探討海馬迴在統計學習歷程的可能作用：個體差異與功能性磁振造影研究

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陳品瑋(Pin-Wei Chen) 查詢紙本館藏

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認知與神經科學研究所

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探討海馬迴在統計學習歷程的可能作用：個體差異與功能性磁振造影研究
(Exploring the Role of the Hippocampus in Statistical Learning: Individual Differences and fMRI Investigation)

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至系統瀏覽論文 (2029-8-22以後開放)

摘要(中)

「統計學習」指的是生物在自然情況下、通過適應環境中的規律性而呈展出的學習效果。儘管這一概念涵蓋了廣泛的學習現象，有研究者提出這些現象皆可用一系列記憶歷程來描述，主要涉及資訊分塊和整合的過程。現有的行為與功能性神經造影研究證據皆表明，統計學習並非由單一的神經機制所支持，而是涉及幾乎所有參與當下信息處理的神經網絡。由於海馬迴所處的信息層級，以及適合進行快速且複雜運算的神經活動特性與結構，在學習與記憶的動態過程中發揮著獨特且重要的作用。當前的研究者普遍認為，海馬迴透過發展共享的記憶痕跡（memory engram）細胞來促進關聯（associations）的習得，而它的模式完成（pattern completion）功能能促進並強化關聯學習；同時，它的模式分離（pattern separation）功能主要用於維持資訊的精確和獨特性，與關聯學習互補。然而，我們認為模式分離功能可能也在關聯習得的過程中產生貢獻。為探究這項可能性，本研究一共採用了兩種取向的實驗：一是檢視個別差異以探討統計學習與其他能力（特別是海馬迴的模式分離功能）之間的相關性，二是觀察與統計學習過程相關的大腦活動變化，特別聚焦於支持模式分離功能的海馬迴和支持基礎視覺處理歷程的主要視覺皮質區。
實驗一旨在了解個體在統計學習作業中的表現是否與受到海馬迴支持的模式分離功能有關。我們採用觀察個體差異的研究方法，透過受試者在一系列作業中表現的相關性來探究兩種認知功能在機制上是否存在顯著的相關性。實驗一分成1a與1b兩個部分，分別以105名年輕受試者和47名長者作為研究對象，來進一步了解老化潛在可能造成的機制變遷。受試者進行的實驗作業包含有：以視覺或聽覺呈現規律的兩項經典統計學習作業、一項動作序列學習作業、一項對模式分離功能敏感的記憶測驗與三項傳統記憶測驗、兩項工作記憶測驗，以及兩項非語文流體智力測驗。
實驗1a的結果顯示，受試者在兩種不同感官輸入下的統計學習作業表現皆與他們在記憶測驗中的相似物區辨（lure discrimination）能力指標呈顯著相關。這項指標被認為與海馬迴的模式分離功能相關，這意味著個人海馬迴在替個別經驗建立正交化編碼的性能優劣或傾向強度，可能會直接或間接地影響他們進行統計學習的效益。我們進一步從實驗1b探究老化對統計學習機制造成的影響中觀察到，年長受試者的相似物區辨能力仍舊與其在視覺統計學習作業中的表現相關，但與其在聽覺統計學習作業中的表現則失去相關性，反而變得與外顯配對關聯學習的能力相關。另外，在年長受試者身上，我們原先在年輕受試者身上觀察到的視覺和聽覺統計學習作業表現之間的相關性也不復存在。這可能表示，長者在進行聽覺統計學習時的表現更受其聽知覺能力的影響，導致由海馬迴機制所支持的模式分離功能對整體表現的影響降低，因而無法觀察到聽覺、視覺統計學習作業共享相同的領域共通（modality-general）學習機制。
實驗二有兩個目的：首先，我們認為海馬迴的模式分離功能有助於減緩偶然習得的配對關聯造成的混淆，想對此進行驗證。其次，我們試圖探索統計學習過程中不同腦區隨時間展現的活動消長。為此，我們進行fMRI實驗，招募了37名受試者，讓他們觀看一系列看似隨機呈現、實際上是以每三項為一組有固定時序關係的「三連體」為隱藏結構呈現的幾何圖形，並記錄他們在熟悉這些規律、也就是視覺統計學習的期間與前後的大腦活動。一方面，我們透過表徵相似性分析檢查在熟悉「三連體」規律結構後，在兩個三連體邊界處相遇的項目是否會因應區隔不同時序組合的需求，表現出表徵「差異性」的強化。另一方面，我們直接觀察海馬迴、三個內側顳葉皮質、以及視覺處理皮質（合併的V1和V2、餘下的lateral occipital cortex、fusiform gyrus、inferior temporal gyrus）在學習歷程中的活動。由於位於三連體內不同時序位置的圖形自然帶有不同屬性：處在「第一」和「第二」個項目位置的圖形具有預測力、而在「第二」和「第三」個項目位置的圖形是可被預測的，因此我們意圖探索這些位於視覺信息處理系統中不同層次的腦區，對於「有預測力」和「可被預測」的刺激項目在學習歷程中是否、又會如何展現不同的活動與互動趨勢。
在實驗二的結果中，我們沒有觀察到任何腦區根據三連體的分組展現顯著的表徵相似性變化。值得注意的是，儘管行為測驗的結果顯示受試者確實有學習到某些刺激材料之間的序列規則，我們也未能重現之前腦造影研究中的結果，即對於在統計學習作業中屬於同一個三連體的刺激材料，其海馬迴系統內表徵的相似性會提升。考慮到與之前的研究相比，我們使用了更簡單、容易記憶的視覺刺激，因此可推測不同類型刺激材料的表徵可能具有不同程度的可塑性。關於第二個目的，我們發現在統計學習期間，不同腦區的活動動態存在差異。不同層級的視覺處理皮質在整個學習過程都有展現顯著活動，其中較高階的視覺皮質（除了合併的V1和V2之外）的活動強度有隨時間下降的趨勢。海馬迴在大部分的時間沒有展現顯著一致的活動，但在學習過程中期對於「有預測力」的刺激項目的活動強度有所提升。這意味它可能在使用模式完成（pattern completion）功能產生預測信號，透過預測編碼來促進統計學習的發生。內側顳葉皮質隨時間的變化趨勢在整體上與海馬迴更接近，但在學習過程中期也對「可被預測」的刺激項目提升活動強度，並且也對「可被預測」的刺激項目展現隨時間的活動程度下降。這複雜的活動模式意味這些腦區可能參與在多種不同機制中，有待後續研究釐清。
總結來說，這項研究的結果，無論是來自行為或是腦造影的實驗證據，皆支持海馬迴在統計學習歷程中有發揮作用，同時這些結果進一步地顯示海馬迴的「模式分離」和「模式完成」兩種功能可能都有助於更好地掌握環境中的規律性。當前的研究者通常將海馬迴參與在統計學習過程中發揮的效用與其模式完成功能連結，後續的研究值得深入探究海馬迴是否在學習的不同階段發揮著不同作用。

