Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/156083
|
| Title: | 以fMRI與機器學習探討數學焦慮的神經網絡
Investigating the Neural Network Underlying Math Anxiety Through fMRI and Machine Learning |
| Authors: | 卓奕呈
Cho, Yi-Cheng |
| Contributors: | 張葶葶
Chang, Ting-Ting
卓奕呈
Cho, Yi-Cheng |
| Keywords: | 數學焦慮
工作記憶
神經連結
功能性磁振造影
預測建模
math anxiety
working memory
neural connectivity
fMRI
predictive modeling |
| Date: | 2025 |
| Issue Date: | 2025-03-03 15:32:20 (UTC+8) |
| Abstract: | 許多行為研究強調了數學焦慮與數學表現兩者之間的負面相關，並常將其歸因於對工作記憶的干擾。儘管相關行為研究已相當廣泛，數學焦慮背後的神經機制仍不清晰，這主要源於各種研究中運用不同的數學任務和方法，以及在任務複雜性區分上的不足。此外，數學焦慮對內在大腦連結性的影響仍未被充分探索，以及整合神經影像數據與認知數據以預測數學焦慮的研究也仍相當稀少，而機器學習方法也可為理解數學焦慮的神經機制來提供更全面的見解，特別是在不同情境下的展現。而鑑於過往行為研究一致發現工作記憶在調節數學焦慮影響中具有關鍵作用，本研究三個實驗均聚焦於與工作記憶和認知控制相關的神經網路。
本研究我們藉由三種互補的方法探討數學焦慮的神經相關性。研究1檢視了算術問題解決過程中的任務相關大腦活化和功能性連結。結果顯示，數學焦慮與數字處理區域（右側內頂葉溝）活化增加有關，這表明高焦慮個體可能需要更多努力來應對更高的認知和情緒需求，此外，數學焦慮干擾了情緒相關腦區（右側杏仁核）與認知控制腦區（如左側下額葉回和輔助運動區）之間的整合，突顯了在處理複雜任務時管理情緒干擾的挑戰。研究2通過靜息態功能性連結（RSFC）分析探索內在神經狀態，結果未發現數學焦慮與內在連結性之間的顯著關聯。然而，在研究1中基於任務的心理生理交互作用（PPI）分析發現數學焦慮與數學相關任務期間的功能性連結有顯著相關性，強調其情境依賴的特性。研究3採用預測建模，結果顯示，任務相關的PPI特徵，特別是結合工作記憶等認知因素後，其預測準確性明顯優於RSFC特徵。關鍵的預測因子（例如右側杏仁核與右側內頂葉溝之間的連結性）突顯了情緒、工作記憶和數字處理之間的交互作用。
這些發現表明，數學焦慮主要透過認知焦慮機制運作，影響注意力與工作記憶，而非源於持續性的情緒過度活躍。此外，結果進一步表明數學焦慮更多仰賴任務特定的神經動態，而非穩定的特質樣態。這強調了任務特定神經連結的失調及其與認知過程交互作用如何影響數學焦慮，為數學相關挑戰中的神經認知基礎提供了全面的視角。
Many behavioral studies have emphasized the adverse link between math anxiety and math performance, frequently attributing it to the disruption of working memory. Despite extensive behavioral research, the neural mechanisms underlying math anxiety remain unclear, with inconsistencies in findings stemming from the use of various tasks with differing methodologies and insufficient differentiation of task complexities. Moreover, the impact of math anxiety on intrinsic brain connectivity remains underexplored. Additionally, there is a need for studies that integrate neuroimaging data with cognitive indicators to predict math anxiety, as this approach could offer deeper insights into the neural mechanisms underlying math anxiety, particularly across varying situational contexts. Given the consistent findings from prior behavioral studies highlighting the critical role of working memory in mediating the effects of math anxiety, all three experiments in this study were designed to focus on neural networks associated with working memory and cognitive control.
