Brain-Based Approaches to the Study of Intelligence
Summary and Keywords
A brain-based approach can provide a framework for intelligence, for integration of biology and cognitive processes that have direct implications for education and brain plasticity. Intelligence is reframed here as a selective cluster of different cognitive processes often localized in broad divisions of the brain. Theories and systems that have guided investigation into the brain mechanisms for cognitive processes are reviewed. The focus is on education and cultural disadvantage, delineating changes in the brain due to learning and its dysfunction. Selected programs for enhancement of neurocognitive abilities are presented. Neuronal changes appear to occur as a consequence of learning throughout life. A brain-based approach not only relates to how intelligence works, but also opens the door to understanding the mind and hence consciousness. One may say that the mind is not an eclectic collection of intellectual functions of the brain. Rather, the ultimate goal of intelligence is to form a better view of self that gives meaning to an individual’s existence.
An Introduction to Brain-Based Studies of Intelligence
The brain consists of a great many modules that process information more or less independently of each other. It seems likely that it will be easier to discover how one of those modules works than to explain the functioning of the brain as a whole.
(Frith, 1997, p. 5)
The study of intelligence based on the brain has a long history. Consider Hippocrates, who suggested in the 5th century bc that the brain might be the basis of human intelligence (Sternberg, 1990). A brain-based approach to intelligence is a biological metaphor, as Sternberg (1990) discusses in his textbook Metaphors of Mind. Metaphors are essentially analogies, a popular mode of inference.
Not everyone needs to agree that the biological explanation of intelligence is merely a metaphor; it is also an explanation of the neurological and cognitive determinants of intelligent behavior. Given that explanations can exist at many levels, “brain-based” theories of intelligence can be approached from the molecular, neural, and cognitive levels; these may be expressed in behaviors such as test performance. At the behavioral level, intelligence can be estimated by tests and observed as a product of intact or impaired brain functions. The PASS theory, comprising Planning, Arousal-Attention, and two categories of processing, Simultaneous and Successive is an example of a neurocognitive theory. PASS provides explanations of cognitive and behavioral expressions of intelligence while its origins remain in physiological levels of brain functions (Das, Kirby, & Jarman, 1975, 1979; Das, Naglieri, & Kirby, 1994).
The biological approach does not exclude experience and the cultural environment in which an individual must function. One of the earlier physiologists, Sechenov, studying the “elements of thought” (1878), was persistent in arguing that we cannot separate biology from culture (see Das, Kirby, & Jarman, 1979 for an extended discussion). There is little disagreement at present that gene to culture transmission is a bidirectional process, that it works both ways even at the molecular level (Heyes, 2012). New research reveals the role that genes play in shaping the evolution of neural circuits in the brain; these influence not only cognitive but also social behavior. Research on the evolution of human cognition that includes intelligence asks what types of thinking are unique to humans and how they have been generated by evolutionary processes. A new kind of thinking on evolution of human cognition advocates a coevolution of the technological, social, and cultural brain (Heyes, 2012).
It is suggested that (1) intelligence should be considered as a conglomeration of cognitive processes for adaptive purpose, that it is not to be defined as IQ, and (2) cognition, unlike IQ, is by nature responsive to learning, be it formal or spontaneously acquired through experiences. Learning links cognitive processes to education because cognitive enhancement can be learned through training (Posner & Rothbart, 2007; Das, 2009).
IQ and General Intelligence
IQ, or intelligence quotient, is a global index of a person’s general ability. Intelligence is cognition, comprising sensory, perceptual, associative, and relational knowledge. Human intelligence is the mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment (Sternberg, 2017).
Do the standard IQ tests based on Binet or Wechsler provide adequate measures of intelligence as defined here? Historically, the earliest tests of IQ, such as Binet, were used to identify children who might need special education; thus, many of the items in the tests measured school-related achievement. IQ tests, as Gardner (1993) has written, rely heavily upon language. These standard measures of IQ depend upon a person’s skill in defining words, in knowing facts about the world, in finding connections (and differences) among verbal concepts. Gardner argues that the intelligence test reveals little about an individual’s potential for further growth. Two examples of the contents of a standard IQ test, depicted in Table 1, support Gardner.
As these examples show, comparison between items in standard IQ measures and achievement test items leaves us wondering which items assess IQ and which ones are part of school achievement tests.
Table 1. Comparisons Between Items in Standard IQ Measures and Achievement Tests.
Wechsler Verbal Scale—Arithmetic item
A boy had twelve books and sold five. How many books did he have left?
Stanford Achievement Test—Math item
Peter counted seventeen lily pads at the pond. There were frogs sitting on five of the lily pads, and the rest were empty. How many lily pads were empty?
Wechsler Intelligence Test: Vocabulary item:
What does scared mean?
(The child answers orally)
Stanford Achievement Test—Reading Vocabulary
Someone who is glad is
It is clear that there are major similarities between two widely used tests of Intelligence (Wechsler Intelligence Scale for Children [WISC-V]) and Achievement (Stanford Achievement test). While WISC-V renames IQ as Cognitive Ability, it retains a substantial overlap of items with Wechsler Individual Achievement Test (WIAT-III).
Naglieri and Bornstein (2003) also comment on how confusing it is to distinguish between the two tests. They state that users of these measures must recognize that the tests have highly similar items. For example, both ask questions that require knowledge of math facts, as well as knowledge of words (Vocabulary and Similarities on the WISC-V and Oral Language, Basic Reading, and Reading Comprehension on the WIAT-III). Obviously, these item similarities ensure a high correlation between “intelligence” and achievement. However, to be fair, there are other items on WISC that do not overlap clearly with achievement.
In my opinion IQ as a unidimensional conceptualization of intelligence has not helped researchers and clinicians understand variations of cognitive functions within special populations such as individuals with learning disabilities, disorders of attention, or intellectual and developmental disabilities. Neither has this conceptualization aided the amelioration of cognitive deficiencies. In contrast, a brain-based approach not only provides an explanation of cognitive assets, as in the gifted and talented, but also leads to construction of programs that are guided by the theoretical explanation for those facing intellectual challenges.
Pioneers in Brain-Based Intelligence
The concept that afferent neurons can have either excitatory or inhibitory functions goes back to early researchers in physiology, the best known among whom is Sechenov (1878) (see also Das, Kirby, & Jarman, 1979). The need to respond positively as well as withhold a response, including blocking a habitual response when necessary, is an essential ingredient for self-regulation.
The neurons of the cerebral cortex contain horizontal and vertical connections. They travel within and between the cortical layers. The response of any individual neuron is determined by the influence of thousands of synaptic inputs. The great majority of these inputs have their origin in networks of both excitatory and inhibitory neurons. These networks in the brain are highly dynamic, constantly receiving inputs from numerous synapses. As a result of this dense connectivity, the brain has the ability to generate persistent activity even in the absence of sensory stimulation. Memory and thinking may thus occur without the external input from a field of stimuli. This has been particularly well studied in the prefrontal cortex (Fuster & Alexander, 1971).
In the cortex, just a small fraction of the neurons are responsible for inhibition, yet they have an important function in regulating activity of principal cells. For example, when inhibition is blocked by drugs, cortical activity becomes disorganized and neurons may lose their selectivity to different stimulus features. Thus the interplay between excitation and inhibition has an important role in determining the cortical computation. Excitatory and inhibitory inputs of a neuron are said to be balanced when the ratio between the two inputs is constant.
