Relevant bibliographies by topics / Implicit learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Implicit learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 March 2025

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Journal articles on the topic "Implicit learning"

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Frick, Robert W., and Yuh-Shiow Lee. "Implicit Learning and Concept Learning." Quarterly Journal of Experimental Psychology Section A 48, no. 3 (August 1995): 762–82. http://dx.doi.org/10.1080/14640749508401414.

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In Experiments 1 and 2, subjects were exposed to letter strings that followed a pattern—the second letter was always the same. This exposure was disguised as a test of immediate memory. Following this training, subjects could discriminate new letter strings following the pattern from letter strings not following the pattern more often than would be expected by chance, which is the traditional evidence for concept learning. Discrimination was also better than would be predicted from subjects’ explicit report of the pattern, demonstrating the co-occurrence of concept learning and implicit learning. In Experiment 3, rules were learned explicitly. Discrimination was worse than would be predicted from subjects’ explicit report, validating the implicit learning paradigm. In Experiment 4, deviations from a prototypical pattern were presented during training. In the test of discrimination, prototypes were as familiar as old deviations and more familiar than new deviations, even when considering only implicit knowledge. Experiment 5 found implicit knowledge of a familiar concept. These results are consistent with the hypothesis that the distinguishing features of a concept can be learned implicitly, and that one type of implicit learning is concept learning.
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Holyoak, Keith J., and Merideth Gattis. "Implicit assumptions about implicit learning." Behavioral and Brain Sciences 17, no. 3 (September 1994): 406–7. http://dx.doi.org/10.1017/s0140525x00035159.

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Seger, Carol Augart. "Implicit learning." Psychological Bulletin 115, no. 2 (1994): 163–96. http://dx.doi.org/10.1037/0033-2909.115.2.163.

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Frensch, Peter A., and Dennis Rünger. "Implicit Learning." Current Directions in Psychological Science 12, no. 1 (February 2003): 13–18. http://dx.doi.org/10.1111/1467-8721.01213.

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Implicit learning appears to be a fundamental and ubiquitous process in cognition. Although defining and operationalizingimplicit learning remains a central theoretical challenge, scientists' understanding of implicit learning has progressed significantly. Beyond establishing the existence of “learning without awareness,” current research seeks to identify the cognitive processes that support implicit learning and addresses the relationship between learning and awareness of what was learned. The emerging view of implicit learning emphasizes the role of associative learning mechanisms that exploit statistical dependencies in the environment in order to generate highly specific knowledge representations.
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El Ghaoui, Laurent, Fangda Gu, Bertrand Travacca, Armin Askari, and Alicia Tsai. "Implicit Deep Learning." SIAM Journal on Mathematics of Data Science 3, no. 3 (January 2021): 930–58. http://dx.doi.org/10.1137/20m1358517.

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Liu, Yan, Yang Liu, Shenghua Zhong, and Songtao Wu. "Implicit Visual Learning." ACM Transactions on Intelligent Systems and Technology 8, no. 2 (January 18, 2017): 1–24. http://dx.doi.org/10.1145/2974024.

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Hendrickx, Hilde, and Jan De Houwer. "Implicit Covariation Learning." Psychologica Belgica 37, no. 1-2 (January 1, 1997): 29. http://dx.doi.org/10.5334/pb.907.

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Ennen, Elizabeth. "Implicit practical learning." Behavioral and Brain Sciences 17, no. 3 (September 1994): 404–5. http://dx.doi.org/10.1017/s0140525x00035135.

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Pham, Theresa, Joel Hosung Kang, Alisha Johnson, and Lisa M. D. Archibald. "Feature-focusing constraints on implicit learning of function word and meaning associations." Applied Psycholinguistics 41, no. 2 (March 2020): 401–26. http://dx.doi.org/10.1017/s0142716420000041.

