 4
SLIe-r
n = 38
P < A < Su
Dysc
n = 25
P < Su < A
SLIe-r
n = 20
P
Dysc
n = 17
Si < P
Comb
n = 25
P < Si
Comb
n = 40
Su/P/A
Dysl
n = 61
Su*
n = 22
Planning 0.03/06 0.04/0.5 0.05/0.5 0.05/0.4 0.04/0.4 0.001/0.9 0.001/1.5 0.04/0.2**
Attention 0.03/0.3 0.04/0.2 NS NS NS 0.05/0.2 0.05/0.2 NS
Successive NS NS NS NS NS NS NS NS
Simultaneous NS NS NS NS NS NS NS NS
Note: *Cognitive weakness at baseline: an individual PASS (planning, attention, successive, simultaneous) score lower than the child’s mean and below normative standard
score 85. P = planning A = attention Su = successive Si = simultaneous Comb in cluster 2 is SLIe + Dysc Comb in cluster 3 is SLIe +Dysc + Dysl. **p paired Student
-test/effect size as average across subjects. Statistical effect size according to Cohen’s δ: trivial (<0.1), small (0.1 - 0.3), moderate (0.3 - 0.5), large difference effect (>0.5). t

F. PEREZ-ALVAREZ ET AL.
considered different cognitive functions. So, for instance,
Stroop and Trail Making tests are considered valid to assess
both attention and executive function.
We must say that the majority of tests have been created ac-
cording to a process consisting of seeing an external behavior
(for instance, being attentive), then elaborating the test, and
then normalizing it statistically. We can assume such test meas-
ures a behavior consequence of a central processing of informa-
tion, but which central processing? For example, inattentive
behavior involves the attention processing when mental activity
is focused on another someone/something, for instance the last
film. Then, we must differentiate attention central processing
from attentional behavior. In fact, this may explain, to a great
part, the heterogeneity of the results of the studies.
In PASS terms (Naglieri & Das, 1997; Das, Kar, & Parrilla,
1996; Das, Naglieri, & Kirby, 1994; Das, Garrido, Gonzalez,
Timoneda, & Perez-Alvarez, 1999), four programs (software)
are always working whenever any cognitive activity takes place
independently of how the information is either entering (input)
or leaving (output) central nervous system. In fact, this is not
different from what the central nervous system (CNS) does
with any kind of information being processed. For instance,
ataxia must be considered a behavior (output) that can be due to
failure in cerebellar neuronal network (program), but also in
vestibular neuronal network (program). The same output can be
due to different central programs. Really, something not differ-
ent from the fact that different sums (programs) can actually
produce the same sum (output) like 3 + 3, 4 + 2, 1 + 5 = 6. In-
stead, different outputs can be due to the same central process-
ing or program. This concept of mental cognitive operation
allow us to intervene, for instance, on a dyslexic problem with-
out using reading as a training material, because the central
program is independent of input and output.
What can be deduced from neurological lesion studies is be-
ing reinforced by growing neurological evidence by using func-
tional neuroimage (Cabeza & Nyberg, 2000; Catani, Jones, &
Fytche, 2005; Davis et al., 2007; Hampson, Peterson, Skudlar-
ski, Gatenby, & Gore, 2002; Greicius, Krasnow, Reiss, &
Menon, 2003; Le Bihan et al., 2001; Maldjian, 2001; Morgane,
Galler, & Mokler, 2005; Perez-Alvarez & Timoneda, 2007;
Pujol et al. 2008; Raichle, 2000, 1991; Shinkareva et al. 2008;
Vannest, Karunanayaka, Schmithorst, Szaflarski, & Holland,
2009; Vuilleumier & Pourtois, 2007). For years it has been well
known the information enters via the senses, is centrally proc-
essed at neurological centers, and leaves via the motor system
with verbal or non-verbal expression (manipulation). In turn,
the central neurological centers constitute a serial network from
the sensorial input to the motor output with the higher proces-
sor in between (Davis et al., 2007; Mesulam, 1998; Swanson,
2007; Swanson, Grant, Hökfelt, & Jones, 2007). The PASS
processing must be considered a processor at the higher central
level.
Functional neuroimage techniques are allowing us to observe
the central processing and distinguish peripheral sensorial net-
work from central high-order network. In this sense, for in-
stance, studies of language have been clearly illustrative, dem-
onstrating how both receptive-perceptive areas (Wernicke) and
expressive-motor areas (Broka) can be differentiated from cen-
tral high-order areas. Broca area operates even in the case of
silent reading, which means that Broca’s neurons are succes-
sively placed in the processing network before the somatic
neurons responsible for motor act of speaking out.
