Статті в журналах: "Human information processing"

1

CALLISTA ROY, SISTER. "Human Information Processing." Annual Review of Nursing Research 6, no. 1 (September 1988): 237–62. http://dx.doi.org/10.1891/0739-6686.6.1.237.

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2

Callaway, Enoch, Roy Halliday, Hilary Naylor, Lovelle Yano, and Karen Herzig. "Drugs and Human Information Processing." Neuropsychopharmacology 10, no. 1 (February 1994): 9–19. http://dx.doi.org/10.1038/npp.1994.2.

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3

Velmans, Max. "Is human information processing conscious?" Behavioral and Brain Sciences 14, no. 4 (December 1991): 651–69. http://dx.doi.org/10.1017/s0140525x00071776.

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AbstractInvestigations of the function of consciousness in human information processing have focused mainly on two questions: (1) Where does consciousness enter into the information processing sequence, and (2) how does conscious processing differ from preconscious and unconscious processing? Input analysis is thought to be initially “preconscious” and “pre-attentive” - fast, involuntary, and automatic. This is followed by “conscious,” “focal-attentive” analysis, which is relatively slow, voluntary, and flexible. It is thought that simple, familiar stimuli can be identified preconsciously, but conscious processing is needed to identify complex, novel stimuli. Conscious processing has also been thought to be necessary for choice, learning and memory, and the organization of complex, novel responses, particularly those requiring planning, reflection, or creativity.The present target article reviews evidence that consciousness performs none of these functions. Consciousness nearly alwaysresultsfrom focal-attentive processing (as a form of output) but does not itselfenter intothis or any other form of human information processing. This suggests that the term “conscious process” needs reexamination. Consciousnessappearsto be necessary in a variety of tasks because they require focal-attentive processing; if consciousness is absent, focal-attentive processing is absent. From afirst-person perspective, however, conscious statesarecausally effective. First-person accounts arecomplementaryto third-person accounts. Although they can be translated into third-person accounts, they cannot be reduced to them.

4

Petrie, Rachel X. A., and Ian J. Deary. "Smoking and human information processing." Psychopharmacology 99, no. 3 (November 1989): 393–96. http://dx.doi.org/10.1007/bf00445565.

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5

Spangenberg, J. F. A., and F. J. N. Nijhuis. "Human information processing in science." Scientometrics 18, no. 5-6 (May 1990): 389–407. http://dx.doi.org/10.1007/bf02020153.

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6

Ishii, Hiroshi. "The Dawn of Kansei Information Processing. Human Interface and 'Kansei' Information Processing." Journal of the Institute of Image Information and Television Engineers 52, no. 1 (1998): 56–59. http://dx.doi.org/10.3169/itej.52.56.

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7

Lynn, Christopher W., Lia Papadopoulos, Ari E. Kahn, and Danielle S. Bassett. "Human information processing in complex networks." Nature Physics 16, no. 9 (June 15, 2020): 965–73. http://dx.doi.org/10.1038/s41567-020-0924-7.

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8

PAAS, FRED G. W. C., and JOS J. ADAM. "Human information processing during physical exercise." Ergonomics 34, no. 11 (November 1991): 1385–97. http://dx.doi.org/10.1080/00140139108964879.

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9

Robertson, Ivan T. "Human information-processing strategies and style." Behaviour & Information Technology 4, no. 1 (January 1985): 19–29. http://dx.doi.org/10.1080/01449298508901784.

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10

Fedzechkina, Maryia, Becky Chu, and T. Florian Jaeger. "Human Information Processing Shapes Language Change." Psychological Science 29, no. 1 (December 1, 2017): 72–82. http://dx.doi.org/10.1177/0956797617728726.

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Human languages exhibit both striking diversity and abstract commonalities. Whether these commonalities are shaped by potentially universal principles of human information processing has been of central interest in the language and psychological sciences. Research has identified one such abstract property in the domain of word order: Although sentence word-order preferences vary across languages, the superficially different orders result in short grammatical dependencies between words. Because dependencies are easier to process when they are short rather than long, these findings raise the possibility that languages are shaped by biases of human information processing. In the current study, we directly tested the hypothesized causal link. We found that learners exposed to novel miniature artificial languages that had unnecessarily long dependencies did not follow the surface preference of their native language but rather systematically restructured the input to reduce dependency lengths. These results provide direct evidence for a causal link between processing preferences in individual speakers and patterns in linguistic diversity.

11

Callaway, Enoch. "The Biology of Human Information Processing." Journal of Psychoactive Drugs 18, no. 4 (October 1986): 315–18. http://dx.doi.org/10.1080/02791072.1986.10472363.

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12

Rugg, Michael D. "Psychophysiological approaches to human information processing." Biological Psychology 22, no. 2 (April 1986): 208–9. http://dx.doi.org/10.1016/0301-0511(86)90073-6.

