Computation

8 Key excerpts on "Computation"

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  Cognitive Psychology and Information Processing

  An Introduction

  • R. Lachman, J. L. Lachman, E. C. Butterfield(Authors)
  • 2015(Publication Date)
  • Psychology Press

    (Publisher)

  There is no question that the development of computers, and the intellectual climate that permitted it, has profoundly enriched our understanding of human cognition. "Computer science" is actually a family of loosely related subspecialties, including algorithm theory, numerical methods, automata theory, programming languages, and artificial intelligence. The relationship between these subspecialties and contemporary cognitive psychology is an intimate one indeed. The two fields have evolved in tandem. Both are derived from seminal work in mathematics that occurred during the first half of the twentieth century, and both are centrally concerned with the nature of intelligent behavior. Consequently, we have chosen to present the influences of computer science on cognitive psychology in this chapter, together with our summary of the information-processing paradigm itself. Before the development of Computational machinery, and the source of ideas it provided, American psychology had already given much attention to the concept of "intelligence." However, most of this attention was directed to the measurement of intelligence rather than to developing theories of its nature. The measurement approach, together with the pervasive influence of neobehaviorist pretheory, had several implications. For one thing, it tended to emphasize individual differences rather than human commonalities in intellectual functioning. Intelligence came to be viewed as some kind of personal characteristic, of which some people have much and others little; however, what it consisted of was never adequately specified. In fact, it was not long ago that students were told that human intelligence is "whatever the intelligence test measures." This facile answer served only to evade, rather than illuminate, a legitimate and centrally important psychological question

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  Essentials of Sensation and Perception

  • George Mather(Author)
  • 2014(Publication Date)
  • Routledge

    (Publisher)

  Figure 5.5 can be described in abstract terms as a Computational operation that transforms one representation into another. The notion of Computation is very general, and is not restricted to arithmetic operations such as addition and subtraction, which can be applied to analog representations. Computations can also be performed on symbolic representations. In this case they typically involve true/false comparisons and equals assignments. An example of a simple symbolic Computation would be:

  IF <OBJECT SOUND=QUACK> AND <OBJECT LOCATION=WATER>

        THEN <OBJECT=DUCK>

  In fact any manipulation of quantities or symbols according to a set of rules can be described as a Computation. A specific set of rules to perform a particular Computation is sometimes called an algorithm. Take the spell-checker in your word processor, email program or text message editor. It works by finding each string of letters enclosed by spaces, and comparing it against a stored list of known words. If a match is found, the word is deemed okay, but if no match is found the word is flagged as a spelling mistake. The spell-checking function is a Computational process. It manipulates and compares representations of word strings according to a set of matching rules to produce an output (correct/incorrect). The same algorithm is implemented on many different hardware and software platforms, including various PCs and phones. A more complicated version of this kind of Computational process must take place in your head when you decide whether a word you are reading is spelled correctly, and even when you decide whether you recognize the person whose face you are looking at. The Computation is more complex because you first have to extract and identify each letter (which can be quite difficult in handwriting), or extract the visual details in a face, before you can begin to compare the word or face against representations stored in your memory. Despite the complications, these mental Computations are largely hidden and apparently effortless and rapid. The challenge of exposing and describing mental Computations is one of the continuing fascinations of research in perception.

  Evaluation

  Internal representations are a central theme of this chapter. However, some theories of perception reject the notion of representation entirely. Connectionist

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  Cognition

  • Thomas A. Farmer, Margaret W. Matlin(Authors)
  • 2023(Publication Date)
  • Wiley

    (Publisher)

  Cognitive Psychology: Overview The term cognition, or mental activity, refers to the acquisition, storage, transformation, and use of knowl- edge. Cognition is inescapable, meaning that your cognitive processes are always at work. They grant you the ability to recognize and interpret stimuli in your environment and to react strategically to such INTRODUCTION TO COGNITIVE PSYCHOLOGY 2 information. Cognitive processes grant you the ability to plan, to create, to interact with others, and to process the thoughts, sensations, and emotions that you experience. If cognition operates every time you acquire some information, place it in storage, transform that information, and use it . . . then cognition includes a wide range of mental processes! This textbook will explore many of these mental processes, such as perception, memory, imagery, language, problem solving, reasoning, and decision making. As you will see throughout this book, your cognitive processes do not function in isolation from one another. Instead, they work together in intricate and highly coordinated ways to create your conscious (and sometimes nonconscious) experiences. For example, you are performing multiple cognitive tasks simultaneously as your read this paragraph. You are using pattern recognition to create words from an assortment of squiggles and lines that form the letters on this page. You are also consulting your memory and your knowledge about language to search for word meanings and to link together the ideas in this paragraph. Additionally, right now, as you think about these cognitive tasks, you are engaging in another cognitive task called metacognition— you are thinking about your own thought processes.