In this study, we investigated the neural correlates of math anxiety through three complementary approaches. Study 1 examined task-related brain activation and functional connectivity during arithmetic problem-solving. The results showed that math anxiety was associated with increased activation in numerical processing regions (right intraparietal sulcus), suggesting an increased cognitive effort to manage heightened cognitive and emotional demands. Furthermore, math anxiety disrupted the integration between emotion-related regions (right amygdala) and cognitive control regions (e.g., left inferior frontal gyrus and supplementary motor area), highlighting challenges in managing emotional interference during complex tasks. Study 2 explored intrinsic neural states using resting-state functional connectivity (RSFC) analysis, finding no significant associations between math anxiety and intrinsic connectivity. In contrast, task-based psychophysiological interaction (PPI) analyses identified significant correlations between math anxiety and functional connectivity during math-related tasks, emphasizing its context-dependent nature. Study 3 employed predictive modeling, demonstrating that task-related PPI features, particularly when combined with cognitive factors like working memory and vocabulary, achieved superior predictive accuracy compared to RSFC features. Key predictors, such as connectivity between the right amygdala and right intraparietal sulcus, underscored the interaction between emotion regulation, working memory, and numerical processing.
These findings suggest that math anxiety predominantly operates through cognitive anxiety mechanisms, disrupting attention and working memory rather than resulting from persistent emotional hyperactivity. Moreover, the results underscore its situational nature, emphasizing that task-specific disruptions in neural connectivity are central to math-related performance deficits. These insights emphasize the need for targeted interventions that address its context-specific triggers and improve both cognitive efficiency and emotional regulation during math-related tasks. |
| Reference: | Alloway, T. P., & Passolunghi, M. C. (2011). The relationship between working memory, IQ, and mathematical skills in children. Learning and Individual Differences, 21(1), 133-137. https://doi.org/https://doi.org/10.1016/j.lindif.2010.09.013
Amunts, K., Kedo, O., Kindler, M., Pieperhoff, P., Mohlberg, H., Shah, N. J., Habel, U., Schneider, F., & Zilles, K. (2005). Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology, 210(5), 343-352. https://doi.org/10.1007/s00429-005-0025-5
Ansari, D. (2008). Effects of development and enculturation on number representation in the brain. Nature Reviews Neuroscience, 9(4), 278-291. https://doi.org/10.1038/nrn2334
Ardekani, B. A., Bermudez, E., Mubeen, A. M., Bachman, A. H., & for the Alzheimer’s Disease Neuroimaging, I. (2017). Prediction of Incipient Alzheimer’s Disease Dementia in Patients with Mild Cognitive Impairment. Journal of Alzheimer's Disease, 55, 269-281. https://doi.org/10.3233/JAD-160594
Artemenko, C., Daroczy, G., & Nuerk, H.-C. (2015). Neural correlates of math anxiety – an overview and implications [Mini Review]. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01333
Ashcraft, M. H. (2002). Math Anxiety: Personal, Educational, and Cognitive Consequences. Current directions in psychological science, 11(5), 181-185. https://doi.org/10.1111/1467-8721.00196
Ashcraft, M. H., & Faust, M. W. (1994). Mathematics anxiety and mental arithmetic performance: An exploratory investigation. Cognition and Emotion, 8(2), 97-125. https://doi.org/10.1080/02699939408408931
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224-237. https://doi.org/10.1037/0096-3445.130.2.224
Ashcraft, M. H., & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243-248. https://doi.org/10.3758/BF03194059
Ashcraft, M. H., & Moore, A. M. (2009). Mathematics Anxiety and the Affective Drop in Performance. Journal of Psychoeducational Assessment, 27(3), 197-205. https://doi.org/10.1177/0734282908330580
Atabek, O., Şavklıyıldız, A., Orhon, G., Colak, O. H., Özdemir, A., & Şenol, U. (2022). The effect of anxiety on mathematical thinking: An fMRI study on 12th-grade students. Learning and Motivation, 77, 101779. https://doi.org/https://doi.org/10.1016/j.lmot.2021.101779
Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., & Daucourt, M. C. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin, 147(2), 134-168. https://doi.org/10.1037/bul0000307