摘要(英)

“Statistical learning” refers to the learning effects exhibited by organisms through adaptation to regularities embedded in their environment under natural settings. While it encompasses a broad range of learning phenomena, researchers have proposed that they can generally be categorized as the extraction and integration of information, with memory processes serving as foundational building blocks for their emergence. Accumulating evidence from behavioral and functional neuroimaging studies suggests that SL is not supported by a single mechanism, but involves nearly all neural networks engaged in the processing of task-related information. The hippocampus, by virtue of its hierarchical position as well as its neural characteristics and architecture, plays a unique and significant role in the dynamic processes of learning and memory. Current researchers generally agree that the hippocampus facilitates the acquisition of associations by developing shared memory engram cells, with its pattern completion function having a synergistic effect. Conversely, its pattern separation function is considered to serve a complementary purpose, primarily responsible for maintaining the precision and distinctiveness of information. However, we speculated that pattern separation may also contribute to the acquisition of associations. To explore this possibility, the current study employed two experimental approaches: First, examining individual differences to investigate correlations between SL and other individual abilities, particularly the pattern separation function of the hippocampus. Second, exploring changes in brain activity associated with SL processes, focusing on the hippocampus (which supports pattern separation) and visual cortices (which are essential for visual processing).
Our first experiment sought to determine whether variation in SL performance across individuals can be associated with the efficacy of their hippocampal pattern separation function. We adopted an individual differences approach to examine whether a significant mechanistic link exists between these two cognitive processes, by analyzing the relationships between participants’ performances across a series of tasks. Experiment 1 comprised two parts, 1a and 1b, involving 105 young adults and 47 older adults, respectively, to further understand the potential the impact of aging on SL mechanisms. Participants performed tasks including two conventional SL tasks in visual or auditory modalities, one motor sequence learning task, a memory test sensitive to pattern separation, three traditional long-term memory tests, two working memory tests, and two fluid intelligence tests.
Results from Experiment 1a demonstrated an interconnection between SL abilities across sensory modalities, with performances on both SL tasks correlated with lure discrimination performance, which is closely related to the competence of hippocampal pattern separation function. This suggests that the superiority of the hippocampus to encode distinct experiences uniquely may influence the efficiency in SL. Experiment 1b further revealed that, among older adults, while the performance in visual SL remained correlated with the ability to discriminate lures in memory, those of auditory SL didn’t and, instead, showed an increased connection with the ability to explicitly learn (auditorily presented) paired associations. Furthermore, with aging, SL abilities across sensory modalities become less predictive of each other. These results indicate that, in elderlies, the capability to perceive and process auditory information may become a dominant factor influencing the variability of SL performance. Consequently, the impact of hippocampal pattern separation appears reduced, leading to a lack of observed modality-general SL mechanisms.
Our second, fMRI experiment had a dual purpose: Firstly, we aimed to identify a signature indicating that hippocampal pattern separation takes a part in SL processes. Secondly, we sought to explore the temporal dynamics across different brain regions during SL. To these ends, we recruited 37 participants, presented them with a series of geometrical shapes that were implicitly structured as triplets (i.e., three items with a fixed temporal order). On one hand, we examined whether representational patterns of shape stimuli sitting astride triplet boundaries would undergo differentiation after being familiarized with such regularity structure, implying pattern separation aids in reducing confusion caused by accidentally experienced associations. On the other hand, we explored whether and how regions at different levels of the visual information processing hierarchy, including the hippocampus, three MTL cortices, and four visual cortices (combined V1 and V2, remaining lateral occipital cortex, fusiform gyrus, inferior temporal gyrus), would exhibit different patterns of activity changes throughout the familiarization phase, possibly contingent upon the predictiveness (the first and the second items within a triplet can predict upcoming stimuli) and predictability (the second and the third items within a triplet can be predicted) of stimuli.
The results of Experiment 2 did not detect any significant changes in representational similarity across brain regions after learning. Notably, despite behavioral evidence of learning triplet structure (or sequential associations among stimuli), we were unable to reproduce previous results showing increased representational similarity for associated stimuli (i.e., items belonging to the same triplet) within the hippocampal system. Considering the use of simpler and more memorable visual stimuli compared to previous studies, we speculate that the plasticity of internal representations may vary depending on the properties of the stimulus materials. Regarding the second purpose, we found that activity dynamics during the learning phase vary across brain regions. Visual cortices were consistently engaged throughout the SL process, with its higher-level parts (excluding combined V1 and V2) showing a decline in activity over time, regardless of whether the stimuli is predictable. The hippocampus showed no consistent participation for most of the time, but increased its activity for predictive stimuli in the middle stages of the learning phase, possibly engaged in predictive coding process, facilitating perceptual processing by generating prediction signals through pattern completion. The MTL cortices exhibited complex activity patterns, roughly resembling those of the hippocampus, but also increased activity for predictable stimuli during the middle phase and decreased its response intensity over time, suggesting their involvement in multiple mechanisms that require further investigation.
In conclusion, the results of this study, both from behavioral and neuroimaging evidence, indicate that the hippocampus plays a role in SL processes. Furthermore, our results suggest that both the “pattern separation” and “pattern completion” functions of the hippocampus contribute to the better grasping of regularities in the environment. While current researchers predominantly associate the hippocampus’s involvement in SL with its pattern completion function, future studies should consider exploring the potential for the hippocampus to serve various functions at different stages of the SL process.

關鍵字(中)

★ 統計學習
★ 海馬迴
★ 模式分離
★ 個體差異
★ 功能性磁振造影

關鍵字(英)

★ Statistical Learning
★ Hippocampus
★ Pattern Separation
★ Individual Differences
★ fMRI

