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Cognition, Affection and Conation: Implications for Pedagogical Issues in Higher

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 Cognition, Affection and Conation: Implications for
Pedagogical Issues in Higher Education

Review of Literature

In the last three decades ‘Cognitive Science’ as a new branch of science

has emerged out of the efforts of researchers in linguistics, computer science,
psychology and neuroscience. This is the science of mind which is concerned
with mental phenomena like perception, thought, learning, understanding
and remembering. Its scope is very wide ranging from observing learning
processes in children to programming computers to solve problems through
‘artificial intelligence’. This cognitive revolution was primarily driven by the
progresses in computers and computer science. Simulation became a powerful
research tool. We can not observe the mental processes directly but can simulate
these by computers. Various cognitive theories emerged from this new paradigm.
The most successful was the information processing theory, and cognitive science
merged with it as a part of computer science. Thus, Cognitive Science views the
human mind as a highly complex information-processing system – that is, a
system which receives, stores, retrieves, transforms and transmits
information.

However, at the very outset cognitive science encounters a deeply

philosophical issue – the ‘mind-body’ problem, which has been plaguing the
minds of philosophers and psychologists for several decades rather centuries ago –
the ontological and the epistemological riddles. In philosophical language, the
ontological questions are –
1. What things really exist in the mind?
2. What is their essential nature?

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Primarily there are two theories which attempt to answer these questions:
1. The ‘Materialist Theory’ holds that only the brain exists and what we
call mental states and mental processes are merely sophisticated states
and processes of a complex physical system called the brain.
2. The ‘Dualist Theory’, on the other hand claims that mental processes
constitute a distinct kind of phenomenon that is essentially non-physical
in nature.

Subsequently, both ‘Metaphysics’and ‘Epistemology’ in philosophy play

very significant role in the development of various cognitive theories. Along with
these another basic discipline ‘sociology’, sociology of knowledge in particular
has also played a very important role in the growth of cognitive science.
According to Thagard (1996), “Cognitive science proposes that people have
mental procedures that operate on mental representations to produce thought and
action”. What is common among the researchers across the various contributing
disciplines is the notion that – the processes that occur during cognition can be
represented abstractly by some type of predictive representation. The nature of
that specific representation depends on the discipline; such as, philosophers rely
on formal logic, artificial intelligence researchers employ computer code,
neuroscientists are guided by biological structure, and cognitive psychologists
often use statistical analysis to fit data resulting from experimentation. Thus, by
building theoretically driven, and empirically tested structures of cognitive
processes, cognitive scientists seek to increase understanding of the mind, as well
as to build systems that are able to understand, predict, and generate human
thought and action (i.e., information processing).

However, the methods employed by cognitive scientists vary greatly. Like

linguists (in Linguistics) are most concerned with developing formal systems of

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syntax, semantics, phonetics and pragmatics (discourse & cognitive approach),
and their work typically consists of comparing sentences and utterances. Often
this is done by examining databases of existing language and computer models.
Psychologists rely primarily on laboratory experiments, aiming to understand how
people form categories, reason, perceive stimuli, and encode, store, and retrieve
memories. To, accomplish these goals, psychologists examine the outcome of
various experimental manipulations, the amount of time it takes an experimental
subject to perform a task, and the various strategies people implement to complete
the task. Computer scientists, very often build algorithms to simulate artificial
intelligence, creating programs that can comprehend or generate language, exhibit
creativity, or solve problems. Cognitive anthropologists and sociologists compare
multiple cultures and societies to assess the universality of mental structures often
using ethnographies, field observations, and some direct manipulation of
experimental variables. Thus, it seems cognitive science spans many disciplines
and methodologies, but researchers across this field seek to answer the same
fundamental question: “how are information processes – represented in the
mind?”

Knowledge, Understanding and Cognition:

So far as knowledge, cognition, understanding and their interrelationships are
concerned, researchers have viewed that knowledge in one sense is the verified
propositions, warranted assertions and a category of truth. It is the category of
cognition, located in recorded language and propositions, which is usually kept in
libraries and computer data banks. In another sense, knowledge means
competencies, states of mind, expertise, learned abilities, located in people and
especially deals with their ability to perform in well informed ways. Thus,
knowledge is also the process of knowing and understanding, conceived of as
the realized ability, to perform adequately in relation to one’s personal
purposes and states of affairs. This makes cognition the same process as

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knowing and understanding that is realized through much practice, care and
learning. However, cognition can be distinguished with respect to levels of
knowing and forms of knowing. Levels of knowing are degrees of extent to which
one has realized the ability to perform adequately in relation to some state of
affairs (refer James E. Christensen). They are degrees of extent to which one
knows. There are at least three levels of knowing, such as 1. Level 1 ( ∝ ) pre-
conventional knowing (Alpha state); 2 – ( β ) Level 2, conventional knowing
(Beta state); 3 – Level 3 (ν ) post – Conventional knowing (Theta state). At
level 1, in pre-conventional knowing stage, the individual experiences a high
degree of disorganization, makes many mistakes, and has a low degree of control.
In this level there are many trials and errors and much self-conscious effort, as
performed by a novice learner. At the level – 2 of knowing that is at conventional
stage of achievement, the individual’s performance becomes habituated and
automatic. There is high level of mastery, control and very little or no self-
conscious effort, the person performs quickly, efficiently and accurately. But the
achievement of level-3 knowing (post-conventional) requires exploration,
inquiry, and creativity, so that one breaks new ground and forms new
standards of performance that extend beyond the conventions of Level – 2
knowing. In addition to these there are also forms of knowing. At least six forms
of knowing can be there which deal with different kinds of performance, such as –
linguistic, emotional, imaginal, physical, physiological, and conative.
Linguistic performances which signify meaning with symbols, include speaking,
reading, writing, reasoning and performing logical operations such as deduction,
reduction, induction and retroduction (Steiner, 1978), may be in silent, spoken or
written form. Emotional performances are feelings of emotion in relation to some
state of affairs, such as the emotional response in a panic situation, feelings of
anguish about being falsely accused, or a sense of ecstasy while experiencing the
nature’s beauty. Imaginal performances are the acts of forming images shapes,
imagined sounds, and imagined relationships in ones awareness or consciousness.
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Physical performances are organized movements and gestures like swimming,
driving or diving etc. Physiological performances are the actions like deliberately
showing one’s heart rate, diminishing one’s blood pressure or blocking out pain.
Conative performances are acts of volition or will. Conation is the state of mind
of having purpose, and conative knowing is choosing or willing to perform in
relation to some set of circumstances or state of affairs. It is a state of knowing –
to, as distinct from knowing – that or knowing how. Conative knowing is the
state of willingness. But when a person achieves a state of ‘knowing – how’, it
includes all the instances of emotional ,imaginal, physical, physiological as
well as linguistic knowing.

Understanding is closely related to knowing, especially linguistic

knowing. Understanding arises from realizing the ability to signify meaning to
one’s self with symbols; through symbolizing that one can make sense out of
various states of affairs in relation to his/her environment. The development of
understanding requires experience and an ability to talk about that experience. At
least there are three levels of understanding i.e., levels of prehension,
apprehension, and comprehension. In the development of understanding,
enunciation precedes adjudication. That is, the act of saying or talking about a
matter must take place before one is able to engage in the act of exercising
competent and adequate judgment about a matter. The three levels of
understanding relate to the acts of uttering, conceiving, enunciating as well as
adjudicating. Thus, prehension is operating with language at the level of uttering
without conceiving much meaning. Apprehension is conceiving symbols with
meaning, but the meaning is restricted largely to denotative meaning. It is the
most expanded level of understanding. Denotative meaning is the relationship
between an object and a word; and connotative meaning is the relationship
between a word (or a set of words) and another word (or set of words).
Enunciation is saying or making a pronouncement about something and

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adjudication is making judgment about something. The levels of understanding
relate to the acts of both enunciation as well as adjudication. At the level of
prehension, well informed judgments are not possible, but this is a precondition
for the development of adjudication. Understanding enables an individual not
only to describe, explain, and predict but also control to some extent the state
of affairs through anticipation, prescription and intervention. As
understanding develops through to the two higher levels, the capacity to make well
informed judgments about something also develops. The realized abilities to
describe, explain predict and prescribe are all linguistic abilities. That is,
understanding is linguistic knowing which is articulated with all other forms of
knowing. Understanding is a system of knowing in which linguistic knowing
guides the other forms of knowing that are functioning within the system. Human
development, considered as the extension of cognitive function, is the process in
which this system of knowing understanding develops from (1) – a restricted and
relatively uncomplicated, undifferentiated function in to (2) – an extensive, higher
complicated and extremely differentiated function.

With regard to the categorizations of cognition by various authors like,

Bloom, Gagne, Piaget, Bruner, Biggs, Collis and others, these are either subsets,
combinations or conflations of the elemental/primary categories of (the above
mentioned) three levels and six forms of knowing and the three levels of
understanding. For example, Bloom et al. (1956) and Krathwohl et. al. (1956)
have used the categories of cognitive, affective and psychomotor domains to
classify abilities that can be learned. Thus, learned cognitive abilities, in
Bloom’s terms, are the same as linguistic knowing. They include the linguistic
(conceptual) abilities to recall comprehend, analyze, apply, synthesize and
evaluate states of affairs by means of signifying meaning with symbols or using
language. Recalling in Bloom’s terms is an instance of understanding at the
‘prehension’ level. Bloom et al. characterize comprehending as the ability to

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understand to the extent that an individual can restate a statement in other words
(translation), reorder the statement (interpretation) or estimate or predict from a
statement (extrapolation). And applying is the realized ability to use general ideas
or procedures appropriately in new situations without help, direction or prompting.
Bloom’s analyzing, synthesizing and evaluating are instances of understanding at
the level of comprehension. Learned psychomotor abilities are knowing in
relation to physical performances and physiological performances. But
psychomotor abilities also include linguistic (conceptual), imaginal,
emotional, and conative knowing, such as in playing tennis one must know the
rules of tennis, willing to play by the rules (conative knowing), must keep his/her
emotions in control (emotional knowing), one must also imagine (anticipate) the
positions of ball (imaginal knowing). Psychomotor knowing, in this way is
actually a complex combination of all these physical, linguistic, emotional,
imaginal, conative and physiological knowing. Krathwohl et al. (1956) have
categorized the learned affective abilities as these involved in the process of
attaching a value to something, holding a strong belief about something, or having
a deep-seated attitude about something. Affective knowing thus is also a
complex phenomenon of linguistic, emotional, imaginal and conative
knowing. Gagne (1977) offers the categories of cognition as a scheme for
classification of learned abilities such as intellectual skills, cognitive
strategies, verbal information, motor skills, and attitudes. Intellectual skills
are instances of linguistic knowing and Gagne categorizes these in a hierarchy of
less complex to more complex: signal learning, stimulus-response learning,
chaining, verbal association, discrimination learning, concept learning, rule
learning, problem solving. The way in which these eight levels of ability relate to
the categories of prehension, apprehension and comprehension is that signal
learning and stimulus – response learning function at the level below prehension;
chaining and verbal association function at the level of prehension; discrimination
and concept learning function at the level of apprehension; and abstract concept

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learning, rule learning, and problem solving learning function at the level of
comprehension. The progression in understanding is from denotative to
connotative linguistic performances. Verbal information is the ability to recall,
cognitive strategies used for solving the problems are all instances of linguistic
knowing. Motor skills are same as psychomotor abilities, and attitudes (of
cooperativeness, aggressive, passive, inquisitive) are closely related to the
category of affective abilities. These are the result of a complex combination of
linguistic, emotional, imaginal, physical, physiological and conative knowing.
Piaget (1971) has classified level of understanding into four categories like – 1)
sensori– motor, 2) pre – operational, 3) concrete – operational and 4) formal
operational stages. The pre-operational level functions at the level of prehension;
the concrete and formal operational level are the instances of linguistic knowing
and functions at the apprehension and comprehension levels of understanding
respectively. Another alternative classification of understanding has also been
proposed by Bruner (1964) and he has conceived the categories as 1) enactive,
2) iconic, and 3) symbolic stages of representation. That is, understanding can
be developed and represented enactively, by physical action (like feel, taste); can
be developed and represented iconically, shape, line, colour and tone. Finally, it
can be developed and represented symbolically with conception of meaning with
symbol systems (words, signs, sentences). Bruner relates these categories of
understanding to periods in childhood when children develop these categories;
enactive understanding is below the level of system of physical knowing. Iconic
understanding is an instance of imaginal knowing, and symbolic understanding is
linguistic knowing at all of its levels. Biggs and Collis (1982) classified the
distinction between developmental stages and learning outcomes. They addressed
the problem of what learning outcomes were possible, and they conceived of five
categories: - 1) prestructural 2) unistructural, 3) multistructural 4) relational,
and 5) extended abstract. Prestructural is pre-conventional linguistic knowing
(level 1- α alpha stage). It is also understanding at the level of prehension.

