MAP602_Experimental Psychology

Signal Detection Theory 195 8.1 Introduction Signal Detection Theory is a means to measure the ability to differentiate between information- bearing patterns called stimulus in living organisms, signal in machines and random patterns that distract from the information called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator. In the field of electronics, the separation of such patterns from a disguising background is referred to as signal recovery. 8.2 Signal Detection Theory According to the theory, there are a number of determiners of how a detecting system will detect a signal, and where its threshold levels will be. The theory can explain how changing the threshold will affect the ability to discern, often exposing how adapted the system is to the task, purpose or goal at which it is aimed. When the detecting system is a human being, characteristics such as experience, expectations, physiological state (e.g., fatigue) and other factors can affect the threshold applied. For instance, a sentry in wartime might be likely to detect fainter stimuli than the same sentry in peacetime due to a lower criterion; however they might also be more likely to treat innocuous stimuli as a threat. Much of the early work in detection theory was done by radar researchers. By 1954, the theory was fully developed on the theoretical side as described by Peterson, Birdsall and Fox and the foundation for the psychological theory was made by Wilson P. Tanner, David M. Green, and John A. Swets, also in 1954. Detection theory was used in 1966 by John A. Swets and David M. Green for psychophysics. Green and Swets criticized the traditional methods of psychophysics for their inability to discriminate between the real sensitivity of subjects and their (potential) response biases. Detection theory has applications in many fields such as diagnostics of any kind, quality control, telecommunications, and psychology. The concept is similar to the signal to noise ratio used in the sciences and confusion matrices used in artificial intelligence. It is also usable in alarm management, where it is important to separate important events from background noise. CU IDOL SELF LEARNING MATERIAL (SLM)

196 Experimental Psychology Illustration of Signal Detection Theory (SDT) The treat legitimate sites as “non-signal,” and phishing sites as “signal.” The sensitivity (d') measures users’ ability to discern signal from noise. Criterion (C) measures users’ decision tendency. The effects of training could be to: (a) make the user shift the decision Criterion and thus increasing alertness; (b) make users increase sensitivity, separating the two distributions better and thus improving people’s ability to distinguish between phishing and legitimate sites; or (c) a combination of both. ceiterion (C) Probability legitimate sites phishing sites (signal) (non signal) true positive false negative No, legitimate sites Yes, phishing sites Criterion Probability true negative phishing sites (signal) false positive legitimate sites (non signal) No, legitimate sites Yes, phishing sites Fig. 8.1: Signal Detection Theory Internal response probability of occurrence curves for noise-alone and signal-plus-noise trials. Since the curves overlap, the internal response for a noise-alone trial may exceed the internal response for a signal-plus-noise trial. Vertical lines correspond to the criterion response. CU IDOL SELF LEARNING MATERIAL (SLM)

Signal Detection Theory 197 8.3 Basic Concept of Signal Detection Theory Signal detection theory (SDT) is used when psychologists want to measure the way we make decisions under conditions of uncertainty, such as how we would perceive distances in foggy conditions or during eyewitness identification. SDT assumes that the decision maker is not a passive receiver of information, but an active decision-maker who makes difficult perceptual judgments under conditions of uncertainty. In foggy circumstances, we are forced to decide how far away from us an object is, based solely upon visual stimulus which is impaired by the fog. Since the brightness of the object, such as a traffic light, is used by the brain to discriminate the distance of an object, and the fog reduces the brightness of objects, we perceive the object to be much farther away than it actually is (see also decision theory). According to SDT, during eyewitness identifications, witnesses base their decision as to whether a suspect is the culprit or not based on their perceived level of familiarity with the suspect. Signal detection theory (SDT) was originally developed to describe the performance of radars, which must detect signals against a background of noise. As radars become more sensitive (capable of detecting weaker and weaker signals), they are increasingly able to correctly detect when signals are present; these events are called hits, and their probability of occurrence is the hit rate. However, radars may also mistake noise for signals; these events are false alarms, and the corresponding probability is the false alarm rate. A challenge similar to the detection of signals by radars arises when humans listen for weak auditory stimuli. A key notion here is that perception involves decision: Was that faint tone simply imagined, or was it actually presented? SDT addresses this problem by recognizing that hit and false alarm rates reflect two factors, sensitivity and bias. Sensitivity is the ability to distinguish the presence of a signal from its absence. For example, sensitivity to an auditory tone increases when the tone becomes louder or when the noise in which it is presented becomes quieter. Bias is the tendency to state that a signal is present, and it also affects hit and false alarm rates. A listener will more likely report hearing a faint tone when each hit earns $10 and each false alarm costs $1 (bias is set to favor hits), than when the rewards and penalties are reversed (bias is set to avoid false alarms). Early SDT publications demonstrated that common performance measures confound sensitivity and bias. For example, the percentage of correct responses is often conceptualized as reflecting sensitivity, but it changes when bias changes. These early SDT publications derived CU IDOL SELF LEARNING MATERIAL (SLM)

198 Experimental Psychology “pure” measures of sensitivity, including d' and A', and “pure” measures of bias, such as ß and c. These measures are now routinely assessed in such diverse areas as memory, medicine and clinical diagnosis, library science, weather forecasting, and hazard detection by motor vehicle operators. Indeed, the literature is filled with publications that apply SDT to a wide range of problems. A frequent goal is to demonstrate how the understanding of a particular phenomenon changes when sensitivity is distinguished from bias. 8.4 Assumptions of Signal Detection Theory Assumptions of Signal Detection Theory are as follows: 1. If there is sufficient evidence in the literature to warrant the assumption of equal variance Gaussian density functions, d' and beta may be employed to describe, respectively, the subject’s ability to discriminate the two classes of events and the subject’s response bias, subject to the considerations. 2. If there is insufficient evidence to support the assumptions of the model (that the density functions are Gaussian and of equal variance), these assumptions should be tested directly in applying the model. 3. The most typical method for testing these assumptions is the use of a rating procedure. This procedure should be used also if the density functions are suspected of being Gaussian, but of unequal variance, and might be employed by careful researchers even when both assumptions are supported by previous research. 4. With the rating procedure, the subject is asked to use N different responses that reflect the subject’s confidence that a Class 1 event has occurred. Typically, five to eight confidence ratings or responses are employed. 5. If the receiver-operating-characteristic curve exhibits a systematic deviation from linearity, the Gaussian assumption may be invalid. 6. If this deviation from linearity is large, but not systematic, there exists an actual deviation from normality and/or a large error factor that may be correlated with the criterion of the subject. CU IDOL SELF LEARNING MATERIAL (SLM)

Signal Detection Theory 199 7. Any criterion-correlated error factor will distort the form of the normalized receiver- operating-characteristic curve. 8. The Gaussian models of signal detection theory are members of a class of models in which the parameters describing the ability of the subject to perform the given task and the subject’s decision rule are dependent on the assumption of certain specific underlying density functions. 8.5 Applications of Signal Detection Theory Signal Detection Theory has wide application, both in humans and animals. Topics include memory, stimulus characteristics of schedules of reinforcement, etc. 1. Sensitivity or Discriminability Conceptually, sensitivity refers to how hard or easy it is to detect that a target stimulus is present from background events. For example, in a recognition memory paradigm, having longer to study to-be-remembered words makes it easier to recognize previously seen or heard words. In contrast, having to remember 30 words rather than 5 make the discrimination harder. One of the most commonly used statistics for computing sensitivity is the so-called sensitivity index or d'. There are also non-parametric measures, such as the area under the ROC-curve. 2. Bias Bias is the extent to which one response is more probable than another. That is, a receiver may be more likely to respond that a stimulus is present or more likely to respond that a stimulus is not present. Bias is independent of sensitivity. For example, if there is a penalty for either false alarms or misses, this may influence bias. If the stimulus is a bomber, then a miss (failing to detect the plane) may increase deaths, so a liberal bias is likely. In contrast, crying wolf (a false alarm) too often may make people less likely to respond, grounds for a conservative bias. 3. Compressed Sensing Another field which is closely related to signal detection theory is called compressed sensing (or compressive sensing). The objective of compressed sensing is to recover high dimensional but CU IDOL SELF LEARNING MATERIAL (SLM)

