Pedagogy of Higher Education:

topsalmonΤεχνίτη Νοημοσύνη και Ρομποτική

23 Φεβ 2014 (πριν από 7 χρόνια και 10 μήνες)

773 εμφανίσεις


Pedagogy of Higher Education:
Research Review

Under the MHRD Project

“National Mission in Education through Information
and Communication Technologies (ICT)


Cognition, Affection and

Conation: Implications for
Pedagogical Issues in Highe
r 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 neuros
This is the science of mind which is concerned
with mental phenomena like perception, thought, learning, understanding

. 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.
became a powerful
research tool. We can not observe the mental processes directly but can simulat
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 informati
processing system

that is,

system which

stores, retrieves, transforms and transmits

However, at the very outset cognitive science encounters a deeply
philosophical issue

the ‘mind
body’ proble
, 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


What things really exist in the


What is their essential nature?


Primarily there are two theories which attempt to answer these questions:


‘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.


The ‘
Dualist Theory’
, on the
other hand

claims that mental processes
constitute a distinct kind of phenomenon that is essentially non
in nature.

Subsequently, both ‘


in philosophy play


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”. W
hat is common among

the researchers across the var
ious contributing
ciplines is

the notion that

the processes that occur during cognition can be

by some type


. The nature of
that specific representation depends on

the discipline; such as, philos
ophers rely
on formal lo
gic, 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 drive
n, 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 process

However, the methods employed by cognitive scientists vary greatly. Like
linguists (in Linguistics) are most concerned with developing formal systems of


syntax, semantics, phonetics and pragmatics (discourse & cognitive approach),
and their work
typically consists of comparing sentences and utterances.

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 manipulation
s, the amount of time it takes a
subject to perform a task, and
the various strate
gies 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 di
and methodologies, but researchers across
this field seek to answer the sa
fundamental question: “
how are information


represented in the

Knowledge, Understanding and Cognition:

So far as knowledge, cognition, unde
rstanding 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 proposi
tions, which is usually kept in
libraries and computer data banks. In another sense,

knowledge means
states of mind, expertise, learned abilities, located in people and
especially deals with their ability to perform in well informed ways. T
knowledge is also the process of knowing and understanding, conceived of as

realized ability, to perform adequately in relation to one
’s personal

and states of affairs
. This makes cognition the sa
me process as


knowing and understanding t
hat 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

one has realized the ability to perform

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

as 1. Level 1 (

) p
conventional knowing (A
lpha state); 2


) Level 2, c
onventional knowing
(Beta state); 3

Level 3 (

) p


knowing (Theta state).

level 1, in pre
conventional knowing stage, the individual experiences a high
degree of disorgan
ization, 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
onal) requires exploration,
inquiry, and creativity, so that one breaks new ground and forms new
standards of performance that extend beyond the

conventions of Level

. In addition to these there are also forms of knowing.
At least
six forms


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, reasonin
g 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 emotio
nal 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 o
nes awareness or consciousness.


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 bl
ocking out pain

onative performances are acts of volition or will.
Conation is the state of mind
of having purpose

and conative knowing is choosing or

to perform in
relation to some set of circumstances or state of affairs. It is a state of k

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

physical, physiological as
well as lin
guistic knowing.

Understanding is closely related to knowing, especially linguistic

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 state
s 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 comprehensi
. 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 th
ree 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
ning, 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 o
f words) and another word (or set of words).
Enunciation is saying or making a pronouncement about something and


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


affairs through anticipation, prescription and intervention
. As
understanding develops through to the two higher levels, the capa
city to make well
informed judg
ments about something also develops. The realized abilities to
describe, explain predict a
nd 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 func
tioning 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 confla
tions of the elemental/primary categories of (the above
mentioned) three levels and six forms of knowing and the three levels of
rstanding. For example,
Bloom e
t al. (1956) and
Krathwohl et.
l. (1956)

used the categories of cognitive, affective
and psychomotor domains to

classify a
bilities 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, synthesiz
e and
evaluate states of affairs by means of signifying meaning with symbols or using
language. Recalling in Bloom

terms is an instance of understanding at the
‘prehension’ level. Bloom et al. characterize comprehending as the ability to


