Attentional bias modification training is commonly employed to regulate attentional bias and alleviate symptoms of mental disorders, but its efficacy is constrained and inconsistent. To address this challenge, we introduced an attention switch training method grounded in the rhythmic theory of attention. A total of 135 university students with negative attentional bias were randomly assigned to one of four groups: with/without rhythm-based training × with/without probability-based training. Each participant completed a single training session and underwent the Self-Assessment of Valence Task and the Dot Probe Task (DPT) before and after training. The results indicated that theta rhythm training led to a greater reduction in negative attentional bias, mainly by enhancing the disengagement of attention from negative stimuli. Regression analysis revealed an inverse relationship between initial attentional scores and training effects, with the rhythm-based training showing the strongest correlation. Overall, the rhythm-based training offers a more effective framework for modifying attentional bias with a mechanism of attention shift based on the theta trough.

Introduction Categorization uncertainty occurs when novel stimuli are classified as category members or non-members, with frontal-parietal and insular regions involved in its monitoring, but graded uncertainty in similarity-based category induction-especially implicit processing without explicit confidence judgments-has not been well characterized.Methods The present study investigated these mechanisms using a similarity-based category induction task combined with functional magnetic resonance imaging (fMRI). Participants simultaneously viewed three stimuli: two reference stimuli (S1 and S2) that defined a target category, and a probe stimulus (S3) for which they judged category membership. Levels of categorization uncertainty were manipulated by varying the degree of feature similarity between the probe and reference stimuli.Results The fMRI data revealed that activity in the left fronto-parietal network-including the medial frontal gyrus (BA11), middle frontal gyrus (BA46), cingulate gyrus (BA32), inferior parietal lobule (BA40), and left insula (BA13)-increased systematically with higher categorization uncertainty. Notably, positive activations were observed in the left cingulate gyrus, middle frontal gyrus, and inferior parietal lobule, whereas negative activations were detected in the left medial frontal gyrus and insula. Psychophysiological interaction (PPI) analyses further demonstrated enhanced functional coupling between the left cingulate cortex and inferior parietal lobule under high uncertainty conditions.Discussion These findings suggest that distributed fronto-parietal and insular systems support the processing of graded uncertainty during similarity-based category induction, even in the absence of explicit confidence judgments.

Abstract Task-based fMRI has traditionally characterized cognitive engagement through spatial patterns of BOLD activation, leaving open whether tasks also organize activity along the frequency dimension. Here we tested whether temporally structured task engagement reshapes infra-slow BOLD dynamics, asking whether task responses are simply superimposed onto ongoing activity or instead redistribute power across the infra-slow spectrum. Across three independent fMRI datasets, task timing systematically reorganized the BOLD power spectrum. In the main visual attention dataset, periodic stimulation produced sharp stimulus-locked peaks at 0.083 and 0.125 Hz; critically, these gains were matched by a reduction concentrated within the dominant intrinsic infra-slow range (0.01-0.04 Hz), indicating redistribution rather than a simple local increase. Independent auditory and visual-motion datasets showed analogous task-frequency responses at their respective timescales, indicating that this reorganization generalizes across modalities and stimulation frequencies. The peaks were spatially related to GLM-derived activation but persisted after removal of the GLM-modeled response, indicating that this frequency-specific organization is not fully reducible to conventional task-evoked activation. These features were functionally informative: task-imposed frequencies distinguished cognitive states, predicted individual reaction time, and preserved subject-specific signatures across runs more effectively than broader spectral ranges or GLM-derived features. Finally, Jansen-Rit simulations coupled to a Balloon-Windkessel model reproduced the peaks, showing that their expression depends on balanced excitatory, inhibitory, and filter-gain regimes rather than simple amplification of input. Together, these findings indicate that task-fMRI reveals not only where cognition is localized, but how temporally structured engagement redistributes infra-slow dynamics across timescales.

