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Journal articles on the topic 'Perceptual learning'

To see the other types of publications on this topic, follow the link: Perceptual learning.

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Wenger, Michael J., and Stephanie E. Rhoten. "Perceptual learning produces perceptual objects." Journal of Experimental Psychology: Learning, Memory, and Cognition 46, no. 3 (March 2020): 455–75. http://dx.doi.org/10.1037/xlm0000735.

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2

Ahissar, Merav. "Perceptual Learning." Current Directions in Psychological Science 8, no. 4 (August 1999): 124–28. http://dx.doi.org/10.1111/1467-8721.00029.

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3

Goldstone, Robert L. "PERCEPTUAL LEARNING." Annual Review of Psychology 49, no. 1 (February 1998): 585–612. http://dx.doi.org/10.1146/annurev.psych.49.1.585.

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4

Lindsey, Delwin T. "Perceptual Learning." Optometry and Vision Science 80, no. 7 (July 2003): 485. http://dx.doi.org/10.1097/00006324-200307000-00006.

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5

Seitz, Aaron R. "Perceptual learning." Current Biology 27, no. 13 (July 2017): R631—R636. http://dx.doi.org/10.1016/j.cub.2017.05.053.

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6

Long, Donlin M. "Perceptual Learning." Neurosurgery Quarterly 13, no. 2 (June 2003): 149–50. http://dx.doi.org/10.1097/00013414-200306000-00011.

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7

Dubin, Mark W., and Victoria S. Pelak. "Perceptual Learning." Journal of Neuro-Ophthalmology 24, no. 4 (December 2004): 350–51. http://dx.doi.org/10.1097/00041327-200412000-00021.

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8

Gold, Joshua I., and Takeo Watanabe. "Perceptual learning." Current Biology 20, no. 2 (January 2010): R46—R48. http://dx.doi.org/10.1016/j.cub.2009.10.066.

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9

Prettyman, Adrienne. "Perceptual learning." Wiley Interdisciplinary Reviews: Cognitive Science 10, no. 3 (December 20, 2018): e1489. http://dx.doi.org/10.1002/wcs.1489.

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10

Garrigan, P., and P. J. Kellman. "Perceptual learning depends on perceptual constancy." Proceedings of the National Academy of Sciences 105, no. 6 (February 4, 2008): 2248–53. http://dx.doi.org/10.1073/pnas.0711878105.

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11

Hirsch, Micah E., Kaitlin L. Lansford, Tyson S. Barrett, and Stephanie A. Borrie. "Generalized Learning of Dysarthric Speech Between Male and Female Talkers." Journal of Speech, Language, and Hearing Research 64, no. 2 (February 17, 2021): 444–51. http://dx.doi.org/10.1044/2020_jslhr-20-00313.

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Purpose Perceptual training is a listener-targeted means for improving intelligibility of dysarthric speech. Recent work has shown that training with one talker generalizes to a novel talker of the same sex and that the magnitude of benefit is maximized when the talkers are perceptually similar. The current study expands previous findings by investigating whether perceptual training effects generalize between talkers of different sex. Method Forty new listeners were recruited for this study and completed a pretest, familiarization, and posttest perceptual training paradigm. Historical data collected using the same three-phase protocol were included in the data analysis. All listeners were exposed to the same talker with dysarthria during the pretest and posttest phases. For the familiarization phase, listeners were exposed to one of four talkers with dysarthria, differing in sex and level of perceptual similarity to the test talker or a control talker. During the testing phases, listener transcribed phrases produced by the test talker with dysarthria. Listener transcriptions were then used to calculate a percent words correct intelligibility score. Results Multiple linear regression analysis revealed that intelligibility at posttest was not predicted by sex of the training talker. Consistent with earlier work, the magnitude of intelligibility gain was greater when the familiarization and test talkers were perceptually similar. Additional analyses revealed greater between-listeners variability in the dissimilar conditions as compared to the similar conditions. Conclusions Learning as a result of perceptual training with one talker with dysarthria generalized to another talker regardless of sex. In addition, listeners trained with perceptually similar talkers had greater and more consistent intelligibility improvement. Together, these results add to previous evidence demonstrating that learning generalizes to novel talkers with dysarthria and that perceptual training is suitable for many listeners.