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  Cognition and Motivation

  Forging an Interdisciplinary Perspective

  • Shulamith Kreitler(Author)
  • 2012(Publication Date)
  • Cambridge University Press

    (Publisher)

  Thus, if meaning matters to the mind, incomplete knowledge definitely does, as well. 8 Cognitive Science and Knowledge Management 331 Starting up with Bruner once More What we want to find out is where exactly (logically speaking and not just historically) Computationalism came into the picture as a means to study the mind. Where eventually did it happen that the construction of meaning got replaced by the processing of information, in the sense of manipulating mean- ingful signs to produce further meaningful signs? Maybe the manipulation of signs was not considered a description of processes in the brain but rather as a paradigm for producing good programs to produce meaningful signs? Bruner (1990, p. 33), however, insists that, “The central concept of a human psychology is meaning and the processes and transactions involved in the construction of meanings.” Furthermore, “One must understand how his experiences and his acts are shaped by his intentional states . . . the form of these intentional states is realized only through participation in the symbolic systems of the culture.” This is Bruner’s way of going “back to his roots,” or in other words, it is his motivation to overcome the incompleteness of a compu- tational approach when it is being taken too literally. The question concerning the development positively as well as negatively of cognitive science we want to pose here, however, goes much further, pre- supposing the direction in which we are looking for a sort of solution.

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  Cities And Regions As Nonlinear Decision Systems

  • Robert W Crosby(Author)
  • 2019(Publication Date)
  • Taylor & Francis

    (Publisher)

  It would appear that human cognitive systems possess important pattern-driven aspects as well as goal-driven aspects. Computing Process Models of Individuals and Aggregates The sets of concepts outlined above may serve as a basis for constructing computing models, and hence computer programs, of individual decision-making behavior. Such a basis is probably adequate to model a great range of human behavior (including the behavior exemplified in Table I) that is difficult to model in other terms. In this section we first indicate briefly some of the past applications of Computational process modeling, both in general contexts and in contexts of interest for URS modeling. We then outline how the concepts and techniques may apply to explaining some of the major characteristics of human decicion-making that are difficult to incorporate into the more traditional models. Models of Individual Decision-making BehavioP 195 Past Applications of Computing Concepts to the Modeling of Individual Decision-Making Behavior The value of the Computational process approach has been exemplified by numerous applications in a variety of domains. These include problem solving in mathematics, physics and medicine, the comprehension and learning of language, the recognition of images, the development of concepts and game playing. Most of these domains of application have involved well-defined, abstract systems possessing a minimal correspondence to physical reality, with a tendency to concentrate on non-spatial forms of cognition and decision-making.

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  Modeling in the Neurosciences

  From Biological Systems to Neuromimetic Robotics

  • G. N. Reeke, R.R. Poznanski, K. A. Lindsay, J.R. Rosenberg, O. Sporns(Authors)
  • 2005(Publication Date)
  • CRC Press

    (Publisher)