This study aimed to assess alterations in functional connectivity (FC) within brain networks in children and adolescents with β-TM major and to explore the intrinsic relationship between network changes and cognitive impairment. This prospective study recruited 70 patients with β-TM and 64 healthy controls. Cognitive function assessments using Montreal Cognitive Assessment (MoCA) and Modified Mini-Mental State Examination (MMMSE), and hematological parameters were collected. Region of interest (ROI)-to-ROI connectivity analysis was conducted to investigate the whole brain FC within and between resting-state networks. Granger causality analysis was utilized to evaluate the effective interactions among them. Patients exhibited significant cognitive impairment compared to controls. Key hematological indicators, such as serum ferritin, were not found to be correlated with cognitive function. Rs-fMRI revealed extensive reductions in functional connectivity, accompanied by several enhancements. These FCs’ alterations significantly correlated with cognitive deficits. Granger causality analysis further indicated effective information flow from these FCs. This study showed significant correlation of cognitive impairment with aberrant FC in brain networks and hematological parameters in patients with β-TM. These results should advance our understanding of the neural mechanism underlying β-TM-related cognitive dysfunction and may serve as potential neuroimaging biomarkers for cognitive functioning in this population.

1IntroductionAll activities of entities occur within specific temporal and spatial scales. These scales are mutually restrictive. The spatiotemporal scales of neural activity are subject to the structural and functional organization of the brain. There is a strong connection between the size of the brain and the frequency of neural activity (Buzsáki et al., 2013), while each brain region or neural circuit has its own spectral characteristics (Keitel & Gross, 2016)(see Fig. 1A). Consequently, cognitive-specific spectral fingerprints should be present if each cognitive function is realized by a distinct neural circuit. This phenomenon is elucidated by the spectral fingerprint hypothesis of cognition (Siegel et al., 2012)(see Fig. 1B). Recent studies have not only uncovered the frequency characteristics of particular brain regions or neural circuits, but also indicated that each cognitive process fluctuates at a preferential frequency (Palva & Palva, 2018).In specificity, if a comprehensive cognitive function is divided into distinct cognitive processing stages, each associated with different neural circuits, then this cognitive function can be characterized by the spatiotemporal structure of nested neural circuits and corresponding spectral profiles. Assessing the level achievable by each cognitive function is contingent upon the resemblance between the spatiotemporal structure of its neural circuit and the optimal spatiotemporal structure. The intrinsic spatiotemporal structure of cognitions is, therefore, crucial for the comprehension of the neural mechanisms governing normal and pathological cognitive processes, as well as for cognitive rehabilitation and interventions in brain function.Insert Fig.1 about hereFigure 1. The intrinsic spatiotemporal structure of cognitions and its implication for the intervention of brain functions. A) The cognitive-specific spatiotemporal brain structure. B) Cognitive fingerprints defined by specific neural circuits and power spectrum. C) Intervening abnormal brain functions through simulated twin spatiotemporal structure of normal brain functions.2Brain stimulation based on the intrinsic spatiotemporal structure of cognitionsBy engaging in a long-term cognitive training, individuals can optimize their cognitive abilities and gradually stabilize the neural circuits associated with those cognitive functions, resulting in the emergence of specific spatiotemporal structures (Yang et al., 2019). The spatiotemporal structure of the neural circuit associated with optimal cognitive functioning can be defined as the intrinsic spatiotemporal structure of the cognitive function. Young adults, prodigies, or individuals with professional training often offer us models that closely approximate the intrinsic spatiotemporal structure (Qiao et al., 2022). Though many cognitive functions may appear to be alike at first glance, they are hard to transfer from one to another due to their exclusive spatiotemporal structure. Different cognitive functions may share many brain regions, networks, and neural oscillations. However, this overlap is insufficient to produce transfer effects across cognitive functions after cognitive training. A specific cognitive function is defined by the complete spatiotemporal structure formed by both these shared and non-shared