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 app
ropriately in new situations without help, direction or prompting.
Bloom’s analyzing, synthesizing and evaluating are instances of understanding at
the level of comprehension.
arned psychomotor abilities are

knowing in
relation to physical performances

and physiological performances. But
psychomotor abilities also include linguistic (conceptual), imaginal


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)
. P
sychomotor knowing, in this way is
actually a complex combination of all these physical, linguistic, emotional,
imaginal, co
native and physi
ological 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
ated attitude about
Affective knowing thus is also a
complex phenomenon of linguistic, emotional, imaginal and conative

Gagne (1977)

offers the
categories of cognition as a scheme for
classification of learned abilities such as intellectual skills


verbal information, motor skills, and attitudes
. Intellectual skills
are instances of linguistic knowing and Gagne categorizes these in a hierarchy of


complex to more complex: signal learning, stimulus
response learning,
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

ponse 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


learning, rule learning, and problem

solving learning function at the level of
comprehension. The progression in understanding is from denotative to
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 combinatio
n of
linguistic, emotional, imaginal, physical, physiological and conative knowing.
Piaget (1971)

has classified level of
understanding into fo
ur categories like


motor, 2) pre

operational, 3) c

operational and
l 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
1964) and he has conceived the categories as 1)

2) iconic, and 3)

ymbolic 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 categorie
s 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 under
standing is
linguistic knowing at all of its levels
. Biggs and Collis (1982)

classified the
between developmental stages and learning outcomes. They addressed
the problem of what learning outcomes were possible, and they conc
eived of

1) prestructural 2) unistructural, 3) multistructural 4) r

5) extended abstract
. Prestructural is pre
conventional linguistic knowing
(level 1

alpha stage). It is also understanding at the level of pre


Unistructural, multistructural, relational, and extended abstract are instantiations
of conventional linguistic knowing (level 2

Beta stage).
Also unistructural

multistructural, and relational are instances of underst
anding 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, G
Bruner, Biggs and Collis as well as Krathwohl et al. ha
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 spec
ifying 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 le
vels of understanding.
Out of

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
ative 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

views rule the cognitive era.

They claim that cognitive
a does not constitute merely the behavioral (stimulus

patterns of a ‘black box’. Constitutive reality agent is today considered highly
relevant for the scientific study of mind.
There a
re predominantly two kinds of
mps: those who believe th
at cognitive faculties are completely specified by


the innate biological reality (Noam Chomsky, Jerry Fodor
, see Nagarjuna G.,
Review Talks

), and those who believe that they develop during
ontogeny based on incompletely specified ‘embryological’ real
ity (
, Susan Carey, Alison Gopnik
, see Nagarjuna G., Review Talks

A striking observation made by cognitive developmental psychologists based
on experimental findings that ‘language is inst
inctive and peculiarly human’.
A leading dev
elopmental psychologist
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 grad
and recurring reencoding of more or less inaccessible (encapsulated) implicit
representations into explicit accessible

representations leads to behavioral


There are very few scholars who believe that cognition is only human,
but often it is a
lso 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., Mer
lin Donald’s three stages of the evolution of
culture and cognition; & Peter Gardenfors’s account of How Homo became
, see
Nagarjuna G., Review Talks

has revealed that most of the
peculiarly human characteristics are strongly correlated to th
e social fabric of
life rather than genetic, neuro
physiological domain
Evidence is
gradually accumulating to suggest that the larger size of human brain

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 t
o enhance much needed memory
capacity for storing cultural heritage, and also for detached processing of
information: explaining thinking. There are two interdependent but



evolving inheritance mechan

biological and social
. The
e of human beings cannot be understood without delineating the two. Many
leading cognitive psychologists (e.g. Alison Gopnik
, see
Nagarjuna G., Review


today believe in a strong working hypothesis called
: theory

theory. According to this vi
ew 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
The above author argues that even infants in the crib are litt
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.
form o
f 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 huma
n 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, incl
uding 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 bo


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.
sum, cultural evolution has a comprehensive influence


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



processing approach,



Connectionist approach

The information

processing approach is sq
uarely 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
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

human intelligence
or cognitive process
. However, there are
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 r
elates 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. Final
ly, the last is concerned with the knowledge base that enters
in to any kind of task solution.