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13

Wilkinson, Thomas M. "Human Information Processing, Health Information Technology, and Medical Outcomes." JAMA 302, no. 13 (October 7, 2009): 1417. http://dx.doi.org/10.1001/jama.2009.1417.

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14

Ramsey, N. F. "Neurophysiological factors in human information processing capacity." Brain 127, no. 3 (November 7, 2003): 517–25. http://dx.doi.org/10.1093/brain/awh060.

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15

Jayles, Bertrand, Ramón Escobedo, Roberto Pasqua, Christophe Zanon, Adrien Blanchet, Matthieu Roy, Gilles Tredan, Guy Theraulaz, and Clément Sire. "Collective information processing in human phase separation." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1807 (July 27, 2020): 20190801. http://dx.doi.org/10.1098/rstb.2019.0801.

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In our digital societies, individuals massively interact through digital interfaces whose impact on collective dynamics can be important. In particular, the combination of social media filters and recommender systems can lead to the emergence of polarized and fragmented groups. In some social contexts, such segregation processes of human groups have been shown to share similarities with phase separation phenomena in physics. Here, we study the impact of information filtering on collective segregation behaviour of human groups. We report a series of experiments where groups of 22 subjects have to perform a collective segregation task that mimics the tendency of individuals to bond with other similar individuals. More precisely, the participants are each assigned a colour (red or blue) unknown to them, and have to regroup with other subjects sharing the same colour. To assist them, they are equipped with an artificial sensory device capable of detecting the majority colour in their ‘environment’ (defined as their k nearest neighbours, unbeknownst to them), for which we control the perception range, k = 1, 3, 5, 7, 9, 11, 13. We study the separation dynamics (emergence of unicolour groups) and the properties of the final state, and show that the value of k controls the quality of the segregation, although the subjects are totally unaware of the precise definition of the ‘environment’. We also find that there is a perception range k = 7 above which the ability of the group to segregate does not improve. We introduce a model that precisely describes the random motion of a group of pedestrians in a confined space, and which faithfully reproduces and allows interpretation of the results of the segregation experiments. Finally, we discuss the strong and precise analogy between our experiment and the phase separation of two immiscible materials at very low temperature. This article is part of the theme issue ‘Multi-scale analysis and modelling of collective migration in biological systems’.

16

Spivey, Michael J. "Redesigning our theories of human information processing." Discourse, Cognition and Communication 15, no. 3 (December 12, 2007): 261–65. http://dx.doi.org/10.1075/idj.15.3.08spi.

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17

KITAGAWA, NORIMICHI, and CHARLES SPENCE. "Audiotactile multisensory interactions in human information processing." Japanese Psychological Research 48, no. 3 (September 2006): 158–73. http://dx.doi.org/10.1111/j.1468-5884.2006.00317.x.

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18

Dickhaut, John, Vernon Smith, Baohua Xin, and Aldo Rustichini. "Human economic choice as costly information processing." Journal of Economic Behavior & Organization 94 (October 2013): 206–21. http://dx.doi.org/10.1016/j.jebo.2013.08.001.

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19

Ren, Fuji. "Affective Information Processing and Recognizing Human Emotion." Electronic Notes in Theoretical Computer Science 225 (January 2009): 39–50. http://dx.doi.org/10.1016/j.entcs.2008.12.065.

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20

Sanders, Andries F. "Human information processing: Measures, mechanisms and models." Acta Psychologica 76, no. 2 (April 1991): 199–200. http://dx.doi.org/10.1016/0001-6918(91)90048-5.

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21

Duncan, James R. "Human Information Processing, Health Information Technology, and Medical Outcomes—Reply." JAMA 302, no. 13 (October 7, 2009): 1417. http://dx.doi.org/10.1001/jama.2009.1418.

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22

Alvandi, Ebrahim Oshni. "Emotions and Information Processing." International Journal of Synthetic Emotions 2, no. 1 (January 2011): 1–14. http://dx.doi.org/10.4018/jse.2011010101.

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An animate system standing in nature and trying to investigate its surroundings for different purposes does a type of cognitive processing. Emotions as mental states are leading human cognitive features that attract life by interactions processed in the world. This paper examines how this cognitive feature process works. By researching history and theories related to emotions and their generation, it becomes clear that information processing is discussed as a tool for their processes. Three different styles of information processing are evaluated for emotional processes. The pragmatic notion of information processing fits as a processing tool in modeling emotions and artificial emotions and explains the emotional process.

23

Rikani, Azadeh A., Zia Choudhry, Adnan Maqsood Choudhry, Nasir Rizvi, and Huma Ikram. "Spatial information processing by the human visual system." El Mednifico Journal 2, no. 3 (July 25, 2014): 309. http://dx.doi.org/10.18035/emj.v2i3.156.