Pavlov (1927) did several experiments to establish different aspects of activation of neurons: the strength of excitation and inhibition; a balance between the two processes; and mobility between excitatory and inhibitory states that allows us to shift from response to no response and back to responding. A dog with a strong and balanced neural system of excitation and inhibition is conditioned quickly and extinguishes fast as well. In an unbalanced system, with strong excitation and weak inhibition, conditioning will be fast, whereas extinction of the conditioned response may take a relatively longer time—that is, it takes repeated trials in which the conditioned stimulus-response connection is not reinforced. Similarly for humans, a habit is formed quickly, but it takes a long time to give up even though the habitual response is no longer rewarded. Mobility from excitation to inhibition and vice versa is easily seen in a task that demands response shift as required in executive functions.
For example, executive functions include shifting and flexibility of response sets, and inhibition of a prepotent response (Miyake et al., 2000). Mobility of excitation to inhibition is clearly required as in response shifts. For example, an experiment may present stimulus items in the shape of a box or a cross. The two figures are either red or blue in color. The participant is asked to name the shape when a cue (#) appears on top of the shape, and to name the color when there is no #. So on some trials, shape is salient, and in others color is salient. Similarly, inhibition plays an important role in attentional control, that is, regulating behavior by proper allocation of attentional resources and not deviating from achieving a goal. Both shifting and inhibition allow us to plan and solve problems, make good decisions, and act appropriately (Das & Misra, 2015). The place of inhibition in our actions is so important that some reviewers, such as Dempster and Corkhill (1999), persuasively argue that it is the most important aspect of intelligent behavior.
In a later publication, Conditioned Reflexes and Psychiatry, Pavlov (1941) demonstrated the interplay of strength, balance, and mobility of neural activation as they are expressed in psychiatric syndromes such as paranoia and obsessive-compulsive neurosis. Later, Luria (1961) used the three aspects of excitation-inhibition in describing the regulation of behavior, and in explaining hyper-arousal and under-arousal among atypical children. Pavlov’s pioneering studies were prescient in anticipating later advances of the three basic properties of our nervous system.
In higher cognitive functions, such as planning/executive functions, we can see the implications of the three aspects of excitation and inhibition in ADHD (attention deficit hyperactive disorder) and impulsivity.
There are, however, limitations to knowledge about how to map the huge amount of connections between neurons in the human brain. One contemporary approach to address this is the Human Connectome Project (Toga, Clark, Thompson, Shattuck, & Van Horn, 2012). The human brain has some 86 billion neurons. The axons that connect neural cells are 100,000 kilometers in length. Brain functions of a specific nature are probably best studied by looking for groups of neurons, such as the neuronal columns of which the different parts of the cortex are composed. Toga et al. aim to explore the structural and functional organization of these groups of neurons in order to map brain functions. The study of excitatory and inhibitory connections in the Human Connectome Project will be a formidable task but should provide spectacular additions to our knowledge about how the brain works.
Later Advances: Hebb and Luria
Donald Hebb and Alexander Luria have greatly influenced the field of brain and intelligence.
Donald O. Hebb
Hebb’s interest in a brain-based approach to intelligence began with lesion studies pioneered in Canada by Penfield (Hebb & Penfield, 1940). Hebb’s seminal contribution to understanding intelligence in terms of brain processes has strongly influenced the course of psychology. His theory is labeled under neuropsychological approach to intelligence by Sternberg (1990), who wrote that his contribution was one of the most respected and influential theories during the 20th century.
The Organization of Behavior (Hebb, 1949) is regarded as his classic contribution to neuropsychology. Like Luria, Hebb was engaged in clinical work on neurologically impaired patients, in association with Penfield, who established the Montreal Neurological Institute. He had gained his expertise in brain processes by working with animals; his mentor was Lashley, who also made significant contributions to intelligence (1929). Hebb was dissatisfied with global measures of intelligence because of their failure to measure cognitive functions that were impaired by brain injury. Like Luria, he questioned the tests’ value, and even constructed special tests: Adult Comprehension and Picture Anomaly (New World Encyclopedia, 2014).
Hebb proposed that whenever an axon of cell A excites cell B and repeatedly or persistently initiates cell B’s firing, a growth process or metabolic change must take place that increases A’s efficiency (Hebb, 1949). The theory is often summarized as “cells that fire together, wire together,” although this is an over-simplification of the nervous system, not to be taken literally, and does not accurately represent Hebb’s original statement on changes in cell connectivity strength. The theory is commonly invoked to explain some types of associative learning in which simultaneous activation of cells leads to pronounced increases in synaptic strength.
On the form and function of cell assemblies, Hebb notes “The general idea is an old one, that any two cells or systems of cells that are repeatedly active at the same time will tend to become “associated,” so that activity in one facilitates activity in the other” (Hebb, 1949, p. 7). Hebb explains cell assemblies and phase sequences as “When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell (Hebb, 1949, p. 63). There are similarities between Pavlov’s conditioning, an instance of associative learning, and Hebb’s account of learning.
Hebbian cell assemblies and phase sequence concepts may not be entirely correct in view of later neurophysiological research, as Sternberg (1990) observes. However, these concepts have influenced explanations of the workings of human brain in remote fields, such as consciousness (see Francis Crick’s The Astonishing Hypothesis, 1994). Crick’s hypothesis is that a set of neurons firing in synchrony at frequencies of approximately 400 to 500 per second, an assembly of synchronous neurons, produces awareness of an object.
Luria was an admirer of Hebb, regarding him as a path breaker who provided insights into how the mind works in terms of the neural processes by proposing the concept of cell assemblies and phase sequences. Luria’s description of the broad functional organization in the brain can be traced to its historical origin in Pavlov. Admittedly, contemporary knowledge about these functions has progressed much since Pavlov and even Luria. Nevertheless, the historical roots sometimes promote a better understanding of later advances in analyses of cognitive processes.
Pavlov’s foundational observations on higher nervous activity (mental processes) provide a background for Luria’s neuropsychology of brain functions. Pavlov (1927) concluded from his experiments that higher nervous activity can be categorized in terms of (a) strength, (b) balance, and (c) movement (mobility). For an example of Pavlov’s influence on Luria, consider Luria’s research on mental retardation:
1. Individuals with mental retardation have a weak nervous system in that they require more training to learn and learning is more susceptible to extinction.
2. Children with attention deficit may be predominantly hypoactive or hyperactive.
3. Pathological changes are noticeable in all sensory and motor functions, a disturbance in analysis and synthesis of sensory and motor functions. Specially observed is relative weakness in speech and language.
4. There are functional segmented processes that could have locations in different anatomical parts of the brain.
Such proposals by Luria (1966) are consistent with Pavlov’s basic observation that specific areas of the brain are associated with specific functions. Luria’s functional organization in the brain that leads to PASS (Planning, Attention-Arousal, Simultaneous, and Successive) theory on segmenting cognitive functions is an affirmation of Pavlov’s observation.
“Planning and Consciousness,” an evening lecture that Luria gave at the 1969 International Congress in Psychology, summarized 50 years of clinical and experimental research on planning and consciousness.
Luria began the talk by referring to the CNS, the Conceptual Nervous System, as dubbed by Hebb and Skinner, two prominent persons in psychology whom he respected. Luria specifically identified the frontal lobes as the powerhouse of organizing consciousness. He ended his lecture by declaring the brain as truly an “organ of freedom,” that is, the workings of the brain are characterized by freedom to regulate higher order thinking and freedom to select strategies for action. (See Goldberg, 2001 for a book-length elaboration of Luria’s initial concepts.)
Pavlov (1941) used language to study conscious processes and the two phases of volition: first arising as an idea in the mind, and subsequently preparing and executing a plan of action. Luria utilized both Pavlov’s teachings and Vygotsky’s insight as he studied the conceptualization of consciousness and its underlying neurophysiological structure throughout his life.
