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Journal articles on the topic 'Selective attention'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Theofilidis, Antonis, and Filipos Kargopoulos. "Mind’s selective attention to previous experience." Clinical Research and Clinical Trials 4, no. 3 (September 29, 2021): 01–07. http://dx.doi.org/10.31579/2693-4779/057.

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The basic arguments for a mental image model of thought are based on neuropsychological evidence. France (2000) found that the same brain regions are activated during both mental representation and actual perception, while Bishiah (1993) found that brain traumas that affected perception, also affected the ability to create mental images. Pylyshyn(2003) on the other hand, argues that all mental images are guided by ‘’cognitive penetrability’’, thus on their very basis, are manipulated by certain propositional elements. Given this difficulty, Kargopoulos (2009) hinted towards further research, featuring shapes and solid objects, for which subjects have no priorextensive knowledge. This would force subjects to use non-semantic strategies of representation, meaning mental imagery. Hinton’s (1979) cube problem conforms to these requirements. Hinton’s problem aligns with the idea that spatial tasks (especially tasks with cubes that change layout) are guided by propositional cues (our knowledge about squares) and supports Pylyshyn’s position. Using one of the simplest objects, a cube, Hinton showed that as soon as this shape changes its mental arrangement in space, even suspicious -as to the nature of the experiment- participants will make mistakes that are not present when they manipulate a mental image of the cube sitting on its typicalarray. Aim: Our goal was to investigate the relationship between spatio-visual skill and the ability for mental partitioning in healthy subjects. Methodology: We used 2 groups (344 participants) a control and an experimental one. In the control group, we presented a Moebius’ strip, in the experimental group, we presented the same Moebius’ strip and asked them to mentally represent it. All participants asked to mentally partition the strip. Results: Of the 344 participants, only 31 managed to give the correct number of vertices in space. Though people had a hard time manipulating the cube’s mental image, their success rates were much higher for the Hinton 1 task in which propositional representation was more accessible. Only 9 of the 344 participants could find the correct answer for the Moebius strip task in which mental manipulation of the strip image was impossible. Conclusions: We come to the conclusion that the relationship between ‘’seeing’’ and ‘’knowing’’ is more complex, not just on the level of the mental image level but also on the level of perception. Our findings bring back to the scientific background the idea that the mind’s selective attention to previous experience and cognitive schemas will decidedly affect human thought.

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2

KASAI, Tetsuko, and Jun-ichiro KAWAHARA. "Selective attention." Japanese Journal of Physiological Psychology and Psychophysiology 33, no. 1 (2015): 1–3. http://dx.doi.org/10.5674/jjppp.1511si.

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3

Rose, Steven. "Selective attention." Nature 360, no. 6403 (December 1992): 426–27. http://dx.doi.org/10.1038/360426b0.

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4

Johnston, W. A., and V. J. Dark. "Selective Attention." Annual Review of Psychology 37, no. 1 (January 1986): 43–75. http://dx.doi.org/10.1146/annurev.ps.37.020186.000355.

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5

Brown, Patrick, and Paul Fera. "Turning selective attention failure into selective attention success." Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 48, no. 1 (1994): 25–57. http://dx.doi.org/10.1037/1196-1961.48.1.25.

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6

Loose, Rainer, Christian Kaufmann, Dorothea P. Auer, and Klaus W. Lange. "Selective attention and divided attention." NeuroImage 11, no. 5 (May 2000): S34. http://dx.doi.org/10.1016/s1053-8119(00)90968-6.

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7

Quigley, C., and M. M. Muller. "Feature-Selective Attention in Healthy Old Age: A Selective Decline in Selective Attention?" Journal of Neuroscience 34, no. 7 (February 12, 2014): 2471–76. http://dx.doi.org/10.1523/jneurosci.2718-13.2014.

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8

Hanus, D., E. Vul, and N. Kanwisher. "Delay of selective attention during the attentional blink." Journal of Vision 8, no. 6 (March 27, 2010): 6. http://dx.doi.org/10.1167/8.6.6.

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9

Vul, Edward, Deborah Hanus, and Nancy Kanwisher. "Delay of selective attention during the attentional blink." Vision Research 48, no. 18 (August 2008): 1902–9. http://dx.doi.org/10.1016/j.visres.2008.06.009.

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10

Shalev, L., I. Davidesco, C. Mevorach, and G. Goelman. "Disentangling selective attention from orienting of attention." Journal of Vision 9, no. 8 (September 3, 2010): 83. http://dx.doi.org/10.1167/9.8.83.

