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statistical learning of orthography-semantics regularities: A developmental neurophysiological study

DEVELOPMENTAL COGNITIVE NEUROSCIENCE 2025年 74卷 101576页

作者： Duan, Rujun Zhao, Wenyuan Tong, Shelley Xiuli Tong, Xiuhong Educ Univ Hong Kong Dept Psychol 10 Lo Ping Rd Hong Kong Peoples R China Univ Hong Kong Fac Educ Human Commun Dev & Informat Sci Hong Kong Peoples R China

Combining event-related potential (ERP) with an artificial orthography learning paradigm, this study examined how 7-11-year-old Chinese children from first to fifth grade implicitly acquired orthography-semantics regularities of artificial characters. We manipulated the semantic consistency levels of radicals embedded in artificial characters at high (100 %), moderate (80 %), and low (60 %). The behavioral results indicated that 8-year-olds in the second grade were able to acquire regularities of semantic radicals within a sequence of artificial characters. The ERP results revealed a N170-P300-N400 pattern. The N170 component in the occipitotemporal area indicated orthographic identification during the early stages of learning, while the P300 and N400 components reflected attentional allocation and semantic activation during the later stages of statistical learning of orthography-semantics connections. The interactions between neural activation of statistical learning and grade suggest a significant developmental change in 7-11-year-old children. These findings support the multicomponent model of statistical learning and the age-related hypothesis.

关键词： statistical learning Orthography-semantics connections Developmental variance N170-P300-N400 pattern

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statistical learning of artificial orthographic regularity arises from coordinated activity across distinct brain regions

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NEUROSCIENCE 2025年 568卷 253-264页

作者： Tong, Xiuhong Lv, Yating Wang, Tiantian Duan, Rujun Tong, Shelley Xiuli Educ Univ Hong Kong Dept Psychol Hong Kong Peoples R China Hangzhou Normal Univ Affiliated Hosp Ctr Cognit & Brain Disorders Hangzhou Zhejiang Peoples R China Univ Hong Kong Fac Educ Human Commun Learning & Dev Hong Kong Peoples R China

The human brain possesses the ability to automatically extract statistical regularities from environmental inputs, including visual-graphic symbols and printed units. However, the specific brain regions underlying the statistical learning of these visual-graphic symbols or artificial orthography remain unclear. This study utilized functional magnetic resonance imaging (fMRI) with an artificial orthography learning paradigm to measure brain activities associated with the statistical learning of radical positional regularities embedded in pseudocharacters containing high (100%), moderate (80%), and low (60%) levels of consistency, along with a series of random abstract figures. Thirty adults passively viewed a continuous stream of these pseudocharacters. fMRI data revealed that the left occipital area and the visual word form area (VWFA) exhibited greater responses at the low consistency level than at the high and moderate levels, suggesting implicit statistical learning of positional regularities. Functional connectivity analysis further revealed significant correlations between the occipital lobe, the VWFA, and other brain regions, such as the middle temporal gyrus (MTG), the superior occipital gyrus (SOG), and the cerebellum. Moreover, neural activity showed a tendency to correlate with behavioral recognition performance. These findings demonstrate that the incidental acquisition of statistical regularities in artificial orthography arises from the coordinated activation of multiple distinct neural circuits.

关键词： statistical learning Imaging study Orthographic regularity VWFA Occipital area

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statistical learning and optimization of the helical milling of the biocompatible titanium Ti-6Al-7Nb alloy

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期125卷 1789-1813页

作者： da Costa, Tomas Barbosa Dutra Pereira, Robson Bruno Lauro, Carlos Henrique Brandao, Lincoln Cardoso Davim, J. Paulo Univ Fed Sao Joao del Rei Ctr Innovat Sustainable Mfg CIMS Dept Mech & Ind Engn 170 Frei Orlando Sq BR-36880000 Sao Joao Del Rei MG Brazil Univ Fed Sao Joao del Rei Ctr Innovat Modelling & Optimizat Syst CIMOS Dept Mech & Ind Engn 170 Frei Orlando Sq BR-36880000 Sao Joao Del Rei MG Brazil Univ Aveiro Dept Mech Engn Aveiro Portugal

