Information – processing approach, and

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 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 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.

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).

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: - 1à 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 involve moving concepts from one part to the other. 2à 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. 3à 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. 4à Trying and succeeding – people can attempt mental actions (e.g. retrieving), but these actions may fail or be successful. 5à Remember and forget – Remembering can be defined as succeeding in retrieving a concept from memory, while forgetting is when a concept becomes inaccessible. 6à 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. 7à 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. à 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. à Similarity judgment model – explains how people judge things to be similar, different, or analogous (e.g., Gentner & Markman, 1997). 3. à Memory Model says about memory storage and retrieval (see Conway, 1997). 4. à Emotion Model states about emotional appraisal and coping strategies (e.g., Gratch & Marsella, 2004). 5. à 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. à Explanation Model (including causes of failure) narrates the process of generating explanations for events and states with unknown causes (e.g., Leake, 1995). 7. à 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. à Theory of Mind Reasoning Model – explains how people reason about the mental states and processes of other people and themselves. 9. à Threat Detection Model analyses how people identify threats and opportunities that may impact the achievement of their goals (e.g., Pryor & Collins, 1992). 10. à Goal Management Model describe how people prioritize and reconsider the goals that they choose to pursue (e.g., Schut et al., 2004). 11. à 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. à Design Model shows how people develop plans for the creation or configuration of an artifact, process information. 13. à Scheduling Model explains how people reason about time and select when they will do the plans that they intend to do. 14. à Decision Making Model describes how people identify choices and make decisions (e.g., Zachary et al., 1998). 15. à 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. à 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 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) à person variables that deal with the individual and others (for example, cognitive psychologists can recall many facts about cognition, whereas most people cannot), 2) à 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) à 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) à Cognitive processes are split into an object – level (cognition), and a meta-level (Metacognition); 2) à The Meta-level contains a dynamic model of the object-level; and 3) à 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

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 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, 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 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 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 (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.

(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 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 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.

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. 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 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 paralle l. 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.

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.

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 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 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 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.

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, 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 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 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 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 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:

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