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11

彭, 鸿博. "Temporal Structure in Attention: Selective Temporal Attention." Advances in Psychology 10, no. 04 (2020): 448–54. http://dx.doi.org/10.12677/ap.2020.104056.

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12

Dayan, Peter, Sham Kakade, and P. Read Montague. "Learning and selective attention." Nature Neuroscience 3, S11 (November 2000): 1218–23. http://dx.doi.org/10.1038/81504.

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13

Schwartzstein, Joshua. "SELECTIVE ATTENTION AND LEARNING." Journal of the European Economic Association 12, no. 6 (October 3, 2014): 1423–52. http://dx.doi.org/10.1111/jeea.12104.

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14

Vul, E., D. Hanus, and N. Kanwisher. "Selective attention and uncertainty." Journal of Vision 8, no. 6 (April 2, 2010): 878. http://dx.doi.org/10.1167/8.6.878.

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15

Moorselaar, Dirk, and Heleen A. Slagter. "Inhibition in selective attention." Annals of the New York Academy of Sciences 1464, no. 1 (March 2020): 204–21. http://dx.doi.org/10.1111/nyas.14304.

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16

Sridharan, Devarajan, Jason S. Schwarz, and Eric I. Knudsen. "Selective attention in birds." Current Biology 24, no. 11 (June 2014): R510—R513. http://dx.doi.org/10.1016/j.cub.2013.12.046.

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17

Schlosser, Nicole, Christoph Mensebach, Nina Rullkötter, Camille Schaffrath, Martin Driessen, Thomas Beblo, and Katja Wingenfeld. "Selective Attention in Depression." Journal of Nervous and Mental Disease 199, no. 9 (September 2011): 696–702. http://dx.doi.org/10.1097/nmd.0b013e318229d6cf.

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18

Theeuwes, Jan. "Selective attention in vision." Acta Psychologica 83, no. 3 (August 1993): 237–42. http://dx.doi.org/10.1016/0001-6918(93)90056-w.

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19

Reeves, Adam. "Attention as a Unitary Concept." Vision 4, no. 4 (November 9, 2020): 48. http://dx.doi.org/10.3390/vision4040048.

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In this paper, I discuss attention in terms of selecting visual information and acting on it. Selection has been taken as a bedrock concept in attention research since James (1890). Selective attention guides action by privileging some things at the expense of others. I formalize this notion with models which capture the relationship between input and output under the control of spatial and temporal attention, by attenuating or discarding certain inputs and by weighing energetic costs, speed, and accuracy in meeting pre-chosen goals. Examples are given from everyday visually guided actions, and from modeling data obtained from visual searches through temporal and spatial arrays and related research. The relation between selection, as defined here, and other forms of attention is discussed at the end.

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20

Tomita, Nozomi, Shoji Imai, Yusuke Kanayama, and Hiroaki Kumano. "Relationships Between Cortically Mediated Attentional Dysfunction and Social Anxiety, Self-Focused Attention, and External Attention Bias." Perceptual and Motor Skills 126, no. 6 (August 6, 2019): 1101–16. http://dx.doi.org/10.1177/0031512519867798.

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Social anxiety disorder is characterized by a marked fear and avoidance of social situations or a fear of being evaluated by others. Although training for top-down attentional control has been an effective treatment for social anxiety disorder, few studies have demonstrated that individuals with social anxiety have top-down attentional dysfunction. This study used dichotic listening (DL) tasks to investigate the relationship between social anxiety and top-down attentional control over relevant brain activities. We also investigated relationships between both social situation-dependent self-focused attention and external attention bias and situation-independent attentional control. Thirty-six healthy participants underwent near-infrared spectroscopy scanning while performing top-down selective and divided attention DL tasks. Then, they undertook a speech task and completed a questionnaire to assess the degrees of their self-focused attention and external attention bias. The results showed that the degree of social fear and self-focused attention during the speech task were negatively correlated with scores on the selective attention task and with the activity of the left pars opercularis during the selective DL task, which were related to each other. These results suggest that a relationship exists between social fear, self-focused attention in a social situation, and top-down selective attentional dysfunction as assessed both behaviorally and by brain activity changes.

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21

Sun, Peng, Charles Chubb, Charles E. Wright, and George Sperling. "Human attention filters for single colors." Proceedings of the National Academy of Sciences 113, no. 43 (October 10, 2016): E6712—E6720. http://dx.doi.org/10.1073/pnas.1614062113.