structures. Due to the difference in intrinsic spatiotemporal structures, each cognitive function is unique, which may be a significant reason for the lack of far transfer effects in cognitive training. We refer to this phenomenon as cognitive isolation. Just like the productive isolation, cognitive isolation impedes the transition between two distinct cognitive functions. This may be an important reason for the absence of the far transfer effect in cognitive trainings (Sala & Gobet, 2019).The intrinsic spatiotemporal structure of cognitive functions may provide advantageous targets for brain stimulations. Current interventions for brain function are limited in their efficacy, as they are restricted to nonspecific neural circuits or frequencies, making it difficult to reach an ideal brain state (Qiao et al., 2022). On the other hand, interventions that target specific spatial or frequency elements of brain function have yielded remarkable outcomes (Cash et al., 2021). Therefore, it is exciting to anticipate the potential outcomes of brain stimulation techniques that are based on intrinsic spatiotemporal structures. In other words, achieving the optimal frequency of activity in each neural circuit, as well as their interactions, may lead to the attainment of optimal cognitive function.With the progression of digital twin technology, it is possible to mirror the twin spatiotemporal structure of every cognitive function in the future. According to the neural entrainment theory, this simulated twin spatiotemporal structure may cause the neural system to resonate in particular patterns (Zhang et al., 2023), thereby optimizing correspondent cognitive functions (see Fig. 1C). It is encouraging that there have been endeavors to combine high-definition transcranial electrical stimulation with broadband or band-removed spectral profiles, resulting in favorable outcomes (Janssens et al., 2022). This has promising implications for brain function rehabilitation and cognitive enhancement.3DiscussionThe implementation of precision intervention is pivotal in improving the efficacy of non-invasive brain stimulation techniques. As each cognitive function is tied to particular neural circuits, stimulating a specific neural circuit at its intrinsic frequency inevitably yields more precise regulation to that cognitive function. However, when multiple cognitive processes or neural circuits are impaired, as commonly observed in mental disorders, the regulation of a single circuit or frequency may not necessarily achieve the desired outcome. Likewise, to enhance holistic cognitive functions, rather than focusing solely on improving a single stage of cognitive processing, it becomes essential to coordinate more circuits at various frequencies. In this instance, the intrinsic spatiotemporal structure of cognitive functions can provide a more optimal solution.4Conflict of InterestThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.5FundingThis research was supported by the National Social Science Foundation of China (BBA200030).6ReferenceBuzsáki, G., Logothetis, N., & Singer, W. (2013). Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron, 80(3), 751-764. Cash, R. F. H., Cocchi, L., Lv, J., Fitzgerald, P. B., & Zalesky, A. (2021). Functional magnetic resonance imaging–guided personalization of transcranial magnetic stimulation treatment for depression. JAMA psychiatry, 78(3), 337-339. Janssens, S. E. W., Oever, S. T., Sack, A. T., & Graaf, T. A. d. (2022). "Broadband Alpha Transcranial Alternating Current Stimulation": Exploring a new biologically calibrated brain stimulation protocol. NeuroImage, 253, 119109. Keitel, A., & Gross, J. (2016). Individual human brain areas can be identified from their characteristic spectral activation fingerprints. PLoS Biology, 14(6), e1002498. Palva, S., & Palva, J. M. (2018). Roles of brain criticality and multiscale oscillations in temporal predictions for sensorimotor processing. Trends in Neurosciences, 41(10), 729-743. Qiao, J., Wang, Y., & Wang, S. (2022). Natural frequencies of neural activities and cognitions may serve as precise targets of rhythmic interventions to the aging brain. Frontiers in aging neuroscience, 14, 988193. Sala, G., & Gobet, F. (2019). Cognitive training does not enhance general cognition. Trends in Cognitive Sciences, 23(1), 9–20. Siegel, M., Donner, T. H., & Engel, A. K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nature Reviews Neuroscience, 13(2), 121-134. Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X.-J. (2019). Task representations in neural networks trained to perform many cognitive tasks. Nature Neuroscience, 22, 297–306. Zhang, S., Qin, Y., Wang, J., Yu, Y., Wu, L., & Zhang, T. (2023). Noninvasive electrical stimulation neuromodulation and digital brain technology: A review. Biomedicines, 11(6), 1513.