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AbstractRecent research has begun to investigate implicit learning at the level of meaning. The general consensus is that implicitly linking a word with a meaning is constrained by existing linguistic knowledge. However, another factor to consider is the extent to which attention is drawn to the relevant meanings in implicit learning paradigms. We manipulated the presence of cue saliency during implicit rule learning for a grammatical form (i.e., articles) linked to meaning (i.e., animacy vs. varying notions of size). In a series of experiments, participants learned four novel words but did not know that article usage also depended on a hidden rule, creating an opportunity for implicit rule learning. We found implicit learning through the use of a highly salient meaning (Experiment 1) or if image size was made salient by being explicitly cued (Experiment 3), but not in a low salient paradigm for intrinsic object size (Experiment 2). The findings suggest that implicit learning of semantic information might not be as constrained as previously argued. Instead, implicit learning might be additionally influenced by feature-focusing cues that make the meaning contrasts more salient and thereby more readily available to learning.
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Kuhn, Gustav, and Zoltán Dienes. "Implicit Learning of Nonlocal Musical Rules: Implicitly Learning More Than Chunks." Journal of Experimental Psychology: Learning, Memory, and Cognition 31, no. 6 (2005): 1417–32. http://dx.doi.org/10.1037/0278-7393.31.6.1417.

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Dissertations / Theses on the topic "Implicit learning"

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Riedel, Beate. "Auditory implicit learning." Thesis, University of Glasgow, 2003. http://theses.gla.ac.uk/4884/.

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It has been suggested that much of the information we acquire from our external environment involves processes that do not require conscious awareness (e.g. Reber, 1989; Reber and Winter, 1994). Such knowledge acquisition has been termed implicit learning and this has been put forward as a fundamental process in allowing learning of complex information (e.g. Reber, 1992; Schmidke and Heuer, 1997). It has been proposed that acquisition of the underlying rule structure of stimulus events provides an indication of such a process as being fundamental and general. In contrast, learning bound to more peripheral processes should only be shown when subjects learn, for example, surface features of stimuli or a sequence of motor responses, but not the underlying rules (e.g. Perruchet and Pacteau, 1990; Seger, 1998). The research in this thesis investigates systematically whether implicit learning of sound stimuli behaves any differently to such learning of visual stimuli. This expands the empirical scope of previous studies in the implicit learning field and allows assessment of such processes as fundamental and general. Chapter 1 provides a background to implicit learning in general and introduces the different concepts involved. Chapters 2 to 4 investigated the generality of findings from visual implicit learning studies in the auditory domain. In particular, they studied the role of rule abstraction in sequence learning (Nissen and Bullemer, 1987) and invariant learning tasks (McGeorge and Burton, 1990). Findings from the sequence learning experiments in Chapters 2 and 3 suggest that subjects were unable to abstract the underlying rule structure of stimuli, as would have been evident from learning of the auditory sequences employed by listening alone. Instead, subjects were only able to learn the relevant associations between their actions (keypress responses) and a set of stimuli. These findings add to evidence from visual implicit learning studies that found peripheral processes involved in such learning. Findings from the invariant learning experiments in Chapter 4 show what types of auditory invariant features subjects can and cannot learn. This identified for the first time the exact information, or rule, that subjects acquire in such a task in an auditory context. Additionally, it provides some evidence that explicit processes may have been involved. Overall, the findings from the experiments in this thesis put into question that implicit learning is a fundamental process, which involves implicit rule abstraction.
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Kelly, Stephen William. "Experiments in implicit learning." Thesis, University of Glasgow, 1997. http://theses.gla.ac.uk/4989/.

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This thesis examines two paradigms from the area of implicit learning in detail. The literature suggests that the invariance detection paradigm of McGeorge and Burton (1990) gives rise to unconscious knowledge held at a conceptual level with the decision process served by a 'nearest-neighbour' similarity mechanism. The experiments in this thesis suggest that several aspects of this task do indeed seem to agree with present conceptions of unconscious knowledge but no evidence could be found that this knowledge is held at a conceptual level or that specific similarity plays any role in this task. Instead the experiments in this thesis suggest that this task may be better understood in terms of an abstraction mechanism which acquires perceptual information. Using the invariance detection paradigm, this thesis examines the effect of two types of task which measure performance above an 'objective threshold' of awareness. Performance on each task was not the same, suggesting that one cannot assume all direct tests measure the same knowledge despite being similar in nature. In addition, the finding that only the more sensitive of the two tasks could elicit information in the invariance detection paradigm suggests that the knowledge is extremely difficult to elicit. This also is a property of implicit learning and points to the digit invariance task being mediated by unconscious mechanisms. The finding of robust invariance detection in laboratory tasks suggests that one might expect to find similar learning for real world invariance. No evidence for this could be found, which suggests that either implicit learning is a laboratory artefact or that real world invariance learning does not operate in the same way that laboratory experiments suggest. These results suggest that laboratory experiments are required which replicate conditions under which real world learning might occur.
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Bright, James. "Issues in implicit learning." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359867.