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Unistructural, multistructural, relational, and extended abstract are instantiations
of conventional linguistic knowing (level 2 - β Beta stage).Also unistructural,
multistructural, and relational are instances of understanding at the level of
apprehension, while extended abstract is an instance of understanding at the level
of comprehension. This is implied here that all these research works of Bloom,
Piaget, Gagne, Bruner, Biggs and Collis as well as Krathwohl et al. have
focused upon the problem of identifying categories or knowing (learning
outcomes) that a learner might undertake to study and learn under guidance.
A system of categories of knowing is important for competently performing
the task of selecting and specifying educational goals, aims, objectives, and
purposes. All these classifications given by different researchers / authors are
actually the subsets, combinations, or conflations of these elementary
categories of levels, forms and range of knowing and levels of understanding.
Out of this prehension, apprehension and comprehension are teachable. The other
six forms of knowing and two level of knowing (pre-conventional and
conventional) can also be taught, but the post – conventional knowing is purely
creative and innovative in nature. Thus, these can give some guidelines to our
educational researchers and planners to think about how to devise curriculum
which would incorporate a clear conception of the levels and forms of cognition,
as well as facilitate the development of affective, psychomotor and conative
domains of the learner.

Different Approaches to Cognition:

Presently, two dominant approaches i.e., rationalists and
constructivists views rule the cognitive era. They claim that cognitive
phenomena does not constitute merely the behavioral (stimulus – response)
patterns of a ‘black box’. Constitutive reality agent is today considered highly
relevant for the scientific study of mind. There are predominantly two kinds of
camps: those who believe that cognitive faculties are completely specified by
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the innate biological reality (Noam Chomsky, Jerry Fodor, see Nagarjuna G.,
Review Talks, 2006), and those who believe that they develop during
ontogeny based on incompletely specified ‘embryological’ reality (Karmiloff-
Smith, Susan Carey, Alison Gopnik, see Nagarjuna G., Review Talks, 2006).
A striking observation made by cognitive developmental psychologists based
on experimental findings that ‘language is instinctive and peculiarly human’.
A leading developmental psychologist Karmiloff-Smith demonstrates that
some behavioral / cognitive modules actually are culminations of
developmental process and not entirely innate. The theory by
‘representational redescription’ (RR) proposed by her explain that gradual
and recurring reencoding of more or less inaccessible (encapsulated) implicit
representations into explicit accessible representations leads to behavioral
mastery. There are very few scholars who believe that cognition is only human,
but often it is also argued that the so called higher modes of cognition such as self-
consciousness, theory of mind, fabrication of tools, language, scientific
knowledge, etc. must be peculiar and defining characteristics of human nature.
The review of research (i.e., Merlin Donald’s three stages of the evolution of
culture and cognition; & Peter Gardenfors’s account of How Homo became
Sapiens, see Nagarjuna G., Review Talks, 2006) has revealed that most of the
peculiarly human characteristics are strongly correlated to the social fabric of
human life rather than genetic, neuro-physiological domain. Evidence is
gradually accumulating to suggest that the larger size of human brain
(encephalization) has mostly to do with the new found socio-cultural context
during phylogeny. Socialization and language go hand in hand, as both are
dependent on each other. Thus, it is hypothesized by the current generation of
researchers that representational redescription is an essential mechanism in
producing external memory space helping to enhance much needed memory
capacity for storing cultural heritage, and also for detached processing of
information: explaining thinking. There are two interdependent but

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superveniently evolving inheritance mechanisms: - biological and social. The
nature of human beings cannot be understood without delineating the two. Many
leading cognitive psychologists (e.g. Alison Gopnik, see Nagarjuna G., Review
Talks, 2006) today believe in a strong working hypothesis called: theory –
theory. According to this view no knowledge worth the name can be non-
theoretical, and the basic mechanism (or methodology) of knowledge
formation and evaluation happens by theory change, and this mechanism is
universal. The above author argues that even infants in the crib are little
theoreticians. The mechanism that makes us know the world around is the same
as the one that makes science. Formal knowledge is an explicitly constructed form
of knowledge in the sense that the rules of construction are overtly specified. This
form of possible world construction creates an idealized description of the
actual world that describes indirectly (mediated by models) the phenomenal
world. Only in this form of construction can we find invariant relativistic
descriptions of various flavors of scientific theories.

Representation of knowledge in memory and the evolution of

consciousness span the range of problems in understanding cognition.
Knowledge representation, probably the most intelligent behavior is the typical
characteristic of human activity. It is unique to humans because of its dependence
on language and other symbolic systems. The full development of language and
thinking is what constitutes intellectual development. One of the central functions
of language is that it frees us to refer to objects without the need to manipulate
them physically; representations of knowledge through language lead to an
explosion of interconnected information. The social-constructivist view places
the evolution of all higher mental functions, including language, firmly in the
lap of culture (Vygotsky, 1962). Language is a good example of cultural
evolution of the mind as well as of the brain. Cultural evolution has accelerated
the development of brain systems that must support the emergence of both

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cognitive and non-cognitive functions. In today’s world of progressive use of
visual modes such as computer and information storage devices, it is hard to
imagine that the brain would not be under pressure to develop new structures
(Donald, 1993). Not only the content of thought and its cortical organization but
also its structure is determined by the culture in which an individual lives. In
sum, cultural evolution has a comprehensive influence on intellectual
activities an influence that is mediated by the tools of cognition and its
architectural basis in the brain.
In contemporary cognitive psychology two main approaches usually
predominate-
1) Information – processing approach, and
2) Connectionist approach
The information – processing approach is squarely rooted in the emergence
of the computing machine. The information psychologists sometimes argue that
the mind works like a computer. This can trace its lineage back to the work in
human factors. The research has demonstrated that humans actively seek
information about the world, and the plans and goals that humans formed for the
world were based on the information they sought and found. The information
processing psychologists have adopted the ‘computer metaphor’ to
understand human intelligence or cognitive process. However, there are
several basic questions that arise in information processing approaches to
intelligence (Sternberg, 1985 a). The first relates to the processes underlying
performance on any intelligent task or test. The second relates to processes.
The third is concerned with the strategies of performing the task, these
strategies being an outcome of a combination of different processes. The
fourth pertains to the mental representations of these processes and
strategies. Finally, the last is concerned with the knowledge base that enters
in to any kind of task solution. These five different issues are a common

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concern of several contemporary theories of intelligence although they may
themselves differ from each other in various ways.

The connectionists, on the other hand, have adopted the “Brain

Metaphor”, and sought to develop computational models of cognition. Their
work is intimately linked to historical roots in neurocomputing and therefore
is very much neuronally inspired. Actually this is an offshoot of the association
theory of learning – (Thorndike’s Connectionism, 1913). This theory suggests
that the most rudimentary type of learning occurs in the formation of
associations or connections between sensory experience and neural impulses.
When a modifiable connection between a situation and a response is made
and is accompanied or followed by a satisfying state of affairs, that
connection strength is increased (Thorndike’s Law of effect, 1913). Thus,
connectionism is a method by which cognitive activities are explained in terms of
interactions between units that resemble neurons (Schneider, 1987). The basic
elements in connectionist models are nodes and links. These are also called
units and connections. The nodes are assumed to be simple, homogeneous
processing devices. Each node takes on a level of activity based on a weighted
sum of input from the environment and from other nodes. However, the nodes do
not individually correspond to external objects or situations; they are characterized
only by levels of activity and by their ability to transmit activation over the links
between nodes. The links provide the means by which the units are able to
interact with each other. The set of nodes and the links that connect them are
typically referred to as a network. The network’s behavior as a whole is a
function of the initial levels of activity of the nodes and of the weights on its
links. The connectionists’ models assume many of the principles of learning
theories based on behavioristic approach (i.e., Hull, Tolman, Gutherie, see
Hilgard & Bower, 1956 etc.). Even though the connectionist models have not
really worked on spatial-temporal network, the recent advancements in

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formulating such networks show the potential of the connectionists’ approach
from simple associations to systematic reasoning from simple associations to
systematic reasoning (Shastri & Ajjanagadde, 1993). At the same time the
information processing approach to intelligent behavior has culminated in
providing models for problem solving and other intelligent behaviors in terms
of artificial intelligence following the pioneering work of Newell and Simon
(1972).

Thus, in the last half-a-century, developments in computer science,

particularly ‘Artificial Intelligence’, have contributed several enlightening
metaphors to cognitive science. The most significant contribution has been in
the area of knowledge representation and memory, drawing mostly from the
centuries of deliberations on epistemology and logic. Today these remain the
least controversial among the proposals on the architecture of mind based on
the information processing approaches. Most notable and highly relevant are
the concepts of ‘modularity’ and ‘encapsulation’, borrowed from object
oriented abstractions of procedural and declarative data modeling. Fodor’s
(1983, 2001) highly influential architecture of mind proposed that the mind is
composed of peripheral (perceptual), domain-specific, dissociable functional
sub-systems that are mandatory, swift and involuntary processing units,
wholly determined by evolutionary selected genetic endowment. However,
the high level central cognitive systems that are involved in belief, creativity,
reasoning etc., are (according to Fodor) a modular and non-encapsulated. A
group of scholars disagree with Fodor and attempt to modularize almost every
cognitive faculty of mind making it entirely modular. Moreover, this notion of
‘informational encapsulation’ has also been challenged by Nagarjuna G. (2006) by
arguing that cross-representation of cognitive dimensions, which is essential for
the formation of concepts of any kind, is totally impossible with encapsulation.

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 Research on computational modeling in cognitive science has two
different pursuits; one is computational ‘cognitive models’, the software
systems that propose testable hypotheses, highlight the inadequacies of
current theories, and predict the behavior of people in simulations. The
second pursuit is the development of ‘inferential theories’, software systems
that propose representation and inference mechanisms that describe the
explanations and predictions that people generate. These are about human
cognition and falls under the heading ‘Commonsense Psychology’, also called
‘naïve psychological reasoning’. Cognitive models are authored to describe
the way people think (the process of human cognition). Inferential theories
about the mind are authored to describe the way people think they think (the
inference that people make about human cognition). These two pursuits have
been widely discussed, in the context of ‘Theory of mind reasoning’, originally
started to investigate as an ability that young children acquire to reason about the
false beliefs of other people (Wimmer & Perner, 1983). This has included a range
of social cognition behaviors, perspective taking, metacognition, and introspection
etc. (Baron – Cohen et al., 2000). Two competing theories of ‘Theory of mind
reasoning’ have been proposed. One, the advocates of ‘Theory of Theory’ have
argued that ‘Theory of Mind Reasoning’ relies on tacit inferential theories about –
mental states and processes (inferential theories), which are manipulated using
more general inferential mechanisms (Gopnik & Meltzoff, 1997; Nichols & Stich,
2002). The proponents of ‘Simulation Theory’ argue that ‘Theory of Mind
Reasoning’ can be better described as a specialized mode of reasoning, where
inferences are generated by employing one’s own reasoning functions
(described as cognitive models) to simulate the mental states and processes of
other people (Goldman, 2000).