Pavlov’s and Luria’s contributions to brain research can be summed up in the following two sentences:
The structure of the brain is best described as a complex functional system.
The brain’s initial mechanism is regulation of action by speech.
Multiple Intelligence (MI): Past and Present
In the 19th century, faculty psychology took hold of the conceptualization of mental functions. The most notable proponent was Gall (1825, cited in Sternberg, 1990). His theory has often been caricatured because it logically ended up in examining separate bumps in the skull to represent separate faculties. However ridiculous such an idea may be, it does appear to have a core of truth. Thurstone (1938), perhaps the best known psychometrician of the first half of the 20th century, proposed separate primary abilities such as verbal, spatial, and fluency. These primary abilities were in opposition to the belief in one general ability, or the g factor. The idea of separate abilities that resemble distinct faculties found a supporter in the work of Fodor (1983). Fodor suggested that the various measures of intellectual functions are modular in nature and relatively independent of each other. Language is the best example of a separate module, independent of general ability. It is true that in linguistics as well as in neuropsychology we frequently come across evidence in support of the modularity of linguistic functions. Thus, the module for language is relatively independent and works in parallel to another module, which could be numerical ability. This has an appeal in a sense that often the so-called intelligent person may be extremely good with numbers but not so with the use of language. Remember that dyslexia, often described as a “verbal or language deficit,” can be found among children of both high and low IQs.
Multiple intelligences are consistent with evidence from lesion studies. Obviously, damage to different parts of the brain destroys basic intellectual functions. Although, admittedly, each type of intelligence identified by MI theorists may not have a brain signature, modularity of certain functions, such as language, object perception, reading, and memory, can be disrupted by injury or disease.
Howard Gardner’s Theory of MI (1983)
According to Gardner, an intelligence is “a biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products that are of value in a culture” (Gardner, 1999, pp. 33–34). Many of Gardner’s “intelligences” correlate with the g factor. He proposed eight to ten separate kinds of intelligence. Several of these resemble Thurstone’s (1938) seven primary mental abilities.
Is Gardner’s theory based on brain processes? Probably the best answer would be that it refers to the brain, but not with any elaboration, except in regard to linguistic intelligence. Gardner (1983) wrote about characteristics for evaluating a theory of human intelligence; the first refers to the brain, specifically the potential isolation of the intelligence by brain damage. Gardner also posited that there must be a distinctive developmental history of the intelligence; that there must exist individuals, or savants, who excel in intelligence; that it must have a plausible evolutionary purpose; that it may be assessed experimentally, psychologically, and psychometrically; and that it can be represented via a symbolic system.
Sternberg’s Triarchic Theory
The first facet of Sternberg’s theory of intelligence concerns the internal world of the individual, specifically the cognitive mechanisms which, in a way, lead to intelligent behavior. It is primarily concerned with information processing. The second facet deals with learning new skills and translating that knowledge into action. Sternberg linked this to the way we approach novel tasks and how we develop automatic responses for familiar tasks. The third facet focuses on intelligence concerning interactions with the external world: how we behave in selecting, adapting to, and shaping our environment.
Sternberg (1985) defined a component as a mental process that may translate a sensory input into a mental representation, translate one mental representation into another mental representation, or translate a mental representation into motor output. In layperson’s terms, when we experience something in the real world, we form our own mental impressions of that experience. Each individual’s private representational mechanism is unique and uses unique symbols, such as what is sometimes called “inner speech” (Luria, 1973). Mental representations lay the groundwork for reasoning and inference, and drive motor activity.
Sternberg put forth three separate components: firstly, the metacomponents used in planning, adopting strategies, and self-evaluating after performing a task; secondly, performance components, including the cognitive processes that were used in performing the task; and, lastly, knowledge acquisition components, concerned with how we attach meaning to words in our own or a new language, and to nonverbal material such as spatial, musical, and physical, or with the knowledge that either is learned in a formal setting or spontaneously acquired through life experiences.
Metacomponents are a part of planning. This includes decision-making, judgement, and evaluation, all dealing with the use of strategies in different types of problem solving. On the other hand, performance components relate to inductive reasoning and other mental activities, such as those used in problem solving. We are able to understand the process of learning through the knowledge acquisition component. These processes operate differently in people who are experts in their field and those who are just beginning to learn about a field. The three components all refer to tasks that are executed in a laboratory or classroom—an essentially artificial environment. Sternberg did, however, consider experience important when taking into account all aspects of intelligence. Here he has drawn on Vygotsky’s ideas of zones of proximal development (1962), that is, a child’s ability to gain from instruction or experience in solving a problem. He saw experience’s impact on intelligence as two distinct parts: the ability to adapt to novelty, and the development of automatic mechanisms for information processing. Sternberg (1985) proposed that intelligence involved “the ability to learn and think within new conceptual systems” (p. 30), more so than within those that are already familiar. This means the ability to develop methods of solving problems even when they are unlike anything a person has previously encountered.
The third and final part of the theory is concerned with an individual’s external world. This is in line with Sternberg’s (1985) general definition of intelligence as “mental activity directed toward purposive adaptation to, and selection and shaping of, real-world environments relevant to one’s life” (p. 45). The ability to adapt to one’s environment is often seen as a mark of intelligence. However, it goes beyond mere adaptation: we also select an appropriate environment for our behavior. Adaptation alone would mean we would not have creative thinkers, or social reformers who imaginatively challenge the status quo. In order to survive as intelligent individuals in society, we adapt, but we also select and shape our environments in such a way that capitalizes on our strengths and compensates for our weaknesses.
Sternberg’s theory of intelligence does not directly refer to the brain. However, each of the three components can be linked to brain functions, within the context of PASS (Planning, Attention-Arousal, Simultaneous, and Successive) theory.
Cognitive Processes: An Alternative to IQ
Here we use the term cognitive processes to replace IQ. In refining IQ as cognitive processes, we do not mean that intelligence is a random collection of such processes. Rather we describe how the mind works while solving problems relating to three major domains: perceptual, memory (mnestic), and conceptual, following Luria (see Figure 1, which diagrams PASS [Planning, Attention-Arousal, Simultaneous, and Successive] theory).
Cognitive processes specifically link with functional systems in the brain (Luria, 1966, 1970; Goldberg, 2001). Functions can have the same initial stimulus and even the same outcome or response, but the processes in between can be quite different (Luria, 1973) and can change. Learning contributes to changes in processing. Also associated with learning are some key mental functions such as attention, planning, and evaluation, as well as analysis and synthesis of information. These are broadly aligned with functional systems in the specific regions of the brain (Das, Naglieri, & Kirby, 1994; C. Frith, 1997; Goldberg, 2001). The Planning, Attention-Arousal, Simultaneous, and Successive (PASS) theory has been proposed following Luria’s cortical organization of the brain function. The cognitive functions in this theory are one example of a brain-based approach to intelligence.
The PASS Theory of Intelligence: A Brain-Based Approach
The Planning, Attention-Arousal, Simultaneous, and Successive (PASS) theory of intelligence (Das, Naglieri, & Kirby, 1994) is derived from Luria. At the same time, it is clearly influenced by contemporary work on cognitive psychology. Its authors argue that a comprehensive theory of intelligence ideally should have three faces: a theory that explains the cognitive processes, one that provides a set of assessment tools for measuring the relative strengths of cognitive processing, and one that guides programs that improve the deficient processes whenever possible. Training programs for the improvement of the weak cognitive processing add to the pragmatic value of the test.
