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Abstract: Previous research has shown that non-Māori Speaking New Zealanders have extensive latent knowledge of Māori, despite not being able to speak it. This knowledge plausibly derives from a memory store of Māori forms (Oh et al., 2020; Panther et al., 2023). Modelling suggests that this ‘proto-lexicon’ includes not only Māori words, but also word-parts; however, this suggestion has not yet been tested experimentally. We present the results of a new experiment in which non-Māori speaking New Zealanders and non-New Zealanders were asked to segment a range of Māori words into parts. We show that the degree to which segmentations of non-Māori speakers correlate to the segmentations of two fluent speakers of Māori is stronger among New Zealanders than non-New Zealanders. This research adds to the growing evidence that even in a largely ‘monolingual’ population, there is evidence of latent bilingualism through long-term exposure to a second language.

This paper aims to assess the difficulty levels of mathematical tasks in relation to the appearance of unconscious patterns of thinking in students' cognitive processes that play a role in learning. The data analyzed using a mixed-methods approach was collected from a population of undergraduate engineering and science students enrolled in Calculus courses at the university while answering three questionnaires as part of their online class activities. Two criteria were used to find categories of difficulty levels: one subjective, given by an evaluation carried out by the subjects, and another behavioral, related to obtaining the correct answers. The relationships of these criteria with the appearance of these unconscious patterns of thinking were identified: a significant and strong correlation was noticed between the number of erroneous unconscious patterns detected and task difficulty levels determined by the percentage of correct answers, as well as a significant and strong correlation between task difficulty levels determined by the subjective evaluation and the number of these patterns recognized. Based on the results obtained, it can be stated that these unconscious erroneous patterns in students' reasoning about a mathematical concept are related to the index of the difficulty of a task and could be considered indicators of mental effort according to the cognitive load theory. The analysis showed the recognition of these unconscious patterns in students' cognitive mechanisms is relevant when solving mathematical tasks that require information processing at a higher level and could play a role in assessing the difficulty levels of a task related to the study of mathematical concepts in Calculus courses, which constitutes the main novelty of this study. [ABSTRACT FROM AUTHOR]

The exploration of what lies beyond the explicitly expressed is a challenge for qualitative research. It involves studying something that is not directly given. This paper presents different methodological solutions and focuses on implicit knowledge in professional contexts and theorizing accounts. I will discuss conceptualizations of tacit knowledge and implicit learning, and then focus on specific aspects of the Documentary Method. The Documentary Method is a research strategy that attempts to explicate implicit collective orientations through group discussions. It is based on Karl Mannheim's sociology of knowledge and aims to uncover the taken-for-granted mutual understanding that results from shared or similar experiences. Based on Mannheim's critique of rationalism, this understanding is assumed to be found in the narrative and descriptive parts of group discussions. However, this article examines whether it can also be found in theorizing accounts. It draws on Hans Blumenberg's insight that metaphors are often contained in technical language and can obscure an underlying system of orientation. In this case, implicit mutual understanding is based on shared or similar experiences that are difficult to fully understand and articulate. An illustrative interpretation of data from a team specializing in sexuality education demonstrates the practical application of these methodological considerations. In essence, the article addresses the question of how implicit knowledge emerges in language and provides a new lens for analyzing theorizing accounts. [ABSTRACT FROM AUTHOR]

In recent years, there has been a proliferation of weakly supervised methods in the field of video anomaly detection. Despite significant progress in existing research, these efforts have primarily focused on addressing this issue within Euclidean space. Conducting weakly supervised video anomaly detection in Euclidean space imposes a fundamental limitation by constraining the ability to model complex patterns due to the dimensionality constraints of the embedding space and lacking the capacity to model long-term contextual information. This inadequacy can lead to misjudgments of anomalous events due to insufficient video representation. However, hyperbolic space has shown significant potential for modeling complex data, offering new insights. In this paper, we rethink weakly supervised video anomaly detection with a novel perspective: transforming video features from Euclidean space into hyperbolic space may enable the network to learn implicit relationships in normal and anomalous videos, thereby enhancing its ability to effectively distinguish between them. Finally, to validate our approach, we conducted extensive experiments on the UCF-Crime and XD-Violence datasets. Experimental results show that our method not only has the lowest number of parameters but also achieves state-of-the-art performance on the XD-Violence dataset using only RGB information. [ABSTRACT FROM AUTHOR]

The aim of the study was to compare the effects of implicit learning using dual task-paradigm, with explicit learning on learning a novel skill, and if the performance is maintained over a prolonged period of time. Twenty-six high school adolescents (n = 26, boys n = 15, girls n = 11, age: 16 ± 0.66 years) performed a four-week front-flip learning program, where participants underwent two hours front flip practice in total between the pre- and post-test session followed by two tests; three and six months after the post-test, in which the front-flip was not practiced. Performance was evaluated by two independent gymnastics judges. Both groups increased performance at the post test, with significantly higher scores in the explicit group compared with the implicit group. Probably benefiting from error correction to select positive action outcomes and avoid negative ones consciously. However, the explicit group was also the only group that significantly decreased performance again at first retention test, suggesting that their reliance on the retrieval of declarative knowledge from working memory was subject to decay. While it seems that performance learned via implicit learning may deteriorate more slowly, but also continuously throughout six months suggesting that the directly accumulated procedural knowledge may need for proper reinforcement and practice. [ABSTRACT FROM AUTHOR]

Although the number of studies into boredom in second and/or foreign language (L2) learning is evidently on the rise and our understanding of this negative emotion has been considerably extended, surprisingly, empirical evidence is still scant with respect to boredom experienced in out-of-school situations. This study addresses this gap by: (1) examining the relative contribution of factors underlying in-class and after-class boredom, (2) investigating relationships among these factors, (3) identifying distinct learner profiles connected with these factors, and (4) exploring the role of group-related variables in this respect. The data were collected from 107 Polish university students majoring in English through two tools designed for this purpose. The results demonstrated that in-class and after-class boredom are distinct yet multidimensional constructs and factors underpinning them cannot be easily separated. Four in-class and after-class boredom-specific cluster groups were identified and the impact of gender and attainment measures for some of these factors was found. Future research directions are discussed as well. [ABSTRACT FROM AUTHOR]