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Cognition and Memory:
Human memory has been widely studied in the history of cognitive
psychology. Many different approaches have been pursued to develop an
understanding of memory process, including the computational cognitive models.
One such model called ‘Similarity – based memory retrieval’ has been authored
by Forbus et al. (1994) to justify its utility in memory processes. In this two-stage
model, a target situation in working memory serves as a retrieval cue for a
possible base situation in long –term memory. In the first stage, a fast comparison
process is done between a target and potential bases using a flat feature – vector
representation, resulting in a number of candidate retrievals. In the second stage,
attempts are to identify deep structural alignments between the target and these
candidates using a graph – comparison algorithm. Based on the strength of the
comparisons made in these two stages, base situations that exceed a threshold are
retrieved. This computational model has helped to explain the empirical evidence
of human memory retrieval performance, including why remindings are
sometimes based only on surface – level similarities, and other times based only
on deep structural analogies. This model has enough simplicity in (its) functional
mode. The system is initialized with a database of situations to be stored in long-
term memory. Its processes are initiated when a target situation is in working
memory. Its role effect on other cognitive processes is the retrieval of base
situations from long-term memory into working memory. Gordon and Hobb
(2003) developed a ‘formal inferential theory’ which explains and encodes a
commonsense view of how people think human memory works (commonsense
theory of human memory). It describes – human memory concerns memories in
the minds of people, which are operated upon by memory processes of storage,
retrieval, memorization, reminding and repression, among others. The key aspects
of this theory are as follows: - 1 Concepts in memory – people have minds
with at least two parts, one where concepts are stored in memory and a second
where concepts can be in the focus of one’s attention. Storage and retrieval

17
involve moving concepts from one part to the other. 2 Accessibility – In
memory the concepts have varying degrees of accessibility, but there is some
threshold beyond which they cannot be retrieved into the focus of attention. 3
Associations – concepts that are in memory may be associated with one another,
and having a concept in the focus of attention increases the accessibility of the
concepts with which it is associated. 4 Trying and succeeding – people can
attempt mental actions (e.g. retrieving), but these actions may fail or be successful.
5 Remember and forget – Remembering can be defined as succeeding in
retrieving a concept from memory, while forgetting is when a concept becomes
inaccessible. 6 Remembering to do- A precondition for executing actions in a
plan at a particular time is that a person remembers to do it, retrieving the action
from memory before its execution. 7 Repressing – People often repress
concepts that they find unpleasant, causing these concepts to become inaccessible.
Then again Hobbs and Gordon (2005) began an effort to develop inferential
theories based on 30 representational areas to support automated commonsense
inference, which have a high degree of overlap with the classes of cognitive
models. The aim of this work is to develop formal (logical) theories that
achieve a high degree of coverage over the concepts related to mental states
and processes, but that also have the necessary inferential competency to support
automated commonsense reasoning in this domain. These theories were
authored as sets of axioms in ‘first-order pedicate calculus’, enabling their
use in existing automated reasoning systems (e.g. resolution theorem –
proving algorithms). These 30 areas are considered as taxonomy of cognitive
models which participate in an integrative cognitive architecture. Underlying
these 30 areas there are 16 functional classes of cognitive models. These are as
follows: - 1.  Knowledge and inference model (Managing knowledge)
describes how people maintain and update their beliefs in the face of new
information (e.g., Byrne & Walsh, 2002). 2.  Similarity judgment model –
explains how people judge things to be similar, different, or analogous (e.g.,

18
Gentner & Markman, 1997). 3.  Memory Model says about memory storage
and retrieval (see Conway, 1997). 4.  Emotion Model states about emotional
appraisal and coping strategies (e.g., Gratch & Marsella, 2004). 5. 
Envisionment (including Execution envisionment) Model explains how people
reason about causality, possibility, and intervention in real and imagined worlds
(e.g., Sloman & Lagnado, 2005). 6.  Explanation Model (including causes of
failure) narrates the process of generating explanations for events and states with
unknown causes (e.g., Leake, 1995). 7.  Expectation Model describes how
people come to expect that certain events and states will occur in the future, and
how they handle expectations violations (e.g., Schank, 1982). 8.  Theory of
Mind Reasoning Model – explains how people reason about the mental states and
processes of other people and themselves. 9.  Threat Detection Model
analyses how people identify threats and opportunities that may impact the
achievement of their goals (e.g., Pryor & Collins, 1992). 10.  Goal
Management Model describe how people prioritize and reconsider the goals that
they choose to pursue (e.g., Schut et al., 2004). 11.  Planning Model deals with
plans, plan elements, planning modalities, planning goals, plan construction, and
plan adaptation and narrates the process of selecting a course of action that will
achieve one’s goals (e.g. Rattermann, 2001). 12.  Design Model shows how
people develop plans for the creation or configuration of an artifact, process
information. 13.  Scheduling Model explains how people reason about time
and select when they will do the plans that they intend to do. 14.  Decision
Making Model describes how people identify choices and make decisions (e.g.,
Zachary et al., 1998). 15.  Monitoring Model explains how people divide their
attention in ways that enable them to wait for, check for, and react to events in the
world and in their minds (e.g., Atkin & Cohen, 1996). 16.  Plan Execution
Model deals with execution modalities, repetitive execution, body interaction,
plan following, observation of execution and defines the way that people put their
plans into action and control their own behavior (e.g., Stein, 1997). However, it is

19
evident that it is only through the parallel development of inferential theories and
cognitive models that we can appropriately assess the strengths and limitations of
each, which can be possible through further research and analysis.

Cognition and Metacognition:

Since Flavell’s (1971) coining of the term ‘Metamemory’ many have
investigated the phenomenon surrounding cognition about cognition.
Developmental psychology has reported the most positive evidence regarding how
cognitive function develops during childhood and the importance of metacognitive
strategies and monitoring in it. Wellman (1992) views human metacognition,
not as a unitary phenomenon, but rather as a multifaceted theory of mind.
Metacognition involves several separate but related cognitive processes and
knowledge structures that share as a common theme the self as referent.
Wellman explains that the theory of mind emerges during childhood from an
awareness of the differences between internal and external worlds , that is from the
perception that there exist both mental states and events that are quite
discriminable from external states and events. A number of psychological
variables (knowledge classes) are in this theory such as 1)  person variables
that deal with the individual and others (for example, cognitive psychologists can
recall many facts about cognition, whereas most people cannot), 2)  task
variables, which concern the type of mental activity (e.g., it is more difficult to
remember non-sense words than familiar words), and 3)  strategy variables
that relate to alternative approaches to a mental task (e.g., to remember a list it
helps to rehearse). This theory also includes a self-monitoring component,
whereby people evaluate their levels of comprehension and mental performance
with respect to the theory and the norms the theory predicts. Nelson and Narens
(1992) present a general information – processing framework for integrating and
better understanding metacognition and metamemory. Their model is based on
three basic principles: 1)  Cognitive processes are split into an object – level
20
(cognition), and a meta-level (Metacognition); 2)  The Meta-level contains a
dynamic model of the object-level; and 3)  A flow of information from the
object-level to the meta-level is considered monitoring whereas information
flowing from the meta-level to the object-level is considered control.
Monitoring informs the meta-level about the state of the object-level and thus
allows the meta-levels’ model of the object level to be undated. Then depending
upon the state of this model, control can initiate, maintain, or terminate object-
level behavior. Object-level behavior consists of cognitive activities such as
problem solving or memory retrieval.
Control

Object Meta –
Level Level

Monitoring
(Nelson et al.’s model of ‘Metacognitive Monitoring and Control of
Cognition’.)

Both the authors (Nelson & Narens, 1992) address knowledge acquisition
(encoding), retention, and retrieval in both monitoring and control directions
of information flow during memory task. Monitoring processes include ease-
of-learning judgments, judgments of learning (JOLs), feelings of knowing (FOKs)
and confidence in retrieved answers. Control processes include selection of the
kind of processes, allocation of study time, termination of study, selection of
memory search strategy, and termination of search. This framework has been
widely used both in psychological research and computational sciences.
Moreover, research examining the relationship between metacognitive skills and
educational instruction has made significant progress. Researchers (Forrest-
Pressley, Mackinnon and Waller, 1985; Garner, 1987) report successful instruction
procedures related to both problem solving and reading comprehension (see also

21
Ram & Leake, 1995 for related topic in computer/ cognitive science).
Metacognition research encompasses studies regarding reasoning about
one’s own thinking, memory and the executive processes that presumably
control strategy selection and processing allocation. Metacognition differs
from standard cognition in that the self is the referent of the processing or the
knowledge (Wellman, 1983). Thus, metaknowledge is knowledge about
knowledge, and metacognition is cognition about cognition. But often
metaknowledge and metamemory (memory about one’s own memory) are
included in the study of metacognition as they are important in self-monitoring
and other metacognitive processes. Many of the roots of metacognition in
computation are influenced by the large body of work in cognitive, developmental,
and social psychology, cognitive aging research, and the educational and learning
sciences.

Problem Solving and Metacognition:

Problem solving is one area where a natural fit exists, to computational
theories in ‘Artificial Intelligence’. Concepts such as executive control and
monitoring are important to problem solving in order to manage problem
complexity and to evaluate progress towards goals. Dörner (1979) reports the
earliest experiment on the effects of cognitive monitoring on human problem
solving. Derry (1989) offers a comprehensive model of reflective problem solving
for mathematical word problems inspired by John Anderson’s ACT * (Anderson,
1983) and PUPS (Anderson& Thompson, 1989) theories of general cognition.
Based on such a theory, Derry and her colleagues developed a computer-based
instructional system to teach word problems to military servicemen. Swanson
(1990) has also established the independence of general problem aptitude from
metacognitive ability. Subjects with relatively low aptitude, but high
metacognitive ability, often use metacognitive skills to compensate for low ability
so that their performance is equivalent to high aptitude subjects. Moreover,
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Davidson, Deuser and Sternberg (1994) from a series of studies show that the use
of metacognitive abilities correlate with standard measures of intelligence. In their
experiments on insight problem-solving they report that, although higher IQ
subjects are slower rather than faster on analyzing the problems and applying their
insights, their performance is higher. They argue that the difference in
performance is due to effective use of metacognitive processes of problem
identification, representation, planning how to proceed, and solution evaluation,
rather than problem solving abilities per se. Dominowski (1998) reviews many
such studies and concludes that although some conflicting evidence exists,
subjects in metacognitive conditions generally do better on problem-solving tasks.
The reason for the difference is not just that subjects are verbalizing their thoughts.
Silent thinking and simple thinking out loud perform equally well. The difference
is that problem-focused attentions of subjects improve local problem-solving
behavior, whereas metacognitive attention allows subjects to be flexible
globally and thus have a greater chance of finding a more complex and
effective problem-solving strategy. Recently the researchers are also
proposing ‘Metareasoning’ strategy. Forbus and Hinrichs (2004) have
proposed a new architecture for “Companion Cognitive Systems” that
employ psychologically plausible models of analogical learning and reasoning
and that maintain self-knowledge in the form of logs of activity. Singh (2005)
has created an architecture called ‘EM-ONE’, that supports layers of
metacognitive activities that monitor reasoning in physical, social, and mental
domains. These layers range from the reactive, deliberative, reflective, self-
reflective, and self-conscious to the self-ideals layer. More recently, the
metacognition community in psychology and cognitive science has started a novel
line of research on metacognition and vision which examines how people think
about their own visual perception. For example Levin and Beck (2004)
demonstrated that not only do people overestimate their visual capabilities but
most interesting, given feedback on their errors, they refuse to believe the

23
evidence “before their eyes”. Brown (1987) has described research into
metacognition as a “many-headed monster of obscure parentage”. This also
equally applies to many approaches of ‘Artificial Intelligence’ that deal with
metacognition, metareasoning and metaknowledge and the interrelationship
among them. But in essence the researchers have concluded that a metacognitive
reasoner is a system (in Artificial Intelligence Programs) that reasons specifically
about itself (its knowledge, beliefs and its reasoning process), not one that simply
uses such knowledge. In the field of education and pedagogy much of the
groundbreaking work in metacognition was conducted by researchers who
desired to understand whether young students could effectively monitor and
regulate their learning, reading, writing and mathematical problem solving.
General models of self-regulated learning – which have largely grown from
an educational perspective attempt to capture all aspects of students’
activities and their environment that may contribute to student scholarship.
Accordingly, educational psychologists are interested in students’ basic
cognitive abilities, along with the integration of these abilities into a
framework that highlights goals settings, self-efficacy, domain knowledge,
motivation, and other factors. The core of these general models, however, is
most often constituted from the two powerhouse concepts in metacognition:
monitoring and control (John Dunlosky & Janet Metcalfe, 2009).

