請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83236
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳恩賜 | zh_TW |
dc.contributor.advisor | Joshua Goh Oon Soo | en |
dc.contributor.author | 蘇煜翔 | zh_TW |
dc.contributor.author | Yu-Shiang Su | en |
dc.date.accessioned | 2023-01-11T17:02:18Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-01-07 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-12-30 | - |
dc.identifier.citation | Abdulrahman, H., & Henson, R. N. (2016). Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis. NeuroImage, 125, 756–766. https://doi.org/10.1016/j.neuroimage.2015.11.009
Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8, 14. https://doi.org/10.3389/fninf.2014.00014 Amer, T., Giovanello, K. S., Grady, C. L., & Hasher, L. (2018). Age differences in memory for meaningful and arbitrary associations: A memory retrieval account. Psychology and Aging, 33(1), 74–81. https://doi.org/10.1037/pag0000220 Amer, T., Wynn, J. S., & Hasher, L. (2022). Cluttered memory representations shape cognition in old age. Trends in Cognitive Sciences, 26(3), 255–267. https://doi.org/10.1016/j.tics.2021.12.002 Auksztulewicz, R., & Friston, K. (2016). Repetition suppression and its contextual determinants in predictive coding. Cortex, 80, 125–140. https://doi.org/10.1016/j.cortex.2015.11.024 Bäckman, L., Lindenberger, U., Li, S.-C., & Nyberg, L. (2010). Linking cognitive aging to alterations in dopamine neurotransmitter functioning: Recent data and future avenues. Neuroscience & Biobehavioral Reviews, 34(5), 670–677. https://doi.org/10.1016/j.neubiorev.2009.12.008 Bang, D., & Fleming, S. M. (2018). Distinct encoding of decision confidence in human medial prefrontal cortex. Proceedings of the National Academy of Sciences, 115(23), 6082–6087. https://doi.org/10.1073/pnas.1800795115 Barron, H. C., Dolan, R. J., & Behrens, T. E. J. (2013). Online evaluation of novel choices by simultaneous representation of multiple memories. Nature Neuroscience, 16(10), 1492–1498. https://doi.org/10.1038/nn.3515 Bartolo, R., & Averbeck, B. B. (2020). Prefrontal Cortex Predicts State Switches during Reversal Learning. Neuron, 106(6), 1044-1054.e4. https://doi.org/10.1016/j.neuron.2020.03.024 Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018). What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior. Neuron, 100(2), 490–509. https://doi.org/10.1016/j.neuron.2018.10.002 Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37(1), 90–101. https://doi.org/10.1016/j.neuroimage.2007.04.042 Bellmund, J. L. S., Gärdenfors, P., Moser, E. I., & Doeller, C. F. (2018). Navigating cognition: Spatial codes for human thinking. Science, 362(6415), eaat6766. https://doi.org/10.1126/science.aat6766 Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., & Goodman, N. D. (2018). Pyro: Deep Universal Probabilistic Programming. ArXiv:1810.09538 [Cs, Stat]. http://arxiv.org/abs/1810.09538 Brodski, A., Paasch, G.-F., Helbling, S., & Wibral, M. (2015). The Faces of Predictive Coding. Journal of Neuroscience, 35(24), 8997–9006. https://doi.org/10.1523/JNEUROSCI.1529-14.2015 Cabeza, R., Albert, M., Belleville, S., Craik, F. I. M., Duarte, A., Grady, C. L., Lindenberger, U., Nyberg, L., Park, D. C., Reuter-Lorenz, P. A., Rugg, M. D., Steffener, J., & Rajah, M. N. (2018). Maintenance, reserve and compensation: The cognitive neuroscience of healthy ageing. Nature Reviews Neuroscience, 19(11), 701. https://doi.org/10.1038/s41583-018-0068-2 Cabeza, R., Anderson, N. D., Locantore, J. K., & McIntosh, A. R. (2002). Aging Gracefully: Compensatory Brain Activity in High-Performing Older Adults. NeuroImage, 17(3), 1394–1402. https://doi.org/10.1006/nimg.2002.1280 