MAP608 Cognitive Psychology_Draft 2-converted-converted

But, if you have no knowledge of how programming languages work at all, then you're out of luck. No amount of playing around with random bits of text is likely to get you to a working computer program. So when there are many, even unlimited, options, other problem-solving methods are sometimes best. Difference Reduction Difference reduction requires you to break down a large task into smaller steps. The first thing you do is ask yourself what step will take you from where you are to as close as possible to the final goal. You take that step and repeat the process until you finally reach the goal. For example, if you need to get from the street to the inside of your home, you might have to: 1. Unlock the gate 2. Swing open the gate 3. Walk to the house 4. Unlock the house 5. Open the door 6. Enter your home Sometimes difference reduction is not the quickest way to get to your goal - sometimes you have to take one step backwards to take a step forwards. For example, the step that will get you closest to being inside your home might be to walk to the door. But if you're locked out of the house, you might first need to visit the neighbor for the spare key. Means-Ends Analysis With means-ends analysis you compare your current situation and the situation you want to arrive at, identify the most significant difference between those two situations, and then create a sub-goal to remove that difference. If you want to work as a doctor, the most significant difference between where you are and where you want to be is having a job as a doctor - that's what would have to be different in your life to make that happen. Gradually, you'll come to the conclusion that you don't yet have the knowledge or the degree necessary, but the biggest difference in your actual life is the job. Means-ends analysis is easier to explain using examples. Let's say that your house is messy. Your goal is for it to be tidy. Here are the steps you would go through to complete a means- ends analysis: • Step 1: What's the biggest difference between these two situations? Nothing is where it should be. • Step 2: What would change this? Moving objects to where they belong, throwing them away, or hiding them so they are no longer in view. 147 CU IDOL SELF LEARNING MATERIAL (SLM)

• Step 3: You decide to move the objects to where they belong. But not everything has particular place that it belongs, like the nice vase your mother just bought you. So you create a new sub-goal: you want each object in your house to have a place it belongs. • Step 4: What's the biggest difference between these two situations? There isn't enough storage space for everything. • Step 5: What would cause there to be enough storage space? Reorganizing things to make a space for each object, buying a storage box or cupboard, or moving house. • Step 6: You decide to buy a new storage box. But you don't have any money, so you create a new sub-goal: you want to have some money. • Step 7: Now, what's the biggest difference between these two situations? And on and on the process continues. While this sounds complicated, means-end analysis is something you do all the time quickly, without realizing it. PROBLEM-SOLVING STRATEGIES Algorithms: The step-by-step procedure involved in figuring out the correct answer to any problem is called algorithm. The step by step procedure involved in solving a mathematical problem using math formula is a perfect example of a problem-solving algorithm. Algorithm is the strategy that results in accurate answer; however, it’s not always practical. The strategy is highly time consuming, and involves taking lots of steps. For instance, attempting to open a door lock using algorithm to find out the possible number combinations would take a really long time. An algorithm is a step-by-step procedure that will always produce a correct solution. A mathematical formula is a good example of a problem-solving algorithm. While an algorithm guarantees an accurate answer, it is not always the best approach to problem-solving. This strategy is not practical for many situations because it can be so time-consuming. For example, if you were trying to figure out all of the possible number combinations to a lock using an algorithm, it would take a very long time! Heuristics: A heuristic is a mental rule-of-thumb strategy that may or may not work in certain situations. Unlike algorithms, heuristics do not always guarantee a correct solution. However, using this problem-solving strategy does allow people to simplify complex problems and reduce the total number of possible solutions to a more manageable set. Heuristics refers to mental strategy based on rule-of thumb. There is no guarantee that it will always work out to produce the best solution. However, the rule of thumb strategy does help to simplify complex problems by narrowing the possible solutions. It makes it easier to reach the correct solution using other strategies. 148 CU IDOL SELF LEARNING MATERIAL (SLM)

Heuristic strategy of problem solving can also be referred to as the mental shortcut. For instance, you need to reach the other part of the city in a limited amount of time. You’ll obviously seek for the shortest route and means of transportation. The rule of thumb allows you to make up your mind about the fastest route depending on your past commutes. You might choose subway instead of hiring a cab. Trial and Error: A trial-and-error approach to problem-solving involves trying a number of different solutions and ruling out those that do not work. This approach can be a good option if you have a very limited number of options available. If there are many different choices, you are better off narrowing down the possible options using another problem-solving technique before attempting trial-and-error. Trial and error strategy is the approach that deals with trying a number of different solutions and ruling out the ones that do not work. Approaching this strategy as the first method in an attempt to solve any problem can be highly time-consuming. So, it’s best to use this strategy as a follow up to figure out the best possible solution, after you have narrowed down the possible number of solution using other techniques. For instance, you’re trying to open a lock. Trying to enter every possible combination directly onto the lock for Trial-and-Error method can be highly time-consuming. Instead, if you’ve narrowed down the possible combinations to 20, you’ll have a much easier time solving the particular problem. Insight: In some cases, the solution to a problem can appear as a sudden insight. According to researchers, insight can occur because you realize that the problem is actually similar to something that you have dealt with in the past, but in most cases, the underlying mental processes that lead to insight happen outside of awareness. Insight is something that just occurs suddenly. Researchers suggest that insight can occur if you’ve dealt with similar problems in the past. For instance, Knowing that you’ve solved a particular algebra question in the past will make it much easier for you to solve the similar questions at present. However, it’s not always necessary that the mental processes be related with past problems. In fact, most cases of mental processes leading to insight happen outside of consciousness. FACTORS AFFECTING IN PROBLEM-SOLVING Of course, problem-solving is not a flawless process. There are a number of different obstacles that can interfere with our ability to solve a problem quickly and efficiently. Researchers have described a number of these mental obstacles, which include functional fixedness, irrelevant information, and assumptions. 149 CU IDOL SELF LEARNING MATERIAL (SLM)

• Functional Fixedness: This term refers to the tendency to view problems only in their customary manner Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution. • Irrelevant or Misleading Information: When you are trying to solve a problem, it is important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. When a problem is very complex, the easier it becomes to focus on misleading or irrelevant information. • Assumptions: When dealing with a problem, people often make assumptions about the constraints and obstacles that prevent certain solutions. • Mental Set: Another common problem-solving obstacle is known as a mental set, which is the tendency people have to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can often work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions. SUMMARY • Problem solving involves mentally working to overcome obstacles that stand in the way of reaching a goal. The key steps of problem solving are problem identification, problem definition and representation, strategy construction, organization of information, allocation of resources, monitoring, and evaluation. • In everyday experiences, these steps may be implemented very flexibly. Various steps may be repeated, may occur out of sequence, or may be implemented interactively. • Although well-structured problems may have clear paths to solution, the route to solution still may be difficult to follow. Some well-structured problems can be solved using algorithms. They may be tedious to implement but are likely to lead to an accurate solution if applicable to a given problem. • Computers are likely to use algorithmic problem-solving strategies. Humans are more likely to use rather informal heuristics (e.g., means–ends analysis, working forward, working backward, and generate and test) for solving problems. • When ill-structured problems are solved, the choice of an appropriate problem representation powerfully influences the ease of reaching an accurate solution. • Additionally, in solving ill-structured problems, people may need to use more than a heuristic or an algorithmic strategy; insight may be required. • Many ill-structured problems cannot be solved without the benefit of insight. There are several alternative views of how insightful problem solving takes place. 150 CU IDOL SELF LEARNING MATERIAL (SLM)

• According to the Gestaltist and the neo-Gestaltist views, insightful problem solving is a special process. It comprises more than the sum of its parts and may be evidenced by the suddenness of realizing a solution. • A mental set (also termed entrenchment) is a strategy that has worked in the past but that does not work for a particular problem that needs to be solved in the present. A particular type of mental set is functional fixedness. It involves the inability to see that something that is known to have a particular use also may be used for serving other purposes. • Transfer may be either positive or negative. It refers to the carryover of problem-solving skills from one problem or kind of problem to another. • Positive transfer across isomorphic problems rarely occurs spontaneously, particularly if the problems appear to be different in content or in context. • Incubation follows a period of intensive work on a problem. It involves laying a problem to rest for a while and then returning to it. In this way, subconscious work can continue on the problem while the problem is consciously ignored. KEY WORDS/ ABBREVIATIONS • Heuristic-A rule of thumb for making decisions of a particular kind whichusually works but does not guarantee a correct solution. LEARNING ACTIVITY 1. Explain in detail the problem solving cycle. 2. What are the different methods and strategies of problem solving? UNIT END QUESTIONS (MCQS AND DESCRIPTIVE) A. Descriptive Questions 1. At some point or another, we all face with situations in which, we have solve problems. Explain one of such situation and your reaction towards solving that situation. 2. We engage in a systematic process when solving our problems. What are the steps involved in problem solving cycle? 151 CU IDOL SELF LEARNING MATERIAL (SLM)

3. Elaborate some of the types of problems as mentioned in cognitive psychology. 4. Some internal and external factors impact the way in which we approach and solve problems. Elaborate on some of these factors. 5. Explain some of the commonly used strategies for problem solving. 6. Means-ends analysis is a common way we use to solve problems. Explain the process and steps involved in means-ends analysis. B. Multiple Choice Questions (MCQs) . 1. Emphasizing what comes to mind firstor most readily/quickly is known as (a)Heuristic (b)Critical Thinking (c)Intuitive Thought (d)Confirmation Bias 2. is one that has a clear goal or solution, and problem solving strategies are easily developed. (a) Heuristic (b) Well defined problem (c)Intuitive Thought (d)Confirmation Bias 3. are the steps we use to find solutions to problems and issues. (a)Heuristic (b) Well defined problem (c) Problem Solving Methods (d) Problem Solving Strategies 4. The step-by-step procedure involved in figuring out the correct answer to any problem is called (a) Heuristic (b) Algorithms 152 CU IDOL SELF LEARNING MATERIAL (SLM)

