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Journal articles on the topic 'Information Processing Theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 June 2025

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1

Johnson, Don H. "Information Theory and Neural Information Processing." IEEE Transactions on Information Theory 56, no. 2 (2010): 653–66. http://dx.doi.org/10.1109/tit.2009.2037047.

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2

IWAI, Sosuke. "Fuzzy Theory and Knowledge Information Processing." Journal of Japan Society for Fuzzy Theory and Systems 2, no. 1 (1990): 29–38. http://dx.doi.org/10.3156/jfuzzy.2.1_29.

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3

Cruces, Sergio, Rubén Martín-Clemente, and Wojciech Samek. "Information Theory Applications in Signal Processing." Entropy 21, no. 7 (2019): 653. http://dx.doi.org/10.3390/e21070653.

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4

Yukalov, Vyacheslav, and Didier Sornette. "Processing Information in Quantum Decision Theory." Entropy 11, no. 4 (2009): 1073–120. http://dx.doi.org/10.3390/e11041073.

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5

Simon, Herbert A. "The information-processing theory of mind." American Psychologist 50, no. 7 (1995): 507–8. http://dx.doi.org/10.1037/0003-066x.50.7.507.

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6

Sinanović, Sinan, and Don H. Johnson. "Toward a theory of information processing." Signal Processing 87, no. 6 (2007): 1326–44. http://dx.doi.org/10.1016/j.sigpro.2006.11.005.

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7

Papademetriou, Rallis C. "Data processing using information theory functionals." Kybernetes 27, no. 3 (1998): 264–72. http://dx.doi.org/10.1108/03684929810209478.

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8

Feixas, Miquel, Anton Bardera, Jaume Rigau, Qing Xu, and Mateu Sbert. "Information Theory Tools for Image Processing." Synthesis Lectures on Computer Graphics and Animation 6, no. 1 (2014): 1–164. http://dx.doi.org/10.2200/s00560ed1v01y201312cgr015.

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9

Swanson, H. Lee. "Information Processing Theory and Learning Disabilities." Journal of Learning Disabilities 20, no. 1 (1987): 3–7. http://dx.doi.org/10.1177/002221948702000102.

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10

Swanson, H. Lee. "Information Processing Theory and Learning Disabilities." Journal of Learning Disabilities 20, no. 3 (1987): 155–66. http://dx.doi.org/10.1177/002221948702000303.

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11

Galla, Tobias. "Theory of Neural Information Processing Systems." Journal of Physics A: Mathematical and General 39, no. 14 (2006): 3849. http://dx.doi.org/10.1088/0305-4470/39/14/b01.

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12

Rudolph, Thomas J., and Elizabeth Popp. "An Information Processing Theory of Ambivalence." Political Psychology 28, no. 5 (2007): 563–85. http://dx.doi.org/10.1111/j.1467-9221.2007.00590.x.

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13

Hyde, Kenneth F. "Information processing and touring planning theory." Annals of Tourism Research 35, no. 3 (2008): 712–31. http://dx.doi.org/10.1016/j.annals.2008.05.001.

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14

Flohr, H. "An information processing theory of anaesthesia." Neuropsychologia 33, no. 9 (1995): 1169–80. http://dx.doi.org/10.1016/0028-3932(95)00056-9.

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15

Bacauskiene, Marija, and Antanas Verikas. "The Evidence Theory Based Post‐Processing of Colour Images." Informatica 15, no. 3 (2004): 315–28. http://dx.doi.org/10.15388/informatica.2004.063.

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16

(Shitz), Shlomo Shamai, and Abdellatif Zaidi. "Information Theory for Data Communications and Processing." Entropy 22, no. 11 (2020): 1250. http://dx.doi.org/10.3390/e22111250.

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This book, composed of the collection of papers that have appeared in the Special Issue of theEntropy journal dedicated to “Information Theory for Data Communications and Processing”,reflects, in its eleven chapters, novel contributions based on the firm basic grounds of informationtheory. The book chapters [1–11] address timely theoretical and practical aspects that carry bothinteresting and relevant theoretical contributions, as well as direct implications for modern currentand future communications systems. [...]

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17

WU, Meng, and Xin-Wen BAI. "Motivated Information Processing Model: Theory and Applications." Advances in Psychological Science 20, no. 11 (2013): 1889–98. http://dx.doi.org/10.3724/sp.j.1042.2012.01889.

