博碩士論文 110825003 詳細資訊




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姓名 黃羿寧(Yih-Ning Huang)  查詢紙本館藏   畢業系所 認知與神經科學研究所
論文名稱 空間預期性對注意力採樣節律之神經機制探討
(Spatial prediction modulates the rhythm of attentional sampling)
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摘要(中) 先前的研究發現視覺空間注意力具有採樣節律的特質,並在4-8 Hz的theta和8-16 Hz的alpha頻率下震盪,且與視覺系統內的神經振盪的相位振幅耦合有關。許多研究指出此注意取樣節奏與不同的試驗需求有關,例如:刺激物數量、干擾物的數量以及試驗難度等因素。然而,目前對於試驗需求與注意力節律及其神經調節之間關聯性的了解仍不充分。本研究旨在研究空間預期心理對注意力採樣節律的影響,並利用腦電波(EEG)探討神經振盪和行為節律的關係。我們使用類Posner注意力實驗,結合不同空間提示效度(100%和50%),並在不同時間點(300毫秒至1300毫秒)呈現閾值Landolt環以形成行為節律,並應用全息希爾伯特頻譜分析(Holo-Hilbert Spectral Analysis; HHSA)和全息希爾伯特跨頻相位聚集網絡(Holo-Hilbert Cross-frequency Phase Clustering; HHCFPC)對腦波數據進行分析,研究注意力採樣期間腦波的動態變化。研究結果顯示,空間預期性提高了回應的精確度並降低注意力採樣節律從beta頻帶(15 Hz和19 Hz)到theta頻帶(4 Hz)。此外,腦波資料顯示,在完全可預期的情況下,枕葉的alpha-band振幅受到前額區域theta-band的調節,而前額區域和頂-枕區域之間的低頻振動(1.2-5.7 Hz)在不同試驗之間呈現顯著的相位對齊。在目標物出現之前的枕葉神經活動,也可以預測不同空間預期性的情形下反應的精確度。總體而言,我們的研究結果顯示,不同程度的空間預期性可以導致不同的準備過程,並能在行為節律中體現出來。這些結果提供了神經振盪和行為節律之間關聯性的證據,並表明它們在大腦中作為一種進行資源分配機制的重要角色。
摘要(英) One characteristics of attention is to facilitate relevant information and inhibit irrelevant distractors. Recent studies demonstrate that behavioral performance during visual spatial attention fluctuates at theta (4-8 Hz) and alpha (8-16 Hz) frequencies, linked to phase amplitude coupling of neural oscillations within the visual system (i.e., frontal-parietal network and visual cortex). Moreover, previous studies suggest that attentional sampling rhythms are task-dependent. This is evidenced with varying behavioral performance at different frequencies when comparing spatial resolutions, numbers of distractors, and task difficulties. However, the role of prior spatial prediction in behavioral rhythms remains unclear. To address this issue, we employed an adaptive Posner-like discrimination task with variable CTOA durations ranging from 300 ms to 1300 ms in steps of 20 ms while manipulating spatial prediction via cue validity (100% & 50%), with concurrently electroencephalography (EEG) recording. An adaptive analytical method, namely the Holo-Hilbert Spectral Analysis (HHSA) and Holo-Hilbert Cross-frequency Phase Clustering (HHCFPC) was applied for the EEG data analysis to explore the dynamic changes of perception during sustained attention. Our findings indicate that informative cues enhance spatial and temporal modulation, improving response precision for near-threshold Landolt rings. Furthermore, spatial prediction influences the speed of behavioral rhythms at the theta-band (4 Hz) with certain predictions and at alpha & beta bands (15 & 19 Hz) with uncertain predictions. We also discovered that spatial prediction strengthens theta-alpha modulations at parietal-occipital areas, frontal theta phase and parietal-occipital alpha amplitude coupling, within frontal theta phase/ alpha amplitude coupling, and the phase alignments of low frequency (1.2-5.7 Hz) oscillations between trials at frontal and parietal-occipital areas. Notably, during the pre-target period, beta-modulated gamma oscillations in parietal-occipital areas predict response precision in spatially uncertain conditions, while frontal theta phase and parietal-occipital alpha amplitude coupling predict response precision in spatially certain conditions. Overall, our results suggest that different levels of spatial certainty lead to distinct preparations for sampling unpredictable targets. Specifically, we found that certain spatial cues promote more robust communication between different brain areas (theta-alpha coupling) and override the phasic contributions of a natural filtering mechanism (i.e.,beta-gamma coupling) within the early visual cortex during perception. In conclusion, our study supports the notion that the speed of periodic sampling in perception depends on the task at hand, indicating that neural rhythms serve as an adaptive resource allocation mechanism in the brain and highlighting the critical role of spatial prediction in attentional sampling rhythms.