(c) Trial and Error Method (d) Problem Solving Strategies 5. involves trying a number of different solutions and ruling out those that do not work. (a) Heuristic (b) Algorithm (c) Problem Solving Methods (d) Trial And Error Approach Answer 1 (a) 2 (b) 3 (c) 4 (b) 5 (d) REFERENCES • Kellogg, R. T. (2003). Cognitive Psychology (2nd ed.). California, USA.: Sage Publications • Neisser, U. (2014). Cognitive Psychology (Classic ed.). New York: Psychology Press • Eysenck, M. W. and Keane, M. T. (2015) Cognitive Psychology: A Student's Handbook (7th ed.). New York: PsychologyPress • Galotti, K.M. (2008), Cognitive Psychology: In and out of the Laboratory.Delhi: Thomson. • Sternberg, R. J. & Sternberg, K. (2012). Cognitive psychology (6th ed.).USA: Wadsworth, Cengage Learning. • Groome, D. (2014). An Introduction to Cognitive Psychology: Processes and Disorders. (3rd ed.). New York: PsychologyPress. • Mazur, J.E. (1986), Learning and Behaviors. (6th ed.). Englewood Cliffs, NewJersey: Prentice Hall. • Galotti, K.M. (1999), Cognitive Psychology: In and Outside Laboratory. Mumbai: Thomson Asia. • Frensch, P. A., & Sternberg, R. J. (1989). Expertise and intelligent thinking: When is it worse to know better? In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence, Vol. 5 (p. 157–188). Lawrence Erlbaum Associates, Inc. 153 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT 9 CONCEPT FORMATION Structure 9.0 Learning Objectives Introduction Nature of Concepts Concept Formation Experimental Studies Piaget’s Observation Formation of Concepts Summary Key Words/ Abbreviations Learning Activity Unit End Questions (MCQs and Descriptive) References LEARNING OBJECTIVES After this unit, you will be able to, • Explain the nature of concepts in cognitive psychology • Identify simple and complex concepts and explain the process by which we make concepts • Describe the process of concept formation • Outline the experimental studies to understand the process of forming concepts • With the help of experiments, describe the steps of making rules and creating concepts from those rules • Describe Jean Piaget’s observation related to concepts • Outline the steps involved in the process of concept formation INTRODUCTION Human beings are capable of abstract thinking. Most of the classifications that we make seem to be concrete discriminations. For example, people may use the same term in a discriminative or conceptual way. A child might use the term policeman in discriminating a man who wears a distinctive uniform, while a lawyer may use the term to represent a civil servant charged with enforcing criminal codes. In practice, people seem to think in ways that combine abstractness and concreteness. They also may blend class membership with assignment along a scale—e.g., such concepts as 154 CU IDOL SELF LEARNING MATERIAL (SLM)

leadership, an abstract quality that people are said to exhibit in varying degrees. The same would apply to vivacity, avarice, and other personality traits. NATURE OF CONCEPTS A concept is a generalization that helps to organize information into categories. For example, the concept \"square\" is used to describe those things that have four equal sides and four right angles. Thus, the concept categorizes things whose properties meet the set requirements. The way young children learn concepts has been studied in experimental situations using so- called artificial concepts such as \"square.\" In contrast, real-life, or natural, concepts have characteristic rather than defining features. For example, a robin would be a prototypical or \"good\" example of the concept \"bird.\" A penguin lacks an important defining feature of this category—flight, and thus is not as strong an example of a \"bird.\" Similarly, for many children the concept \"house\" represents a squarish structure with walls, windows, and a chimney that provides shelter. In later development, the child's concept of house would be expanded to include nontypical examples, such as \"teepee\" or \"igloo,\" both of which have some but not all of the prototypical characteristics that the children have learned for this concept. Natural concepts are often learned through the use of prototypes, highly typical examples of a category— like the robin cited above. The other major method of concept learning is through the trial-and-error method of testing hypotheses. People will guess or assume that a certain item is an instance of a particular concept; they then learn more about the concept when they see whether their hypothesis is correct or not. People learn simple concepts more readily than complex ones. For example, the easiest concept to learn is one with only a single defining feature. The next easiest is one with multiple features, all of which must be present in every case, known as the conjunctive concept. In conjunctive concepts, and links all the required attributes. For example, the concept square is defined by four sides and four 90-degree angles. It is more difficult to master a so-called disjunctive concept, when either one feature or another must be present. People also learn concepts more easily when they are given positive rather than negative examples of a concept (e.g., shown what it is rather than what it is not). CONCEPT FORMATION Concept formation is a process by which a person learns to sort specific experiences into general rules or classes. With regard to action, a person picks up a particular stone or drives a specific car. With regard to thought, however, a person appears to deal with classes. For instance, one knows that stones (in general) sink and automobiles (as a class) are powered by engines. In other words, these things are considered in a general sense beyond any particular stone or automobile. Awareness of such classes can help guide behavior in new situations. 155 CU IDOL SELF LEARNING MATERIAL (SLM)

Thus two people in a bakery may never have met before, but, if one can be classified as customer and the other as clerk, they tend to behave appropriately. Similarly, many people are able to drive almost any automobile by knowing how to drive a specific automobile. The term concept formation describes how a person learns to form classes, whereas the term conceptual thinking refers to an individual’s subjective manipulation of those abstract classes. A concept is a rule that may be applied to decide if a particular object falls into a certain class. The concept “citizen of the United States” refers to such a decision rule, meaning any person who was born in U.S. territory or who is a child of a U.S. citizen or who has been legally naturalized. The rule suggests questions to ask in checking the citizenship of any particular individual. As most concepts do, it rests on other concepts; “U.S. citizen” is defined in terms of the concepts “child” and “territory.” Many scientific or mathematical concepts cannot be understood until the terms by which they are defined have been grasped. In this way concept formation builds on itself. Conceptual classification may be contrasted with another type of classification behavior called discrimination learning. In discrimination learning, objects are classified on the basis of directly perceived properties such as physical size or shape. The emphasis on concrete physical features in discrimination learning can be contrasted with the more abstract nature of concept formation. When a stimulus is perceived to match several different past experiences, however, the response may be a compromise, because an object need not bear an all-or-none relation to a set of others in discrimination learning; for example, there is no absolute distinction between tall and short people. While human beings are capable of abstract thought, many of the classifications people make seem to be concrete discriminations. For example, people may use the same term in a discriminative or conceptual way. A child might use the term policeman in discriminating a man who wears a distinctive uniform, while a lawyer may use the term to represent a civil servant charged with enforcing criminal codes. In practice, people seem to think in ways that combine abstractness and concreteness. They also may blend class membership with assignment along a scale—e.g., such concepts as leadership, an abstract quality that people are said to exhibit in varying degrees. The same would apply to vivacity, avarice, and other personality traits. People seem to develop more-complex sets of classes than do other animals, but this does not necessarily mean that human modes of learning are unique. It may be that all animals have the same basic biochemical machinery for learning but human animals exhibit it in greater variety. Yet, it seems no more appropriate to account for human concept formation in terms of discrimination learning alone than it does to reduce the functions of a piston engine to chemical reactions. 156 CU IDOL SELF LEARNING MATERIAL (SLM)

EXPERIMENTAL STUDIES Because careful observation of informal, everyday behavior is difficult, most evidence about human concept formation comes from laboratory subjects. For example, each subject is asked to learn a rule for classifying geometric figures (see table9.1). Table 9.1: Experimental results for concept formation The experimenter may concoct the rule that all green objects are called GEK. The subject is shown some of the figures, told which are named GEK, and asked to infer the rule or to apply it to other figures. This is roughly akin to teaching a young child to identify a class of barking animals with the name DOG. In both cases a general rule is derived from specific examples. The problem of discovering that GEK = GREEN is almost trivial when four GEK and four NOT GEK figures are presented at once, but the problem becomes surprisingly difficult if the figures are presented one at a time and need to be remembered. Furthermore, when two concepts are to be learned together (e.g., JIG = TRIANGLE and GEK = GREEN), memory for each concept tends to be mixed, and it becomes a formidable task to solve either problem. This suggests that short-term memory is important to concept learning and that short-term memory can often serve as a limiting factor in performance. The mastery of more-complex concept learning often depends on allotting enough time for the information to be fixed in memory. 157 CU IDOL SELF LEARNING MATERIAL (SLM)

Most such experiments involve very simple rules. They properly concern concept identification (rather than formation) when the learner is asked to recognize rules he already knows. Adult subjects tend to focus on one stimulus attribute after another (e.g., shape or color) until the answer is found. (This represents problem solving with a minimum of thinking; they simply keep guessing until they are right.) People tend to avoid repeating errors but seem to make surprisingly little use of very recent short-term experience. Most people try out attributes in an orderly manner, first considering such striking features as size, shape, and color and only later turning to the more abstract attributes (e.g., number of similar figures, or equilateral versus isosceles triangles). This suggests that there is no sharp distinction between discrimination learning (relatively concrete) and concept formation (more abstract); instead, one progresses from the concrete to the abstract. Study can shift from concept identification to concept learning by requiring combinations of previously learned rules. A conjunctive concept (in which the rule is based on the joint presence of two or more features; e.g., GEK patterns now are LARGE and GREEN) is fairly easy to learn when the common characteristics stand out. But learning a disjunctive rule (e.g., GEK objects now are either LARGE or GREEN but not both) is quite difficult; there is no invariant, relatively concrete feature on which to rely. Concept learning in adults may be understood as a two-step process: first the discovery of which attributes are relevant, then the discovery of how they are relevant. In the conjunctive illustration used here, the learner is likely first to notice that size and color have something to do with the answer and then to determine what it is. This two-step interpretation presupposes that the subject has already learned rules for color, size, shape, or similar dimensions. In an example of what is called “intra-dimensional” shift, initially the subject learns that GEK = GREEN; then, without warning, the experimenter changes the rule to GEK = RED. The same attribute or dimension (colour) is still relevant, but the way in which it is used has been changed. In “extra-dimensional” shift, the relevant dimension is changed (e.g., from GEK = GREEN to GEK = TRIANGLE), but the classification of some objects does not change (GREEN TRIANGLE is a GEK under both rules). The relative ease with which subjects handle such problems suggests something about how they learn. If they tend to learn simply by associating GEK with specific figures without considering the selected attribute, then they should find extra-dimensional-shift problems easier, since only some of their associations need be relearned. But if they have learned stepwise in terms of relevant attributes (e.g., to say “What is the colour? Ah, that colour means it is GEK”), intra-dimensional shift should be easier, since only the “how” phase of the two-step process need be relearned. College students tend to find intra-dimensional-shift problems easier, indicating that they are prone to use the two-step process. On the other hand, suppose a rat initially is rewarded when it runs into the right-hand side of a maze for food, then a change is made by rewarding entries 158 CU IDOL SELF LEARNING MATERIAL (SLM)

