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Journal articles on the topic 'Implicit learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 March 2025

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1

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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2

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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3

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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4

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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6

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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7

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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8

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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9

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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Leung, Janny H. C., and John N. Williams. "THE IMPLICIT LEARNING OF MAPPINGS BETWEEN FORMS AND CONTEXTUALLY DERIVED MEANINGS." Studies in Second Language Acquisition 33, no. 1 (February 21, 2011): 33–55. http://dx.doi.org/10.1017/s0272263110000525.

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The traditional implicit learning literature has focused primarily on the abstraction of statistical regularities in form-form connections. More attention has been recently directed toward the implicit learning of form-meaning connections, which might be crucial in the acquisition of natural languages. The current article reports evidence for implicit learning of a mapping between a novel set of determiners and thematic roles, obtained using a newly developed reaction time methodology. The results conclude that contextually derived form-meaning connections might be implicitly learned.

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Willingham, Daniel B., and Kelly Goedert-Eschmann. "The Relation Between Implicit and Explicit Learning: Evidence for Parallel Development." Psychological Science 10, no. 6 (November 1999): 531–34. http://dx.doi.org/10.1111/1467-9280.00201.

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Much research has focused on the separability of implicit and explicit learning, but less has focused on how they might interact. A recent model suggests that in the motor-skill domain, explicit knowledge can guide movement, and the implicit system learns in parallel, based on these movements. Functional imaging studies do not support that contention, however; they indicate that learning is exclusively implicit or explicit. In the experiment reported here, participants learned a motor sequencing task either implicitly or explicitly. At transfer, most of the stimuli were random, but the sequence occasionally appeared; thus, it was not obvious that explicit knowledge could be applied to the task. Nevertheless, participants with explicit training showed sequence knowledge equivalent to those with implicit training, implying that implicit knowledge had been acquired in parallel with explicit knowledge. This result has implications for the development of automaticity and of motor-skill learning.

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Kerz, Elma, Daniel Wiechmann, and Florian B. Riedel. "IMPLICIT LEARNING IN THE CROWD." Studies in Second Language Acquisition 39, no. 4 (April 3, 2017): 711–34. http://dx.doi.org/10.1017/s027226311700002x.

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AbstractA growing field of research has made use of a semiartificial language paradigm to investigate the role of awareness in L2 acquisition. A central and empirically still unresolved issue in this field concerns the possibility of learning implicitly, that is, without intention to learn and without awareness of what has been learned. Up until now, studies on implicit learning have mainly been conducted in laboratory settings under highly controlled conditions with university students as participants. The present study investigated whether and to what extent the results obtained in such settings can be extrapolated to the general population. Building on Williams (2005), we designed two crowdsourcing experiments that examined the learning of novel form-meaning mappings under incidental conditions in 163 participants. Our design allowed us to disentangle the effects of awareness at the level of noticing and understanding. The results of the two experiments demonstrated the implicit learning effect outside the lab in a more varied sample of participants and indicated that awareness at both levels appears to have a facilitative effect on learning outcomes.

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14

Abrahamse, Elger. "Editorial to the special issue Implicit Serial Learning." Advances in Cognitive Psychology 8, no. 2 (June 28, 2012): 70–72. http://dx.doi.org/10.5709/acp-0104-2.

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Wittschen, Cameron G., and Christopher A. Was. "Influence of mind wandering and increased attentional demands on multitasking and implicit learning." Europe’s Journal of Psychology 21, no. 1 (February 28, 2025): 65–76. https://doi.org/10.5964/ejop.14605.