Helical milling has been applied for hole-making in titanium alloys, especially in the Ti-6Al-4V alloy, considering the aims of the aeronautic, automobile, and other sectors. When considering hole-making in Ti-alloys for biomedical applications, few studies have been carried out. Besides, intelligent approaches for modeling and optimization of this process in these special alloys are demanded to achieve the best results in terms of hole surface quality, and productivity. This work presents an approach for modeling and optimization of helical milling for hole-making of Ti-6Al-7Nb biocompatible titanium alloy. The surface roughness of the holes was measured to quantify the hole quality. Principal component analysis was performed for dimensionality reduction of the roughness outputs. For modeling, a learning procedure was proposed considering polynomial response surface regression, tree-based methods, and support vector regression. Cross-validation is used for learning and model selection. The results pointed out that the support vector regression model was the best one. Multi-objective evolutionary optimization was performed considering the support vector regression model and the deterministic model of the material removal rate. The Pareto set and the Pareto frontier were plotted and discussed concerning practical aspects of the helical milling process. The proposed learning and optimization approach enabled the achievement of the best results of the helical milling in the biocompatible Ti-6Al-7Nb alloy and can be applied to other intelligent manufacturing applications.

关键词： Helical milling Biocompatible titanium alloy statistical learning Machine learning Support vector regression Multi-objective evolutionary optimization

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statistical learning-Based Adaptive Network Access for the Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2023年第14期10卷 12219-12233页

作者： Raza, Muhammad Ahmad Abolhasan, Mehran Lipman, Justin Shariati, Negin Ni, Wei Jamalipour, Abbas Univ Technol Sydney Fac Engn & Informat Technol RF & Commun Technol Res Lab Ultimo NSW 2007 Australia Commonwealth Sci & Ind Res Org Data61 Sydney NSW 2122 Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Industrial Internet of Things (IIoT) applications generate data in varying amounts with diverse quality of service requirements. The adaptive network access approach and distributed resource management in IIoT networks can reduce the communication overheads caused by centralized resource management approaches. In this regard, statistical learning is a promising tool for addressing decision-making problems in a dynamic environment. This article considers uplink dominant IIoT networks in which massive devices generate delay-sensitive and delay-tolerant data and communicate over shared radio resources. We propose a novel grant-free access scheme using a statistical learning approach that enables IIoT entities to perform delay-sensitive and delay-tolerant transmissions over dynamically partitioned resources in a prioritized manner. In order to improve utilization of available radio resources, we design an adaptive network access mechanism operating in a semi-distributed manner. This mechanism enables end devices to use their transmission history to choose between static and dynamic resource allocation-based grant-free schemes in a dynamic environment. Simulation results show that average latency and resource utilization vary in grant-free access schemes employing static and dynamic resource allocations. Thus, compared to a single transmission scheme, the proposed adaptive network access offers better channel utilization while meeting the application-specific latency bound in IIoT networks.

关键词： Index Terms-Adaptive networks grant-free access Industrial Internet of Things (IIoT) statistical learning

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statistical learning from Brazilian fake news

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EXPERT SYSTEMS 2023年第3期40卷 e13171-e13171页

作者： Lima, Gabriel B. Chaves, Thiago de M. Freitas, Wanessa W. L. de Souza, Renata M. C. R. Univ Fed Pernambuco Ctr Informat Recife PE Brazil

Fake news is information that does not represent reality but is commonly shared on the internet as if it were true, mainly because of its dramatic, appealing, and controversial content. Therefore, a relevant issue is to find characteristics that can assist in identifying Fake News, mainly nowadays, where an increasing number of fake news is spread all over the internet every day. This work aims to extract knowledge from Brazilian fake news data based on statistical learning. Initially, an exploratory data analysis is performed for the available variables to extract insights from the differences between fake and true news. Then, the prediction and modelling are carried out. The learning phase aims to build a model and measure the features that best explain the behaviour of misleading texts, which leads to a parsimonious model. Finally, the test phase estimates the fitted model accuracy based on 10-fold cross-validation in the Monte Carlo framework. The results show that four variables are significant to explain fake news. Moreover, our model achieved comparable results with state-of-the-art, 0.941 F-measure, for a single classifier while having the advantage of being a parsimonious model. This work's details and code can be found at .