200 Experimental Psychology with low complexity entities from only a few measurements. Thus, one of the most important applications of compressed sensing is in the recovery of high dimensional signals which are known to be sparse (or nearly sparse) with only a few linear measurements. The number of measurements needed in the recovery of signals is by far smaller than what Nyquist sampling theorem requires provided that the signal is sparse, meaning that it only contains a few non-zero elements. There are different methods of signal recovery in compressed sensing including basis pursuit, expander recovery algorithm, CoSaMP and also fast non-iterative algorithm. In all of the recovery methods mentioned above, choosing an appropriate measurement matrix using probabilistic constructions or deterministic constructions, is of great importance. In other words, measurement matrices must satisfy certain specific conditions such as RIP (Restricted Isometry Property) or Null Space Property in order to achieve robust sparse recovery. 4. Performance Decrements in Monitoring Performance decrements in monitoring (vigilance) tasks result either from a decrement in observer sensitivity or from changes in decision criteria. A taxonomic approach is outlined which predicts which of these performance trends will occur in a particular monitoring situation. This enables an identification of those cases where sensitivity decrements occur, for which design changes may be needed and where training techniques are unlikely to be helpful in enhancing performance. 5. Poor Reader Performance in Medical Diagnosis Poor reader performance in medical diagnosis may result from inappropriate reporting criteria, especially when disease prevalence and decision goals vary. A study is reported in which staff radiologists, but not radiology residents, were found to vary their reporting criteria appropriately in response to a change in disease prevalence. Staff radiologists were superior to the residents in disease detection and classification, but the residents were not significantly worse if both first and second choice diagnoses were analyzed. ROC measures of diagnostic accuracy were independent of disease prevalence and reporting criteria. The results have implications for the identification of training criteria and the assessment of system accuracy in diagnostic radiology. These two studies illustrate the general usefulness of SDT in identifying target areas for the application of human engineering principles. CU IDOL SELF LEARNING MATERIAL (SLM)

Signal Detection Theory 201 8.6 Summary Signal Detection Theory is a means to measure the ability to differentiate between information- bearing patterns called stimulus in living organisms, signal in machines and random patterns that distract from the information called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator. In the field of electronics, the separation of such patterns from a disguising background is referred to as signal recovery. According to the theory, there are a number of determiners of how a detecting system will detect a signal, and where its threshold levels will be. The theory can explain how changing the threshold will affect the ability to discern, often exposing how adapted the system is to the task, purpose or goal at which it is aimed. When the detecting system is a human being, characteristics such as experience, expectations, physiological state (e.g., fatigue) and other factors can affect the threshold applied. For instance, a sentry in wartime might be likely to detect fainter stimuli than the same sentry in peacetime due to a lower criterion; however they might also be more likely to treat innocuous stimuli as a threat. Detection theory has applications in many fields such as diagnostics of any kind, quality control, telecommunications, and psychology. The concept is similar to the signal to noise ratio used in the sciences and confusion matrices used in artificial intelligence. It is also usable in alarm management, where it is important to separate important events from background noise. Signal detection theory (SDT) is used when psychologists want to measure the way we make decisions under conditions of uncertainty, such as how we would perceive distances in foggy conditions or during eyewitness identification. SDT assumes that the decision-maker is not a passive receiver of information, but an active decision-maker who makes difficult perceptual judgments under conditions of uncertainty. In foggy circumstances, we are forced to decide how far away from us an object is, based solely upon visual stimulus which is impaired by the fog. Since the brightness of the object, such as a traffic light, is used by the brain to discriminate the distance of an object, and the fog reduces the brightness of objects, we perceive the object to be much farther away than it actually is (see also decision theory). According to SDT, during eyewitness identifications, witnesses base their decision as to whether a suspect is the culprit or not based on their perceived level of familiarity with the suspect. CU IDOL SELF LEARNING MATERIAL (SLM)

202 Experimental Psychology Signal detection theory (SDT) was originally developed to describe the performance of radars, which must detect signals against a background of noise. As radars become more sensitive (capable of detecting weaker and weaker signals), they are increasingly able to correctly detect when signals are present; these events are called hits, and their probability of occurrence is the hit rate. However, radars may also mistake noise for signals; these events are false alarms, and the corresponding probability is the false alarm rate. A challenge similar to the detection of signals by radars arises when humans listen for weak auditory stimuli. A key notion here is that perception involves decision: Was that faint tone simply imagined, or was it actually presented? SDT addresses this problem by recognizing that hit and false alarm rates reflect two factors, sensitivity and bias. Sensitivity is the ability to distinguish the presence of a signal from its absence. For example, sensitivity to an auditory tone increases when the tone becomes louder or when the noise in which it is presented becomes quieter. Bias is the tendency to state that a signal is present, and it also affects hit and false alarm rates. A listener will more likely report hearing a faint tone when each hit earns $10 and each false alarm costs $1 (bias is set to favor hits), than when the rewards and penalties are reversed (bias is set to avoid false alarms). Early SDT publications demonstrated that common performance measures confound sensitivity and bias. For example, the percentage of correct responses is often conceptualized as reflecting sensitivity, but it changes when bias changes. These early SDT publications derived “pure” measures of sensitivity, including d' and A', and “pure” measures of bias, such as ß and c. These measures are now routinely assessed in such diverse areas as memory, medicine and clinical diagnosis, library science, weather forecasting, and hazard detection by motor vehicle operators. Indeed, the literature is filled with publications that apply SDT to a wide range of problems. A frequent goal is to demonstrate how the understanding of a particular phenomenon changes when sensitivity is distinguished from bias. Bias is the extent to which one response is more probable than another. That is, a receiver may be more likely to respond that a stimulus is present or more likely to respond that a stimulus is not present. Bias is independent of sensitivity. For example, if there is a penalty for either false alarms or misses, this may influence bias. If the stimulus is a bomber, then a miss (failing to detect the plane) may increase deaths, so a liberal bias is likely. In contrast, crying wolf (a false alarm) too often may make people less likely to respond, grounds for a conservative bias. CU IDOL SELF LEARNING MATERIAL (SLM)

Signal Detection Theory 203 Conceptually, sensitivity refers to how hard or easy it is to detect that a target stimulus is present from background events. For example, in a recognition memory paradigm, having longer to study to-be-remembered words makes it easier to recognize previously seen or heard words. In contrast, having to remember 30 words rather than 5 make the discrimination harder. One of the most commonly used statistics for computing sensitivity is the so-called sensitivity index or d'. There are also non-parametric measures, such as the area under the ROC curve. Bias is the extent to which one response is more probable than another. That is, a receiver may be more likely to respond that a stimulus is present or more likely to respond that a stimulus is not present. Bias is independent of sensitivity. For example, if there is a penalty for either false alarms or misses, this may influence bias. If the stimulus is a bomber, then a miss (failing to detect the plane) may increase deaths, so a liberal bias is likely. In contrast, crying wolf (a false alarm) too often may make people less likely to respond, grounds for a conservative bias. Performance decrements in monitoring (vigilance) tasks result either from a decrement in observer sensitivity or from changes in decision criteria. A taxonomic approach is outlined which predicts which of these performance trends will occur in a particular monitoring situation. This enables an identification of those cases where sensitivity decrements occur, for which design changes may be needed and where training techniques are unlikely to be helpful in enhancing performance. Poor reader performance in medical diagnosis may result from inappropriate reporting criteria, especially when disease prevalence and decision goals vary. A study is reported in which staff radiologists, but not radiology residents, were found to vary their reporting criteria appropriately in response to a change in disease prevalence. Staff radiologists were superior to the residents in disease detection and classification, but the residents were not significantly worse if both first and second choice diagnoses were analyzed. ROC measures of diagnostic accuracy were independent of disease prevalence and reporting criteria. The results have implications for the identification of training criteria and the assessment of system accuracy in diagnostic radiology. These two studies illustrate the general usefulness of SDT in identifying target areas for the application of human engineering principles. CU IDOL SELF LEARNING MATERIAL (SLM)

204 Experimental Psychology 8.7 Key Words/Abbreviations  Signal Detection Theory: Detect a signal.  Concept of Signal Detection Theory: Signal detection theory (SDT) is used when psychologists want to measure the way we make decisions under conditions of uncertainty.  Compressed Sensing: Another field which is closely related to signal detection theory is called compressed sensing.  Performance Decrements: Performance decrements in monitoring (vigilance) tasks result either from a decrement in observer sensitivity or from changes in decision criteria.  Medical Diagnosis: Poor reader performance in medical diagnosis may result from inappropriate reporting criteria. 8.8 Learning Activity 1. You are required to identify the basic concept of Signal Detection Theory. _________________________________________________________________ _________________________________________________________________ 2. You are suggested to prepare the report on “Applications of Signal Detection Theory”. _________________________________________________________________ _________________________________________________________________ 8.9 Unit End Exercises (MCQs and Descriptive) A. Descriptive Type Questions 1. Discuss in details about Signal Detection Theory. 2. Explain the basic concept of Signal Detection Theory. 3. Discuss various assumptions of Signal Detection Theory. 4. Explain the applications of Signal Detection Theory. 5. Write note on Performance decrements and Medical diagnosis. CU IDOL SELF LEARNING MATERIAL (SLM)

Signal Detection Theory 205 B. Multiple Choice Questions 1. Which of the following is a means to measure the ability to differentiate between information- bearing patterns called stimulus? (a) Signal Detection Theory (b) Experience (c) Expectations (d) All the above 2. When the detecting system is a human being, characteristics such as __________. (a) Experience (b) Expectations (c) Physiological state (d) All the above 3. Which of the following is the assumption of Signal Detection Theory? (a) If there is sufficient evidence in the literature to warrant the assumption of equal variance (b) If there is insufficient evidence to support the assumptions of the model (c) The most typical method for testing these assumptions is the use of a rating procedure (d) All the above 4. Which of the following in monitoring tasks result either from a decrement in observer sensitivity or from changes in decision criteria? (a) Performance decrement (b) Efficiency (c) Proficiency (d) All the above 5. Which of the following is the application of Signal Detection Theory? (a) Sensitivity or discriminability (b) Compressed sensing (c) Performance decrements in monitoring (d) All the above Answers: 1. (a), 2. (b), 3. (d), 4. (a), 5. (c) 8.10 References References of this unit have been given at the end of the book.  CU IDOL SELF LEARNING MATERIAL (SLM)