Knowledge-based visual question answering (KB-VQA) requires to answer questions according to the given image with the assistance of external knowledge. Recently, researchers generally tend to design different multimodal networks to extract visual and text semantic features for KB-VQA. Despite the significant progress, 'caption' information, a textual form of image semantics, which can also provide visually non-obvious cues for the reasoning process, is often ignored. In this paper, we introduce a novel framework, the Knowledge Based Caption Enhanced Net (KBCEN), designed to integrate caption information into the KB-VQA process. Specifically, for better knowledge reasoning, we make utilization of caption information comprehensively from both explicit and implicit perspectives. For the former, we explicitly link caption entities to knowledge graph together with object tags and question entities. While for the latter, a pre-trained multimodal BERT with natural implicit knowledge is leveraged to co-represent caption tokens, object regions as well as question tokens. Moreover, we develop a mutual correlation module to discern intricate correlations between explicit and implicit representations, thereby facilitating knowledge integration and final prediction. We conduct extensive experiments on three publicly available datasets (i.e., OK-VQA v1.0, OK-VQA v1.1 and A-OKVQA). Both quantitative and qualitative results demonstrate the superiority and rationality of our proposed KBCEN. [ABSTRACT FROM AUTHOR]

AbstractIntroductionPurposeMethodsResultsConclusionPatients with stroke often face cognitive impairments, they may find explicit learning challenging. Implicit learning, which unfolds unconsciously, offers an alternative approach.This systematic review aims to provide a comprehensive overview of the effects of implicit motor learning in stroke rehabilitation.Following the PRISMA guidelines, an electronic search was conducted across five databases for studies published between January 2000 and December 2023.A total of 1,096 studies were initially identified, with nine RCTs and crossover trials meeting the inclusion criteria. Notably, six out of nine studies reported positive effects of implicit learning on motor performance compared to explicit learning. Most studies focused on chronic stroke patients, with interventions ranging from 3 to 4 weeks or assessing immediate effects.Implicit learning shows promise in enhancing motor performance in stroke patients. Therapists should tailor interventions to individual cognitive abilities to maximize the benefits of implicit learning. [ABSTRACT FROM AUTHOR]

We investigated whether allowing individuals to exchange verbal information during dyadic practice changed the effect of analogy instructions intended to strengthen explicit motor learning by an implicit means. Forty-three right-handed college students performed golf putting, aiming at a target three meters away. Participants were assigned to one of two groups: Dyadic Practice Analogy Instruction or Individual Practice Analogy Instruction. Participants in the Dyadic Practice group were allowed to communicate with one another about the task during their practice. Before practice, participants performed a working memory capacity test. Both groups performed 180 trials of golf distributed across three days. On each day, there were four blocks of 15 trials. On the third day, participants reported the explicit rules they used to learn the task and they completed the Intrinsic Motivation Inventory. On the fourth day, they took three learning tests: retention, dual-task transfer, and social pressure transfer tests. Results of the retention test indicated that both groups learned the task comparably. Similarly, there were no significant group differences between the participants' number of explicit rules learned and their motivation levels on either of the transfer tests. Finally, only the participants in the Dyadic Practice Analogy Group showed a significant correlation between their performance on the dual-task transfer test and their working memory capacity. Overall, we found that dyadic practice did not interfere with the implicit type of motor learning promoted by analogy instruction (i.e., implicit learning). [ABSTRACT FROM AUTHOR]

We focus on learning unknown dynamics from data using ODE-nets templated on implicit numerical initial value problem solvers. First, we perform inverse modified error analysis of the ODE-nets using unrolled implicit schemes for ease of interpretation. It is shown that training an ODE-net using an unrolled implicit scheme returns a close approximation of an inverse modified differential equation (IMDE). In addition, we establish a theoretical basis for hyperparameter selection when training such ODE-nets, whereas current strategies usually treat numerical integration of ODE-nets as a black box. We thus formulate an adaptive algorithm which monitors the level of error and adapts the number of (unrolled) implicit solution iterations during the training process, so that the error of the unrolled approximation is less than the current learning loss. This helps accelerate training while maintaining accuracy. Several numerical experiments are performed to demonstrate the advantages of the proposed algorithm compared to nonadaptive unrollings and validate the theoretical analysis. We also note that this approach naturally allows for incorporating partially known physical terms in the equations, giving rise to what is termed "gray box" identification. [ABSTRACT FROM AUTHOR]

It remains unclear whether late second language learners (L2ers) can acquire sufficient knowledge about unique-to-L2 constructions through implicit learning to build anticipations during real-time processing. To tackle this question, we conducted a visual world paradigm experiment to investigate high-proficiency late first-language Dutch second-language Mandarin Chinese learners' online processing of syntactic cues to count vs. mass interpretations in Chinese which are unique-to-L2 and never explicitly taught. The results showed that late Dutch–Mandarin learners were sensitive to a mass-biased syntactic cue in real-time processing, and exhibited some native-like anticipatory behaviour. These findings indicate that late L2ers can acquire unique-to-L2 constructions through implicit learning, and can automatically use this knowledge to make predictions. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the delivery of rehabilitation using implicit motor learning principles in an acute stroke setting. Design: Pilot, assessor-blind, cluster randomised controlled trial with nested qualitative evaluation. Setting: Eight inpatient stroke units, UK. Participants: People within 14 days of stroke onset, presenting with lower limb hemiplegia. Interventions: Participants at control clusters received usual care. Participants at intervention clusters received rehabilitation using an Implicit Learning Approach (ILA); primarily consisting of reduced frequency instructions/feedback, and promotion of an external focus of attention. Video recording was used to understand the ability of intervention site therapists to adhere to the implicit learning principles, and to compare differences between groups. Measures: Ability to recruit and retain clusters/participants; suitability and acceptability of data collection processes; appropriateness of fidelity monitoring methods; and appropriateness of chosen outcome measures. Results: Eight stroke units participated, with four assigned to each group (intervention/control). Fifty-one participants were enrolled (intervention group 21; control group 30). Mean time since stroke was 6 days (SD 3.42; 0–14); mean age was 73 years (SD 14, 25–94). Of those approached to take part, 72% agreed. We found clear differences between groups with respect to the frequency and type of instructional statement. The ILA was acceptable to both patients and therapists. Conclusion: It is feasible to evaluate the application and effectiveness of motor learning principles within acute stroke rehabilitation, using a cluster randomised design. A larger study is required to evaluate the benefits of each approach; we provide a range of sample size estimates required for this. [ABSTRACT FROM AUTHOR]