Functional neuroimage techniques of connectivity (Greicius,
Krasnow, Reiss, & Menon, 2003; Hampson, Peterson, Skud-
larski, Gatenby, & Gore, 2002; Maldjian, 2001; Swanson, 2007)
as well as neuroimaging tractography (Catani, Jones, & Fytche,
2005; Le Bihan et al., 2001) are allowing us to observe concrete
neurological networks. The existence of a functional connection
between Broca’s area and Wernicke’s area has been demon-
strated at rest. An increase in this functional connection when
the language system is actively engaged (when subjects are
continuously listening to narrative text) has been also con-
firmed. A correlation between Broca’s area and a region in left
premotor cortex has been found to be significant at rest and to
increase during continuous listening. These findings suggest
that the neuroimage technology can reveal the presence and
strength of functional connections in high-level cognitive sys-
tems (Hampson, Peterson, Skudlarski, Gatenby, & Gore, 2002).
Also, neuroimage (Cabeza & Nyberg, 2000, Raichle, 2000,
1991) is contributing to support the PASS principle that input
and output of information are independent of central processing.
For instance, both a complex mental arithmetic task and a task
consisting of strategic searching of a missing card within a pack
of cards activate dorsolateral prefrontal cortex. Two apparent
different tasks are resolved by the same neuronal area. On the
contrary, two tasks like which number is between 3 and 5 and
which day is between Monday and Wednesday behave differ-
ently. These two apparently similar tasks do not activate the
same neurological areas. The first task activates left parietal,
whereas the second one does non-parietal area. Then, what our
external observation tells us is different from or equal to is not
always the same in the eyes of the neuron.
Multiple studies based not only on functional neuroimage but
also on acoustic analysis of temporal processing of information
(Perez-Alvarez, Fabregas, & Timoneda, 2009; Tallal, Miller, &
Fitch, 1993) and on other methods have conclusively shown
that central processing is independent of input and output of
information, just as the essential principle of the PASS theory
affirms. Similar evidence has been obtained with many differ-
ent tasks, namely, mathematics, reading, music and so on,
which allows us to deduce similar central programs operate
independently of the nature of the task. We can observe input
and output, but we must deduce the central processing. Input
and output, either verbal or manipulative, may be both succes-
sive and simultaneous. Both input and output may be succes-
sive or simultaneous and, instead, central processing be simul-
taneous or successive respectively. Vice versa is also true.
Coincidentally, PASS mathematical factorial analysis valida-
tion (Das, Kar, & Parrilla, 1996; Das, Naglieri, & Kirby, 1994)
tells us: “A is higher than B, B is higher than C. Which one is
higher? Which one lower?” is resolved by using PASS simul-
taneous processing. Instead, “A is higher than B, C is higher
than A, B is higher than C. True or false?” is resolved by using
PASS planning. Also, to get to reach a toy that is far away be-
hind an obstacle by removing the obstacle and pulling the fabric
where the object is placed is a behavior a 9 months old infant
can do. We can see this behavior involves some kind of strategy,
but it has been scientifically (factorial analysis) verified this
action does not demand PASS planning, a processing that is not
operative before 5 years old.
Another neurological principle is that the more central
(higher processor), the less concentration of neurons (Vannest,
karunanayaka, Schmithhorst, Szaflarski, & Holland, 2009). In
fact, this is in accordance with what was years ago demon-
766

F. PEREZ-ALVAREZ ET AL.
strated by using electrical stimulation of neurons in conscious
patients being operated because of lesion in brain. Since Pen-
field we know with local anesthesia it is possible to test which
effect on language follows after the stimulation in different
areas of the brain. Therefore, although each PASS processing
has been associated with particular centers, namely, planning-
prefrontal, attention-prefrontal/reticular system, successive-
prefrontal/temporal, simultaneous-parietal/occipital (Das, Kar,
& Parrilla, 1996; Das, Naglieri, & Kirby, 1994; McCrea, 2009),
the fact is that every processing woks as a high-order one with a
network distributed throughout the cortex. Thus, fMRI and
Event Related Potential studies demonstrate that neurological
processing is a complex process that cannot be related to a sin-
gle brain regions, but rather it implicates an interactive network
with distributed interactive activity in time and space (Morgane,
Galler, & Mokler, 2005; Vuilleumier & Purtois, 2007). Ac-
cording to Cajal’s law of neural avalanche, “every peripheral
impression received by the dendrites (sensory) of a single cell is
propagated towards the centers in the fashion of an avalanche;
or, in other words, the number of neurons concerned in the
conduction increases progressively from the periphery to the
cerebrum”. Likewise, evidence on oscillatory synchrony tells us
that different neurological regions work in the coordination of
long-distance neuronal communication during higher cognitive
processes (Sederberg, Kahana, Howard, Donner, & Madsen,
2003).