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Gebauer, Guido Felix. "Implicit learning and intelligence." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619521.

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Paciorek, Albertyna. "Implicit learning of semantic preferences." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/244632.

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The research presented in this PhD dissertation examines the phenomenon of semantic implicit learning, using semantic preferences of novel verbs as a test case. Implicit learning refers to the phenomenon of learning without intending to learn or awareness that one is learning at all. Semantic preference (or selectional preference – as preferred in computational linguistics) is the tendency of a word to co-occur with words sharing similar semantic features. For example, ‘drink’ is typically followed by nouns denoting LIQUID, and the verb ‘chase’ is typically followed by ANIMATE nouns. The material presented here spans across disciplines. It examines a well-documented psychological phenomenon - implicit learning – and applies it in the context of language acquisition, thereby providing insights into both fields. The organisation of this dissertation groups its experiments by their methodology. Chapter 1 provides an overview of the current psychological and linguistic literature. Chapter 2 includes a pen-and-paper study carried out in a classroom environment on Polish learners of English, where awareness is assessed by subjective measures taken at each test question as well as a post-experiment questionnaire. Chapter 3 includes a collection of 5 computer-based experiments based on a false-memory paradigm. After exposure to sentential contexts containing novel verbs, participants are shown to endorse more previously unseen verb-noun pairings that follow the correct semantic preference patterns to the pairings that violate it. The result holds even when participants do not reveal any explicit knowledge of the patterns in the final debriefing. Awareness is additionally assessed using indirect measures examining correlations of confidence judgements with performance. Chapter 4 examines whether implicit learning of novel verb semantic preference patterns is automatic. To this end, a reaction time procedure is developed based on two consecutive decisions (“double decision priming”). The method reveals that semantic implicit learning, at least in the described cases, exerts its influence with a delay, in post-processing. Chapter 5 comprises research done in collaboration with Dr Nitin Williams, University of Reading. It documents an attempt at finding neural indices of implicit learning using a novel single-trial analysis of an electroencephalographic (EEG) signal, based on empirical mode decomposition (EMD) denoising. Chapter 6 presents a final discussion and indications for future research. The main contribution of this dissertation to the general field of implicit learning research consists in its challenging the predominant view that implicit learning mainly relies on similarity of forms presented in training and test. The experiments presented here require participants to make generalisations at a higher, semantic level, which is largely independent of perceptual form. The contribution of this work to the field of Second Language Acquisition consists of empirical support for the currently popular but seldom tested assumptions held by advocates of communicative approaches to language teaching, namely that certain aspects of linguistic knowledge can develop without explicit instruction and explanation. At the same time, it challenges any view assuming that vocabulary learning necessarily relies on explicit mediation. The experiments collected here demonstrate that at least word usage in context can be learnt implicitly. A further contribution of this dissertation is its demonstration that the native language may play a key role in determining what is learnt in such situations. A deeper understanding of the phenomenon of semantic implicit learning promises to shed light on the nature of word and grammar learning in general, which is crucial for an account of the processes involved in the development of a second language mental lexicon.
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Huddy, Vyvyan. "Active processing in implicit learning." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390696.

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Wall, Matthew Bryan. "Emotional influences on implicit learning." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611312.

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Al-Hoorie, Ali. "Implicit attitudes in language learning." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/42571/.