Chan, S. C. Y., Niv, Y., & Norman, K. A. (2016). A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex. Journal of Neuroscience, 36(30), 7817–7828. https://doi.org/10.1523/JNEUROSCI.0659-16.2016 Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477 Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215. https://doi.org/10.1038/nrn755 Costa, V. D., Tran, V. L., Turchi, J., & Averbeck, B. B. (2015). Reversal Learning and Dopamine: A Bayesian Perspective. Journal of Neuroscience, 35(6), 2407–2416. https://doi.org/10.1523/JNEUROSCI.1989-14.2015 d’Acremont, M., Fornari, E., & Bossaerts, P. (2013). Activity in Inferior Parietal and Medial Prefrontal Cortex Signals the Accumulation of Evidence in a Probability Learning Task. PLOS Computational Biology, 9(1), e1002895. https://doi.org/10.1371/journal.pcbi.1002895 Davis, S. W., Dennis, N. A., Daselaar, S. M., Fleck, M. S., & Cabeza, R. (2008). Qué PASA? The Posterior–Anterior Shift in Aging. Cerebral Cortex, 18(5), 1201–1209. https://doi.org/10.1093/cercor/bhm155 De Martino, B., Fleming, S. M., Garrett, N., & Dolan, R. J. (2013). Confidence in value-based choice. Nature Neuroscience, 16(1), 105–110. https://doi.org/10.1038/nn.3279 Desmurget, M., Reilly, K. T., Richard, N., Szathmari, A., Mottolese, C., & Sirigu, A. (2009). Movement Intention After Parietal Cortex Stimulation in Humans. Science, 324(5928), 811–813. https://doi.org/10.1126/science.1169896 Erixon-Lindroth, N., Farde, L., Robins Wahlin, T.-B., Sovago, J., Halldin, C., & Bäckman, L. (2005). The role of the striatal dopamine transporter in cognitive aging. Psychiatry Research: Neuroimaging, 138(1), 1–12. https://doi.org/10.1016/j.pscychresns.2004.09.005 Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS ONE, 12(9), e0184661. https://doi.org/10.1371/journal.pone.0184661 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 Farashahi, S., Donahue, C. H., Khorsand, P., Seo, H., Lee, D., & Soltani, A. (2017). Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty. Neuron, 94(2), 401-414.e6. https://doi.org/10.1016/j.neuron.2017.03.044 Fitzgerald, J. K., Freedman, D. J., Fanini, A., Bennur, S., Gold, J. I., & Assad, J. A. (2013). Biased Associative Representations in Parietal Cortex. Neuron, 77(1), 180–191. https://doi.org/10.1016/j.neuron.2012.11.014 FitzGerald, T. H. B., Hämmerer, D., Friston, K. J., Li, S.-C., & Dolan, R. J. (2017). Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions. PLOS Computational Biology, 13(5), e1005418. https://doi.org/10.1371/journal.pcbi.1005418 FitzGerald, T. H. B., Seymour, B., & Dolan, R. J. (2009). The Role of Human Orbitofrontal Cortex in Value Comparison for Incommensurable Objects. Journal of Neuroscience, 29(26), 8388–8395. https://doi.org/10.1523/JNEUROSCI.0717-09.2009 Fjell, A. M., Sneve, M. H., Grydeland, H., Storsve, A. B., Amlien, I. K., Yendiki, A., & Walhovd, K. B. (2017). Relationship between structural and functional connectivity change across the adult lifespan: A longitudinal investigation. Human Brain Mapping, 38(1), 561–573. https://doi.org/10.1002/hbm.23403 Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time Discounting and Time Preference: A Critical Review. Journal of Economic Literature, 40(2), 351–401. https://doi.org/10.1257/002205102320161311 Freedman, D. J., & Assad, J. A. (2016). Neuronal Mechanisms of Visual Categorization: An Abstract View on Decision Making. Annual Review of Neuroscience, 39(1), 129–147. https://doi.org/10.1146/annurev-neuro-071714-033919 Freedman, D. J., & Ibos, G. (2018). An Integrative Framework for Sensory, Motor, and Cognitive Functions of the Posterior Parietal Cortex. Neuron, 97(6), 1219–1234. https://doi.org/10.1016/j.neuron.2018.01.044 Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127. https://doi.org/10.1038/nrn2787 Gelman, A. (2006). Multilevel (Hierarchical) Modeling: What It Can and Cannot Do. Technometrics, 48(3), 432–435. https://doi.org/10.1198/004017005000000661 Gershman, S. J., Blei, D. M., & Niv, Y. (2010). Context, learning, and extinction. Psychological Review, 117(1), 197–209. https://doi.org/10.1037/a0017808 Gläscher, J., Daw, N., Dayan, P., & O’Doherty, J. P. (2010). States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning. Neuron, 66(4), 585–595. https://doi.org/10.1016/j.neuron.2010.04.016 Goh, J. O., Suzuki, A., & Park, D. C. (2010). Reduced neural selectivity increases fMRI adaptation with age during face discrimination. NeuroImage, 51(1), 336–344. https://doi.org/10.1016/j.neuroimage.2010.01.107 Grady, C. (2012). The cognitive neuroscience of ageing. Nature Reviews Neuroscience, 13(7), 491–505. https://doi.org/10.1038/nrn3256 Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In Cambridge Handbook of Computational Cognitive Modeling (p. 49). Cambridge University Press. Hämmerer, D., Schwartenbeck, P., Gallagher, M., FitzGerald, T. H. B., Düzel, E., & Dolan, R. J. (2019). Older adults fail to form stable task representations during model-based reversal inference. Neurobiology of Aging, 74, 90–100. https://doi.org/10.1016/j.neurobiolaging.2018.10.009 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 Hoffman, M. D., & Gelman, A. (2011). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. ArXiv:1111.4246 [Cs, Stat]. http://arxiv.org/abs/1111.4246 Hunt, L. T., & Hayden, B. Y. (2017). A distributed, hierarchical and recurrent framework for reward-based choice. Nature Reviews Neuroscience, 18(3), 172–182. https://doi.org/10.1038/nrn.2017.7 Jocham, G., Hunt, L. T., Near, J., & Behrens, T. E. J. (2012). A mechanism for value-guided choice based on the excitation-inhibition balance in prefrontal cortex. Nature Neuroscience, 15(7), 960–961. https://doi.org/10.1038/nn.3140 Josefsson, M., de Luna, X., Pudas, S., Nilsson, L.-G., & Nyberg, L. (2012). Genetic and Lifestyle Predictors of 15-Year Longitudinal Change in Episodic Memory. Journal of the American Geriatrics Society, 60(12), 2308–2312. https://doi.org/10.1111/jgs.12000 Kaasinen, V., Vilkman, H., Hietala, J., Någren, K., Helenius, H., Olsson, H., Farde, L., & Rinne, J. O. (2000). Age-related dopamine D2/D3 receptor loss in extrastriatal regions of the human brain. Neurobiology of Aging, 21(5), 683–688. https://doi.org/10.1016/S0197-4580(00)00149-4 Karlsson, M. P., Tervo, D. G. R., & Karpova, A. Y. (2012). Network Resets in Medial Prefrontal Cortex Mark the Onset of Behavioral Uncertainty. Science, 338(6103), 135–139. https://doi.org/10.1126/science.1226518 Kennedy, K. M., Rodrigue, K. M., Bischof, G. N., Hebrank, A. C., Reuter-Lorenz, P. A., & Park, D. C. (2015). Age trajectories of functional activation under conditions of low and high processing demands: An adult lifespan fMRI study of the aging brain. NeuroImage, 104, 21–34. https://doi.org/10.1016/j.neuroimage.2014.09.056 Kiani, R., & Shadlen, M. N. (2009). Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex. Science, 324(5928), 759–764. https://doi.org/10.1126/science.1169405 Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719. https://doi.org/10.1016/j.tins.2004.10.007 Knudsen, E. B., & Wallis, J. D. (2022). Taking stock of value in the orbitofrontal cortex. Nature Reviews Neuroscience, 1–11. https://doi.org/10.1038/s41583-022-00589-2 Knutson, B., Taylor, J., Kaufman, M., Peterson, R., & Glover, G. (2005). Distributed Neural Representation of Expected Value. Journal of Neuroscience, 25(19), 4806–4812. https://doi.org/10.1523/JNEUROSCI.0642-05.2005 Kobayashi, K., & Hsu, M. (2017). Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty. Journal of Neuroscience, 37(29), 6972–6982. https://doi.org/10.1523/JNEUROSCI.0535-17.2017 Koen, J. D., Hauck, N., & Rugg, M. D. (2019). The Relationship between Age, Neural Differentiation, and Memory Performance. Journal of Neuroscience, 39(1), 149–162. https://doi.org/10.1523/JNEUROSCI.1498-18.2018 Koen, J. D., & Rugg, M. D. (2019). Neural Dedifferentiation in the Aging Brain. Trends in Cognitive Sciences, 23(7), 547–559. https://doi.org/10.1016/j.tics.2019.04.012 Kolossa, A., Kopp, B., & Fingscheidt, T. (2015). A computational analysis of the neural bases of Bayesian inference. NeuroImage, 106, 222–237. https://doi.org/10.1016/j.neuroimage.2014.11.007 Körding, K. (2007). Decision Theory: What “Should” the Nervous System Do? Science, 318(5850), 606–610. https://doi.org/10.1126/science.1142998 Korn, C. W., Prehn, K., Park, S. Q., Walter, H., & Heekeren, H. R. (2012). Positively Biased Processing of Self-Relevant Social Feedback. Journal of Neuroscience, 32(47), 16832–16844. https://doi.org/10.1523/JNEUROSCI.3016-12.2012 Kouststaal, W., Reddy, C., Jackson, E. M., Prince, S., Cendan, D. L., & Schacter, D. L. (2003). False recognition of abstract versus common objects in older and younger adults: Testing the semantic categorization account. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(4), 499–510. https://doi.org/10.1037/0278-7393.29.4.499 Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—Connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2. https://doi.org/10.3389/neuro.06.004.2008 Kruschke, J. K. (2018). Rejecting or Accepting Parameter Values in Bayesian Estimation. Advances in Methods and Practices in Psychological Science, 1(2), 270–280. https://doi.org/10.1177/2515245918771304 Kumar, M., Michael Anderson, Antony, J., Baldassano, C., Brooks, P. P., Cai, M. B., Chen, P.-H. C., Ellis, C. T., Henselman-Petrusek, G., Huberdeau, D., Hutchinson, B., Li, Y. P., Lu, Q., Manning, J. R., Mennen, A. C., Nastase, S. A., Richard, H., Schapiro, A. C., Schuck, N. W., … Norman, K. A. (2020). BrainIAK: The Brain Imaging Analysis Kit. OSF Preprints. https://doi.org/10.31219/osf.io/db2ev Lebreton, M., Abitbol, R., Daunizeau, J., & Pessiglione, M. (2015). Automatic integration of confidence in the brain valuation signal. Nature Neuroscience, 18(8), 1159–1167. https://doi.org/10.1038/nn.4064 Lerner, Y., Honey, C. J., Silbert, L. J., & Hasson, U. (2011). Topographic Mapping of a Hierarchy of Temporal Receptive Windows Using a Narrated Story. Journal of Neuroscience, 31(8), 2906–2915. https://doi.org/10.1523/JNEUROSCI.3684-10.2011 Li, H.-J., Hou, X.-H., Liu, H.-H., Yue, C.-L., Lu, G.-M., & Zuo, X.-N. (2015). Putting age-related task activation into large-scale brain networks: A meta-analysis of 114 fMRI studies on healthy aging. Neuroscience & Biobehavioral Reviews, 57, 156–174. https://doi.org/10.1016/j.neubiorev.2015.08.013 Li, S.-C., Lindenberger, U., & Sikström, S. (2001). Aging cognition: From neuromodulation to representation. Trends in Cognitive Sciences, 5(11), 479–486. https://doi.org/10.1016/S1364-6613(00)01769-1 Lindenberger, U. (2014). Human cognitive aging: Corriger la fortune? Science, 346(6209), 572–578. https://doi.org/10.1126/science.1254403 Lindenberger, U., & Baltes, P. B. (1994). Sensory functioning and intelligence in old age: A strong connection. Psychology and Aging, 9(3), 339–355. https://doi.org/10.1037//0882-7974.9.3.339 McGuire, J. T., Nassar, M. R., Gold, J. I., & Kable, J. W. (2014). Functionally Dissociable Influences on Learning Rate in a Dynamic Environment. Neuron, 