Having argued such a conceptual explanation, the next cru-
cial point is the result in a test (or academic task) can be modi-
fied for better according to the central program is being used.
Then, the intervention must focus on the central program, but
not on the result. For instance, be the task to remember the
input 633435. The same subject with dysfunctional successive
may do it: a) by recalling the series with no other association
( relationship) but only the lineal association, one digit after the
following (successive), something like rote memorization; b) by
recalling it as 63 34 35, in which case you are using the succes-
sive for recording three units, that is, 63/34/35, but each unit
has been mentally elaborated with simultaneous , which allows
us to establish the relationship 6 + 3 = 63; c) by recalling it with
the following strategy (planning): 63/34/35 is as if 34, 35 and
36 in consecutive order, but turning 36 into 63 and translating
the last unit to the first one in the series. It is evident that our
dysfunctional successive subject needs options (b) and (c).
According to this, the same subject will produce different IQ
result depending on which processing is operating each time.
Terms as phonologic processing, both receptive and expres-
sive language processing, vocabulary processing, semantic
processing, syntactic processing, phonologic processing, prag-
matic processing, discourse processing, auditive discrimination,
visual perception, number processing, arithmetic processing,
music processing, auditive memory, verbal memory, visual
memory, short term memory, long term memory, episodic
memory, biographic memory, and so on can all be accounted
for in the light of PASS cognitive processing (Figure 1) The
PASS processes are not memories, but memory works using the
PASS processes. The matter is that these terms are defined by
the input of information.
We can explain how learning happens by understanding how
information processing takes place. We assume this conceptu-
alization is crucial for a efficient intervention. The most simple
learning, consisting of memorizing many apparently isolated
facts, is rote memorization, which plays an important role in the
Figure 1.
PASS versus NON-PASS processing.
initial stages of learning. For instance, preschoolers often first
learn to use numbers mechanically, or by trial-and-error prob-
lem solving, and then gradually discover or construct deeper
and deeper understanding (insight). In PASS terms, rote memo-
rization is mainly linked to successive processing.
Learning is a process of memorization, but meaningful
learning is a different process from learning by rote memoriza-
tion. Children may accurately imitate computational routines
without understanding. Understanding is learning by insight.
Insight requires thought. For instance, meaning of the plus sign
(+) or minus sign (−) or times sign (×) or equals sign (=) hap-
pens by connecting symbol to their concept. From the begin-
ning, associations—relationships take place. In PASS terms,
association is simultaneous. This way, thinking skill matures to
reach deductive reasoning or the use of rules or principles to
logically prove points. The discovery of relationships by exam-
ining cases characterizes inductive insight. In PASS terms, this
operation implies planning.
Forming associations involves making connections with ex-
isting knowledge. Initially, the existing knowledge has to do
with informal knowledge linked to personal experience. Infor-
mal knowledge is basically a concrete-tangible knowledge.
Meaningful learning is necessarily dependent on what an indi-
vidual already knows, and it takes place by relating the formal
knowledge (symbolism, definition, and so on) to real knowl-
edge (objects, things). Anyone is prone to forget information
that is not personally meaningful. Thus, knowledge base be-
comes a reality. Therefore the role of memorized knowledge
base is substantial for learning.
With development, children learn more relationships and
their knowledge forms a more complete logical system (knowl-
edge becomes more interconnected) to reason deductively,
applying general abstracted principles or rules to solve specific
problems. Children evolve from the simpler form of learning by
rote memorization to more complex forms of learning and
thinking (planning). Gradually, they are able to manage more
cognitively complicated tasks. They reach a more advanced
stage in thinking ability. For instance, counting backward is
more difficult than counting forward for young children. Or at
about 3 years children discover that higher count term is asso-
ciated with larger magnitude. They realize that 2 not only fol-
lows 1 but also represents a larger quantity than does 1. It is
well known that language, weather verbal or written, is under-
stood by the listener or reader according to the meaning shared
by the transmitter and receptor of the language, something to-
tally dependent on the frame of reference (knowledge base).