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The field of language motivation is almost 60 years old. Throughout these decades, one idea has been persistent: Motivation is assumed to be a conscious process on which the learner can exert direct control. That this conscious conceptualization might not give the full picture has not been seriously entertained. An important consequence ensuing from this approach is the overreliance on self-report measures, such as questionnaires and interviews. Thus, in effect, the individual’s conscious reflection on their own attitudes and motivation has been the primary source of empirical data for our field. This thesis challenges this hegemony of conscious motivation. It provides an extensive review of the various paradigms of unconscious attitudes and motivation. It traces back their origins, highlights some of their major findings, and reviews the instruments used within each paradigm to circumvent direct self-report (as well as the controversies surrounding these instruments). The review also demonstrates that the adoption of an unconscious perspective is not inconsistent with major theoretical frameworks in the field. It then selects one of these paradigms, namely implicit attitudes, to apply in the context of language learning. Two studies were conducted on two independent samples (with almost 700 participants in total), in two different contexts (the UK and Saudi Arabia), and with different instruments of implicit attitudes (the Implicit Association Test and the Single-Target Implicit Association Test). Study 1 found that openness to language speakers at the implicit level is associated with more openness at the explicit level. Study 2 successfully replicated this finding, and extended it to language achievement—showing that learners with more favorable attitudes toward language speakers at the implicit level achieved higher grades in their English class. This finding could not be explained away by either social desirability or cognitive confounds. The results from these two studies were also meta-analyzed using Bayes factors in order to give an overall picture of the findings. The Discussion chapter wraps up this thesis by highlighting the relevance of this unconscious approach to the field more broadly. This chapter reviews a number of recent studies that have yielded similar findings to those from the current thesis. Some of these findings are then critically reanalyzed and reinterpreted in the context of unconscious motivation, thus demonstrating how adopting an unconscious approach helps view existing findings in a new light. In some cases, the analysis casts doubt on established ideas that have been taken for granted for decades. The overall message of this thesis is not that conscious motivation should be disregarded. Instead, conscious motivation should be complemented with a consideration of the role of unconscious motivation. A conscious-only approach would offer a limited window into human attitudes and motivation.
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CAMPOLONGO, NICOLO'. "ADAPTIVE AND IMPLICIT ONLINE LEARNING." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/823932.

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This thesis is dedicated to the study of online learning algorithms. In particular, after reviewing fundamental concepts in the theory of convex and online linear optimization, we provide a refined analysis of Implicit updates in the framework of Online Mirror Descent. We design a new adaptive algorithm based on it and carefully study its regret bound in the static case, linking it to the variability of the sequence of loss functions. Furthermore, we extend its application to the dynamic setting, studying its dynamic regret. In particular, we show that it achieves the optimal dynamic regret bound, when the quantities of interest are observable or known beforehand. On the other hand, in order to have a fully adaptive algorithm we show how to combine strongly adaptive algorithms with a simple greedy strategy. Finally, we focus on the well known problem of learning with expert advice. We review existing algorithm and describe an existing open problem. We provide some recent results and partial progress on how this problem could be addressed.
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Rebuschat, Patrick. "Implicit learning of natural language syntax." Thesis, University of Cambridge, 2009. https://www.repository.cam.ac.uk/handle/1810/237038.