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The goal of the current study was to replicate resent findings that suggest mind wandering is associated with impaired explicit learning but not implicit learning, and to extend those finding by investigating whether explicit learning is impaired under attentional load, but implicit learning is not. We used a sequential learning task, specifically a serial reaction task (SRT), to determine if mind wandering would interfere with learning a task that does not require attentional resources (implicit learning). Participants completed the serial reaction time task while watching a 13-minute video lecture. At the end of the video participants answered 10 multiple-choice questions regarding the content presented in the video. At specific intervals during the task, participants responded to mind wandering probes. The probes required participants to report where their attention was in the moments before the probe appeared. Implicit learning was measured by decreased reaction time over the course of several blocks of trials of the SRT. In two experiments, it was observed that participants implicitly learned a sequence of 12 items, regardless of their performance on the multiple-choice item regarding the concurrent video content. Even those who appeared to actively engage with the video and performed well on the multiple-choice questions showed improved performance on the implicit learning task (SRT). These results suggest implicit learning can occur when one is engaged in a concurrent explicit learning task.

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Jenkins, Holly E., Phyllis Leung, Faye Smith, Nick Riches, and Benjamin Wilson. "Assessing processing-based measures of implicit statistical learning: Three serial reaction time experiments do not reveal artificial grammar learning." PLOS ONE 19, no. 9 (September 20, 2024): e0308653. http://dx.doi.org/10.1371/journal.pone.0308653.

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Implicit statistical learning, whereby predictable relationships between stimuli are detected without conscious awareness, is important for language acquisition. However, while this process is putatively implicit, it is often assessed using measures that require explicit reflection and conscious decision making. Here, we conducted three experiments combining an artificial grammar learning paradigm with a serial reaction time (SRT-AGL) task, to measure statistical learning of adjacent and nonadjacent dependencies implicitly, without conscious decision making. Participants viewed an array of six visual stimuli and were presented with a sequence of three auditory (nonsense words, Expt. 1; names of familiar objects, Expt. 2) or visual (abstract shapes, Expt. 3) cues and were asked to click on the corresponding visual stimulus as quickly as possible. In each experiment, the final stimulus in the sequence was predictable based on items earlier in the sequence. Faster responses to this predictable final stimulus compared to unpredictable stimuli would provide evidence of implicit statistical learning, without requiring explicit decision making or conscious reflection. Despite previous positive results (Christiansen et al. 2009 and Misyak et al. 2010) we saw little evidence of implicit statistical learning in any of the experiments, suggesting that in this case, these SRT-AGL tasks were not an effective measure implicit statistical learning.

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Berry, Dianne C., and Zoltan Dienes. "The relationship between implicit memory and implicit learning." British Journal of Psychology 82, no. 3 (August 1991): 359–73. http://dx.doi.org/10.1111/j.2044-8295.1991.tb02405.x.

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Folia, Vasiliki, Julia Uddén, Christian Forkstam, Martin Ingvar, Peter Hagoort, and Karl Magnus Petersson. "Implicit Learning and Dyslexia." Annals of the New York Academy of Sciences 1145, no. 1 (December 2008): 132–50. http://dx.doi.org/10.1196/annals.1416.012.

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Tseng, Philip, Tzu-Yu Hsu, Ovid J. L. Tzeng, Daisy L. Hung, and Chi-Hung Juan. "Probabilities in Implicit Learning." Perception 40, no. 7 (January 2011): 822–29. http://dx.doi.org/10.1068/p6833.

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Ashworth, PD. "Handbook of Implicit Learning." Physiotherapy 84, no. 5 (May 1998): 240. http://dx.doi.org/10.1016/s0031-9406(05)65559-9.

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Nakamura, Daisuke. "Methodological considerations in studying awareness during learning: Part 1: Implicit learning." Polish Psychological Bulletin 44, no. 1 (March 1, 2013): 102–17. http://dx.doi.org/10.2478/ppb-2013-0011.

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AbstractMethodological problems of how awareness during learning should be measured have been extensively discussed and investigated in cognitive psychology. This review considers; 1)whether amnesics can perform implicit learning tasks at a similar level to normal controls, 2) whether differences in instructional orientations create dissociations in performance in tests of implicit and explicit knowledge, and 3) whether participants can retrospectively verbalise the learning outcomes. The paper concludes that; (1) amnesics’ implicit learning abilities differ from the normal controls, (2) instructions on implicit learning do not guarantee the occurrence of implicit learning, and (3) objective and subjective awareness measures used in the literature face inherent problems and so the awareness controversy remains unsettled.