Motor learning involves both explicit and implicit processes that are fundamental for acquiring and adapting complex motor skills. However, stroke may damage the neural substrates underlying explicit and/or implicit learning, leading to deficits in overall motor performance. Although both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke when tested during a task that elicits dissociable contributions from both. Here, we compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning (i.e., strategy-use) using visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. After the first 40 strides of split-belt walking, we removed the visual feedback and instructed individuals to walk comfortably, a manipulation intended to minimize contributions from explicit learning. We used a multirate state-space model to characterize individual explicit and implicit process contributions to overall behavioral change. The computational and behavioral analyses revealed that, compared with controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning, a result that runs counter to prior work testing each process individually during gait. Since poststroke locomotor rehabilitation involves interventions that rely on both explicit and implicit motor learning, future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning. NEW & NOTEWORTHY: Motor learning involves both implicit and explicit processes, the underlying neural substrates of which could be damaged after stroke. Although both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke. Using a locomotor task that elicits dissociable contributions from both processes and computational modeling, we found evidence that chronic stroke causes deficits in both explicit and implicit locomotor learning. [ABSTRACT FROM AUTHOR]

It is estimated that 1 in 36 children are affected by autism spectrum disorder (ASD) in the United States, which is nearly a twofold increase from a decade ago. Recent genetic studies have identified de novo loss‐of‐function (dnLoF) mutations in the Down Syndrome Cell Adhesion Molecule (DSCAM) as a strong risk factor for ASD. Previous research has shown that DSCAM ablation confers social interaction deficits and perseverative behaviors in mouse models. However, it remains unknown to what extent DSCAM underexpression captures the full range of behaviors, specifically cognitive phenotypes, presented in ASD. Here, we conducted a comprehensive cognitive behavioral phenotyping which revealed that loss of one copy of DSCAM, as in the DSCAM2J+/−, that is, DSCAM heterozygous mice, displayed hyperactivity, increased anxiety‐like behavior, and motor coordination deficits. Additionally, hippocampal‐dependent learning and memory was affected, including impairments in working memory, long‐term memory, and contextual fear learning. Interestingly, implicit learning processes remained intact. Therefore, DSCAM LoF produces autistic‐like behaviors that are similar to those observed in human cases of ASD. These findings further support a role for DSCAM dnLoF mutations in ASD and suggest DSCAM2J+/− as a suitable model for ASD research. [ABSTRACT FROM AUTHOR]

This study investigates the cognitive mechanisms underlying vigilance and pattern recognition using a novel adaptation of Mackworth's Clock Test. We aimed to quantify the time it takes for temporal patterns detected unconsciously through implicit learning to surface in the conscious mind within a dynamic vigilance task environment. Forty-eight participants detected random and non-disclosed rhythmic anomalous clock hand movements in this setting. Our results indicate significant variability in detection accuracy, reaction times, and the ability to recognize the hidden pattern among participants. Notably, 23% of all participants and 56% of those who consciously reported the pattern exhibited statistically lower reaction times indicative of knowledge of the pattern 40 s before conscious identification. These findings provide valuable insights into the transition from unconscious to conscious detection, highlighting the complexity of sustained attention and pattern recognition. The study's implications extend to designing training programs and tasks for high-stakes professions requiring prolonged vigilance. Future research should further explore the cognitive and neural correlates of these processes and the impact of task complexity on performance. [ABSTRACT FROM AUTHOR]

Most implicit sentiment sentences in aspect level implicit sentiment analysis lack emotional words, but there are still potential emotional clues. Current research mainly utilizes contextual information and external knowledge of text for semantic enhancement, thereby improving implicit sentiment analysis. However, in these models, the semantic correlation between words decays as the distance between the two increases, leading to weak and difficult to capture emotional cues at long distances, presenting ambiguity in emotional polarity and reducing the performance of sentiment analysis. To solve this problem, in this paper, we propose aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement. Firstly, the semantic wave-based aspect word context information enhancement method uses the fluctuation property of semantic wave to perform weight adaptive enhancement of semantically related long-distance context information. This method essentially utilizes a sine wave function to significantly reduce the weight of words that are close but semantically unrelated, thereby allowing the weight of words that are close and semantically similar to be relatively reflected, in order to solve the problem of hiding emotional clues at long distances. Then, the implicit sentiment analysis method based on supervised contrastive pre-training is used to capture potential sentiment cues from context information and embed them into the sentence vector by weak feature learning of implicit sentiment. Finally, multi-feature interaction-based knowledge enhancement method is used to focus on the context content of sentences that are more related to background knowledge in a feature interaction way, and the information scaling factor is combined to enhance the dependency between aspect words and sentiment cues, thereby solving the problem of weak features in long-distance emotional clues and accurately identifying the emotional polarity of aspect words. The experiment shows that the model proposed in this paper has made significant improvements on the Restaurant and Laptop datasets in SemEval 2014 Task 4, with an accuracy increase of 3.38% and 4.57% in implicit emotions, respectively, achieving the best performance so far. At the same time, generalization and robustness testing experiments were conducted on the model, achieving preferable performance improvement. [ABSTRACT FROM AUTHOR]

It has been proposed that an implicit learning deficit explains the difficulties with grammar commonly observed in children with Developmental Language Disorder (DLD). The present study further investigates this link in two ways. Firstly, we investigate whether kindergartners with DLD have more difficulties with preposition understanding and production as compared to their typically developing peers because they have not (yet) learned to weigh implicit structural information (word order) over more explicit semantic information (noun animacy; Study 1). Secondly, we investigate whether kindergartners with DLD learn to comprehend and produce locative prepositions from an implicit learning context (Study 2). In Study 1 we observed that Dutch kindergartners with DLD (n = 32) made more errors in preposition comprehension (picture-matching task) and preposition production (semi-spontaneous production task) as compared to their typically developing peers (n = 30). We have no evidence that these differences can be explained by a difference in cue weighing or implicit learning ability (serial reaction time task). In Study 2 we observed that the storytelling context led to an increase in preposition production in children with DLD, but we found no evidence that it also led to a better understanding and more semantically accurate productions of the prepositions. We conclude that Dutch kindergartners with DLD have difficulties with locative prepositions but cannot conclude that differences in cue-weighing or implicit learning play a role herein. [ABSTRACT FROM AUTHOR]