Beilock, S. L., & Carr, T. H. (2005). When High-Powered People Fail: Working Memory and "Choking under Pressure" in Math. Psychological Science, 16(2), 101-105. http://www.jstor.org/stable/40064185
Biatchford, P. (1996). Pupils’ views on school work and school from 7 to 16 years. Research Papers in Education, 11(3), 263-288. https://doi.org/10.1080/0267152960110305
Cabral, C., & Silveira, M. (2013, 3-7 July 2013). Classification of Alzheimer's disease from FDG-PET images using favourite class ensembles. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Carey, E., Hill, F., Devine, A., & Szücs, D. (2016). The Chicken or the Egg? The Direction of the Relationship Between Mathematics Anxiety and Mathematics Performance [Mini Review]. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01987
Cargnelutti, E., Tomasetto, C., & Passolunghi, M. C. (2017). The interplay between affective and cognitive factors in shaping early proficiency in mathematics. Trends in Neuroscience and Education, 8-9, 28-36. https://doi.org/https://doi.org/10.1016/j.tine.2017.10.002
Chang, T.-T., Lee, P. H., & Metcalfe, A. W. S. (2018). Intrinsic insula network engagement underlying children's reading and arithmetic skills. NeuroImage, 167, 162-177. https://doi.org/10.1016/j.neuroimage.2017.11.027
Chang, T.-T., Metcalfe, A. W. S., Padmanabhan, A., Chen, T., & Menon, V. (2016). Heterogeneous and nonlinear development of human posterior parietal cortex function. NeuroImage, 126, 184-195. https://doi.org/https://doi.org/10.1016/j.neuroimage.2015.11.053
Chang, T. T., Rosenberg-Lee, M., Metcalfe, A. W., Chen, T., & Menon, V. (2015). Development of common neural representations for distinct numerical problems. Neuropsychologia, 75, 481-495. https://doi.org/10.1016/j.neuropsychologia.2015.07.005
Chinn, S. (2009). Mathematics anxiety in secondary students in England. Dyslexia, 15(1), 61-68. https://doi.org/https://doi.org/10.1002/dys.381
Chiu, L.-h., & Henry, L. L. (1990). Development and validation of the Mathematics Anxiety Scale for Children. Measurement and Evaluation in Counseling and Development, 23(3), 121-127.
Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res, 29(3), 162-173. https://doi.org/10.1006/cbmr.1996.0014
CUI Fang, L. X., YANG Jiawang, LIU Jie. (2023). The neural mechanism of the impact of mathematical anxiety on the math conceptual knowledge: Evidence from a resting-state fMRI study. Acta Psychologica Sinica, 55(6), 968-977. https://doi.org/10.3724/sp.J.1041.2023.00968
Demedts, F., Reynvoet, B., Sasanguie, D., & Depaepe, F. (2022). Unraveling the role of math anxiety in students’ math performance [Original Research]. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.979113
Detterman, D. K., & Daniel, M. H. (1989). Correlations of mental tests with each other and with cognitive variables are highest for low IQ groups. Intelligence, 13(4), 349-359. https://doi.org/https://doi.org/10.1016/S0160-2896(89)80007-8
Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics Anxiety: What Have We Learned in 60 Years? [Review]. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.00508
Engle, R. W. (2002). Working Memory Capacity as Executive Attention. Current directions in psychological science, 11(1), 19-23. https://doi.org/10.1111/1467-8721.00160
Etkin, A., Büchel, C., & Gross, J. J. (2015). The neural bases of emotion regulation. Nature Reviews Neuroscience, 16(11), 693-700. https://doi.org/10.1038/nrn4044
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336-353. https://doi.org/10.1037/1528-3542.7.2.336
Facon, B. (2006). Does age moderate the effect of IQ on the differentiation of cognitive abilities during childhood? Intelligence, 34(4), 375-386. https://doi.org/https://doi.org/10.1016/j.intell.2005.12.003
Faust, M. W. (1996). Mathematics Anxiety Effects in Simple and Complex Addition. Mathematical Cognition, 2(1), 25-62. https://doi.org/10.1080/135467996387534
Finell, J., Sammallahti, E., Korhonen, J., Eklöf, H., & Jonsson, B. (2021). Working Memory and Its Mediating Role on the Relationship of Math Anxiety and Math Performance: A Meta-Analysis. Front Psychol, 12, 798090. https://doi.org/10.3389/fpsyg.2021.798090
Fortune, A., & Spangenberg, E. D. (2023). Trait-and state-components of mathematics anxiety versus perceived mathematics anxiety. International Journal of Education in Mathematics, Science and Technology, 11(6), 1366-1385.