Cognition and Intelligence:

Intelligence is cognition comprising sensory, perceptual, associative, and
relational knowledge. According to Das, Naglieri, and Kirby (1994) intelligence
is the ability to plan and structure one’s behavior with an end in view. If the end is
social one, then it is the most parsimonious solution to a problem for common
good. Sternberg (2005) defined intelligence as a number of components that allow
one to adapt, select, and shape one’s environment. Gardner (1999) defined
intelligence as the ability to create an effective product or offer a service that is
24
valued in a culture; a set of skills that make it possible for a person to solve
problems in life. Contemporary theories about intelligence may be broadly divided
into two closes, psychometric and cognitive types. The quantitative approach to
intelligence is better reflected in psychometric theories of which Charles
Spearman’s two factor theory (‘g’ – general ability &‘s’ – specific ability) is an
early example. In contrast, cognitive theories are both qualitative and quantitative.
Following Spearman, and even his predecessor, Galton, (Jensen, 2006) is perhaps
the chief advocate of general intelligence or “psychometric g”. His evidence for
‘g’ goes beyond factor analysis and seeks validity in reaction time studies of
elementary mental processes. He is poised to launch a movement for finding a
“super G” or all inclusive general ability, picking up where Galton left off (A.R.
Jensen, 2008). A popular way to divide intelligence is “Fluid Intelligence” and
“Crystallized Intelligence” as advanced by Cattell and Hunt. Fluid intelligence is
the ability to deal with novel intellectual problems, whereas crystallized
intelligence is the ability to apply learned solutions to new problems (Hunt, 1997).
Psychometric approach to general intelligence has continued to advance. A recent
classification of abilities has been proposed comprising verbal, perceptual , and
image rotation abilities with general intelligence or “g” at its top. But all the
psychometric classification of intelligence has a common weakness, that is – “The
weakness of psychometric models is related to their strength. They stand on an
impressive mathematical model of analysis of a given set of tests, without any
clear stance about what the tests should be in the first place” (E. Hunt, 2008).

Intelligence as cognitive processing is a common theme for all cognitive

theories of intelligence. These theories also advance the idea that intelligence has
multiple categories. Such as both the modern cognitivists like Sternberg and
Gardner view intelligence as neither a single nor biologically determined factor,
but as a number of domains that represent the interaction of the individuals’
biological predispositions with the environment and cultural context. Das et al’s

25
(1994) PASS (Planning, Attention, Simultaneous, Successive) theory of
intelligence is a further developmental step in this direction. The most recent
theories of intelligence with the cognitive processing (information processing)
approach are, of Gardner’s ‘Theory of Multiple Intelligences’, Sternberg’s,
‘Triarchic Theory’ and Das et al’s ‘PASS Theory’.

The theory of “Multiple Intelligences”, developed by Gardner (1999),

proposes seven separate kinds of intelligences comprising linguistic, logical
mathematical, spatial, musical, bodily-kinesthetic, interpersonal, and intrapersonal
domains as well as two recent additions such as naturalistic and existential
intelligence. Even though these nine types of intelligences are highly popular, the
theory lacks much empirical support. Earl Hunt remarked that the theory of
‘Multiple Intelligences’ cannot be evaluated by the canons of science until it is
made specific enough to generate measurement models. Thus, if one cannot
operationalize the concept intelligence, it cannot be evaluated.

R.J.Sternberg’s “Triarchic Theory” (Sternberg, 2005) proposes three

components of intelligence. The first relates to the internal world of the individual
that specifies the cognitive mechanisms which result in intelligent behavior; its
components are concerned with information processing. Learning how to do
things, and actually doing them, is the essential characteristic of the second
component of Sternberg’s theory. This part is concerned with the way people deal
with novel tasks and how they develop automatic routine, responses for well-
practiced tasks. The third component is concerned with practical intelligence.
More recently, Sternberg has expanded the three dimensions of intelligence adding
to these a measure of creativity. This latest edition is called “Theory of
Successful Intelligence”, which is still evolving.

26
 (Intelligence comprises: Analytical, Creative, & Practical abilities)

The ‘PASS’ theory of intelligence (Das et al; 1994) proposes that cognition
is organized in three systems and four processes. The first is the ‘Planning’
system, which involves executive functions responsible for controlling and
organizing behavior, selecting and constructing strategies, and monitoring
performance. The second is the ‘Attention’ system, which is responsible for
maintaining arousal levels and alertness, and ensuring focus on relevant stimuli.
The third system is the “Information processing’ system, which employs
‘simultaneous’ and ‘successive’ processing to encode, transform, and retain
information. Simultaneous processing is engaged when the relationship between
items and their integration into whole units of information is required, e.g.,
recognizing figures such as a triangle within a circle versus a circle within a
triangle. Successive processing is required for organizing separate items in a
sequence as for example remembering a sequence of words or actions exactly in
the order in which they had just been presented. These four processes are
functions of four areas of the brain. Plannings are broadly located in the front part
of our brains, the frontal lobe. Attention and arousal are a function of the frontal
lobe and the lower part of the cortex, although some other parts are also involved

27
in attention as well. Simultaneous processing and successive processing occur in
the posterior region or the back of the brain. Simultaneous processing is broadly
associated with the occipital and the parietal lobes, successive with the frontal-
temporal lobes. Das and Naglieri (1997) have also developed a psychometric test
battery called “Cognitive Assessment System” based on their PASS model of
intelligence, through which all these above processes (four) can be assessed.
These tests have been widely used for understanding, assessment (diagnosis) and
intervention of different educational problems like mental retardation, reading
disability, autism, attention-deficit etc, as well as cognitive changes in ageing
process.

In recent times the PASS theory has the support of both psychometric
measures as well as empirical findings of brain functions (in favor of). However,
the significance of brain studies awaits further discussion in the context of biology
of intelligence. The biology of intelligence is concerned with explaining how
intelligence is related to specific areas of the brain and the connections between

28
them (connectionists approach). A brain network of general intelligence involving
the parietal and frontal lobes has been recently suggested by Jung and Haier
(2007). Their “Parieto – Frontal Integration Theory” attempts to explain
individual differences in reasoning. Earl Hunt expresses his confidence over this
theory in explaining individual differences in intelligence. However, it still
considers intelligence as a general ability and is unable to explain how emotions
impact reasoning.

Cognition and Creativity:

Creativity is a multifaceted phenomenon. People are creative by virtue of a
combination of intellectual, personality and motivational attributes whose outcome
also depends on the environment. R.J. Sternberg says (1998) “Creativity can take
many forms and come in many blends. Some people have more of the intellectual
attributes and still others more of the personality attributes”. Intelligence is seen
as related to both creativity and wisdom, although more to wisdom
(Sternberg, 1985). The making of a new, different and aesthetically stimulating
product is more salient in conceptions of creativity than of wisdom, whereas
balanced judgment and skillful and undistorted appraisal of meaning is more
salient in conceptions of wisdom. The creative personality is dynamic; the wise
personality is balanced and virtuous (Sternberg, 2001; Baltes & Staudinger,
2000). Research findings have shown that both creativity and wisdom show
much evidence of openness and complexity (including intelligence).
Originality being more saliently associated with creativity and meaning –
making with wisdom; furthermore, ambition, autonomy, and perseverance
are more associated with creativity and benevolence with wisdom (Helson &
Srivastava, 2002). However, cognitive – affective vitality is an essential
component of both creativity and wisdom.

29
 In cognitive science Terry Dartnall (2007) (author of the book “An
Interaction: Creativity, Cognition and Knowledge”) holds the view that an
account of creativity is the ultimate test for cognitive science. A system is said
to be creative if it can articulate its domain-specific skills to itself as
structures that it can reflect upon and change. Such an account will provide
an explanation of how our creative products emerge, not out of combination
of elements but out of our knowledge and ability. Dartnall (2007) further
argues that cognitive science is in need of a new epistemology that re-
evaluates the role of representations in cognition and accounts for the
flexibility and fluidity of creative thought. In fact such an epistemology is
already with us in some leading edge models of human creativity. The
various aspects of creativity are – mundane creativity, representational re-
description, analogical thinking, fluidity and dynamic binding, input vs.
output creativity, emergent memory and emergence. The author argues that
we construct representation in the imagination, rather than copy them from
experience. It gives us the fluidity and flexibility that we need about creative
cognition. Rather, cognition emerges out of our knowledge about a domain
and our ability to express this knowledge as explicit, accessible thought.
Hence, we need an epistemology which could account for the way in which we
can understand the properties of the objects and vary them in the imagination.
This is called “property epistemology” which recognizes the role of representation
or knowledge about the properties of objects in the world. The representations are
constructed in our mind by the knowledge and the conceptual capabilities that we
acquire in making sense of the world. We do this by redeploying capabilities that
we first acquired in learning and problem solving.

In concurrence with this the researchers like Prinz and Barsalou (2002)
have emphasized concept acquisition as a form of creativity. The
representations we form contribute to an ever-growing repertoire of concepts.

30
They develop an account of concept acquisition and explore prospects of
constructing computational model of perceptual symbols using current
strategies and / technologies. They argue a more promising account such as
perceptual symbols (a class of non-arbitrary symbols) are derived from the
representations generated in perceptual input systems and therefore can be
systematically combined and transformed. Perceptual symbols are multimodal
and schematic and can represent dynamic symbols which can be changed
according to the context. When the perceptual symbols modify or accommodate
each other in combination, new things can be discovered. For constructing
perceptual systems computationally, the authors have chosen connectionist models
because these are good at acquiring symbols, modeling perceptual input systems,
are context sensitive as well as address information semantically. The authors
have suggested that a model of perceptual symbols must include mechanisms for
grouping together multimodal symbols. Perceptual symbol systems yield multiple
perceptual representations concurrently. Integration mechanisms convert these
perceptual representations in to symbols and group them together to form concepts
that can be assessed by higher level systems.