206 Experimental Psychology UNIT 9 LEARNING THEORIES Structure: 9.0 Learning Objectives 9.1 Introduction 9.2 The Concept of Learning 9.3 Types of Learning 9.4 Principles of Learning 9.5 FactorsAffecting Learning 9.6 Learning Theories 9.7 Hull 9.8 Tolman 9.9 Guthrie’s Theory of Learning 9.10 Summary 9.11 Key Words/Abbreviations 9.12 LearningActivity 9.13 Unit End Exercises (MCQs and Descriptive) 9.14 References CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 207 9.0 Learning Objectives After studying this unit, you will be able to:  Explain the Classical Conditioning  Explain the Instrumental Conditioning  Discuss about Hull  Elaborate the Tolman and Guthrie 9.1 Introduction Learning Theory describes how students absorb, process, and retains knowledge during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a world view, is acquired or changed and knowledge and skills retained. Behaviorists look at learning as an aspect of conditioning and advocate a system of rewards and targets in education. Educators who embrace cognitive theory believe that the definition of learning as a change in behaviour is too narrow, and study the learner rather than their environment and in particular the complexities of human memory. Those who advocate constructivism believe that a learner’s ability to learn relies largely on what they already know and understand, and the acquisition of knowledge should be an individually tailored process of construction. Transformative learning theory focuses on the often-necessary change required in a learner’s preconceptions and world view. Geographical learning theory focuses on the ways that contexts and environments shape the learning process. 9.2 The Concept of Learning Learning can be defined as a process that brings together cognitive, emotional and environmental influences and experiences for acquiring, enhancing or making changes in one’s knowledge, skills, values and world views. Learning as a process focuses on what happens when the learning takes place. Explanations of what happens constitute learning theories. A learning theory is an attempt to describe how people and animals learn; thereby helping us understands the inherently complex process of learning. CU IDOL SELF LEARNING MATERIAL (SLM)

208 Experimental Psychology Definitions of Learning Gales defined “Learning as the behavioural modification which occurs as a result of experience as well as training”. Crow and Crow defined “Learning as the process of acquisition of knowledge, habits and attitudes”. According to E.A. Peel, “Learning can be described as a change in the individual which takes place as a result of the environmental change”. H.J. Klausmeir described “Learning as a process which leads to some behavioural change as a result of some experience, training, observation, activity, etc.” John B. Watson defined Learning as “any relatively permanent change in that occurs as a result of practice and experience”. Nature of Learning 1. Nature of learning is the features of learning. 2. Learning involves change in the behavior of an individual but it may or may not lead to the guarantee improvement in an individual. 3. Learning is a lifelong process and is permanent in nature. This change in the behavior is mainly due to the result in past experience, practice and training. Whatever we learn generally reflects through our behavior. 4. Learning is described as a key process in human. All human beings learn. Each and every individual learns as he grows from past experience and training. 5. An individual learns by interacting with others and his behavior is influenced by the environment in which he lives. This experience makes him to change or modify his in order to deal efficiently with it. We can conclude that, learning is a change in, influenced by past. 6. An individual’s skills, knowledge, habits, attitudes, interests and other personality characteristics are mainly because of learning. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 209 Characteristics of Learning 1. Learning is a fundamental process of life. 2. It is a continuous process it affects all modes of behaviour. 3. Learning is change in response or behaviour, may be favorable or unfavorable. 4. It is a process of change not a product in the form of changed behaviour. 5. Learning takes place when an organism reacts in a situation. 6. Learning is universal. 7. Learning is total reaction of the individual to the total situation. 8. Learning is transferable. 9. Learning is a process and not a product. 10. The process of learning is determined by conscious as well as unconscious experiences. Significance of Learning 1. Learning is essential to all organisms and without learning a living soul is of no use. One who does not understand his environment at least will be dead in no time. Imagine being born and not learning to breathe in the open air! You would be dead in no time. 2. Learning helps us understand basic necessities of life and gives us a way of acquiring and mastering them. A lion that cannot prey or a gazelle that cannot run fast are both going to have a swift end, unless they meet each other and one survives. Similarly, a person cannot be spoon fed throughout his life. As he grows, he needs to learn how food is eaten and then find ways to earn his food. 3. Learning helps to adapt to a new environment. If only we know how to change our ways according to changes in our locale, will we survive. This is evident in the rise of modern humans as opposed to the Neanderthals and other primitive humanoids. This can also be demonstrated with the spread of human beings to arctic regions and the deserts where the people have adapted their lifestyles to match their environment. Not just extremes of CU IDOL SELF LEARNING MATERIAL (SLM)

210 Experimental Psychology temperatures or conditions, we have to adapt ourselves every day to new people, places, jobs and relations. Only learning can assist us in this. 4. Learning helps respond to dangers and react. If you ever see a child stuck in the middle of a road, you will find that it sits still when the road is empty, but starts crying as soon as the traffic approaches. However, the reaction of an adult, in a similar situation, is to run and save him/her. This is because the grown up has learned to avert danger by action, whereas the baby has only learned to cry and attract attention, so that someone saves it, eventually averting danger. Survival even in normal life is impossible without learning. 5. Learning helps in becoming more efficient and helps attain great positions. Each time you read a resume and run through the title ‘work experience’, know that the information added reflects learning that the individual indulged in, apart from educational qualifications. This is based on the assumption that the time you spent doing something also helped you to “learn” that craft. 6. Greater learning can provide you with deeper knowledge of a subject, which cannot be imparted from bookish education. We must remember that some of the most prominent personalities in history were not educated, but learned and well versed in their own trade, just by the virtue of learning and not education. How you learn something can take you from a common man's position to that of a leader. 7. In totality your ability and inclination to learn determines the course that your life takes and the success that you achieve. You have to use your learning of concepts, people and situations in handling day to day stuff. Remember, winners don't do anything different; they just do the same things differently. 8. Learning is never complete unless we have both experience and education. A lack of either can impair the use of other. Unless we have all of these, we cannot become better individuals. 9. Learning is not just important to ensure that we keep up-to-date with developments in our particular field. It is also an important source of motivation, stimulation and job satisfaction. For example, somebody who works in a particular place for three years and during that time continues to learn, grow and develop is likely to experience far greater job satisfaction CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 211 than someone who stays in the same post for three years, simply repeating the basic tasks in the same way without any growth or development over that time. Learning should therefore be seen as something positive and worthwhile in its own right, not just something that we have to do to meet other people’s expectations of us. 10. Learning is, therefore, important because it helps us to keep in tune with trends and developments in our own field. It provides stimulation and job satisfaction and also helps to keep us on our toes to make sure that we do not become blasé and thus more likely to make mistakes. Learning should therefore not be seen as an additional burden on top of what is already perhaps a heavy workload, but rather something to be welcomed as a means of dealing as effectively as possible with that heavy workload. 9.3 Types of Learning Learning is of various types which are explained as under: 1. Motor Learning The day-to-day activities of an individual refer to motor activities. In order to maintain regular life each and every individual learns all these activities which include walking, running, skating, driving, climbing, etc. All these activities involve the powerfully built coordination. 2. Verbal Learning It refers to the language we speak and communicate. It also includes the various communication devices which we use on a day-to-day basis. They mainly include signs, pictures, symbols, words, figures, sounds, etc. which are considered to be the tools used in such activities. Words are used for the purpose of communication. 3. Concept Learning It is the form of learning which requires higher order mental processes like thinking, reasoning, intelligence, etc. We learn different concepts from birth. Concept learning involves two processes, which include construction and simplification. This learning is very useful in order to understand and identify different things. CU IDOL SELF LEARNING MATERIAL (SLM)