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24

Lynn, Christopher W., Lia Papadopoulos, Ari E. Kahn, and Danielle S. Bassett. "Author Correction: Human information processing in complex networks." Nature Physics 16, no. 12 (July 7, 2020): 1238. http://dx.doi.org/10.1038/s41567-020-0985-7.

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25

Driver, Michael J., Katarina Svensson, Roy P. Amato, and Larry E. Pate. "A Human-Information-Processing Approach to Strategic Change." International Studies of Management & Organization 26, no. 1 (March 1996): 41–58. http://dx.doi.org/10.1080/00208825.1996.11656673.

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26

No authorship indicated. "Review of Eye Movements and Human Information Processing." Contemporary Psychology: A Journal of Reviews 31, no. 10 (October 1986): 820. http://dx.doi.org/10.1037/024205.

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27

Lounasmaa, O. V., M. Hamalainen, R. Hari, and R. Salmelin. "Information processing in the human brain: magnetoencephalographic approach." Proceedings of the National Academy of Sciences 93, no. 17 (August 20, 1996): 8809–15. http://dx.doi.org/10.1073/pnas.93.17.8809.

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28

Truta, Meda, and Mihai Popescu. "Intelligent Control System Based on Human Information Processing." IFAC Proceedings Volumes 34, no. 3 (May 2001): 117–20. http://dx.doi.org/10.1016/s1474-6670(17)34336-7.

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29

Howes, Andrew, Richard L. Lewis, and Satinder Singh. "Utility Maximization and Bounds on Human Information Processing." Topics in Cognitive Science 6, no. 2 (March 20, 2014): 198–203. http://dx.doi.org/10.1111/tops.12089.

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30

Seo, Chang-Jin. "Artificial Vision System using Human Visual Information Processing." Journal of Digital Convergence 12, no. 11 (November 28, 2014): 349–55. http://dx.doi.org/10.14400/jdc.2014.12.11.349.

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31

Coles, MichaelG H. "Psychophysiology and contemporary models of human information processing." Biological Psychology 20, no. 3 (May 1985): 189–90. http://dx.doi.org/10.1016/0301-0511(85)90066-3.

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32

Ramanujan, S., and RB Cooper. "A human information processing perspective on software maintenance." Omega 22, no. 2 (March 1994): 185–203. http://dx.doi.org/10.1016/0305-0483(94)90078-7.

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33

Czigler, István, Gergely Csibra, and Ágnes Ambró. "Age and Information Processing." European Psychologist 2, no. 3 (January 1997): 247–57. http://dx.doi.org/10.1027/1016-9040.2.3.247.

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This paper reviews our recent studies on the effects of aging on human information processing. In these studies the event-related potentials of the brain (ERPs) recorded in visual discrimination tasks were compared in younger and older groups of subjects in four experiments. We obtained a slight age-related delay of the NA component of the ERP. This component is a correlate of elementary pattern-identification processes. Obvious latency differences appeared on the anterior positivity, selection negativity, and N2b components in tasks where the target stimuli were defined by two stimulus characteristics. These components are correlates of attentional processes, i.e., the results support the view emphasizing age-related decline of the attentional processes. In the elderly the late positivity was less sensitive to stimulus probability, and in the older groups this component was more evenly distributed over the scalp. These results are considered as an indication that the structure of stimulus sequences was less efficiently represented in the older subjects.

34

Yang, C. Y. David, and Jon D. Fricker. "Using Human Information Processing Principles to Design Advanced Traveler Information Systems." Transportation Research Record: Journal of the Transportation Research Board 1759, no. 1 (January 2001): 1–8. http://dx.doi.org/10.3141/1759-01.

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35

Fagan, Joseph, and Robert E. Ployhart. "The information processing foundations of human capital resources: Leveraging insights from information processing approaches to intelligence." Human Resource Management Review 25, no. 1 (March 2015): 4–11. http://dx.doi.org/10.1016/j.hrmr.2014.09.003.

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36

Diamant, Emanuel. "Modeling human-like intelligent image processing: An information processing perspective and approach." Signal Processing: Image Communication 22, no. 6 (July 2007): 583–90. http://dx.doi.org/10.1016/j.image.2007.05.007.

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37

K.N., Sridevi, and Prakasha S. "Doc-To-Tokens based Pre-Processing in Information Retrieval System." Webology 18, SI05 (October 30, 2021): 570–79. http://dx.doi.org/10.14704/web/v18si05/web18247.

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38

Burgin, Mark, Eugene Eberbach, and Rao Mikkilineni. "Processing Information in the Clouds." Proceedings 47, no. 1 (May 7, 2020): 25. http://dx.doi.org/10.3390/proceedings2020047025.

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Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.