In unsupervised domain adaptation (UDA), knowledge is transferred from label-rich source domains to relevant but unlabeled target domains. Current most popular state-of-the-art works suggest that performing domain alignment from the class perspective can alleviate domain shift. However, most of them based on domain adversarial which is hard to train and converge. In this paper, we propose a novel contrastive learning to improve diversity and discriminability for domain adaptation, dubbed as IDD_ICL, which improve the discriminativeness of the model while increasing the sample diversity. To be precise, we first design a novel implicits contrastive learning loss at sample-level by implicit augment sample of the source domain. While augmenting the diversity of the source domain, we can cluster the samples of the same category in the source domain together, and disperse the samples of different categories, thereby improving the discriminative ability of the model. Furthermore, we show that our algorithm is effective by implicitly learning an infinite number of similar samples. Our results demonstrate that our method doesn't require complex technologies or specialized equipment, making it readily adoptable and applicable in practical scenarios. [ABSTRACT FROM AUTHOR]

Light field salient object detection (LF SOD) has recently received increasing attention. However, most current works typically rely on an individual focal stack backbone for feature extraction. This manner ignores the characteristic of blurred saliency-related regions and contour within focal slices, resulting in insufficient or even inaccurate saliency responses. Aiming at addressing this issue, we rethink the feature mining (i.e., exploration) within focal slices and focus on exploiting informative focal slice features and fully leveraging contour information for accurate LF SOD. First, we observe that the geometric relation between different regions within the focal slices is conducive to useful saliency feature mining if utilized properly. In light of this, we propose an implicit graph learning (IGL) approach. The IGL constructs graph structures to propagate informative geometric relations within the focal slices and all-focus features, and promotes crucial and discriminative focal stack feature mining via graph feature distillation. Second, unlike previous works that rarely utilize contour information, we propose a reciprocal refinement fusion (RRF) strategy. This strategy encourages saliency features and object contour cues to effectively complement each other. Furthermore, a contour hint injection mechanism is introduced to refine the feature expressions. Extensive experiments showcase the superiority of our approach over previous state-of-the-art models with an efficient real-time inference speed. Codes are available at https://github.com/gbliao/IRNet and https://openi.pcl.ac.cn/OpenVision/IRNet. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Human motor learning involves cognitive strategies in addition to implicit adaptation. Differences in systems‐level neurophysiology between strategy‐based and implicit learning remain poorly understood. We asked how the P3 event‐related potential, an electroencephalography signal known to increase during early motor learning, relates to strategy‐based learning and implicit adaptation. We re‐analysed data from two experiments, in which participants (n = 64) reached towards a visual target, with online visual feedback replacing vision of their moving hand. We induced learning by rotating the visual feedback. In the first experiment, feedback rotations were turned on during pairs of two consecutive trials, interspersed between non‐rotated trials. In one condition, feedback was rotated relative to the actual movement, allowing participants to develop a re‐aiming strategy on the second trial of each pair, while it was rotated relative to the target in the other condition, rendering re‐aiming futile. P3 amplitude increased in the first rotated trial in both conditions, but this increase was more pronounced in the re‐aiming condition. In the second experiment, a constant visuomotor rotation was turned on for many consecutive trials. We instructed one group beforehand how to re‐aim successfully, while the other group had to develop a strategy by themselves. P3 amplitude increased during early adaptation only in the latter group. These findings collectively suggest that in the context of motor learning, the P3 ERP is associated with a need to develop, or adjust, a cognitive strategy. [ABSTRACT FROM AUTHOR]

To enhance and sustain movement accuracy, humans make corrections in subsequent trials based on previous errors. Trial-by-trial learning occurs unconsciously and has mostly been studied using reaching movements. Goal-directed projection movements, such as archery, have an inherent delay between releasing an object and observing an outcome (e.g. the arrival position of the object), and this delay may prevent trial-by-trial implicit learning. We aimed to investigate the learning in the projection movement and the impacts of the inherent delay. During the experiment, a joystick was flicked once to transport a cursor from the starting location to a target. To manipulate the length of the delay between the cursor release and outcome observation, the speed of the cursor movement was varied: a fast speed can lead to a short delay. We found trial-by-trial implicit learning under all speed conditions, and the error sensitivity was not significantly different across speed conditions. Furthermore, the error sensitivity depended on the target location, that is, the movement direction. The results indicate that trial-by-trial implicit learning occurred in goal-directed projection movement, despite the length of the inherent delay. Additionally, the degree of this learning was affected by the movement direction. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to compare the effects of explicit learning with implicit learning using a dual-task paradigm on learning a novel skill and whether the performance was maintained over an extended period. Methods: Forty-four young children from a local gymnastics club (n = 44, boys n = 10, girls n = 34, age: 10 ± 2.9 years) completed four 30 min front-flip practice sessions over four weeks between pre- and post-test, followed by two retention tests three and six months after the post-test, in which no front-flip practice occurred. Results: Comparable improvements were found beyond baseline performance for both learning conditions over the six-month hiatus. While both groups increased performance at the post-test, neither explicit nor implicit learning groups were able to maintain the achieved performance level into six months of retention. In particular, the explicit group showed a more marked decrease than the implicit group after three months, which was probably caused by the decay of their reliance on the retrieval of declarative knowledge from working memory. Conclusions: The current findings highlight the importance of conducting retention tests over an extended period to monitor performance development after the acquisition of a novel task by different learning methods, as they can differ over time. [ABSTRACT FROM AUTHOR]