關鍵字(中) ★ 空間注意力
★ 採樣節律
★ 空間預期性
★ 提示效度
★ 腦波相位振幅耦合
關鍵字(英) ★ spatial prediction
★ attentional sampling rhythms
★ cue validity
★ phase-amplitude coupling
★ HHSA
論文目次 摘要 i
Abstract ii
致謝 iv
Table of Contents v
List of Figures vii
List of Tables viii
Chapter I General introduction 1
Chapter II Literature review 4
2-1 Rhythmic Sampling Behavior 4
2-1-1 Perceptual Alpha Rhythms 5
2-1-2 Attentional Theta Rhythms 6
2-1-3 Tentative Models for Perceptual and Attentional Rhythms 10
2-2 Spatial Prediction 11
2-2-1 Selective Attention and its Neural Correlates 11
2-2-2 Spatial Prediction with Temporal Expectation 13
2-3 Research Aims and Expectations 19
Chapter III Methodology 21
3-1 Participants 21
3-2 Apparatus and Data Acquisition 21
3-3 Design and Procedure 21
3-4 Behavioral Analysis and Statistics 24
3-4-1 Foreperiod and Spectral Analysis 24
3-5 EEG Analysis and Statistics 25
3-5-1 EEG Pre-processing 25
3-5-2 Holo-Hilbert Spectral Analysis (HHSA) 26
3-5-3 Inter-trial Phase Coherence (ITPC) 30
3-5-4 Holo-Hilbert Cross-frequency Phase Clustering (HHCFPC) 31
3-5-5 Event-Related Mode (ERM) 33
3-5-6 Statistical Analysis 35
Chapter IV Results 36
4-1 Behavioral results 36
4-1-1 Overall Cuing Effects 36
4-1-2 Temporal Structure of Selective Attention 37
4-2 Neural Correlates of Spatial Cues 43
4-2-1 Asymmetric Alpha Oscillations 43
4-2-2 Lateralized ERP Components 46
4-3 Cue Validity Effects on Neural Communications 49
4-3-1 Interareal Phase Amplitude Coupling 49
4-3-2 Phase Alignments Across Trials 51
4-4 Neural Correlates Preceding the Target 53
4-4-1 Spectral Power Predicts Response Precision 53
4-4-2 Interareal Phase Amplitude Coupling Predicts Response Precision 55
Chapter V Discussion 59
Chapter VI Conclusions 66
References 67
參考文獻 Abdalaziz, M., Redding, Z. V., & Fiebelkorn, I. C. (2023). Rhythmic temporal coordination of neural activity prevents representational conflict during working memory. Current Biology, 33(9), 1855-1863. e1853.
Arjona, A., Escudero, M., & Gómez, C. M. (2016). Cue validity probability influences neural processing of targets. Biological Psychology, 119, 171-183.
Arnal, L. H., & Giraud, A.-L. (2012). Cortical oscillations and sensory predictions. Trends in cognitive sciences, 16(7), 390-398.
Balestrieri, E., Ronconi, L., & Melcher, D. (2022). Shared resources between visual attention and visual working memory are allocated through rhythmic sampling. European Journal of Neuroscience, 55(11-12), 3040-3053.
Bashinski, H. S., & Bacharach, V. R. (1980). Enhancement of perceptual sensitivity as the result of selectively attending to spatial locations. Perception & psychophysics, 28, 241-248.
Bauer, M., Stenner, M.-P., Friston, K. J., & Dolan, R. J. (2014). Attentional modulation of alpha/beta and gamma oscillations reflect functionally distinct processes. Journal of Neuroscience, 34(48), 16117-16125.
Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., & Moulines, E. (1997). A blind source separation technique using second-order statistics. IEEE Transactions on signal processing, 45(2), 434-444.