Another author Donald M. Peterson (2002) advocates for representational

redescription as the explanation for understanding creativity. He holds that the
concept of creativity can be better understood as “representations”, that is cases in
which we increase our knowledge by figuring knowledge which we already
possess. Representational re-description hypothesis describes that the mind is
endogenously driven to go beyond what behavioral mystery and to re-
describe and represent its knowledge to itself in increasingly abstract forms.
It does this without any external pressure. In the course of development this
knowledge is re-described as explicit, declarative knowledge that becomes
available to other procedures, nor to the system as a whole. This approach to
knowledge gives a description of the cognitive processes behind our thoughts

31
and the recurring changes. It is an explication of knowledge, that is
rearrangement or re-representation, which produces new output from old
structures. Explication is creative where its access output at issue is new, but the
procedure / knowledge accessed is not. When drawing procedures become
accessible and manipulable new drawings become possible, so that the
performance can be altered in a flexible manner. Two other researchers Halford
and Wilson (2002) think that creativity requires explicit representations that
are accessible to and modified by other cognitive processes without need of
external processes. They believe that creativity requires the ability to
represent and recursively modify explicit complex relations in parallel. John
E. Hummel and Keith J. Holyoak (2002) think creativity as mapping a
problematic situation onto a structurally similar situation that we are
familiar with. Such analogies play an important role in creative thinking as it
enables us to draw inferences in the sense of generating hypotheses.
Analogical thinking has four components: accessing a useful potential source
analog, mapping the source to the target to identify systematic
correspondence, using the mapping to draw new inferences about the target
and inducing a generalized schema that captures the commonalities between
the source and the target. Induction also depends on mechanisms that access
and use relevant prior knowledge from outside the immediate of the problem at
hand like reasoning by analogy. The central part of induction is the discovery of
systematic correspondences among existing elements and using those
correspondences to guide inference. The authors have developed a computational
model of analogy called ‘LISA’ (Learning and inference with schemas and
analogies) which fulfils some essential requirements for creativity. Structure
mapping and schema induction involve the ability to appreciate abstract relational
similarities between situations and the ability to induce a more general principle
from those relational similarities. Actually this is the first step in creative
thinking.

32
 Derek Partridge and John Rowe (2002) have presented a computational
study of the nature and process of creativity, the model called “GENESIS”
also features a representationally fluid emergent memory mechanism. These
two authors primarily focus on two psychological theories of human creativity, the
‘cortical arousal’, or “special mechanism”, theory and the theory that creativity
does not involve a special mechanism, and that it is just normal problem solving.
They have distinguished between input and output creativity. Input creativity
helps in solving problems and makes sense of the world while output
creativity helps us when we deploy our knowledge to create something on our
own. Thus the mechanisms and inner capabilities that are put into place during
the input creativity phase are re-deployed in the output creativity phase. On the
other hand, Chris Thornton (2002) has tried to carry out a logical analysis of the
operational characteristics of basic learning procedures and to use this analysis to
find out some interesting facts about the relationship between learning some types
of creativity. The key idea to be worked out is our ability to be creative might be
partly founded on our ability to learn. He argues that certain creative processes
may be viewed as learning processes running away out of control. He further
clarifies that the generative aspect of creativity may be understood in terms of a
particular type of learning. Author observes that the identification of a
relationship within certain data effectively recodes those data. The relational
learning always implicitly recodes the data, thus generates new data, and thus can
potentially be applied recursively. Authors like Gary McGraw and Douglas
Hofstadter (2002) have tried to implement the findings of a project called “Letter
Spirit Project”. According to them, it is difficult to quantify and model
creativity. The ‘Letter Spirit Project’ is an attempt to model central aspects
of human high-level perception and creativity on a computer. It is based on
the idea that creativity is an automatic outcome of the existence of sufficiently
flexible and context sensitive concepts or fluid concepts.

33
 Author Richard McDonough (2002) suggests that ‘emergentism’ offers the
possibility of a kind of creativity that involves the birth of something genuinely
new. This means that more can come out of an organism than can be accounted
for by what is materially/ mechanically internal to the organism. Emergent
materialism is the view that life and mind are emergent characteristics of matter,
but emergence is neither a necessary nor sufficient condition for creativity.
Author Terry Dartnall (2002) suggests that currently cognitive science needs to get
lessons from classical empiricism by claiming that it is our knowledge about the
domain that does the hard cognitive work, and representations are constructed out
of this knowledge. Current research in cognitive science also supports the
view that representations are not mere stored copies in mind. However, this
novel epistemological approach seems especially useful when it comes to
accounting for complex cognition when creativity emerges where
representations are not practically possible because they are not spatio-
temporally present, such as having an idea a thousand sided plane figure (a
chiliagon). However, here one’s creative imagination gets a boost by the
extent to which one knows that ‘a chiliagon is a thousand sided figure’.

The above discussion gives us a comprehensive summary of the current

research on creativity and cognitive science.

Emotional Intelligence and Emotional Creativity:

Intelligence is primarily associated with one’s level of academic
achievement and professional accomplishment. It is the capacity to reason validly
about a domain of information, and typically requires converging on a single
answer. On the other hand, creativity is associated with the degree to which a
person engages in novel endeavors. It requires generation of multiple alternatives
that are both novel and appropriate alternatives that are both novel and appropriate

34
alternatives that are both novel and appropriate ( Lubart, 1994). With regard to the
relationship between intelligence and creativity a number of views have come up,
like – ‘creativity is a subset of intelligence’ (Guilford, 1975); that creativity and
intelligence are related or partially overlapping constructs (Barron & Harrington,
1981); and these two constructs are mostly distinct mental abilities (Torrance,
1975; Runco & Albert; 1986). Over the last few decades the research on these
concepts have also incorporated the affective domain and the concepts like
‘Emotional Intelligence’ and ‘Emotional Creativity’ have emerged. Emotional
intelligence (EI), is defined as the ability to perceive emotions accurately, use
emotions to enhance thinking, understand and label emotions, and regulate
emotions in the self and others (Mayer & Salovey, 1997). Similar to cognitive
intelligence, EI require reasoning skills, and analytical skills. Parallel to EI,
one new domain of creativity has been introduced called ‘Emotional
Creativity’ (EC). Emotional Creativity (EC) is the ability to experience and
express original, appropriate and authentic combinations of emotions (Averill
& Thomas-Knowles, 1991). Similar to cognitive creativity, EC requires
divergence from the norm/ standard. Where as EI pertains to how a person
reasons with emotions, EC pertains to the richness of a person’s emotional
life. As such, a person with high EI will have knowledge of and may use a
variety of regulation strategies, whereas a person with high EC will
experience more complex emotions. Both EI and EC have been compared to
cognitive abilities, such as verbal intelligence (Mayer, Salovey, Caruso, &
Sitarenjos, 2003; Averill & Thomas Knowles, 1991). But the question arises
whether the relationship between EI and EC is parallel to that of cognitive
intelligence and creativity. That is, will these two abilities be mostly
uncorrelated, or will they be more highly related? Studies have shown that both
EI and EC may be related to creative behavior. In their study Gutbezahl and
Averill (1996) have found that emotional creativity is related to behavioral
creativity that involved expression of emotion (e.g., writing a love narrative). One

35
component of EI is the ability to use emotions to facilitate thought processes, such
as when directing one’s efforts in to activities best performed in certain emotional
states (Palfai & Salovey, 1993; Mayer, 2001; Mayer & Salovey, 1997). Another
EI ability concerns the regulation of emotion to reduce negative or maintain
positive emotions. Positive emotions can enhance creativity by increasing
flexibility and breadth of thinking (Estrada, Isen, & Young, 1994; Isen, 1999).
Both the EI and EC have been analysed to describe the emotional abilities.
Emotional intelligence pertains to how an individual reasons about and with
emotions. It includes four component abilities: the perception, use, understanding,
and regulation of emotion (Mayer & Salovey, 1997). Perception of emotions is
the ability to accurately identify emotional content in faces and pictures. Use of
emotions concerns the utilization of emotion as information to assist thinking and
decision making. Understanding emotion involves adequately labeling emotions
and understanding their progress. Finally, regulation of emotion pertains to
effective managing of feelings in oneself and others to enhance well-being in self
and others. Emotional creativity is the ability to experience and express novel and
effective blends of emotions. There are three criteria for EC: novelty (i.e., the
variations of common emotions and generation of new emotions), effectiveness
(i.e., appropriateness for the situation or beneficial consequences), and authenticity
(i.e., honest expression of one’s experiences and values). Another condition for
EC is emotional preparedness, which reflects a person’s understanding of
emotions and willingness to explore emotions (Averill, 1999 a, 1999 b). While EI
requires analytical ability and convergence to one best answer to an emotional
problem, EC involves the ability to diverge from the common and generate a novel
emotional reaction. Emotional creativity can involve a manipulation and
transformation of experience that leads to problem solving in the domain of
emotions, but experience alone, rather than problem solving, is sufficient for a
response to be considered emotionally creative (Averill, 1999 b). Regarding the
relationship between EI and EC several theoretical predictions have emerged, such

36
as EC is a component (subset) of EI; EI and EC are partially overlapping abilities;
EI and EC are two distinct sets of abilities so on. Most recently Ivcevic, Brackett,
and Mayer (2007) in their study found that EI and EC are indeed distinct
abilities. Their study also revealed that EC showed low, but significant,
correlations with personality attributes like ‘Agreeableness’ and moderate
correlations with ‘Verbal Intelligence’. On the other hand, EC was mostly
uncorrelated with cognitive intelligence, and it was highly correlated with
‘Openness to Experience’ personality trait. The authors have suggested that EI is
not directly related to creative behavior in the arts. Now the question is how can
EI be used to enhance creative thinking? They offer two explanations for the
role of EI in creativity. The first hypothesis is that EI would be important for
certain classes of creative behaviors. Activities that call for generation and
manipulation of emotions, such as acting on stage, could be more relevant
criteria to examine the contribution of EI to creativity. Alternatively, EI
might moderate the relationship between emotional traits and creativity.
Emotional creativity is an ability that significantly predicted involvement in
the arts. This was more strongly related to artistic expression and appreciation in
performing arts than to artistic activity in writing and visual arts in which the
expression of emotions is not always necessary. The authors have concluded that
emotional abilities play a significant role in creativity only when the products
express emotional content. However, they have further suggested that the
relationship between EI and EC could be investigated by examining open-ended
descriptions of problem solving in emotional situations that would vary in
explicitness of problem definition and in the format of successful solutions
(Correctness vs. fluency and originality criteria). Moreover, to investigate the role
of emotional abilities in creativity it would be crucial to develop a variety of
different criteria for creativity.

37
 When we consider creativity as a process and try to translate it into
teaching–learning process, automatically Torrance’s (1993) “Incubation
Model of Teaching” comes to our mind. This is a three-stage model that provides
opportunities for incorporating creative thinking abilities and skills into any
discipline at any level from preschool to graduate and professional educations.
The three stages in the model are: heightening expectations and motivation,
deepening expectations or digging deeper, and going beyond or keeping it going.
The purpose of the first stage is to create desire to know, to learn or to discover; to
arouse curiosity; to stimulate the imagination, and to give purpose and motivation.
The goals of the second stage it to go beyond the surface or warm-up and to look
more deeply into the new information. For Creative thinking to occur, there must
be ample opportunity for one thing to lead another. This involves deferring
judgment, making use of all the senses, opening new doors, and forgetting
problems to be considered or solutions to try. The objective of the third stage is to
genuinely encourage creative thinking beyond the learning environment in
order for the new information or skills to be incorporated into daily lives. It
is found that those teachers who have applied this instructional model have
reported that teaching becomes an exciting experience to them and their students.
Torrance has further confirmed that this model can be applied not only to
“teaching”, but to lectures, sermons, workshops, seminars and conferences. Some
field reports indicate that this program resulted in more reading, more books
checked out of libraries, more seeking information through interviews and
experiments, and discovery learning. Research has also highlighted another model
called “Interactive Learning Model” (Johnston, 1996, 1998) which proposes that
learning is a process occurring because of the continuous interaction of no less
than three mental processes: Cognition (thinking), Affectation (feeling) and
Conation (willingness to act). Researchers, have found that ‘Interactive
Learning Model’ (ILM) gives an opportunity to teachers, learners as well as
policy makers (a means) to identify how each student processes information,

38
uses his/her personal tools for learning, and develops as a confident and
successful life-long learner. These three mental processes (cognition,
affectation & conation) form patterns of behavior within each learner. It’s
also found that different learners learn in different settings and therefore not
all learners learn best in a non-traditional setting and vice versa (Zelezny,
1999). More recently, the researchers such as Vanhear Jacqueline and Pace Paul J
(2008) have confirmed that for a learner to take interest in learning, the
teacher must be aware of the learner’s own preferred way of learning
(learning style) in order to address his/her needs and enhance his/her learning
experience. Empirical research has already shown that new meaningful
knowledge does not occur in a vacuum, and thus prior knowledge has to be
taken into consideration if we expect meaningful learning to take place (Bruer,
1993; Johnston, 1996, 1998; Novak 1998). Jacqueline and Paul (2008) found
that the integration of some of the meta-cognitive tools such as heuristic
(Moria’s vee Heuristic), concept mapping along with an understanding of
learner’s learning style (preferred learning mode) can provide the teacher
with a clear picture of how the learner responds to and act upon incoming
information. These meta-cognitive teaching strategies, if adopted by the
teacher can easily shift the control from him (teacher) to the learner.
Consequently, learners become the agents of their own learning and actively
participate in the learning process. They even exhibit their planning for
future learning activities, and this is very important/ useful for the teacher to
be able to collaboratively build a learning program which would be relevant
to the learner’s style of responding to new information and can be truly
motivating, meaningful and innovative/creative.