To date, extensive work has been devoted to incorporating computational thinking in K‐12 education. Recognizing students' computational thinking stages in game‐based learning environments is essential to capture unproductive learning and provide appropriate scaffolding. However, few reliable and valid computational thinking measures have been developed, especially in games, where computational knowledge acquisition and computational skill construction are implicit. This study introduced an innovative approach to explore students' implicit computational thinking through various explicit factors in game‐based learning, with a specific focus on Zoombinis, a logical puzzle‐based game designed to enhance students' computational thinking skills. Our results showed that factors such as duration, accuracy, number of actions and puzzle difficulty were significantly related to students' computational thinking stages, while gender and grade level were not. Besides, findings indicated gameplay performance has the potential to reveal students' computational thinking stages and skills. Effective performance (shorter duration, fewer actions and higher accuracy) indicated practical problem‐solving strategies and systematic computational thinking stages (eg, Algorithm Design). This work helps simplify the process of implicit computational thinking assessment in games by observing the explicit factors and gameplay performance. These insights will serve to enhance the application of gamification in K‐12 computational thinking education, offering a more efficient method to understanding and fostering students' computational thinking skills. Practitioner notesWhat is already known about this topic Game‐based learning is a pedagogical framework for developing computational thinking in K‐12 education.Computational thinking assessment in games faces difficulties because students' knowledge acquisition and skill construction are implicit.Qualitative methods have widely been used to measure students' computational thinking skills in game‐based learning environments.What this paper adds Categorize students' computational thinking experiences into distinct stages and analyse recurrent patterns employed at each stage through sequential analysis. This approach serves as inspiration for advancing the assessment of stage‐based implicit learning with machine learning methods.Gameplay performance and puzzle difficulty significantly relate to students' computational thinking skills. Researchers and instructors can assess students' implicit computational thinking by observing their real‐time gameplay actions.High‐performing students can develop practical problem‐solving strategies and exhibit systematic computational thinking stages, while low‐performing students may need appropriate interventions to enhance their computational thinking practices.Implications for practice and/or policy Introduce a practical method with the potential for generalization across various game‐based learning to better understand learning processes by analysing significant correlations between certain gameplay variables and implicit learning stages.Allow unproductive learning detection and timely intervention by modelling the reflection of gameplay variables in students' implicit learning processes, helping improve knowledge mastery and skill construction in games.Further investigations on the causal relationship between gameplay performance and implicit learning skills, with careful consideration of more performance factors, are expected. [ABSTRACT FROM AUTHOR]

In the Serial Reaction Time Task, participants respond to several stimuli usually being unaware that the stimuli follow a predefined sequence while still learning the sequence. In the present study, we aimed to clearly separate explicit intentional learning from implicit incidental learning by either informing participants about all details of the sequence or not informing participants about the existence of the sequence. Further, we explored the influence of anticipatory cues during practice while anticipatory cues were either presented (extrinsically triggered anticipation) or not presented (self-reliant intrinsic anticipation). Participants were tested before and after practice in the Practice Sequence and a Control Sequence. To test automatization, tests were performed in Single-Task and Dual-Task Blocks. Results showed that after learning with explicit instructions, participants memorized the sequence more deeply and executed the sequence faster than after learning without explicit instructions. Further, by learning with anticipatory cues, participants memorized the sequence less deeply and executed the sequence slower than by learning without anticipatory cues. Unexpectedly, automatization was sequence-unspecific and independent of the practice conditions. In conclusion, detailed explicit prior information about the sequence facilitates sequence learning while anticipatory online cues during practice hamper sequence learning. [ABSTRACT FROM AUTHOR]

Visual search usually improves with repeated exposure to a search display. Previous research suggests that such a "contextual cueing" effect may be supported even by aspects of the search display that participants have been explicitly asked to ignore. Based on this evidence, it has been suggested that the development of contextual cueing over trials does not depend on selective attention. In the present series of experiments, we show that the most common strategy used to prevent participants from paying attention to task-irrelevant distractors often results in suboptimal selection. Specifically, we show that visual search is slower when search displays include many irrelevant distractors. Eye-tracking data show that this happens, at least in part, because participants fixate on them. These results cast doubts on previous demonstrations that contextual cueing is independent of selective attention. Public Significance Statement: Can we learn about stimuli we are trying to ignore? Previous research suggests that, occasionally, people learn statistical relations between visual stimuli they have been instructed to ignore, even if paying attention to them is counterproductive. This is consistent with the common assumption that some types of learning are relatively automatic and require no attention. However, can we be sure that people actually ignore stimuli just because they are instructed to do so? In the present study, we show that participants do pay some attention to task-irrelevant stimuli, putting a question mark on previous studies concluding that learning does not require attention. [ABSTRACT FROM AUTHOR]

Collocations (e.g., target audience, highly controversial, abject poverty, abysmal failure, extenuating circumstances, obviate the need) have continued to attract a lot of attention from corpus linguists, applied linguists, and second language acquisition (SLA) researchers, due to their ubiquitousness in academic English. Furthermore, the effective use of collocations has been perceived to be of paramount importance to English as a second (ESL) and English as a foreign language (EFL) learners' language proficiency, especially as evidenced from the empirical studies that have emerged copiously during the last three decades or so. However, mastering collocations is considered as one of the most formidable aspects of vocabulary acquisition, especially for adult ESL/EFL learners. Therefore, in the general domain of vocabulary learning and teaching, the issue of 'how to address collocations' has remained a point of contention among researchers and pedagogically-oriented scholars, not to mention its status as a persistent professional concern for many English language teaching (ELT) teachers, particularly in EFL contexts. The most perennial question raised in this regard has been the extent to which L2 learners will learn collocations in an implicit laissez-faire atmosphere of enriched surrounding contexts -- i.e., incidental learning -- than in an instructed sequence of direct attention to them -- i.e., explicit instruction. In this narrative overview, I provide theoretical, linguistic, and pedagogical evidence which buttress several arguments for the explicit teaching of collocations in EFL contexts. These arguments are used as evidence against the claims made by Reynolds (2019, 2022) against teaching collocations. [ABSTRACT FROM AUTHOR]

This paper explores the reliability of moral intuitions through the lens of utilitarian ethics, examining both their biological and cultural underpinnings. It argues that while human moral intuitions have a biological basis that aligns with utilitarian principles, these intuitions are significantly influenced and often altered by cultural and societal norms. The first section discusses the biological foundations of moral intuitions, highlighting research on genetic predispositions and neurological factors that support fairness and cooperation as fundamental moral principles. The second section examines how socialization and cultural exposure shape and sometimes distort these intuitions, leading to ethical practices that may diverge from utilitarian standards. Case studies, including the practice of ritual cannibalism, illustrate the conflict between innate moral responses and culturally derived values. The paper concludes that while biological intuitions provide a fundamental ethical framework, the influence of cultural norms necessitates a critical evaluation of moral beliefs to ensure alignment with utilitarian ethics. This underscores the importance of not solely relying on intuitions shaped by cultural contexts but rather assessing them against objective ethical standards. [ABSTRACT FROM AUTHOR]

Dataflow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically typed programming languages like Python present implicit dataflow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit dataflow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs' in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit dataflow problems. To further enhance the accuracy of LLMs, we design a five-step chain of thought (CoT) and break it down into an Artificial Intelligence (AI) chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach's performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit dataflow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve. [ABSTRACT FROM AUTHOR]