The PASS cognitive processing model is described as a modern theory of neurocognitive processes within the information-processing framework (Das, Naglieri, & Kirby, 1994; Papadopoulos, Parrila, & Kirby, 2015). It is based on Luria’s analyses of brain structures (1966, 1970, 1973) that have been updated by Goldberg (2001). Luria described human cognitive processes within the framework of three functional units comprising planning, attention-arousal, and the two information processing categories—simultaneous and successive. The function of the first unit is cortical arousal and attention; the second unit codes information using simultaneous and successive processes; and the third unit provides for planning, self-monitoring, and structuring of cognitive activities. Recent updates of planning, including executive functions, have expanded the perspective of Luria’s original conceptualization. For example, Petrides (2005) presents considerable evidence for the role of the prefrontal cortex in higher order control processes that regulate cognition and behavior. Self-control and self-regulation have been identified as a component of directed attention (Kaplan & Berman, 2010). Focused attention is the central process in mindfulness; awareness is consciousness, we can say, and consciousness can direct attention as in mindful meditation (Dietrich, 2007; Das, 2014). Each of the four PASS components, especially simultaneous and successive processes, has direct implications for education.
Because thorough reviews of the PASS theory and related research are presented elsewhere (Das, Kirby, & Jarman, 1979; Das, Naglieri, & Kirby, 1994; Das & Naglieri, 2001; Das & Misra, 2015), only a brief summary is provided here.
The cognitive processes that occur within the three functional units are responsible for and involved in all cognitive activity.
The first functional unit, attention-arousal, is located primarily in the brainstem, the diencephalon, and the medial regions of the cortex (Luria, 1973). This unit provides the brain with the appropriate level of arousal or cortical tone and “directive and selective attention” (p. 273). When a multidimensional stimulus array is presented to a person who is then required to pay attention to only one dimension, the inhibition of responding to other (often more salient) stimuli, and the allocation of attention to the central dimension, depends on the resources of the first functional unit. Luria (1973) stated that optimal conditions of arousal are needed before the more complex forms of attention involving “selective recognition of a particular stimulus and inhibition of responses to irrelevant stimuli” (p. 271) can occur. Moreover, only when individuals are sufficiently aroused and their attention is adequately focused can they utilize processes in the second and third functional units. Individuals vary in their levels of arousal, with some having a hyper level and others a hypo level. The majority of people, however, have a balanced level of arousal. Individual differences in the efficiency with which one may utilize attentional resources are also observed.
The second functional unit is associated with the occipital, parietal, and temporal lobes posterior to the central sulcus of the brain. This unit is responsible for receiving, processing, and retaining information a person obtains from the external world. This unit involves simultaneous processing and successive processes. Simultaneous processing involves integrating stimuli into groups such that the interrelationships among the components are understood. Whereas simultaneous processing involves working with stimuli that are interrelated, successive processing involves information that is linearly organized and integrated into a chain-like progression. For example, successive processing is involved in the decoding of unfamiliar words, production of syntagmatic aspects of language, and speech articulation. Following a sequence such as the order of operations in a math problem is another example of successive processing. In contrast, simultaneous processing involves integration of separate elements into groups.
Simultaneous and successive processing have been studied extensively in relation to reading and comprehension
The third functional unit in Luria’s division of the brain functions involves the frontal lobes. Planning and its associated processes such as decision-making, evaluating, programming, and regulating present and future behavior are the essential functions of frontal lobes. The frontal lobes are the last acquisition of the evolutionary process and occupy nearly one-third of the human brain hemispheres. They are intimately related to the reticular formation of the brainstem, being densely supplied with ascending and descending fibers. They have intimate connections with the motor cortex and with the structures of the second block; their structures become mature only during the fourth to fifth year of life, and they develop rapidly during a period of significance for the first forms of conscious control of behavior (Luria cited in Sapir & Nitzburg, 1973, p. 118).
Knowledge base is an integral component of the PASS model, and, therefore, all processes are embedded within this dimension. The base of knowledge included in the PASS figure is intended to represent all information obtained from the cultural and social background of the individual, because this determines the form of mental activity. The importance of social interactions is perhaps most clearly presented by Luria (1976); he states, “the significance of schooling lies not just in the acquisition of new knowledge, but the creation of new motives and formal modes of discursive verbal and logical thinking divorced from immediate practical experience” (p. 133). This statement emphasizes the role of knowledge, as well as planning processes (e.g., the creation of motives), in all forms of cognitive activity. Recognizing the importance of the base of knowledge obtained from all sources (formal as well as informal, practical as well as theoretical, etc.), we have incorporated this component within the PASS theory. Vygotsky (Luria, 1978) called knowledge base the history of an individual’s life.
Recent research has supported the PASS theory of intelligence. An experiment from Japan (Okuhata, Okazaki, & Maekawa, 2009) studied the two types of processing using EEG coherence. EEG coherence patterns were recorded when the participants were engaged in performing simultaneous or successive tasks. Two distinguishable coherence patterns corresponding to the two types of processes were obtained. The coherence patterns of the simultaneous processing tasks were long-range connections crisscrossing the left and right hemispheres of the brain. In contrast, the EEG patterns of successive tasks were short-range connections within each hemisphere. Thus, simultaneous processing is reflected across both hemispheres, crossing the left-right hemisphere division of the brain, whereas successive processing is associated with almost identical patterns of coherence in each hemisphere. Both processes are localized in the posterior part of the brain, as Luria had suggested.
These findings have applications in diagnosis and rehabilitation of neurologically impaired patients. For example, McCrea (2009) has demonstrated the clinical use of the Cognitive Assessment System (CAS) with stroke patients, providing support for the hypothesis that the two hemispheres of the brain are capable of successive and simultaneous processing, although in distinctly different patterns (McCrea, 2009, p. 96).
Critiques of PASS
A critique of PASS was published seven years before the final version of the Das-Naglieri Cognitive Assessment System (Telzrow, 1990). It concerns the validity of the PASS theory based on its initial presentation in Das, Kirby, and Jarman (1975). In this otherwise sympathetic critique, the only critical remark was that the authors lacked supporting evidence for attention as a separate cognitive process. In the same issue of the Journal of Psychoeducational Assessment, Lambert (1990) reviews the Das-Naglieri Cognitive Assessment System positively: “Criterion-based and predictive validity studies are still in their exploratory stages, but preliminary evidence indicates that performance on the test improves with age and that there is a significant relationship between performance on the tests and reading” (p. 338).
On the other hand, a negative review by Kranzler and Keith (1999) has been given a certain importance. Their main point was that they could not “independently support” the factor analysis that yielded the four factors—planning, attention, simultaneous, and successive. Naglieri and Das have responded to their paper in various articles, the strongest being one by Naglieri (1999b) in School Psychology Review. In a scholarly review of PASS processes and scales by Carlson and Hunt (2015), it was also suggested that attention in PASS ought to be dropped because planning and attention did not exhibit differential validity as assessed in CAS.
Without delving into an overly detailed discussion, suffice it to say that the attention scale has a high correlation with planning in factor analysis. However, that is not a reason why the two scales should be merged.
The basic attention processes, at the level of cognition and the brain, can be separated from planning or executive functions. At the level of brain functions, automatic attention, such as orienting to a sudden noise or a pinprick, is controlled by the posterior part of the brain in the parietal lobe. In contrast, when information load increases, the anterior attentional network is engaged; attentional control is located in the prefrontal cortex. Executive attention or attentional control is heavily loaded with information and controls the allocation of attentional resources during decision-making (Das & Misra, 2015). The trail-making test in CAS, in contrast to automatic attention to a red dot in a field of black dots, activates the prefrontal cortex. To answer the critiques directly, the argument for separating attention and planning is based on neuroanatomy and the independence of low-level attention from high-level attentional control, the latter being more pre-frontal. As suggested in Das and Misra (2015), additional tests of executive functions for planning scale in CAS will improve the contrast between attention and planning. Thus, there are many reasons besides factor analysis for regarding planning and attention as distinct but interdependent processes.