So far as the role of emotion in decision making is concerned Prof. J. P.

Das (2008) has narrated about his cognitive planning model and stated that
emotions and conations interact with cognition. This is the recent received

39
view that decision making no longer assumes a rational information processor, be
it in business management or entrepreneurship. Rationality is bounded by
emotions and in any case, emotions cannot be separated from rationality in
either personal or business decision-making. Both emotion and cognitive
functions are integrated to determine a basic component in making decisions,
which is working memory (Gray et al; 2002). It’s a common fact that today’s
forward looking corporation actively strive to determine what employee
characteristics are of greatest value in enhancing organizational effectiveness and
efficiency. Empirical research findings also boost the fact that the prospective
employers mostly want/seek communication, emotional and interpersonal skills in
their employees. It’s a corporate notion that IQ gets you hired, but EQ gets you
promoted. However, EQ should not be considered as substitute for intellect, but
rather as an enhancer for work skills and employment opportunities. Goleman
(1999) has asserted that emotional intelligence abilities were about four times
more important than IQ in determining professional success and prestige, even for
those with a scientific background. emotional intelligence (EQ) covers a range of
skills like self-awareness, self-regulation, emotional resilience, motivation,
empathy, decisiveness, conscientiousness, communication, influence and a
persuasive skill which has considerable impact on individual’s personal
competence, social competence as well as job performance. EQ can be nurtured
and stimulated. A person’s EQ level can have a considerable impact on learning.
This indicates that education has a prime role to play in enhancing the EQ levels
of students that should reflect in the behavior are improved working abilities of
graduates (Riemer, 2003). Goleman has pointed out that engineering education
has ignored this range of EQ skills that incorporate communication, and
collaborative abilities, teamwork, selling an idea, accepting criticism and
feedback, learning to adapt, and leadership. He further explained that when the
graduate engineers are promoted to leadership positions, they often lack the
requisite leadership and managerial skills. Hence, such EQ related skills need to

40
be integrated urgently into engineering curricula for engineering to regain
relevance in education, across disciplines and in society. Of course, in our present
curriculum engineering students are supposed to take some humanities and
management subjects as their breadth electives. Academicians (Riemer, 2003; &
Jaeger, 2003) have suggested that incorporating elements of EQ learning in
studies, rather than as a separate study unit or module will link learning and work
attitudes, including motivation, creativity and interpersonal skills, with the tasks at
hand, such as project work, group assignment etc. Learning EQ skills seem to be
in line with experiential learning and a constructivist approach to studies, as EQ by
nature implies an experiential approach. Thus, encouraging students to learn these
new skills through, collaborative learning, problem based learning, project work
activities and in student – centered learning will succeed more than would a
standalone lecture on EQ theory without practice in real life situation. Research
findings have also indicated that in a graduate professional education course, by
the end of the semester, the students in the EI (Emotional Intelligence) curriculum
section had higher average emotional intelligence scores than those in non-EI
curriculum (Jaeger, 2003). Analysis revealed that changes in students’ emotional
intelligence levels were related to the type of curriculum offered. The EI-
curriculum section had a higher average change score in overall emotional
Intelligence (9.9.) compared to the non-EI curriculum sections (1.7). These
findings also suggest that students, who are generally attuned to their emotions
and feelings and can adapt to emotionally driven situations, were more likely
to attain higher levels of academic achievement in the course. It is the
combination of emotion and cognition and their influence on decision making
that connects them to the learning process. Emotion impels memory and
attention drives learning. Thus, it is important to ensure that learners
become emotionally involved in what is taught. This research shows that
emotional competence can be increased in a classroom setting and is strongly
correlated with student academic performance. With regard to EI, it has

41
been a well conceived and consensus view that if graduate professional
schools begin addressing emotional intelligence within the academic
environment, corporations will not need to invest millions of money to
improve EI of their employees (Cherniss & Goleman, 1998). Moreover, the
sustainability of increased levels of emotional intelligence and implementation of
EI curriculum are the more vital issues, needed to be addressed by the current
researchers. Along with EI, creativity and innovation is also recognized as a vital
component of entrepreneurship now-a-days. Hence, the researchers and educators
struggle today to reform the enterprise pedagogy. In one of the study Berglund
and Wennberg (2006) found that engineering students tended to emphasize
incremental development and solving existing problems, while business students
tended to focus on the radically new and generally were more market – oriented in
their creative styles. In the business context creative novelty and appropriateness
is often translated into idea development (Ward, 2004), new product innovations
(Amabile, 1996), and adapting or improving existing innovations (Kirton, 1987).
Methodologically, creativity in entrepreneurship and innovation has been
explained through cognitive processes, attitudes, motivation, existing knowledge,
work environment and personality traits (Amabile, 1996; Walton, 2003; Ward,
2004). Much research also addresses the question of different kinds of creativity,
such as Sternberg and Lubart (1995) distinguish between uppercase ‘c’ or genius
creativity and lowercase ‘c’ or mundane creativity. Boutaiba (2004) takes another
approach by stating that – ‘we need to recognize that entrepreneurial activity is an
inherent part of everyday life, and even the seemingly trivial activities of everyday
life have great capacity to move us in new and unexpected directions’. Thus,
some suggestions have been given by the researchers for engineering
entrepreneurship education. It may be advisable to include more elements that
emphasize market orientation and a focus on the bigger commercial picture (H.
Berglund & K. Wennberg, 2006). Engineering students generally have higher
creative potential and if these energies can also be geared towards more

42
commercial pursuits, students should end up better prepared for the realities of
entrepreneurial life. One way of such learning could be to actively mix
engineering students with students from business schools. This would lead to a
pooling of creative strengths as well as induce learning between individuals.
Another way could be more successful if the educational structure is flexible
enough to formulate heterogeneous entrepreneurs’ group. The pedagogy should
cater to both group and individual needs by allowing both the extremely creative
individual and others to thrive and develop in collaborative learning situations.

Motivation, Cognitive Processing and Achievement:

Over the years many studies have shown that apart from cognitive factors,
motivation and emotion significantly influenced educational outcomes. According
to the ‘Expectancy – value model of motivation’ (Jacobs & Newstead, 2000;
Walters & Pintrich, 1998), motivation consists of three components, namely a)
– an expectancy component which concerns the students; belief about his or
her ability to perform the task (e.g. can I do the task?), b) – a value–
component, which refers to the students’ goals and beliefs about the
importance and interest of the task (e.g. why am I doing this task?), and
finally c) – an affective component, which refers to the students’ emotional
responses to the task (e.g. how do I feel performing this task?). The
relationship between expectancy and achievement may be mediated by the
use of cognitive and meta-cognitive strategies. The students who believe that
they are capable of performing a task tend to use more, and more
appropriate, cognitive and meta-cognitive strategies. These students are
more likely to persist in performing the task, resulting in higher levels of
achievement (Vollmeyer & Rheinberg, 2000). In this regard the mastery goal
perspective assumes that mastery goals, which increase one’s competency,
understanding and appreciation for what is learned, are beneficial for
learning whereas performance goals are not. The multiple goal perspective,
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on the other hand, argues that both mastery and performance goals might be
beneficial for learning (Barron & Harackiewicz, 2003; Harackiewicz, Barron.
Pintrich, Elliot, & Thrash, 2002). Moreover, the goal difficulty also determines
the amount of effort to attain a goal; effort is greater when attaining difficult
goals. Studies on the affective – component have shown that various emotions
influence both the quality of thinking and cognitive information – processing
(Meyer & Turner, 2002; Wolters & Pintrich, 1998). Positive emotions, such as
curiosity, generally enhance motivation, and facilitate learning and performance.
Negative emotions, like mild anxiety, can also enhance learning and performance
by focusing the learner’s attention on a particular task. However, intense negative
emotions, like anxiety, panic, insecurity and related thoughts, feeling incompetent,
generally adversely affect motivation, interfere with learning and contribute to a
lower performance (Sarason, as cited in Kuyper, Van Der Werf & Lubbers, 2000).
The existing literature in this area indicates that cognitive and meta-cognitive
strategy uses may mediate the relationship between expectancy, values, and affect
on the one hand, and achievement on the other. However, more information is
needed on whether the effects of motivation on achievement change over years
and if so, how these effects change over years (Jacobs & Newstead, 2000). In this
context one longitudinal study has shown that the students’ expectancy and value
influenced the total achievement credits directly, and higher motivation resulted in
higher academic achievement (Bruinsma, M., 2004). But a deep information
processing approach did not automatically result in a higher total number of
credits. Rather, the deep information processing was found to be negatively
related to achievement. Some other studies have also found similar results like no
significant relationship existed between self-efficacy and performance when
cognitive engagement variables were included in the design/analyses (Pintrich &
De Groot, 1990). Even though self-efficacy might play an important
facilitative role in a cognitive engagement, cognitive engagement itself might
be seen more as an indicator of actual behavior and less of achievement in

44
terms of grade point average (GPA). Furthermore, cognitive engagement
could be enhanced by teaching students about different cognitive and
metacognitive strategies, while enhancing a students motivation would
enhance the frequency of use of these strategies. In another study on the
relationship between perception of the learning environment and academic
outcomes, Lizzio, Wilson and Simons (2002) found a positive relationship
between surface approach and ‘Grade Point Average’ (GPA) for commerce
students, but not for humanities students. They suggested that characteristics
of the learning environment, such as a job-specific and narrow vocational
focus, might be an important intermediating variable. These above studies
imply that teachers can influence motivation and deep information processing
strategies by adopting instruction and curricula accordingly, such as by
developing and maintaining students interest in the subject matter, by
providing high-quality learning environments, by illustrating the meaning
and purposes of the course and by indicating the reasons for learning etc.

Intelligence / The general ability, and Pedagogy:

Out of the many features of general intelligence, two most important
characteristics are: (1) its general component which operates across all
contexts and domains, (2) its plasticity, or amenability to enhancement, or
acceleration, in response to appropriate environmental influences and this
plasticity goes as far as the brain itself. When we accept the fact that the
functioning of the general intellectual processor in the mind can be improved by
education, then the construct of intelligence becomes more acceptable by the
researchers. It no longer has ultimate control over our students’ ability to learn.
Thus, the educators now have it in their power to raise their students’ general
cognitive ability, and so raise all of their academic performance. Recently a group
of researchers (Adey, et al, 2007) have recommended for action plan for raising
general cognitive ability in terms of certain principles as well as approach.

