Resources for teaching cognitive psychology: supplementary material for " Cognitive psychology: a student's handbook", 4. ed. by Michael W Eysenck; Mark T. Rigorously researched and accessibly written, Cognitive Psychology: A Student's Handbook is widely regarded as the leading undergraduate textbook in the. Cognitive psychology: a student's handbook / Michael W. Eysenck, Mark T. Keane whilst getting edition information, textStatus=error,errorThrown= undefined.
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Editorial Reviews. Review. 'Eysenck and Keane have reached a remarkable and almost Cognitive Psychology: A Student's Handbook 7th Edition, Kindle Edition . by Additional gift options are available when downloading one eBook at a time. Cognitive psychology: a student's handbook by Michael W Eysenck by Michael W Eysenck; Mark T Keane. eBook: Document. English. 7th ed. London. Showing all editions for 'Cognitive psychology: a student's handbook', Sort by: Title / Author, Type, Language, Date / Edition, Publication eBook: Document.
Of all the changes, the most dramatic has been the huge increase in the number of studies making use of sophisticated techniques e. During the s, such studies probably increased tenfold, and are set to increase still further during the early years of the third millennium. As a result, we now have four major approaches to cognitive psychology: experimental cognitive psychology based mainly on laboratory experiments; cognit- ive neuropsychology, which points up the effects of brain damage on cognition; cognitive science, with its emphasis on computational modelling; and cognitive neuroscience, which uses a wide range of techniques to study brain functioning. It is a worthwhile but challenging business to try to integrate information from these four approaches, and that it is exactly what we have tried to do in this book. As before, our busy professional lives have made it essential for us to work hard to avoid chaos. For example, the first author wrote several parts of the book in China, and other parts were written in Mexico, Poland, Russia, Israel, and the United States. I Michael Eysenck would like to express my profound gratitude to my wife Christine, to whom this book in common with the previous edition is appropriately dedicated.
This is the companion website for the seventh edition of Cognitive Psychology: Eysenck and Mark T.
Rigorously researched and accessibly written, Cognitive Psychology: The book is clearly organised, and offers comprehensive coverage of all the key areas of cognitive psychology. With a strong focus on considering human cognition in context, the book has been designed to help students develop a thorough understanding of the fundamentals of cognitive psychology, providing them with detailed knowledge of the very latest advances in the field.
Cognitive Psychology: Those taking courses in computer science, education, linguistics, physiology and medicine will also find it an invaluable resource.
Michael W. During the s, theorists such as Neisser argued that nearly all cognitive activity consists of interactive bottom-up and top-down processes occurring together see Chapter 4.
Perception and remembering might seem to be exceptions, because perception depends heavily on the precise stimuli presented and thus on bottom-up processing , and remembering depends crucially on stored information and thus on top-down processing. By the end of the s, most cognitive psychologists agreed that the information-processing paradigm was the best way to study human cognition see Lachman et al.
Many of these ideas stemmed from the view that human cognition resembles the functioning of computers. As Herb Simon , p. The evidence for that commonality is now over-whelming. The information-processing framework is continually developing as information technology develops.
The computational metaphor is always being extended as computer technology develops. In the s and 1. Many different programming languages had been developed by the s, leading to various aspects of computer software and languages being used e. Information processing: Diversity Cognitive science is a trans-disciplinary grouping of cognitive psychology, artificial intelligence, linguistics, philosophy, neuroscience, and anthropology.
The common aim of these disciplines is the understanding of the mind. To simplify matters, we will focus mainly on the relationship between cognitive psychology and artificial intelligence. There are various reasons why these distinctions are less neat and tidy in reality than we have implied.
First, terms such as cognitive science and cognitive neuroscience are sometimes used in a broader and more inclusive way than we have done. Second, there has been a rapid increase in recent years in studies that combine elements of more than one approach. Third, some have argued that experimental cognitive psychologists and cognitive scientists are both endangered species, given the galloping expansion of cognitive neuropsychology and cognitive neuroscience.
In this book, we will provide a synthesis of the insights emerging from all four approaches. The approach taken by experimental cognitive psychologists has been in existence for several decades, so we will focus mainly on the approaches of cognitive scientists, cognitive neuropsychologists, and cognitive neuroscientists in the following sections.
Before doing so, however, we will consider some traditional ways of obtaining evidence about human cognition. This approach has proved to be very useful, and the data thus obtained have been used in the development and subsequent testing of most theories in cognitive psychology.
However, there are two major potential problems with the use of such data: 1. Measures of the speed and accuracy of performance provide only indirect information about the internal processes and structures of central interest to cognitive psychologists. Behavioural data are usually gathered in the artificial surroundings of the laboratory.
The ways in which people behave in the laboratory may differ greatly from the ways they behave in everyday life see Chapter Cognitive psychologists do not rely solely on behavioural data to obtain useful information from their participants.
In spite of this, it is often assumed that introspection can provide useful evidence about some mental processes. Nisbett and Wilson argued that introspection is practically worthless, supporting their argument with examples. In one study, participants were presented with a display of five essentially identical pairs of stockings, and decided which pair was the best. After they had made their choice, they indicated why they had chosen that particular pair.