84(4), 870–881. https://doi.org/10.1016/j.neuron.2014.10.013 Monge, Z. A., & Madden, D. J. (2016). Linking cognitive and visual perceptual decline in healthy aging: The information degradation hypothesis. Neuroscience & Biobehavioral Reviews, 69, 166–173. https://doi.org/10.1016/j.neubiorev.2016.07.031 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 Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15(1), 1–25. https://doi.org/10.1002/hbm.1058 Nyberg, L., Karalija, N., Salami, A., Andersson, M., Wåhlin, A., Kaboovand, N., Köhncke, Y., Axelsson, J., Rieckmann, A., Papenberg, G., Garrett, D. D., Riklund, K., Lövdén, M., Lindenberger, U., & Bäckman, L. (2016). Dopamine D2 receptor availability is linked to hippocampal–caudate functional connectivity and episodic memory. Proceedings of the National Academy of Sciences, 113(28), 7918–7923. https://doi.org/10.1073/pnas.1606309113 Nyberg, L., & Pudas, S. (2019). Successful Memory Aging. Annual Review of Psychology, 70(1), 219–243. https://doi.org/10.1146/annurev-psych-010418-103052 Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17(2), 299–320. Park, D. C., Polk, T. A., Park, R., Minear, M., Savage, A., & Smith, M. R. (2004). Aging reduces neural specialization in ventral visual cortex. Proceedings of the National Academy of Sciences, 101(35), 13091–13095. https://doi.org/10.1073/pnas.0405148101 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 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830. Phan, D., Pradhan, N., & Jankowiak, M. (2019). Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro. ArXiv:1912.11554 [Cs, Stat]. http://arxiv.org/abs/1912.11554 Piazza, M., Izard, V., Pinel, P., Le Bihan, D., & Dehaene, S. (2004). Tuning Curves for Approximate Numerosity in the Human Intraparietal Sulcus. Neuron, 44(3), 547–555. https://doi.org/10.1016/j.neuron.2004.10.014 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 Qiu, M., & Johns, B. T. (2020). Semantic diversity in paired-associate learning: Further evidence for the information accumulation perspective of cognitive aging. Psychonomic Bulletin & Review, 27(1), 114–121. https://doi.org/10.3758/s13423-019-01691-w Ramscar, M., Hendrix, P., Shaoul, C., Milin, P., & Baayen, H. (2014). The Myth of Cognitive Decline: Non-Linear Dynamics of Lifelong Learning. Topics in Cognitive Science, 6(1), 5–42. https://doi.org/10.1111/tops.12078 Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., Dahle, C., Gerstorf, D., & Acker, J. D. (2005). Regional Brain Changes in Aging Healthy Adults: General Trends, Individual Differences and Modifiers. Cerebral Cortex, 15(11), 1676–1689. https://doi.org/10.1093/cercor/bhi044 Rich, E. L., & Wallis, J. D. (2016). Decoding subjective decisions from orbitofrontal cortex. Nature Neuroscience, 19(7), 973–980. https://doi.org/10.1038/nn.4320 Salthouse, T. A. (2019). Trajectories of normal cognitive aging. Psychology and Aging, 34(1), 17–24. https://doi.org/10.1037/pag0000288 Sarafyazd, M., & Jazayeri, M. (2019). Hierarchical reasoning by neural circuits in the frontal cortex. Science, 364(6441), eaav8911. https://doi.org/10.1126/science.aav8911 Schuck, N. W., Cai, M. B., Wilson, R. C., & Niv, Y. (2016). Human Orbitofrontal Cortex Represents a Cognitive Map of State Space. Neuron, 91(6), 1402–1412. https://doi.org/10.1016/j.neuron.2016.08.019 Schuck, N. W., Frensch, P. A., Schjeide, B.-M. M., Schröder, J., Bertram, L., & Li, S.