Beliefs become an important part of knowledge base.

F. PEREZ-ALVAREZ ET AL.
In essence, relationship learning has to do with discriminat-
ing “same as” (equivalence) from “different from” (inequiva-
lence). That’s why discrimination is more difficult in cases of
high similarity. The more defining characteristics that are
shared, the more likely that a child o any person will confuse.
For instance, for a child learning reading, letters like “f” and “t”
or “n” and “h”, and “p” and “b” and “d”. Also, numbers like 6
and 9. This discrimination is based on cognitive network higher
complex than perceptual-motor network, which is involved in
fine visual-motor integration processing responsible for the
proper coordination of eyes and hand movements (Mesulam,
1998).
Another essential point has to do with the fact that the cen-
tral-neurological processing happens more frequently uncon-
sciously than consciously between the either consciously or
unconsciously processed sensorial input (stimulus) and the
either consciously or unconsciously processed output (response)
(Das & Kendrick, 1997; Das, Kar, & Parrilla, 1996; Das, Na-
glieri, & Kirby, 1994; Das, Garrido, Gonzalez, Timoneda, &
Perez-Alvarez, 1999; Davis et al., 2007, Perez-Alvarez & Ti-
moneda, 2007; Pujol et al., 2008 ). For instance, a child is pre-
sented with single separated letters, concretely, “u, b, s,” (con-
sciously processed input). He/she is asked to pronounce the
successive combination “b, u, s,” and he/she answers (output)
correctly (consciously processed output). Then, he/she is asked
to pronounce the presented sequence “q, u, s,” and the answer
(output) is again /b s/. Incorrect answer, but incorrect reason-
ing ? If we ask him/her: “How did you do it?” He/she will an-
swer : “I did it this way” (consciously processed output).
His/her explanation will be elaborated by his/her thinking
brain taking via sensorial gates the information coming from
outside in real time. In fact, it is a posteriori response to the
answer being formulated. It is about a posteriori consciously
thinking response with respect to the first previous unconscious
mechanisms responsible for the resolution of the task. Probably,
the verbal explanation being reported (consciously processed)
will not correspond to the real reason for the response (uncon-
sciously processed). Really, his/her unconsciously processed
knowledge, not susceptible to be consciously and verbally re-
ported, is the symbol “b” sounds /b/ whether right side up or
not. Then, correct reasoning happened. If the same error in
identification persists, we may shake our head in disbelief,
unable to understand how the error could persist despite re-
peated correction (Bargh & Ferguson, 2000; Dobbins, Schnyer,
Verfaellie, & Schacter, 2004).
Evaluation that exclusively examines resulting-external pro-
duct is not accurate, and even may overestimate a child's com-
petence in case of “false success” in academic learning. For
instance, with a choice of only two answers to a question, you
have a 50 - 50 chance of getting any particular question right
just by guessing. On average, guessing should permit a pupil to
get about 5 of the 10 correct. In fact, a correct response does
not guarantee a deep appreciation of the rule, principle or
knowledge. A focus on performance overlooks invaluable in-
formation needed to diagnose incomplete or inaccurate under-
standing or reasoning and to design an effective remedial plan.
Errors provide important clues about underlying processes and
the meaning of errors can disclose we are in the presence of a
“false failure.” Practically, we are always facing rule-governed
learning. They may be logical, although incorrect.
Body language and, for instance, eye language may be very
informative (Das, Kar, & Parrilla, 1996; Das, Naglieri, & Kirby,
1994; Das, Garrido, Gonzalez, Timoneda, & Perez-Alvarez,
1999; Perez-Alvarez & Timoneda, 2007). For instance, eyes up
and to the left or to the right indicates simultaneous processing,
eyes level and to the left or to the right successive processing,
eyes down and to the left or to the right body sensations. And
other body expressions are informative: wrinkled forehead,
contracted jaw, shoulders thrown back, breading shallow in the
chest, a fix grin, indicate all tension-concentration. On the con-
trary, shoulders relaxed and drooped breading deeply in ab-
dominal area as breading from diaphragm indicate tranquility,
relax. Therefore many body expressions tells us about cognition
and emotion: unusual posture, specific hand movements, head
turns, leaning to one side, rocking back and forth or side to side,
rigid body, facial expression (mouth and eyebrows), startled
look, big grin on the face, eye contact, yawning, particular
words or phrases, voice quality and pitch, tone, volume, inflec-
tion, speed, tempo (rhythmic, choppy), and so on. We are not
interested so much in what someone is saying as in how it is
been said.