論文目次

Chinese Abstract ii
English Abstract v
Acknowledgments ix
Table of Contents xi
List of Figures xiv
List of Tables xvi
CHAPTER 1: INTRODUCTION 1
1.1 Statistical Learning 1
1.1.1 Unraveling the multifaceted nature of SL 2
1.1.2 Defining categories and properties of regularities 5
1.1.3 Discussing mechanistic accounts of SL 7
1.1.4 Revealing modulational properties embodied in SL processes 11
1.1.5 Gaining insight into SL through the individual differences approach 13
1.1.6 Comprehending SL from the influence of age 17
1.2 The neural foundations of SL 20
1.2.1 Recognizing the hippocampal system 22
1.2.2 Cracking the neural basis of learning and memory 25
1.2.3 Highlighting evidence of hippocampal involvement in SL 27
1.2.4 Questioning the significance of hippocampal engagement in SL 29
1.2.5 Including evidence of rapid cortical learning 31
1.2.6 Disentangling the complex role of hippocampus in SL 34
1.3 Summary and the aim of the present study 36
CHAPTER 2: EXPERIMENT 1a 38
2.1 Methods 39
2.1.1 Participants 39
2.1.2 Implicit/Statistical learning tasks 39
2.1.3 Mnemonic similarity task (MST) 42
2.1.4 Conventional long-term memory tests 44
2.1.5 General cognitive ability measures 45
2.1.6 Procedure 47
2.2 Results 48
2.2.1 Missing values 48
2.2.2 Performance on each task 49
2.2.3 Correlations within the same task domain 50
2.2.4 Correlations between I/SL and long-term memory abilities 53
2.2.5 Correlations with general cognitive abilities 54
2.3 Discussions 55
2.3.1 Hippocampal pattern separation function may play a role in SL processes 55
2.3.2 The disparities between the two SL paradigms may lead to the adoption of distinct learning mechanisms 56
CHAPTER 3: EXPERIMENT 1b 60
3.1 Methods 62
3.1.1 Participants 62
3.1.2 Tasks and procedure 62
3.2 Results 64
3.2.1 Missing values and data quality 64
3.2.2 Performance and relationship with age on each task 66
3.2.3 Correlations within task domain 70
3.2.4 Correlations between I/SL and long-term memory abilities 72
3.2.5 Correlations between I/SL and general cognitive abilities 73
3.3 Discussions 74
3.3.1 Aging affects SL mechanisms underlying the triplet segmentation tasks 74
3.3.2 Age-related declines in sequence learning on SRT tasks 76
CHAPTER 4: EXPERIMENT 2 79
4.1 Methods 81
4.1.1 Participants 81
4.1.2 fMRI task stimuli and design 82
4.1.3 Offline behavioral test 84
4.1.4 Procedure 85
4.1.5 MRI data acquisition 86
4.2 Analysis 87
4.2.1 Behavioral data analysis 87
4.2.2 MRI data preprocessing 88
4.2.3 Regions of interest (ROIs) definitions 89
4.2.4 Exploring changes in brain activity over time during the exposure phase 90
4.2.5 Exploring context-dependent interactions between the hippocampus and MTL/visual cortices during the exposure phase 93
4.2.6 Exploring changes in representational similarity after exposure phase 94
4.3 Results 97
4.3.1 Missing values 97
4.3.2 Behavioral performance of SL 97
4.3.3 Change in activity intensity throughout the exposure phase (Univariate) 98
4.3.4 Change in functional connectivity throughout the exposure phase (gPPI) 105
4.3.5 Representational change from before to after exposure phase (RSA) 108
4.3.6 Neural correlates with behavior familiarity score 112
4.4 Discussions 114
4.4.1 Whether the hippocampal system exhibits representational similarity changes that reflect the disambiguation of different triplets 114
4.4.2 Whether and how the hippocampus, alongside other cortices at different levels of the ventral visual information processing system, exhibit different patterns of activity 116
CHAPTER 5: GENERAL DISCUSSIONS 122
5.1 Summary of key results 122
5.2 Adaptive and versatile hippocampal processing during SL 123
5.3 Competition and cooperation between multiple brain systems during SL 126
5.4 Study limitation and future suggestions 128
5.5 Conclusions 129
REFERENCE 130