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These principles are: 1 Learning activities must be generative of cognitive
stimulation, that is they must have the potential to create challenge rather
than being comfortably within the reach of the learner’s current processing
capability. 2 Learning should be collaborative in the sense that learners
learn to listen to one another, to changing their positions. 3 We need
continually to raise awareness in students of what may be abstracted from
any particular domain specific learning, such as: a) factors in the concept, such
as the organization or the quantity of information that cause difficulties in
representing and processing; b) connecting the present concept to others already in
their possession, as they differentiate it from other concepts, and even as they
decide that some concepts need to be unlearned; c) control of the thinking and
learning processes as such, thereby transferring mental power from the teacher to
the thinker. Gradually students become self-reliant and self regulating rather than
depending on the teacher. 4 The present learning experience needs to be
connected to the concept space and the learning space of the past.
Considering the above principles the researchers (Adey et al, 2007) have
advocated for “Powerful learning environments”. This approach emphasizes
the metacognitive, self-regulated and motivational aspects of the learning
environment with a special emphasis on the mastering of the mental
operations and concepts related to a particular domain. In this learning
environment, students are led to plan their learning or problem-solving acts
from the start, reflect on what they know, what they can do, and what they do
not know about the problem and the domain, build relations between the
problem and their prior knowledge and systematically and continuously
monitor and regulate the process from the start through to the end. The
focus is to guide the students to make full use of their intellectual abilities for
learning in specific domains. However, it does not seem to pay sufficient
attention to either the processing or the developmental constraints of learning

46
at particular phases of life by particular individuals. Moreover, teaching for
cognitive stimulation is far more demanding.

Recent Trend in Learning and Instruction: Cognitive Load Theory

Over the years the teaching learning practice has been evolving and the
recent findings in cognitive science suggest that knowledge acquisition is a
constructing process of building coherent mental representations within
conceptual frameworks, and that such representations may be built from
verbal and pictorial symbols (Bransford, Brown, & Cocking, 2000). In this
regard multimedia offer many possibilities to facilitate knowledge
construction (Mayer, 2005a, 2005b). Constructive theories of learning assume
that students use their prior knowledge to construct new knowledge from the
presented information that is relevant for their individual goals and which is
based on their individual experiences. This is a widespread belief that
knowledge construction is facilitated by technology using multiple channels of
communication (see Verhoeven & Graesser, 2008). Now the question is how
students acquire new knowledge in multimedia environments with
multimodal (visual & auditory) presentation of information when working on
multiple tasks and how cognitive overload can be reduced by attuning
instructional design to the characteristics of the student. Thus, the cognitive
load theory emerged in the field of learning and instruction (through
multimedia mode).

Cognitive load has been defined as the load that performing a task
imposes on the learner’s cognitive system. The components of cognitive load
theory have been explained by number of researchers (Pass, Renkl, & Sweller
2003; Sweller, 2005).

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 Aspects of Cognitive Load Theory

This theory explains that learning processes that lead to knowledge

construction and automation are determined by the goal, the required mental
representations, the learner’s inventory of cognitive schemata and processing
strategies. Performance of learning tasks and the associated learning processes
impose a cognitive load on the learner’s working memory. The main focus of this
theory is the distinction between intrinsic load, which is due to the task, and
extraneous load, which is due to sub-optimal instruction. Intrinsic load involves
element activity which is determined by the nature of the task demands in relation
to the expertise and motivation of the learner. Instructional design may result in
extraneous load (which is ineffective for learning) and in ‘Germane’ load
(which is effective for learning). Extraneous cognitive load is defined as
unnecessary extra load due to poorly designed instruction. Germane load is

48
defined as load that contributes to learning such as, self-explanation.
Cognitive load theory primarily focuses on how constraints of working
memory have to be taken into account in order to optimize learning
processes. This is concerned with techniques of adopting cognitive load by
optimizing the use of working memory capacity in order to facilitate changes in
long-term memory associated with schema acquisition. This theory has many
implications for instructional design, such as the learning materials should keep
the students’ extraneous cognitive load at a minimum and germane load at a
maximum during the learning process. A recent reconceptualization of
cognitive load theory by Schnotz and Kürschner (2007) suggests that germane
load should not simply be maximized, but rather adapted to the intrinsic load
of the learning task within the constraints of working memory.

Now the important question is how cognitive load influences knowledge

construction in interactive learning environments. Interactive knowledge
construction is normally facilitated in an environment that stimulates meaningful,
social and strategic learning processes (Bransford et al, 2000; Verhoeven et al,
2006). Meaningful learning presupposes that students attend to the essential
aspects of the presented material, organize it into a coherent cognitive structure,
and integrate it with what they already know. Social learning occurs when the
students learn by observing teachers, tutors, or other students and when they
receive feedback on their own activities. Strategic learning occurs when students
can identify and apply, consciously or unconsciously, the intelligent procedures,
processes, skills and strategies that help them to master the learning material and
to transfer these strategies from one situation to another. Information
Communication Technologies (ICT) are often expected to facilitate knowledge
construction (Graesser, Chipman & King, 2008; Jonassen, 2004; Mayer, 2005a),
but how learning environments can be designed to optimally facilitate students’
knowledge construction and elaboration. A common way to ground learning in

49
meaningful context is to anchor the learning experience in an information rich,
coherent, realistic, problem scenario (Leu & Kinzer, 2000). These environments
with anchored problem-based learning provide an authentic context for students to
identify and define problems, to execute strategies to solve the problems, to
specify reasons for attempted solutions, and to observe results.

Research on multimedia comprehension has evolved various models in

which cognitive, memory-based, and constructive views are integrated. The
memory based view considers comprehension as a product of processing of
explicitly presented information, whereas constructivist theories emphasize
the roles of world knowledge and inferences (Verhoeven & Perfetti, 2008).
The point of debate is how multimedia processing takes place and how
students learn to develop multimedia comprehension skills. The
comprehension of multimedia demands specific strategies of information
utilization and is highly vulnerable to goal competition and task difficulty (De
Stefano & LeFevre, 2007). The cognitive theory of multimedia learning (Mayer,
2005b) is based on the idea that there are separate processing systems for both
kinds of information, such as verbal and visual channels in the working memory
with limited capacities. The integrative model of text and picture comprehension
assumes channels on two different levels. On the perceptual level, the model
includes sensory channels (auditory & visual), and on the cognitive level, it
assumes representational channels, namely a descriptive channel and a depictive
channel in working memory (Schnotz, 2005). Mayer (2005a, 2005b) argues that a
higher quantity of learning (i.e., capacity to transfer what is learned to new
situations) is attained when text and pictures are presented in an auditory – visual
mode as opposed to a visual-visual mode (Schnotz, 2005). The additive learning
effect of pictures accompanying oral or written text is referred to as the
multimedia effect (Mayer, 2005b). For hypermedia comprehension, the student
must combine the meaning of each unit with the message accumulated up to that

50
point on the basis of prior units and their mutual links. Knowledge integration
involves a large number of component skills that are not always adequately
covered in instructional design. One such skill is the chunking of multiple
information elements into a single unit or into cognitive schemas that can
subsequently be automated and stored in long-term memory. The information that
becomes integrated may stem from different information sources such as text and
pictures. These integrative processes may impose high working memory load on
the student’s working memory (Paas et al, 2003). The review of recent studies
concludes that the knowledge base in learner long-term memory (LTM)
provides executive guidance in the process of knowledge elaboration.
Accordingly, the role of external instructional guidance could be described as
providing a substitute for missing LTM knowledge structures in a schema-
based framework for knowledge construction and elaboration. It is also
argued that adaptive learning environments based on rapid diagnostic
methods could provide instructional support at different stages of knowledge
elaboration in order to optimize cognitive load. Continuous balancing of
executive function is seen as essential for optimizing cognitive load by
presenting required guidance at the appropriate time and removing
unnecessary redundant support as learner proficiency in a domain increases.

Moreover, learning outcomes are dependent on personal

characteristics also such as prior knowledge, motivation, and perspective-
taking. Furthermore, it appears to be mediated by task demands, which are
the result of instructional design. Concept map structures and interactive
animations in the instructional design can be seen to impact the learning
outcomes. Finally, the success of learning is also clearly related to
environmental factors such as interactivity, locus of control, opportunities for
collaboration etc.. Thus, in order to optimize cognitive load adaptive learning
environments should be implemented in which task demands and

51
instructional support levels are attuned to the expertise and memory
capacities of the individual learner (Salden, Paas, & Van Merriënboer, 2006)
Based on the re-analysis of these above factors, Schnotz and Kürschner (2007)
have also identified the need for research on more sensitive ways of assessing
learner characteristics, both prior to and during instruction, in order to understand
learning processes and outcomes. The same learning environment is differentially
demanding and produces different results depending on characteristics of the
learners, most importantly their knowledge in the task domain. Goldman (2009)
has also indicated that to optimize learning outcomes, theories of instructional
design and learning need to be more adaptive and reflect the nuances of
interactions among learners, tasks and instructional supports. Researchers have
extensively worked on ‘Cognitive Load Theory’ (CLT) in order to contribute to a
global understanding of how individual, task and environment variables interact in
shaping the learners’ activity and the associated cognitive load. These messages
have lots of implications and guidelines for instructional designers. Such as,
Kalyuga (2009) demonstrates that task instructions have to be carefully tailored to
fit the learner’s level of prior knowledge. Segers and Verhoeven (2009) suggest
that “a layer of structure between the child and the Web is a useful addition to
education”. Amadieu et al.’s (2009) results point to the need to design content
representations that are easy to interpret and to use (as apposed to complex /
confusing network concept maps). Similarly, Schnotz and Rasch (2009) show the
importance of designing visualizations that facilitate the processing of contents in
a way that is consistent with task demands. Scheiter et al. (2009) illustrate the
need for flexible environments that will accommodate students’ strategies.
Moreno’s (2009) conclusions support the view that collaborative scenarios should
be kept simple and should not bypass students’ individual work on the subject-
matter. Another important aspect of this research domain is the novelty and
versatility of the tools, representations and learning contents that are presently
being investigated.

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 Beyond the level of knowledge construction, knowledge elaboration is a
process of using prior knowledge to continuously expand and refine new
material based on the processes like organizing, restructuring,
interconnecting, integrating new elements of information, identifying
relations between them and relating the new material to the learner’s prior
knowledge. These processes are essential for meaningful learning as they
allow the learners to organize knowledge into a coherent structure and
integrate new information with existing knowledge structures. According to
cognitive load theory, two key functional components of our cognitive
architecture are responsible for these processes (Sweller, 2003, 2004; Van
Merriënboer & Sweller, 2005). One is our long-term memory (LTM) the
permanent store of organized information and the other is working memory
(WM), the immediate storage of information, at hand and its processing. The
knowledge structures in LTM are essential for preventing working memory (WM)
overload and for guiding cognitive processes. Accordingly, the role of external
instructional guidance in the process of knowledge elaboration could be described
as providing a substitute for missing LTM structures. Thus, knowledge
elaboration processes require executive guidance that is shared between the
learner and instructional means (or another expert). Specifically, three
processes are identified:
1) The available knowledge base in learner LTM is used to provide
executive guidance in the process of knowledge elaboration;
2) External instructional guidance substitutes for missing LTM schema –
based guidance;
3) Adaptive learning environment based on rapid diagnostic methods
could be effective means for tailoring knowledge elaboration processes
to changing characteristics of individual learners and optimizing
executive guidance at different stages of knowledge elaboration.