Most participants chose the rightmost pair, and so their decisions were actually affected by relative spatial position. However, the participants strongly denied that spatial position had played any part in their decision, referring instead to slight differences in colour, texture, and so on among the pairs of stockings as having been important.
Nisbett and Wilson , p. The limitations of introspective evidence are becoming increasingly clear. For example, consider research on implicit learning, which involves learning complex material without the ability to verbalise what has been learned.
There is reasonable evidence for the existence of implicit learning see Chapter 7. There is even stronger evidence for implicit memory, which involves memory in the absence of conscious recollection. Normal and brain-damaged individuals can exhibit excellent memory performance even when they show no relevant introspective evidence see Chapter 7.
Ericsson and Simon , argued that Nisbett and Wilson had overstated the case against introspection. Careful consideration of the studies that Nisbett and Wilson regarded as striking evidence of the worthlessness of introspection reveals that participants generally provided retrospective interpretations about information that had probably never been fully attended to.
In sum, introspection is sometimes useful, but there is no conscious awareness of many cognitive processes or their products. This point is illustrated by the phenomena of implicit learning and implicit memory, but numerous other examples of the limitations of introspection will be presented throughout this book.
A decent computational model can show us that a given theory can be specified and allow us to predict behaviour in new situations. Mathematical models were used in experimental psychology long before the emergence of the informationprocessing paradigm e. These models can be used to make predictions, but often lack an explanatory component. For example, committing three traffic violations is a good predictor of whether a person is a bad risk for car insurance, but it is not clear why.
One of the major benefits of the computational models developed in cognitive science is that they can provide both an explanatory and predictive basis for a phenomenon e. We will focus on computational models in this section, because they are the hallmark of the cognitive science approach.
This made it hard to decide whether the evidence fitted the theory. In contrast, cognitive scientists produce computer programs to represent cognitive theories with all the details made explicit. In the s and s, cognitive psychologists tended to use flowcharts rather than programs to characterise their theories.
Computer scientists use flowcharts as a sort of plan or blue-print for a program, before they write the detailed code for it. Flowcharts are more specific than verbal descriptions, but can still be underspecified if not accompanied by a coded program.
An example of a very inadequate flowchart is shown in Figure 1. This is a flowchart of a bad theory about how we understand sentences. It assumes that a sentence is encoded in some form and then stored. After that, a decision process indicated by a diamond determines if the sentence is too long. If it is too long, then it is broken up and we return to the encode stage to re-encode the sentence.
If it is ambiguous, then its two senses are distinguished, and we return to the encode stage. If it is not ambiguous, then it is stored in long-term memory. After one sentence is stored, we return to the encode stage to consider the next sentence. Such comments point to genuine criticisms. For example, after deciding that only a certain length of sentence is acceptable, it may turn out that it is impossible to decide whether the sentence portions are ambiguous without considering the entire sentence.
Thus, the boxes may look all right at a superficial glance, but real contradictions may appear when their contents are specified. In similar fashion, exactly what goes down the arrows is critical. In addition, it may have to record the fact that an item is either a sentence or a possible meaning of a sentence. The gaps in the flowchart show some similarities with those in the formula shown in Figure 1.
Not all theories expressed as flowcharts possess the deficiencies of the one described here. However, implementing a theory as a program is a good method for checking that it contains no hidden assumptions or vague terms. In the previous example, this would involve specifying the form of the input sentences, the nature of the storage mechanisms, and the various decision processes e. Palmer and Kimchi argued that it should be possible to decompose a theory successively through a number of levels from descriptive statement to flowchart to specific functions in a program until one reaches a written program.
In addition, they argued that it should be possible to draw a line at some level of decomposition, and say that everything above that line is psychologically plausible or meaningful, whereas everything below it is not. This issue of separating psychological aspects of the program from other aspects arises because there will always be parts of the program that have little to do 1.
Reproduced with permission of the author. However, no-one would argue that such print commands form part of the psychological model. Cooper et al. Is it possible to separate psychological aspects of a program f rom other aspects? Are there differences in reaction time between programs and human participants?
This would be a very precise language, like a logic, that would be directly executable as a program. For example, it is seldom meaningful to relate the speed of the program doing a simulated task to the reaction time taken by human participants, because the processing times of programs are affected by psychologically irrelevant features.
At the very least, the program should be able to reproduce the same outputs as participants when given the same inputs. Computational modelling techniques The general characteristics of computational models of cognition have been discussed at some length. It is now time to deal with some of the main types of computational model that have been used in recent years. Three main types are outlined briefly here: semantic networks; production systems; and connectionist networks.
Semantic networks Consider the problem of modelling what we know about the world see Chapter 9.
There is a long tradition from Aristotle and the British empiricist school of philosophers Locke, Hume, Mill, Hartley, Bain which proposes that all knowledge is in the form of associations.
There is a whole class of cognitive models owing their origins to these ideas; they are called associative or semantic or declarative networks. Thus, for example, a dog and a cat node may be connected by a link with an activation of 0.