參考文獻

1. Augustinack, J. C., Huber, K. E., Stevens, A. A., Roy, M., Frosch, M. P., van der Kouwe, A. J. W., Wald, L. L., Van Leemput, K., McKee, A. C., Fischl, B., & Alzheimer’s Disease Neuroimaging Initiative. (2013). Predicting the location of human perirhinal cortex, Brodmann’s area 35, from MRI. NeuroImage, 64, 32–42. https://doi.org/10.1016/j.neuroimage.2012.08.071
2. Avants, B. B., Tustison, N., & Johnson, H. (2009). Advanced Normalization Tools (ANTS).
3. Bach, D. R., & Dolan, R. J. (2012). Knowing how much you don’t know: A neural organization of uncertainty estimates. Nature Reviews Neuroscience, 13(8), 572–586.
4. Baker, C. I., Olson, C. R., & Behrmann, M. (2004). Role of Attention and Perceptual Grouping in Visual Statistical Learning. Psychological Science, 15(7), 460–466. https://doi.org/10.1111/j.0956-7976.2004.00702.x
5. Barak, O., Rigotti, M., & Fusi, S. (2013). The sparseness of mixed selectivity neurons controls the generalization-discrimination trade-off. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 33(9), 3844–3856. https://doi.org/10.1523/JNEUROSCI.2753-12.2013
6. Bear, M. F., Connors, B. W., & Paradiso, M. A. (2016). Neuroscience: Exploring the brain (4. ed). Wolters Kluwer.
7. Benson, N. C., Butt, O. H., Brainard, D. H., & Aguirre, G. K. (2014). Correction of Distortion in Flattened Representations of the Cortical Surface Allows Prediction of V1-V3 Functional Organization from Anatomy. PLoS Computational Biology, 10(3), e1003538. https://doi.org/10.1371/journal.pcbi.1003538
8. Benson, N. C., & Winawer, J. (2018). Bayesian analysis of retinotopic maps. eLife, 7, e40224. https://doi.org/10.7554/eLife.40224
9. Bogaerts, L., Siegelman, N., & Frost, R. (2016). Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities. Psychonomic Bulletin & Review, 23(4), 1250–1256. https://doi.org/10.3758/s13423-015-0996-z
10. Borzello, M., Ramirez, S., Treves, A., Lee, I., Scharfman, H., Stark, C., Knierim, J. J., & Rangel, L. M. (2023). Assessments of dentate gyrus function: Discoveries and debates. Nature Reviews. Neuroscience, 24(8), 502–517. https://doi.org/10.1038/s41583-023-00710-z
11. Bulgarelli, F., Weiss, D. J., & Dennis, N. A. (2021). Cross-situational statistical learning in younger and older adults. Aging, Neuropsychology, and Cognition, 28(3), 346–366. https://doi.org/10.1080/13825585.2020.1759502
12. Cai, D. J., Aharoni, D., Shuman, T., Shobe, J., Biane, J., Song, W., Wei, B., Veshkini, M., La-Vu, M., Lou, J., Flores, S. E., Kim, I., Sano, Y., Zhou, M., Baumgaertel, K., Lavi, A., Kamata, M., Tuszynski, M., Mayford, M., … Silva, A. J. (2016). A shared neural ensemble links distinct contextual memories encoded close in time. Nature, 534(7605), 115–118. https://doi.org/10.1038/nature17955
13. Campbell, K. L., Zimerman, S., Healey, M. K., Lee, M. M. S., & Hasher, L. (2012). Age differences in visual statistical learning. Psychology and Aging, 27(3), 650–656. https://doi.org/10.1037/a0026780
14. Canada, K. L., Ngo, C. T., Newcombe, N. S., Geng, F., & Riggins, T. (2019). It’s All in the Details: Relations Between Young Children’s Developing Pattern Separation Abilities and Hippocampal Subfield Volumes. Cerebral Cortex, 29(8), 3427–3433. https://doi.org/10.1093/cercor/bhy211
15. Cattell, R. B. (1940). A culture-free intelligence test. I. Journal of Educational Psychology, 31(3), 161–179. https://doi.org/10.1037/h0059043
16. Chomsky, N. (1959). [Review of Review of Verbal behavior, by B. F. Skinner]. Language, 35(1), 26–58. https://doi.org/10.2307/411334
17. Choucry, A., Nomoto, M., & Inokuchi, K. (2024). Engram mechanisms of memory linking and identity. Nature Reviews. Neuroscience, 25(6), 375–392. https://doi.org/10.1038/s41583-024-00814-0
18. Christiansen, M. H. (2019). Implicit Statistical Learning: A Tale of Two Literatures. Topics in Cognitive Science, 11(3), 468–481. https://doi.org/10.1111/tops.12332
19. Christiansen, M. H., Allen, J., & Seidenberg, M. S. (1998). Learning to Segment Speech Using Multiple Cues: A Connectionist Model. Language and Cognitive Processes, 13(2–3), 221–268. https://doi.org/10.1080/016909698386528
20. Cohen, N. J., & Squire, L. R. (1980). Preserved Learning and Retention of Pattern-Analyzing Skill in Amnesia: Dissociation of Knowing How and Knowing That. Science, 210(4466), 207–210. https://doi.org/10.1126/science.7414331
21. Conway, C. M., & Christiansen, M. H. (2005). Modality-Constrained Statistical Learning of Tactile, Visual, and Auditory Sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(1), 24–39. https://doi.org/10.1037/0278-7393.31.1.24
22. Conway, C. M., & Christiansen, M. H. (2006). Statistical Learning Within and Between Modalities: Pitting Abstract Against Stimulus-Specific Representations. Psychological Science, 17(10), 905–912. https://doi.org/10.1111/j.1467-9280.2006.01801.x
23. Corkin, S. (1968). Acquisition of motor skill after bilateral medial temporal-lobe excision. Neuropsychologia, 6(3), 255–265. https://doi.org/10.1016/0028-3932(68)90024-9
24. Corsi, P. M. (1972). Human memory and the medial temporal region of the brain (Vol. 34, Issues 2-B, p. 891). ProQuest Information & Learning.
25. Covington, N. V., Brown-Schmidt, S., & Duff, M. C. (2018). The Necessity of the Hippocampus for Statistical Learning. Journal of Cognitive Neuroscience, 30(5), 680–697. https://doi.org/10.1162/jocn_a_01228
26. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. The Behavioral and Brain Sciences, 24(1), 87–114; discussion 114-185. https://doi.org/10.1017/s0140525x01003922
27. Cox, J. A., & Aimola Davies, A. M. (2022). Age differences in visual statistical learning: Investigating the effects of selective attention and stimulus category. Psychology and Aging, 37(6), 698–714. https://doi.org/10.1037/pag0000697
28. Cox, R. W. (1996). AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research, 29(3), 162–173. https://doi.org/10.1006/cbmr.1996.0014
29. Cox, R. W. (2019). Equitable Thresholding and Clustering: A Novel Method for Functional Magnetic Resonance Imaging Clustering in AFNI. Brain Connectivity, 9(7), 529–538. https://doi.org/10.1089/brain.2019.0666
30. DeKraker, J., Haast, R. A. M., Yousif, M. D., Karat, B., Lau, J. C., Köhler, S., & Khan, A. R. (2022). Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold. eLife, 11, e77945. https://doi.org/10.7554/eLife.77945
31. Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
32. Duncan, K. D., & Schlichting, M. L. (2018). Hippocampal representations as a function of time, subregion, and brain state. Neurobiology of Learning and Memory, 153, 40–56. https://doi.org/10.1016/j.nlm.2018.03.006
33. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1016/0364-0213(90)90002-E
34. Emberson, L. L., Conway, C. M., & Christiansen, M. H. (2011). Timing is everything: Changes in presentation rate have opposite effects on auditory and visual implicit statistical learning. Quarterly Journal of Experimental Psychology, 64(5), 1021–1040. https://doi.org/10.1080/17470218.2010.538972