to the left (intra-dimensional shift) or by rewarding entries to any brightly lighted alley regardless of location (extra-dimensional shift). The rat will perform best on the extra- dimensional-shift problem. Among children, performance depends substantially on age. Preschool children are likely to do best with extra-dimensional shifts (as rats do), but children beyond kindergarten age tend to find the intra-dimensional shift easiest. Concepts need not be limited to simple classifications. They also can be interpreted as models or rules that reflect crucial possibilities for change. To take a simple case, an adult is not apt to think that the volume of water changes when it is poured into a container of different shape. Young children may claim that it does. In the adult’s concept, volume is not synonymous with the shape of a container but is based on a model of how fluids behave. Concepts offer a basis for deciding if certain changes will have significant effects. PIAGET’S OBSERVATIONS Through clinical observations, Swiss psychologist Jean Piaget initiated considerable study of how young children learn concepts that help them cope with their physical surroundings. As models for defining feasible change, concepts are at least as important in such contexts as they are for classification. Piaget stressed that infants must first learn to distinguish themselves from the external environment. Next they form understandings of the physical world (for example, identifying objects that fall) that allow further exploration of the world. Later in the preschool period, children grasp the concept of spatial localization—objects that are separated in space. Piaget characterized this period of learning as classifying objects only on the basis of perceptually attractive, concrete physical features (in agreement with laboratory studies of intra-dimensional and extra-dimensional shift). He and others who used his methods reported that preschool children are apt to explain external change in terms of their own needs: a four-year-old is likely to say that a cloud moves “because the sun is in my eyes.” Among children in early primary grades, other interpretations of cause and effect might be expressed by saying a moving cloud “wants to hide the sun.” In later primary grades, volitional and passive movement usually become conceptually distinct. By adolescence, children develop an ability to deal analytically with objects apart from their immediate perceptual characteristics. This marks an understanding of the hierarchies of subclasses within more general classes—for example, a normal child of eleven applies the properties of all living things to the class called birds. Given proper information, by the age of six many children display significant concept- forming abilities. They ordinarily have considerable linguistic competence, using (though often not being able to explain) such abstract qualifications as present and past tense. Rules of formal logic (such as “new math”) can be taught in the elementary grades. Progressive use of abstract concepts seems to reflect both maturation and learning. 159 CU IDOL SELF LEARNING MATERIAL (SLM)

The role of instruction in concept formation remains poorly understood, yet practically all cultural heritage is explicitly taught. Better knowledge of how to instruct and of the role of imitation in transmitting cultural concepts is needed. In addition, some linguists believe that language itself guides how concepts will be formed; if a language has no words for a concept, they assert, it is unlikely that a speaker of that language will think of that concept. FORMATION OF CONCEPTS There are four main steps involved in the formation of concepts. The steps are: 1. Observation 2. Generalisation 3. Discrimination or Differentiation 4. Abstraction Step # 1. Observation: The first stage in the formation of concepts is the observation of an event, object or an experience. This can also be called the stage of becoming aware. This can be either direct or indirect. The child can directly see a dog and become aware of it. On the other hand, he also hears stories about devils and giants from his parents and grandparents; here the awareness is indirect. Thus, all of us have some knowledge or awareness of primitive people (or at least we believe we have) even though most of us have not seen them. Generally repeated experiences provide the basis for the development of concepts. Step # 2. Generalisation: Repeated experiences or observations of different objects result in a tendency to form a general idea. Thus, a child first sees one dog, then another dog, then a third and so on and begins to form the general idea of a dog. This is called the process of generalisation. The process of generalisation explains how the child acquires many concepts like the concepts of gender, shape, number, etc. Step # 3. Discrimination or Differentiation: Along with generalisation and the observation and organisation of similarities among things and objects, the child also becomes aware of the differences between them. Thus, all dogs are alike and all cows are alike. Dogs run on four legs and cows also do the same. At the same time dogs and cows are different from each other and big dogs are different from small dogs, and bulls are different from cows. It is this type of sequential operation of generalisation and differentiation in interaction that leads to the formation of concepts. Step # 4. Abstraction: 160 CU IDOL SELF LEARNING MATERIAL (SLM)

From the description of the above processes the operation of abstraction becomes evident. The child has seen dogs and he happens to see a cow on a different occasion. He does not observe them at the same time but inwardly he compares his experiences on the two occasions. The perceptions and the experiences are now inwardly analysed and re-experienced in the absence of the objects. This results in an appreciation of similarities and differences. This process by which the experience is analysed in the absence of actual situations is known as abstraction. It is abstraction which actually transforms comparable and contrasting experiences into concepts. This ability to respond to concrete situations in the absence of the actual situations is known as abstract thinking ability. It can be seen that as the child grows older, the process of abstraction plays an increasingly important role in the development of concepts. It is this process of abstraction which helps us to form ideas of the future and far off objects. SUMMARY • The growth of science, in particular, and knowledge, in general and, perhaps, the growth of culture and civilisation, have all been possible because of our ability to form abstract concepts. • Concepts like force, energy, mind, truth are all examples of abstract concepts. Literary creations, masterpieces in art and other fields, are all embodiments of abstract concepts. • The ability to form abstract concepts is related to the intellectual ability of an individual and the richness of his experience. To a large extent performance in intelligence tests also reflects the ability to form abstract concepts. • The development of concepts proceeds from general and undifferentiated concepts to differentiated concepts. • For example, when a child looks at an object for the first time, he forms a vague and general idea of the object as a whole. This is why, a child’s concepts are not very clear. Gradually the details of the concepts become clear. One of the authors remembers that once upon a time his little niece referred to a pig as a big rat. • It was only subsequently that the little girl developed both the concepts and was able to differentiate a pig from a rat. The formation of clear concepts, therefore, involves the three processes – generalization, differentiation and abstraction. • The greater, the wider and the richer an individual’s experience with different objects and stimuli the better is the process of formation of concepts. 161 CU IDOL SELF LEARNING MATERIAL (SLM)

• The reader will, no doubt, understand the importance of the formation of clear concepts for proper adjustment of short-term memory and the importance of a rich and varied environment for enhancing the development of concepts in children. • Experiments in psychology have shown that certain factors like labelling or giving a name influences the process of the formation of concepts. • In a very interesting experiment, years ago, Heidbreder showed how the addition of certain labels to vague visual stimuli influenced concept formation. The effectiveness of social stereotypes again provides an illustration of the same. We form ideas of people on the basis of what we hear about them. • Experimental efforts to understand concepts were made initially by psychologists who by training or intellectual kinship were related to Wilhelm Wundt. • Jean Piaget has spent over forty years investigating the development of thought, or as he sometimes refers to it, “conceptual operations,” in children. • The formulation is based on semi-naturalistic observations of behaviour of children in response to ingeniously designed problems and adroit questioning. In general the findings suggest to Piaget and his co-workers that intellectual development can be characterized as a series of stages in which each stage lays the foundation for the successor. KEY WORDS/ ABBREVIATIONS • Concept formation- The process of learning or acquiring a concept from particular instances, some of which are examples of the concept and some of which are not. • Concept formation and learning- In education, concept formation in which a concept can be applied to new situations and related to other concepts is differentiated from simple rote learning, in which a response is given to a single stimulus or a few stimuli but does not become an integrated part of the individual’s general knowledge. LEARNING ACTIVITY 1. Explain the concept of concept formation with experimental studies. 2. Explain the steps involved in concept formation. 162 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT END QUESTIONS (MCQS AND DESCRIPTIVE) A. Descriptive Questions 1. Explain the meaning and nature of concept as explained by cognitive psychologists. 2. Experiments have been conducted to understand the process which we use to form concepts. Explain the experiments and analyse the results of the same. 3. Concept formation is a systematic process. Enlist and describe the steps involved in formation of concepts. 4. Explain in detail, Piaget’s observations on concept formation. 5. When we form a specific concept, we are to apply it to different settings. This is called generalization. Explain the nature of generalization and the process by which we generalize concepts. B. Multiple Choice Questions (MCQs) 1. Because it has the external features associated with the concept of dog, a wolf is perceived as a dog. This is an example of– (a) Centration (b) Equilibration (c) Object Permanence (d) Prototype 2. is a generalization that helps to organize information intocategories. (a)Concept (b)Concept Formation (c)Heuristics (d) Prototype 3. is a process by which a person learns to sort specific experiences into general rules or classes. (a)Concept (b)Concept Formation (c)Heuristics 163 CU IDOL SELF LEARNING MATERIAL (SLM)

(d) Prototype 4. Repeated experiences or observations of different objects result in a tendency to form a general idea. This is called as (a)Concept (b)Concept Formation (c)Heuristics (d)Generalization 5. is the ability to respond to concrete situations in the absence of the actual situations (a) Abstraction (b)Concept Formation (c)Heuristics (d)Generalization Answer 1. (d) 2 (a) 3 (b) 4 (d) 5 (a) REFERENCES • Kellogg, R. T. (2003). Cognitive Psychology (2nd ed.). California, USA.: Sage Publications • Neisser, U. (2014). Cognitive Psychology (Classic ed.). New York: Psychology Press • Eysenck, M. W. and Keane, M. T. (2015) Cognitive Psychology: A Student's Handbook (7th ed.). New York: PsychologyPress • Galotti, K.M. (2008), Cognitive Psychology: In and out of the Laboratory.Delhi: Thomson. • Sternberg, R. J. & Sternberg, K. (2012). Cognitive psychology (6th ed.).USA: Wadsworth, Cengage Learning. • Groome, D. (2014). An Introduction to Cognitive Psychology: Processes and Disorders. (3rd ed.). New York: PsychologyPress. • Mazur, J.E. (1986), Learning and Behaviors. (6th ed.). Englewood Cliffs, NewJersey: Prentice Hall. • Galotti, K.M. (1999), Cognitive Psychology: In and Outside Laboratory. Mumbai: Thomson Asia. 164 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT 10 REASONING Structure 10.0 Learning Objectives Introduction Reasoning: Meaning and Definition Deductive Reasoning and Inductive Reasoning Theoretical Approaches to Reasoning Abstract rule theories of deduction Mental model theory Domain specific rule theories Heuristics and bias accounts Probability theory Errors in Reasoning Summary Key Words/ Abbreviations Learning Activity Unit End Questions (MCQs and Descriptive) References LEARNING OBJECTIVES After this unit, you will be able to, • Explain the concept of reasoning • Describe inductive and deductive reasoning • Outline the theoretical aspects of reasoning • Identify errors in reasoning INTRODUCTION If you wanted to invest your money in the stock market,would you rather rely on a Nobel laureate’s strategy oron a simple heuristic (which is kind of a rule of thumb)? Researchers compared the levels ofsuccess of 14 portfolio management strategies andcompared them with the success of the simple 1/Nheuristic. This heuristic simply suggests that you distributeyour assets evenly among a given number of options. That is, each of the N options receives 1/N ofthe total investment short term memory. Among the other strategies evaluatedwas Nobel laureate Harry Markowitz’s mean variancemodel, according to which investors shouldoptimize the trade-off between the mean and varianceof a portfolio return. Markowitz suggested you minimizeyour risk and maximize your return by 165 CU IDOL SELF LEARNING MATERIAL (SLM)