Belouchrani, A., Abed-Meraim, K., Cardoso, J., & Moulines, E. (1993). Second-order blind separation of temporally correlated sources. Proc. Int. Conf. Digital Signal Processing,
Benwell, C. S., Tagliabue, C. F., Veniero, D., Cecere, R., Savazzi, S., & Thut, G. (2017). Prestimulus EEG power predicts conscious awareness but not objective visual performance. ENeuro, 4(6).
Brainard, D. H., & Vision, S. (1997). The psychophysics toolbox. Spatial vision, 10(4), 433-436.
Breska, A., & Deouell, L. Y. (2017). Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment. PLoS biology, 15(2), e2001665.
Busch, N. A., Dubois, J., & VanRullen, R. (2009). The phase of ongoing EEG oscillations predicts visual perception. J Neurosci, 29(24), 7869-7876. https://doi.org/10.1523/JNEUROSCI.0113-09.2009
Busch, N. A., & VanRullen, R. (2010). Spontaneous EEG oscillations reveal periodic sampling of visual attention. Proc Natl Acad Sci U S A, 107(37), 16048-16053. https://doi.org/10.1073/pnas.1004801107
Canolty, R. T., & Knight, R. T. (2010). The functional role of cross-frequency coupling. Trends in cognitive sciences, 14(11), 506-515.
Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in cognitive sciences, 18(8), 414-421.
Chacko, R. V., Kim, B., Jung, S. W., Daitch, A. L., Roland, J. L., Metcalf, N. V., Corbetta, M., Shulman, G. L., & Leuthardt, E. C. (2018). Distinct phase-amplitude couplings distinguish cognitive processes in human attention. Neuroimage, 175, 111-121.
Chang, C.-F., Liang, W.-K., Lai, C.-L., Hung, D. L., & Juan, C.-H. (2016). Theta oscillation reveals the temporal involvement of different attentional networks in contingent reorienting. Frontiers in Human Neuroscience, 10, 264.
Chen, A., Wang, A., Wang, T., Tang, X., & Zhang, M. (2017). Behavioral oscillations in visual attention modulated by task difficulty. Frontiers in psychology, 8, 1630.
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature reviews neuroscience, 3(3), 201-215.
Cravo, A. M., Rohenkohl, G., Wyart, V., & Nobre, A. C. (2011). Endogenous modulation of low frequency oscillations by temporal expectations. Journal of neurophysiology, 106(6), 2964-2972.
Cravo, A. M., Rohenkohl, G., Wyart, V., & Nobre, A. C. (2013). Temporal expectation enhances contrast sensitivity by phase entrainment of low-frequency oscillations in visual cortex. Journal of Neuroscience, 33(9), 4002-4010.
Cruzat, J., Torralba, M., Ruzzoli, M., Fernández, A., Deco, G., & Soto-Faraco, S. (2021). The phase of Theta oscillations modulates successful memory formation at encoding. Neuropsychologia, 154, 107775.
Daume, J., Wang, P., Maye, A., Zhang, D., & Engel, A. K. (2021). Non-rhythmic temporal prediction involves phase resets of low-frequency delta oscillations. Neuroimage, 224, 117376.
Doherty, J. R., Rao, A., Mesulam, M. M., & Nobre, A. C. (2005). Synergistic effect of combined temporal and spatial expectations on visual attention. Journal of Neuroscience, 25(36), 8259-8266.
Drewes, J., & VanRullen, R. (2011). This is the rhythm of your eyes: the phase of ongoing electroencephalogram oscillations modulates saccadic reaction time. Journal of Neuroscience, 31(12), 4698-4708.
Dugué, L., Marque, P., & VanRullen, R. (2011). The phase of ongoing oscillations mediates the causal relation between brain excitation and visual perception. Journal of Neuroscience, 31(33), 11889-11893.
Dugué, L., Marque, P., & VanRullen, R. (2015). Theta oscillations modulate attentional search performance periodically. J Cogn Neurosci, 27(5), 945-958. https://doi.org/10.1162/jocn_a_00755
Dugué, L., & VanRullen, R. (2017). Transcranial magnetic stimulation reveals intrinsic perceptual and attentional rhythms. Frontiers in Neuroscience, 11, 154.