These five different issues are a common


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


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 neuro
computing and therefore
is very much

neuronally inspir
. Actually this is an offshoot of the association
theory of learning

(Thorndike’s Connectionism, 1913).
This theory suggests
that the most rudime
ntary type of learning occurs in

the formation of
associations or connections between sensory experience
and neural impulses
hen 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

between units that resemble neuron
s (Schneider, 1987).
The basic
elements in connectionist models are nodes and links. These are also called
units and


The nodes a
re 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 situation
s; 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 connec
t 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

links. The connectionists’

models assume many of the principles o
f learning
theories b
ased on behavioristic approach (i.e., Hull, To
man, Gutherie


& Bower, 1956
etc.). Even though the connectionist models have not
really worked on spatial
temporal network, the recent advancements in


formulating such networks show the potential

of the connectionist

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 i
providing models for problem solving and other intelligent behaviors in terms
of artificial intelligence following the pioneering work of Newell and Simon

Thus, in the last half
century, developments in computer science,
particularly ‘Artif
icial 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 an
d 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
systems that are ma
ndatory, 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 an
d non
. 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


arguing that cross
representation of cognitive dimensions, which is essential for
the formation of concepts of any kind, is totally impossible with encapsulation.


Research on computational m

in c
tive s
cience has two
different pursuits; o
ne is computational ‘cognitive models’, the software
systems that propose testable hypotheses,
highlight the inadequacies of
rent theories, and predict the behavior of people in simulations. The
second pursuit is the development of ‘inferential theorie
s’, 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 ‘Commonsen
se Psychology’, also
‘naïve psychol
ogical 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 c
ognition). 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, m
etacognition, and introspection

Cohen et al., 2000). Two competing theories of ‘Theory of mind
reasoning’ have been proposed. One, the advocates of ‘
Theory of Th

argued that

Theory of Mind

relies on tacit inferential theories about

mental states and processes (inferential theories), which are manipulated using
more general inferential mechanisms

& Meltzoff,

1997; Nichols & Stich,
02). The proponents of ‘
Simulation Theory’

argue that

Theory of Mind

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 simul
ate the mental states and processes of
other people

(Goldman, 2000).


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 ‘

based memory retrieval’

has been authored
by Fo
bus et al. (1994) to justify its utility in memory processes. In this two
model, a target situation in workin
g 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

representation, resulting in a number of candid
ate retrievals. In the second stage,
attempts are to identify deep structural alignments between the target and these
candidates using a graph

comparison algorithm

ased 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
n 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
term memory. Its processes are initiated when a target situation is in working
Its role effect on other cognitive processes is the retrieval of base
situations from long
term memor
y into working memory. Gordon and

(2003) developed

formal inferential theory

which explains and encodes a
commonsense view of how people think hu
man 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
of this theory are as follows:


oncepts 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


involve moving co
ncepts from one part to the other.


memory the concepts have varying degrees of accessibility, but there is some
threshold beyond which they cannot be retrieved into the focus of attention.


concepts that are in memo
ry 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.


people can
attempt mental


(e.g. retrieving), but these action
s may fail or be successful.

Remember and forget

Remembering can be defined as succeeding in
retrieving a concept from memory, while forgetting is when a concept becomes


A precondition for executing actions in a
lan at a particular
time is that a person remembers to do it, retrieving the action
from memory before its execution.


People often repress
concepts that they find unpleasant, causing these concepts to become inaccessible.
Then again Hobbs

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 we
authored as sets of axioms in

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


䭮ow汥摧攠 慮搠 i湦敲敮捥c m潤敬

(Managing knowledge)
describes how people
maintain and update their beliefs
in the face
of new
information (e.g., Byrne & Walsh, 2002).

Similarity judgment model

explains how people judge things to be similar, different, or analogous (e.g.,


Gentner & Markman, 1997).

Memory Model

says about memory storage
and retrieva
l (see Conway, 1997).


states about emotional
appraisal and coping strategies (e.g., Gratch & Marsella, 2004).


(including Execution envisionment) Model explains how people
reason about causality,
ossibility, and inter
vention in real and imagined worlds
(e.g., Sloman & Lagnado, 2005).