Abstract To enhance and sustain movement accuracy, humans make corrections in subsequent trials based on previous errors. Trial-by-trial learning occurs unconsciously and has mostly been studied using reaching movements. Goal-directed projection movements, such as archery, have an inherent delay between releasing an object and observing an outcome (e.g. the arrival position of the object), and this delay may prevent trial-by-trial implicit learning. We aimed to investigate the learning in the projection movement and the impacts of the inherent delay. During the experiment, a joystick was flicked once to transport a cursor from the starting location to a target. To manipulate the length of the delay between the cursor release and outcome observation, the speed of the cursor movement was varied: a fast speed can lead to a short delay. We found trial-by-trial implicit learning under all speed conditions, and the error sensitivity was not significantly different across speed conditions. Furthermore, the error sensitivity depended on the target location, that is, the movement direction. The results indicate that trial-by-trial implicit learning occurred in goal-directed projection movement, despite the length of the inherent delay. Additionally, the degree of this learning was affected by the movement direction.

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induces implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Visual errors induced by movement drive implicit corrections of that movement. When similar errors are experienced consecutively, does sensitivity to the error remain consistent each time? This study aimed to investigate the modulation of implicit error sensitivity through continuous exposure to the same errors. In the reaching task using visual error-clamp feedback, participants were presented with the same error in direction and magnitude for four consecutive trials. We found that implicit error sensitivity decreased after exposure to the second error. These results indicate that when visual errors occur consecutively, the sensorimotor system exhibits different responses, even for identical errors. The continuity of errors may be a factor that modulates error sensitivity.

What is the relationship between literacy skills and implicit learning? To address previous mixed findings, we compared school‐aged readers, typical (CON, n = 54) and with dyslexia (DYS, n = 53), in relation to their performance on a serial reaction time task. For the first time, we also included an isolated spelling deficit group (ISD, n = 30) to control for distinctive effects of reading and spelling deficits. A linear reaction times analysis did not reveal between‐group differences in implicit learning. However, further examination revealed that most CON (65%) and ISD (63%) were implicit learners, whereas most DYS were nonlearners (64%). Brain activity showed differences in early learning phases: CON learners and DYS nonlearners activated the left inferior frontal gyrus (IFG) and left insula more than other groups. Our findings imply that implicit learning is more frequently disrupted in children with dyslexia than in typical readers, and that activation of the left IFG and insula contributes to effective learning in the latter group but it does not in the former. A one‐page Accessible Summary of this article in nontechnical language is freely available in the Supporting Information online and at https://oasis‐database.org [ABSTRACT FROM AUTHOR]

In the wave of research on autonomous driving, 3D object detection from the Bird's Eye View (BEV) perspective has emerged as a pivotal area of focus. The essence of this challenge is the effective fusion of camera and LiDAR data into the BEV. Current approaches predominantly train and predict within the front view and Cartesian coordinate system, often overlooking the inherent structural and operational differences between cameras and LiDAR sensors. This paper introduces CL-FusionBEV, an innovative 3D object detection methodology tailored for sensor data fusion in the BEV perspective. Our approach initiates with a view transformation, facilitated by an implicit learning module that transitions the camera's perspective to the BEV space, thereby aligning the prediction module. Subsequently, to achieve modal fusion within the BEV framework, we employ voxelization to convert the LiDAR point cloud into BEV space, thereby generating LiDAR BEV spatial features. Moreover, to integrate the BEV spatial features from both camera and LiDAR, we have developed a multi-modal cross-attention mechanism and an implicit multi-modal fusion network, designed to enhance the synergy and application of dual-modal data. To counteract potential deficiencies in global reasoning and feature interaction arising from multi-modal cross-attention, we propose a BEV self-attention mechanism that facilitates comprehensive global feature operations. Our methodology has undergone rigorous evaluation on a substantial dataset within the autonomous driving domain, the nuScenes dataset. The outcomes demonstrate that our method achieves a mean Average Precision (mAP) of 73.3% and a nuScenes Detection Score (NDS) of 75.5%, particularly excelling in the detection of cars and pedestrians with high accuracies of 89% and 90.7%, respectively. Additionally, CL-FusionBEV exhibits superior performance in identifying occluded and distant objects, surpassing existing comparative methods. [ABSTRACT FROM AUTHOR]

This study investigates the cognitive mechanisms underlying vigilance and pattern recognition using a novel adaptation of Mackworth’s Clock Test. We aimed to quantify the time it takes for temporal patterns detected unconsciously through implicit learning to surface in the conscious mind within a dynamic vigilance task environment. Forty-eight participants detected random and non-disclosed rhythmic anomalous clock hand movements in this setting. Our results indicate significant variability in detection accuracy, reaction times, and the ability to recognize the hidden pattern among participants. Notably, 23% of all participants and 56% of those who consciously reported the pattern exhibited statistically lower reaction times indicative of knowledge of the pattern 40 s before conscious identification. These findings provide valuable insights into the transition from unconscious to conscious detection, highlighting the complexity of sustained attention and pattern recognition. The study’s implications extend to designing training programs and tasks for high-stakes professions requiring prolonged vigilance. Future research should further explore the cognitive and neural correlates of these processes and the impact of task complexity on performance.

Abstract Dual-task performance holds significant relevance in real-world scenarios. Implicit learning is a possible approach for improving dual-task performance. Analogy learning, utilizing a single metaphor to convey essential information about motor skills, has emerged as a practical method for fostering implicit learning. However, evidence supporting the effect of implicit learning on gait-cognitive dual-task performance is insufficient. This exploratory study aimed to examine the effects of implicit and explicit learning on dual-task performance in both gait and cognitive tasks. Tandem gait was employed on a treadmill to assess motor function, whereas serial seven subtraction tasks were used to gauge cognitive performance. Thirty healthy community-dwelling older individuals were randomly assigned to implicit or explicit learning groups. Each group learned the tandem gait task according to their individual learning styles. The implicit learning group showed a significant improvement in gait performance under the dual-task condition compared with the explicit learning group. Furthermore, the implicit learning group exhibited improved dual-task interference for both tasks. Our findings suggest that implicit learning may offer greater advantages than explicit learning in acquiring autonomous motor skills. Future research is needed to uncover the mechanisms underlying implicit learning and to harness its potential for gait-cognitive dual-task performance in clinical settings.