Transcranial Direct Current Stimulation (tDCS) has been employed to enhance executive control (EC), thereby improving cognitive functions and mental health. However, the effects of tDCS on EC remain inconclusive, and the mechanisms involved in its impact on baseline and conflict processing are not well understood. This study applied high-definition tDCS (HD-tDCS) to the left dorsolateral prefrontal cortex DLPFC) to investigate the distinct effects of tDCS on baseline and conflict processing. Compared to the sham group, tDCS significantly reduced reaction time variability in both conditions and decreased mean reaction time and error rate in the conflict condition. These findings demonstrate significant enhancements in general behavioral stability and conflict processing respectively. This study demonstrates a significant enhancement of tDCS on EC, elucidating the dual mechanisms of tDCS in modulating the baseline state and EC, providing valuable insights into the mechanisms of tDCS intervention on cognitive functions.

The physiological information carried by brain signals is distinguished by their mean and variability. Research has indicated that both the variability of local signals and the spatial mean of the whole-brain signal (known as the global signal, GS) are sensitive to brain development. This raises the question of whether the spatial variability of the whole-brain signal, referred to as global variability (GV), could potentially serve as a more specific marker of brain development. We first established the reliability of GV and its topography (GVtopo) using data from the Human Connectome Project (HCP). Then, we examined the age-related patterns of GV and GVtopo in the Nathan Kline Institute Rockland Sample (NKI-RS; N = 968, ages ranging from 6 to 85 years) and validated these findings in an independent dataset from Southwest University (SALD; N = 492, ages ranging from 19 to 80 years). Our results demonstrated the robustness of GV and GVtopo, with intra-class correlation coefficients surpassing 0.61. Both GV and GVtopo exhibited distinct non-linear developmental trajectories, differring from those of GS and its topography. Furthermore, GV demonstrated substantial age-predictive capability, underscoring its potential as a valuable marker of brain development and its significance for future age-related research. This study reveals that global variability (GV) and its topography (GVtopo) capture unique brain functional patterns across the lifespan, outperforming global signal (GS) in age prediction and offering new insights into age-related neural changes.

持续性注意是个体在一段时间内将注意保持在某个客体或活动上的能力，是顺利完成日常活动和工作学习的关键。然而，随着时间的推移，注意水平会不断波动，对正在进行的活动产生负面影响。持续性注意的正常发展，及其在神经、精神类疾病患者中的异常波动发生在多个亚慢波频率。现有研究把持续性注意的波动简化为有限认知资源的权衡和分配，难以涵盖其多种认知成分、多个波动频率的复杂特征。本研究旨在探究持续性注意低频波动的认知神经机制，包括：（1）探究持续性注意核心认知成分波动的脑时空特征，建构持续性注意的认知成分波动假说；（2）通过亚慢波经颅电刺激，从频率（时间）和靶点（空间）两方面探索基于持续性注意成分低频波动特征的干预机制，并验证提出的持续性注意的认知成分波动假说；（3）考察持续性注意和注意网络之间的交互作用，探索持续性注意的成分波动假说的外延。本研究有助于深化对持续性注意认知结构和神经波动时空特征的理解，并为持续性注意波动的精准干预提供重要参考。

Backgrounding:The executive functions (EFs) involve multiple subcomponents including inhibition, updating, and shifting. These subcomponents are mediated by distinct brain networks, each linked to specific neural oscillations. Frequency-specific stimulation is a key approach to achieving precise intervention on different cognitive functions through affecting specific spatiotemporal organizations of brain networks. Objective:We aimed to explore the modulation of different brain networks and EFs' subcomponents by stimulation at frequencies of 0.02 Hz and 0.05 Hz, which are closely linked to whole-brain dynamics. Method:In a randomized, placebo-controlled, cross-over study, we applied anodal oscillatory transcranial direct current stimulation (O-tDCS) to the left DLPFC to investigate the frequency-specific modulation on oxy-hemoglobin (HbO) and offline EF scores (Experiment 1, N = 54), as well as online EF scores (Experiment 2, N = 48). Result:Near the stimulation frequency, brain signals were significantly enhanced. Specifically, an increase in power at 0.02 Hz was associated with enhanced inhibitory function, while an increase in power at 0.05 Hz was linked to decreased updating function. Compared to the sham condition, 0.02 Hz stimulation increases PLV within the frontal lobe, whereas 0.05 Hz increases PLV between the frontal and parietal lobes, indicating the presence of distinct spatiotemporal structures within cognitive-related brain networks. Conclusion:The frequency-specific modulation of O-tDCS on brain networks and EF subcomponents suggests that different EFs are supported by brain networks with specific spatiotemporal architectures, bolstering the spectral fingerprint hypothesis of cognition. The spatiotemporal structure of cognitive-specific brain networks offers novel insights and targets for non-invasive interventions targeting diverse cognitive functions.