212 Experimental Psychology 4. Discrimination Learning Discrimination refers to learning to differentiate between stimuli and showing a proper and correct response to these stimuli. Example, sound horns of different vehicles like bus, car, ambulance, etc. 5. Learning of Principles Individuals learn certain principles, rules and laws related to science, mathematics, grammar, etc. in order to manage and do their work effectively and efficiently. These principles help in showing the relationship between two or more concepts. 6. Problem Solving Problem solving is considered to be a higher order learning process. This type of learning requires the use of cognitive abilities which include thinking, reasoning, observation, imagination, generalization, etc. It is very useful to overcome difficult problems which are encountered by the people. 7. Attitude Learning Attitude is a tendency which helps us in understanding behaviour and as such is highly suchceptible to change. Every individual develops different kinds of attitudes from birth about different people, objects and everything around him. The behavior which an individual has may be positive or negative depending upon his attitudes. 9.4 Principles of Learning Principles of Learning are as follows: (i) Learning is active: Active learning goes beyond providing opportunities for hands-on, experiential learning. It involves engaging the learner’s mind as well as they are encouraged to question and evaluate information critically. The emphasis on enquiry, processes and skills promotes transference. (ii) Learning is holistic: Learning is discipline-based but interconnectedness of knowledge is promoted. Learning is also made more coherent with regular opportunities to see the connection of learning to real life. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 213 (iii) Learning requires met cognition: All learners are capable of being proactive, self-reflecting and self-regulating. Met cognitive processes should be taught explicitly and opportunities must be made available for learners to self-regulate. (iv) Learning is social: Learning is influenced by social interactions, interpersonal relations and communication with others. (v) Learning is contextual: Learning is facilitated by conducive environmental factors. 9.5 Factors Affecting Learning Learning is completely based upon certain important factors that help in deciding what changes will be caused by this experience. These are the main factors that influence what a person learns and are considered to be the root level for our behavior and everything we do is connected to what we learn. The various key elements or the main factors that affect learning are: 1. Motivation The encouragement, the support one gets to complete a task, to achieve a goal is known as motivation or encouragement. It is a very important aspect of learning as it acts gives us a positive energy to complete a task. 2. Practice There is very good saying that “Practice makes us perfect”. In order to be a perfectionist or at least complete the task, it is very important to practice what we have learnt continuously. 3. Environment Each and every individual learns from his surroundings and learn from the people around him. The two types of environment are internal and external. What we learn at home is treated as our internal environment and what we learn from outside by interacting with people both friends and at workplace is referred as external environment. CU IDOL SELF LEARNING MATERIAL (SLM)

214 Experimental Psychology 4. Mental Group It describes our thinking by the group of people we chose to be with. Every person likes to connect with people who he is comfortable with. It can be for a social cause where people with the same mentality work in the same direction. 9.6 Learning Theories Various Learning Theories are: 1. Classical Conditioning 2. Instrumental Conditioning 3. Observational Learning 1. Classical Conditioning Classical conditioning (also known as Pavlovian conditioning) is learning through association and was discovered by Pavlov, a Russian physiologist. In simple terms two stimuli are linked together to produce a new learned response in a person or animal. John Watson proposed that the process of classical conditioning (based on Pavlov’s observations) was able to explain all aspects of human psychology. Everything from speech to emotional responses was simply patterns of stimulus and response. Watson denied completely the existence of the mind or consciousness. Watson believed that all individual differences in behavior were due to different experiences of learning. He famously said, “Give me a dozen healthy infants, well-formed, and my own specified world to bring them up in and I’ll guarantee to take any one at random and train him to become any type of specialist I might select – doctor, lawyer, artist, merchant-chief and, yes, even beggar-man and thief, regardless of his talents, penchants, tendencies, abilities, vocations and the race of his ancestors”. Classical Conditioning Flow Chart based on pavlov’s observation are given below: Models display the stimuli, responses and their interactions during stages of conditioning. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 215 Using Pavlov’s experiment, define each of the following types of stimuli and responses, and describe how they interact and are used to create a learned response. Before conditioning No Response NS (Bell) US UR (Food) (Salivation) During conditioning US UR (Food) (Salivation) NS (Bell) CR (Salivation) After conditioning NS (Bell) Fig. 9.1: Classical Conditioning Flow Chart Applying the Model of Classical Conditioning to Human Behavior For each example listed below, create a model similar to that used to analyze Pavlov’s experiment in order to show how classical conditioning plays a role in learned human behavior. Background information is found in the link below. Classical Conditioning Examples Little Albert (conditioned fear) Peter Rabbit (unlearned fear) CU IDOL SELF LEARNING MATERIAL (SLM)

216 Experimental Psychology spiders (relaxed anxiety) autoimmune disorders (mind over body) Classical conditioning generally requires repeated and closely associated pairings of stimuli to have desired effects, but can a response be conditioned in a single occurrence? Consider a conditioned taste aversion where a particular food and an illness are paired together. Can conditioning take place in one pairing? Create a classical model to represent the impact of this unfortunate event. There are three stages of classical conditioning. Stages of Classical Conditioning At each stage, the stimuli and responses are given special scientific terms: Stage 1: Before Conditioning In this stage, the unconditioned stimulus (UCS) produces an unconditioned response (UCR) in an organism. In basic terms, this means that a stimulus in the environment has produced a behavior/response which is unlearned (i.e., unconditioned) and therefore is a natural response which has not been taught. In this respect, no new behavior has been learned yet. For example, a stomach virus (UCS) would produce a response of nausea (UCR). In another example, a perfume (UCS) could create a response of happiness or desire (UCR). This stage also involves another stimulus which has no effect on a person and is called the neutral stimulus (NS). The NS could be a person, object, place, etc. The neutral stimulus in classical conditioning does not produce a response until it is paired with the unconditioned stimulus. Stage 2: During Conditioning During this stage, a stimulus which produces no response (i.e., neutral) is associated with the unconditioned stimulus at which point it now becomes known as the conditioned stimulus (CS). For example, a stomach virus (UCS) might be associated with eating a certain food such as chocolate (CS). Also, perfume (UCS) might be associated with a specific person (CS). CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 217 For classical conditioning to be effective, the conditioned stimulus should occur before the unconditioned stimulus, rather than after it, or during the same time. Thus, the conditioned stimulus acts as a type of signal or cue for the unconditioned stimulus. Often during this stage, the UCS must be associated with the CS on a number of occasions, or trials, for learning to take place. However, one trail learning can happen on certain occasions when it is not necessary for an association to be strengthened over time (such as being sick after food poisoning or drinking too much alcohol). Stage 3: After Conditioning Now, the conditioned stimulus (CS) has been associated with the unconditioned stimulus (UCS) to create a new conditioned response (CR). For example, a person (CS) who has been associated with nice perfume (UCS) is now found attractive (CR). Also, chocolate (CS) which was eaten before a person was sick with a virus (UCS) now produces a response of nausea (CR). Little Albert Experiment (Phobias) Ivan Pavlov showed that classical conditioning applied to animals. Did it also apply to humans? In a famous (though ethically dubious) experiment, Watson and Rayner (1920) showed that it did. Little Albert was a 9-month-old infant who was tested on his reactions to various stimuli. He was shown a white rat, a rabbit, a monkey and various masks. Albert described as “on the whole stolid and unemotional” showed no fear of any of these stimuli. However, what did startle him and cause him to be afraid was if a hammer was struck against a steel bar behind his head. The sudden loud noise would cause “little Albert to burst into tears”. Little Albert Classical Conditioning When Little Albert was just over 11 months old, the white rat was presented, and seconds later the hammer was struck against the steel bar. This was done seven times over the next seven weeks, and each time Little Albert burst into tears. By now little Albert only had to see the rat and he immediately showed every sign of fear. He would cry (whether or not the hammer was hit against the steel bar) and he would attempt to crawl away. CU IDOL SELF LEARNING MATERIAL (SLM)

218 Experimental Psychology Fig. 9.2: Little Albert Classical Conditioning In addition, the Watson and Rayner found that Albert developed phobias of objects which shared characteristics with the rat; including the family dog, a fur coat, some cotton wool and a Father Christmas mask! This process is known as generalization. Watson and Rayner had shown that classical conditioning could be used to create a phobia. A phobia is an irrational fear, i.e., a fear that is out of proportion to the danger. Over the next few weeks and months, Little Albert was observed and ten days after conditioning his fear of the rat was much less marked. This dying out of a learned response is called extinction. However, even after a full month it was still evident, and the association could be renewed by repeating the original procedure a few times. Classical Conditioning in the Classroom The implications of classical conditioning in the classroom are less important than those of operant conditioning, but there is a still need for teachers to try to make sure that students associate positive emotional experiences with learning. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 219 If a student associates negative emotional experiences with school, then this can obviously have bad results, such as creating a school phobia. For example, if a student is bullied at school they may learn to associate the school with fear. It could also explain why some students show a particular dislike of certain subjects that continue throughout their academic career. This could happen if a student is humiliated or punished in class by a teacher. Critical Evaluation Classical conditioning emphasizes the importance of learning from the environment, and supports nurture over nature. However, it is limiting to describe behavior solely in terms of either nature or nurture, and attempts to do this underestimate the complexity of human behavior. It is more likely that behavior is due to an interaction between nature (biology) and nurture (environment). Strength of classical conditioning theory is that it is scientific. This is because it is based on empirical evidence carried out by controlled experiments. For example, Pavlov (1902) showed how classical conditioning could be used to make a dog salivate to the sound of a bell. Classical conditioning is also a reductionist explanation of behavior. This is because a complex behavior is broken down into smaller stimulus-response units of behavior. Supporters of a reductionist approach say that it is scientific. Breaking complicated behaviors down to small parts means that they can be scientifically tested. However, some would argue that the reductionist view lacks validity. Thus, while reductionism is useful, it can lead to incomplete explanations. A final criticism of classical conditioning theory is that it is deterministic. This means that it does not allow for any degree of free will in the individual. Accordingly, a person has no control over the reactions they have learned from classical conditioning, such as a phobia. The deterministic approach also has important implications for psychology as a science. Scientists are interested in discovering laws which can then be used to predict events. However, by creating general laws of behavior, deterministic psychology underestimates the uniqueness of human beings and their freedom to choose their own destiny. CU IDOL SELF LEARNING MATERIAL (SLM)