Background. A particularly wide choice of approaches and methods are characteristic of modern teaching practice. The growing popularity of implicit teaching techniques based on the concept of learning without awareness of what has been learned, is a bright example of this kind of approaches. Implicit teaching stipulates a more conscious awareness of learners’ characteristics and teaching process conditions which might significantly affect the effectiveness of the approach application. Objectives. The aim is to identify characteristics of learners, namely, Russian-speaking students of a non-linguistic faculty, which proved to be significant for successful acquisition of specific grammar material when implicit and explicit approaches were used. The focus is on the pedagogical tools contributing to the effectiveness of the approaches. Methods. The article describes the experience of teaching syntactic structures fronting (structures starting with something else but the grammatical subject) to Russian-speaking students using implicit and explicit approaches. The effectiveness of the material acquisition was evaluated on the basis of two control tests. To determine students’ characteristics which proved to be essential for the effectiveness of the approaches, as well as the useful pedagogical techniques the methods of survey, experiment, comparison and analysis of the data obtained were used. Results. The principles of leading international concepts concerning the use of implicit and explicit approaches in teaching a second language are considered. Educational materials aimed at students’ implicit acquisition of the targeted grammatical structures have been developed. Texts have been selected and adapted; exercises have been constructed to develop the understanding of the texts. Educational materials have been compiled for the explicit introduction of grammar. A survey was developed and conducted to identify factors that influenced the effectiveness of educational materials. Conclusions. As a result of the research, it was found that the effectiveness of the implicit approach is limited by such students’ characteristics as a high degree of explicit instruction expectation, trust in this approach, and the habit of relying on the rule formulated by the teacher. The combination of the implicit approach with subsequent Guided Discovery strategies and noticing techniques has shown high efficiency.

Data-driven fault diagnosis, identifying abnormality causes using collected industrial data, is one of the challenging tasks for intelligent industry safety management. It is worth noting that practical industrial data are usually related to a mixture of several physical attributes, such as the operating environment, product quality and working conditions. However, the traditional models may not be sufficient to leverage the coherent information for diagnostic performance enhancement, due to their shallow architecture. This paper presents a hierarchical matrix factorization (HMF) that relies on a succession of matrix factoring to find an efficient representation of industrial data for fault diagnosis. Specifically, HMF consecutively decomposes data into several hierarchies. The intermediate hierarchies play the role of analysis operators which automatically learn implicit characteristics of industrial data; the final hierarchy outputs high-level and discriminative features. Furthermore, HMF is also extended in a nonlinear manner by introducing activation functions, referred as NHMF, to deal with nonlinearities in practical industrial processes. The applications of HMF and NHMF to fault diagnosis are evaluated by the multiple-phase flow process. The experimental results show that our models achieve competitive performance against the considered shallow and deep models, consuming less computing time than deep models. [ABSTRACT FROM AUTHOR]

Background: Intellectual development disorder (IDD) is a challenging disorder that professionals, most notably teachers, are confronted with daily. Supporting oral language in the classroom is a complex challenge as teachers are expected to implement effective methods to support students' language. However, they face a persistent scarcity of scientific evidence to draw upon in their practice. Aims: This research provides a first overview of instructional methods and their effectiveness in this population. Method: Our search was conducted through various databases, generating a total of 931 articles. Data from the 14 selected studies were analyzed. Outcomes and results: The results reveal limited, heterogenous, and hardly comparable studies in terms of their characteristics (samples, language area, strategies, and effectiveness measures) suggesting a low level of evidence regarding the effectiveness of these practices. Conclusion and implications: We discuss the implications of these findings in light of the different factors contributing to the interventions' effectiveness, as well as considering the issue of transfer and generalization of acquired skills in IDD. We also reveal the urgent need for developing methods addressing cognitive load limitations such as implicit learning approaches, to enhances effectiveness and compensate the gap of available resources tailored to improve oral language outcomes in the IDD population. [ABSTRACT FROM AUTHOR]

Dual-task performance holds significant relevance in real-world scenarios. Implicit learning is a possible approach for improving dual-task performance. Analogy learning, utilizing a single metaphor to convey essential information about motor skills, has emerged as a practical method for fostering implicit learning. However, evidence supporting the effect of implicit learning on gait-cognitive dual-task performance is insufficient. This exploratory study aimed to examine the effects of implicit and explicit learning on dual-task performance in both gait and cognitive tasks. Tandem gait was employed on a treadmill to assess motor function, whereas serial seven subtraction tasks were used to gauge cognitive performance. Thirty healthy community-dwelling older individuals were randomly assigned to implicit or explicit learning groups. Each group learned the tandem gait task according to their individual learning styles. The implicit learning group showed a significant improvement in gait performance under the dual-task condition compared with the explicit learning group. Furthermore, the implicit learning group exhibited improved dual-task interference for both tasks. Our findings suggest that implicit learning may offer greater advantages than explicit learning in acquiring autonomous motor skills. Future research is needed to uncover the mechanisms underlying implicit learning and to harness its potential for gait-cognitive dual-task performance in clinical settings. [ABSTRACT FROM AUTHOR]

Reducing the radiation dose leads to the x-ray computed tomography (CT) images suffering from heavy noise and artifacts, which inevitably interferes with the subsequent clinic diagnostic and analysis. Leading works have explored diffusion models for low-dose CT imaging to avoid the structure degeneration and blurring effects of previous deep denoising models. However, most of them always begin their generative processes with Gaussian noise, which has little or no structure priors of the clean data distribution, thereby leading to long-time inference and unpleasant reconstruction quality. To alleviate these problems, this paper presents a Structure-Aware Diffusion model (SAD), an end-to-end self-guided learning framework for high-fidelity CT image reconstruction. First, SAD builds a nonlinear diffusion bridge between clean and degraded data distributions, which could directly learn the implicit physical degradation prior from observed measurements. Second, SAD integrates the prompt learning mechanism and implicit neural representation into the diffusion process, where rich and diverse structure representations extracted by degraded inputs are exploited as prompts, which provides global and local structure priors, to guide CT image reconstruction. Finally, we devise an efficient self-guided diffusion architecture using an iterative updated strategy, which further refines structural prompts during each generative step to drive finer image reconstruction. Extensive experiments on AAPM-Mayo and LoDoPaB-CT datasets demonstrate that our SAD could achieve superior performance in terms of noise removal, structure preservation, and blind-dose generalization, with few generative steps, even one step only. [ABSTRACT FROM AUTHOR]