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18

Hersh, Benjamin. "The Paradox of Social Information Processing Theory." International Journal of Science in Society 3, no. 1 (2012): 53–74. http://dx.doi.org/10.18848/1836-6236/cgp/v03i01/51305.

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19

Price, Donald D., and Stephen W. Harkins. "An information processing theory of chronic pain." APS Journal 2, no. 3 (1993): 179–81. http://dx.doi.org/10.1016/s1058-9139(05)80085-3.

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20

Ariel, Shlomo. "An information processing theory of family dysfunction." Psychotherapy: Theory, Research, Practice, Training 24, no. 3S (1987): 477–95. http://dx.doi.org/10.1037/h0085745.

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21

Egelhoff, William G. "Information-Processing Theory and the Multinational Enterprise." Journal of International Business Studies 22, no. 3 (1991): 341–68. http://dx.doi.org/10.1057/palgrave.jibs.8490306.

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22

Hann, Il-Horn, Kai-Lung Hui, Sang-Yong Tom Lee, and Ivan P. L. Png. "Overcoming Online Information Privacy Concerns: An Information-Processing Theory Approach." Journal of Management Information Systems 24, no. 2 (2007): 13–42. http://dx.doi.org/10.2753/mis0742-1222240202.

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23

Atick, Joseph J. "Could information theory provide an ecological theory of sensory processing?" Network: Computation in Neural Systems 3, no. 2 (1992): 213–51. http://dx.doi.org/10.1088/0954-898x_3_2_009.

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24

Atick, Joseph J. "Could information theory provide an ecological theory of sensory processing?" Network: Computation in Neural Systems 22, no. 1-4 (2011): 4–44. http://dx.doi.org/10.3109/0954898x.2011.638888.

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25

Gellatly, Angus. "Perception and information processing." Behavioral and Brain Sciences 22, no. 3 (1999): 377–78. http://dx.doi.org/10.1017/s0140525x9935202x.

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Perception and cognition can be understood either as conscious experience, thought, and behaviour or as bodily functions executed at the level of information processing. Whether or not they are cognitively penetrable depends on the level referred to. Selective attention is the mechanism by which cognition affects perception, theory influences observation and observational reports, culture biases experience, and current knowledge determines what inferences are made. Seeing must be distinguished from seeing as.

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26

Li, Yuan, and William Kettinger. "An Evolutionary Information-Processing Theory of Knowledge Creation." Journal of the Association for Information Systems 7, no. 9 (2006): 593–617. http://dx.doi.org/10.17705/1jais.00101.

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27

Zhou, Honggeng. "An Empirical Test of the Information Processing Theory." International Journal of Information Systems and Supply Chain Management 4, no. 1 (2011): 45–59. http://dx.doi.org/10.4018/jisscm.2011010103.

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According to the propositions in the information processing theory, this study tests the relationship between task uncertainty and three organizational design strategies, i.e., creation of lateral relationships, investment in information systems, and creation of self-contained tasks. Data from 125 North American manufacturing firms are used and business environment uncertainty is employed to measure task uncertainty. Sourcing practice and delivery practice measure the creation of lateral relationships, while Information quality measures the investment in information systems. Also, just-in-time production and human resource management measure the creation of self-contained tasks. Regression analysis shows that business environment uncertainty has significant positive influence on sourcing practice, delivery practice, information quality, just-in-time production, and human resource management. While the information processing theory was proposed more than thirty years ago, this study empirically extends the relevance of information processing theory to today’s supply chain environment.

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28

Mayer, M. A. "Analysis of Information Processing and Cognitive Disability Theory." American Journal of Occupational Therapy 42, no. 3 (1988): 176–83. http://dx.doi.org/10.5014/ajot.42.3.176.

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29

Farfán, Fernando D., Julio C. Politti, and Carmelo J. Felice. "Evaluation of EMG processing techniques using Information Theory." BioMedical Engineering OnLine 9, no. 1 (2010): 72. http://dx.doi.org/10.1186/1475-925x-9-72.

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30

Buck, John R. "Information theory for matched field processing: preliminary results." Journal of the Acoustical Society of America 109, no. 5 (2001): 2319. http://dx.doi.org/10.1121/1.4744154.

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31

Bolon, Douglas S. "Information processing theory: implications for health care organisations." International Journal of Technology Management 15, no. 3/4/5 (1998): 211. http://dx.doi.org/10.1504/ijtm.1998.002605.