220 Experimental Psychology 2. Instrumental Conditioning Instrumental conditioning is another term for operant conditioning, a learning process first described by B.F. Skinner. In instrumental conditioning, reinforcement or punishment are used to either increase or decrease the probability that a behavior will occur again in the future. Examples of Operant Conditioning For example, if a student is rewarded with praise every time she raises her hand in class, she becomes more likely to raise her hand again in the future. If she is also scolded when she speaks out of turn, she becomes less likely to interrupt the class. In these examples, the teacher is using reinforcement to strengthen the hand-raising behavior and punishment to weaken the talking out of turn behavior. Instrumental conditioning is often used in animal training as well. For example, training a dog to shake hands would involve offering a reward every time the desired behavior occurs. History of Operant Conditioning Psychologist E.L. Thorndike was one of the first to observe the impact of reinforcement in puzzle box experiments with cats. During these experiments, Thorndike observed a learning process that he referred to as “trial-and-error” learning. The experiments involved placing a hungry cat in a puzzle box and in order to free itself, the cat had to figure out how to escape. Thorndike then noted how long it took the cats to free themselves on each experimental trial. Initially, the cats engaged in ineffective escape methods, scratching and digging at the sides or top of the box. Eventually, trial-and-error would lead the cats to successfully push or pull the escape route. After each successive trial, the cats engaged less and less in the ineffective escape behaviors and more quickly responded with the correct escape actions. Thorndike referred to his observations as the Law of Effect. The strength of a response increases when it is immediately followed by a “satisfier” (reinforcer). On the other hand, actions that are followed by unpleasant effects are more likely to be weakened. In Thorndike’s puzzle box experiments, escaping the box was the satisfier. Every time the cats successfully escaped the box, the behavior that immediately preceded the escape was reinforced and strengthened. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 221 Thorndike’s work had a tremendous effect on B.F. Skinner’s later research on operant conditioning. Skinner even created his own version of Thorndike’s puzzle boxes which he referred to as an operant chamber, also known as a Skinner box. Functioning of Operant Conditioning at Workplace Skinner identified two key types of behaviors. The first type is respondent behaviors. These are simply actions that occur reflexively without any learning. If you touch something hot, you will immediately draw your hand back in response. Classical conditioning focuses on these respondent behaviors. In Pavlov’s classic experiments with dogs, salivating to the presentation of food was the respondent behavior. By forming an association between the sound of a bell and the presentation of food, however, Pavlov was able to train dogs to actually salivate simply at the sound of that bell. Skinner realized that while classical conditioning could explain how respondent behaviors could lead to learning, it could not account for every type of learning. Instead, he suggested that it was the consequences of voluntary actions that lead to the greatest amount of learning. The second type of behaviors is what Skinner referred to as operant behaviors. He defined these as any and every voluntary behavior that acts upon the environment to create a response. These are the voluntary behaviors that are under our conscious control. These are also actions that can be learned. The consequences of our actions play an important role in the learning process. Reinforcement and Punishment Skinner identified two key aspects of the operant conditioning process. Reinforcement serves to increase the behavior while punishment serves to decrease the behavior. There are also two different types of reinforcement and two different types of punishment. Positive reinforcement involves presenting a favorable outcome, such as giving a child a treat after she cleans her room. Negative reinforcement involves the removal of an unpleasant stimulus, like telling a child that if she eats all her potatoes, then she will not have to eat her broccoli. Since the child considers broccoli an unpleasant consequence and eating the potatoes leads to the removal of this undesirable consequence, eating the potatoes is then negatively reinforced. CU IDOL SELF LEARNING MATERIAL (SLM)

222 Experimental Psychology Positive punishment means applying an unpleasant event after a behavior. Spanking, for example, is a common example of positive punishment. This type of punishment is often referred to as punishment by application. A negative consequence is directly applied to reduce the unwanted behavior. Negative punishment involves taking away something pleasant after a behavior occurs. For example, if a child fails to clean her room, her parents might tell her that she cannot go to the mall with her friends. Taking away the desirable activity acts as a negative punisher on the preceding behavior. 3. Observational Learning The Observational Learning process was propounded by Albert Bandura in his Social Learning Theory, which focused on learning by imitation or observing people’s behaviour. For observational learning to take place effectively, four important elements will be essential: Motivation, Attention, Memory and Motor Skills. Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based on various processes. In humans, this form of learning seems to not need reinforcement to occur, but instead, requires a social model such as a parent, sibling, friend or teacher with surroundings. Particularly in childhood, a model is someone of authority or higher status in an environment. In animals, observational learning is often based on classical conditioning, in which an instinctive behavior is elicited by observing the behavior of another (e.g. mobbing in birds), but other processes may be involved as well. Stages of Observational Learning Bandura’s social cognitive learning theory states that there are four stages involved in observational learning: Stage-1: Attention Observers cannot learn unless they pay attention to what is happening around them. This process is influenced by characteristics of the model, such as how much one likes or identifies with the model, and by characteristics of the observer, such as the observer's expectations or level of emotional arousal. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 223 Stage-2: Retention/Memory Observers must not only recognize the observed behavior but also remember it at some later time. This process depends on the observer’s ability to code or structure the information in an easily remembered form or to mentally or physically rehearse the model’s actions. Stage-3: Initiation/Motor Observers must be physically and/intellectually capable of producing the act. In many cases’ the observer possesses the necessary responses. But sometimes, reproducing the model's actions may involve skills the observer has not yet acquired. It is one thing to carefully watch a circus juggler, but it is quite another to go home and repeat those acts. Stage-4: Motivation The observer must have motivation to recreate the observed behavior. Perhaps, the most important aspect of observational learning involves motivation. If the human or animal does not have a reason for imitating the behavior, then no amount of attention, retention, or reproduction will overcome the lack of motivation. Bandura identified several motivating factors for imitation. These include knowing that the model was previously reinforced for the behavior, being offered an incentive to perform, or observing the model receiving reinforcement for the behavior. These factors can also be negative motivations. For instance, if the observer knew that the model was punished for the behavior, was threatened for exhibiting the behavior, or observed the model being punished for the behavior, then the probability of mimicking the behavior is less. Applications of Observational Learning Modeling has been used successfully in many therapeutic conditions. Many therapists have used forms of modeling to assist their patients to overcome phobias. For example, adults with claustrophobia may observe a model in a video as they move closer and closer to an enclosed area before entering it. Once the model reaches the enclosed area, for instance a closet, he or she will open the door, enter it, and then close the door. The observer will be taught relaxation techniques and be told to practice them anytime he or she becomes anxious while watching the film. The end result is to continue observing the model until the person can enter the closet himself or herself. CU IDOL SELF LEARNING MATERIAL (SLM)

224 Experimental Psychology Bandura’s findings in the Bobo doll experiments have greatly influenced children’s television programming. Bandura filmed his students physically attacking the Bobo doll, an inflatable doll with a rounded bottom that pops back up when knocked down. A student was placed in the room with the Bobo doll. The student punched the doll, yelled “sockeroo” at it, kicked it, hit it with hammers, and sat on it. Bandura then showed this film to young children. Their behavior was taped when in the room with the doll. The children imitated the behaviors of the student and at times even became more aggressive toward the doll than what they had observed. Another group of young children observed a student being nice to the doll. Ironically, this group of children did not imitate the positive interaction of the model. Bandura conducted a large number of varied scenarios of this study and found similar events even when the doll was a live clown. These findings have prompted many parents to monitor the television shows their children watch and the friends or peers with which they associate. Unfortunately, the parental saying “Do as I say, not as I do” does not hold true for children. Children are more likely to imitate the behaviors versus the instructions of their parents. One of the most famous instances of observational learning in animals involves the blue tit, a small European bird. During the 1920s and through the 1940s, many people reported that the cream from the top of the milk being delivered to their homes was being stolen. The cream-stealing incidents spread all over Great Britain. After much speculation about the missing cream, it was discovered that the blue tit was the culprit. Specifically, one bird had learned to peck through the foil top of the milk container and suck the cream out of the bottle. It did not take long before other blue tit birds imitated the behavior and spread it through the country. 9.7 Hull Hull developed a version of behaviorism in which the stimulus (S) affects the organism (O) and the resulting response (R) depends upon characteristics of both O and S. In other words, Hull was interested in studying intervening variables that affected behavior such as initial drive, incentives, inhibitors, and prior training (habit strength). Like other forms of behavior theory, reinforcement is the primary factor that determines learning. However, in Hull’s theory, drive reduction or need satisfaction plays a much more important role in behavior than in other frameworks (i.e., connectionism, operant conditioning). CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 225 Hull’s theoretical framework consisted of many postulates stated in mathematical form; They include: (1) organisms possess a hierarchy of needs which are aroused under conditions of stimulation and drive, (2) habit strength increases with activities that are associated with primary or secondary reinforcement, (3) habit strength aroused by a stimulus other than the one originally conditioned depends upon the closeness of the second stimulus in terms of discrimination thresholds, (4) stimuli associated with the cessation of a response become conditioned inhibitors, and (5) the more the effective reaction potential exceeds the reaction threshold, the shorter the latency of response. As these postulates indicate, Hull proposed many types of variables that accounted for generalization, motivation, and variability (oscillation) in learning. One of the most important concepts in Hull’s theory was the habit strength hierarchy: for a given stimulus, an organism can respond in a number of ways. The likelihood of a specific response has a probability which can be changed by reward and is affected by various other variables (e.g., inhibition). In some respects, habit strength hierarchies resemble components of cognitive theories such as schema and production systems. Applications of Hull’s Theory Hull’s theory is meant to be a general theory of learning. Most of the research underlying the theory was done with animals, except for Hull et al. (1940) which focused on verbal learning. Miller and Dollard (1941) represents an attempt to apply the theory to a broader range of learning phenomena. As an interesting aside, Hull began his career researching hypnosis – an area that landed him in some controversy at Yale (Hull, 1933). Example Here is an example described by Miller and Dollard (1941): A six-year-old girl who is hungry and wants candy is told that there is candy hidden fewer than one of the books in a bookcase. The girl begins to pull out books in a random manner until she finally finds the correct book (210 seconds). She is sent out of the room and a new piece of candy is hidden under the same book. In her next search, she is much more directed and finds the candy in 86 seconds. By the ninth repetition of this experiment, the girl finds the candy immediately (2 seconds). The girl exhibited a drive for the candy and looking under books represented her responses to reduce this drive. When she eventually found the correct book, this particular response was rewarded, forming a habit. On subsequent trials, the CU IDOL SELF LEARNING MATERIAL (SLM)