35. Endress, A. D., & Bonatti, L. L. (2007). Rapid learning of syllable classes from a perceptually continuous speech stream. Cognition, 105(2), 247–299. https://doi.org/10.1016/j.cognition.2006.09.010
36. Endress, A. D., & Bonatti, L. L. (2016). Words, rules, and mechanisms of language acquisition. WIREs Cognitive Science, 7(1), 19–35. https://doi.org/10.1002/wcs.1376
37. Erickson, L. C., Kaschak, M. P., Thiessen, E. D., & Berry, C. A. S. (2016). Individual Differences in Statistical Learning: Conceptual and Measurement Issues. Collabra, 2(1). https://doi.org/10.1525/collabra.41
38. Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116. https://doi.org/10.1038/s41592-018-0235-4
39. Farrell Pagulayan, K., Busch, R. M., Medina, K. L., Bartok, J. A., & Krikorian, R. (2006). Developmental normative data for the Corsi Block-tapping task. Journal of Clinical and Experimental Neuropsychology, 28(6), 1043–1052. https://doi.org/10.1080/13803390500350977
40. Favila, S. E., Chanales, A. J. H., & Kuhl, B. A. (2016). Experience-dependent hippocampal pattern differentiation prevents interference during subsequent learning. Nature Communications, 7, 11066. https://doi.org/10.1038/ncomms11066
41. Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
42. Fischl, B., Stevens, A. A., Rajendran, N., Yeo, B. T. T., Greve, D. N., Van Leemput, K., Polimeni, J. R., Kakunoori, S., Buckner, R. L., Pacheco, J., Salat, D. H., Melcher, J., Frosch, M. P., Hyman, B. T., Grant, P. E., Rosen, B. R., van der Kouwe, A. J. W., Wiggins, G. C., Wald, L. L., & Augustinack, J. C. (2009). Predicting the location of entorhinal cortex from MRI. NeuroImage, 47(1), 8–17. https://doi.org/10.1016/j.neuroimage.2009.04.033
43. Fiser, J., & Aslin, R. N. (2001). Unsupervised Statistical Learning of Higher-Order Spatial Structures from Visual Scenes. Psychological Science, 12(6), 499–504. https://doi.org/10.1111/1467-9280.00392
44. Fonov, V., Evans, A., McKinstry, R., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102. https://doi.org/10.1016/S1053-8119(09)70884-5
45. Forest, T. A., Schlichting, M. L., Duncan, K. D., & Finn, A. S. (2023). Changes in statistical learning across development. Nature Reviews Psychology, 2(4), 205–219. https://doi.org/10.1038/s44159-023-00157-0
46. French, R. M., Mareschal, D., Mermillod, M., & Quinn, P. C. (2004). The Role of Bottom-Up Processing in Perceptual Categorization by 3- to 4-Month-Old Infants: Simulations and Data. Journal of Experimental Psychology: General, 133(3), 382–397. https://doi.org/10.1037/0096-3445.133.3.382
47. Frost, R., Armstrong, B. C., & Christiansen, M. H. (2019). Statistical learning research: A critical review and possible new directions. Psychological Bulletin, 145(12), 1128–1153. https://doi.org/10.1037/bul0000210
48. Frost, R., Armstrong, B. C., Siegelman, N., & Christiansen, M. H. (2015). Domain generality versus modality specificity: The paradox of statistical learning. Trends in Cognitive Sciences, 19(3), 117–125. https://doi.org/10.1016/j.tics.2014.12.010
49. Gheysen, F., Gevers, W., De Schutter, E., Van Waelvelde, H., & Fias, W. (2009). Disentangling perceptual from motor implicit sequence learning with a serial color-matching task. Experimental Brain Research, 197(2), 163–174. https://doi.org/10.1007/s00221-009-1902-6
50. Gheysen, F., Van Opstal, F., Roggeman, C., Van Waelvelde, H., & Fias, W. (2010). Hippocampal contribution to early and later stages of implicit motor sequence learning. Experimental Brain Research, 202(4), 795–807. https://doi.org/10.1007/s00221-010-2186-6
51. Gheysen, F., Van Opstal, F., Roggeman, C., Van Waelvelde, H., & Fias, W. (2011). The Neural Basis of Implicit Perceptual Sequence Learning. Frontiers in Human Neuroscience, 5. https://doi.org/10.3389/fnhum.2011.00137
52. Goldstein, R., & Vitevitch, M. S. (2014). The influence of clustering coefficient on word-learning: How groups of similar sounding words facilitate acquisition. Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.01307
53. Gómez, R. L. (2002). Variability and Detection of Invariant Structure. Psychological Science, 13(5), 431–436. https://doi.org/10.1111/1467-9280.00476
54. Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., … Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1), Article 1. https://doi.org/10.1038/sdata.2016.44
55. Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63–72. https://doi.org/10.1016/j.neuroimage.2009.06.060
56. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009). PyMVPA: A Python Toolbox for Multivariate Pattern Analysis of fMRI Data. Neuroinformatics, 7(1), 37–53. https://doi.org/10.1007/s12021-008-9041-y
57. Harrison, L. M., Duggins, A., & Friston, K. J. (2006). Encoding uncertainty in the hippocampus. Neural Networks: The Official Journal of the International Neural Network Society, 19(5), 535–546. https://doi.org/10.1016/j.neunet.2005.11.002
58. Hasson, U. (2017). The neurobiology of uncertainty: Implications for statistical learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711), 20160048. https://doi.org/10.1098/rstb.2016.0048
59. Hasson, U., Chen, J., & Honey, C. J. (2015). Hierarchical process memory: Memory as an integral component of information processing. Trends in Cognitive Sciences, 19(6), 304–313. https://doi.org/10.1016/j.tics.2015.04.006
60. Hauser, M. D., Newport, E. L., & Aslin, R. N. (2001). Segmentation of the speech stream in a non-human primate: Statistical learning in cotton-top tamarins. Cognition, 78(3), B53–B64. https://doi.org/10.1016/S0010-0277(00)00132-3
61. Hebb, D. O. (1949). The organization of behavior; a neuropsychological theory (pp. xix, 335). Wiley.
62. Henke, K. (2010). A model for memory systems based on processing modes rather than consciousness. Nature Reviews Neuroscience, 11(7), 523–532. https://doi.org/10.1038/nrn2850
63. Hindy, N. C., Ng, F. Y., & Turk-Browne, N. B. (2016). Linking pattern completion in the hippocampus to predictive coding in visual cortex. Nature Neuroscience, 19(5), 665–667. https://doi.org/10.1038/nn.4284
64. Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554
65. Howard, D. V., & Howard, J. H. (1989). Age differences in learning serial patterns: Direct versus indirect measures. Psychology and Aging, 4(3), 357–364. https://doi.org/10.1037/0882-7974.4.3.357
66. Howard, D. V., & Howard, J. H. (1992). Adult age differences in the rate of learning serial patterns: Evidence from direct and indirect tests. Psychology and Aging, 7(2), 232–241. https://doi.org/10.1037/0882-7974.7.2.232
67. Howard, J. H., & Howard, D. V. (1997). Age differences in implicit learning of higher order dependencies in serial patterns. Psychology and Aging, 12(4), 634–656. https://doi.org/10.1037//0882-7974.12.4.634
68. Howard, J. H., & Howard, D. V. (2013). Aging mind and brain: Is implicit learning spared in healthy aging? Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00817