The learning based on facilitating rote memory by repetition
is not an efficient one. A child may learn a procedure mechani-
cally (rote memory/rotely memorized) but he/she does not
really understand why the procedure woks. This is a non mean-
ingful learning, a senseless procedure where the lack of rela-
tionships or associations does not makes far transfer possible
and achievable. The new knowledge is not internalized to be
used (far transferred) in a new task with no apparent relation-
ship. The mechanical use of rotely learned procedures means
that the rotely learned rules cannot transfer. Children fail to see
any connection with a known procedure.
The more efficient learning is founded on minimizing suc-
cessive processing (working memory/short term memory) and
maximizing simultaneous processing (associations/long term
memory) by tutored planning training (strategies/problem solv-
ing/decision-making). Indeed, tutored training is superior to
non-tutored training, which is more linked to intuitive learn-
ing. The intuitive learning leading to intuitive knowledge fits
into the existing pattern of thought. Young children, for in-
stance, presented with a container with 5 items and another
with 9 ones to which we add 4 and 2 more items respectively,
think 5 + 4 is “more than” 9 + 2 because they saw “more”
added to the first container. Clearly, intuitive arithmetic is im-
precise, but the performance is coherent with the previous
knowledge. Also, by observing that adding objects to a set
“makes more”, a child intuitively concludes that when a coin is
added to a cup with five coins, that cup then contains more than
a cup of eight to which nothing has been added. Or a child con-
cluding that his longer row of 7 has more than his shorter row
of 8 is using perceptual criterion of length to conclude that the
longer row has more quantity. He/she must realize that the
number of items in a set does not change because the appear-
ance of the set has changed. The work of making the child in-
ternalize and transfer the new knowledge that substitute the old
knowledge (change of knowledge) is the aim of an efficient
intervention. The efficient intervention requires much more
than just practice. Practice in itself does not guarantee learning.
According to neurological evidence, we know that signal
transmission through synapsis is facilitated by the repetition of
activity of the synapses (Guyton & Hall, 1996). Since we know
the long term potentiation phenomenon (Hebb, 1949), we also
know that the effect of a stimulus becomes more potent when
previous stimuli have been applied. Therefore repetition works
768

F. PEREZ-ALVAREZ ET AL.
by itself, but obviously temporal summation-rote memorization
is a mental activity less efficient than global PASS mental
processing. The spatial summation phenomenon must be con-
sidered neuronal expression of simultaneous processing. And
conditioning is simultaneous processing and consequently
learning may be considered as a conditioning phenomenon.
In fact, practice is important to make thinking skills auto-
matic once learning took place. Empirical evidence indicates
that the amount of practice (pointless drills, interviews, con-
versations) is not predictive of mastery. On the contrary, mas-
tery is more directly linked to the development of meaningful
knowledge than to practice frequency, although you’ll learn by
doing, by performing. It may take time to see, assimilate new
information to what is known, and build up a network of rela-
tionships. Whereas some relationships are relatively easy to see
and are quickly internalized (comprehension), others are not
easily abstracted and require time to master. Children go at
their own pace. Moreover, it is very frustrating for children to
continue practicing when they can see that they are doing it
incorrectly but do not know how to correct it.
Next, we will present an example of how PASS planning
training (Das & Kendrick, 1997) operates in order to get to
construct or change a strategy. A child experiences that, for
instance, 5 − 4 = 1; 8 − 7 = 1; 23 − 22 = 1. The child may real-
ize that the answer is always one. Then the rule: “the subtrac-
tion of two number neighbors produces a difference of one” can
be internalized and transferred. The children can abstract a
general rule or principle that enables them to respond effi-
ciently even to previously unencountered problem (far transfer).
Inductive learning works from the concrete and specific to the
abstract and general. The abstract principle is to find something
common to all the items. Abstraction is a question of degree
such that the higher degree of abstraction is required for the
higher cognitive concept.