226 Experimental Psychology strength of this habit was increased until it became a single stimulus-response connection in this setting. 9.8 Tolman Tolman argued that humans engage in this type of learning everyday as we drive or walk the same route daily and learn the locations of various buildings and objects. Only when we need to find a building or object does learning become obvious. Tolman conducted experiments with rats and mazes to examine the role that reinforcement plays in the way those rats learn their way through complex mazes. These experiments eventually led to the theory of latent learning. Cognitive maps as an example of latent learning in rats. Tolman coined the term cognitive map, which is an internal representation (or image) of external environmental feature or landmark. He thought that individuals acquire large numbers of cues (i.e., signals) from the environment and could use these to build a mental image of an environment (i.e., a cognitive map). By using this internal representation of a physical space, they could get to the goal by knowing where it is in a complex of environmental features. Short-cuts and changeable routes are possible with this model. In their famous experiments, Tolman and Honzik (1930) built a maze to investigate latent learning in rats. The study also shows that rats actively process information rather than operating on a stimulus response relationship. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 227 Tolman’s Maze Door Curtain START Food Box Plan of Maze Fig. 9.3: Cognitive Maps Aim To demonstrate that rats could make navigational decisions based on knowledge of the environment, rather than their directional choices simply being dictated by the effects of rewards. Procedure In their study, three groups of rats had to find their way around a complex maze. At the end of the maze, there was a food box. Some groups of rats got to eat the food, some did not, and for some rats the food was only available after 10 days. Group 1: Rewarded Day 1 - 17: Every time they got to end, given food (i.e., reinforced). Group 2: Delayed Reward Day 1 - 10: Every time they got to end, taken out. Day 11 - 17: Every time they got to end, given food (i.e., reinforced). CU IDOL SELF LEARNING MATERIAL (SLM)

228 Experimental Psychology Group 3: No Reward Day 1 - 17: Every time they got to end, taken out. Results The delayed reward group learned the route on days 1 to 10 and formed a cognitive map of the maze. They took longer to reach the end of the maze because there was no motivation for them to perform. From day 11 onwards, they had a motivation to perform (i.e., food) and reached the end before the reward group. Critical Evaluation The behaviorists stated that psychology should study actual observable behavior, and that nothing happens between stimulus and response (i.e., no cognitive processes take place). Fig. 9.4: Critical Evaluation Edward Tolman (1948) challenged these assumptions by proposing that people and animals are active information processes and not passive learners as behaviorism had suggested. Tolman developed a cognitive view of learning that has become popular in modern psychology. Tolman believed individuals do more than merely respond to stimuli; they act on beliefs, attitudes, changing conditions, and they strive toward goals. Tolman is virtually the only behaviorists who found the stimulus-response theory unacceptable, because reinforcement was not necessary for learning to occur. He felt behavior was mainly cognitive. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 229 9.9 Guthrie’s Theory of Learning Guthrie’s contiguity theory specifies that “a combination of stimuli which has accompanied a movement will on its recurrence tend to be followed by that movement”. According to Guthrie, all learning was a consequence of association between a particular stimulus and response. Furthermore, Guthrie argued that stimuli and responses affect specific sensory-motor patterns; what are learned are movements, not behaviors. In contiguity theory, rewards or punishment play no significant role in learning since they occur after the association between stimulus and response has been made. Learning takes place in a single trial (all or none). However, since each stimulus pattern is slightly different, many trials may be necessary to produce a general response. One interesting principle that arises from this position is called “postremity” which specifies that we always learn the last thing we do in response to a specific stimulus situation. Contiguity theory suggests that forgetting is due to interference rather than the passage of time; stimuli become associated with new responses. Previous conditioning can also be changed by being associated with inhibiting responses such as fear or fatigue. The role of motivation is to create a state of arousal and activity which produces responses that can be conditioned. Application Contiguity theory is intended to be a general theory of learning, although most of the research supporting the theory was done with animals. Guthrie did apply his framework to personality disorders (e.g., Guthrie, 1938). Example The classic experimental paradigm for Contiguity theory is cats learning to escape from a puzzle box (Guthrie and Horton, 1946). Guthrie used a glass paneled box that allowed him to photograph the exact movements of cats. These photographs showed that cats learned to repeat the same sequence of movements associated with the preceding escape from the box. Improvement comes about because irrelevant movements are unlearned or not included in successive associations. CU IDOL SELF LEARNING MATERIAL (SLM)

230 Experimental Psychology Principles (i) In order for conditioning to occur, the organism must actively respond (i.e., do things). (ii) Since learning involves the conditioning of specific movements, instruction must present very specific tasks. (iii) Exposure too many variations in stimulus patterns is desirable in order to produce a generalized response. (iv) The last response in a learning situation should be correct since it is the one that will be associated. 9.10 Summary Learning Theory describes how students absorb, process, and retains knowledge during learning. Cognitive, emotional and environmental influences, as well as prior experience, all play a part in how understanding, or a world view, is acquired or changed and knowledge and skills retained. Behaviorists look at learning as an aspect of conditioning and advocate a system of rewards and targets in education. Educators who embrace cognitive theory believe that the definition of learning as a change in behaviour is too narrow, and study the learner rather than their environment and in particular the complexities of human memory. Those who advocate constructivism believe that a learner's ability to learn relies largely on what they already know and understand, and the acquisition of knowledge should be an individually tailored process of construction. Transformative learning theory focuses on the often-necessary change required in a learner’s preconceptions and world view. Geographical learning theory focuses on the ways that contexts and environments shape the learning process. Learning can be defined as a process that brings together cognitive, emotional and environmental influences and experiences for acquiring, enhancing or making changes in one’s knowledge, skills, values and world views. Learning as a process focuses on what happens when the learning takes place. Explanations of what happens constitute learning theories. A learning theory is an attempt to describe how people and animals learn; thereby helping us understands the inherently complex process of learning. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 231 The day-to-day activities of an individual refer to motor activities. In order to maintain regular life, each and every individual learns all these activities which include walking, running, skating, driving, climbing, etc. All these activities involve the powerfully built coordination. It refers to the language we speak and communicate. It also includes the various communication devices which we use on a day-to-day basis. They mainly include signs, pictures, symbols, words, figures, sounds, etc. which are considered to be the tools used in such activities. Words are used for the purpose of communication. It is the form of learning which requires higher order mental processes like thinking, reasoning, intelligence, etc. We learn different concepts from birth. Concept learning involves two processes, which include construction and simplification. This learning is very useful in order to understand and identify different things. Discrimination refers to learning to differentiate between stimuli and showing a proper and correct response to these stimuli. Example, sound horns of different vehicles like bus, car, ambulance, etc. Individuals learn certain principles, rules and laws related to science, mathematics, grammar, etc. in order to manage and do their work effectively and efficiently. These principles help in showing the relationship between two or more concepts. Problem solving is considered to be a higher order learning process. This type of learning requires the use of cognitive abilities which include thinking, reasoning, observation, imagination, generalization, etc. It is very useful to overcome difficult problems which are encountered by the people. Every individual develops different kinds of attitudes from birth about different people, objects and everything around him. The behavior which an individual has may be positive or negative depending upon his attitudes. CU IDOL SELF LEARNING MATERIAL (SLM)