A Location in the Brain for Intelligence
Biological correlates of general intelligence (g) have been sought using both brain-imaging techniques and statistical analyses. In an investigative study conducted by Kievit et al. (2012), results of both brain-imaging and statistical analyses were considered to find out how different neurological activities in the brain may contribute to a neurological picture of intelligence. Their results support the idea that several neurological properties together determine individual differences in g. However, these properties by themselves are not unified. Thus there is little evidence to support a “neural g.” Rather, multiple indicators of multiple causes best describe a neurological picture of “intelligence.”
The Search for a Neural g: A General Ability Is Not Supported by Neural Activity
The search for a neural location for general intelligence resembling Spearman’s g leads to multiple locations. Consider, for instance, a spatial task that has a high g-loading in Spearman’s g theory, a brain imaging study by Duncan et al. (2000), which showed that “the strongest high-g activations occurred bilaterally in the lateral prefrontal cortex” (p. 458). The research report continued:
In a recent analysis of imaging findings, indeed, we have shown that diverse forms of demand, including task novelty, response competition, working memory load, and perceptual difficulty, produce broadly similar lateral frontal activity . . . all these demands are also associated with specific recruitment of the dorsal part of the anterior cingulate, close to the medial frontal activity seen here only for the spatial problem-solving task. (p. 459)
The Duncan et al. report has not gone without challenge. It has been criticized in regard to locating general intelligence so narrowly in the brain, specifically by Jung and Haier (2007). The authors offer an alternative: they propose to broaden the location to an integration of parietal (P) and frontal activities (FIT).
Brain-Imaging Approach to Intelligence (P-FIT)
Integration of brain structures involving parietal and frontal regions of the brain is proposed as a basis for general intelligence (Jung & Haier, 2007). Specifically, it is proposed that individuals with more gray matter as well as white matter in frontal and parietal areas show higher scores in psychometric tests of intelligence. Gray matter is simply described as constituting the amount of neurons, synapses, and dendrites, whereas white matter consists of axons, both long and short. These form the hardware for intelligence. The proposed P-FIT theory is based on findings from 37 brain-imaging studies. Intelligence is also described as how efficiently the brain works. Individuals with higher intelligence scores apparently use less energy than those with lower intelligence, that is, consume less glucose in the brain when solving a hard problem in Raven’s Progressive Matrices, a test of reasoning. This is interpreted to support the hypothesis that they have better neural efficiency. The efficiency depends on disuse of several brain areas that are irrelevant for the task, and a better use of the areas that are specifically relevant.
It is important to note that P-FIT theory and the neural efficiency proposal have had many modifications and refutations (Neubauer & Fink, 2012). In spite of the neuro-imaging evidence for P-FIT, we must be cautious about what the evidence really supports. Have we found a location in the brain for g or general intelligence? We should be cautious of accepting the evidence as a neural g simply because individuals who score highly in standard psychometric measures of IQ are found to have better neural efficiency. More gray and white matter in the brain underpins the correlation. The relationship is a tenuous one, raising questions regarding the specific measures of gray and white matter and the implication that all of these connections are more efficiently used for a task of reasoning. Correlations, even when reliable, do not provide a causal connection.
Thus, the P-FIT view has several critics. As one of the critical replies to Jung and Haier’s main article suggests, posterior regions are involved only when executive demands are minimal and domain general functions, such as working memory, can be mapped on to specific regions of prefrontal cortex (Prabhakaran & Rypma, 2007). Involvement of the parietal lobe in planning and executive functions, such as cognitive set switching, that also involve working memory seems to be well accepted (Crone, Bunge, Latenstein, & van der Molen, 2005). According to the results of a functional MRI study, the medial prefrontal cortex, including supplementary motor area and cingulated motor area, is consistently engaged during task switching, along with the superior parietal cortex. Executive functions like the planning factor in PASS, however, are not general intelligence or g. Simply stated, intelligence is the outcome of a distributed collection of processes, which are differentially involved depending on the task.
In Search of a Biological Measure of General Intelligence: Speed of Processing and Mental Chronometry
Individuals differ in their speed of processing information. Correlation is reasonably strong between speed of processing and general intelligence measure (Jensen, 1987, 2006). Although this seems like a reasonable statement, it leads to several questions.
1. Hierarchies: Given that it is possible to divide processing into cognitively easier or more difficult within the same type of tasks, does solution time vary according to cognitive load demanded by the task? The answer is yes, as the tasks vary in types of cognitive load and processing. See the discussion on separating attention and planning for an example.
2. Types of cognitive processing: Is it possible to divide all of the speed of processing tasks within the processing categories or framework of planning, attention, simultaneous, and successive processing? The answer is once again yes: a common “speed” of processing factor does not emerge when cognitive tasks measuring the four processes are included. In that case, speed divides itself according to the types of cognitive processes required (Das, 2014).
It is only when extremely simple elementary cognitive tasks are involved that a general factor of speed as measured in reaction time is obtained (Jensen, 2006; Deary, 2012; for a critical summary, see Das, 2015).
Mental chronometry asks how we relate human thought processes to measurable events in the brain. It holds promise for measuring the time course of information processing, relating it to the brain. Posner (2005) presents an up-to-date review of mental chronometry. However, he does not include the kind of construct that Jensen has discussed at length, based on reaction time speed and its correlation with psychometric intelligence. Portioning mental processing speed can be seen in the simple task of deciding whether a presented digit is above or below five. Dehaene (1997) argued that the task could be divided into four stages. These stages are:
• Encoding, obtaining the identity of the probe input (the number is an Arabic numeral, 5, or spelled digit, “five”),
• Comparison against the stored representation of the digit five (compare digits that are close to 5, such as 6, or distant from 5, such as 9),
• Response selection (press a key by right or left hand), and
• Error monitoring, checking the output (error trials compared with correct response trials).
According to additive factor theory, a variable that affects overall reaction time by varying the time to complete one stage will be additive with the effects of variables that affect other stages. EEG showed a separate latency (time) for each stage—no general “speed” of response was considered. Also, importantly, fMRI (functional MRI) data have confirmed the precise location for each of the stages (Posner, 2005). To conclude, the chronometric design that divides processing speed does not support a general speed factor as a biological marker of IQ.
On the whole, the various attempts at supporting a unified brain function like g appear to be unsuccessful (Das & Misra, 2015). It is therefore worthwhile to approach intelligence in terms of brain processes. This conclusion is quite consistent with C. Frith’s (1997) suggestion that we can learn more by looking at how different parts of the brain, such as the prefrontal areas, contrasting with dorsolateral and orbito-frontal areas, are differentially activated by cognitive tasks demanding purely logical thinking versus thinking involving affect. Similarly, in regard to the neural correlates of ADHD as discussed earlier, a frontal lobe dysfunction has been supported—children with ADHD did not demonstrate a generalized cognitive impairment (Shue & Douglas, 1992). Thus, to repeat, we can learn more by looking at different parts of the brain and how these contribute to “intelligence” than by studying the brain as a whole in search of finding a general ability.
Development of Brain and Intelligence
Parents and psychologists have been long aware of the stages of intellectual development in children. Piaget’s observations of how children develop are highly influential. How does the brain change?