Functional declines in cognitive and motor performance as a result of aging can increase the risk of falls and the incidence of traumatic brain injury (TBI), mild TBI (mTBI), or subsequent post-concussion syndrome (PCS) in older adults. Head injuries or PCS can lead to a longer recovery and a greater possibility of developing neurodegenerative diseases, such as dementia or Alzheimer’s disease (AD). This targeted review utilized a rigorous, focused, evidence-based methodology, necessitating adherence to a structured approach to ensure the identification and inclusion of the most pertinent studies. Empirical evidence demonstrates the effects of physical or mental activity on the cognitive and motor functions of older adults. Studies on functional deterioration, balance problems, fall accidents, and neurological abnormalities are reported. The implications of TBI/mTBI, along with the neurological mechanisms of exercise for behavioural improvements and the prospective effects of physical or mental activity on PCS therapy, are examined. Most importantly, evaluating the rehabilitation outcomes of non-traditional exercises, including mindfulness, yoga, and Tai Chi, is crucial for individuals experiencing mTBI or PCS. Finally, the paper provides research suggestions and possible mind-body exercise interventions for mTBI and PCS in older adults.

Thalamic structural and functional abnormalities in major depressive disorder (MDD) are linked to impairments in diverse cognitive and emotional functions via the thalamo-cortical circuit. Given the constraints of temporal and spatial factors on information exchange, investigating frequency-specific effective connectivity (EC) is essential for elucidating the abnormal mechanisms of spatiotemporal information communication in patients with MDD. We employed a large-scale, multicenter resting-state functional magnetic resonance imaging (fMRI) dataset comprising individuals with MDD and matched healthy controls. Frequency-specific EC between the thalamic subregions and cortical/subcortical regions was assessed using spectral Granger causality in four frequency bands: slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.185 Hz), and a classic frequency range (0.01–0.08 Hz). Support vector regression (SVR) models were employed to evaluate the predictive value of altered EC for clinical symptom scores. Individuals with MDD exhibited significant and frequency-dependent abnormalities in thalamocortical and thalamo-subcortical EC, with the most pronounced disruptions observed in the slow-5 band. These abnormalities originate from the specific thalamic subregions and extend to cortical and subcortical regions. Among the frequency bands analyzed, EC alterations in the slow-5 band showed the strongest association with clinical severity and yielded the highest predictive performance in SVR models. Frequency-specific EC disruptions, particularly within the slow-5 band, may reflect fundamental spatiotemporal communication deficits in MDD. These findings highlight the slow-5 thalamocortical and thalamo-subcortical EC as a potential neurobiological marker for diagnosis and a target for treatment strategies in MDD.

In order to address the difficulty in accurately predicting the load of distribution networks due to strong load fluctuations and high randomness of user behavior, this paper proposes a short-term load forecasting method for distribution networks based on sample entropy (SE) secondary decomposition and deep learning. First, the random forest (RF) algorithm is used to extract features from external factors affecting load data. Then, time-varying filtering empirical mode decomposition (TVFEMD) is applied to preliminarily decompose the load sequence into subsequences. Combining SE and singular spectrum analysis (SSA), highly complex subsequences are further decomposed to improve the quality of the sequences as inputs to the prediction model. Next, the subsequences and load influencing factors are input into a bidirectional long short-term memory (BiLSTM) model, whose hyperparameters are optimized by an improved dung beetle optimization (IDBO) algorithm, for load forecasting. Finally, the predicted results of the output subsequences are superimposed to obtain the final prediction result.