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 Elaborating higher-level knowledge requires cognitive resources for dealing
with flexible, non-routine aspects of performance. Acquisition of task specific
skills is an essential condition for the release of such resources. Continuous
balancing of executive guidance is essential for reducing or eliminating other
sources of cognitive over-load by presenting required guidance at the appropriate
time and removing unnecessary redundant support as learner proficiency in a
domain increases. Adaptive learning environments that dynamically tailor levels
of external instructional support to changing individual levels of learner
knowledge could effectively optimize executive guidance during knowledge
elaboration processes (Kalyuga, 2009). Some other researchers (Calcaterra,
Antonietti, & Underwood, 2005) have examined the influence of cognitive style,
spatial orientation and computer expertise on hypertext navigation patterns and
learning outcomes when participants interacted with a hypermedia presentation.
Their results indicated that hypermedia navigation behavior was linked to
computer skills rather than to cognitive style and that learning outcomes were
unaffected by cognitive style or by computer skills. However, learning outcomes
were positively affected by specific search patterns, such as by re-visiting
hypermedia sections and visiting overview sections in the early stages of
hypermedia browsing. Further, navigating overview sections and holistic
processing fostered knowledge representation in the form of maps. These findings
suggest that individual differences can affect hypermedia navigation even though
their role in learning is complex and the impact of cognitive style on learning
outcomes was proved to be lass important than initially predicted. Researchers
like P. Jamieson, J. Dane, and P. C. Lippman (2005) have worked on the issue
‘‘what type of design layouts can promote the diverse ways in which students
create knowledge and develop skills?” ‘‘What would be the future of the
‘classroom’ as a paradigm for teaching and learning settings within the
university’’? They have proposed the notion of ‘learning spaces’ as layered

54
transactional settings for liberating our thinking and our approach to spatial design
in order to achieve dynamic learning environments, and to meet current and future
needs of teachers and ever increasing students.

Problem – based Learning and Pedagogy:

Problem solving in real-world contexts involves multiple ways of knowing
and learning. Thus, intelligence in the real world involves not only learning how
to do things effectively but also more importantly the ability to deal with novelty
and growing our capacity to adapt, select and shape our interactions with the
environment. Knowledge in this new millennium is increasingly characterized by
the creative integration of information and learning from diverse disciplines.
Hence, educators, policy makers and researchers need to be aware of new
approaches of dealing with knowledge and information where problems can be
used innovatively in pedagogies. Problem based learning is an inquiry-based
pedagogy that is best rooted in sound understanding of the psychological
processes of problem solving and the development of cognition. The ability to
learn when plunged into an unfamiliar situation and to adapt positively to
rapidly changing demands is a reality for every worker today. Our students
now-a-days not only need to learn to confront problems as a matter of
necessity but also need to develop a positive mindset of observation and
taking on ‘problems’ as a matter of inquisitiveness to improve and invent
processes and products. Thus, problem solving in real world contexts
involves multiple perspectives and multiple ways of knowing and multi-
disciplinary learning (Tan, 2003). In present times one of the most important
things today is the ability to gain different perspectives, develop multi-
viewpoints, be aware of different worldviews and paradigms and different
ways of reasoning and thinking so that we can highly be flexible in our
thinking in new environments.

55
 Research on memory and knowledge points out that memory is not just
associations, but more importantly the connections and meaningful coherent
structures of learning experiences. Learning is not just about being systematic and
breaking things into small parts but also seeing the big / whole / total picture. The
whole is more meaningful than sum of its parts is not a new concept, but learning
to get an overview first and learning to get into important details more selectively
as and when we need was not the common practice in pedagogy. Now we can
know more about “novice” learners and “expert” learners. We can develop better
learning in individuals by providing opportunities for acquisition of procedures
and skills through dealing with information in a problem space and learning of
general strategies of problem solving. We need to talk aloud – thinking processes
and strategies and not just content or factual knowledge. Moreover, individuals
can be taught meta-cognitive processes and self – regulatory thinking. Initially we
need a structured and organized approach for acquiring fundamental knowledge
and foundations. Our brain and mind are wired in such a way that we learn well
through pattern recognition, observation and imitation. The mind can also be
highly stimulated through novelty – dealing with situation of newness. Often
mind seeks for change and new challenges. This calls for a different perspective
in thinking that would require a more holistic and integrative approach.

Once upon a time, good pedagogy was about making content

knowledge visible to students. This was probably started by behaviorist
psychology where specific behavioral objectives followed by the management
and reinforcement of learning led to the attainment of the desired knowledge
and skills. Teaching involved providing clear explanation to students in
disseminating knowledge and solving problems. In 1960s Jean Piaget’s work
on cognitive development gained momentum which was based on three
interrelated conceptions such as 1) – the relation between action and thought, 2) –
the construction of the cognitive structure, and 3) – the role of self – regulation.

56
According to Piaget, logical thinking and reasoning about complex situations
represent the highest form of cognitive development. In the 1970s, cognitive
psychology gained new ground as interest in “mentalism” grew. Vygotsky (1978)
believed that intelligence begins in the social environment and directs itself inward
and that all psychological processes are in genesis essentially social processes,
initially shared among people. He posited that higher mental processes are
functions of mediated activity. According to Vygotsky’s explanation in his
‘theory of internalization’, in the classroom, an expert teacher may model many
approaches of a problem – solving process for the student. The students will need
to internalize these processes as their own problem – solving activities if they are
to develop effective self – regulation and meta-cognitive abilities. In 1980s the
emphasis was on the “teaching of thinking” as a relatively new concept (Costa &
Lowery, 1989; Resnick, 1987). Staff development in teaching thinking was
stressed, and making teachers’ thinking visible was in many ways the next wave of
good pedagogy. Thus, towards the last decade of 20th century, effective
teaching was characterized by modeling the process of learning so that
students could observe and learn process skills, problem – solving skills, and
thinking skills while acquiring content knowledge. In 1990s, instead of being
concerned about what students failed to learn, Feuerstein, (1990; see Oon-
Seng Tan, 2007) turned his focus to what they could learn, and the inner
structure of cognition. He was more concerned with cognitive processes
pertaining to learning to learn and thinking about thinking. He preferred to
search for a more proximal and optimistic determinant of cognitive
development, the presence of a competent mediator by helping learners
discover their learning potentials and gain awareness of their thinking and
thinking about thinking. His theory of mediated learning experience,
provides the psychological basis for pedagogy that helps to make student’s
thinking visible. The use of challenging learning environments, as in PBL
activities, encourages questioning and overcomes the fear of making mistakes.

57
Pintrich (2000) described self-regulated learning as a process by which
students engage in different strategies to regulate their cognition, motivation,
and behavior, as well as the context. Problem – based learning processes call
for strategies that are goal–directed and self–directed in the context of
problem. Facilitating the acquisition of self–regulated learning strategies is
an important aspect of metacognition. In the 21st century, the knowledge–
based economy fueled by information explosion and accessibility,
globalization and rapid proliferation of technology demands new
competencies, thus calls for a different paradigm in pedagogy. Currently
educators have to confront new ways of looking at knowledge and
participation in the learning process. Pedagogy in the 21st century has to go
beyond making content visible and making teachers thinking visible. Good
pedagogy today is about making students’ thinking visible. The challenge of
education is to design learning environments and processes where students’
ways of thinking and knowing are manifested in active collaborative, self–
regulated, and self directed learning. The role of the teacher is to enable
students to recognize the state, repertoire, and depth of various dimensions of
their thinking and to sharpen their abilities to deal with real world problems.
The “Visibility” of students’ cognition is a prerequisite for effective mediation
and facilitation (O.S. Tan, 2007). Thus, the progressive challenges of
pedagogy can be summed up as –
a) Making content knowledge visible to learners
b) Making teachers’ thinking visible to learners
c) Making learners’ thinking visible to themselves, their peers, and the
teacher.
Problem – based learning focuses on all the above – mentioned
challenges. PBL process embraces / incorporates progressive active learning,
learner – centred approach and the use of metacognition and self –regulation in the
context of real world or simulated complex problems. It is a pedagogy based on

58
constructivism in which realistic problems are used in conjunction with the design
of a learning environment where inquiry activities, self – directed learning,
information mining, dialogue and collaborative problem solving are incorporated
(Tan, 2004a). PBL has certain characteristics as following (Tan 2003, 2005).

i) Use of real – world problem as the starting point of learning, which

calls for multiple perspectives.
ii) Self – directed learning is primary.
iii) The problem calls for identification of learning needs and new areas
of learning.
iv) Harnessing of a variety of knowledge sources and the use and
evaluation of information resources are essential PBL processes.
v) Learning is collaborative, communicative and cooperative.
vi) Development of inquiry and problem – solving skills is as important
as content knowledge acquisition for the solution of the problem.
vii) PBL tutor facilitates and coaches through questioning and cognitive
coaching.
viii) Closure in the PBL process includes synthesis and integration of
learning and concludes with an evaluation and review of the learner’s
experience and the learning processes.

Research in PBL approaches found that students trained in PBL were

more likely to use versatile and meaningful approaches to studying,
compared to non – PBL students (Major and Palmer, 2001). PBL create an
intrinsic interest and enhance self – directed learning skills (Morrison, 2004).
By reflecting upon prior learning, students are able to analyze and synthesize
the contextual information, acquire further knowledge and assimilate it into
their existing knowledge base (Nelson et al., 2004).

59
 Recent, research on student-centred learning and its pedagogical implications
revealed that good teaching should be understood not as a set of performance
skills which may only be opportunistically related to students’ extant
conceptualizations , but as the locus through which students confront their own
epistemic beliefs also. In addition to this, teaching practices at higher education
must focus explicitly on the difficult issue of what counts as evidence in order to
boost students’ reasoning ability/process. Thus, if student-centred learning is to
grow, students need to be socialized to ask genuine questions and to rely on
themselves and their peers as resources in solving the problems they identify.
Therefore, pedagogical approaches which support this kind of endeavour, must
determine / appraise the extent to which student – centred discussion activities or
hands – on learning activities are purposefully / intentionally included in the
pedagogical practices of teachers who genuinely subscribe to a constructivist view
of learning (Maclellan & Soden, 2004). In addition to this research and theory
into cognitive load and technology – enhanced learning suggests that complex
information environments may well impose a barrier on student learning.
However, teachers have the capacity to mitigate against cognitive load through
the way they prepare and support students engaging with complex information
environments. Thus, learning is enhanced when integrating pedagogies are
employed to mitigate against high–load information environments (Bahr & Bahr,
2009). This suggests that a mature policy framework for ICTs in education needs
to be considered for the development of pedagogical practices as well as
professional competencies to effectively design and integrate technologies for
learning. Of course, this enhanced teaching practice or professional competency is
dependent upon teachers problematizing the ways and contexts in which they
learn and make sense of that practice. In this knowledge – based society self study
and lifelong learning have been strongly advocated as the cornerstone of effective
professional practice (Clarke & Erickson, 2007), and instrumental in strengthening

60
the relationship between teaching and learning irrespective of developmental
stages and levels of education.

Conclusion: -
From the above discussion, it can be concluded that insightful, flexible,
inventive, and breakthrough thinking develops best when people are immersed in
solving a problem over an extended period of time. The pedagogy of PBL helps to
make visible or explicit the thinking as well as the richness of the cognitive
structuring and the processes involved. Moreover, in order to boost the
technology enhanced student–centred learning, teachers should genuinely
subscribe to a constructive view of learning by intentionally incorporating various
student-centred discussion/participatory activities and an optimum level of ICT
based hands-on learning activities in pedagogical practices. At last, but not the
least our teachers have to constantly engage themselves in self-study and life-long
learning in order to achieve professional competency and academic excellence.

61
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Submitted by

DR. ATASI MOHANTY

CENTRE FOR EDUCATIONAL TECHNOLOGY
I.I.T. KHARAGPUR, INDIA

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