关键词： exploratory data analysis fake news logistic regression statistical learning

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statistical learning of target and distractor spatial probability shape a common attentional priority computation

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CORTEX 2023年 169卷 95-117页

作者： Ferrante, Oscar Chelazzi, Leonardo Santandrea, Elisa Univ Verona Dept Neurosci Biomed & Movement Sci Str Grazie 8 I-37134 Verona Italy Natl Inst Neurosci Verona Unit Verona Italy Univ Birmingham Ctr Human Brain Hlth Sch Psychol Birmingham W Midlands England

Converging evidence recently put forward the notion that dedicated neurocognitive mechanisms do exist for the suppression of salient, but irrelevant distractors. Along this line, it is plausible to hypothesize that, in appropriate contexts, experience-dependent forms of attentional learning might selectively induce plastic changes within this dedi-cated circuitry, thus allowing an independent shaping of priorities at the service of attentional filtering. Conversely, previous work suggested that statistical learning (SL) of both target and distractor spatial probability distributions converge in adjusting only the overall attentional priority of locations: in fact, in the presence of an independent manip-ulation, either related to the target or to the distractor only, SL induces indirect effects (e.g., changes in filtering efficiency due to an uneven distribution of targets), suggesting that SL -induced plastic changes affect a shared neural substrate. Here we tested whether, when (conflicting) target-and distractor-related manipulations are concurrently applied to the very same locations, dedicated mechanisms might support the selective encoding of spatial priority in relation to the specific attentional operation involved. In three related experiments, human healthy participants discriminated the direction of a target arrow, while ignoring a salient distractor, if present;both target and distractor spatial probability distributions were concurrently manipulated in relation to each single location. Critically, the selection bias produced by the target-related SL was marginally reduced by an adverse distractor contingency, and the suppression bias generated by the distractor-related SL was erased, or even reversed, by an adverse target contingency. Our results suggest that even conflicting target-and distractor-related SL manipulations result in the adjustment of a unique spatial priority computation, likely because the process directly relies on direct plastic alter

关键词： Visual selective attention Spatial priority maps Target selection Distractor suppression statistical learning

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statistical learning in patients in the minimally conscious state

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CEREBRAL CORTEX 2023年第6期33卷 2507-2516页

作者： Xu, Chuan Li, Hangcheng Gao, Jiaxin Li, Lingling He, Fangping Yu, Jie Ling, Yi Gao, Jian Li, Jingqi Melloni, Lucia Luo, Benyan Ding, Nai Zhejiang Univ Affiliated Hosp 1 Sch Med Dept Neurol Hangzhou 310003 Zhejiang Peoples R China Hangzhou Mingzhou Brain Rehabil Hosp Dept Rehabil Hangzhou 311215 Peoples R China Zhejiang Univ Coll Biomed Engn & Instrument Sci Key Lab Biomed Engn Minist Educ Hangzhou Peoples R China Res Ctr Adv Artificial Intelligence Theory Zhejiang Lab Hangzhou 311121 Peoples R China NYU Comprehens Epilepsy Ctr 223 34th St New York NY 10016 USA

When listening to speech, cortical activity can track mentally constructed linguistic units such as words, phrases, and sentences. Recent studies have also shown that the neural responses to mentally constructed linguistic units can predict the outcome of patients with disorders of consciousness (DoC). In healthy individuals, cortical tracking of linguistic units can be driven by both long-term linguistic knowledge and online learning of the transitional probability between syllables. Here, we investigated whether statistical learning could occur in patients in the minimally conscious state (MCS) and patients emerged from the MCS (EMCS) using electroencephalography (EEG). In Experiment 1, we presented to participants an isochronous sequence of syllables, which were composed of either 4 real disyllabic words or 4 reversed disyllabic words. An inter-trial phase coherence analysis revealed that the patient groups showed similar word tracking responses to real and reversed words. In Experiment 2, we presented trisyllabic artificial words that were defined by the transitional probability between words, and a significant word-rate EEG response was observed for MCS patients. These results suggested that statistical learning can occur with a minimal conscious level. The residual statistical learning ability in MCS patients could potentially be harnessed to induce neural plasticity.