Dugue, L., Roberts, M., & Carrasco, M. (2016). Attention Reorients Periodically. Curr Biol, 26(12), 1595-1601. https://doi.org/10.1016/j.cub.2016.04.046
Egly, R., Driver, J., & Rafal, R. D. (1994). Shifting visual attention between objects and locations: evidence from normal and parietal lesion subjects. Journal of Experimental Psychology: General, 123(2), 161.
Esghaei, M., Treue, S., & Vidyasagar, T. R. (2022). Dynamic coupling of oscillatory neural activity and its roles in visual attention. Trends in Neurosciences.
Fakche, C., VanRullen, R., Marque, P., & Dugué, L. (2022). α phase-amplitude tradeoffs predict visual perception. ENeuro, 9(1).
Fiebelkorn, I. C., & Kastner, S. (2019). A Rhythmic Theory of Attention. Trends Cogn Sci, 23(2), 87-101. https://doi.org/10.1016/j.tics.2018.11.009
Fiebelkorn, I. C., Pinsk, M. A., & Kastner, S. (2018). A Dynamic Interplay within the Frontoparietal Network Underlies Rhythmic Spatial Attention. Neuron, 99(4), 842-853 e848. https://doi.org/10.1016/j.neuron.2018.07.038
Fiebelkorn, I. C., Saalmann, Y. B., & Kastner, S. (2013). Rhythmic sampling within and between objects despite sustained attention at a cued location. Curr Biol, 23(24), 2553-2558. https://doi.org/10.1016/j.cub.2013.10.063
Haegens, S., Händel, B. F., & Jensen, O. (2011). Top-down controlled alpha band activity in somatosensory areas determines behavioral performance in a discrimination task. Journal of Neuroscience, 31(14), 5197-5204.
Hayward, D. A., & Ristic, J. (2013). Measuring attention using the Posner cuing paradigm: the role of across and within trial target probabilities. Frontiers in Human Neuroscience, 7, 205.
Helfrich, R. F., Fiebelkorn, I. C., Szczepanski, S. M., Lin, J. J., Parvizi, J., Knight, R. T., & Kastner, S. (2018). Neural Mechanisms of Sustained Attention Are Rhythmic. Neuron, 99(4), 854-865 e855. https://doi.org/10.1016/j.neuron.2018.07.032
Holcombe, A. O., & Chen, W. Y. (2013). Splitting attention reduces temporal resolution from 7 Hz for tracking one object to <3 Hz when tracking three. J Vis, 13(1), 12. https://doi.org/10.1167/13.1.12
Hsu, C.-H., Lee, C.-Y., & Liang, W.-K. (2016). An improved method for measuring mismatch negativity using ensemble empirical mode decomposition. Journal of neuroscience methods, 264, 78-85.
Hsu, C. Y., Liu, T. L., Lee, D. H., Yeh, D. R., Chen, Y. H., Liang, W. K., & Juan, C. H. (2023). Amplitude modulating frequency overrides carrier frequency in tACS‐induced phosphene percept. Human Brain Mapping, 44(3), 914-926.
Huang, D., Liang, H., Xue, L., Wang, M., Hu, Q., & Chen, Y. (2017). The time course of attention modulation elicited by spatial uncertainty. Vision research, 138, 50-58.
Huang, N. E., Hu, K., Yang, A. C., Chang, H. C., Jia, D., Liang, W. K., Yeh, J. R., Kao, C. L., Juan, C. H., Peng, C. K., Meijer, J. H., Wang, Y. H., Long, S. R., & Wu, Z. (2016). On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data. Philos Trans A Math Phys Eng Sci, 374(2065), 20150206. https://doi.org/10.1098/rsta.2015.0206
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On instantaneous frequency. Advances in adaptive data analysis, 1(02), 177-229.
Huang, Y., Chen, L., & Luo, H. (2015). Behavioral oscillation in priming: competing perceptual predictions conveyed in alternating theta-band rhythms. Journal of Neuroscience, 35(6), 2830-2837.
Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural networks, 13(4-5), 411-430.
Jensen, O., & Mazaheri, A. (2010). Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci, 4, 186. https://doi.org/10.3389/fnhum.2010.00186
Jia, J., Liu, L., Fang, F., & Luo, H. (2017). Sequential sampling of visual objects during sustained attention. PLoS biology, 15(6), e2001903.