Over the last decade, the additional singleton task has been widely used to study visual statistical learning. In this paradigm, participants are instructed to find a target while ignoring a series of distractors. In some trials, a salient singleton distractor is added to the search display, making the task more difficult. However, if the singleton appears more frequently in one particular location of the display, participants eventually learn to suppress attention towards this location. It has been suggested that this type of learning is probably implicit and independent of working memory (WM) resources. To our knowledge, only one study has explored the impact of WM in suppression effect (Gao & Theeuwes, Psychonomic Bulletin & Review, 27, 96–104, 2020). However, there are reasons to suspect that the amount and type of WM load used in that study may have been suboptimal to detect any effects on distractor suppression. The aim of the present study was to explore the impact of WM load on distractor suppression addressing these issues. Contrary to our expectations, our results confirm that this type of learning is indeed highly resilient even to strong manipulations of WM load. [ABSTRACT FROM AUTHOR]

Electrical Impedance Tomography (EIT) is a widely employed imaging technique in industrial inspection, geophysical prospecting, and medical imaging. However, the inherent nonlinearity and ill-posedness of EIT image reconstruction present challenges for classical regularization techniques, such as the critical selection of regularization terms and the lack of prior knowledge. Deep generative models (DGMs) have been shown to play a crucial role in learning implicit regularizers and prior knowledge. This study aims to investigate the potential of three DGMs – variational autoencoder networks, normalizing flow, and score-based diffusion model – to learn implicit regularizers in learning-based EIT imaging. We first introduce background information on EIT imaging and its inverse problem formulation. Next, we propose three algorithms for performing EIT inverse problems based on corresponding DGMs. Finally, we present numerical and visual experiments, which reveal that (1) no single method consistently outperforms the others across all settings, and (2) when reconstructing an object with two anomalies using a well-trained model based on a training dataset containing four anomalies, the conditional normalizing flow (CNF) model exhibits the best generalization in low-level noise, while the conditional score-based diffusion model (CSD*) demonstrates the best generalization in high-level noise settings. We hope our preliminary efforts will encourage other researchers to assess their DGMs in EIT and other nonlinear inverse problems. [ABSTRACT FROM AUTHOR]

Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action. [ABSTRACT FROM AUTHOR]

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In English-medium instruction (EMI), students learn content knowledge through their additional language. Despite the implicit English learning goals, EMI content subject teachers have been urged to provide more language support for their students. Such expectations may not be something that EMI teachers were professionally prepared for. It is then worth examining how EMI teachers (re)construct their identity when facing such new expectations. Based on Wenger’s [Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press] identity construction framework, this qualitative multiple case study reveals how three EMI teachers in Hong Kong and Mainland China secondary schools negotiated their identity when being supported to incorporate language teaching in their content lessons. From data collected in co-planning meetings, lesson observations and interviews, all the teachers were engaged in constructing the identity of language aware content teachers, but some of them appeared to experience identity conflicts. The contrast of the three cases reveals that teachers’ professional training, school context and curriculum requirement are important in shaping teachers’ professional identity. Teachers’ agency also impacts on how teachers negotiate their identity, especially when facing contextual constraints. These findings underscore the importance of preparing teachers both psychologically and practically in EMI teacher education. [ABSTRACT FROM AUTHOR]

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning-based methods demand huge training data and are difficult to generalize. Here, we propose to conduct Biomedical data compRession with Implicit nEural Function (BRIEF) by representing the target data with compact neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves 2-3 orders of magnitude compression on diverse biomedical data at significantly higher fidelity than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, and supports customized spatially varying fidelity. BRIEF's multifold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate low-bandwidth data sharing and promote collaboration and progress in the biomedical field. [ABSTRACT FROM AUTHOR]

A college of education established a research-based framework, Principled Innovation (PI), to support education professionals to consider the role character plays when innovating. Three instructors who authored this study integrated the four assets and eight practices of the PI framework into their courses and wanted to support one another in identifying bright spots for how PI was used and expanding upon them. They used a protocol they created, the Appreciative Tuning Protocol (ATP), to help one another expand on their original efforts. This study involved a triangulated, qualitative review of the three ATP meeting transcripts, natural artifacts from the courses, and their reflections. Findings revealed that all three instructors were successful in integrating PI, that all three instructors decided to integrate PI implicitly by applying the PI assets and practices in activities, and that PI supported the course learning objectives in all courses. This study has implications for other instructors who wish to utilize PI in their courses. Research is needed to determine how PI can be integrated into other higher education topics and whether the addition of PI can impact student achievement. [ABSTRACT FROM AUTHOR]

The unpredictable nature of our world can introduce a variety of errors in our actions, including sensory prediction errors (SPEs) and task performance errors (TPEs). SPEs arise when our existing internal models of limb-environment properties and interactions become miscalibrated due to changes in the environment, while TPEs occur when environmental perturbations hinder achievement of task goals. The precise mechanisms employed by the sensorimotor system to learn from such limb- and task-related errors and improve future performance are not comprehensively understood. To gain insight into these mechanisms, we performed a series of learning experiments wherein the location and size of a reach target were varied, the visual feedback of the motion was perturbed in different ways, and instructions were carefully manipulated. Our findings indicate that the mechanisms employed to compensate SPEs and TPEs are dissociable. Specifically, our results fail to support theories that suggest that TPEs trigger implicit refinement of reach plans or that their occurrence automatically modulates SPE-mediated learning. Rather, TPEs drive improved action selection, that is, the selection of verbally sensitive, volitional strategies that reduce future errors. Moreover, we find that exposure to SPEs is necessary and sufficient to trigger implicit recalibration. When SPE-mediated implicit learning and TPE-driven improved action selection combine, performance gains are larger. However, when actions are always successful and strategies are not employed, refinement in behavior is smaller. Flexibly weighting strategic action selection and implicit recalibration could thus be a way of controlling how much, and how quickly, we learn from errors. Humans learn from errors in their movements. This study shows that learning mechanisms triggered by limb-related sensory prediction errors and task-related performance errors are dissociable. While prediction errors drive implicit updates to motor plans, performance errors trigger deliberative selection of error-reducing actions. [ABSTRACT FROM AUTHOR]

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating one’s effector, supported by Bayesian cue integration, underpins the sensorimotor system’s implicit adaptation. [ABSTRACT FROM AUTHOR]