69. Ihle-Hansen, H., Vigen, T., Berge, T., Einvik, G., Aarsland, D., Rønning, O. M., Thommessen, B., Røsjø, H., Tveit, A., & Ihle-Hansen, H. (2018). Montreal Cognitive Assessment in a 63- to 65-year-old Norwegian Cohort from the General Population: Data from the Akershus Cardiac Examination 1950 Study. Dementia and Geriatric Cognitive Disorders Extra, 7(3), 318–327. https://doi.org/10.1159/000480496
70. Isbilen, E. S., McCauley, S. M., Kidd, E., & Christiansen, M. H. (2020). Statistically Induced Chunking Recall: A Memory-Based Approach to Statistical Learning. Cognitive Science, 44(7), e12848. https://doi.org/10.1111/cogs.12848
71. Janacsek, K., & Nemeth, D. (2013). Implicit sequence learning and working memory: Correlated or complicated? Cortex, 49(8), 2001–2006. https://doi.org/10.1016/j.cortex.2013.02.012
72. Janacsek, K., & Nemeth, D. (2015). The puzzle is complicated: When should working memory be related to implicit sequence learning, and when should it not? (Response to Martini et al.). Cortex, 64, 411–412. https://doi.org/10.1016/j.cortex.2014.07.020
73. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841. https://doi.org/10.1016/s1053-8119(02)91132-8
74. Karuza, E. A., Emberson, L. L., Roser, M. E., Cole, D., Aslin, R. N., & Fiser, J. (2017). Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes. Journal of Cognitive Neuroscience, 29(12), 1963–1976. https://doi.org/10.1162/jocn_a_01182
75. Karuza, E. A., Thompson-Schill, S. L., & Bassett, D. S. (2016). Local Patterns to Global Architectures: Influences of Network Topology on Human Learning. Trends in Cognitive Sciences, 20(8), 629–640. https://doi.org/10.1016/j.tics.2016.06.003
76. Kassambara, A. (2023). Rstatix: Pipe-Friendly Framework for Basic Statistical Tests_. R package version 0.7.2. https://CRAN.R-project.org/package=rstatix
77. Kim, G., Lewis-Peacock, J. A., Norman, K. A., & Turk-Browne, N. B. (2014). Pruning of memories by context-based prediction error. Proceedings of the National Academy of Sciences, 111(24), 8997–9002. https://doi.org/10.1073/pnas.1319438111
78. Kim, G., Norman, K. A., & Turk-Browne, N. B. (2017). Neural Differentiation of Incorrectly Predicted Memories. The Journal of Neuroscience, 37(8), 2022–2031. https://doi.org/10.1523/JNEUROSCI.3272-16.2017
79. Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition, 83(2), B35–B42. https://doi.org/10.1016/S0010-0277(02)00004-5
80. Kirwan, C. B., Hartshorn, A., Stark, S. M., Goodrich-Hunsaker, N. J., Hopkins, R. O., & Stark, C. E. L. (2012). Pattern separation deficits following damage to the hippocampus. Neuropsychologia, 50(10), 2408–2414. https://doi.org/10.1016/j.neuropsychologia.2012.06.011
81. Kirwan, C. B., & Stark, C. E. L. (2007). Overcoming interference: An fMRI investigation of pattern separation in the medial temporal lobe. Learning & Memory (Cold Spring Harbor, N.Y.), 14(9), 625–633. https://doi.org/10.1101/lm.663507
82. Klein, A., Ghosh, S. S., Bao, F. S., Giard, J., Häme, Y., Stavsky, E., Lee, N., Rossa, B., Reuter, M., Chaibub Neto, E., & Keshavan, A. (2017). Mindboggling morphometry of human brains. PLOS Computational Biology, 13(2), e1005350. https://doi.org/10.1371/journal.pcbi.1005350
83. Klein, A., & Tourville, J. (2012). 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol. Frontiers in Neuroscience, 6. https://doi.org/10.3389/fnins.2012.00171
84. Knowlton, B. J., Squire, L. R., & Gluck, M. A. (1994). Probabilistic classification learning in amnesia. Learning & Memory, 1(2), 106–120. https://doi.org/10.1101/lm.1.2.106
85. Kóbor, A., Horváth, K., Kardos, Z., Nemeth, D., & Janacsek, K. (2020). Perceiving structure in unstructured stimuli: Implicitly acquired prior knowledge impacts the processing of unpredictable transitional probabilities. Cognition, 205, 104413. https://doi.org/10.1016/j.cognition.2020.104413
86. Kóbor, A., Janacsek, K., Hermann, P., Zavecz, Z., Varga, V., Csépe, V., Vidnyánszky, Z., Kovacs, G., & Nemeth, D. (2022). Finding pattern in the noise: Persistent implicit statistical knowledge impacts the processing of unpredictable stimuli [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/axq49
87. Kok, P., Jehee, J. F. M., & de Lange, F. P. (2012). Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex. Neuron, 75(2), 265–270. https://doi.org/10.1016/j.neuron.2012.04.034
88. Kriegeskorte, N. (2008). Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience. https://doi.org/10.3389/neuro.06.004.2008
89. Liao, Y. C., Yeh, T. L., Yang, Y. K., Lu, F.-H., Chang, C.-J., Ko, H. C., & Lo, C. M. (2004). Reliability and validation of the Taiwan geriatric depression scale. Taiwanese Journal of Psychiatry, 18, 30–41.
90. Lu, J.-F. R., Tseng, H.-M., & Tsai, Y.-J. (2003). Assessment of health-related quality of life in Taiwan (I): Development and psychometric testing of SF-36 Taiwan version. Taiwan Journal of Public Health, 22(6), 501–511.
91. Lukács, Á., & Kemény, F. (2015). Development of Different Forms of Skill Learning Throughout the Lifespan. Cognitive Science, 39(2), 383–404. https://doi.org/10.1111/cogs.12143
92. Manns, J. R., Howard, M. W., & Eichenbaum, H. (2007). Gradual changes in hippocampal activity support remembering the order of events. Neuron, 56(3), 530–540. https://doi.org/10.1016/j.neuron.2007.08.017
93. Marr, D. (1971). Simple Memory: A Theory for Archicortex. Philosophical Transactions of the Royal Society of London Series B, 262(841), 23–81.
94. Martini, M., Sachse, P., Furtner, M. R., & Gaschler, R. (2015). Why should working memory be related to incidentally learned sequence structures? Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 64, 407–410. https://doi.org/10.1016/j.cortex.2014.05.016
95. Maye, J., Werker, J. F., & Gerken, L. (2002). Infant sensitivity to distributional information can affect phonetic discrimination. Cognition, 82(3), B101–B111. https://doi.org/10.1016/S0010-0277(01)00157-3
96. McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419–457. https://doi.org/10.1037/0033-295X.102.3.419
97. McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. NeuroImage, 61(4), 1277–1286. https://doi.org/10.1016/j.neuroimage.2012.03.068
98. Meulemans, T., & Van der Linden, M. (1997). Associative chunk strength in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(4), 1007–1028. https://doi.org/10.1037/0278-7393.23.4.1007
99. Midford, R., & Kirsner, K. (2005). Implicit and Explicit Learning in Aged and Young Adults. Aging, Neuropsychology, and Cognition, 12(4), 359–387. https://doi.org/10.1080/13825580500246894
100. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
101. Milner, B. (1966). Amnesia following operation on the temporal lobes. Amnesia, 109–133.
102. Milner, B., Corkin, S., & Teuber, H.-L. (1968). Further analysis of the hippocampal amnesic syndrome: 14-year follow-up study of H.M. Neuropsychologia, 6(3), 215–234. https://doi.org/10.1016/0028-3932(68)90021-3