We can use concrete procedures like blocks, fingers or marks
for 5 − 2. That is, for problems with addends of 5 or less, for
instance, a finger-pattern procedure can be useful. So, for 3 + 5,
child puts up finger patterns of 3 and 5 on separate hands and
then counts all 8 fingers. However, this strategy cannot be used
with problems , such as 3 + 9 and 4 + 10. If so, another efficient
mental computing procedure, the most economical mental pro-
cedure to minimize cognitive effort, is needed. A child may
have no difficulty with 4 × 2 by doing four counted two times,
but he may be overwhelmed by the problem 2 × 4 by doing two
counted four times. He needs to see that 4 × 2 is equivalent to 2
× 4, that is, that multiplication is commutative. When ready,
children will abandon concrete procedures in favor of mental
procedures. The child should be weaned from activities that
rely on concrete objects (using concrete objects to compute the
sum, for instance), visible clues (by pushing counted objects
away into a clearly separate pile), and so on, and required to
solve the problems mentally, gradually going from concrete to
abstract representation, and running the bridge between con-
crete but limited direct perception and abstract but general ideas.
Obviously, a concrete strategy becomes inefficient, even im-
possible, according to what is required.
Even children with learning difficulties can see ways of us-
ing their existing knowledge to shortcut cognitive effort, in-
venting more efficient workable strategies (planning), more
powerful mental strategies (McCloskey, Caramaza, & Basili,
1985; Shalev, Manor, Auerbach, & Gross-Tsur, 1998). The
same is true for very young or disadvantaged or mentally
handicapped children.
In conclusion, we have discussed the process that explains
the product, that is, our results. SLI, dyslexia, and dyscalculia
are not homogenous entities in PASS terms. A kind of dyslexia
is clearly defined by isolated successive weakness. SLI-ex-
pressive and a minority of both dyslexia and dyscalculia appear
linked to successive weakness although associated with plan-
ning and additionally with attention in the case of SLI-expres-
sive, which means these entities share common pathogenesis.
SLI-expressive-receptive and Dyscalculia appear linked to si-
multaneous weakness, although associated with planning weak-
ness. Other kind of SLIe-r appears linked to isolated planning
weakness. Other types of SLIe-r and Dyscalculia appear liked
to combined planning +successive + attention weakness. There-
fore, SLIe-r and dyscalculia show higher heterogeneity. Iso-
lated dysfunctional attention does not appear in any case. After
6 months of intervention, planning improves statistically in all
cases. Efficient intervention is based on planning. Attention
improves in few cases. Successive and simultaneous do not
improve. The best result is in dyslexics and SLI-expressive and
a minority of Dyscalculia. The worst result is in those without
cognitive deficiency. SLIe-r and dyscalculia are in the middle.
The effect of intervention at 6 months remains with minor
changes at 12 months after 6 months without intervention.
This study has addressed some hypotheses regarding the di-
agnosis, treatment, and prognosis of cognitive function asso-
ciated with learning difficulties. We anticipated what has been
further elucidated and defined by this study concerning unex-
plored questions. It has provided valuable data and we hope the
application of these results will be a step towards providing a
better understanding of the topic. Likewise, we hope our results
will stimulate significant further investigations into the field.
The most important limitations of this paper would be the
sample size of some subsamples.
Acknowledgements
We thank the staff of the Fundació Carme Vidal de NeuroP-
sicoPedagogia for aid in the study and their care and interest in
the investigation.
REFERENCES
Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Bev-
erly Hills, CA: Sage Publications.
American Psychiatric Association (2000). Diagnostic and statistical
manual of mental disorders (4th ed.). Washington, DC: APA.
Bargh, J. A., & Ferguson, M. L. (2000). Beyond behaviorism: On the
automaticity of higher mental processes. Psychological Bulletin, 126,
925-945. http://dx.doi.org/10.1037/0033-2909.126.6.925
Bishop, D. (1994). Is specific language impairment a valid diagnostic
category? Genetic and psycholinguistic evidence. The acquisition
and dissolution of language. Philosophical Transactions: Biological
Sciences, 346, 105-111. http://www.jstor.org/stable/56025
http://dx.doi.org/10.1098/rstb.1994.0134
Bishop, D., & Leonard, L. (2001). Speech and language impairments in
children. Hove: Psychology Press.
Bravo, L. (1979). Cuestionario de evaluación de problemas de aprendi-
zaje CEPA. Estudios Pedagógicos, 4, 113-123.
Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: An empirical
review of 275 PET and fMRI studies. Journal of Cognitive Neuro-
science, 12, 1-47.
http://dx.doi.org/10.1162/08989290051137585
Catani, M., Jones, D. K., & Fytche, D. H. (2005). Perisylvian language

F. PEREZ-ALVAREZ ET AL.
770
networks of human brain. Annals of Neurology, 57, 8-16.
http://dx.doi.org/10.1002/ana.20319
Das, J. P., & Kendrick, M. (1997). PASS reading enhancement program
(PREP): A short manual for teachers. Journal of Cognitive Education,
5, 193-208.