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12

Cousineau, Denis. "Review: Perceptual Learning." Perception 32, no. 2 (February 2003): 255–56. http://dx.doi.org/10.1068/p3202rvw.

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13

Kellman, Philip J. "Unifying Perceptual Learning." i-Perception 2, no. 4 (May 2011): 409. http://dx.doi.org/10.1068/ic409.

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14

Goldstone, Robert L., Ji Y. Son, and Lisa Byrge. "Early Perceptual Learning." Infancy 16, no. 1 (December 10, 2010): 45–51. http://dx.doi.org/10.1111/j.1532-7078.2010.00054.x.

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15

Gilbert, C. D. "Early perceptual learning." Proceedings of the National Academy of Sciences 91, no. 4 (February 15, 1994): 1195–97. http://dx.doi.org/10.1073/pnas.91.4.1195.

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16

Moore, D. R. "Auditory Perceptual Learning." Learning & Memory 10, no. 2 (March 1, 2003): 83–85. http://dx.doi.org/10.1101/lm.59703.

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17

Gold, J. M., P. J. Bennett, and A. B. Sekuler. "Visualizing perceptual learning." Journal of Vision 2, no. 7 (March 15, 2010): 559. http://dx.doi.org/10.1167/2.7.559.

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18

Bueti, Domenica, and Dean V. Buonomano. "Temporal Perceptual Learning." Timing & Time Perception 2, no. 3 (2014): 261–89. http://dx.doi.org/10.1163/22134468-00002023.

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Our interaction with the environment and each other is inherently time-varying in nature. It is thus not surprising that the nervous systems of animals have evolved sophisticated mechanisms to not only tell time, but to learn to discriminate and produce temporal patterns. Indeed some of the most sophisticated human behaviors, such as speech and music, would not exist if the human brain was unable to learn to discriminate and produce temporal patterns. Compared to the study of other forms of learning, such as visual perceptual learning, the study of the learning of interval and temporal pattern discrimination in the subsecond range is relatively recent. A growing number of studies over the past 15 years, however, have established that perceptual and motor timing undergo robust learning. One of the principles to have emerged from these studies is that temporal learning is generally specific to the trained interval, an observation that has important implications to the neural mechanisms underlying our ability to tell time.

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19

Lu, Zhong-Lin, Tianmiao Hua, Chang-Bing Huang, Yifeng Zhou, and Barbara Anne Dosher. "Visual perceptual learning." Neurobiology of Learning and Memory 95, no. 2 (February 2011): 145–51. http://dx.doi.org/10.1016/j.nlm.2010.09.010.

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20

Ahissar, Merav. "Perceptual Learning 2012." Vision Research 61 (May 2012): 1–3. http://dx.doi.org/10.1016/j.visres.2012.04.014.

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21

McGuire, Grant. "Perceptual cue learning." Journal of the Acoustical Society of America 120, no. 5 (November 2006): 3174. http://dx.doi.org/10.1121/1.4787943.

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22

Grzeczkowski, Lukasz, Elisa M. Tartaglia, Fred W. Mast, and Michael H. Herzog. "Linking perceptual learning with identical stimuli to imagery perceptual learning." Journal of Vision 15, no. 10 (October 23, 2015): 13. http://dx.doi.org/10.1167/15.10.13.

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23

Gong, Xizi, Qian Wang, and Fang Fang. "Configuration perceptual learning and its relationship with element perceptual learning." Journal of Vision 22, no. 13 (December 1, 2022): 2. http://dx.doi.org/10.1167/jov.22.13.2.

