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Journal articles on the topic 'Attentional learning'

Author: Grafiati

Published: 11 March 2023

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1

Anderson, Brian A. "The attention habit: how reward learning shapes attentional selection." Annals of the New York Academy of Sciences 1369, no. 1 (November 23, 2015): 24–39. http://dx.doi.org/10.1111/nyas.12957.

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2

Vidnyanszky, Z., and W. Sohn. "Attentional learning: learning to bias sensory competition." Journal of Vision 3, no. 9 (March 16, 2010): 174. http://dx.doi.org/10.1167/3.9.174.

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3

Leber, A. B., and J. i. Kawahara. "Abstract learning of attentional set." Journal of Vision 8, no. 6 (April 2, 2010): 874. http://dx.doi.org/10.1167/8.6.874.

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4

Wierzchoń, Michał, Vincent Gaillard, Dariusz Asanowicz, and Axel Cleeremans. "Manipulating attentional load in sequence learning through random number generation." Advances in Cognitive Psychology 8, no. 2 (June 28, 2012): 179–95. http://dx.doi.org/10.5709/acp-0114-0.

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5

Okumura, Yuko, Yasuhiro Kanakogi, Tessei Kobayashi, and Shoji Itakura. "Do attentional cues affect infant learning?" Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 1EV—079–1EV—079. http://dx.doi.org/10.4992/pacjpa.82.0_1ev-079.

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6

Ahissar, M., and S. Hochstein. "Attentional control of early perceptual learning." Proceedings of the National Academy of Sciences 90, no. 12 (June 15, 1993): 5718–22. http://dx.doi.org/10.1073/pnas.90.12.5718.

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7

Lefebvre, C., A. Seitz, T. Watanabe, and P. Jolicoeur. "Learning blinks during the attentional blink." Journal of Vision 5, no. 8 (September 1, 2005): 1065. http://dx.doi.org/10.1167/5.8.1065.

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8

Olivers, Christian, and Artem Belopolsky. "Oculomotor Measures of Learning Attentional Templates." Journal of Vision 16, no. 12 (September 1, 2016): 120. http://dx.doi.org/10.1167/16.12.120.

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9

Livesey, E., I. Harris, and J. Harris. "Implicit learning and the attentional blink." Journal of Vision 9, no. 8 (March 21, 2010): 159. http://dx.doi.org/10.1167/9.8.159.

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10

Shanks, David R., Lee A. Rowland, and Mandeep S. Ranger. "Attentional load and implicit sequence learning." Psychological Research Psychologische Forschung 69, no. 5-6 (April 23, 2005): 369–82. http://dx.doi.org/10.1007/s00426-004-0211-8.

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11

Yamakawa, Hiroshi. "Attentional Reinforcement Learning in the Brain." New Generation Computing 38, no. 1 (January 2, 2020): 49–64. http://dx.doi.org/10.1007/s00354-019-00081-z.

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AbstractRecently, attention mechanisms have significantly boosted the performance of natural language processing using deep learning. An attention mechanism can select the information to be used, such as by conducting a dictionary lookup; this information is then used, for example, to select the next utterance word in a sentence. In neuroscience, the basis of the function of sequentially selecting words is considered to be the cortico-basal ganglia-thalamocortical loop. Here, we first show that the attention mechanism used in deep learning corresponds to the mechanism in which the cerebral basal ganglia suppress thalamic relay cells in the brain. Next, we demonstrate that, in neuroscience, the output of the basal ganglia is associated with the action output in the actor of reinforcement learning. Based on these, we show that the aforementioned loop can be generalized as reinforcement learning that controls the transmission of the prediction signal so as to maximize the prediction reward. We call this attentional reinforcement learning (ARL). In ARL, the actor selects the information transmission route according to the attention, and the prediction signal changes according to the context detected by the information source of the route. Hence, ARL enables flexible action selection that depends on the situation, unlike traditional reinforcement learning, wherein the actor must directly select an action.

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12

Banerjee, Soham, Trafton Drew, Megan K. Mills, and William F. Auffermann. "Perceptual training: learning versus attentional shift." Journal of Medical Imaging 7, no. 02 (December 31, 2019): 1. http://dx.doi.org/10.1117/1.jmi.7.2.022407.