Gray, K. R., Aljabar, P., Heckemann, R. A., Hammers, A., & Rueckert, D. (2013). Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. NeuroImage, 65, 167-175. https://doi.org/https://doi.org/10.1016/j.neuroimage.2012.09.065
Guendelman, S., Medeiros, S., & Rampes, H. (2017). Mindfulness and Emotion Regulation: Insights from Neurobiological, Psychological, and Clinical Studies [Review]. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00220
Gunderson, E. A., Park, D., Maloney, E. A., Beilock, S. L., & Levine, S. C. (2018). Reciprocal relations among motivational frameworks, math anxiety, and math achievement in early elementary school. Journal of cognition and development, 19(1), 21-46. https://doi.org/10.1080/15248372.2017.1421538
Hembree, R. (1990). The Nature, Effects, and Relief of Mathematics Anxiety. Journal for Research in Mathematics Education JRME, 21(1), 33-46. https://doi.org/10.5951/jresematheduc.21.1.0033
Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16(3), 174-180. https://doi.org/10.1016/j.tics.2012.01.006
Hopko, D. R., Ashcraft, M. H., Gute, J., Ruggiero, K. J., & Lewis, C. (1998). Mathematics Anxiety and Working Memory: Support for the Existence of a Deficient Inhibition Mechanism. Journal of Anxiety Disorders, 12(4), 343-355. https://doi.org/https://doi.org/10.1016/S0887-6185(98)00019-X
Lebedev, A. V., Westman, E., Van Westen, G. J. P., Kramberger, M. G., Lundervold, A., Aarsland, D., Soininen, H., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B., Lovestone, S., & Simmons, A. (2014). Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness. NeuroImage: Clinical, 6, 115-125. https://doi.org/https://doi.org/10.1016/j.nicl.2014.08.023
Liebert, R. M., & Morris, L. W. (1967). Cognitive and Emotional Components of Test Anxiety: A Distinction and Some Initial Data. Psychological Reports, 20(3), 975-978. https://doi.org/10.2466/pr0.1967.20.3.975
Liu, S., Wei, W., Chen, Y., Hugo, P., & Zhao, J. (2021). Visual–Spatial Ability Predicts Academic Achievement Through Arithmetic and Reading Abilities [Original Research]. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.591308
Long, J., Song, X., Wang, C., Peng, L., Niu, L., Li, Q., Huang, R., & Zhang, R. (2024). Global-brain functional connectivity related with trait anxiety and its association with neurotransmitters and gene expression profiles. Journal of Affective Disorders, 348, 248-258. https://doi.org/https://doi.org/10.1016/j.jad.2023.12.052
Lyons, I. M., & Beilock, S. L. (2012a). Mathematics Anxiety: Separating the Math from the Anxiety. Cerebral Cortex, 22(9), 2102-2110. https://doi.org/10.1093/cercor/bhr289
Lyons, I. M., & Beilock, S. L. (2012b). When Math Hurts: Math Anxiety Predicts Pain Network Activation in Anticipation of Doing Math. PLOS ONE, 7(10), e48076. https://doi.org/10.1371/journal.pone.0048076
Ma, X. (1999). A Meta-Analysis of the Relationship Between Anxiety Toward Mathematics and Achievement in Mathematics. Journal for Research in Mathematics Education JRME, 30(5), 520-540. https://doi.org/10.2307/749772
Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences, 15(10), 483-506. https://doi.org/10.1016/j.tics.2011.08.003
Miller, E. K., & Cohen, J. D. (2001). An Integrative Theory of Prefrontal Cortex Function. Annual Review of Neuroscience, 24(Volume 24, 2001), 167-202. https://doi.org/https://doi.org/10.1146/annurev.neuro.24.1.167
Moradi, E., Pepe, A., Gaser, C., Huttunen, H., & Tohka, J. (2015). Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. NeuroImage, 104, 398-412. https://doi.org/https://doi.org/10.1016/j.neuroimage.2014.10.002
Morris, L. W., Davis, M. A., & Hutchings, C. H. (1981). Cognitive and emotional components of anxiety: Literature review and a revised worry–emotionality scale. Journal of Educational Psychology, 73(4), 541-555. https://doi.org/10.1037/0022-0663.73.4.541
OECD. (2013). PISA 2012 Results: Ready to Learn (Volume III). https://doi.org/doi:https://doi.org/10.1787/9789264201170-en