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32

Snyder, Robert. "Information Processing: A Visual Theory for Television News." Journal of Visual Literacy 14, no. 1 (1994): 69–76. http://dx.doi.org/10.1080/23796529.1994.11674491.

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33

Ransom, Robert. "Biological Information Processing: Current Theory and Computer Simulation." Mathematical Biosciences 84, no. 1 (1987): 123–24. http://dx.doi.org/10.1016/0025-5564(87)90047-2.

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34

Castellano, Sergio. "Towards an information-processing theory of mate choice." Animal Behaviour 78, no. 6 (2009): 1493–97. http://dx.doi.org/10.1016/j.anbehav.2009.10.002.

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35

Khabbazi Oskouei, Samad, Stefano Mancini, and Mark M. Wilde. "Union bound for quantum information processing." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2221 (2019): 20180612. http://dx.doi.org/10.1098/rspa.2018.0612.

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In this paper, we prove a quantum union bound that is relevant when performing a sequence of binary-outcome quantum measurements on a quantum state. The quantum union bound proved here involves a tunable parameter that can be optimized, and this tunable parameter plays a similar role to a parameter involved in the Hayashi–Nagaoka inequality (Hayashi & Nagaoka 2003 IEEE Trans. Inf. Theory 49 , 1753–1768. ( doi:10.1109/TIT.2003.813556 )), used often in quantum information theory when analysing the error probability of a square-root measurement. An advantage of the proof delivered here is that it is elementary, relying only on basic properties of projectors, Pythagoras' theorem, and the Cauchy–Schwarz inequality. As a non-trivial application of our quantum union bound, we prove that a sequential decoding strategy for classical communication over a quantum channel achieves a lower bound on the channel's second-order coding rate. This demonstrates the advantage of our quantum union bound in the non-asymptotic regime, in which a communication channel is called a finite number of times. We expect that the bound will find a range of applications in quantum communication theory, quantum algorithms and quantum complexity theory.

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36

Mathiassen, Lars, and Carsten Soreness. "Towards a Theory of Organizational Information Services." Journal of Information Technology 23, no. 4 (2008): 313–29. http://dx.doi.org/10.1057/jit.2008.10.

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The use of information technology (IT) in organizations has undergone dramatic changes during the past 30 years. As a result, it has become increasingly common to adopt services rather than traditional systems perspective to more accurately capture contemporary practices. There is, however, a lack of theories that can help us understand, assess, and design information services in organizational contexts. On this backdrop, we combine general notions of information processing options and requirements to outline a contingency theory of organizational use of information services. The theory suggests that information services are configurations of heterogeneous information processing capabilities; these services are evoked by organizational actors to help execute tasks, and evoking different configurations may lead to equally satisfactory outcomes. The theory distinguishes between four types of services computational, adaptive, networking, and collaborative services, and it suggests that organizational actors need portfolios of information services that are suited to the equivocality and uncertainty profile of the information processing they face. The paper defines four types of services and how they relate to information processing requirements; it applies the theory to a study of information services in response to vehicle policing; and it outlines how the theory relates to standardization and unintended consequences of information services. We conclude by discussing the theory and its implications for research and practice.

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37

Ognjenovic, Predrag. "Processing of Aesthetic Information." Empirical Studies of the Arts 9, no. 1 (1991): 1–9. http://dx.doi.org/10.2190/kc25-jwtn-nrx4-c7a1.

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38

Xu, Feng, and Simon X. Yang. "Information Security and Processing." Intelligent Automation & Soft Computing 19, no. 2 (2013): 109–10. http://dx.doi.org/10.1080/10798587.2013.786916.

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39

Dozier, V. Casey, and Debra Osborn. "Cognitive information processing theory: Applications in research and practice." Journal of the National Institute for Career Education and Counselling 41, no. 1 (2018): 39–47. http://dx.doi.org/10.20856/jnicec.4107.

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40

Ogarok, A. L. "METHODICAL BASIS OF THE THEORY OF SEMANTIC INFORMATION PROCESSING." Informatization and communication, no. 1 (March 20, 2019): 84–91. http://dx.doi.org/10.34219/2078-8320-2019-10-1-84-91.

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The problem of developing a methodological basis for the theory of semantic information processing is considered. An analysis of existing approaches to the automation of semantic processing of information in computer systems is carried out. The results of research on creating a methodological basis for the analysis and synthesis of unstructured textual information in computer systems are presented.