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13

Wulf, Gabriele, Nancy H. McNevin, Thomas Fuchs, Florian Ritter, and Tonya Toole. "Attentional Focus in Complex Skill Learning." Research Quarterly for Exercise and Sport 71, no. 3 (September 2000): 229–39. http://dx.doi.org/10.1080/02701367.2000.10608903.

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14

Angulo, Rocío, and Gumersinda Alonso. "Attentional changes in human perceptual learning." Behavioural Processes 98 (September 2013): 61–68. http://dx.doi.org/10.1016/j.beproc.2013.05.009.

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15

Navon, David, and Ronen Kasten. "Incidental learning of secondary attentional cueing." Acta Psychologica 127, no. 2 (February 2008): 459–75. http://dx.doi.org/10.1016/j.actpsy.2007.08.009.

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16

Choi, H., and T. Watanabe. "Learning with attention eliminates attentional blink on a long-term basis." Journal of Vision 9, no. 8 (September 3, 2010): 856. http://dx.doi.org/10.1167/9.8.856.

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17

Vickery, T. J., R. S. Sussman, and Y. Jiang. "Selective attention and general attentional resources in the learning of spatial context." Journal of Vision 6, no. 6 (March 24, 2010): 844. http://dx.doi.org/10.1167/6.6.844.

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18

Shalev, Lilach, Tamar Kolodny, Nir Shalev, and Carmel Mevorach. "Attention Functioning Among Adolescents With Multiple Learning, Attentional, Behavioral, and Emotional Difficulties." Journal of Learning Disabilities 49, no. 6 (August 4, 2016): 582–96. http://dx.doi.org/10.1177/0022219415579125.

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19

Declerck, Carolyn, and Bert De Brabander. "Relationship between Attentional Orientation and Associative Learning." Perceptual and Motor Skills 91, no. 3_suppl (December 2000): 1076–82. http://dx.doi.org/10.2466/pms.2000.91.3f.1076.

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20

Sun, Chuxiong, Kaijie Zhou, Cong Cong, Kai Li, Rui Wang, and Xiaohui Hu. "Learning Attentional and Gated Communication via Curiosity." Computational Intelligence and Neuroscience 2022 (April 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/2951193.

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Due to the partial observability in decentralized multi-agent systems, communication is critical for cooperation. Furthermore, the ability to decide when and whom to communicate is important to achieve efficient communication. However, the existing methods are typically driven by extrinsic rewards. Hence, when the reward from environment is sparse, delayed, or noisy, the communication performance of these methods would be restricted. Furthermore, it would introduce additional difficulty named credit assignment when using extrinsic reward to train communication and sample policies together. To tackle these difficulties, we introduce the mechanism of intrinsic motivation from psychology to multi-agent communication. We hold the view that the observations with more uncertainty and curiosity are more valuable for communication. It can help agent find useful information from observations. It is a good complement to existing extrinsic driven methods. Concretely, at sending end, we learn a curiosity from local observations to model the communication importance. Then, we design a heuristic mechanism to prune unnecessary messages. It can solve the problem of when to communicate. Then, the ability to gate unnecessary message can reduce the cost and improve the efficiency of communication, which is important to apply to real-world scenarios. Furthermore, at receiving end, we utilize the intrinsic importance to differentiate information, which can be helpful for local decisions. It could solve the problem of whom to communicate. The ability to pay attention to useful information can efficiently improve the performance of communication behaviors. At last, we evaluate our method on a variety of multi-agent scenarios. The experiments of full communication demonstrate that the curiosity is capable to model the communication importance, and the results of gated communication further prove the conclusion.

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21

Hilchey, Matthew D., Blaire J. Weidler, and Jay Pratt. "Statistical learning can modulate contingent attentional capture." Journal of Vision 19, no. 10 (September 6, 2019): 139c. http://dx.doi.org/10.1167/19.10.139c.

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22

Song, J. H., and P. Bedard. "Attentional load effects on visuo-motor learning." Journal of Vision 11, no. 11 (September 23, 2011): 1035. http://dx.doi.org/10.1167/11.11.1035.