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24

Volbrecht, Vicki J. "Perceptual learning meets associative learning." New Ideas in Psychology 11, no. 2 (July 1993): 285–86. http://dx.doi.org/10.1016/0732-118x(93)90042-c.

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25

Sagi, D. "Perceptual learning: learning to see." Current Opinion in Neurobiology 4, no. 2 (1994): 195–99. http://dx.doi.org/10.1016/0959-4388(94)90072-8.

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26

Caruso, Valeria C., and Evan Balaban. "Auditory Perceptual Category Formation Does Not Require Perceptual Warping." Journal of Cognitive Neuroscience 27, no. 8 (August 2015): 1659–73. http://dx.doi.org/10.1162/jocn_a_00806.

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Categorical perception occurs when a perceiver's stimulus classifications affect their ability to make fine perceptual discriminations and is the most intensively studied form of category learning. On the basis of categorical perception studies, it has been proposed that category learning proceeds by the deformation of an initially homogeneous perceptual space (“perceptual warping”), so that stimuli within the same category are perceived as more similar to each other (more difficult to tell apart) than stimuli that are the same physical distance apart but that belong to different categories. Here, we present a significant counterexample in which robust category learning occurs without these differential perceptual space deformations. Two artificial categories were defined along the dimension of pitch for a perceptually unfamiliar, multidimensional class of sounds. A group of participants (selected on the basis of their listening abilities) were trained to sort sounds into these two arbitrary categories. Category formation, verified empirically, was accompanied by a heightened sensitivity along the entire pitch range, as indicated by changes in an EEG index of implicit perceptual distance (mismatch negativity), with no significant resemblance to the local perceptual deformations predicted by categorical perception. This demonstrates that robust categories can be initially formed within a continuous perceptual dimension without perceptual warping. We suggest that perceptual category formation is a flexible, multistage process sequentially combining different types of learning mechanisms rather than a single process with a universal set of behavioral and neural correlates.

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27

Yang, Jia, Fang-Fang Yan, Lijun Chen, Jie Xi, Shuhan Fan, Pan Zhang, Zhong-Lin Lu, and Chang-Bing Huang. "General learning ability in perceptual learning." Proceedings of the National Academy of Sciences 117, no. 32 (July 23, 2020): 19092–100. http://dx.doi.org/10.1073/pnas.2002903117.

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Developing expertise in any field usually requires acquisition of a wide range of skills. Most current studies on perceptual learning have focused on a single task and concluded that learning is quite specific to the trained task, and the ubiquitous individual differences reflect random fluctuations across subjects. Whether there exists a general learning ability that determines individual learning performance across multiple tasks remains largely unknown. In a large-scale perceptual learning study with a wide range of training tasks, we found that initial performance, task, and individual differences all contributed significantly to the learning rates across the tasks. Most importantly, we were able to extract both a task-specific but subject-invariant component of learning, that accounted for 38.6% of the variance, and a subject-specific but task-invariant perceptual learning ability, that accounted for 36.8% of the variance. The existence of a general perceptual learning ability across multiple tasks suggests that individual differences in perceptual learning are not “noise”; rather, they reflect the variability of learning ability across individuals. These results could have important implications for selecting potential trainees in occupations that require perceptual expertise and designing better training protocols to improve the efficiency of clinical rehabilitation.

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28

Adolph, Karen E., Ludovic M. Marin, and Frederic F. Fraisse. "Learning and exploration: Lessons from infants." Behavioral and Brain Sciences 24, no. 2 (April 2001): 213–14. http://dx.doi.org/10.1017/s0140525x01223942.

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Based on studies with infants, we expand on Stoffregen & Bardy's explanation of perceptual motor errors, given the global array. Information pick-up from the global array is not sufficient without adequate exploratory movements and learning to support perceptually guided activity.