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The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is feature-based attention (FBA). FBA is best described by attention filters that specify precisely the extent to which items containing attended features are selectively processed and the extent to which items that do not contain the attended features are attenuated. The centroid-judgment paradigm enables quick, precise measurements of such human perceptual attention filters, analogous to transmission measurements of photographic color filters. Subjects use a mouse to locate the centroid—the center of gravity—of a briefly displayed cloud of dots and receive precise feedback. A subset of dots is distinguished by some characteristic, such as a different color, and subjects judge the centroid of only the distinguished subset (e.g., dots of a particular color). The analysis efficiently determines the precise weight in the judged centroid of dots of every color in the display (i.e., the attention filter for the particular attended color in that context). We report 32 attention filters for single colors. Attention filters that discriminate one saturated hue from among seven other equiluminant distractor hues are extraordinarily selective, achieving attended/unattended weight ratios >20:1. Attention filters for selecting a color that differs in saturation or lightness from distractors are much less selective than attention filters for hue (given equal discriminability of the colors), and their filter selectivities are proportional to the discriminability distance of neighboring colors, whereas in the same range hue attention-filter selectivity is virtually independent of discriminabilty.

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22

Raymond, Jane E., and Jennifer L. O'Brien. "Selective Visual Attention and Motivation." Psychological Science 20, no. 8 (August 2009): 981–88. http://dx.doi.org/10.1111/j.1467-9280.2009.02391.x.

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Learning to associate the probability and value of behavioral outcomes with specific stimuli (value learning) is essential for rational decision making. However, in demanding cognitive conditions, access to learned values might be constrained by limited attentional capacity. We measured recognition of briefly presented faces seen previously in a value-learning task involving monetary wins and losses; the recognition task was performed both with and without constraints on available attention. Regardless of available attention, recognition was substantially enhanced for motivationally salient stimuli (i.e., stimuli highly predictive of outcomes), compared with equally familiar stimuli that had weak or no motivational salience, and this effect was found regardless of valence (win or loss). However, when attention was constrained (because stimuli were presented during an attentional blink, AB), valence determined recognition; win-associated faces showed no AB, but all other faces showed large ABs. Motivational salience acts independently of attention to modulate simple perceptual decisions, but when attention is limited, visual processing is biased in favor of reward-associated stimuli.

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23

Jiang, Yuhong, and Marvin M. Chun. "Selective attention modulates implicit learning." Quarterly Journal of Experimental Psychology Section A 54, no. 4 (November 2001): 1105–24. http://dx.doi.org/10.1080/713756001.

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The effect of selective attention on implicit learning was tested in four experiments using the “contextual cueing” paradigm (Chun & Jiang, 1998, 1999). Observers performed visual search through items presented in an attended colour (e.g., red) and an ignored colour (e.g., green). When the spatial configuration of items in the attended colour was invariant and was consistently paired with a target location, visual search was facilitated, showing contextual cueing (Experiments 1, 3, and 4). In contrast, repeating and pairing the configuration of the ignored items with the target location resulted in no contextual cueing (Experiments 2 and 4). We conclude that implicit learning is robust only when relevant, predictive information is selectively attended.

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24

Brand, N., L. Verspui, and A. Oving. "Induced Mood and Selective Attention." Perceptual and Motor Skills 84, no. 2 (April 1997): 455–63. http://dx.doi.org/10.2466/pms.1997.84.2.455.

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Subjects ( N = 60) were randomly assigned to an elated, depressed, or neutral mood-induction condition to assess the effect of mood state on cognitive functioning. In the elated condition film fragments expressing happiness and euphoria were shown. In the depressed condition some frightening and distressing film fragments were presented. The neutral group watched no film. Mood states were measured using the Profile of Mood States, and a Stroop task assessed selective attention. Both were presented by computer. The induction groups differed significantly in the expected direction on the mood subscales Anger, Tension, Depression, Vigour, and Fatigue, and also in the mean scale response times, i.e., slower responses for the depressed condition and faster for the elated one. Differences between conditions were found in the errors on the Stroop: in the depressed condition were the fewest errors and significantly longer error reaction times. Speed of error was associated with self-reported fatigue.

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25

Foster, Jonathan, K. "Selective attention in Alzheimer's disease." Frontiers in Bioscience 6, no. 1 (2001): d135. http://dx.doi.org/10.2741/foster.