(including causes of
failure) narrates the process of generating explanations for events and states with
unknown causes (e.g., Leake, 1995).



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.

周敯特 潦

explains how people reason about the mental states and
cesses of other people and themselves.


etection Model

analyses how people identify threats and opportunities that may impact the
achievement of their goals (e.g., Pryor & Collins, 1992).


describe how people priori
tize and reconsider the goals that
they choose to pursue (e.g., Schut et al., 2004).


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).

䑥獩杮 䵯M敬

shows how
people develop plans for the creation or
configuration of an artifact, process

Scheduling Model

explains how people reason about time
and select when they will do the plans that they intend to do.


describes how people identify choices and make decisions (e.g.,
Zachary et al., 1998).

䵯湩M潲楮o 䵯摥d

explains how people divide their
attention in ways th
at enable them to wait for, check for, and react to events in the
world and in

their minds (e.g., Atkin & Cohe
n, 1996).


deals with execution modalities, repetitive execution, body interaction,
plan following, observation of exec
ution and defines the way

that people put their
plans in
to action and control their own behavior (e.g., Stein, 1997). However, it is


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

Cognition and


Since Fla
vell’s (1971) coining of the ter
m ‘Metamemory’ many have
investigated the phenomenon surrounding co
gnition 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 metacogni
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 inter
nal 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

person variables

that deal with the individual and others (for example, cognitive psychologists can
recall many facts about cognition, whereas most people cannot),


which concern the type of mental activity (e.g., it is more difficult to
remember non
sense words than familiar words), and

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
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 inform

processing framework for integr
ating and
better understanding m
etacognition and metamemory. Their model is based on
three basic principles: 1)

r潣敳獥猠慲攠s灬楴p楮瑯 慮a潢橥捴o



(cognition), and a meta
level (Metacognition); 2)


dynamic model of the object
level; and 3)

䄠晬fw 潦o楮景牭慴楯渠晲fm 瑨e
汥癥l 瑯tt桥h浥瑡
汥癥氠i猠捯c獩摥牥搠m潮楴潲楮o w桥牥慳h楮景i浡瑩潮

flowing from the meta
level to the object
level is considered control.

itoring 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.
level behavior consists of cognitive activities such as
problem solving or memory retrieval.



et al.’s model of ‘
Metacognitive Monitoring and Control of

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.

processes include ease
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, selec
tion 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 instructi
on has made significant p
rogress. Researchers (Forrest
ressley, Mackinnon and Waller, 1985; Garner, 1987) report successful instruction
procedures related to both problem solving and reading comprehension (see also






Ram & Leake, 1995 for related topic in
computer/ cognitive science).

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 st
rd 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 o
ne’s own memory) are
included in the study of metacognition as they are important in self
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

Problem Solving and


Problem solving is one area where a natural fit exists, to computational
theories in ‘Artificial Intelligence’. Concepts su
ch as executive control and
monitoring are important to problem solving in order to manage problem
complexity and to evaluate progress towards goals.

(1979) reports the
earliest experiment on the effects of cognitive monitoring on human problem
ving. 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, Der
ry and her colleagues developed a computer
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.


Davidson, Deuser and Sternberg (1994) from a series of studies show that t
he use
of metacognitive abilities correlate with standard measures of intelligence. In their
experiments on insight problem
they report that, although higher IQ
are slower rather than faster
n analyzing the problems and applying their
sights, 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 s
ubjects are verbalizing their thoughts.
Silent thinking and simple thinking out loud perform equally well. The
is that
attentions of subjects improve

local problem
behavior, whereas metacognitive
attention allows subjec
ts to be flexible
lobally 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 n
ew architecture
for “Companion Cognitive S
ystems” that
employ psychologically plausible models of analogical learning and reasoning
and that maintain self
knowledge in the form of logs