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23

Curran, Tim, and Steven W. Keele. "Attentional and nonattentional forms of sequence learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 19, no. 1 (1993): 189–202. http://dx.doi.org/10.1037/0278-7393.19.1.189.

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24

Kersten, Alan W., Robert L. Goldstone, and Alexandra Schaffert. "Two competing attentional mechanisms in category learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 24, no. 6 (November 1998): 1437–58. http://dx.doi.org/10.1037/0278-7393.24.6.1437.

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25

Leber, Andrew B., Jun-Ichiro Kawahara, and Yuji Gabari. "Long-term abstract learning of attentional set." Journal of Experimental Psychology: Human Perception and Performance 35, no. 5 (2009): 1385–97. http://dx.doi.org/10.1037/a0016470.

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26

Rerup, Claus. "Attentional Triangulation: Learning from Unexpected Rare Crises." Organization Science 20, no. 5 (October 2009): 876–93. http://dx.doi.org/10.1287/orsc.1090.0467.

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27

Passingham, R. "Prefrontal cortex: associative learning and attentional selection." NeuroImage 13, no. 6 (June 2001): 346. http://dx.doi.org/10.1016/s1053-8119(01)91689-1.

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28

Carmel, D., A. Khesin, and M. Carrasco. "Attentional facilitation of perceptual learning without awareness." Journal of Vision 10, no. 7 (August 3, 2010): 357. http://dx.doi.org/10.1167/10.7.357.

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29

Kelley, T., and N. Lavie. "Attentional learning: The role of distractor expectancy." Journal of Vision 8, no. 6 (March 20, 2010): 239. http://dx.doi.org/10.1167/8.6.239.

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30

Zhao, Jinyuan, Yanna Wang, Baihua Xiao, Cunzhao Shi, Jingzhong Jiang, and Chunheng Wang. "Adversarial learning based attentional scene text recognizer." Pattern Recognition Letters 138 (October 2020): 217–22. http://dx.doi.org/10.1016/j.patrec.2020.07.027.

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31

Green, C. S., and D. Bavelier. "Learning, Attentional Control, and Action Video Games." Current Biology 22, no. 6 (March 2012): R197—R206. http://dx.doi.org/10.1016/j.cub.2012.02.012.

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32

Liang, Bin, Chaofeng Sha, Dong Wu, Bo Xu, Yanghua Xiao, and Wei Wang. "ACCF: Learning Attentional Conformity for Collaborative Filtering." IEEE Access 7 (2019): 148541–49. http://dx.doi.org/10.1109/access.2019.2921853.

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33

Song, J. H., and P. Bedard. "Effects of attentional states on visuomotor learning." Journal of Vision 13, no. 9 (July 25, 2013): 655. http://dx.doi.org/10.1167/13.9.655.

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34

Issa, Bernard, Kara Morgan-Short, Briana Villegas, and Gary Raney. "An Eye-tracking Study on the Role of Attention and its Relationship with Motivation." EUROSLA Yearbook 15 (July 31, 2015): 114–42. http://dx.doi.org/10.1075/eurosla.15.05iss.

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This study aimed to assess whether attentional allocation to direct object pronouns in L2 Spanish was influenced by external or internal manipulations of attention and whether these manipulations caused learning of the form. Attention was measured by fixation duration and skipping rate on the pronouns, and learning was measured with a sentence interpretation task. Results provided empirical evidence that both types of manipulations direct attention to target forms, but in different ways, and bring about learning. In addition to examining the role of attention, the present study examined how different types of motivation, (i.e., integrative, intrinsic and extrinsic) were related to both attentional allocation and learning and found that intrinsic and extrinsic motivation were related to different attentional manipulations. Results are informative for models of L2 acquisition that posit a role for attention, instructed L2 acquisition and L2 motivation research.

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35

Luque, David, Miguel A. Vadillo, María J. Gutiérrez-Cobo, and Mike E. Le Pelley. "The blocking effect in associative learning involves learned biases in rapid attentional capture." Quarterly Journal of Experimental Psychology 71, no. 2 (January 1, 2018): 522–44. http://dx.doi.org/10.1080/17470218.2016.1262435.