consideringseveral factors, such as that sometimes certain groupsof stocks go up in price whereas others go down (e.g.,if the oil price goes up, airline profits will go down). The researchers found that the simple 1/N heuristicactually outperformed all 14 other models. In this chapter,you will learn more about how humans make decisionsand what shortcuts (heuristics) they use when theyare faced with uncertainty or more information thanthey can process. REASONING: MEANING AND DEFINITION Reasoning is one of the oldestareas of research in psychology. Reasoning is concerned itself with a key question about human nature: “Are human beings rational?” Philosophers tend to answer this question with a resounding “yes”, with arguments that the laws of logic are the laws of thought. This basic idea has been usedin the psychology of reasoning, although, in more sophisticated forms. The psychology of reasoning covers both deductive and inductive reasoning. When people carry out deductive reasoning they usually determine what conclusion, if any, necessarily follows when certain statements or premises are assumed to be true. In inductive reasoning, people make a generalized conclusion from premises that describe particular instances. Reasoning is used not only when we want to solve an immediate problem but also when we anticipate future problems.Reasoning is one of the best forms of controlled thinking consciously towards the solution of a problem. Reasoning is realistic in the sense that the solution is sought always in reference to the reality of the situation. We can solve many problems in our day-dreams, dreams and imaginations but they are unrealistic solutions. As Sherman defined, “Reasoning is a process of thinking during which the individual is aware of a problem identifies, evaluates, and decides upon a solution”.Reasoning is known to have played a significant role in one’s adjusting short term memory to the environment. The process of reasoning not only determines one’s cognitive activities but also has an influence the behavior and personality. Definitions of Reasoning: 1. “Reasoning is a stepwise thinking with a purpose or goal in mind” —Garrett. 2. “Reasoning is the term applied to highly purposeful, controlled and selective thinking”— Gates. 3. “Reasoning is the word used to describe the mental recognition of cause and effect relationships, it may be the prediction of an event from an observed cause or the inference of a cause from an observed event”—Skinner. 166 CU IDOL SELF LEARNING MATERIAL (SLM)

DEDUCTIVE REASONING AND INDUCTIVE REASONING Deductive Reasoning Deductive reasoning is the process of reasoning from one or more general statementsregarding what is known to reach a logically certain conclusion. It often involves reasoning from one or moregeneral statements regarding what is known to a specific application of the generalstatement. Deductive reasoning requires one to start with a few general ideas. These ideas are called premises. After forming premises, they are applied to a specific situation. Recognized rules, laws, theories, and other widely accepted truths are used to prove that a conclusion is right. The concept of deductive reasoning is often expressed visually using a funnel that narrows a general idea into a specific conclusion. In practice, the most basic form of deductive reasoning is the syllogism, where two premises that share some idea support a conclusion. It may be easier to think of syllogisms as the following theorem: If A=B and C=A, then B=C. Deductive Reasoning inTheory: Deductive Reasoning in Deductive Reasoning Theory: inPractice: General Ideas A is B All muscles are made outof Specific Conclusion C is A living tissue. Therefore, B is C. All humans have muscles. Therefore, all humans aremade out of living tissue. Table 10.1 Deductive and inductive reasoning Deductive reasoning is based on logical propositions. A proposition is basicallyan assertion, and this proposition could be either true or false. For example, sentences like “Cognitive psychologystudents are brilliant,” “Cognitive psychology students wear shoes,” or “Cognitivepsychology students like peanut butter.” In a logical argument, premises are propositionsabout which arguments are made. Henry has categorized three types of deductive reasoning: i. Conditioned reasoning: Conditioned reasoning is the reasoning tied down by some specific condition such as the following. For example, if there is a solar eclipse, the street will be dork. There is a solar eclipse 167 CU IDOL SELF LEARNING MATERIAL (SLM)

... The streets are dark. ii. Categorical reasoning: Categorical reasoning is a type of reasoning is based on some categorical statements. For example, all Robins are birds. All birds lay eggs. ... All Robins lay eggs. iii. Linear reasoning: Linear reasoning involves straight forward relationships among elements. For example, If Ram is taller than Mohan and Mohan is taller than Sohan, Ram is the tallest. Cognitive psychologists are interested in propositions that when connected in ways requiring people to drawreasoned conclusions. In other words, deductive reasoning is useful because it helps peopleconnect various propositions to draw conclusions. Cognitive psychologists are interested toknow how people connect propositions to draw conclusions. Some of these conclusionsare well reasoned; others are not. Inductive Reasoning Inductive reasoning, on the other hand, uses a set of specific observations to reach an overarching conclusion. Hence we can say that it is the opposite of deductive reasoning. So, a few particular premises create a pattern which gives way to a broad idea that is likely true. This is commonly shown using an inverted funnel (or a pyramid) that starts at the narrow premises and expands into a wider conclusion. There is no equivalent to a syllogism in inductive reasoning, meaning there is no basic standard format. All forms of inductive reasoning, though, are based on finding a conclusion that is most likely to fit the premises and is used when making predictions, creating generalizations, and analysing cause and effect. Inductive Reasoning in Theory: Inductive Reasoning in Practice: Specific Observations My neighbour’s cat hisses at me daily. Broad Conclusion At the pet store, all the cats hiss at me. Therefore, all cats probably hate me. 168 CU IDOL SELF LEARNING MATERIAL (SLM)

Inductive reasoning is the process of reasoning from specific facts or observations toreach a likely conclusion that may explain the facts. The inductive reasoner thenmay use that probable conclusion to attempt to predict future specific instances(Johnson-Laird, 2000). The key feature distinguishing inductive from deductive reasoningis that, in inductive reasoning, we never can reach a logically certain conclusion.We only can reach a particularly well- founded or probable conclusion. Withdeductive reasoning, in contrast, reaching logically certain—deductively valid—conclusionsis possible. For example, suppose that you notice that all the people enrolled in your cognitivepsychology course are on the dean’s list (or honour roll). From these observations,you could reason inductively that all students who enrol in cognitivepsychology are excellent students (or at least earn the grades to give that impression). However, unless you can observe the grade-point averages of all people whoever have taken or ever will take cognitive psychology, you will be unable to proveyour conclusion. Furthermore, a single poor student who happened to enrol in acognitive psychology course would disprove your conclusion. Still, after large numbersof observations, you might conclude that you had made enough observations toreason inductively. Cognitive psychologists probably agree on at least two of the reasons why peopleuse inductive reasoning. First, it helps them to become increasingly able to makesense out of the great variability in their environment. Second, it also helps themto predict events in their environment, thereby reducing their uncertainty. Thus,cognitive psychologists seek to understand the how rather than the why of inductivereasoning. We may (or may not) have some innate schema-acquisition device. Butwe certainly are not born with all the inferences we manage to induce. THEORETICAL APPROACHES TO REASONING Deductive reasoning research covers a wide variety of tasks from syllogistic reasoning, to reasoning withspatial connectives, to reasoning with propositional connectives (e.g., if, or, and not). Any adequate theoryof deduction should be able to explain the phenomena arising from this research. At this point there areprobably only two real candidate theories to meet this challenge (the abstract rule and mental modelstheories), although there are other accounts that cover smaller sets of phenomena. Each of these are brieflyoutlined next and then some of them are expanded on in subsequent sections. 169 CU IDOL SELF LEARNING MATERIAL (SLM)

10.4.1 Abstract rule theories of deduction The abstract rule theory generally takes logical notions of validity as its normative model of reasoning (seeprevious section). It assumes that people reason validly by applying abstract, content-free rules of inference,in a manner that is similar to the derivation of proofs in logic. In short, people employ a form of mentallogic to derive conclusions from premises. However, people can make mistakes because some derivationsare more complex than others (and exceed working memory) or because they misunderstand the premises ofa given deductive problem. The main proponents of this view are Braine and O’Brien. Mental models theory The mental models theory also, in essence, assumes logical notions of validity as its normative model. It assumes that people reason by manipulatingmental models of a set of premises, in a manner akin to semantic methods of proof in logic. In short, peopleconstruct mental models that represent possible states-of-affairs in the world, and then they describe andverify these models to reach valid conclusions. A conclusion is valid if there are no counterexamples to it;that is, if there is no state of affairs in which the premises are true but the conclusion is false. Again,however, people may make mistakes if they have to represent a large number of models that exceed theirworking memory. The main exponents of this view are Johnson-Laird and Byrne. Domain-specific rule theories Most domain-specific rule theories are essentially dual-process theories. They assume that some basiclogical competence is handled by some core mechanism (be it an abstract rule or mental models one), butthat there is a second mechanism using domain-specific rules that handles certain effects. Thus, reasoning is,in part, based on rules that are sensitive to the content of different situations; rules that are encoded indomain-specific schemata. There is a wide variety of such theories that propose differentflavours of rules from pragmatic reasoning schemata to social exchange schemata. Heuristics and bias accounts Most heuristic/bias accounts are also essentially dual-process theories. They assume that people have abasic logical competence, which is sometimes over-ridden by various heuristics or biases. Reasoning is seenas being, in part, due to non-logical tendencies based on a response to superficial aspects of a task situation(e.g., the presence of matching negatives, the position of an item on a test screen). We have called theseaccounts rather than theories, as they are a loose collection of ideas rather than a coherent theory. They alsocarry a theoretical health warning in that they can involve reified phenomena as cognitive processes; that is,the tendency to turn the description of a phenomenon into a theory. For example, one might find in a particularreasoning problem that people always choose the conclusion on the top left of the screen and then “explain”this by saying that people have a left-bias heuristic, when we 170 CU IDOL SELF LEARNING MATERIAL (SLM)

really should be seeking a deeper explanationof why such a bias might occur. Evans (1989; Wason & Evans, 1975) has been most active inexploring this approach in a reasoning context. Probabilistic theory Unlike all the aforementioned theories, the probabilistic theory does not rely on logic for its normativemodel, but rather draws on probability theory (e.g., Bayesian probability theory). Reasoning is not aboutvalidity but is about maximising information gain to reduce uncertainty. A maximally informative statementis one that tells you something improbable or surprising, relative to your prior knowledge. People have thecognitive goal of reducing uncertainty by increasing informativeness, and they make conclusions designedto maximise informativeness. ERRORS IN REASONING There are many strategies that can make solving a problem easier and more efficient. Two of them, algorithms and heuristics, are of particularly great psychological importance. • Heuristic A heuristic is a rule of thumb, a strategy, or a mental shortcut that generally works for solving a problem (particularly decision-making problems). It is a practical method, one that is not a hundred percent guaranteed to be optimal or even successful, but is sufficient for the immediate goal. The advantage of heuristics is that they often reduce the time and cognitive load required to solve a problem; the disadvantage is that they cannot always be relied on to solve the problem—just most of the time. • Algorithm An algorithm is a series of sets of steps for solving a problem. Unlike a heuristic, you are guaranteed to get the correct solution to the problem; however, an algorithm may not necessarily be the most efficient way of solving the problem. Additionally, you need to know the algorithm (i.e., the complete set of steps), which is not usually realistic for the problems of daily life. The difference between an algorithm and a heuristic can be summed up in the example of trying to find a Starbucks (or some other national chain) in a city. An algorithm would be a series of steps: “Walk in an increasingly large grid pattern around the city blocks until you find a Starbucks or you have looked at every street.” But a heuristic could simply be, “Well, usually they’re at busy intersections; I’ll just walk to the nearest busy intersection.” • Appeal to Ignorance: 171 CU IDOL SELF LEARNING MATERIAL (SLM)