The activities of the human brain vary across different timescales, exhibiting scale-free dynamics. Previous research has highlighted the psychological and physiological significance of brain dynamical fluctuations across the Delta to Gamma bands. However, there has been less focus on infra-slow scale-free dynamics, e.g. power law exponent (PLE), and neural variability, e.g. standard deviation (SD), and sample entropy (SE), in mediating brain-behavior connection during attention. In this study, we recruited 49 participants and recorded functional magnetic resonance imaging (fMRI) resting-state and task data during a sustained attention task paradigm to investigate how the three measures-SD, SE, and PLE-modulate the dynamics of behavioral performance. Our findings demonstrate the following: (i) PLE, SD, and SE exhibit differential topographic distribution with a hierarchical structure from sensory to associative networks, during their rest-task modulation. (ii) PLE, SD, and SE show different topographic extensions from visual cortex to default-mode network in their relationship with behavioral variability. (iii) The relationship between SD and SE is mediated by PLE in the empirical data, which (iv) is further confirmed in simulation. Collectively, our results highlight the topographically- and dynamically-layered mechanisms of distinct neurodynamical features during attention processing: scale-free dynamics modulate neural and behavioral variability.

BackgroundReliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI.MethodsChildren with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling.ResultsThe final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95% CI = 0.772-0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model.ConclusionsThe nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

BACKGROUND:Attentional bias modification training (ABMT) is commonly employed to regulate negative attentional bias (NAB) and, in turn, to prevent or alleviate depressive symptoms. Recent advancements in attention switch theory have facilitated the development of a novel training paradigm that may enhance the efficacy of such interventions. METHODS:A total of fifty-seven college students were assigned to two groups: one exhibiting NAB and the other without. Both groups underwent training with a novel paradigm integrating theta rhythm with the traditional dot-probe task (DPT). The DPT was also administered as a pre- and post-test measure. RESULTS:For individuals with NAB, rhythmic DPT effectively alleviates their NAB. Additionally, within the training procedure's DPT, flashing negative stimuli elicits faster responses when the probe appears at the positive stimulus' location. Baseline attention scores can negatively predict changes in subsequent corresponding attentional performance. CONCLUSIONS:This study presents a novel training paradigm-the theta rhythm-based DPT-that effectively modifies NAB. The mechanism underlying this intervention may be driven by positive salient stimuli at the critical trough, facilitating the switch of attention from negative to positive stimuli.

Background Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear.Methods We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed.Results The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group.Conclusion The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.

The development of attentional functions is a fundamental issue of human cognitive development, but the available evidence for its developmental trajectory is inconsistent due to the diversity and low reliability of measurement paradigms. The study examined the development of attentional functions and attentional collaborations in 281 Chinese primary school children (109 girls, 5.98-13.24 years old) using the self-designed High Reliability-Composite Attention Test. Results showed that the executive control continued to develop prior to the age of 10. It further contributed to the linear development of attentional collaborations. Each of these scores exhibited a split-half reliability exceeding 0.82. Therefore, we effectively demonstrated a mechanism for attentional development that revolves around executive control.

注意是个体对特定对象的指向与集中。警觉、定向、执行控制等基本注意功能和选择性注意、持续性注意等复杂注意功能有着各不相同的发生、发展和衰退轨迹。本文回顾了基本注意功能和复杂注意功能的毕生发展轨迹，提出了注意功能发展的阶段论：注意的生命全程发展大致包含三个阶段：注意功能的萌芽期（0-1岁）、注意功能的发展期（1岁-成年）和注意功能的衰退期（成年-老年）。此外，本文强调了注意功能之间的相互协作对理解注意发展的意义：复杂注意功能依赖于基本注意功能的协作，其发展进程也依赖后者的发展。未来应使用对注意水平敏感的、高信效度的测量工具加强注意发展的纵向研究、青少年研究和注意功能之间相互协作的研究，完善注意发展的理论