103. Miyashita, Y. (1988). Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature, 335(6193), 817–820. https://doi.org/10.1038/335817a0
104. Miyashita, Y. (1993). Inferior temporal cortex: Where visual perception meets memory. Annual Review of Neuroscience, 16, 245–263. https://doi.org/10.1146/annurev.ne.16.030193.001333
105. Molitor, R. J., Sherrill, K. R., Morton, N. W., Miller, A. A., & Preston, A. R. (2021). Memory Reactivation during Learning Simultaneously Promotes Dentate Gyrus/CA 2,3 Pattern Differentiation and CA 1 Memory Integration. The Journal of Neuroscience, 41(4), 726–738. https://doi.org/10.1523/JNEUROSCI.0394-20.2020
106. Mumford, J. A., Turner, B. O., Ashby, F. G., & Poldrack, R. A. (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59(3), 2636–2643. https://doi.org/10.1016/j.neuroimage.2011.08.076
107. Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment. Journal of the American Geriatrics Society, 53(4), 695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x
108. Neger, T. M., Rietveld, T., & Janse, E. (2014). Relationship between perceptual learning in speech and statistical learning in younger and older adults. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00628
109. Neger, T. M., Rietveld, T., & Janse, E. (2015). Adult age effects in auditory statistical learning. 18th International Congress of Phonetic Sciences (ICPhS 2015). https://doi.org/10/component/file_2195943/ICPHS0124.pdf
110. Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. Cognitive Psychology, 19(1), 1–32. https://doi.org/10.1016/0010-0285(87)90002-8
111. Palmer, S. D., Hutson, J., & Mattys, S. L. (2018). Statistical Learning for Speech Segmentation: Age-Related Changes and Underlying Mechanisms. Psychology and Aging, 33(7), 1035–1044. https://doi.org/10.1037/pag0000292
112. Park, D. C., & Reuter-Lorenz, P. (2009). The Adaptive Brain: Aging and Neurocognitive Scaffolding. Annual Review of Psychology, 60(1), 173–196. https://doi.org/10.1146/annurev.psych.59.103006.093656
113. Peña, M., Bonatti, L. L., Nespor, M., & Mehler, J. (2002). Signal-driven computations in speech processing. Science (New York, N.Y.), 298(5593), 604–607. https://doi.org/10.1126/science.1072901
114. Perruchet, P. (2019). What Mechanisms Underlie Implicit Statistical Learning? Transitional Probabilities Versus Chunks in Language Learning. Topics in Cognitive Science, 11(3), 520–535. https://doi.org/10.1111/tops.12403
115. Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: One phenomenon, two approaches. Trends in Cognitive Sciences, 10(5), 233–238. https://doi.org/10.1016/j.tics.2006.03.006
116. Perruchet, P., & Vinter, A. (1998). PARSER: A Model for Word Segmentation. Journal of Memory and Language, 39(2), 246–263. https://doi.org/10.1006/jmla.1998.2576
117. Poldrack, R. A., & Rodriguez, P. (2004). How do memory systems interact? Evidence from human classification learning. Neurobiology of Learning and Memory, 82(3), 324–332. https://doi.org/10.1016/j.nlm.2004.05.003
118. Raslau, F. D., Mark, I. T., Klein, A. P., Ulmer, J. L., Mathews, V., & Mark, L. P. (2015). Memory Part 2: The Role of the Medial Temporal Lobe. American Journal of Neuroradiology, 36(5), 846–849. https://doi.org/10.3174/ajnr.A4169
119. Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6(6), 855–863. https://doi.org/10.1016/S0022-5371(67)80149-X
120. Reber, A. S. (1976). Implicit learning of synthetic languages: The role of instructional set. Journal of Experimental Psychology: Human Learning and Memory, 2(1), 88–94. https://doi.org/10.1037/0278-7393.2.1.88
121. Reber, P. J. (2013). The neural basis of implicit learning and memory: A review of neuropsychological and neuroimaging research. Neuropsychologia, 51(10), 2026–2042. https://doi.org/10.1016/j.neuropsychologia.2013.06.019
122. Ritvo, V. J. H., Turk-Browne, N. B., & Norman, K. A. (2019). Nonmonotonic Plasticity: How Memory Retrieval Drives Learning. Trends in Cognitive Sciences, 23(9), 726–742. https://doi.org/10.1016/j.tics.2019.06.007
123. Rogerson, T., Cai, D. J., Frank, A., Sano, Y., Shobe, J., Lopez-Aranda, M. F., & Silva, A. J. (2014). Synaptic tagging during memory allocation. Nature Reviews. Neuroscience, 15(3), 157–169. https://doi.org/10.1038/nrn3667
124. Rosenthal, C. R., Andrews, S. K., Antoniades, C. A., Kennard, C., & Soto, D. (2016). Learning and Recognition of a Non-conscious Sequence of Events in Human Primary Visual Cortex. Current Biology, 26(6), 834–841. https://doi.org/10.1016/j.cub.2016.01.040
125. Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical Learning by 8-Month-Old Infants. Science, 274(5294), 1926–1928. https://doi.org/10.1126/science.274.5294.1926
126. Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70(1), 27–52. https://doi.org/10.1016/s0010-0277(98)00075-4
127. Saffran, J. R., Newport, E. L., Aslin, R. N., Tunick, R. A., & Barrueco, S. (1997). Incidental language learning: Listening (and learning) out of the corner of your ear. Psychological Science, 8(2), 101–105. https://doi.org/10.1111/j.1467-9280.1997.tb00690.x
128. Saffran, J. R., & Thiessen, E. D. (2007). Domain-General Learning Capacities. In E. Hoff & M. Shatz (Eds.), Blackwell Handbook of Language Development (pp. 68–86). Blackwell Publishing Ltd. https://doi.org/10.1002/9780470757833.ch4
129. Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M., & Turk-Browne, N. B. (2014). The Necessity of the Medial Temporal Lobe for Statistical Learning. Journal of Cognitive Neuroscience, 26(8), 1736–1747. https://doi.org/10.1162/jocn_a_00578
130. Schapiro, A. C., Kustner, L. V., & Turk-Browne, N. B. (2012). Shaping of Object Representations in the Human Medial Temporal Lobe Based on Temporal Regularities. Current Biology, 22(17), 1622–1627. https://doi.org/10.1016/j.cub.2012.06.056
131. Schapiro, A. C., Rogers, T. T., Cordova, N. I., Turk-Browne, N. B., & Botvinick, M. M. (2013). Neural representations of events arise from temporal community structure. Nature Neuroscience, 16(4), 486–492. https://doi.org/10.1038/nn.3331
132. Schapiro, A. C., Turk-Browne, N. B., Botvinick, M. M., & Norman, K. A. (2017). Complementary learning systems within the hippocampus: A neural network modelling approach to reconciling episodic memory with statistical learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711), 20160049. https://doi.org/10.1098/rstb.2016.0049
133. Schapiro, A. C., Turk-Browne, N. B., Norman, K. A., & Botvinick, M. M. (2016). Statistical learning of temporal community structure in the hippocampus. Hippocampus, 26(1), 3–8. https://doi.org/10.1002/hipo.22523
134. Schendan, H. E., Searl, M. M., Melrose, R. J., & Stern, C. E. (2003). An FMRI study of the role of the medial temporal lobe in implicit and explicit sequence learning. Neuron, 37(6), 1013–1025. https://doi.org/10.1016/s0896-6273(03)00123-5