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Blocking refers to the finding that less is learned about the relationship between a stimulus and an outcome if pairings are conducted in the presence of a second stimulus that has previously been established as a reliable predictor of that outcome. Attentional models of associative learning suggest that blocking reflects a reduction in the attention paid to the blocked cue. We tested this idea in three experiments in which participants were trained in an associative learning task using a blocking procedure. Attention to stimuli was measured 250 ms after onset using an adapted version of the dot probe task. This task was presented at the beginning of each learning trial (Experiments 1 and 2) or in independent trials (Experiment 3). Results show evidence of reduced attention to blocked stimuli (i.e. “attentional blocking”). In addition, this attentional bias correlated with the magnitude of blocking in associative learning, as measured by predictive-value judgments. Moreover, Experiments 2 and 3 found evidence of an influence of learning about predictiveness on memory for episodes involving stimuli. These findings are consistent with a central role of learned attentional biases in producing the blocking effect, and in the encoding of new memories.

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36

Gao, Ya, and Jan Theeuwes. "Independent effects of statistical learning and top-down attention." Attention, Perception, & Psychophysics 82, no. 8 (September 9, 2020): 3895–906. http://dx.doi.org/10.3758/s13414-020-02115-x.

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Abstract It is well known that spatial attention can be directed in a top-down way to task-relevant locations in space. In addition, through visual statistical learning (VSL), attention can be biased towards relevant (target) locations and away from irrelevant (distractor) locations. The present study investigates the interaction between the explicit task-relevant, top-down attention and the lingering attentional biases due to VSL. We wanted to determine the contribution of each of these two processes to attentional selection. In the current study, participants performed a search task while keeping a location in spatial working memory. In Experiment 1, the target appeared more often in one location, and appeared less often in other location. In Experiment 2, a color singleton distractor was presented more often in location than in all other locations. The results show that when the search target matched the location that was kept in working memory, participants were much faster at responding to the search target than when it did not match, signifying top-down attentional selection. Independent of this top-down effect, we found a clear effect of VSL as responses were even faster when target (Experiment 1) or the distractor (Experiment 2) was presented at a more likely location in visual field. We conclude that attentional selection is driven by implicit biases due to statistical learning and by explicit top-down processing, each process individually and independently modulating the neural activity within the spatial priority map.

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37

Zhao, Yu, and Shuying Liu. "A Deep Learning Model with Virtual Reality Technology for Second Language Acquisition." Mobile Information Systems 2022 (March 12, 2022): 1–7. http://dx.doi.org/10.1155/2022/9686725.

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Attention is considered a sufficient condition for transforming input into absorption in the field of second language acquisition and is a major cognitive factor influencing second language learning. The temporal characteristics of attentional shift are a more accurate reflection of second language learners’ thinking processes. Based on this, this study uses deep learning techniques and VR technology to explore the attentional patterns of the second language (English) learners when processing online tasks. The experiments show that the linear attentional control model of young second language learners is closely related to their online task performance, which can visually explain the effect of their linear attentional control on online task completion. The model also has a high regression/prediction accuracy.

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38

Cutting, Joe, and Ioanna Iacovides. "Learning by Doing: Intrinsic Integration Directs Attention to Increase Learning In Games." Proceedings of the ACM on Human-Computer Interaction 6, CHI PLAY (October 25, 2022): 1–18. http://dx.doi.org/10.1145/3549503.

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Educational games have long been seen as having great potential, but evidence for their effectiveness is mixed, suggesting deficiencies in our theoretical understanding of learning in games and associated design principles. The principle of "Intrinsic integration" of learning content with game mechanics (Habgood and Ainsworth, 2011) increases learning in educational games, but the theoretical mechanisms behind the principle are unclear, leading to implementation issues. In response, we performed a pre-registered study (n=210) to test possible motivational, cognitive load or attentional mechanisms for moderating learning at an abstract learning task within an educational game similar to Pacman. Learning was higher in the intrinsically integrated version with no significant effects of motivation or cognitive load leading to the conclusion that intrinsic integration increased learning via an attentional mechanism where players only pay attention to features needed for the game task and ignore task-irrelevant information. We discuss theoretical implications for game learning as well as insights for designers of educational games.