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22

Curran, Tim. "Higher-Order Associative Learning in Amnesia: Evidence from the Serial Reaction Time Task." Journal of Cognitive Neuroscience 9, no. 4 (July 1997): 522–33. http://dx.doi.org/10.1162/jocn.1997.9.4.522.

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Patients with anterograde amnesia are commonly believed to exhibit normal implicit learning. Research with the serial reaction time (SRT) task suggests that normal subjects can implicitly learn visuospatial sequences through a process that is sensitive to higher-order information that is more complex than pairwise associations between adjacent stimuli. The present research reexamined SRT learning in a group of amnesic patients with a design intended to specifically address the learning of higher-order information. Despite seemingly normal learning effects on average, the results suggest that amnesic patients do not learn higher-order information as well as control subjects. These results suggest that amnesic patients have an associative learning impairment, even when learning is implicit, and that the medial temporal lobe and/or diencephalic brain areas typically damaged in cases of amnesia normally contribute to implicit sequence learning.

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Bankieris, Kaitlyn R., Ting Qian, and Richard N. Aslin. "Synesthetes perseverate in implicit learning: Evidence from a non-stationary statistical learning task." Quarterly Journal of Experimental Psychology 72, no. 7 (December 11, 2018): 1771–79. http://dx.doi.org/10.1177/1747021818816285.

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Synesthetes automatically and consistently experience additional sensory or cognitive perceptions in response to particular environmental stimuli. Recent evidence suggests that the propensity to develop synesthesia is genetic while the particular associations experienced by a given synesthete are influenced by learning. Despite the potential role of implicit learning in the formation of synesthetic associations, there has been minimal investigation of synesthetes’ implicit learning abilities. In this study, we examine linguistic-colour synesthetes’ ability to implicitly learn from and adjust to non-stationary statistics in a domain unrelated to their particular form of synesthesia. Engaging participants in a computer game Whack-the-mole, we utilise the online measure of reaction time to assess the time course of learning. Participants are exposed to “worlds” of probabilities that, unbeknownst to them, undergo unannounced changes, creating unpredictable statistical shifts devoid of accompanying cues. The same small set of probability worlds are repeated throughout the experiment to investigate participants’ ability to retain and learn from this repetitive probabilistic information. The reaction time data provide evidence that synesthetes require more information than nonsynesthetes to benefit from the non-stationary probability distributions. These findings demonstrate that linguistic-colour synesthetes’ implicit learning abilities—in a domain far from their synesthetic experiences—differ from those of nonsynesthetes.

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Foti, F., F. De Crescenzo, G. Vivanti, D. Menghini, and S. Vicari. "Implicit learning in individuals with autism spectrum disorders: a meta-analysis." Psychological Medicine 45, no. 5 (August 15, 2014): 897–910. http://dx.doi.org/10.1017/s0033291714001950.

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Background.Individuals with autism spectrum disorders (ASDs) are characterized by social communication difficulties and behavioural rigidity. Difficulties in learning from others are one of the most devastating features of this group of conditions. Nevertheless, the nature of learning difficulties in ASDs is still unclear. Given the relevance of implicit learning for social and communicative functioning, a link has been hypothesized between ASDs and implicit learning deficit. However, studies that have employed formal testing of implicit learning in ASDs provided mixed results.Method.We undertook a systematic search of studies that examined implicit learning in ASDs using serial reaction time (SRT), alternating serial reaction time (ASRT), pursuit rotor (PR), and contextual cueing (CC) tasks, and synthesized the data using meta-analysis. A total of 11 studies were identified, representing data from 407 individuals with ASDs and typically developing comparison participants.Results.The results indicate that individuals with ASDs do not differ in any task considered [SRT and ASRT task: standardized mean difference (SMD) −0.18, 95% confidence interval (CI) −0.71 to 0.36; PR task: SMD −0.34, 95% CI −1.04 to 0.36; CC task: SMD 0.27, 95% CI −0.07 to 0.60].Conclusions.Based on our synthesis of the existing literature, we conclude that individuals with ASDs can learn implicitly, supporting the hypothesis that implicit learning deficits do not represent a core feature in ASDs.