Singh (2005)
has created an architecture called



that supports l
ayers of
metacognitive activities that monitor reasoning in physical, social, and mental
These layers range from the reactive, deliberative, reflective

reflective, and self
conscious to the self
ideals layer. More recently, the
on 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 ove
restimate their visual capabilities but
most interesting, given feedback
on their
errors, they refuse to believe the


evidence “before their eyes”. Brown (1987) has described research into
metacognition as a “many
headed monster of obscure parentage”
. Thi
s 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 sys
tem (in Artificial Intelligence Programs) that reasons specifically
about itself (its knowledge, beliefs and its reasoning process), not one t
hat simply
uses such knowledge.
In the field of education and pedagogy much of the
groundbreaking work in metacog
nition 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 g
rown 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, alo
ng 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 concep
ts in metacognition:

and control (John Dunlosky & Ja
net 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


valued in a culture; a set of skills that make it possible for a person to solve
blems 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

specific abilit
y) is an
early example. In contrast, cognitive theories are both qualitative and quantitative.
Following Spearm
an, and even his predecessor, G
alton, (Jensen, 2006) is perhaps
the chief advocate of gene
ral intelligence or “psychometric g”. His evidence for
‘g’ goes beyond factor analysis and seeks validity in reaction time s
tudies 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”
“Crystallized Intelligence” as advanced by Cattell and Hunt.

Fluid intelligence is
the ability to deal with novel intellectual problem
s, 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 ha
s been proposed comprising verbal,


, and
mage rotation abilities with general intelligence or “g” at its top. But all the
psychometric classification of intelligence has a common weakness, that is

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


Hunt, 2008).

Intelligence as cognitive processing is a common theme for all cognitive
theories of int
elligence. Th
ese 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 domain
s that represent the interaction of the individuals

biological predispositions with the environment and cultural context.
Das et al’s


(1994) PASS (Planning, Attention, Simultaneous, Successive) theory of
intelligence is a further developmental step in th
is direction. The most recent

theories of intelligence with the cognitive processing (information processing)
approach are, of Gardner’s ‘Th
eory of Multiple Intelligence


archic Theory’ and Das et al’s ‘
PASS Theory’.

The theory of “
ultiple 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 intellige
nce. 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
ngs, and actually doing them, is

the essential characteristic of the
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

Intelligence”, which is still evolving.



comprises: Analytical,



The ‘PASS’ theory of intelligence (Das et al; 1994) proposes that cognition
is organized in three systems and four

processes. The first is the ‘P
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

The third system is the “Information processing
’ system, which employs
imultaneous’ and


processing to encode, transform, and retain
information. Simultaneous processing is engaged when the relationship between
items and their int
egration 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. T
hese four processes are
functions of four areas of the brain
. P


broadly located in the front part
of our brains, the frontal lobe. Attention and arous
al are a function of the frontal
lobe and the lower part of the cortex, although some other parts are also involved


in attention as well. Simultaneous processing and successive processing occur in
the posterior region or the back of the brain. Simultaneo
us 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 S

based on their PASS mod
el 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

autism, attention
deficit etc, as well as cognitive changes in ageing

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


them (connectionists approa
ch). 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 e
xpresses 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 i
s a multifaceted phenomenon. People are creative by virtue of a
combination of intellectual, personality and motivational attributes whose outcome
also depends on t
he environment. R.J. Sternberg

says (1998)

Creativity can take
many forms and come in man
y 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

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, 20
01; Baltes & Staudinger,
2000). Research findings have shown that
both creativity and wisdom show
much evidence of open
s and complexity (including intelligence).
Originality being more salien
tly associated with creativity and meaning

making with wisdom; furthermore, ambition, autonomy, and perseverance
more associated with creativity and benevolence with wisdom

elson &
Srivastava, 2002
). However, cognitive

affective vitality is an e
component of both creativity and wisdom.


In cognitive science
Terry Dartnall (2007) (author of the book “An
Interaction: Creativity
, Cognition and K
nowledge”) holds the view that an
account of creativity is the ultimate
test for cognitive scie
nce. A s
ystem 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 e
lements 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 thoug
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

Rather, cogni
tion 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 th
em 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 capab
ilities 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 P
rinz and Barsalou

have emphasized
concept acquisi
tion as a form of creativity
. The
representations we form contribute to an ever
growing repertoire of concepts.


They develop an account of
concept acquisition and explore prospects of
constructing computational model of perceptual symbols using current
trategies 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

nsformed. 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 con
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
suggested that a model of perceptual symbols must include mechanisms for
grouping together multimodal symbols. Perceptual symbol systems yield multiple

representations concurrently.

mechanisms convert these
perceptual representation
s in to symbols and group

them together to form concepts
that can be assessed by higher level systems.