关键词： statistical learning disorders of consciousness stimulus-locked activity

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statistical learning for train delays and influence of winter climate and atmospheric icing

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JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2023年 26卷

作者： Wang, Jianfeng Mantas-Nakhai, Roberto Yu, Jun Umea Univ Dept Math & Math Stat Umea Sweden Lulea Univ Technol Dept Comp Sci Elect & Space Engn Lulea Sweden

This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains. Inhomogeneous Markov chain model and stratified Cox model were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling used covariates weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that temperature, snow depth, ice/snow precipitation, and train operational direction, significantly impacted the arrival delay. Further, by partitioning the train line into three segments as per transition intensity, the model identified that the middle segment had the highest chance of a transfer from punctuality to delay, and the last segment had the lowest probability of recovering from delayed state. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method, which indicated that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. With the model performance, the fitted model could be beneficial for both travellers to plan their trips reasonably and railway operators to design more efficient and wiser train schedules as per weather condition.

关键词： statistical learning Inhomogeneous Markov chain model Stratified Cox model Arrival delay Primary delay Walk-forward validation

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statistical learning of target location guides attention proactively

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PSYCHONOMIC BULLETIN & REVIEW 2025年 1-10页

作者： Golan, Aidai Ramgir, Aniruddha Lamy, Dominique Tel Aviv Univ Sch Psychol Sci Tel Aviv Israel Tel Aviv Univ Sagol Sch Neurosci Tel Aviv Israel Tel Aviv Univ Dept Psychol POB 39040 IL-69978 Tel Aviv Israel

Our perceptual system is highly sensitive to statistical regularities in our environment. In particular, we respond faster to targets that appear in frequently attended locations-a phenomenon known as target-location probability learning (LPL). Is attention proactively aligned with the high-probability target location, or reactively allocated to that location when the learning context is detected? The studies that addressed this question tested whether a spatial attentional bias learned in one task transfers to another. However, they yielded conflicting findings and were open to alternative accounts. We reexamined whether LPL-guided attention is proactively allocated to the high-probability target location while addressing these previous studies' potential caveats, in two experiments. During learning, the search target appeared more often at one location than elsewhere, and during extinction, all search target locations were equiprobable. In both learning and extinction, letter-probe trials were interspersed among the search trials. We found that LPL acquired during search transferred to the letter-probe task during both learning and extinction. Importantly, during extinction, participants continued to prioritize the previously high-probability location on both search and letter-probe trials, even when they were informed after the learning phase that the bias would be discontinued and were asked to start their search at the location indicated by an arrow precue. We conclude that LPL guides attention proactively and inflexibly.

关键词： statistical learning Proactive attentional guidance Task context Selection history Visual search

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statistical learning of spatiotemporal regularities dynamically guides visual attention across space

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ATTENTION PERCEPTION & PSYCHOPHYSICS 2023年第4期85卷 1054-1072页

作者： Xu, Zhenzhen Theeuwes, Jan Los, Sander A. Vrije Univ Amsterdam Dept Expt & Appl Psychol Van der Boechorststr 7 NL-1081 BT Amsterdam Netherlands Inst Brain & Behav Amsterdam iBBA Amsterdam Netherlands

In dynamic environments, statistical learning of spatial and temporal regularities guides visual attention in space and time. In the current study, we explored whether and how combined spatiotemporal regularities regarding target events guide visual attention. In three experiments, participants performed the additional singleton task. They were asked to search for a target stimulus with a unique shape among five non-target distractors and respond to the orientation of a line inside the target. Unbeknownst to the participants, the moment in time that the search display was presented was predictive of the target location. Specifically, the target was more likely to be presented at one high-probability location after a short interval and at another high-probability location after a long interval. The results showed that participants' performance was better for high-probability locations than for low-probability locations. Moreover, visual search efficiency was greater when the target appeared at the high-probability location after its associated interval than when it occurred there after its nonassociated interval, regardless of whether the distribution of intervals was uniform (Experiment 1), exponential (Experiment 2), or anti-exponential (Experiment 3). Taken together, the results indicate that implicitly learned spatiotemporal regularities dynamically guide visual attention towards the probable target location.

关键词： Visual attention Spatial attention Temporal attention Spatiotemporal regularities statistical learning

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