135. Schevenels, K., Altvater-Mackensen, N., Zink, I., De Smedt, B., & Vandermosten, M. (2021). Aging effects and feasibility of statistical learning tasks across modalities. Aging, Neuropsychology, and Cognition, 30(2), 201–230. https://doi.org/10.1080/13825585.2021.2007213
136. Schlichting, M. L., Mumford, J. A., & Preston, A. R. (2015). Learning-related representational changes reveal dissociable integration and separation signatures in the hippocampus and prefrontal cortex. Nature Communications, 6(1), 8151. https://doi.org/10.1038/ncomms9151
137. Scoville, W. B., & Milner, B. (1957). LOSS OF RECENT MEMORY AFTER BILATERAL HIPPOCAMPAL LESIONS. Journal of Neurology, Neurosurgery & Psychiatry, 20(1), 11–21. https://doi.org/10.1136/jnnp.20.1.11
138. Sherman, B. E., & Turk-Browne, N. B. (2020). Statistical prediction of the future impairs episodic encoding of the present. Proceedings of the National Academy of Sciences, 117(37), 22760–22770. https://doi.org/10.1073/pnas.2013291117
139. Siegelman, N., Bogaerts, L., Elazar, A., Arciuli, J., & Frost, R. (2018). Linguistic entrenchment: Prior knowledge impacts statistical learning performance. Cognition, 177, 198–213. https://doi.org/10.1016/j.cognition.2018.04.011
140. Siegelman, N., Bogaerts, L., & Frost, R. (2017). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. Behavior Research Methods, 49(2), 418–432. https://doi.org/10.3758/s13428-016-0719-z
141. Siegelman, N., Bogaerts, L., & Frost, R. (2019). What Determines Visual Statistical Learning Performance? Insights From Information Theory. Cognitive Science, 43(12), e12803. https://doi.org/10.1111/cogs.12803
142. Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: Theoretical perspectives and empirical evidence. Journal of Memory and Language, 81, 105–120. https://doi.org/10.1016/j.jml.2015.02.001
143. Staresina, B. P., & Davachi, L. (2009). Mind the Gap: Binding Experiences across Space and Time in the Human Hippocampus. Neuron, 63(2), 267–276. https://doi.org/10.1016/j.neuron.2009.06.024
144. Stark, S. M., Kirwan, C. B., & Stark, C. E. L. (2019). Mnemonic Similarity Task: A Tool for Assessing Hippocampal Integrity. Trends in Cognitive Sciences, 23(11), 938–951. https://doi.org/10.1016/j.tics.2019.08.003
145. Stark, S. M., Yassa, M. A., Lacy, J. W., & Stark, C. E. L. (2013). A task to assess behavioral pattern separation (BPS) in humans: Data from healthy aging and mild cognitive impairment. Neuropsychologia, 51(12), 2442–2449. https://doi.org/10.1016/j.neuropsychologia.2012.12.014
146. Thiessen, E. D. (2017). What’s statistical about learning? Insights from modelling statistical learning as a set of memory processes. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711), 20160056. https://doi.org/10.1098/rstb.2016.0056
147. Thiessen, E. D., & Erickson, L. C. (2013). Beyond Word Segmentation: A Two- Process Account of Statistical Learning. Current Directions in Psychological Science, 22(3), 239–243. https://doi.org/10.1177/0963721413476035
148. Thiessen, E. D., Kronstein, A. T., & Hufnagle, D. G. (2013). The extraction and integration framework: A two-process account of statistical learning. Psychological Bulletin, 139(4), 792–814. https://doi.org/10.1037/a0030801
149. Thiessen, E. D., & Pavlik, P. I. (2013). iMinerva: A Mathematical Model of Distributional Statistical Learning. Cognitive Science, 37(2), 310–343. https://doi.org/10.1111/cogs.12011
150. Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). mediation: R package for causal mediation analysis. UCLA Statistics/American Statistical Association. https://dspace.mit.edu/handle/1721.1/91154
151. Treves, A., & Rolls, E. T. (1992). Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus, 2(2), 189–199. https://doi.org/10.1002/hipo.450020209
152. Tsai, C.-F., Lee, W.-J., Wang, S.-J., Shia, B.-C., Nasreddine, Z., & Fuh, J.-L. (2012). Psychometrics of the Montreal Cognitive Assessment (MoCA) and its subscales: Validation of the Taiwanese version of the MoCA and an item response theory analysis. International Psychogeriatrics, 24(4), 651–658. https://doi.org/10.1017/S1041610211002298
153. Tseng, H.-M., Lu, J.-F. R., & Tsai, Y.-J. (2003). Assessment of health-related quality of life in Taiwan (II): Norming and validation of SF-36 Taiwan version. Taiwan Journal of Public Health, 22(6), 512–518.
154. Turk-Browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2009). Neural Evidence of Statistical Learning: Efficient Detection of Visual Regularities Without Awareness. Journal of Cognitive Neuroscience, 21(10), 1934–1945. https://doi.org/10.1162/jocn.2009.21131
155. Turk-Browne, N. B., Scholl, B. J., Johnson, M. K., & Chun, M. M. (2010). Implicit Perceptual Anticipation Triggered by Statistical Learning. The Journal of Neuroscience, 30(33), 11177–11187. https://doi.org/10.1523/JNEUROSCI.0858-10.2010
156. Turk-Browne, N., Jungé, J., & Scholl, B. (2005). The Automaticity of Visual Statistical Learning. Journal of Experimental Psychology. General, 134, 552–564. https://doi.org/10.1037/0096-3445.134.4.552
157. Turner, B. O. (2010). A comparison of methods for the use of pattern classification on rapid event-related fMRI data. [Poster]. Annual Meeting of the Society for Neuroscience.
158. Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908
159. Voss, J. L., Bridge, D. J., Cohen, N. J., & Walker, J. A. (2017). A Closer Look at the Hippocampus and Memory. Trends in Cognitive Sciences, 21(8), 577–588. https://doi.org/10.1016/j.tics.2017.05.008
160. Wammes, J., Norman, K. A., & Turk-Browne, N. (2022). Increasing stimulus similarity drives nonmonotonic representational change in hippocampus. eLife, 11, e68344. https://doi.org/10.7554/eLife.68344
161. Ware, Jr., J. E., & Gandek, B. (1994). The SF-36 Health Survey: Development and Use in Mental Health Research and the IQOLA Project. International Journal of Mental Health, 23(2), 49–73. https://doi.org/10.1080/00207411.1994.11449283
162. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), Article 6684. https://doi.org/10.1038/30918
163. Wechsler, D. (1997a). Wechsler Adult Intelligence Scale—Third Edition (WAIS-III). San Antonio, TX: The Psychological Corporation.
164. Wechsler, D. (1997b). Wechsler Memory Scale—Third Edition (WMS-III). San Antonio, TX: The Psychological Corporation.
165. Yassa, M. A., & Stark, C. E. L. (2011). Pattern separation in the hippocampus. Trends in Neurosciences, 34(10), 515–525. https://doi.org/10.1016/j.tins.2011.06.006
166. Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17(1), 37–49. https://doi.org/10.1016/0022-3956(82)90033-4
167. Yu, C., & Smith, L. B. (2007). Rapid word learning under uncertainty via cross-situational statistics. Psychological Science, 18(5), 414–420. https://doi.org/10.1111/j.1467-9280.2007.01915.x
168. Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57. https://doi.org/10.1109/42.906424

指導教授

吳嫻(Denise H. Wu)

審核日期

2024-8-23

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