Juan, C. H., Nguyen, K. T., Liang, W. K., Quinn, A. J., Chen, Y. H., Muggleton, N. G., Yeh, J. R., Woolrich, M. W., Nobre, A. C., & Huang, N. E. (2021). Revealing the Dynamic Nature of Amplitude Modulated Neural Entrainment With Holo-Hilbert Spectral Analysis. Front Neurosci, 15, 673369. https://doi.org/10.3389/fnins.2021.673369
Kasten, F. H., Wendeln, T., Stecher, H. I., & Herrmann, C. S. (2020). Hemisphere-specific, differential effects of lateralized, occipital-parietal alpha- versus gamma-tACS on endogenous but not exogenous visual-spatial attention. Sci Rep, 10(1), 12270. https://doi.org/10.1038/s41598-020-68992-2
Kawashima, T., Hayashi, M. J., & Amano, K. (2022). Attentional rhythmic blink: Theta/Alpha balance in neural oscillations determines the rhythmicity in visual sampling. bioRxiv, 2022.2004. 2015.488436.
Keitel, C., Ruzzoli, M., Dugué, L., Busch, N. A., & Benwell, C. S. (2022). Rhythms in cognition: The evidence revisited. In (Vol. 55, pp. 2991-3009): Wiley Online Library.
Kelly, S. P., Lalor, E. C., Reilly, R. B., & Foxe, J. J. (2006). Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. Journal of neurophysiology, 95(6), 3844-3851.
Kienitz, R., Schmid, M. C., & Dugué, L. (2022). Rhythmic sampling revisited: experimental paradigms and neural mechanisms. European Journal of Neuroscience, 55(11-12), 3010-3024.
Lachaux, J. P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194-208.
Landau, A. N., & Fries, P. (2012). Attention samples stimuli rhythmically. Curr Biol, 22(11), 1000-1004. https://doi.org/10.1016/j.cub.2012.03.054
Landau, A. N., Schreyer, H. M., van Pelt, S., & Fries, P. (2015). Distributed Attention Is Implemented through Theta-Rhythmic Gamma Modulation. Curr Biol, 25(17), 2332-2337. https://doi.org/10.1016/j.cub.2015.07.048
Leszczyński, M., Fell, J., & Axmacher, N. (2015). Rhythmic working memory activation in the human hippocampus. Cell reports, 13(6), 1272-1282.
Liang, W. K., Tseng, P., Yeh, J. R., Huang, N. E., & Juan, C. H. (2021). Frontoparietal Beta Amplitude Modulation and its Interareal Cross-frequency Coupling in Visual Working Memory. Neuroscience, 460, 69-87. https://doi.org/10.1016/j.neuroscience.2021.02.013
Lin, W. M., Oetringer, D. A., Bakker‐Marshall, I., Emmerzaal, J., Wilsch, A., ElShafei, H. A., Rassi, E., & Haegens, S. (2022). No behavioural evidence for rhythmic facilitation of perceptual discrimination. European Journal of Neuroscience, 55(11-12), 3352-3364.
Los, S. A., Knol, D. L., & Boers, R. M. (2001). The foreperiod effect revisited: Conditioning as a basis for nonspecific preparation. Acta Psychologica, 106(1-2), 121-145.
Makeig, S., Jung, T.-P., Ghahremani, D., & Sejnowski, T. J. (1996). Independent component analysis of simulated ERP data. Institute for Neural Computation, University of California: technical report INC-9606.
Malhotra, P., Coulthard, E. J., & Husain, M. (2009). Role of right posterior parietal cortex in maintaining attention to spatial locations over time. Brain, 132(3), 645-660.
Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods, 164(1), 177-190.
Mathewson, K. E., Gratton, G., Fabiani, M., Beck, D. M., & Ro, T. (2009). To see or not to see: prestimulus alpha phase predicts visual awareness. J Neurosci, 29(9), 2725-2732. https://doi.org/10.1523/JNEUROSCI.3963-08.2009
Mathewson, K. E., Lleras, A., Beck, D. M., Fabiani, M., Ro, T., & Gratton, G. (2011). Pulsed out of awareness: EEG alpha oscillations represent a pulsed-inhibition of ongoing cortical processing. Front Psychol, 2, 99. https://doi.org/10.3389/fpsyg.2011.00099
McDonald, J. J., Störmer, V. S., Martinez, A., Feng, W., & Hillyard, S. A. (2013). Salient sounds activate human visual cortex automatically. Journal of Neuroscience, 33(21), 9194-9201.