Orbach, L., Herzog, M., & Fritz, A. (2019). Relation of State- and Trait-Math Anxiety to Intelligence, Math Achievement and Learning Motivation. Journal of Numerical Cognition, 5(3), 371-399. https://doi.org/10.5964/jnc.v5i3.204
Orbach, L., Herzog, M., & Fritz, A. (2020). State- and trait-math anxiety and their relation to math performance in children: The role of core executive functions. Cognition, 200, 104271. https://doi.org/https://doi.org/10.1016/j.cognition.2020.104271
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Pellizzoni, S., Cargnelutti, E., Cuder, A., & Passolunghi, M. C. (2022). The interplay between math anxiety and working memory on math performance: a longitudinal study. Annals of the New York Academy of Sciences, 1510(1), 132-144. https://doi.org/https://doi.org/10.1111/nyas.14722
Pessoa, L. (2017). A Network Model of the Emotional Brain. Trends in Cognitive Sciences, 21(5), 357-371. https://doi.org/10.1016/j.tics.2017.03.002
Pintelas, E. G., Kotsilieris, T., Livieris, I. E., & Pintelas, P. (2018). A review of machine learning prediction methods for anxiety disorders Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, Thessaloniki, Greece. https://doi.org/10.1145/3218585.3218587
Pizzie, R. G., & Kraemer, D. J. M. (2017). Avoiding math on a rapid timescale: Emotional responsivity and anxious attention in math anxiety. Brain and Cognition, 118, 100-107. https://doi.org/https://doi.org/10.1016/j.bandc.2017.08.004
Pizzie, R. G., McDermott, C. L., Salem, T. G., & Kraemer, D. J. M. (2020). Neural evidence for cognitive reappraisal as a strategy to alleviate the effects of math anxiety. Social Cognitive and Affective Neuroscience, 15(12), 1271-1287. https://doi.org/10.1093/scan/nsaa161
Pizzie, R. G., Raman, N., & Kraemer, D. J. M. (2020). Math anxiety and executive function: Neural influences of task switching on arithmetic processing. Cognitive, Affective, & Behavioral Neuroscience, 20(2), 309-325. https://doi.org/10.3758/s13415-020-00770-z
Pletzer, B., Kronbichler, M., Nuerk, H.-C., & Kerschbaum, H. H. (2015). Mathematics anxiety reduces default mode network deactivation in response to numerical tasks [Original Research]. Frontiers in Human Neuroscience, 9. https://doi.org/10.3389/fnhum.2015.00202
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142-2154. https://doi.org/10.1016/j.neuroimage.2011.10.018
Ramirez, G., Gunderson, E. A., Levine, S. C., & Beilock, S. L. (2013). Math Anxiety, Working Memory, and Math Achievement in Early Elementary School. Journal of cognition and development, 14(2), 187-202. https://doi.org/10.1080/15248372.2012.664593
Ramirez, G., Shaw, S. T., & Maloney, E. A. (2018). Math Anxiety: Past Research, Promising Interventions, and a New Interpretation Framework. Educational Psychologist, 53(3), 145-164. https://doi.org/10.1080/00461520.2018.1447384
Rezaei, S., Gharepapagh, E., Rashidi, F., Cattarinussi, G., Sanjari Moghaddam, H., Di Camillo, F., Schiena, G., Sambataro, F., Brambilla, P., & Delvecchio, G. (2023). Machine learning applied to functional magnetic resonance imaging in anxiety disorders. Journal of Affective Disorders, 342, 54-62. https://doi.org/https://doi.org/10.1016/j.jad.2023.09.006
Richardson, F. C., & Suinn, R. M. (1972). The Mathematics Anxiety Rating Scale: Psychometric data. Journal of Counseling Psychology, 19(6), 551-554. https://doi.org/10.1037/h0033456
Sajjadian, M., Lam, R. W., Milev, R., Rotzinger, S., Frey, B. N., Soares, C. N., Parikh, S. V., Foster, J. A., Turecki, G., Müller, D. J., Strother, S. C., Farzan, F., Kennedy, S. H., & Uher, R. (2021). Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis. Psychological Medicine, 51(16), 2742-2751. https://doi.org/10.1017/S0033291721003871