Das, J. P., Kar, R., & Parrila, R. K. (1996). Cognitive planning. The
psychological basis of intelligent behavior. London: Sage Publica-
tions Ltd.
Das, J. P., Naglieri, J. A., & Kirby, J. R. (1994). Assessment of cogni-
tive processes. The PASS theory of intelligence. Massachussets: Al-
lyn & Bacon, Inc.
Das, J. P., Garrido, M. A., González, M., Timoneda, C., & Pérez-
Álvarez, F. (1999). Dislexia y dificultades de lectura. Barcelona:
Ediciones Paidós Ibérica SA.
Davis, M. H., Coleman, M. R., Absalom, A.R., Rodd, J. M, Johnsrude,
I. S., Matta, B. F. et al. (2007). Dissociating speech perception and
comprehension at reduced levels of awareness. Proceedings of the
National Academy of Sciences of the United States of America, 104,
16032-16037. http://dx.doi.org/10.1073/pnas.0701309104
Dobbins, I. G., Schnyer, D. M., Verfaellie, M., & Schacter, D. L.
(2004). Cortical activity reductions during repetition priming can re-
sult from rapid response learning. Nature, 428, 316-9.
http://dx.doi.org/10.1038/nature02400
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Func-
tional connectivity in the resting brain: A network analysis of the de-
fault mode hypothesis. Proceedings of the National Academy of Sci-
ences of the United States of America, 100, 253-258.
http://dx.doi.org/10.1073/pnas.0135058100
Guyton, A. C. & Hall, J. E. (1996). Textbook of medical physiology (9th
ed.). Philadelphia, PA: W.B. Saunders Company.
Hampson, M., Peterson, B. S., Skudlarski, P., Gatenby, J. C., & Gore, J.
C. (2002). Detection of functional connectivity using temporal cor-
relations in MR Images. Human Brain Mapping, 15, 247-262.
http://dx.doi.org/10.1002/hbm.10022
Le Bihan, D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S.,
Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging:
Concepts and applications. Journal of Magnetic Resonance Imaging,
13, 534-546. http://dx.doi.org/10.1002/jmri.1076
Maggiolo, M., & Pavez, M. (2000). Test para evaluar los procesos
fonológicos de simplificación (TEPROSIF). Santiago: Universidad de
Chile.
Maldjian, J.A. (2001). Functional connectivity MR imaging: Fact or
artifact? AJNR American Journal of Neuroradiology, 22, 239-240.
Matute, E., Rosselli, M., Ardila, A., & Ostrosky-Solis, F. (2005).
Evaluación neuropsicológica infantil. México: Universidad de Gua-
dalajara.
Mc Crea, S. M. (2009). A review and empirical study of the composite
scales of the Das-Naglieri cognitive assessment system. Psychology
Research and Behavior Management, 2, 59-79.
http://dx.doi.org/10.2147/PRBM.S5074
McCloskey, M., Caramaza, A., & Basili, A. (1985). Cognitive mecha-
nism in number processing and calculation: evidence from dyscalcu-
lia. Brain Cognition, 4, 171-196.
http://dx.doi.org/10.1016/0278-2626(85)90069-7
Mesulam, M. M. (1998). From sensation to cognition. Brain, 121,
1013-1052. http://dx.doi.org/10.1093/brain/121.6.1013
Morgane, P. J., Galler, J. R., & Mokler, D. J. (2005). A review of sys-
tems and networks of the limbic forebrain/limbic midbrain. Progress
in Neurobiology, 75, 143-160.
http://dx.doi.org/10.1016/j.pneurobio.2005.01.001
Naglieri, J. A., & Das, J. P. (1997). Cognitive assessment system. Roll-
ing Meadows, IL: Riverside Publishing.
Naglieri, J. A., & Das, J. P. (1995). A reply to Kranzler and Weng’s
shooting in the dark. Journal of School Psychology, 33, 159-167.
http://dx.doi.org/10.1016/0022-4405(95)00005-7
Pérez-Álvarez, F., & Timoneda, C. (2007). A better look at intelligent
behavior. Hauppauge, NY: Nova Science Publishers, Inc.
Perez-Alvarez, F., Fàbregas, M., & Timoneda, C. (2009). Proce-
samiento cognitivo, fonémico o temporal? Neurologia, 24, 40-44.
Perez-Alvarez, F., Serra-Amaya, C., & Timoneda-Gallart, C. (2009).