Using an opponent’s inability to disprove a conclusion as proof of the conclusion’s correctness. Sometimes wrong ideas are so entrenched or hard to disprove that people of special ability are needed to make the case against such ideas. • Confusing Correlation with Causation: When two things happen together, and especially when one occurs just before the other, students commonly think that one thing causes the other. Without other more direct evidence of causation, this assumption is invalid. Both events could be caused by something else. In case students need convincing, just remind them of this example: rain and lightning go together, but neither causes the other. • Emotional Reasoning: Making decisions and arguments based on how you feel rather than objective reality. People who allow themselves to get caught up in emotional reasoning can become completely blinded to the difference between feelings and facts. For example, scientists sometimes unduly value a position because it is “parsimonious,” or elegant, or easily understood (or even complex and sophisticated), etc. Irrelevant information is information that is presented as part of a problem, but which is unrelated or unimportant to that problem and will not help solve it. Typically, it detracts from the problem-solving process, as it may seem pertinent and distract people from finding the most efficient solution. An example of a problem hindered by irrelevant information is this: 15% of people in Topeka have unlisted telephone numbers. You select 200 names at random from the Topeka phone book. How many of these people have unlisted phone numbers? The answer, of course, is none of them: if they are in the phone book, they do not have unlisted numbers. But the extraneous information at the beginning of the problem makes many people believe they have to perform a mathematical calculation of some sort. This is the trouble that irrelevant information can cause. • Jumping to Conclusions: This error occurs under a variety of situations. The most common cause is failure to consider alternatives. An associated cause is failure to question and test assumptions used to arrive at a conclusion. • Over-Generalization: It is illogical to assume that what is true for one is true for all. Example: some scientists studying free will claim that the decision-making process for making a button press is the same for more complex decisions. 172 CU IDOL SELF LEARNING MATERIAL (SLM)

SUMMARY • Reasoning is the capacity for consciously making sense of things, applying logic, establishing and verifying facts, and changing or justifying practices, institutions, and beliefs based on new or existing information. • It is considered to be a definitive characteristic of human nature, and it is associated with a wide range of fields, from science to philosophy. • Reason and reasoning (i.e., the ability to apply reason) are associated with thinking, cognition, and intelligence. Like habit or intuition, reason is one of the ways that an idea progresses to a related idea, helping people understand concepts like cause and effect, or truth and falsehood. • We use reason to form inferences—conclusions drawn from propositions or assumptions that are supposed to be true. • The two major types of reasoning, deductive and inductive • Deductive reasoning is a logical process wherein a conclusion is based on the concordance of multiple premises that are generally assumed to be true. This is sometimes referred to as top-down logic. • Inductive reasoning is the process in which we make generalized decisions after observing, or witnessing, repeated specific instances of something. • The most common errors in human discourse are called 'fallacies of argument'. This also refers to as arguments generated from repetition. In other words, it means to assume something must be true because it has been repeated so many times. • The opposite of the above error is argument from absence. In this case, conclusions are based on lack of evidence, rather than existence of evidence. • Judgment and reasoning involve thinking through the options, followed by making a judgment or conclusion and finally making a decision. Making judgments may involve heuristics. Which or efficient strategies that usually lead you to the right answers. • The most common heuristics used are attribute substitution, the availability heuristic, the representativeness heuristic and the anchoring heuristic – these all aid in quick reasoning and work in most situations. Heuristics allow for errors, a price paid to gain efficiency. • Other errors in judgment, therefore affecting reasoning, include errors in judgment about co-variation – a relationship between two variables such that the presence and magnitude of one can predict the presence and magnitude of the other. 173 CU IDOL SELF LEARNING MATERIAL (SLM)

KEY WORDS/ ABBREVIATIONS • Availability heuristic: When a person makes a judgment about the probability of an event based on the ease with which it comes to mind. • Functional fixedness: When the intended purpose of an object hinders a person’s ability to see its potential other uses. • Mental set: An unconscious tendency to approach a problem in a particular way. • Logic: Step-by-step thinking about how a problem can be solved or a conclusion can be reached. • Inference: A conclusion drawn from true or assumed-true facts. • Syllogism: A type of deductive reasoning, often in the form “All A are B; C is A; therefore, C is B.” • Reason: The capacity for consciously making sense of the world based on logic and evidence. • Reasoning- 1. Thinking in a linear and logical manner to draw conclusions from facts or the classification of things or events using general principles to infer order in the information. LEARNING ACTIVITY 1. What are the different types of reasoning? 2. What are the theories of reasoning? UNIT END QUESTIONS (MCQS AND DESCRIPTIVE) A. Descriptive Questions 1. When we take a decision, we support it with a set of reasons. Describe how cognitive psychologists explain the concept of reasoning. 2. Identify the type of reasoning wherein we reason from one or moregeneral statements to a specific application. Explain it detail. 3. Inductive reasoning uses a set of specific observations to reach an overarching conclusion. Explain this statement. 4. While facing a specific problem, there are certain factors that influence our process of solving them. What are some of the errors that we make while solving them. 174 CU IDOL SELF LEARNING MATERIAL (SLM)

5. Different people use different strategies while solving problems. List some of these strategies. B. Multiple Choice Questions (MCQs) 1. is a process of thinking during which the individual is aware of a problem identifies, evaluates, and decides upon a solution (a) Deductive Reasoning (b) Problem Solving (c) Reasoning (d) Thinking 2. involves reasoning from one or moregeneral statements regarding what is known to a specific application of the generalstatement. (a) Deductive Reasoning (b) Problem Solving (c) Reasoning (d) Inductive Reasoning 3. uses a set of specific observations to reach an overarching conclusion (a) Deductive Reasoning (b) Problem Solving (c) Reasoning (d) Inductive Reasoning 4. is an unconscious tendency to approach a problem in a particular way. (a) Mental Set (b) Problem Solving (c) Reasoning (d) Heuristics 175 CU IDOL SELF LEARNING MATERIAL (SLM)

5. The capacity for consciously making sense of the world based on logic and evidence is called. (a) Thinking (b) Problem Solving (c) Reasoning (d) Heuristics Answer 1 (c) 2 (a) 3 (d) 4 (a) 5 (c) REFERENCES • Kellogg, R. T. (2003). Cognitive Psychology (2nd ed.). California, USA.: Sage Publications • Neisser, U. (2014). Cognitive Psychology (Classic ed.). New York: Psychology Press • Eysenck, M. W. and Keane, M. T. (2015) Cognitive Psychology: A Student's Handbook (7th ed.). New York: PsychologyPress • Galotti, K.M. (2008), Cognitive Psychology: In and out of the Laboratory.Delhi: Thomson. • Sternberg, R. J. & Sternberg, K. (2012). Cognitive psychology (6thed.). USA: Wadsworth, Cengage Learning. • Groome, D. (2014). An Introduction to Cognitive Psychology: Processes and Disorders. (3rd ed.). New York: PsychologyPress. • Mazur, J.E. (1986), Learning and Behaviors. (6th ed.). Englewood Cliffs, NewJersey: Prentice Hall. • Galotti, K.M. (1999), Cognitive Psychology: In and Outside Laboratory. Mumbai: Thomson Asia. • 19Wason, P. C., Evans, J. St. B. T. (1975). Dual processes in reasoning? Cognition, 3, 141–154. • Evans, J. St. B. T. (1998). Matching bias in conditional reasoning: Do we understand it after 25 years? Thinking & Reasoning, 4, 45–82. 176 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT 11 LANGUAGE Structure 11.0 Learning Objectives Introduction Language: Meaning and Definition Properties of Language The Structure of Language Process of Language Acquisition Summary Key Words/ Abbreviations Learning Activity Unit End Questions (MCQs and Descriptive) References LEARNING OBJECTIVES After this unit, you will be able to, • Explain the concept of language • Describe the properties of language • Analyse the structure of language • Explain how we use phonemes, morphemes, lexemes, syntax, and context to use comprehensive language • Outline the process of acquiring language • Describe rules of grammar as given by Chomsky. • Use the rules of grammar and demonstrate the process of forming simple sentences • Combine basic rules of grammar and demonstrate the process of transforming forming simple sentences into complex ones. INTRODUCTION Cognitive psychologistsare concerned with thought as well as language. This approach has been popular since the 1950s. Before that, however, many psychologists believed that the scientific method could not be applied towards study of a process as private as thinking. From ancient Greek times, only philosophers and metaphysicians studied the nature of language and thought. The metaphysician René Descartes, for example, famously argued, “I think, therefore I am.” Today, thanks to advancement in technology and increasingly sophisticated tools for studying brain activity, cognitive psychology is a thriving science. Cognitive psychologists are able to 177 CU IDOL SELF LEARNING MATERIAL (SLM)