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26

Poletti, Martina, Michele Rucci, and Marisa Carrasco. "Selective attention within the foveola." Nature Neuroscience 20, no. 10 (August 14, 2017): 1413–17. http://dx.doi.org/10.1038/nn.4622.

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27

Fenske, Mark J., and Jane E. Raymond. "Affective Influences of Selective Attention." Current Directions in Psychological Science 15, no. 6 (December 2006): 312–16. http://dx.doi.org/10.1111/j.1467-8721.2006.00459.x.

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28

Schupp, H. T., J. Stockburger, M. Codispoti, M. Junghofer, A. I. Weike, and A. O. Hamm. "Selective Visual Attention to Emotion." Journal of Neuroscience 27, no. 5 (January 31, 2007): 1082–89. http://dx.doi.org/10.1523/jneurosci.3223-06.2007.

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29

Krauzlis, Richard, and Lupeng Wang. "Visual selective attention in mice." Journal of Vision 18, no. 10 (September 1, 2018): 1218. http://dx.doi.org/10.1167/18.10.1218.

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30

Zhang, Xilei, Wenming Zheng, and Xiangyong Yuan. "Selective Attention Desynchronizes Automatic Movements." Journal of Vision 19, no. 10 (September 6, 2019): 109. http://dx.doi.org/10.1167/19.10.109.

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31

Vuilleumier, Patrik. "Facial expression and selective attention." Current Opinion in Psychiatry 15, no. 3 (May 2002): 291–300. http://dx.doi.org/10.1097/00001504-200205000-00011.

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32

Marinovic, W., P. Dux, and D. Arnold. "Selective attention and multisensory integration." Journal of Vision 11, no. 11 (September 23, 2011): 266. http://dx.doi.org/10.1167/11.11.266.

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33

Lemelin, S., P. Baruch, J. Everett, R. H. Bouchard, E. Pourcher, and P. Vincent. "SELECTIVE ATTENTION DEFICITS AND SCHIZOPHRENIA." Clinical Neuropharmacology 15 (1992): 239B. http://dx.doi.org/10.1097/00002826-199202001-00459.

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34

Ghimire, Nisha, Bishnu Hari Paudel, Rita Khadka, Parash Nath Singh, and Asim Das. "Electroencephalographic changes during selective attention." Asian Journal of Medical Sciences 6, no. 2 (September 21, 2014): 51–56. http://dx.doi.org/10.3126/ajms.v6i2.11122.

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Background: Though many studies are conducted during attention process, it is still not clear how brain deals with attention. So we conducted a study to find out the electroencephalographic changes during selective attention.Methods: Thirty healthy right handed male students aged 23.1±2.8 yrs were asked to read congruent (red printed in red ink) and incongruent (red printed in blue ink) words printed in cards. EEG was recorded for ninety seconds during baseline (eye open) and reading of both cards. EEG epoch was analyzed by fast fourier transformation. Friedman test was used to compare EEG power spectra among baseline, congruent and incongruent reading followed by Wilcoxon’s Sign Rank Test. Data were expressed as median with inter-quartile range.Results: Compared to congruent test during incongruent test there was selective increment of theta power at Fz [36.04 (28.30-46.19) vs. 47.89 (31.65-48.1)], Cz[36.13 (27.20-46.41) vs. 45.66 (37.15-49.4)] and C4 [25.11 (19.14-30.06) vs. 30.16 (21.43-33.8)] sites but it decreased at F7 [17.88 (14.49-20.93) vs. 11.31(8.96-15.975)] and F8 [19.23 (13.61-25.79) vs. 13.95 (10.40-16.67) sites. Also during incongruent card reading, alpha1 power significantly decreased in F8 [3.39(2.63-4.63) to 2.75 (1.93-4.7)] and alpha 2 power significantly decreased in P3 6.84 [(4.88-10.46) to 5.74 (4.78-19.95)] sites.Conclusion: During selective attention, theta gets synchronized at fronto-central regions and alpha2 desynchronized at parietal regions. The theta and alpha1 at inferior frontal regions were also desynchronized in selective attention. DOI: http://dx.doi.org/10.3126/ajms.v6i2.11122Asian Journal of Medical Sciences Vol.6(2) 2015 52-57

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35

Keil, Thomas, Markus Lang, and Dirk Martignoni. "Is Selective Attention Always Beneficial?" Academy of Management Proceedings 2014, no. 1 (January 2014): 13225. http://dx.doi.org/10.5465/ambpp.2014.13225abstract.