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39

Rausei, Valeria, Tal Makovski, and Yuhong V. Jiang. "Attention Dependency in Implicit Learning of Repeated Search Context." Quarterly Journal of Experimental Psychology 60, no. 10 (October 2007): 1321–28. http://dx.doi.org/10.1080/17470210701515744.

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How much attention is needed to produce implicit learning? Previous studies have found inconsistent results, with some implicit learning tasks requiring virtually no attention while others rely on attention. In this study we examine the degree of attentional dependency in implicit learning of repeated visual search context. Observers searched for a target among distractors that were either highly similar to the target or dissimilar to the target. We found that the size of contextual cueing was comparable from repetition of the two types of distractors, even though attention dwelled much longer on distractors highly similar to the target. We suggest that beyond a minimal amount, further increase in attentional dwell time does not contribute significantly to implicit learning of repeated search context.

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40

van Moorselaar, Dirk, Jan Theeuwes, and Christian N. L. Olivers. "Learning changes the attentional status of prospective memories." Psychonomic Bulletin & Review 23, no. 5 (February 18, 2016): 1483–90. http://dx.doi.org/10.3758/s13423-016-1008-7.

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41

Bucker, Berno, and Jan Theeuwes. "Pavlovian reward learning underlies value driven attentional capture." Attention, Perception, & Psychophysics 79, no. 2 (November 30, 2016): 415–28. http://dx.doi.org/10.3758/s13414-016-1241-1.

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42

Dixon, Matthew L., Justin Ruppel, Jay Pratt, and Eve De Rosa. "Learning to ignore: Acquisition of sustained attentional suppression." Psychonomic Bulletin & Review 16, no. 2 (April 2009): 418–23. http://dx.doi.org/10.3758/pbr.16.2.418.

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43

Rose, Marina M., Douglas E. H. Hartley, and David R. Moore. "Perceptual learning and attentional cues in nonsimultaneous masking." Journal of the Acoustical Society of America 109, no. 5 (May 2001): 2289. http://dx.doi.org/10.1121/1.4744014.

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44

Wulf, Gabriele, Matthias Weigelt, Damian Poulter, and Nancy McNevin. "Attentional Focus on Suprapostural Tasks Affects Balance Learning." Quarterly Journal of Experimental Psychology Section A 56, no. 7 (October 2003): 1191–211. http://dx.doi.org/10.1080/02724980343000062.

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45

Addleman, Douglas A., Gordon E. Legge, and Yuhong V. Jiang. "Spatial attentional learning in simulated central vision loss." Journal of Vision 20, no. 11 (October 20, 2020): 577. http://dx.doi.org/10.1167/jov.20.11.577.

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46

Cosman, Joshua D., and Shaun P. Vecera. "Establishment of an attentional set via statistical learning." Journal of Experimental Psychology: Human Perception and Performance 40, no. 1 (February 2014): 1–6. http://dx.doi.org/10.1037/a0034489.

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47

Bucker, Berno, and Jan Theeuwes. "Pavlovian reward learning underlies value driven attentional capture." Journal of Vision 16, no. 12 (September 1, 2016): 80. http://dx.doi.org/10.1167/16.12.80.

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48

Ogawa, H., and K. Watanabe. "Implicit learning of attentional guidance modulates visual preference." Journal of Vision 8, no. 6 (April 2, 2010): 870. http://dx.doi.org/10.1167/8.6.870.

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49

Bissonette, Gregory B., and Elizabeth M. Powell. "Reversal learning and attentional set-shifting in mice." Neuropharmacology 62, no. 3 (March 2012): 1168–74. http://dx.doi.org/10.1016/j.neuropharm.2011.03.011.

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50

Strand, Steve C. "Attentional aspects of classroom behavior and discrimination learning." Research in Developmental Disabilities 12, no. 3 (January 1991): 229–41. http://dx.doi.org/10.1016/0891-4222(91)90009-h.

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