explore questions such as how language affects thought, whether it is possible to create a “thinking” machine, and why humans are motivated to create art. These are the basic processesinvolved in the initial stages of reading and listening to speech. The main objective of this chapter is to complete our account of reading and listening to speech, dealing with the ways in which phrases, sentences, and entire stories are processed and understood. LANGUAGE: MEANING AND DEFINITION Language is a system that contains of conventional spoken, manual, or written symbols by means of which human beings, as members of a social group and participants in its culture express themselves. The functions of language include communication, the expression of identity, play, imaginative expression, and emotional release. Language is the use of an organized means of combining words to communicate with those around us. It also makes it possible to think about things and processes we currently cannot see, hear, feel, touch, or smell. These things include ideas that may not have any tangible form. As Helen Keller demonstrated, the words we use may be written, spoken, or otherwise signed (e.g., via American Sign Language [ASL]). Even so, not all communication— exchange of thoughts and feelings—is through language. Communication encompasses other aspects—nonverbal communication, such as gestures or facial expressions, can be used to embellish or to indicate. Glances may serve many purposes. For example, sometimes they are deadly, other times, seductive. Communication can also include touches, such as handshakes, hits, and hugs. These are only a few of how we can communicate. Psycholinguistics is the psychology of our language as it interacts with the human mind. It considers both production and comprehension of language (Grenache& Kauchak, 2003a, 2003b; Wheeldon, Meyer, & Smith, 2003). Four areas of study have contributed greatly to an understanding of psycholinguistics: • linguistics, the study of language structure and change; • neuro-linguistics, the study of the relationships among the brain, cognition, and language; • socio-linguistics, the study of the relationship between social behavior and language and • computational linguistics and psycho-linguistics, the study of language via computational methods Language is the ability to produce and comprehend both spoken and written (and in the case of sign language, signed) words. Understanding how language works means reaching across many branches of psychology—everything from basic neurological functioning to high-level cognitive processing. Language shapes our social interactions and brings order to our lives. 178 CU IDOL SELF LEARNING MATERIAL (SLM)

Complex language is one of the defining factors that makes us human. Two of the concepts that make language unique are grammar and lexicon. PROPERTIES OF LANGUAGE The physical aspects of human teeth, larynx and so on are unique to human beings and are not shared by other creatures. This could explain why only human beings have the capacity for speech. However, we did not suggest that human beings were the only creature whocould communicate. All creatures can communicate with other members of their species. Nevertheless given below are some properties of language that are only present uniquely in humans. 1. Displacement Displacement allows human beings to make the use of language to talk about things and events not present in the immediate environment. Humans can communicate about things or events occurred in the past or which may occur in the future. They can also communicate about non-physical or abstract concepts. These characteristics are not seen in any species other than humans. Animal communication on the other hand, is generally considered to lack this property. 2. Arbitrariness Arbitrariness is the case that there is no natural connection between a linguistic form and its meaning. A combination of sounds may carry one meaning in one language, but, it may change its meaning when used in another. For instance, the sound combination ‘na’ and ‘da’ carries the meaning of “nothing” in the Spanish language and the meaning “thread” in the Hindi language. There is nothing about the word nada itself that forces Hindi speakers to convey the idea of “thread”, or the idea of “nothing” for Spanish speakers. Recognizing this general fact about language leads us to conclude that a property of linguistic signs is their arbitrary relationship with the objects they are used to indicate. 3. Productivity Productivity is alsoknown as “creativity or open-endedness”. Creativity is that aspect of language which is linked to the fact that the potential number of utterances in any human language is infinite. This gives rise to innumerable combinations of sounds thus resulting in the vast collection of words. This may also be responsible for differences in intonations and slangs. 4. Cultural Transmission 179 CU IDOL SELF LEARNING MATERIAL (SLM)

Language is not something a person can inherit from his/her parents. We acquire the skill of using a language in its culturalcontext. While communicating with other speakers and not from parental genes we develop our knowledge. A baby born in Japan to Japanese parents when is adopted and brought up from birth by English speakers in the United States will inevitably speak English. On the other hand a baby born in English to Japanese parents will be able to learn both Japanese (due to exposure in home and family environment) and English languages (due to exposure in community or society). 5. Discreteness Discreteness refers to being distinct or specific. The sounds used in language are meaningfully distinct. The fact that the pronunciation of the forms back and back leads to a distinction in meaning can only be due to the difference between the “p” and “b” sounds in English. Each sound in the language is treated as discrete. 6. Duality Language is organized at two levels or layers simultaneously. while speaking in terms of speech production, we have the physical level. Through which we can produce individual sounds like “n”, “b” and “i”. It is obvious that, although may be able to produce woof, it does not seem to be a feature of the canine repertoire that the “w”, “or” and “f” elements can be separated out as a distinct level of production. If your dog could operate with the double level “i.e. duality”, then you might expect to hear oowf and even foow, each with different meanings. 7. Grammar Since languagesfollow a set of combinatory rules, we can communicate an infinite number of concepts. Every language has a separate set of rules. And all languages do obey these rules. These rules can also be known as grammar. Speakers of a language have internalized the rules and exceptions for that language’s grammar. There are rules for every level of language. Word formation (for instance, native speakers of English have internalized the general rule that -ed is the ending for past-tense verbs, so even when they encounter a brand-new verb, they automatically know how to put it into past tense); phrase formation (for example, knowing that when you use the verb “buy,” it needs a subject and an object; “She buys” is wrong, but “She buys a gift” is okay); and sentence formation. THE STRUCTURE OF LANGUAGE All languages have underlying structural rules that make meaningful communication possible. 180 CU IDOL SELF LEARNING MATERIAL (SLM)

The five main components of language are phonemes, morphemes, lexemes, syntax, and context. Along with grammar, semantics, and pragmatics, these components work together to create meaningful communication among individuals. These pieces all work together to create meaningful communication among individuals. • A phoneme is the smallest unit of sound that may cause a change of meaning within a language but that doesn’t have meaning by itself. • A morpheme is the smallest unit of a word that provides a specific meaning to a string of letters (which is called a phoneme). There are two main types of morpheme: free morphemes and bound morphemes. • A lexeme is the set of all the inflected forms of a single word. • Syntax is the set of rules by which a person constructs full sentences. • Context is how everything within language works together to convey a meaning. Figure 11.1: Structure of Human Language Phonemes A phoneme is the basic unit of phonology. It is the smallest unit of sound that may cause a change of meaning within a language. However, that doesn’t have meaning by itself. For example, in the words “bake” and “brake,” only one phoneme has been altered, but a change in meaning has been triggered. The phoneme /r/ has no meaning on its own, but by appearing in the word it has changed the word’s meaning entirely. Phonemes correspond to the sounds of the respective alphabet. Although, there may not always a one-to-one relationship between a letter and a phonemethat is the sound made when you say the word. For example, the word “dog” has three phonemes: /d/, /o/, and / g /. 181 CU IDOL SELF LEARNING MATERIAL (SLM)

However, the word “shape,” despite having five letters, has only three phonemes: /sh/, /long- a/, and /p/. The English language has about 45 different phonemes.These correspond to letters or combinations of letters. Through the process of segmentation, a phoneme can have a pronunciation in one word and a slightly different pronunciation in another. Morphemes Morphemes are the basic unit of morphology. They are the smallest meaningful unit of language. Thus, a morpheme is a series of phonemes which has a special meaning. If a morpheme is altered in any way, the entire meaning of the word can be changed. Some morphemes are individual words (such as “eat” or “water”). These are also known as free morphemes since they can exist on their own. Other morphemes are prefixes, suffixes, or other linguistic pieces which aren’t full words on their own but do affect meaning. These include morphemes such as the “-s” used at the end of “cats” or the “re-” which is used at the beginning of “redo.Since these morphemes must be attached to another word to have meaning, they are called bound morphemes. Within the category of bound morphemes, there are two additional subtypes: derivational and inflectional. Derivational morphemes change the meaning or part of speech of a word when they are used together. For example, the word “sad” changes from an adjective to a noun when “-ness” (sadness) is added to it. “Action” changes in meaning when the morpheme “re- ” is added to it, creating the word “reaction.” Inflectional morphemes modify either the tense of a verb or the number value of a noun; for example, when you add an “-s” to “cat,” the number of cat’s changes from one to more than one. Lexemes Lexemes are the set of inflected forms taken by a single word. For example, members of the lexeme RUN include “run” (the uninflected form), “running” (inflected form), and “ran.” This lexeme excludes “runner (a derived term—it has a derivational morpheme attached). Another way to think about lexemes is that they are the set of words that would be included under one entry in the dictionary—” running” and “ran” would be found under “run,” but “runner” would not. Syntax Syntax refers to a set of rules for constructing full sentences out of words and phrases. Every language has a specific set of rules related to syntax. However, all languages have some form of syntax. In English, the smallest form of a sentence is called as a noun phrase and a verb phrase.A noun phrase can be either a noun or a pronoun) and a verb phrase may be a single verb or a supporting verb accompanying the primary verb. Adjectives and adverbs can be added to the sentence to provide further meaning. ‘Word order’makes a big difference in English language. Nonetheless, in some languages, order is of less importance. For example, the English sentences “The baby ate the 182 CU IDOL SELF LEARNING MATERIAL (SLM)

carrot” and “The carrot ate the baby” do not mean the same thing, even though they contain the exact same words. In languages like Finnish, word order doesn’t matter for general meaning. Hence, different word orders are used to emphasize various parts of the sentence. Context Context refers to the way in which everything within language works together in order to convey a meaning. Context includes tone of voice, body language, and the words being used. Depending on how a person says something, holds his or her body, or emphasizes certain points of a sentence, a variety of different messages can be conveyed. For instance, the word “awesome,” when said with a big smile, means the person is excited about a situation. “Awesome,” said with crossed arms, rolled eyes, and a sarcastic tone, means the person is not thrilled with the situation. PROCESSES OF LANGUAGE ACQUISITION Noam Chomsky is considered as one of the most influential linguists of the twentieth century. Even today he dominates the scene of theoretical linguistics. He is most famous for his unique linguistic philosophy. He revolutionized the discipline of linguistics with his much- talked-about theory of Transformational Generative Grammar (TGG). This theory emphasizes the mental capacity of generating sentences with the use of unconscious knowledge of language. This knowledge of language is referred to as Universal Grammar (UG). Noam Chomsky says, Transformational Generative Grammar (TGG) attempts to specify ‘what the speaker actually knows’. He asserts that human brain is biologically programmed to learn language, and hence language faculty is innate for us. For us, our mind works while learning a language. These innatist and mentalist views made his theory distinct. They place him in head-on collision with behaviorism, which was much in fashion during the first half of the twentieth century. Chomsky gave a serious blow to behaviorism.And so, the stimulus- response theory of language learning was abandoned. This in turn gave a boost to cognitive psychology. This paradigm shift in the history of linguistics is recognized as Chomskyan Revolution. Chomsky’s kind of philosophy is also known as Chomskyan Hierarchy.Transformational grammar is a theory of grammar that accounts for the constructions of a language by linguistic transformations and phrase structures. This is also known as transformational- generative grammar or T-G or TGG. Noam Chomsky published his book called Syntactic Structures in 1957. After this book was published, transformational grammar dominated the field of linguistics for the next few decades. \"The era is called the Era of Transformational-Generative Grammar. It signifies a sharp break with the linguistic tradition of the first half of the 20thcentury both in Europe and America. This theory has its principal objective, the formulation of a finite set of basic and 183 CU IDOL SELF LEARNING MATERIAL (SLM)