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Stark-Inbar, Alit, Meher Raza, Jordan A. Taylor, and Richard B. Ivry. "Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning." Journal of Neurophysiology 117, no. 1 (January 1, 2017): 412–28. http://dx.doi.org/10.1152/jn.01141.2015.

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In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain.NEW & NOTEWORTHY We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the alternating serial reaction time task, exhibited good test-retest reliability in measures of learning and performance. However, the learning measures did not correlate between the two tasks, arguing against a shared process for implicit motor learning.

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Yang, Weiqi, and Michael Spece. "Implicit Adaptation to Low Rank Structure in Online Learning." International Journal of Machine Learning and Computing 11, no. 5 (September 2021): 339–44. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1058.

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Desmottes, Lise, Thierry Meulemans, and Christelle Maillart. "Implicit Spoken Words and Motor Sequences Learning Are Impaired in Children with Specific Language Impairment." Journal of the International Neuropsychological Society 22, no. 5 (April 11, 2016): 520–29. http://dx.doi.org/10.1017/s135561771600028x.

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AbstractObjectives: This study aims to compare verbal and motor implicit sequence learning abilities in children with and without specific language impairment (SLI). Methods: Forty-eight children (24 control and 24 SLI) were administered the Serial Search Task (SST), which enables the simultaneous assessment of implicit spoken words and visuomotor sequences learning. Results: Results showed that control children implicitly learned both the spoken words as well as the motor sequences. In contrast, children with SLI showed deficits in both types of learning. Moreover, correlational analyses revealed that SST performance was linked with grammatical abilities in control children but with lexical abilities in children with SLI. Conclusions: Overall, this pattern of results supports the procedural deficit hypothesis and suggests that domain general implicit sequence learning is impaired in SLI. (JINS, 2016, 22, 1–10)

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Wang, Huijun, and Kunlin Wei. "Transcranial Direct-Current Stimulation Does Not Affect Implicit Sensorimotor Adaptation: A Randomized Sham-Controlled Trial." Brain Sciences 12, no. 10 (September 29, 2022): 1325. http://dx.doi.org/10.3390/brainsci12101325.

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Humans constantly calibrate their sensorimotor system to accommodate environmental changes, and this perception-action integration is extensively studied using sensorimotor adaptation paradigms. The cerebellum is one of the key brain regions for sensorimotor adaptation, but previous attempts to modulate sensorimotor adaptation with cerebellar transcranial direct current stimulation (ctDCS) produced inconsistent findings. Since both conscious/explicit learning and procedural/implicit learning are involved in adaptation, researchers have proposed that ctDCS only affects sensorimotor adaptation when implicit learning dominates the overall adaptation. However, previous research had both types of learning co-exist in their experiments without controlling their potential interaction under the influence of ctDCS. Here, we used error clamp perturbation and gradual perturbation, two effective techniques to elicit implicit learning only, to test the ctDCS effect on sensorimotor adaptation. We administrated ctDCS to independent groups of participants while they implicitly adapted to visual errors. In Experiment 1, we found that cerebellar anodal tDCS had no effect on implicit adaptation induced by error clamp. In Experiment 2, we applied both anodal and cathodal stimulation and used a smaller error clamp to prevent a potential ceiling effect, and replicated the null effect. In Experiment 3, we used gradually imposed visual errors to elicit implicit adaptation but still found no effect of anodal tDCS. With a total of 174 participants, we conclude that the previous inconsistent tDCS effect on sensorimotor adaptation cannot be explained by the relative contribution of implicit learning. Given that the cerebellum is simultaneously involved in explicit and implicit learning, our results suggest that the complex interplay between the two learning processes and large individual differences associated with this interplay might contribute to the inconsistent findings from previous studies on ctDCS and sensorimotor adaptation.