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The findings reported in this dissertation have several implications for our understanding of language acquisition and for future research. Firstly, while the precise form of the knowledge acquired in these experiments is unclear, the findings provided no evidence for rule learning in the vast majority of subjects. It suggests that subjects in these types of experiments (and perhaps in natural language acquisition) do not acquire linguistic rules. The results support Shanks (1995; Johnstone & Shanks, 2001), who argues against the possibility of implicit rule learning. Secondly, while adults can acquire knowledge implicitly, the work reported in this dissertation also demonstrates that adult syntactic learning results predominantly in a conscious (but largely unverbalizable) knowledge base. Finally, from a methodological perspective, the results of the experiments confirm that relying on verbal reports as a measure of awareness is not sufficient. The verbal reports collected at the end of the experiment were helpful in determining what aspects of the semi-artificial grammar subjects had consciously noticed. At the same time, verbal reports were clearly not sensitive enough to assess whether subjects were aware of the knowledge they had acquired. Confidence ratings and source attributions provided a very useful method for capturing low levels of awareness and to observe the conscious status of both structural and judgment knowledge. Future experiments on language acquisition would benefit from the introduction of this relatively simple, but effective way of assessing awareness. The results of the experiments indicate that adult learners are able to acquire syntactic structures of a novel language under both incidental and intentional learning conditions, while processing sentences for meaning, without the benefit of corrective feedback and after shortviexposure periods. That is, the findings demonstrate that the implicit learning of natural language is not restricted to infants and child learners. In addition, the experiments also show that subjects are able to transfer their knowledge to stimuli with the same underlying structure but new surface features. The measures of awareness further suggest that, in experiments 3 to 6 at least, learning resulted in both conscious and unconscious knowledge. While subjects did not become aware of all the information they have acquired, it was clear that higher levels of awareness were associated with improved performance. Participants in experiments 1-5 were exposed to the semi-artificial system under incidental learning conditions by means of different training tasks. In experiments 1 and 2, an auditory plausibility judgment task was used to expose participants to the stimulus sentences. In experiment 3, elicited imitations were used in addition to the plausibility judgment task. The training phase in experiment 4 consisted solely of elicited imitations, while training in experiment 5 consisted of a classification task which required participants to identify the syntactic structure of each stimulus item, followed by plausibility judgments. Participants in experiment 6, on the other hand, were exposed to the semi-artificial grammar under intentional learning conditions. These participants were told that the word order of the stimulus sentences was governed by a complex rule-system and instructed to discover syntactic rules. After training, participants in all six experiments took part in a testing phase which assessed whether learning took place and to what extent they became aware of the knowledge they had acquired. Grammaticality judgments were used as a measure of learning. Awareness was assessed by means of verbal reports, accuracy estimates, confidence ratings and source attributions. Control participants did not take part in the training phase. The present dissertation focuses on the question of how humans acquire syntactic knowledge without intending to and without awareness of what they have learned. The aim is to apply the theoretical concepts and the methodological framework provided by implicit learning research to the investigation of language acquisition. The results of six experiments are reported. In terms of design, all experiments consisted of (i) a training phase, during which subjects were trained on a miniature linguistic system by means of different exposure conditions, (ii) an unexpected testing phase, during which learning and awareness were assessed, and (iii) a debriefing session. A semi-artificial grammar, which consisted of English words and German syntax, was employed to generate the stimulus material for experiments 1, 2, 3, 5 and 6; in the case of experiment 4, nonsense syllables were used instead of English words. The linguistic focus was on verb placement rules. Native speakers of English with no background in German (or any other V2 language) were recruited to take part in the experiments.
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Books on the topic "Implicit learning"

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name, No. Attention and implicit learning. Amsterdam, Netherlands: John Benjamins Pub., 2003.

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Jiménez, Luis, ed. Attention and Implicit Learning. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.

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1964-, Jiménez Luis, ed. Attention and implicit learning. Amsterdam: John Benjamins Publishing Company, Netherlands, 2002.

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A, Stadler Michael, and Frensch Peter A, eds. Handbook of implicit learning. Thousand Oaks: Sage Publications, 1998.

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P, Dienes Zoltan, ed. Implicit learning: Theoretical and empirical issues. Hove, UK: L. Erlbaum Associates, 1993.

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Dianne, Berry, ed. How implicit is implicit learning?: Edited by Dianne C. Berry. Oxford: Oxford University Press, 1997.

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Rebuschat, Patrick, ed. Implicit and Explicit Learning of Languages. Amsterdam: John Benjamins Publishing Company, 2015. http://dx.doi.org/10.1075/sibil.48.

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Rebuschat, Patrick. Implicit and explicit learning of languages. Amsterdam: John Benjamins Publishing Company, 2015.

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C, Ellis Nick, ed. Implicit and explicit learning of languages. London: Academic Press, 1994.

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Axel, Cleeremans, ed. Implicit learning and consciousness: An empirical, philosophical, and computational consensus in the making. New York: Psychology Press, 2002.

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Book chapters on the topic "Implicit learning"

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Yi, Hu. "Implicit Learning." In The ECPH Encyclopedia of Psychology, 1–2. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6000-2_1091-1.

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Yi, Hu. "Implicit Learning." In The ECPH Encyclopedia of Psychology, 702–3. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7874-4_1091.

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Meier, Beat, and Josephine Cock. "Implicit Sequence Learning." In Encyclopedia of the Sciences of Learning, 1506–9. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_515.