232 Experimental Psychology Classical conditioning is learning through association and was discovered by Pavlov, a Russian physiologist. In simple terms, two stimuli are linked together to produce a new learned response in a person or animal. John Watson proposed that the process of classical conditioning (based on Pavlov’s observations) was able to explain all aspects of human psychology. Instrumental conditioning is another term for operant conditioning, a learning process first described by B.F. Skinner. In instrumental conditioning, reinforcement or punishment are used to either increase or decrease the probability that a behavior will occur again in the future. The Observational Learning process was propounded by Albert Bandura in his Social Learning Theory, which focused on learning by imitation or observing people’s behaviour. For observational learning to take place effectively, four important elements will be essential: Motivation, Attention, Memory and Motor Skills. Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based on various processes. In humans, this form of learning seems to not need reinforcement to occur, but instead, requires a social model such as a parent, sibling, friend, or teacher with surroundings. Particularly in childhood, a model is someone of authority or higher status in an environment. In animals, observational learning is often based on classical conditioning, in which an instinctive behavior is elicited by observing the behavior of another (e.g., mobbing in birds), but other processes may be involved as well. Modeling has been used successfully in many therapeutic conditions. Many therapists have used forms of modeling to assist their patients to overcome phobias. For example, adults with claustrophobia may observe a model in a video as they move closer and closer to an enclosed area before entering it. Once the model reaches the enclosed area, for instance a closet, he or she will open the door, enter it, and then close the door. The observer will be taught relaxation techniques and be told to practice them anytime he or she becomes anxious while watching the film. The end result is to continue observing the model until the person can enter the closet himself or herself. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 233 Hull developed a version of behaviorism in which the stimulus (S) affects the organism (O) and the resulting response (R) depends upon characteristics of both O and S. In other words, Hull was interested in studying intervening variables that affected behavior such as initial drive, incentives, inhibitors, and prior training (habit strength). Like other forms of behavior theory, reinforcement is the primary factor that determines learning. However, in Hull’s theory, drive reduction or need satisfaction plays a much more important role in behavior than in other frameworks (i.e., connectionism, operant conditioning). Tolman argued that humans engage in this type of learning everyday as we drive or walk the same route daily and learn the locations of various buildings and objects. Only when we need to find a building or object does learning become obvious. Tolman conducted experiments with rats and mazes to examine the role that reinforcement plays in the way those rats learn their way through complex mazes. These experiments eventually led to the theory of latent learning. Cognitive maps as an example of latent learning in rats. Tolman coined the term cognitive map, which is an internal representation (or image) of external environmental feature or landmark. He thought that individuals acquire large numbers of cues (i.e., signals) from the environment and could use these to build a mental image of an environment (i.e., a cognitive map). Guthrie’s contiguity theory specifies that “a combination of stimuli which has accompanied a movement will on its recurrence tend to be followed by that movement”. According to Guthrie, all learning was a consequence of association between a particular stimulus and response. Furthermore, Guthrie argued that stimuli and responses affect specific sensory-motor patterns; what are learned are movements, not behaviors. In contiguity theory, rewards or punishment play no significant role in learning since they occur after the association between stimulus and response has been made. Learning takes place in a single trial (all or none). However, since each stimulus pattern is slightly different, many trials may CU IDOL SELF LEARNING MATERIAL (SLM)

234 Experimental Psychology be necessary to produce a general response. One interesting principle that arises from this position is called “postremity” which specifies that we always learn the last thing we do in response to a specific stimulus situation. Contiguity theory suggests that forgetting is due to interference rather than the passage of time; stimuli become associated with new responses. Previous conditioning can also be changed by being associated with inhibiting responses such as fear or fatigue. The role of motivation is to create a state of arousal and activity which produces responses that can be conditioned. 9.11 Key Words/Abbreviations  Concept of Learning: Learning can be defined as a process that brings together cognitive, emotional and environmental.  Motor Learning: The day-to-day activities of an individual refer to motor activities.  Verbal Learning: It refers to the language we speak and communicate.  Concept Learning: It is the form of learning which requires higher order mental processes like thinking, reasoning, intelligence, etc.  Discrimination Learning: Discrimination refers to learning to differentiate between stimuli and showing a proper and correct response to these stimuli.  Learning of Principles: Individuals learn certain principles, rules and laws related to science, mathematics, grammar, etc.  Problem Solving: Problem solving is considered to be a higher order learning process.  Classical Conditioning: Classical conditioning (also known as Pavlovian conditioning) is learning through association.  Instrumental Conditioning: Instrumental conditioning is another term for operant conditioning. CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 235  Observational Learning: The Observational Learning process was propounded by Albert Bandura in his Social Learning Theory. 9.12 Learning Activity 1. You are required to prepare a live report on significance of learning. _________________________________________________________________ _________________________________________________________________ 2. You are suggested to identify various types of learning and the positive impacts. _________________________________________________________________ _________________________________________________________________ 9.13 Unit End Exercises (MCQs and Descriptive) A. Descriptive Type Questions 1. Discuss the concept of Learning. 2. Explain nature of the Learning. 3. Discuss significance of learning. 4. Explain various types of Learning. 5. Discuss the principles of Learning. 6. Explain in details about factors affecting Learning. 7. Discuss in details about Classical Conditioning. 8. Explain in brief about Instrumental Conditioning. 9. Discuss in details about Observational Learning. 10. Explain applications of Hull’s Theory. CU IDOL SELF LEARNING MATERIAL (SLM)

236 Experimental Psychology B. Multiple Choice Questions 1. Which of the following describes how students absorb, process, and retains knowledge during learning? (a) Learning Theory (b) Cognitive Theory (c) Perception Theory (d) None of the above 2. Who defined “Learning as the behavioral modification which occurs as a result of experience as well as training”? (a) Gales (b) Crow and Crow (c) H.J. Klausmeir (d) John B. Watson 3. Which of the following is the nature of Learning? (a) Learning involves change in the behavior of an individual but it may or may not lead to the guarantee improvement in an individual (b) Learning is a lifelong process and is permanent in nature (c) Learning is described as a key process in human (d) All the above 4. Which of the following is the characteristic of Learning? (a) Learning is a fundamental process of life (b) It is a continuous process it affects all modes of behaviour (c) Learning is universal (d) All the above CU IDOL SELF LEARNING MATERIAL (SLM)

Learning Theories 237 5. Which of the following is the type of Learning? (a) Motor learning (b) Verbal learning (c) Discrimination learning (d) All the above Answers: 1. (a), 2. (a), 3. (d), 4. (d), 5. (d) 9.14 References References of this unit have been given at the end of the book.  CU IDOL SELF LEARNING MATERIAL (SLM)

238 Experimental Psychology UNIT 10 DISCRIMINATION LEARNING Structure: 10.0 Learning Objectives 10.1 Introduction 10.2 The Concept of Discrimination Learning 10.3 Phenomena of Discrimination Learning 10.4 Paradigms of Discrimination Learning 10.5 Applications ofDiscrimination Learning 10.6 Summary 10.7 Key Words/Abbreviations 10.8 LearningActivity 10.9 Unit End Exercises (MCQs and Descriptive) 10.10 References 10.0 Learning Objectives After studying this unit, you will be able to:  Discuss about discrimination learning and related aspect  Explain the concept of discrimination learning. CU IDOL SELF LEARNING MATERIAL (SLM)

Discrimination Learning 239 10.1 Introduction Discrimination learning is defined in psychology as the ability to respond differently to different stimuli. This type of learning is used in studies regarding operant and classical conditioning. Operant conditioning involves the modification of a behavior by means of reinforcement or punishment. Application of discrimination procedures permits description of the sensory acuities of laboratory animals. Examples: Discrimination learning can be studied in both humans and animals. Animals can use discrimination learning to help them survive, be trained for assisting humans in tasks, and much more. A dog might be trained to use discrimination learning to detect differences in complex odor compounds so that they are able to sniff out different drugs to assist police. Predator can also use discrimination learning to distinguish between two camouflaged prey. Discrimination learning teaches us more about what animals are capable of conceptual thought. Humans can use discrimination learning to detect danger, learn about differences, and more. One example of discrimination learning in humans would be a baby who reacts differently to their mother’s voice than to a stranger’s voice. Discovering different abilities of animals or humans who are unable to communicate. Discrimination learning can be used to see what differences an animal will respond to. For example, since we are unable to have general two-way communication with dogs, we could show a dog two different stimuli that are the same in every way other than one, such as color. We could then use discrimination learning to see which colors a dog can discriminate between. 10.2 The Concept of Discrimination Learning Karl Lashley, a psychologist who studied under John B. Watson, focused mainly on studying learning and discrimination. He published “Brain Mechanisms and Intelligence” in 1929. Lashey’s research on two-alternative forced choice gave a foundation of study to psychologists like Kenneth Spence. Kenneth Spence expanded on the knowledge we had on two-choice discrimination learning. He made two major publications on the subject, The Nature of Discrimination Learning in Animals in 1936 and Continuous Versus Non-continuous Interpretations of Discrimination Learning in 1940. Spence’s research discussed the theory that applying excitation and inhibition to a stimulus and CU IDOL SELF LEARNING MATERIAL (SLM)