Smith, D. D., Meca, A., Bottenhorn, K. L., Bartley, J. E., Riedel, M. C., Salo, T., Peraza, J. A., Laird, R. W., Pruden, S. M., Sutherland, M. T., Brewe, E., & Laird, A. R. (2023). Task-based attentional and default mode connectivity associated with science and math anxiety profiles among university physics students. Trends in Neuroscience and Education, 32, 100204. https://doi.org/https://doi.org/10.1016/j.tine.2023.100204
Soltanlou, M., Artemenko, C., Dresler, T., Fallgatter, A. J., Ehlis, A.-C., & Nuerk, H.-C. (2019). Math Anxiety in Combination With Low Visuospatial Memory Impairs Math Learning in Children [Original Research]. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.00089
Sorvo, R., Kiuru, N., Koponen, T., Aro, T., Viholainen, H., Ahonen, T., & Aro, M. (2022). Longitudinal and situational associations between math anxiety and performance among early adolescents. Annals of the New York Academy of Sciences, 1514(1), 174-186. https://doi.org/https://doi.org/10.1111/nyas.14788
Sorvo, R., Koponen, T., Viholainen, H., Aro, T., Räikkönen, E., Peura, P., Dowker, A., & Aro, M. (2017). Math anxiety and its relationship with basic arithmetic skills among primary school children. British Journal of Educational Psychology, 87(3), 309-327. https://doi.org/https://doi.org/10.1111/bjep.12151
Soysal, D., Bani-Yaghoub, M., & Riggers-Piehl, T. A. (2022). A Machine Learning Approach to Evaluate Variables of Math Anxiety in STEM Students. Pedagogical Research, 7(2).
Spielberger, C. D. (1972). Anxiety: current trends in theory and research. Academic Press.
Suinn, R. M., Taylor, S., & Edwards, R. W. (1988). Suinn Mathematics Anxiety Rating Scale for Elementary School Students (MARS-E): Psychometric and normative data. Educational and Psychological Measurement, 48(4), 979-986. https://doi.org/10.1177/0013164488484013
Supekar, K., Iuculano, T., Chen, L., & Menon, V. (2015). Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring. The Journal of Neuroscience, 35(36), 12574-12583. https://doi.org/10.1523/jneurosci.0786-15.2015
Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257-285. https://doi.org/https://doi.org/10.1207/s15516709cog1202_4
Tripoliti, E. E., Fotiadis, D. I., & Argyropoulou, M. (2007, 22-26 Aug. 2007). A supervised method to assist the diagnosis of Alzheimer's Disease based on functional Magnetic Resonance Imaging. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,
Van der Ven, S. H. G., Prast, E. J., & Van de Weijer-Bergsma, E. (2023). Towards an Integrative Model of Math Cognition: Interactions between Working Memory and Emotions in Explaining Children’s Math Performance. Journal of Intelligence, 11(7), 136. https://www.mdpi.com/2079-3200/11/7/136
Wechsler, D. (2004). The Wechsler intelligence scale for children - fourth edition. Pearson Assessment.
Wechsler, D. (2008). Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV). Pearson Assessment.
Wu, S., Amin, H., Barth, M., Malcarne, V., & Menon, V. (2012). Math Anxiety in Second and Third Graders and Its Relation to Mathematics Achievement [Original Research]. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00162
Yi, H. S., & Na, W. (2020). How are maths-anxious students identified and what are the key predictors of maths anxiety? Insights gained from PISA results for Korean adolescents. Asia Pacific Journal of Education, 40(2), 247-262. https://doi.org/10.1080/02188791.2019.1692782
Young, C. B., Wu, S. S., & Menon, V. (2012). The neurodevelopmental basis of math anxiety. Psychol Sci, 23(5), 492-501. https://doi.org/10.1177/0956797611429134
Zhang, W., Dutt, R., Lew, D., Barch, D. M., & Bijsterbosch, J. (2024). Higher amplitudes of visual networks are associated with trait-but not state-depression. bioRxiv, 2024.2003.2025.584801. https://doi.org/10.1101/2024.03.25.584801 |
| Description: | 碩士
國立政治大學
心理學系
111752014 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111752014 |
| Data Type: | thesis |
| Appears in Collections: | [心理學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 201401.pdf | | 2689Kb | Adobe PDF | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