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Paciorek, Albertyna, and John N. Williams. "Semantic implicit learning." In Implicit and Explicit Learning of Languages, 69–90. Amsterdam: John Benjamins Publishing Company, 2015. http://dx.doi.org/10.1075/sibil.48.04pac.

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Mulligan, Neil W., and Alan S. Brown. "Attention and implicit memory." In Attention and Implicit Learning, 297–334. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.16mul.

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Jiménez, Luis. "Introduction." In Attention and Implicit Learning, 1–7. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.03jim.

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Shanks, David R. "Attention and awareness in “implicit” sequence learning." In Attention and Implicit Learning, 11–42. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.05sha.

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Jiménez, Luis. "Intention, attention, and consciousness in probabilistic sequence learning." In Attention and Implicit Learning, 43–68. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.06jim.

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Hazeltine, Eliot, and Richard B. Ivry. "Neural structures that support implicit sequence learning." In Attention and Implicit Learning, 71–107. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.08haz.

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Ashby, F. Gregory, and Michael B. Casale. "The cognitive neuroscience of implicit category learning." In Attention and Implicit Learning, 109–41. Amsterdam: John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.09ash.

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Conference papers on the topic "Implicit learning"

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Liu, Heping, Songbai Liu, Junkai Ji, Qiuzhen Lin, Jianyong Chen, and Kay Chen Tan. "Personalized Federated Learning with Enhanced Implicit Generalization." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651164.

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Fan, Shen, and Przemyslaw Musialski. "Optimizing 3D Geometry Reconstruction from Implicit Neural Representations." In 2024 International Conference on Machine Learning and Applications (ICMLA), 816–21. IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00117.

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Zhou, Xiaoling, Wei Ye, Yidong Wang, Chaoya Jiang, Zhemg Lee, Rui Xie, and Shikun Zhang. "Enhancing In-Context Learning via Implicit Demonstration Augmentation." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2810–28. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.155.

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Weitl-Harms, Sherri, John D. Hastings, and Jonah Lum. "Using LLMs to Establish Implicit User Sentiment of Software Desirability." In 2024 International Conference on Machine Learning and Applications (ICMLA), 1645–50. IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00254.

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Ravari, Amirhossein, Seyede Fatemeh Ghoreishi, and Mahdi Imani. "Implicit Human Perception Learning in Complex and Unknown Environments." In 2024 American Control Conference (ACC), 5453–59. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644975.

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Rong, Yuetong, and Yijun Mo. "NCPrompt: NSP-Based Prompt Learning and Contrastive Learning for Implicit Discourse Relation Recognition." In Findings of the Association for Computational Linguistics: EMNLP 2024, 1159–69. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.63.

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Oechsle, Michael, Michael Niemeyer, Christian Reiser, Lars Mescheder, Thilo Strauss, and Andreas Geiger. "Learning Implicit Surface Light Fields." In 2020 International Conference on 3D Vision (3DV). IEEE, 2020. http://dx.doi.org/10.1109/3dv50981.2020.00055.

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Prather, Richard W., and Martha W. Alibali. "Implicit learning of arithmetic principles." In 2008 7th IEEE International Conference on Development and Learning (ICDL 2008). IEEE, 2008. http://dx.doi.org/10.1109/devlrn.2008.4640833.

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Luo, Haozheng, Tianyi Wu, Colin Feiyu Han, and Zhijun Yan. "IGN : Implicit Generative Networks." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00097.

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Plassard, Andrew J., L. Taylor Davis, Allen T. Newton, Susan M. Resnick, Bennett A. Landman, and Camilo Bermudez. "Learning implicit brain MRI manifolds with deep learning." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2018. http://dx.doi.org/10.1117/12.2293515.

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Reports on the topic "Implicit learning"

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Bawaskar, Neerja. Analog Implicit Functional Testing using Supervised Machine Learning. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2097.

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2

Jiang, Yuhong V. Implicit Learning of Complex Visual Contexts Under Non-Optimal Conditions. Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada482119.

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3

Rupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1808822.