240 Experimental Psychology having the likelihood of responding to that stimulus be the result of the net excitation strength (excitation minus inhibition). Ivan Pavlov is very influential when it comes to studying discrimination learning. His studies involving salivating dogs demonstrated an ability in the dogs to differentiate a stimulus that would elicit a reward and a stimulus that would not. This can be contrasted with the Little Albert studies where Albert’s lack of discrimination between animals exhibited the psychological and learning phenomenon of generalization learning, which is discrimination learning’s polar opposite. Discrimination learning can be studied in both humans and animals. Animals can use discrimination learning to help them survive, be trained for assisting humans in tasks, and much more. A dog might be trained to use discrimination learning to detect differences in complex odor compounds so that they are able to sniff out different drugs to assist police. Discrimination learning teaches us more about what animals are capable of conceptual thought. Humans can use discrimination learning to detect danger, learn about differences, and more. One example of discrimination learning in humans would be a baby who reacts differently to their mother’s voice than to a stranger’s voice. Discovering different abilities of animals or humans who are unable to communicate. Discrimination learning can be used to see what differences an animal will respond to. For example, since we are unable to have general two-way communication with dogs, we could show a dog two different stimuli that are the same in every way other than one, such as color. We could then use discrimination learning to see which colors a dog can discriminate between. Discrimination learning has limitations. One limitation is the relative-validity effect. This effect states that organisms learn to give more attention to the stimuli that are of more importance to them. Another limitation is the blocking effect. This effect will occur if there is a discriminative stimulus, such as a cat hearing the sound of a bell ringing, that is presented by itself and then it is followed by a reinforcement, such as food for the cat. We would repeat this until the cat starts to salivate when the bell rings. If we add a stimulus of a flash of light after the bell rings, and then followed it by reinforcement (the cat food), it may result in little to no response to a second stimulus. CU IDOL SELF LEARNING MATERIAL (SLM)

Discrimination Learning 241 10.3 Phenomena of Discrimination Learning Discrimination learning can be used as a part of training for more difficult tasks, including the judgement bias tasks and Iowa gambling task described earlier in the chapter. It can, however, also be used as a task in and of itself, to determine the ability of animals to discriminate between two stimuli and the capacity of animals to learn and perform tasks based on discrimination in different modalities. Visual discrimination is frequently used in discrimination learning in various species. This can entail the use of lights, including discrimination between light color, intensity, or frequency of flashing lights. Visual stimuli can also include the use of pictures or patterns. Pigs have visual acuity which is inferior to humans, sheep, and cattle but which should, in theory, be quite sufficient for learning visual discriminations. In practice, however, discrimination based on visual stimuli in pigs has proven quite difficult, requiring lengthy training to show operant responses to distinct 2D shapes. Discrimination of conspecifics based on photographs, which has been demonstrated in domestic sheep and cows did not seem to be possible in pigs. Discrimination tasks based on auditory stimuli have been more successful, with pigs showing distinct operant responses to auditory stimuli of different frequencies. Other modalities, such as odor cues or tactile cues, have yet to be tested in pigs. Given their strong olfactory and tactile abilities (the snout is particularly sensitive), this may be an interesting avenue to explore to improve discrimination learning. Learning discriminative image representations from data have evolved as a promising research area. A powerful image representation captures the prior distributions of data by learning the image features. These features are usually hierarchical in nature (low and high level features) and hence the image representations learn to define the more abstract concepts in terms of the less abstract ones. A good learned representation should be simple (usually linearly dependent), sparse, and possess spatial and temporal coherence. The depth of a network is also an important aspect in the representation learning. Representations learned from the higher layers of deep networks encode high level features of data. CU IDOL SELF LEARNING MATERIAL (SLM)

242 Experimental Psychology Image representations extracted from CNNs, trained on large data sets such as Image Net and fine-tuned on domain specific data sets, have shown state-of-the-art performance in numerous image classification problems. These learned features can be used as universal image representations and have produced outstanding performances in computer vision tasks, e.g., image classification, object detection, fine grained recognition, attribute detection, and instance retrieval. The activations of the first fully connected layer of CNNs are the preferred choice of most researchers. However, the activations of intermediate convolutional layers have also shown comparable performances. In, sub arrays of convolutional layer activations are extracted and used as region descriptors in a ‘local feature’ setting. The extracted local features from two consecutive convolutional layers are then pooled together and included in the resulting feature vector. This approach, termed “cross-convolutional layer pooling,” achieved significant performance improvements in scene classification tasks. 10.4 Paradigms of Discrimination Learning Discrimination learning is a process of learning to behave differently when given different, or unique, stimuli. Elizabeth used whistles with different pitches because she wanted to separate behaviors. She knew her cats would have no idea what she wanted them to do if she used one whistle to encourage every behavior. So, she used two tones: one tone that would tell the cats that it was time to eat and a second tone that would encourage the cats to go out the cat door. Conditioning is the ability to train an animal to perform a task given specific stimuli. Russian physiologist Ivan Pavlov was the first to demonstrate this in an experiment. He conditioned dogs to understand that the bell he was ringing meant that they were to be fed. He did not want the dogs to respond to another stimulus, such as the sound of a buzzer; he wanted them to learn to respond to a specific tone. This is where conditioning and discrimination learning intersect. Elizabeth was an animal lover who had amassed a herd of cats because she took in everybody else’s strays and tried to find them good homes. Her husband understood, but Elizabeth was exasperated over the problems ten healthy felines could cause. She decided she had to train her cats to come only when it was time for dinner or treats and to disappear when it was time to clean the litter boxes. CU IDOL SELF LEARNING MATERIAL (SLM)

Discrimination Learning 243 Elizabeth remembered something from school about a man teaching a bunch of dogs to salivate when they heard a bell. She wondered if she could do the same thing. So, she bought two whistles that had different pitches. Then, she started to train. Eventually, she was able to get the cats to respond to feeding or treat time with one whistle and got them to go out the cat door with the differently pitched whistle. Whether she realized it or not, Elizabeth had used discrimination learning to condition her cats. Elizabeth did the same thing as Pavlov. She wanted her cats to distinguish between different tones and respond correctly depending on the tone they heard. So, she conditioned them to discriminate between different tones and respond with the connected behavior. 10.5 Applications of Discrimination Learning (a) Discrimination learning is used almost every subfield of psychology as it is a basic form of learning that is at the core of human intelligence. Examples of this include but are not limited to, cognitive psychology, personality psychology, developmental psychology, etc. (b) It was a classic topic in the psychology of learning from the 1920s to the 1970s, and was particularly investigated within: (c) Comparative psychology, where a key issue was whether continuous or discontinuous learning processes were concerned in the acquisition of discriminations d) The experimental analysis of behaviour, where a key issue was whether discriminations could be trained without the necessity for the subject to make errors (e) Developmental psychology, where a key issue was the changes that occur in the process of discrimination as a function of age. (f) Cross-cultural psychology, where a key issue was the role that the cultural appropriateness of the stimuli to be discriminated played in the rate of acquisition of effective discrimination (g) Mathematical psychology, where attempts were made to formalize the distinctions being drawn in other branches of psychology. CU IDOL SELF LEARNING MATERIAL (SLM)

244 Experimental Psychology (h) Discrimination learning can almost become an unconscious process for many people. It becomes integrated into daily routines. Examples of discrimination learning in everyday life can include grocery shopping, determining how to decipher between the types of bread or fruit, being able to tell similar stimuli apart, differentiating between different parts while listening to music, or perhaps deciphering the different notes and chords being played. (i) While interest in the learning of discriminations has continued in many fields, from about 1980 onwards the phrase “discrimination learning” was used less often as the main description either of individual studies or of a field of investigation. (j) Instead, investigations of the learning of discriminations have tended to be described in other terms such as pattern recognition or concept discrimination. This change partly reflects the increasing diversity of studies of discrimination, and partly the general expansion of the topic of cognition within psychology, so that learning is not now the central organizing topic that it was in the mid-20th century. 10.6 Summary Discrimination learning is defined in psychology as the ability to respond differently to different stimuli. This type of learning is used in studies regarding operant and classical conditioning. Operant conditioning involves the modification of a behavior by means of reinforcement or punishment. Ivan Pavlov is very influential when it comes to studying discrimination learning. His studies involving salivating dogs demonstrated an ability in the dogs to differentiate a stimulus that would elicit a reward and a stimulus that would not. This can be contrasted with the Little Albert studies where Albert’s lack of discrimination between animals exhibited the psychological and learning phenomenon of generalization learning, which is discrimination learning’s polar opposite. Discrimination learning can be studied in both humans and animals. Animals can use discrimination learning to help them survive, be trained for assisting humans in tasks, and much more. A dog might be trained to use discrimination learning to detect differences in complex odor compounds so that they are able to sniff out different drugs to assist police. Discrimination learning teaches us more about what animals are capable of conceptual thought. Humans can use discrimination learning to CU IDOL SELF LEARNING MATERIAL (SLM)