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Khalifa, AlBara, Tsuneo Kato, and Seiichi Yamamoto. "Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System." International Journal of Emerging Technologies in Learning (iJET) 14, no. 02 (January 30, 2019): 105. http://dx.doi.org/10.3991/ijet.v14i02.9212.

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The introduction of robots into language learning systems has been highly useful, especially in motivating learners to engage in the learning process and in letting human learners converse in more realistic conversational situations. This paper describes a novel robot-assisted language learning system that induces the human learner into a triad conversation with two robots through which he or she improves practical communication skills in various conversational situations. The system applies implicit learning as the main learning style for conveying linguistic knowledge, in an indirect way, through conversations on several topics. A series of experiments was conducted using 80 recruited participants to evaluate the effect of implicit learning and the retention effect in a joining-in-type robot-assisted language learning system. The experimental results show positive effects of implicit learning and repetitive learning in general. Based on these experimental results, we propose an improved method, integrating implicit learning and tutoring with corrective feedback in an adaptive way, to increase performance in practical communication skills even for a wide variety of proficiency of L2 learners.

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Shanks, David R., and Mark F. St. John. "Characteristics of dissociable human learning systems." Behavioral and Brain Sciences 17, no. 3 (September 1994): 367–95. http://dx.doi.org/10.1017/s0140525x00035032.

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AbstractA number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning.

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French, Margaret A., Susanne M. Morton, Charalambos C. Charalambous, and Darcy S. Reisman. "A locomotor learning paradigm using distorted visual feedback elicits strategic learning." Journal of Neurophysiology 120, no. 4 (October 1, 2018): 1923–31. http://dx.doi.org/10.1152/jn.00252.2018.

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Distorted visual feedback (DVF) during locomotion has been suggested to result in the development of a new walking pattern in healthy individuals through implicit learning processes. Recent work in upper extremity visuomotor rotation paradigms suggest that these paradigms involve implicit and explicit learning. Additionally, in upper extremity visuomotor paradigms, the verbal cues provided appear to impact how a behavior is learned and when this learned behavior is used. Here, in two experiments in neurologically intact individuals, we tested how verbal instruction impacts learning a new locomotor pattern on a treadmill through DVF, the transfer of that pattern to overground walking, and what types of learning occur (i.e., implicit vs. explicit learning). In experiment 1, we found that the instructions provided impacted the amount learned through DVF, but not the size of the aftereffects or the amount of the pattern transferred to overground walking. Additionally, the aftereffects observed were significantly different from the baseline walking pattern, but smaller than the behavior changes observed during learning, which is uncharacteristic of implicit sensorimotor adaptation. Thus, experiment 2 aimed to determine the cause of these discrepancies. In this experiment, when VF was not provided, individuals continued using the learned walking pattern when instructed to do so and returned toward their baseline pattern when instructed to do so. Based on these results, we conclude that DVF during locomotion results in a large portion of explicit learning and a small portion of implicit learning. NEW & NOTEWORTHY The results of this study suggest that distorted visual feedback during locomotor learning involves the development of an explicit strategy with only a small component of implicit learning. This is important because previous studies using distorted visual feedback have suggested that locomotor learning relies primarily on implicit learning. This paradigm, therefore, provides a new way to examine a different form of learning in locomotion.

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Tikhonov, R. V., and N. V. Moroshkina. "Implicit Learning in Dyadic Interaction." Vestnik of Saint Petersburg University. Psychology 6, no. 4 (2016): 75–87. http://dx.doi.org/10.21638/11701/spbu16.2016.406.

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Jarvis, Peter. "Human Learning: implicit and explicit." Educação & Realidade 40, no. 3 (September 2015): 809–23. http://dx.doi.org/10.1590/2175-623648387.