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4

Sun, Ron, and Robert C. Mathews. Exploring the Interaction of Implicit and Explicit Processes to Facilitate Individual Skill Learning. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada435130.

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5

Yu, Wanchi. Implicit Learning of Children with and without Developmental Language Disorder across Auditory and Visual Categories. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7460.

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6

Matsekh, Anna M., Luis Chacon, HyeongKae Park, and Guangye Chen. Machine Learning for Memory Reduction in the Implicit Monte Carlo Simulations of Thermal Radiative Transfer. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1618308.

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7

Lyuckanova, Miglena, Lora Lyamova, Yordanka Lalova, Antoaneta Kalonkina, Vasil Kolev, and Juliana Yordanova. The Role of Implicit Sequence Learning in the Auditory Modality for Phonological Awareness in Children. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, September 2018. http://dx.doi.org/10.7546/crabs.2018.09.07.

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8

Zeidenstein, Sondra, and Kirsten Moore. Learning About Sexuality: A Practical Beginning. Population Council, 1996. http://dx.doi.org/10.31899/pgy1996.1007.

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“Learning About Sexuality: A Practical Beginning” is divided into three main parts. The first includes approaches that program staff, activists, and researchers are taking to understand people’s experiences of sexuality. The second explores the explicit and implicit links among health-seeking behavior, contraceptive practice, reproductive health, and sexuality. The chapters in part three focus on activities that challenge entrenched attitudes and behavior about sexuality that have real and potentially harmful effects on women’s and men’s reproductive health. The book features program and research work in all regions of the world with women, men, girls, and boys. The chapters are written by authors from over a dozen countries, with over half the contributions coming from developing countries. Collectively, these chapters represent an exploration of the relationship of sexuality to reproductive health, contraceptive practice, and overall well-being. For all their variety of place, approach, and focus, a number of common themes emerge.
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9

McKnight, Katherine, Nitya Venkateswaran, Jennifer Laird, Rita Dilig, Jessica Robles, and Talia Shalev. Parent Teacher Home Visits: An Approach to Addressing Biased Mindsets and Practices to Support Student Success. RTI Press, September 2022. http://dx.doi.org/10.3768/rtipress.2022.op.0077.2209.

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Research has shown educators’ implicit biases to be a key factor in creating and perpetuating disparities in students’ experiences of schooling, learning, and longer-term outcomes, including job opportunities, wealth, and health. Current school reform and transformation efforts are aimed at addressing institutionalized racism in school policies, practices, and cultural systems by implementing implicit bias training for teachers and staff. In this paper, we explain how a school home visits program, Parent Teacher Home Visits (PTHV), is a promising intervention for counteracting implicit biases and improving outcomes for families and students. The PTHV “relational” home visit model focuses on promoting mutually supportive and accountable relationships between educators and families. We present data from a study examining the experiences of 107 educators and 68 family members who participated in PTHV, showing how educators shifted their deficit assumptions about families and students. Although the PTHV model was not created to address implicit biases, we found that the key components of these home visits align with strategies that psychological research has demonstrated effectively counteracting implicit biases and reducing discriminatory behaviors.
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10

Baquero-Rey, Luis E., and Juan C. Ayala. Exploración y análisis de datos de texto masivos relativos a la crisis del COVID-19. Escuela Tecnológica Instituto Técnico Central, 2023. http://dx.doi.org/10.55411/2023.10.

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La pandemia del COVID-19 trajo consigo grandes implicaciones para la humanidad, desde poner el mundo en jaque frente a las restricciones de movilidad, hasta llegar a implicar nuevos retos para los científicos; con el fin de tratar y hacerle frente a la situación. Esto conllevó, entre otras cosas, al desarrollo de nuevas vacunas en tiempo récord, apoyados desde luego, también en áreas de las ciencias de la computación, como lo es la Inteligencia Artificial. El aprendizaje automático o machine learning es una rama de la inteligencia artificial que permite que las máquinas aprendan sin ser expresamente programadas para ello. Como apoyo a este esta investigación se plantea el uso de algunos de estos algoritmos para examinar texto real de los resúmenes de publicaciones científicas para identificar comportamientos, patrones y superposiciones, especialmente en el contexto de la exploración de datos que implica la temática abordada.
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