Merholz, G., Grabot, L., VanRullen, R., & Dugué, L. (2022). Periodic attention operates faster during more complex visual search. Scientific Reports, 12(1), 6688.
Michail, G., Toran Jenner, L., & Keil, J. (2022). Prestimulus alpha power but not phase influences visual discrimination of long-duration visual stimuli. Eur J Neurosci, 55(11-12), 3141-3153. https://doi.org/10.1111/ejn.15169
Michel, R., Dugué, L., & Busch, N. A. (2022). Distinct contributions of alpha and theta rhythms to perceptual and attentional sampling. European Journal of Neuroscience, 55(11-12), 3025-3039.
Mohan, U. R., Zhang, H., & Jacobs, J. (2022). The direction and timing of theta and alpha traveling waves modulate human memory processing. bioRxiv, 2022.2002. 2007.479466.
Nguyen, K. T., Liang, W. K., Lee, V., Chang, W. S., Muggleton, N. G., Yeh, J. R., Huang, N. E., & Juan, C. H. (2019). Unraveling nonlinear electrophysiologic processes in the human visual system with full dimension spectral analysis. Sci Rep, 9(1), 16919. https://doi.org/10.1038/s41598-019-53286-z
Nguyen, T. V., Hsu, C. Y., Jaiswal, S., Muggleton, N. G., Liang, W. K., & Juan, C. H. (2021). To Go or Not to Go: Degrees of Dynamic Inhibitory Control Revealed by the Function of Grip Force and Early Electrophysiological Indices. Front Hum Neurosci, 15, 614978. https://doi.org/10.3389/fnhum.2021.614978
Nobre, A. C., & van Ede, F. (2018). Anticipated moments: temporal structure in attention. Nat Rev Neurosci, 19(1), 34-48. https://doi.org/10.1038/nrn.2017.141
Näätänen, R. (1972). Time uncertainty and occurence uncertainty of the stimulus in a simple reaction time task. Acta Psychologica, 36(6), 492-503.
Osipova, D., Hermes, D., & Jensen, O. (2008). Gamma power is phase-locked to posterior alpha activity. PLoS One, 3(12), e3990. https://doi.org/10.1371/journal.pone.0003990
Paneri, S., & Gregoriou, G. G. (2017). Top-down control of visual attention by the prefrontal cortex. Functional specialization and long-range interactions. Frontiers in Neuroscience, 11, 545.
Pelli, D. G., & Vision, S. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial vision, 10, 437-442.
Peylo, C., Hilla, Y., & Sauseng, P. (2021). Cause or consequence? Alpha oscillations in visuospatial attention. Trends Neurosci, 44(9), 705-713. https://doi.org/10.1016/j.tins.2021.05.004
Plöchl, M., Fiebelkorn, I., Kastner, S., & Obleser, J. (2022). Attentional sampling of visual and auditory objects is captured by theta‐modulated neural activity. European Journal of Neuroscience, 55(11-12), 3067-3082.
Posner, M. I. (1980). Orienting of attention. Quarterly journal of experimental psychology, 32(1), 3-25.
Posner, M. I., & Cohen, Y. (1984). Components of visual orienting. Attention and performance X: Control of language processes, 32, 531-556.
Posner, M. I., Rafal, R. D., Choate, L. S., & Vaughan, J. (2007). Inhibition of return: Neural basis and function. Cognitive Neuropsychology, 2(3), 211-228. https://doi.org/10.1080/02643298508252866
Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992). Temporary suppression of visual processing in an RSVP task: An attentional blink? Journal of experimental psychology: Human perception and performance, 18(3), 849.
Re, D., Inbar, M., Richter, C. G., & Landau, A. N. (2019). Feature-Based Attention Samples Stimuli Rhythmically. Curr Biol, 29(4), 693-699 e694. https://doi.org/10.1016/j.cub.2019.01.010
Rohenkohl, G., Gould, I. C., Pessoa, J., & Nobre, A. C. (2014). Combining spatial and temporal expectations to improve visual perception. Journal of vision, 14(4), 8-8.
Sack, A. T. (2009). Parietal cortex and spatial cognition. Behavioural brain research, 202(2), 153-161.