Another author Donald M.



advocates for representational
redescription as the explanation for understanding creativity. He holds that

concept of creativity can be better understood as “representations”, that is cases in
which we increase our knowledge by figuring knowledge which we already
Representational re
description hypothesis describes that the mind is
endogenously d
riven to go beyond what behavioral

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, decla
rative knowledge that becomes
le to other procedures, nor

to the system as a whole. This approach to
knowledge gives a description of the cognitive processes behind our thoughts


and the recurring

changes. It is an explication of knowledge, that is

rearrangement or re
representation, which produces new output from old

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


think that
creativity requires explicit representations that
are accessible to and modified by other cognitive proce
sses without need of
external processes.

They believe that
creativity requires the ability to
represent and recursively modify explicit

complex relations in paralle
E. Hummel and Keith J. Holyoak

creativity as m
apping a
problematic si
tuation 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: access
ing a

useful potential source
, 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

which fulfils some essential requirements for creativity.

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 creativ


Derek Partridge and John Rowe


have presented
a computational
study of the nature and process of creativity, the model called “GENESIS”
also features a representationally fluid emergent memory mechanism.

two authors primarily fo
cus 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 solv
ing problems and makes sens
e of the world while output
creativity helps us when we deploy our knowledge to create something on our

Thus the mechanisms and inner capabilities that are put int
o place during
the input creativity phase are re
deployed in the output creativity phase. On the
other hand, Chris Thornton


has tried to carry out a logical analysis of the
operational characteristics of basic learning procedures and to use this an
alysis 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 proce
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 wit
hin 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


have tried to im
plement 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 c
omputer. 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.


Author Richard McDonough

suggests that


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 intern
al to the organism. Emergent
materialism is the view that life an
d mind are emergent characteristics of matter,
but emergence is neither a necessary nor sufficient condition for creativity.
Author Terry Dartnall


suggests that currently cognitive science needs to get
lessons from classical empir
icism 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 s
ummary of the cur

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
out a domain of
, 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 nov
el and appropriate alternatives that are both novel and appropriate


alternatives that are both novel and appropriate ( Lubart, 1994). With regard to the
relationship between i
ntelligence and creativity a number

of views have come up,

‘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 lik
‘Emotional Intelligence’ and
‘Emotional Creativity’ have emerged
. Emotional
intelligence (EI), is defined as the ability to percei
ve 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 r
equire reasoning skills, and analytical skills. Parallel

to EI,
one new domain of creativity has been introduced called ‘Emotional

(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 (Ma
yer, Salovey, Caruso, &
os, 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? Stu
dies 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 narrati
ve). One


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).

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 ha
ve been

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 & Salov
ey, 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 ad
equately 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 e
xpress novel and
effective blends of emotions. There are three

for EC: novelty (i.e., the
variations of common emotions and generation of new emotions), effectiven
(i.e., appropriateness for t
he situation or beneficial consequences), and authe
(i.e., honest expression of one’s experiences and values). Another condition for
EC is emotional preparedn
ess, which reflects a person’
s understanding of
emotions and willingness to expl
ore emotions (Averill, 1999 a,
1999 b). While EI
requires a
nalytical 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


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 ar
e indeed distinct

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
with cognitive intelligence, and
it was highly correlated with
penness 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 crea
tive 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 o
n 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 involveme
nt 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
otional 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
descriptions of problem so
lving 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 wou
ld be crucial to develop a variety of
different criteria for creativity.


When we consider
creativity as a process and try to translate it into

arning process
, automatically
rrance’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 expect
ations 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 purp
ose 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 defer
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 th
at 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 throu
gh 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 l
than three mental processes: Cognition (thinking), Affectation (feeling) and
Conation (willingness to act). Researchers, have found that ‘
Learning Model’ (ILM) gives an opportunity to teachers, learners as well as
policy makers (a means) t
o identify how each student processes information


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 ea
ch learner

also found that
different learners

in different settings and therefore not
all learners learn best in a non
traditional setting and vice versa

1999). More recently, the researchers such
as Vanhear Jacqueline and Pace P
l 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

Empirical resea
rch has already shown th
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