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Building on Polanyi's insight in The Tacit Dimension that we know more than we can tell, this paper argues that we actually experience more of reality than that of which we are conscious. Our conscious experience becomes the basis of explicit learning but that which we experience but of which we are not conscious is the basis of implicit learning and tacit knowledge.

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Mizuhara, Yukio. "Implicit learning of event sequence." Japanese journal of psychology 65, no. 5 (1994): 383–88. http://dx.doi.org/10.4992/jjpsy.65.383.

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Udden, Julia, Vasiliki Folia, and Karl Magnus Petersson. "The Neuropharmacology of Implicit Learning." Current Neuropharmacology 8, no. 4 (December 1, 2010): 367–81. http://dx.doi.org/10.2174/157015910793358178.

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Edwards, Kathleen S., and Myoungju Shin. "Media multitasking and implicit learning." Attention, Perception, & Psychophysics 79, no. 5 (May 1, 2017): 1535–49. http://dx.doi.org/10.3758/s13414-017-1319-4.

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Neal, Andrew, and Beryl Hesketh. "Episodic knowledge and implicit learning." Psychonomic Bulletin & Review 4, no. 1 (March 1997): 24–37. http://dx.doi.org/10.3758/bf03210770.

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Stadler, Michael A. "Distinguishing implicit and explicit learning." Psychonomic Bulletin & Review 4, no. 1 (March 1997): 56–62. http://dx.doi.org/10.3758/bf03210774.

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Whittlesea, Bruce W. A., and Michael D. Dorken. "Implicit learning: Indirect, not unconscious." Psychonomic Bulletin & Review 4, no. 1 (March 1997): 63–67. http://dx.doi.org/10.3758/bf03210775.

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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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Abstract:

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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De Houwer, Jan, Frank Baeyens, and Hilde Hendrickx. "Implicit Learning of Evaluative Associations." Psychologica Belgica 37, no. 1-2 (January 1, 1997): 115. http://dx.doi.org/10.5334/pb.911.

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Seger, Carol A. "Does Implicit Learning Require Attention?" Contemporary Psychology 49, no. 6 (December 2004): 717–19. http://dx.doi.org/10.1037/004853.

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Horan, William P., Michael F. Green, Barbara J. Knowlton, Jonathan K. Wynn, Jim Mintz, and Keith H. Nuechterlein. "Impaired implicit learning in schizophrenia." Neuropsychology 22, no. 5 (2008): 606–17. http://dx.doi.org/10.1037/a0012602.

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Williams, John N. "The Neuroscience of Implicit Learning." Language Learning 70, S2 (June 2020): 255–307. http://dx.doi.org/10.1111/lang.12405.

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Thompson, Kelsey R., Daniel J. Sanchez, Abigail H. Wesley, and Paul J. Reber. "Ego Depletion Impairs Implicit Learning." PLoS ONE 9, no. 10 (October 2, 2014): e109370. http://dx.doi.org/10.1371/journal.pone.0109370.

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Reber, Arthur S. "Implicit learning and tacit knowledge." Journal of Experimental Psychology: General 118, no. 3 (1989): 219–35. http://dx.doi.org/10.1037/0096-3445.118.3.219.

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Andrade, Jackie. "Is learning during anaesthesia implicit?" Behavioral and Brain Sciences 17, no. 3 (September 1994): 395–96. http://dx.doi.org/10.1017/s0140525x00035044.

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Carlson, Richard A. "Is implicit learning about consciousness?" Behavioral and Brain Sciences 17, no. 3 (September 1994): 400. http://dx.doi.org/10.1017/s0140525x00035093.

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Gao, Xiaoqing, and Hugh R. Wilson. "Implicit learning of geometric eigenfaces." Vision Research 99 (June 2014): 12–18. http://dx.doi.org/10.1016/j.visres.2013.07.015.

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Jamieson, Randall K., John R. Vokey, and D. J. K. Mewhort. "Implicit learning is order dependent." Psychological Research 81, no. 1 (October 20, 2015): 204–18. http://dx.doi.org/10.1007/s00426-015-0715-4.

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