As is widely known, Piaget proposed four stages or periods of development. These begin with the sensorimotor (birth to age 2), followed by preoperational from age 2 to 7, during which language and imagery concepts of color and size develop, and concrete-operational (age 7–12), during which concepts like conservation develop (children realize that when something changes in size or appearance, it is still the same). Finally, in the formal-operational stage, which begins at age 12, logical and abstract thinking begin to develop. The stages of development, especially the age ranges, are not supported entirely, and some may dismiss the stage theory, but many educators find these to be a rough guide to determine when children are best prepared to learn certain academic skills.
One of the weaknesses of Piaget’s theory was its inability to explain the aspects of cognitive development that are not universal. Can brain development support the changes in development of cognition? Among authors who accept Piaget’s theory in a modified form, Case (1992) presents the mediating role of the frontal lobes. The underlying mechanism that generates changes between 1 and 5 years of age, and between ages 5 and 10, coincides with the changes in EEG coherence during the same time period, as shown in Thatcher’s (1991) experiment. Cortical functions are reorganized in early childhood in step with growth spurts. Stuss (1992) also offers support relating to the development of executive functions (planning) commensurate with biological maturation of the frontal lobes.
Further support for cognitive development in stages is found in Epstein (2001). Cognitive development is associated with increased neural growth as additional neural connections create multiple branches (arborization). This is significantly influenced by experience and learning (Epstein, 2001), especially with speech and language development. This reaffirms Vygotsky’s and Luria’s observations regarding the regulation of behavior by speech. At an earlier stage of development, speech accompanies action. The children talk to themselves as they are doing a task, but then their own internalized speech begins to guide their action (Vygotsky, 1962; Luria, 1979).
Research in neuroscience affirms the biological bases of human intelligence (Deary, Penke, & Johnson, 2010). Blakemore notes, “The idea that the brain is somehow fixed in early childhood, is completely wrong. There’s no evidence that the brain is somehow set and can’t change after early childhood. In fact, it goes through this very large development throughout adolescence and right into the 20s and 30s, and even after that it’s plastic forever. Plasticity is a baseline state, no matter how old you are. That has implications for things like intervention programs and educational programs for teenagers” (Blakemore, 2012).
There is little doubt that the physical structure of the brain changes remarkably beyond childhood, including an explosive period of change during adolescence, and into old age.
Education and the Human Brain
In Educating the Human Brain, Posner and Rothbart (2007) discuss how knowledge about brain research may be applied for use in education. Persistent problems in educating children with special needs demand solutions from brain researchers. Authorities including Dehaene (1997), Goswami (2006), and Posner (Posner & Rothbart, 2007) have addressed this subject.
School readiness is supported by social-cultural advantage. Not all children enter school with appropriate knowledge and skills. So-called disadvantaged children may not have a literacy-rich environment at home or in their community or have been exposed to numbers and numeracy. Such children are considered disadvantaged because they live in poverty, and illiteracy and innumeracy may accompany poverty.
Are we attaching too much weight to poverty as a factor? Do genetics play a part in acquisition of literacy, specially vocabulary, and knowledge of syntax? Research on language problems in twins showed that environmental factors associated with poverty, as in low SES (socioeconomic status) families, are more powerful than genetic factors in accounting for similarities in language development in children in the same family (Turkheimer, Haley, & Waldron, 2003). Furthermore, 60% of the variance in cognitive abilities, including language development, was accounted for by shared environmental factors among children living in poverty, with the genetic contribution close to zero (Fernald, Marchman, & Weisleder, 2013). In addition, a frequently quoted study by Hart and Risley (1995) found that children from more affluent homes heard 30 million more words by age 3 than children from more impoverished environments. Maternal speech specifically influences vocabulary, sentence structure, and complexity of produced speech.
Conditions that promote poverty and illiteracy diminish children’s ability to learn to read or calculate; also, prolonged deprivation or chronic poverty produces the stress hormone, cortisol (Evans & Kims, 2007). In regard to the harmful effect of low income on cognitive performance, Noble et al. (2015) showed that low income especially affected brain regions supporting language, reading, executive functions, and spatial skills. These are major causes of creating communities of disadvantaged children. Thus both biological and cultural influences are recognized in a brain-based approach to academic learning.
Language and the Brain—A Synopsis
A PET scan picture presented years ago (Posner & Raichle, 1994) is still quite relevant for understanding language representation in the brain. It shows that distinct areas of the brain become active when seeing a printed word, listening to the word spoken, or speaking any word.
Word reading difficulties or speech production difficulties can be located in very different parts of the brain. Any task or function utilizes a set of brain areas that forms an interconnected, parallel, and distributed hierarchy. Each area within the hierarchy makes a specific contribution to the performance of the task (Fiez & Petersen, 1993, p. 287). Extending the conclusion further, no single area of the brain is devoted to reading. Language is probably the most remarkable and unique capacity humans have, and the one that differentiates us from all other animals.
Human linguistic capacity is represented in the left hemisphere of the human brain in both right- and left-handers, except in extremely rare cases. Uta Frith (1997) identifies four seats of language in the perisylvian gyrus becoming specifically tuned to recurring properties of a writing system (p. 293) (see Figure 4). Recently, Dahhan, Kirby, and Munoz (2016) have contributed further to understanding reading and its difficulties by using naming speed tasks and neuroimaging techniques to investigate how “incorporating cognitive psychology with neuroimaging techniques, under the guidance of educational theories, can further the understanding of learning and instruction, and may lead to the identification of the neural signatures of reading difficulties that might be hidden from view earlier in development” (p. 1).
Visual Word Form: A Specific Area in the Brain
Posner and Rothbart (2007) demonstrate how brain localization of specific aspects of reading have direct implications for understanding word reading as well as for guiding remedial programs to accelerate the development of visual word form area. An important paper by McCandliss, Cohen, and Dehaene (2003), and an earlier publication by Posner and McCandliss (1999), finds that years after children first learn to decode letters into words, a form of perceptual expertise emerges in which groups of letters are rapidly and effortlessly conjoined into integrated visual percepts—a process that is crucial to fluent reading ability. McCandliss et al. review the evidence in support of a link between visual word form perception and a functional specialization in a brain region that they name the “Visual Word Form Area” (VWFA). It is a portion of the left fusiform gyrus that is particularly responsive to visual words (McCandliss et al., 2003, p. 293).
They report evidence from brain imaging studies that reliably localize a region of the visual cortex that is especially responsive to visual words. Although language or reading cannot be specifically localized in one area of the brain, numerous neuroimaging studies have provided converging evidence in support of a central finding: perception of visual words and pseudowords reliably activates the left fusiform gyrus (p. 294). This brain specialization is essential to rapid reading ability.
Generally, by age 10, the VWFA is formed and begins to resemble that of adults. The implication of development of the VWFA by age 10 means that before that age, a child may not be expected to depend on the full functioning of this area, or to behave like an adult reader. Posner and Rothbart add that teaching better phonological analysis to children who had poor reading skills improved their reading. Changes expected in the brain region identified with visual word forms were seen following a year of training (Shaywitz, 2003, reported in Posner & Rothbart, 2007).
Recent advances in neuroscience have influenced explanations of dyslexia in terms of brain activation. Pre-readers are prepared for acquisition of reading in terms of cortical functions, which are activated as reading develops and matures. As Goswami (2006) writes, young readers primarily depend on the left posterior superior temporal cortex. This is the specific area that is involved in phonological processes. We know now that phonological decoding is the core deficit in dyslexia. As facility in reading is acquired, the VFWA located in the left hemisphere gets engaged, and the areas initially active in the right hemisphere are disengaged. However, as we would expect, the transition is not so clear among dyslexics. Goswami, like some other researchers, suggests that if remediation is provided through intensive instruction in phonological skills and in letter-sound conversion, activity in the left temporal and parietal areas that are typical for successful reading is likely to normalize.