transformational rules that explain how the native speaker of a language can generate and comprehend all its possible grammatical sentences. Moreover, it focuses mostly on syntax and not on phonology or morphology, as structuralism does\". Chomsky’s grammar Although, Noam Chomsky’s system of transformational grammar, it was developed on the basis of his work with Harris, it differed from Harris’s in a number of respects. It was Chomsky’s system that attracted the most attention and received the most extensive exemplification and further development. As outlined in Syntactic Structures (1957), it comprised three sections, or components: the phrase-structure component, the transformational component, and the morphophonemic component. Each of these components consisted of a set of rules operating upon a certain “input” to yield a certain “output.” The notion of phrase structure may be dealt with independently of its incorporation in the larger system. In the following system of rules, S stands for Sentence, NP for Noun Phrase, VP for Verb Phrase, Det for Determiner, Aux for Auxiliary (verb), N for Noun, and V for Verb stem. Figure 11.2: Different rules related to phrases of language Given in the figure above is a simple phrase-structure grammar. It generates and thereby defines as grammatical such sentences as “The man will hit the ball”.It assigns meaning to each sentence and this meaning generates a structural description. The kind of structural description assigned by a phrase-structure grammar is, in fact, a constituent structure analysis of the sentence. In these rules, the arrow can be interpreted as an instruction to rewrite. This is to be considered as a technical term.The symbol that appears to the left of the arrow, is the symbol or string of symbols that appears to the right of the arrow. 184 CU IDOL SELF LEARNING MATERIAL (SLM)

For example, rule (2) rewrites the symbol VP as the string of symbols Verb + NP, and it thereby defines Verb + NP to be a construction of the type VP. Or, alternatively and equivalently, it says that constructions of the type VP may have as their immediate constituents constructions of the type Verb and NP (combined in that order). Rule (2) can be thought of as creating or being associated with the tree structure in Figure 11.3. Figure 11.3: Rule (2) of phrases of language Rules (1)–(8) do not operate in isolation but constitute an integrated system. The symbol S (standing mnemonically for “sentence”) is designated as the initial symbol. This information is not given in the rules (1)–(8). However, it can be assumed in two ways, 1. This is given in a kind of protocol statement preceding the grammatical rules 2. There is a universal convention according to which S is always the initial symbol. It is necessary to begin with a rule that has the initial symbol on the left. Thereafter any rule may be applied in any order until no further rule is applicable; in doing so, a derivation can be constructed of one of the sentences generated by thegrammar. If the rules are applied in the following order: (1), (2), (3), (3), (4), (5), (5), (6), (6), (7), (8), then assuming that “the” is selected on both applications of (5), “man” on one application of (6), and “ball” on the other, “will” on the application of (7), and “hit” on the application of (8), the following derivation of the sentence “The man will hit the ball” will have been constructed: 185 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 11.4: Structure based on rules for phases of language Many similar derivations of this sentence are possible. These derivations depend on the order in which the rules are applied. The crucial point is that all these different derivations are equivalent in that they can be reduced to the same tree diagram; namely, the one shown in Figure 11.5. If this is compared with the system of rules, it will be seen that each application of each rule creates or is associated with a portion (or subtree) of the tree. The tree diagram, or phrase marker, may now be considered as a structural description of the sentence “The man hit the ball.” It is a description of the constituent structure, or phrase structure, of the sentence, and it is assigned by the rules that generate the sentence. Figure 11.5: Structural description of the sentence 186 CU IDOL SELF LEARNING MATERIAL (SLM)

It is important that we interpret the term ‘generate’ in a static sense and not in a dynamic sense. The statement that the grammar generates a sentence means that the sentence is one of the totalities of sentences. It also means that the grammar defines to be grammatical or well formed. All the sentences are generated simultaneously. The notion of generation must be interpreted as would be a mathematical formula containing variables. For example, in evaluating the formula y 2 + y for different values of y, one does not say that the formula itself generates these various resultant values (2, when y = 1; 5, when y = 2; etc.) one after another or at various times; one says that the formula generates them all simultaneously or, better still perhaps, timelessly. The situation is kind of similar in case of a generative grammar. Although one sentence rather than another can be derived on some occasion by making one choice rather than another at places in the grammar, the grammar must be thought of as generating all sentences statically or timelessly. Thus even if we were to create sentence A instead of sentence B by using a particular rule of grammar, the concept of grammar is related to formation of sentences A and B simultaneously. It is also noted that, whereas a phrase-structure grammar is one that consists entirely of phrase-structure rules, a transformational grammar (as formalized by Chomsky) includes both phrase-structure and transformational rules (as well as morphophonemic rules). The transformational rules depend upon the prior application of the phrase-structure rules and have the effect of converting, or transforming, one phrase marker into another. What is meant by this statement may be clarified first with reference to a purely abstract and very simple transformational grammar, in which the letters stand for constituents of a sentence (and S stands for “sentence”): Figure 11.6: Phase- structure rules The first five rules are phrase-structure rules (PS rules); rule (6) is a transformational rule (T rule). The output of rules (1)– (5) is the terminal string a + b + c + e + f + d + g + h, which 187 CU IDOL SELF LEARNING MATERIAL (SLM)

has associated with it the structural description indicated by the phrase marker shown in Figure 5 (left). Rule (6) applies to this terminal string of the PS rules and the associated phrase marker. It has the effect of deleting C (and the constituents of C) and permuting A and D (together with their constituents). The result is the string of symbols d + g + h + a + b, with the associated phrase marker shown in Figure 5 (right). Figure 11.7: Phase markers The phrase marker shown in Figure 5 (left) may be described as underlying, and the phrase marker shown in Figure 5 (right) as derived from the rule (6). One of the principal characteristics of a transformational rule is its transformation of an underlying phrase marker into a derived phrase marker in this way. Transformational rules, in contrast with phrase- structure rules, are also formally more heterogeneous and may have more than one symbol on the left-hand side of the arrow. The linguistic importance of these abstract considerations may be explained with reference to the relationship that holds in English between active and passive sentences. Chomsky’s rule for relating active and passive sentences (as given in Syntactic Structures) is very similar, at first sight, to Harris’s, discussed above. Chomsky’s rule is: This rule, called the passive transformation, presupposes and depends upon the prior application of a set of phrase-structure rules. For simplicity, the passive transformation may first be considered in relation to the set of terminal strings generated by the phrase-structure rules (1)– (8) given earlier. The string “the + man + will + hit + the + ball” (with its associated phrase marker, as shown in Figure 4) can be treated not as an actual sentence but as the structure underlying both the active sentence “The man will hit the ball” and the corresponding passive “The ball will be hit by the man.” The passive transformationis 188 CU IDOL SELF LEARNING MATERIAL (SLM)

applicable under the condition that the underlying, or “input,” string is analyzable in terms of its phrase structure as NP - Aux - V - NP (the use of subscript numerals to distinguish the two NPs in the formulation of the rule is an informal device for indicating the operation of permutation). In the phrase marker in Figure 4, “the” + “man” are constituents of NP, “will” is a constituent of Aux, “hit” is a constituent of V, and “the” + “ball” are constituents of NP. The whole string is therefore analyzable in the appropriate sense, and the passive transformation converts it into the string “the + ball + will + be + en + hit + by + the + man.” A subsequent transformational rule will permute “en + hit” to yield “hit + en,” and one of the morphophonemic rules will then convert “hit + en” to “hit” (as “ride + en” will be converted to “ridden”; “open + en” to “opened,” and so on). Every transformational rule has the effect of converting an underlying phrase marker into a derived phrase marker. The way the transformational rules assign derived constituent structure to their input strings is one of the major theoretical problems in the formalization of transformational grammar. Here it can be assumed not only that “be + en” is attached to Aux and “by” to NP (as indicated by the plus signs in the rule as it has been formulated above) but also that the rest of the derived structure is as shown in Figure 6. The phrase marker in Figure 6 formalizes the fact, among others, that “the ball” is the subject of the passive sentence “The ball will be hit by the man,” whereas “the man” is the subject of the corresponding active “The man will hit the ball” (compare Figure 4). Figure 11.8: Anexample of derived phrase marker for a passive sentence Although the example above is a very simple one, and only a single transformational rule has been considered independently of other transformational rules in the same system, the passive transformation must operate, not only upon simple noun phrases like “the man” or “the ball,” but upon noun phrases that contain adjectives (“the old man”), modifying phrases (“the man in the corner”), relative clauses (“the man who checked in last night”), and so forth. The incorporation, or embedding, of these other structures with the noun phrase will be brought about by the prior application of other transformational rules. It should also be clear that the 189 CU IDOL SELF LEARNING MATERIAL (SLM)