  Just why is it that the Computational analogy is inadequate? Surely it is correct that on–off signals (neural spikes) enter the brain via multiple sensory pathways and leave via motor pathways, just as binary signals enter and leave a computer via its input/output devices. Inside, various complex transformations are applied to the input signals in order to arrive at appropriate behavioral outputs. As Marr (1982) famously proposed, these transformations (call them Computations if you wish) may be analyzed at the level of their informational requirements (without certain inputs, certain outputs can never be unambiguously obtained), at the level of algorithm (by what steps the appropriate transformations can be efficiently carried out), and at the level of implementation (what kind of devices are needed to carry out the algorithms). These levels of analysis apply also to, indeed, were 1 2 Modeling in the Neurosciences derived from, processes carried out in computers. Thus, what Marr has proposed is to take what has already been learned about Computation, beginning with the work of Turing (1950) and von Neumann, and apply this information to understanding the brain. The problem arises when this analogy is pushed too far, to the point where one falls into the temptation of calling everything the brain does a Computation. One thus arrives at the curious circularity of logic espoused by Churchland and Sejnowski (1992, p. 61), who put it this way: “Notice in particular that once we understand more about what sort of computers nervous systems [authors’ emphasis] are, and how they do whatever it is they do, we shall have an enlarged and deeper understanding of what it is to compute and represent.” In this view, the study of the brain, which is already a big enough problem, takes on the additional burden of providing a new underpinning for computer science as well. This is necessary because conventional computer science cannot adequately explain what it is that the brain does.

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  Mathematical Frontiers Of The Social And Policy Sciences

  • Loren Cobb(Author)
  • 2019(Publication Date)
  • Routledge

    (Publisher)

  , ,. ,,c ) C: : ': ... :-·-· .-:-·:·-· ;) Perceptual systems Memory vectors ., ,,_,_ ,, ,, ,,, , , :) t .-. ·-·=· -~ · . -. .:_ :... ··, ,:: 0) EO ;: c ···z :o ·:.:':l Response Decision process Memory system Figure 1. Flowchart for an information-processing model of perception and memory. From D. A. Norman and D. E. Rurnelhart, A system for perception and memory. In D. A. Norman (Ed.), Models of Human Memory, Academic Press, 1970. Reprinted by permission. Knowledge Representation in Cognitive Psychology 7 opposed to the earlier approaches using the mathematics of stochastic processes and continuous numeric variables, mark the development of a new mathematics in cognitive psychology. Introduction Cognitive psychology is a branch of human experimental psychology that is interested in inferring and modelling, on the basis of behavioral data, the internal mechanisms in-volved in complex mental activities such as reading and problem-solving. This area has been a distinct discipline within experimental psychology for roughly 15 years. Since this paper is concerned with issues of formal theory, it is important to note that cognitive psychology has always been closely associated with formal approaches to theory, because many of its proponents are also members of the discipline known as mathematical psychology. Human Information Processing The first decade of modern cognitive psychology was largely concerned with models of information processing, many of which were in mathematical form. An example is the model of perception and memory proposed by Norman and Rumel-hart in 1970, illustrated in flow-chart form in Figure 1. The human, in performing various perception and memory tasks, was treated as a series of processing stages, with informa-tion traveling from one stage to the next, being transformed and manipulated along the way.

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  The Human Mind through the Lens of Language

  Generative Explorations

  • Nirmalangshu Mukherji(Author)
  • 2022(Publication Date)
  • Bloomsbury Academic

    (Publisher)

  The Computational form of explanation in generative grammar opened the investigation into the generative mind. For the first time in the history of thought, we had a glimpse of what it means for language to be a ‘mirror of mind’ (Chomsky 2006, 67). The insights of generative grammar may now be carefully expanded to reach a fuller description of the human mind. 67 The Mind in Cognitive Science 2.3 Neuroscientism The suggested restrictions on Computational explanations are particularly noteworthy in view of how the cognitive sciences developed beyond classical Turing Computationalism. The new computerism, aiming for a ‘realistic model’ with properties of ‘situatedness’, ‘embodiment’ and the like, soon joined hands with neuroscience. It does not require elaborate conspiracy theory to understand why two very influential books in that direction were published almost simultaneously: Churchland (1986) and Rumelhart and McClelland (1986). The effort led to the grand ‘unified’ discipline boldly called cognitive neuroscience, meaning that properties of cognition are to be studied by examining the brain. It is not difficult to see that studies on organic behaviour assumed astronomical proportions. The brain, of course, is the epicentre of virtually every form of behaviour. There is likely to be some perturbation in the brain or in the nervous system whenever an organism is doing something: reaching for food, reacting to sunlight, exhibiting depth perception, weaving nests, undergoing a headache, watching others eating a banana, meditating, falling in love or humming a tune. 5 So a sample survey can be conducted and brain signals measured for any apparently ‘cognitive’ behaviour that catches a researcher’s fancy. The research effort no doubt generates tons of impressive charts and pictures.

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