39

Burgin, Mark, Eugene Eberbach, and Rao Mikkilineni. "Processing Information in the Clouds." Proceedings 47, no. 1 (May 7, 2020): 25. http://dx.doi.org/10.3390/proceedings47010025.

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Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.

40

Jackson, Russell E., and Dustin P. Calvillo. "Evolutionary Relevance Facilitates Visual Information Processing." Evolutionary Psychology 11, no. 5 (December 2013): 147470491301100. http://dx.doi.org/10.1177/147470491301100506.

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Visual search of the environment is a fundamental human behavior that perceptual load affects powerfully. Previously investigated means for overcoming the inhibitions of high perceptual load, however, generalize poorly to real-world human behavior. We hypothesized that humans would process evolutionarily relevant stimuli more efficiently than evolutionarily novel stimuli, and evolutionary relevance would mitigate the repercussions of high perceptual load during visual search. Animacy is a significant component to evolutionary relevance of visual stimuli because perceiving animate entities is time-sensitive in ways that pose significant evolutionary consequences. Participants completing a visual search task located evolutionarily relevant and animate objects fastest and with the least impact of high perceptual load. Evolutionarily novel and inanimate objects were located slowest and with the highest impact of perceptual load. Evolutionary relevance may importantly affect everyday visual information processing.

41

Chakarov, Vihren E., Krassimir T. Atanassov, and Anthony G. Shannon. "Generalized Net Model of Auditory Information Processing." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 5 (June 20, 2007): 452–56. http://dx.doi.org/10.20965/jaciii.2007.p0452.

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In a series of papers we have described the human body, its separate systems, and their behavior using Generalized Nets (GNs). Here we construct a GN-model that represents auditory information processing - the first on our research on the structure and behavior of the human nervous system.

42

Massaro, Dominic W., and Donald A. Norman. "Memory and Attention: An Introduction to Human Information Processing." American Journal of Psychology 107, no. 4 (1994): 597. http://dx.doi.org/10.2307/1423002.

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43

Hatamoto, K., M. Nagamachi, K. Ito, and T. Tsuji. "Human visual information processing on formation of subjective contours." Japanese journal of ergonomics 25, Supplement (1989): 112–13. http://dx.doi.org/10.5100/jje.25.supplement_112.

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44

Godlewska, Magdalena. "Smart Document-Centric Processing of Human Oriented Information Flows." Computing and Informatics 37, no. 3 (2018): 673–92. http://dx.doi.org/10.4149/cai_2018_3_673.

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45

Rappelsberger, P., N. Vath, S. Weiss, E. Möller, G. Grießbach, H. Witte, and B. Schack. "EEG Frequency and Phase Coupling during Human Information Processing." Methods of Information in Medicine 40, no. 02 (2001): 106–11. http://dx.doi.org/10.1055/s-0038-1634470.

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AbstractNeuronal activity during information processing is represented by oscillations within local or widespread neuronal networks. These oscillations may be recorded by the EEG (electroencephalogram). The oscillatory interaction between neuronal ensembles may be at one single frequency or at different frequencies due to non-linear coupling. The investigation of momentary coherence and phase enables the examination of synchronized oscillatory network activity during fast-changing cognitive processes. On this basis information transfer from occipital areas towards frontal areas could be described during processing of visual presented words. Non-linear phase coupling between oscillations with different frequencies during memory processing was detected by means of cross-bicoherence.

46

Foss, Donald J. "Review of Human Information Processing: Measures, Mechanisms, and Models." Contemporary Psychology: A Journal of Reviews 36, no. 3 (March 1991): 263. http://dx.doi.org/10.1037/029570.

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47

Sobol, M. G., and G. Klein. "New graphics as computerized displays for human information processing." IEEE Transactions on Systems, Man, and Cybernetics 19, no. 4 (1989): 893–98. http://dx.doi.org/10.1109/21.35357.

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48

Strawser, Jerry R. "Human Information Processing the Consistency of Audit Risk Judgments." Accounting and Business Research 21, no. 81 (December 1990): 67–75. http://dx.doi.org/10.1080/00014788.1990.9729405.

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49

Buot, Anne, Marie-Laure Welter, Carine Karachi, Jean-Baptiste Pochon, Eric Bardinet, Jérôme Yelnik, and Luc Mallet. "Processing of emotional information in the human subthalamic nucleus." Journal of Neurology, Neurosurgery & Psychiatry 84, no. 12 (October 25, 2012): 1331–39. http://dx.doi.org/10.1136/jnnp-2011-302158.

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50

ADOLPHS, RALPH. "Is the Human Amygdala Specialized for Processing Social Information?" Annals of the New York Academy of Sciences 985, no. 1 (January 24, 2006): 326–40. http://dx.doi.org/10.1111/j.1749-6632.2003.tb07091.x.

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