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36

Poletti, Martina, Marisa Carrasco, and Michele Rucci. "Selective attention within the foveola." Journal of Vision 15, no. 12 (September 1, 2015): 177. http://dx.doi.org/10.1167/15.12.177.

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37

Melara, Robert. "Mechanisms of auditory selective attention." Journal of the Acoustical Society of America 122, no. 5 (2007): 2969. http://dx.doi.org/10.1121/1.2942592.

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38

Liverence, Brandon M., and Brian J. Scholl. "Selective Attention Warps Spatial Representation." Psychological Science 22, no. 12 (November 17, 2011): 1600–1608. http://dx.doi.org/10.1177/0956797611422543.

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Selective attention not only influences which objects in a display are perceived, but also directly changes the character of how they are perceived—for example, making attended objects appear larger or sharper. In studies of multiple-object tracking and probe detection, we explored the influence of sustained selective attention on where objects are seen to be in relation to each other in dynamic multi-object displays. Surprisingly, we found that sustained attention can warp the representation of space in a way that is object-specific: In immediate recall of the positions of objects that have just disappeared, space between targets is compressed, whereas space between distractors is expanded. These effects suggest that sustained attention can warp spatial representation in unexpected ways.

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39

Sridharan, D., D. L. Ramamurthy, J. S. Schwarz, and E. I. Knudsen. "Visuospatial selective attention in chickens." Proceedings of the National Academy of Sciences 111, no. 19 (April 21, 2014): E2056—E2065. http://dx.doi.org/10.1073/pnas.1316824111.

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40

Patterson, Karalyn. "The pitfalls of selective attention." Behavioral and Brain Sciences 8, no. 4 (December 1985): 721. http://dx.doi.org/10.1017/s0140525x0004588x.

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41

Chelazzi, Leonardo, Andrea Perlato, Elisa Santandrea, and Chiara Della Libera. "Rewards teach visual selective attention." Vision Research 85 (June 2013): 58–72. http://dx.doi.org/10.1016/j.visres.2012.12.005.

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42

Posner, Michael I., and Jon Driver. "The neurobiology of selective attention." Current Biology 2, no. 5 (May 1992): 235–36. http://dx.doi.org/10.1016/0960-9822(92)90355-e.

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43

Steele-Russell, Ian, M. I. Russell, J. A. Castiglioni, J. A. Reuter, and M. W. van Hof. "Selective attention and Pavlovian conditioning." Experimental Brain Research 173, no. 4 (April 21, 2006): 587–602. http://dx.doi.org/10.1007/s00221-006-0404-z.

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44

Hoffmann, Joachim. "Semantic control of selective attention." Psychological Research 49, no. 2-3 (August 1987): 123–29. http://dx.doi.org/10.1007/bf00308677.

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45

Neumann, Odmar, A. H. C. van der Heijden, and D. Alan Allport. "Visual selective attention: Introductory remarks." Psychological Research 48, no. 4 (December 1986): 185–88. http://dx.doi.org/10.1007/bf00309082.

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46

Hillyard, Steven A. "Electrophysiology of human selective attention." Trends in Neurosciences 8 (January 1985): 400–405. http://dx.doi.org/10.1016/0166-2236(85)90142-0.

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47

Posner, Michael I., and David E. Presti. "Selective attention and cognitive control." Trends in Neurosciences 10, no. 1 (January 1987): 13–17. http://dx.doi.org/10.1016/0166-2236(87)90116-0.

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48

Myles-Worsley, Marina, William A. Johnston, and Paul H. Wender. "Spontaneous selective attention in schizophrenia." Psychiatry Research 39, no. 2 (November 1991): 167–79. http://dx.doi.org/10.1016/0165-1781(91)90085-4.

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49

Chokron, Sylvie, Adam M. Brickman, Tsechung Wei, and Monte S. Buchsbaum. "Hemispheric asymmetry for selective attention." Cognitive Brain Research 9, no. 1 (January 2000): 85–90. http://dx.doi.org/10.1016/s0006-8993(99)02169-1.

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50

Tracy, Joseph I., Amy E. McGrory, and Richard C. Josiassen. "Selective attention asymmetries in schizophrenia." Schizophrenia Research 15, no. 1-2 (April 1995): 137. http://dx.doi.org/10.1016/0920-9964(95)95425-9.

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