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29

Szokolszky, Agnes, Catherine Read, Zsolt Palatinus, and Kinga Palatinus. "Ecological approaches to perceptual learning: learning to perceive and perceiving as learning." Adaptive Behavior 27, no. 6 (June 13, 2019): 363–88. http://dx.doi.org/10.1177/1059712319854687.

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In this theoretical review article, our primary goal is to contribute to the post-cognitivist understanding of learning to perceive and perceiving as learning, by discussing a framework for perception and perceptual learning initiated by James J Gibson, and extended by Eleanor J Gibson and others. This Ecological Psychology has a coherent set of assumptions based on the concept of mutualism between the perceiving organism and its surroundings, and the idea of affordances as action possibilities of the surround that are perceptible by the organism. At the same time, Ecological Psychology, broadly construed, consists of different perspectives that take different routes to address questions related to the core concepts of perceptual learning. In this article, we focus on three theoretical stances within Ecological Psychology on the issue of perceptual learning: that of Eleanor J Gibson, the current theory of direct learning by Jacobs and Michaels, and the “organicist” approach based on ideas of organicist biology and developments in evolutionary biology. We consider perceptual learning as embedded in development and evolution, and we explore perceptual learning in more depth in the context of tool use and language development. We also discuss the relation between Ecological Psychology and Enactivism on the nature of perception. In conclusion, we summarize the benefits of Ecological Psychology, as a robust but still developing post-cognitivist framework, for the study of perceptual learning and cognitive science in general.

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30

Gilbert, Charles D. "Learning: Neuronal dynamics and perceptual learning." Current Biology 4, no. 7 (July 1994): 627–29. http://dx.doi.org/10.1016/s0960-9822(00)00138-x.

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31

Fecteau, Jillian H., Pieter Roelfsema, Chris I. De Zeeuw, and Stavroula Kousta. "Perceptual learning, motor learning, and automaticity." Trends in Cognitive Sciences 14, no. 1 (January 2010): 1. http://dx.doi.org/10.1016/j.tics.2009.11.003.

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32

Bakhtiari, Shahab. "Can Deep Learning Model Perceptual Learning?" Journal of Neuroscience 39, no. 2 (January 9, 2019): 194–96. http://dx.doi.org/10.1523/jneurosci.2209-18.2018.

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33

Chui, Yin-To, Susu Lai, and Zhen Qin. "Distributional learning of non-native tone contrasts by older adults after training and overnight consolidation." Journal of the Acoustical Society of America 155, no. 3_Supplement (March 1, 2024): A269. http://dx.doi.org/10.1121/10.0027457.

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Despite decline in psychoacoustic and statistical learning (SL) abilities, older adults demonstrate remarkably intact perceptual learning in both L2 (tone-word learning) and L1 settings (perceptual adaptation to accented/noise-vocoded speech) but show limited transfer of learning to untrained stimuli. This study tests whether perceptual learning is maintained in an implicit statistical learning task where older adults learn L2 tonal contrasts through exposure to probability distributions of tonal tokens, which may pose higher requirements on both psychoacoustic and SL abilities, and whether sleep-dependent consolidation helps the generalization of perceptual knowledge. L1-Cantonese older adults learned to discriminate a perceptually difficult level-falling tone contrast following a pre-test, training, post- training overnight interval. Training stimuli were synthesized by interpolating naturally produced Mandarin level and high-falling tones into six equidistant steps. Participants either heard a bimodal (two-peak resembling level-falling categories) or unimodal distribution (single-peak) consisting of 256 tokens. ABX discrimination task was administered for testing, with tokens by two genders and on two pseudo-syllables to test generalization. Pilot data of 14 participants showed a trend of group effect with the bimodal group outperforming the unimodal group after training and sleep-dependent consolidation, showing that perceptual learning is maintained in a paradigm that relies heavily on psychoacoustic and SL abilities.

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34

Moore, John W., and Geoffrey Hall. "Perceptual and Associative Learning." American Journal of Psychology 107, no. 3 (1994): 465. http://dx.doi.org/10.2307/1422887.