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41

Derakshan, Nazanin, Michael W. Eysenck, and Lynn B. Myers. "Emotional information processing in repressors: The vigilance–avoidance theory." Cognition & Emotion 21, no. 8 (2007): 1585–614. http://dx.doi.org/10.1080/02699930701499857.

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42

O'Shaughnessy, Douglas, Li Deng, and Haizhou Li. "Speech Information Processing: Theory and Applications [Scanning the Issue]." Proceedings of the IEEE 101, no. 5 (2013): 1034–37. http://dx.doi.org/10.1109/jproc.2013.2252718.

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43

Hill, Margaret Duval. "Adaptive Information Processing Theory: Origins, Principles, Applications, and Evidence." Journal of Evidence-Based Social Work 17, no. 3 (2020): 317–31. http://dx.doi.org/10.1080/26408066.2020.1748155.

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44

Tangen, Jodi L., and L. DiAnne Borders. "Applying Information Processing Theory to Supervision: An Initial Exploration." Counselor Education and Supervision 56, no. 2 (2017): 98–111. http://dx.doi.org/10.1002/ceas.12065.

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45

Venturino, Michael, and F. Thomas Eggemeier. "Capacity Limitations in Human Information Processing: Theory and Applications." Proceedings of the Human Factors Society Annual Meeting 31, no. 6 (1987): 672–73. http://dx.doi.org/10.1177/154193128703100613.

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Two factors that are critical aspects of complex system performance are system design and operator training. The contribution of each of these factors becomes paramount as increases in system complexity demand more sophisticated operator timesharing skills to monitor and control system operations safely and efficiently. In order to increase human operator skill levels, improvements in system design must be achieved to make aspects of monitoring and controlling tasks more commensurate with human abilities. Secondly, more effective and efficient training programs must be developed to allow human operators to acquire and maintain appropriate skill levels. Before these improvements can be accomplished, however, a greater understanding of the capabilities and limitations of the human information processing system must be obtained. In a typical modern system (e.g., aircraft cockpit, centralized control room of a power plant or battle management system) the human may perform many tasks concurrently, including visual search, memory search, retention and recall, comprehension, reasoning, judgement and decision making, and executing fine motor movements. Although some combinations of these kinds of tasks may be compatible and non-interfering, other task combinations may interfere and compete for the human's limited information processing capacity, creating overload conditions. The consequence of the human operator's low threshold for overload leads to increased probability of error, with subsequent deteriorations in system performance. There are two theoretical approaches to human attention and information processing that have implications for system design and operator training issues in the context of timesharing and high workload situations. Multiple resource theory and automatic/controlled processing theory have each stimulated much empirical work that provided useful data on human timesharing abilities. Such data have provided the basis for predicting task combinations and training requirements that allow high levels of performance under timesharing and high workload situations. The purpose of this symposium is to describe recent empirical developments that may further our understanding of human information processing capabilities and limitations, particularly under timesharing and high workload conditions. The set of papers includes theoretical and applied treatments of multiple resource and automaticity theories. The issues discussed include the advantages and disadvantages of spreading information input over different sensory modalities (e.g., visual and auditory), development of training methods to acquire sophisticated high level cognitive skills quickly and efficiently, measurement of information processing capacity required by specific task combinations, and a delineation of the characteristics and limitations of working memory.

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46

Slater, Michael D., Helen Chipman, Garry Auld, Thomas Keefe, and Patricia Kendall. "Information Processing and Situational Theory: A Cognitive Response Analysis." Journal of Public Relations Research 4, no. 4 (1992): 189–203. http://dx.doi.org/10.1207/s1532754xjprr0404_1.

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47

Taormina, Robert J. "Organizational analysis of information processing using living systems theory." Behavioral Science 36, no. 3 (1991): 196–223. http://dx.doi.org/10.1002/bs.3830360304.

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48

Santos-Pinto, Luís. "Asymmetries in Information Processing in a Decision Theory Framework." Theory and Decision 66, no. 4 (2007): 317–43. http://dx.doi.org/10.1007/s11238-007-9088-5.

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49

Reardon, Robert C., and Laura K. Wright. "The Case of Mandy: Applying Holland's Theory and Cognitive Information Processing Theory." Career Development Quarterly 47, no. 3 (1999): 195–203. http://dx.doi.org/10.1002/j.2161-0045.1999.tb00730.x.

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50

Matsuoka, T., and T. Ulrych. "Information theory measures with application to model identification." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 3 (1986): 511–17. http://dx.doi.org/10.1109/tassp.1986.1164851.

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