For example, in learning that two concepts are similar, the activation of a link between them may be increased. Part of a very simple network model is shown in Figure 1.
It corresponds closely to the semantic network model proposed by Collins and Loftus Such models have been successful in accounting for a various findings. Ayers and Reder have used semantic networks to understand misinformation effects in eyewitness testimony see Chapter 8. At their best, semantic networks are both flexible and elegant modelling schemes.
Production systems Another popular approach to modelling cognition involves production systems. There is also a working memory i. Consider a very simple production system operating on lists of letters involving As and Bs see Figure 1. The system has two rules: 1. If we give this system different inputs in the form of different lists of letters, then different things happen.
If we give it CCC, this will be stored in working memory but will remain unchanged, because it does not match either of the IF-parts of the two rules. If we give it A, then it will be notified by the rules after the A is stored in working memory. This A is a list of one item and as such it matches rule 1. On the next cycle, AB does not match rule 1 but it does match rule 2. As a result, the B is replaced by an A, leaving an AA in working memory.
Newell and Simon first established the usefulness of production system models in characterising cognitive processes like problem solving and reasoning see Chapter However, these models have a wider applicability. Anderson has modelled human learning using production systems see Chapter 14 , and others have used them to model reinforcement behaviour in rats, and semantic memory Holland et al.
Connectionist networks Connectionist networks, neural networks, or parallel distributed processing models as they are variously called, are relative newcomers to the computational modelling scene. All previous techniques were marked by the need to program explicitly all aspects of the model, and by their use of explicit symbols to represent concepts. Furthermore, connectionist modellers often reject the use of explicit rules and symbols and use distributed representations, in which concepts are characterised as patterns of activation in the network see Chapter 9.
Early theoretical proposals about the feasibility of learning in neural-like networks were made by McCulloch and Pitts and by Hebb However, the first neural network models, called 1. Input patterns can be encoded, if there are enough hidden units, in a form that allows the appropriate output pattern to be generated from a given input pattern.
Reproduced with permission from David E. McClelland, Parallel distributed processing: Explorations in the microstructure of cognition Vol. By the late s, hardware and software develpments in computing offered the possibility of constructing more complex networks overcoming many of these original limitations e. Connectionist networks typically have the following characteristics see Figure 1. In order to understand connectionist networks fully, let us consider how individual units act when activation impinges on them.
Any given unit can be connected to several other units see Figure 1. Each of these other units can send an excitatory or an inhibitory signal to the first unit. This unit generally takes a weighted sum of all these inputs. If this sum exceeds some threshold, it produces an output.
Figure 1. These networks can model cognitive behaviour without recourse to the kinds of explicit rules found in production systems. They do this by storing patterns of activation in the network that associate various inputs with certain outputs. The models typically make use of several layers to deal with complex behaviour. One layer consists of input units that encode a stimulus as a pattern of activation in those units.
Another layer is an output layer, which produces some response as a pattern of activation. However, no such rules exist explicitly in the model.
Networks learn the association between different inputs and outputs by modifying the weights on the links between units in the net. In Figure 1. Various learning rules modify these weights in systematic ways.
When we apply such learning rules to a network, the weights on the links are modified until the net produces the required output patterns given certain input patterns. BackProp allows a network to learn to associate a particular input pattern with a given output pattern. At the start of the learning period, the network is set up with random weights on the links among the units. During the early stages of learning, after the input pattern has been presented, the output units often produce the incorrect pattern or response.
BackProp compares the imperfect pattern with the known required response, noting the errors that occur.
It then back-propagates activation through the network so that the weights between the units are adjusted to produce the required pattern. This process is repeated with a particular stimulus pattern until the network produces the required response pattern.
Thus, the model can be made to learn the behaviour with which the cognitive scientist is concerned, rather than being explicitly programmed to do so. Networks have been used to produce very interesting results.
Several examples will be discussed throughout the text see, for examples, Chapters 2, 10, and 16 , but one concrete example will be mentioned here. Sejnowski and Rosenberg produced a connectionist network called NETtalk, which takes an English text as its input and produces reasonable English speech output.
Some researchers might object to our classification of connectionist networks as merely one among 1. This is a book I have recommended to many starting colleagues asked to teach a course of cognitive psychology. Both the experimental and technical aspects as well as the content areas are explained on the basis of numerous well-chosen examples.
Importantly, the authors also integrate many current references in an elegant way, providing an up-to-date account of the field. I wish I had had this when I was a student. Even my graduate students and post-docs will benefit from reviewing relevant chapters in this handbook. Written in a highly accessible style with additional impressive web site support it is a text that any student would benefit from using. Particularly impressive is the coverage of neuroscience and neuropsychology along with excellent sections on computational models of cognition.
It shares the excitement of our rapidly evolving understanding of the mind and invites the reader to savour recent insights into the workings of vision, memory, problem solving, emotion, and the mysteries of consciousness. It will be the standard text for some years to come. I have been using this text for longer than I wish to remember, and although as expected the material is brought up to date, the pedagogy and clarity of exposition also improve with each edition.
The highest recommendation though is that my students tell me they find this book to be invaluable. Michael W.