Samaha, J., & Postle, B. R. (2015). The speed of alpha-band oscillations predicts the temporal resolution of visual perception. Current Biology, 25(22), 2985-2990.
Senoussi, M., Moreland, J. C., Busch, N. A., & Dugue, L. (2019). Attention explores space periodically at the theta frequency. J Vis, 19(5), 22. https://doi.org/10.1167/19.5.22
Song, K., Meng, M., Chen, L., Zhou, K., & Luo, H. (2014). Behavioral oscillations in attention: rhythmic alpha pulses mediated through theta band. J Neurosci, 34(14), 4837-4844. https://doi.org/10.1523/JNEUROSCI.4856-13.2014
Spaak, E., de Lange, F. P., & Jensen, O. (2014). Local entrainment of alpha oscillations by visual stimuli causes cyclic modulation of perception. Journal of Neuroscience, 34(10), 3536-3544.
Su, Z., Wang, L., Kang, G., & Zhou, X. (2021). Reward makes the rhythmic sampling of spatial attention emerge earlier. Atten Percept Psychophys, 83(4), 1522-1537. https://doi.org/10.3758/s13414-020-02226-5
Szczepanski, S. M., Crone, N. E., Kuperman, R. A., Auguste, K. I., Parvizi, J., & Knight, R. T. (2014). Dynamic changes in phase-amplitude coupling facilitate spatial attention control in fronto-parietal cortex. PLoS Biol, 12(8), e1001936. https://doi.org/10.1371/journal.pbio.1001936
Thut, G., Nietzel, A., Brandt, S. A., & Pascual-Leone, A. (2006). α-Band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. Journal of Neuroscience, 26(37), 9494-9502.
Tomassini, A., Ambrogioni, L., Medendorp, W. P., & Maris, E. (2017). Theta oscillations locked to intended actions rhythmically modulate perception. Elife, 6. https://doi.org/10.7554/eLife.25618
Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP),
Tsai, C.-C., & Liang, W.-K. (2021). Event-related components are structurally represented by intrinsic event-related potentials. Scientific Reports, 11(1), 5670.
Ungerleider, S. K., & G, L. (2000). Mechanisms of visual attention in the human cortex. Annual review of neuroscience, 23(1), 315-341.
van Der Werf, O. J., Ten Oever, S., Schuhmann, T., & Sack, A. T. (2022). No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance. European Journal of Neuroscience, 55(11-12), 3100-3116.
Van Dijk, H., Schoffelen, J.-M., Oostenveld, R., & Jensen, O. (2008). Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability. Journal of Neuroscience, 28(8), 1816-1823.
Van Ede, F., De Lange, F., Jensen, O., & Maris, E. (2011). Orienting attention to an upcoming tactile event involves a spatially and temporally specific modulation of sensorimotor alpha-and beta-band oscillations. Journal of Neuroscience, 31(6), 2016-2024.
VanRullen, R. (2016). Perceptual cycles. Trends in cognitive sciences, 20(10), 723-735.
VanRullen, R., & Koch, C. (2003). Is perception discrete or continuous? Trends Cogn Sci, 7(5), 207-213. https://doi.org/10.1016/s1364-6613(03)00095-0
Vossel, S., Mathys, C., Daunizeau, J., Bauer, M., Driver, J., Friston, K. J., & Stephan, K. E. (2014). Spatial attention, precision, and Bayesian inference: a study of saccadic response speed. Cerebral cortex, 24(6), 1436-1450.
Vossel, S., Thiel, C. M., & Fink, G. R. (2006). Cue validity modulates the neural correlates of covert endogenous orienting of attention in parietal and frontal cortex. Neuroimage, 32(3), 1257-1264.
Voytek, B., Samaha, J., Rolle, C. E., Greenberg, Z., Gill, N., Porat, S., Kader, T., Rahman, S., Malzyner, R., & Gazzaley, A. (2017). Preparatory encoding of the fine scale of human spatial attention. Journal of Cognitive Neuroscience, 29(7), 1302-1310.
Wang, T., Zhang, M., Yu, Q., & Zhang, H. (2012). Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. Journal of Applied Geophysics, 83, 29-34.
White, C. T. (1963). Temporal numerosity and the psychological unit of duration. Psychological Monographs: General and Applied, 77(12), 1.
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01), 1-41.
指導教授 阮啟弘(Chi-Hung Juan) 審核日期 2023-7-18
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