Cognitive versus behavioral ADHD phenotype: What is it all about?
Neuropediatrics, 40, 32-38.
http://dx.doi.org/10.1055/s-0029-1231055
Pujol, J., Reixach, J., Harrison, B. J., Timoneda-Gallart, C., Vilanova, J.
C., & Pérez-Alvarez, F. (2008). Posterior cingulate activation during
moral dilemma in adolescents. Human Brain Mapping, 29, 910-921.
http://dx.doi.org/10.1002/hbm.20436
Raichle, M.E. (1991). Memory mechanisms in the processing of words
and word-like symbols. Ciba Foundation Symposium, 163, 198-204.
Raichle, M.E.. (2000). A brief history of human functional brain map-
ping. In: Toga, A. W., & Mazziotta, J. C. (Eds.), Brain mapping: The
systems (pp. 33-75). San Diego, CA: Academic Press.
http://dx.doi.org/10.1016/B978-012692545-6/50004-0
Rapin, I. (1998). Understanding childhood language disorders. Current
Opinion in Pediatrics, 10, 561-566.
http://dx.doi.org/10.1097/00008480-199810060-00004
Rapin, I., & Allen, D.A. (1983). Developmental language disorders:
Nosologic considerations. In: U. Kirk (Ed.), Neuropsychology of
language, reading and spelling. San Diego, CA: Academic Press.
Rosselli-Cock, M. E., Matute-Villaseñor, E., Ardila-Ardila, A., Botero-
Gómez, V. E., Tangarife-Salazar, G. A., Echeverría-Pulido, S. E., et
al. (2004). Una batería neuropsicológica para la evaluación de niños
con edades entre los 5 y 16 años. Un estudio normativo. Rev Neurol,
38, 720-731.
Sederberg, P. B., Kahana, M. J., Howard, M. W., Donner, E. J., &
Madsen, J. R. (2003). Theta and gamma oscillations during encoding
predict subsequent recall. Journal of Neuroscience, 23, 10809-10814.
Shalev, R. S., Manor, O., Auerbach, J., & Gross-Tsur, V. (1998). Per-
sistence of developmental dyscalculia: What counts? Results from a
3-year prospective follow-up study. The Journal of Pediatrics, 133,
358-362. http://dx.doi.org/10.1016/S0022-3476(98)70269-0
Shinkareva, S. V., Mason, R. A., Malave, V. L., Wang, W., Mitchell, T.
M., & Just, M. A. (2008). Using fMRI brain activation to identify
cognitive states associated with perception of tools and dwellings.
Plos One, 3, e1394. http://dx.doi.org/10.1371/journal.pone.0001394
Swaiman, K. F., Ashwal, S., & Ferriero, D. M. (2006). Pediatric neu-
rology. Principles & practice (4th ed.). Philadelphia, PA: Mosby Inc.
Swanson, L. W. (2007). Quest for the basic plan of nervous system
circuitry. Brain Research Reviews, 55, 356-372.
http://dx.doi.org/10.1016/j.brainresrev.2006.12.006
Swanson, L. W., Grant, G., Hökfelt, T., & Jones, E. G. (2007). Morri-
son JH. A century of neuroscience discovery: Reflecting on the No-
bel Prize awarded to Golgi and Cajal in 1906. Brain Research Re-
views, 55, 191-192.
http://dx.doi.org/10.1016/j.brainresrev.2007.07.001
Tallal, P., Miller, S., & Fitch, R. (1993). Neurobiological basis of
speech: A case for the preeminence of temporal processing. Annals
of the New York Academy of Sciences, 682, 27-47.
http://dx.doi.org/10.1111/j.1749-6632.1993.tb22957.x
Vannest, J., Karunanayaka, P. R., Schmithorst, V. J., Szaflarski, J. P., &
Holland, S. K. (2009). Language networks in children: Evidence
from functional MRI studies. American Journal of Roentgenology,
192, 1190-1196. http://dx.doi.org/10.2214/AJR.08.2246
Vuilleumier, P., & Pourtois, G. (2007). Distributed and interactive brain
mechanisms during emotion face perception: Evidence from func-
tional neuroimaging. Neuropsychologia, 45, 174-194.
http://dx.doi.org/10.1016/j.neuropsychologia.2006.06.003
World Health Organization (1993). International statistical classifica-
tion of diseases and related health problems: Diagnostic criteria for
research. Tenth Revision (ICD-10), Geneva: World Health Organi-
zation.