phrase-structure rules require extension to allow for the various forms of the verb (“is hitting,” “hit,” “was hitting,” “has hit,” “has been hitting,” etc.) and for the distinction of singular and plural. It is important to note that, unlike Harris’s, Chomsky’s system of transformational grammar does not convert one sentence into another: the transformational rules operate upon the structures underlying sentences and not upon actual sentences. A further point is that even the simplest sentences (i.e., kernel sentences) require the application of at least some transformational rules. Corresponding active and passive sentences, affirmative and negative sentences, declarative and interrogative sentences, and so on are formally related by deriving them from the same underlying terminal string of the phrase-structure component. The difference between kernel sentences and non-kernel sentences in Syntactic Structures (in a later system of Chomsky the category of kernel sentences is not given formal recognition at all) resides in the fact that kernel sentences are generated without the application of any optional transformations. Non-kernel sentences require the application of both optional and obligatory transformations, and they differ one from another in that a diverse selection of optional transformations is made. Modifications in Chomsky’s grammar Chomsky’s system of transformational grammar was substantially modified in 1965. Perhaps the most important modification was the incorporation, within the system, of a semantic component, in addition to the syntactic component and phonological component. (The phonological component may be thought of as replacing the morphophonemic component of Syntactic Structures.) The rules of the syntactic component generate the sentences of the language and assign to each not one but two structural analyses: a deep structure analysis as represented by the underlying phrase marker, and a surface structure analysis, as represented by the final derived phrase marker. The underlying phrase marker is assigned by rules of the base (roughly equivalent to the PS [Phrase-Structure] rules of the earlier system); the derived phrase marker is assigned by the transformational rules. The interrelationship of the four sets of rules is shown diagrammatically in Figure 7. The meaning of the sentence is derived (mainly, if not wholly) from the deep structure by means of the rules of semantic interpretation; the phonetic realization of the sentence is derived from its surface structure by means of the rules of the phonological component. The grammar (“grammar” is now to be understood as covering semantics and phonology, as well as syntax) is thus an integrated system of rules for relating the pronunciation of a sentence to its meaning. The syntax, and more particularly the base, is at the “heart” of the system, as it were: it is the base component (as the arrows in the diagram indicate) that generates the infinite class of structures underlying the well-formed sentences of a language. These structures are then given a semantic and phonetic “interpretation” by the other components. 190 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 11.8: Diagrammatic representation of a transformational grammar The base consists of two parts: a set of categorical rules and a lexicon. Taken together, they fulfill a similar function to that fulfilled by the phrase-structure rules of the earlier system. But there are many differences of detail. Among the most important is that the lexicon (which may be thought of as a dictionary of the language cast in a form) lists, in principle, all the vocabulary words in the language and associates with each all the syntactic, semantic, and phonological information required for the correct operation of the rules. This information is represented in terms of what are called features. For example, the entry for “boy” might say that it has the syntactic features: [+ Noun], [+ Count], [+ Common], [+ Animate], and [+ Human]. The categorical rules generate a set of phrase markers that have in them, as it were, a number of “slots” to be filled with items from the lexicon. With each such “slot” there is associated a set of features that define the kind of item that can fill the “slot.” If a phrase marker is generated with a “slot” for the head of a noun phrase specified as requiring an animate noun (i.e., a noun having the feature [+ Animate]), the item “boy” would be recognized as being compatible with this specification and could be inserted in the “slot” by the rule of lexical substitution. Similarly, it could be inserted in “slots” specified as requiring a common noun, a human noun, or a countable noun, but it would be excluded from positions that require an abstract noun (e.g., “sincerity”) or an uncountable noun (e.g., “water”). By drawing upon the syntactic information coded in feature notation in the lexicon, the categorical rules might permit such sentences as “The boy died,” while excluding (and thereby defining as ungrammatical) such non-sentences as “The boy elapsed.” One of the most controversial topics in the development of transformational grammar was the relationship between syntax and semantics. Scholars working in the field agreed that there is a considerable degree of interdependence between the two, and the problem was how to formalize this interdependence. One school of linguists, called generative semanticists, accepted the general principles of transformational grammar but challenged Chomsky’s conception of deep structure as a separate and identifiable level of syntactic 191 CU IDOL SELF LEARNING MATERIAL (SLM)

representation. In their opinion, the basic component of the grammar should consist of a set of rules for the generation of well-formed semantic representations. These would then be converted by a succession of transformational rules into strings of words with an assigned surface-structure syntactic analysis, there being no place in the passage from semantic representation to surface structure identifiable as Chomsky’s deep structure. Chomsky himself denied that there is any real difference between the two points of view and has maintained that the issue is purely one of notation. That this argument could be put forward by one party to the controversy and rejected by the other is perhaps a sufficient indication of the uncertainty of the evidence. Of greater importance than the overt issues, in so far as they are clear, was the fact that linguists were now studying much more intensively than they had in the past the complexities of the interdependence of syntax, on the one hand, and semantics and logic, on the other. The role of the phonological component of a generative grammar of the type outlined by Chomsky is to assign a phonetic “interpretation” to the strings of words generated by the syntactic component. These strings of words are represented in a phonological notation (taken from the lexicon) and have been provided with a surface-structure analysis by the transformational rules (see Figure 11.8). The phonological elements out of which the word forms are composed are segments consisting of what are referred to technically as distinctive features (following the usage of the Prague school, see below The Prague school). For example, the word form “man,” represented phonologically, is composed of three segments: the first consists of the features [+ consonantal], [+ bilabial], [+ nasal], etc.; the second of the features [+ vocalic], [+ front], [+ open], etc.; and the third of the features [+ consonantal], [+ alveolar], [+ nasal], etc. (These features should be taken as purely illustrative; there is some doubt about the definitive list of distinctive features.) Although these segments may be referred to as the “phonemes” /m/, /a/, and /n/, they should not be identified theoretically with units of the kind discussed in the section on Phonology under Structural linguistics. They are closer to what many American structural linguists called “morphophonemes” or the Prague school linguists labelled “archiphonemes,” being unspecified for any feature that is contextually redundant or predictable. For instance, the first segment of the phonological representation of “man” will not include the feature [+ voice]; because nasal consonants are always phonetically voiced in this position in English, the feature [+ voice] can be added to the phonetic specification by a rule of the phonological component. Generative phonology refers to phonology carried out within the framework of an integrated generative grammar. An important aspect of generative phonology is its dependence on syntax. Most American structural phonologists have made a point of principle that the phonemic analysis of an utterance should be carried out without regard to its grammatical 192 CU IDOL SELF LEARNING MATERIAL (SLM)

structure. This principle, however, was controversial among American linguists. Hence it was not generally accepted outside America. This principle was also rejected by the generative grammarians. Further, they made the phonological description of a language much more dependent upon its syntactic analysis than any other aspect of language. They claimed that the phonological rules that assign different degrees of stress to the vowels in English words and phrases and alter the quality of the relatively unstressed vowel concomitantly must make reference to the derived constituent structure of sentences and not merely to the form class of the individual words or the places in which the word boundaries occur. SUMMARY • Language plays a very unique role in our life as it enables us to process, convey or communicate our thoughts. Language can be either spoken or written and it can consist of sounds and written symbols. • Spoken language is dependent a lot on how the sounds are produced. The physiology of the sound and the meaning attached to the sound are two main aspects of spoken language. • Language whether spoken or written has certain components that make the process of communication meaningful. These components are phonemes, morphemes, lexemes, syntax, and context. • However, language becomes meaningful only when the people involved in the process share certain basic knowledge and rules related to use of that knowledge. • While analysing grammar, we understand that here are two levels of analysing comprehension of sentences. • He has given certain rules that are used while we form sentences. • First, there is an analysis of the syntactical (grammatical) structure of each sentence; this is known technically as parsing. What exactly is grammar? It is concerned with the way in which words are combined. • According to Along, the term memoryann, “Grammar is the way in which words are combined’. Grammar is important, and has meaning, only if both the speaker and the hearer (or the writer and the reader) share some common knowledge regarding the significance of these combinations. • Second,analysing the meaning of the sentence. It is important to note that the intended meaning of a sentence may not be the same as its literal meaning. The study of intended meaning is known as pragmatics. • The relationship between syntactic and semantic analysis has been a matter of controversy. • Noam Chomsky is considered as one of the influential persons in conducting the research related to acquisition of language. 193 CU IDOL SELF LEARNING MATERIAL (SLM)

• His theory of transformational grammar emphasis on our capacity to create or formulate sentences unconsciously. KEY WORDS/ ABBREVIATIONS • Grammar- All natural languages are principled systems, and the principles governing use for a given language are collectively referred to as that language’s grammar. • Language- Language is the implicit system that links an external linguistic signal, acoustic or written, and the message carried by that signal. LEARNING ACTIVITY 1. Explain Chomsky’s Transformational Grammar in detail. 2. Explain the structure of Language. UNIT END QUESTIONS (MCQS AND DESCRIPTIVE) A. Descriptive Questions 1. Explain the concept of knowledge as understood by cognitive psychologists. Enlist some of the properties of language. 2. Language is made of five components. Enlist and describe the five components of language 3. Explain the contribution of Noam Chomsky to the field of language. 4. According to Noam Chomsky, his system consisted of the three components or structures; the phrase-structure component, the transformational component, and the morphophonemic component. Describe these components in detail. 5. Chomsky has given many rules of grammar, which are used to make sentences. Explain in detail any one rule with the help of a tree diagram. B. Multiple Choice Questions (MCQs) 1. Which of the following is a characteristic of Chomsky’s theory? (a) Development of language is between three and five years of age 194 CU IDOL SELF LEARNING MATERIAL (SLM)

(b) Language development is dependent on the reinforcement received by the child (c) Children acquire mistakes in language by observing their parents and others around them (d) Children have an innate mental grammar 2. The modern psycholinguistic theory wasdeveloped by . (a) Chomsky (b) Kolher (c) Piaget (d)Kohlberg 3. is the basic unit of phonology. (a) Morphemes (b) Syntax (c) Phonemes (d) Context 4. is a set of rules for constructing full sentences out of words and phrases. (a) Morphemes (b) Syntax (c) Phonemes (d) Context 5. are the basic unit of morphology and the smallest meaningful unit of language. (a) Morphemes (b) Syntax (c) Phonemes (d) Context Answer 195 CU IDOL SELF LEARNING MATERIAL (SLM)

1 (d) 2 (a) 3 (c) 4 (b) 5 (a) REFERENCES • Chomsky, N. (1957). Syntactic Structures. California: Mouton • Kellogg, R. T. (2003). Cognitive Psychology (2nd ed.). California, USA.: Sage Publications • Neisser, U. (2014). Cognitive Psychology (Classic ed.). New York: Psychology Press • Eysenck, M. W. and Keane, M. T. (2015) Cognitive Psychology: A Student's Handbook (7th ed.). New York: PsychologyPress • Galotti, K.M. (2008), Cognitive Psychology: In and out of the Laboratory.Delhi: Thomson. • Sternberg, R. J. & Sternberg, K. (2012). Cognitive psychology (6thed.). USA: Wadsworth, Cengage Learning. • Groome, D. (2014). An Introduction to Cognitive Psychology: Processes and Disorders. (3rd ed.). New York: PsychologyPress. • Mazur, J.E. (1986), Learning and Behaviors. (6th ed.). Englewood Cliffs, NewJersey: Prentice Hall. • Galotti, K.M. (1999), Cognitive Psychology: In and Outside Laboratory. Mumbai: Thomson Asia. 196 CU IDOL SELF LEARNING MATERIAL (SLM)