However, we should approach intensive tutoring for phonological training with caution. Extensive phonemic training may not be recommended for all dyslexic children. It is not claimed to be the magic bullet for treatment of dyslexia (see Posner & Rothbart, 2007 and Das, 2009 for further discussion). A neurological study (Pugh et al., 2000) explains why phonemic training may not be effective (Das, 2002). Among people with dyslexia, the angular gyrus is still important for reading tasks, and its connections to the areas in the back of the left hemisphere of the brain are inactive only when the reading task demands active phonological processing, such as in determining if two words rhyme or not. But when this is not demanded, dyslexics and normal readers show similar neural connections. Such research implies that if active phonological exercises are not demanded of them, dyslexics can utilize supporting neural connections that are intact. It is unclear whether dyslexics can ever read as effectively as non-dyslexics, however.
Appropriate remediation or intervention programs, such as PREP (PASS Reading Enhancement Program (Das, 2009), that do not teach phonics and do not require oral reading, but still enhance successive processing, can be effective (see Das & Misra, 2015 for a recent review). If such programs are instituted during the developmental period, true dyslexics can also make use of the compensatory mechanisms available through the posterior parts of the right hemisphere.
Relevance of Brain Processes in Education: Critiques and Responses
Bowers (2016) argues that understanding the role of different structures of the brain does not actually help improve teaching or assessing how children progress in a classroom setting. Indeed, he writes forcefully that educational neuroscience has little to offer schools or children’s education and that schools are investing in expensive interventions because they claim a neuroscientific basis. The problems faced by classroom teachers dealing with learning difficulties, Bowers maintains, can be diagnosed and addressed only through behavioral methods.
Bowers’s assertion has met with a forceful response, as anticipated, by a distinguished group of researchers (Howard-Jones et al., 2016). Their comment may be summed up as follows: Bowers’s assertions misrepresent the nature and aims of the work in the new field of neuroscience in education. The authors suggest that the two levels of explanation—psychological and neural—complement rather than compete with each other. In their article, they “set out the well-documented goals of research in Educational Neuroscience, and show how, in collaboration with educators, significant progress has already been achieved, with the prospect of even greater progress in the future” (p. 620).
We should not try to narrowly localize a psychological function in the brain. Luria (1966) was one of the earliest neuropsychologists to suggest broad functional organizations in the brain: “Mental functions as complex functional systems cannot be localized in the narrow zones of the cortex or in isolated cell groups” (Luria, 1973, p. 31).
Furthermore, Gonsalves and Cohen (2010) made an important suggestion—research should show how neuroimaging data have provided unique insights not only into brain organization but also into the organization of the mind. Here and in Goswami (2004) some evidence has been presented to show that neuroimaging data have indeed begun to do this. As Goswami mentioned, “Cognitive developmental neuroscience has established a number of neural ‘markers’ that can be used to assess development, for example of the language system. These markers may be useful for investigating educational questions” (p. 12). However she concedes—“bridges need to be built between neuroscience and basic research in education”—suggesting that Bowers’s concerns were not totally irrelevant.
Applications of PASS Theory in Education
A unique feature of PASS (Planning, Attention-Arousal, Simultaneous, and Successive) theory is an assessment system for the four cognitive processes, the Cognitive Assessment System (CAS) (Naglieri & Das, 1997), which stems from the neurocognitive base described but is a psychometric measure for the general population. It has 12 tests divided into four processes, with three subtests in each. Each individual may have a profile with a distinct pattern of cognitive processing. The CAS has been recently revised (CAS2) to recognize executive functions and working memory as derived clusters of CAS2 measures (Naglieri, Das, & Goldstein, 2014). CAS is advanced as an alternative to IQ testing for the purpose of providing assessment of specific cognitive processes such as planning, executive functions, and working memory. The ability to explain common cognitive and learning difficulties is one of the other features that distinguishes CAS from IQ.
Often CAS profiles are specific for individuals with dyslexia (poor in successive); attention deficit, as in fetal alcohol syndrome (planning and attention); or math difficulties regarding problem-solving (poor in simultaneous) and calculations (planning). Furthermore, profiles of neurological impairments have been identified; for example, patients with epilepsy (poor in successive), Down syndrome, and focal brain lesions have distinctive patterns (Das, 2009; McCrea, 2009; Naglieri, 1999b; Naglieri & Otero, 2012).
Selected Programs for Cognitive Remediation
A comprehensive approach to neurocognitive assessment can be conceptualized as a cube with three faces. As mentioned in introducing PASS (Planning, Attention-Arousal, Simultaneous, and Successive) theory, it provides norms for age, gender, and culture, processes for underlying performance in the tests, and a prescription for intervention or programs for remediation whenever possible.
The general basis of neural changes following cognitive remediation is fairly well supported. Two articles on the effect of remediation of learning disability or reading dysfunction are briefly reviewed.
The first report, by Keller and Just (2009), examined “whether 100 hours of intensive remedial instruction affected the white matter of 8–10-year-old poor readers. Prior to instruction, poor readers had significantly lower FA than good readers in a region of the left anterior centrum semiovale. The instruction resulted in a change in white matter (significantly increased FA)” (p. 624). The second study, by S. Shaywitz and B. Shaywitz (2001), examined the changes in the “word form” located in the occipital-temporal region. The word form system appears to take over when a reader has become fluent.
PASS Intervention Programs: Reading and Math
PASS theory has enabled the construction of intervention programs that ameliorate cognitive difficulties related to learning:
• For facilitating literacy acquisition, a prerequisite for school readiness: COGENT (Cognition Enhancement Training). The program consists of modules that train attention and orienting to instruction, metalinguistic concepts, phonemic discrimination, syntax, semantics, inhibition, and automaticity in recognizing shapes, colors, and letters.
• For building the cognitive foundations of reading and comprehension, specifically focused on simultaneous and successive processing: PREP (PASS Reading Enhancement Program). The program consists of successive and simultaneous processing modules, which develop reading strategies such as rehearsal, categorization, monitoring of performance, prediction, revision of prediction, sounding, and sound blending.
• For learning mathematics: Modules for Math. The program focuses on foundational mathematical concepts, comprising size and value, number lines, numerosity, accurate and approximate number systems, and working memory. PASS processes of planning/executive functions, simultaneous processing and pattern recognition are explicitly used in specific modules (Das, 2009; Das & Misra, 2015).
Recent reviews of the efficacy of the programs are published in Das and Misra (2015). Overall, PREP and COGENT programs have reasonable support for improvement of early reading and literacy. Math Module efficacy studies are under way.
Tools of the Mind (Bodrova and Leong): Implementing the Vygotskian Approach in American Early Childhood and Primary Classrooms
In a program called Tools of the Mind, its authors reiterate several Vygotskian concepts in the context of education in school. Vygotsky believed that just as physical tools extend our physical abilities, mental tools extend our mental abilities, enabling us to solve problems and create solutions in the modern world. This means that to successfully function in school and beyond, children need to learn more than a set of facts and skills. They need to master a set of mental tools—tools of the mind (Bodrova & Leong, 2007).
Both Vygotsky and Luria discussed the importance of language and the social-cultural environment in learning and generally in education. As presented by Ratner (1997), Vygotsky and Luria argued that a significant cultural reconstruction has to take place in order for the child to shift from the stage of primitive perceptions to the stage of competent forms of adaptation to the external world. This cultural reconstruction involves other people prompting, guiding, rewarding, punishing, restraining, imitating, and modeling the child’s behavior. Higher mental functions such as attention, planning, and the two main modes of processing information, simultaneous and successive processing, also require social-cultural interactions.