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35

Samuel, Arthur G., and Tanya Kraljic. "Perceptual learning for speech." Attention, Perception, & Psychophysics 71, no. 6 (August 2009): 1207–18. http://dx.doi.org/10.3758/app.71.6.1207.

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36

Lu, Zhong-Lin, and Barbara Anne Dosher. "Mechanisms of perceptual learning." Learning & Perception 1, no. 1 (June 2009): 19–36. http://dx.doi.org/10.1556/lp.1.2009.1.3.

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37

Mackintosh, N. J. "Varieties of perceptual learning." Learning & Behavior 37, no. 2 (May 1, 2009): 119–25. http://dx.doi.org/10.3758/lb.37.2.119.

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38

Herzog, M., T. Otto, and H. Ogmen. "Non-retinotopic perceptual learning." Journal of Vision 11, no. 11 (September 23, 2011): 1022. http://dx.doi.org/10.1167/11.11.1022.

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39

Sowden, Paul, Ian Davies, David Rose, and Martin Kaye. "Perceptual Learning of Stereoacuity." Perception 25, no. 9 (September 1996): 1043–52. http://dx.doi.org/10.1068/p251043.

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40

Werner, B., N. Yamagishi, A. R. Seitz, N. Goda, S. L. Sheremata, M. Kawato, and T. Watanabe. "Interference in perceptual learning." Journal of Vision 4, no. 8 (August 1, 2004): 301. http://dx.doi.org/10.1167/4.8.301.

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41

Kim, R. S., A. Seitz, and L. Shams. "Sound aids perceptual learning." Journal of Vision 6, no. 6 (March 18, 2010): 151. http://dx.doi.org/10.1167/6.6.151.

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42

Watanabe, Takeo, José E. Náñez, and Yuka Sasaki. "Perceptual learning without perception." Nature 413, no. 6858 (October 2001): 844–48. http://dx.doi.org/10.1038/35101601.

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43

Nardini, Marko, Pete Jones, Linnea Landin, Mordechai Juni, Laurence Maloney, and Tessa Dekker. "Learning efficient perceptual sampling." Journal of Vision 15, no. 12 (September 1, 2015): 743. http://dx.doi.org/10.1167/15.12.743.

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44

Dupuis-Roy, N., and F. Gosselin. "Perceptual learning without signal." Journal of Vision 3, no. 9 (March 18, 2010): 672. http://dx.doi.org/10.1167/3.9.672.

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45

Yotsumoto, Y., R. Ni, L. H. Chang, Y. Sasaki, T. Watanabe, and G. Andersen. "Aging and perceptual learning." Journal of Vision 9, no. 8 (September 3, 2010): 861. http://dx.doi.org/10.1167/9.8.861.

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46

Norris, D. "Perceptual learning in speech." Cognitive Psychology 47, no. 2 (September 2003): 204–38. http://dx.doi.org/10.1016/s0010-0285(03)00006-9.

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47

Dupuis-Roy, Nicolas, and Frédéric Gosselin. "Perceptual learning without signal." Vision Research 47, no. 3 (February 2007): 349–56. http://dx.doi.org/10.1016/j.visres.2006.10.016.

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48

Watanabe, T., J. E. Nanez, and Y. Sasaki. "Perceptual learning without perception." Journal of Vision 1, no. 3 (March 14, 2010): 467. http://dx.doi.org/10.1167/1.3.467.

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49

Dosher, Barbara Anne, and Zhong-Lin Lu. "Mechanisms of perceptual learning." Vision Research 39, no. 19 (October 1999): 3197–221. http://dx.doi.org/10.1016/s0042-6989(99)00059-0.

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50

Freyer, F., R. Becker, H. R. Dinse, and P. Ritter. "State-Dependent Perceptual Learning." Journal of Neuroscience 33, no. 7 (February 13, 2013): 2900–2907. http://dx.doi.org/10.1523/jneurosci.4039-12.2013.

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