Psychological functions, including language, are important cognitive tools in human evolution. Individual differences in psychological functions are therefore due to differences in exposure to social experiences that provide the social means for performing psychological activities. Individuals think, evaluate, analyze, synthesize, abstract, and select from social influences (Das, 2009).
The tools are used in kindergarten and in Grade 1. The program differs from traditional kindergarten in that it:
1. Uses intentional make-believe play as an effective educational approach, and
2. Uses scaffolding and peer support through interaction to support reflective, higher-order thinking skills and to assist children in developing social competence and intrinsic motivation to accomplish academic tasks and goals.
Scaffolding is a metaphor for support that is adjustable and temporary. It provides supports for students to be able to complete increasingly difficult tasks, and can be removed when it is no longer necessary (Brown & Campione, 1985). A related strategy is reciprocal teaching; a teacher and a student form a pair. Through an interactive dialogue, the teacher helps the student, provides social support and quickly transfers the responsibility of learning to the student. Brown and Campione used scaffolding and reciprocal teaching in order to expand a learner’s capacity, thus exploring the learner’s zones of proximal development, a key concept in Vygotsky (Palincsar, 2003).
Tools of the Mind makes use of all of the concepts and integrates these features with classroom practices in several schools in the United States and some international locations. Tools of the Mind classrooms facilitate social interaction within learning activities and games. Children work primarily in pairs with materials that are self-correcting and prompt children to help one another (Bodrova & Leong, 2001).
It has been identified as a successful program based on a well-designed study conducted by Blair and Raver (2014). In their study, children showed positive effects on executive functions, reasoning ability, and the control of attention as well as in levels of salivary cortisol and alpha amylase. A number of effects were specific to high-poverty schools. An important objective of Tools of the Mind is to close the gap in achievements between average and disadvantaged children.
This remedial program for learning dysfunctions has been criticized by some well-known scholars in the field of learning disabilities, but it is a serious attempt to use a selected group of skills based on Luria’s writings.
The school program was initiated by Barbara Arrowsmith Young in Toronto, Canada. It is a holistic program of exercises that she began devising for herself in 1977 and which she has said enabled her to overcome her own severe learning difficulties. Her own struggle with learning disability and the rationale for her program are described in her 2012 book The Woman Who Changed Her Brain (Arrowsmith Young, 2012). She was greatly influenced by reading Luria’s The Man with a Shattered World, originally published in 1971.
According to the schools’ website:
Luria concluded that complex cognitive activities, such as reading, writing and mathematics, require the interaction of several areas of the brain and that each individual brain area has a very specific role to play. If one brain area that is part of a specific learning activity is impaired, the performance of that learning activity will be impaired in a way particular to the contribution of the weaker brain area. This is assumed to be the source of a specific learning dysfunction.
The philosophy of the Arrowsmith Program is that these cognitive areas may be improved through strenuous cognitive exercises, resulting in strengthened learning abilities.
Each student in the Arrowsmith Program has his or her specific schedule of tasks and exercises to be completed during the course of a day in the Arrowsmith Program. The exercises for each student differ depending upon that student’s identified learning profile. These include written, visual, auditory and computer exercises.
(Arrowsmith Program, n.d.)
Additionally, as a part of the development of the child as a whole, ecological outings, yoga, and mindful meditation are integral parts of the school curriculum.
It is not certain how far the program succeeds in making changes in the brain, but as addressed previously, the idea that exercises in cognitive remediation lead to neural changes is well supported.
Brain Plasticity and Malleability of Intelligence
If there is an overarching assumption that permeates cognitive training and intervention, it is that intelligence and, by default, the brain, changes itself as we learn. Retraining can be effective because “the brain has a highly robust and well-developed capacity to change” (Organisation for Economic Co-operation and Development [OECD], 2007, p. 55). Norman Doidge (2007) wrote the award-winning book The Brain That Changes Itself (2007) to prove this point.
The idea that the brain can change its own structure and function through thought and activity, as Doidge wrote in the preface, is no longer new. Indeed, we can argue that it had not been new for a very long time, as evidenced by Vygotsky, Luria, and indeed Pavlov’s conditioned reflex specifically referring to the human capacity for forming the second signaling system that is language. Luria’s clinical methods were applied to individuals as well as enabling him to form general laws of cortical functions located in large blocks of the brain (Luria, 1973).
The central idea of neuroplasticity, or brain plasticity, is that changes may occur all through life; the brain does not cease to change after early childhood. Neuroplastic changes may occur on small scales, such as in individual neurons, or on a whole-brain scale, after brain injury. Neuroplastic change can also be caused by behavior (drug addiction), other environmental stimuli, emotions, and thought. Consider the cases of individuals who were addicted to drugs or alcohol and one day got determined to give it up—a compelling reason may be saving a baby in the womb, or the threat of total disconnect with their family, the only people in the world who care for them. They go through the pain of withdrawal, and get rid of the scourge.
Doidge (2007) divides neuroplasticity into adaptations with positive and negative consequences. The adaptiveness that allows an individual to regain normal levels of function after a stroke or detoxification is an instance of “positive plasticity” whereas the maladaptive synaptic rewiring seen in drug addiction and obsessive compulsive disorder would be an example of “negative plasticity.” Finally, Doidge believes that brain may be seen to change even in a short period. Consider a 2005 study of medical students studying for exams—a significant increase in gray matter in the posterior and lateral parietal cortex was observed.
Neuronal changes occur throughout the developmental period. The frontal lobes have a central role in effecting the changes. Therefore damage to frontal lobes in early childhood adversely affects cognitive and social-emotional functions. The frontal lobes are engaged in pruning of excess synapses, thus periodically reorganizing the synaptic environment of the posterior parts of the cortex from the prenatal stage to age 16 (Thatcher, 1991). Brain plasticity is not a new idea.
The brain changes itself continuously, simply described as a consequence of the interaction between genetic programs and the environment. That the brain changes itself is evidenced in conditioned reflexes (Pavlov, 1927, 1941), Sperry’s (1993) seminal work on split-brain functions, Francis Crick’s The Astonishing Hypothesis (1994), and in the hypothesis of a self-conscious mind (especially, Eccles in Popper & Eccles, 1977). All of these pioneers shared the notion that the working mind is firmly grounded in brain mechanisms. Pavlov, for instance, regarded the brain as a machine, but an amazing machine that repairs itself (Pavlov, 1941). Sperry thought that consciousness arises out of the mechanisms of the brain, but having emerged, influences the workings of the brain. All of them believed in the integrative action of the central nervous system—and all four of them received the Nobel Prize.
Schools in many countries explicitly teach kindness and compassion and techniques to promote mindfulness. Training in mindfulness increases attentional control, especially as it is used to treat ADHD (Das, 2014). Research and writings on the psychobiological correlates of mindfulness have recognized benefits of mindfulness practice that coincide with essential changes in neural functions and the immune system. The frontal lobes including the specific regions—dorsolateral prefrontal, and orbital cortex and anterior cingulate, as well as the specific parts of the limbic system—are the structures that are modified as a consequence of mindfulness practice (Siegel, 2007).
The hope is that the children, having been conditioned to practice mindfulness, empathy, and compassion, will be less prone to antisocial behavior as adults. The better angels of human nature will have a chance to prevail. Human intelligence, then, would be closer to its ultimate goal by helping us to develop a better “self.”
The brain’s open modules respond positively to contemplation and urge people to get back to their self. We learn about ourselves continuously through interactions with others (Frith, 2010). When that happens, we have to acknowledge the necessity of a self-conscious mind (Popper & Eccles, 1977).
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