-C. (2013). Effects of aging and dopamine genotypes on the emergence of explicit memory during sequence learning. Neuropsychologia, 51(13), 2757–2769. https://doi.org/10.1016/j.neuropsychologia.2013.09.009 Schwartenbeck, P., FitzGerald, T. H. B., & Dolan, R. (2016). Neural signals encoding shifts in beliefs. NeuroImage, 125, 578–586. https://doi.org/10.1016/j.neuroimage.2015.10.067 Sharot, T., Korn, C. W., & Dolan, R. J. (2011). How unrealistic optimism is maintained in the face of reality. Nature Neuroscience, 14(11), 1475–1479. https://doi.org/10.1038/nn.2949 Smith, S. M., Elliott, L. T., Alfaro-Almagro, F., McCarthy, P., Nichols, T. E., Douaud, G., & Miller, K. L. (2020). Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. ELife, 9, e52677. https://doi.org/10.7554/eLife.52677 Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1), 83–98. https://doi.org/10.1016/j.neuroimage.2008.03.061 Spreng, R. N., Wojtowicz, M., & Grady, C. L. (2010). Reliable differences in brain activity between young and old adults: A quantitative meta-analysis across multiple cognitive domains. Neuroscience & Biobehavioral Reviews, 34(8), 1178–1194. https://doi.org/10.1016/j.neubiorev.2010.01.009 Stern, E. R., Gonzalez, R., Welsh, R. C., & Taylor, S. F. (2010). Updating Beliefs for a Decision: Neural Correlates of Uncertainty and Underconfidence. Journal of Neuroscience, 30(23), 8032–8041. https://doi.org/10.1523/JNEUROSCI.4729-09.2010 Su, Y.-S., Chen, J.-T., Tang, Y.-J., Yuan, S.-Y., McCarrey, A. C., & Goh, J. O. S. (2018). Age-related differences in striatal, medial temporal, and frontal involvement during value-based decision processing. Neurobiology of Aging, 69, 185–198. https://doi.org/10.1016/j.neurobiolaging.2018.05.019 Summerfield, C., & de Lange, F. P. (2014). Expectation in perceptual decision making: Neural and computational mechanisms. Nature Reviews Neuroscience, 15(11), 745–756. https://doi.org/10.1038/nrn3838 Summerfield, C., Luyckx, F., & Sheahan, H. (2020). Structure learning and the posterior parietal cortex. Progress in Neurobiology, 184, 101717. https://doi.org/10.1016/j.pneurobio.2019.101717 Summerfield, C., Trittschuh, E. H., Monti, J. M., Mesulam, M.-M., & Egner, T. (2008). Neural repetition suppression reflects fulfilled perceptual expectations. Nature Neuroscience, 11(9), 1004–1006. https://doi.org/10.1038/nn.2163 Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3(1), 9–44. https://doi.org/10.1007/BF00115009 Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. A Bradford Book. Todorovic, A., & Lange, F. P. de. (2012). Repetition Suppression and Expectation Suppression Are Dissociable in Time in Early Auditory Evoked Fields. Journal of Neuroscience, 32(39), 13389–13395. https://doi.org/10.1523/JNEUROSCI.2227-12.2012 Tomov, M. S., Dorfman, H. M., & Gershman, S. J. (2018). Neural Computations Underlying Causal Structure Learning. Journal of Neuroscience, 38(32), 7143–7157. https://doi.org/10.1523/JNEUROSCI.3336-17.2018 Trudel, N., Scholl, J., Klein-Flügge, M. C., Fouragnan, E., Tankelevitch, L., Wittmann, M. K., & Rushworth, M. F. S. (2021). Polarity of uncertainty representation during exploration and exploitation in ventromedial prefrontal cortex. Nature Human Behaviour, 5(1), 83–98. https://doi.org/10.1038/s41562-020-0929-3 Tymula, A., Belmaker, L. A. R., Ruderman, L., Glimcher, P. W., & Levy, I. (2013). Like cognitive function, decision making across the life span shows profound age-related changes. Proceedings of the National Academy of Sciences, 110(42), 17143–17148. https://doi.org/10.1073/pnas.1309909110 Umanath, S., & Marsh, E. J. (2014). Understanding How Prior Knowledge Influences Memory in Older Adults. Perspectives on Psychological Science, 9(4), 408–426. https://doi.org/10.1177/1745691614535933 Vaidya, A. R., Jones, H. M., Castillo, J., & Badre, D. (2021). Neural representation of abstract task structure during generalization. ELife, 10, e63226. https://doi.org/10.7554/eLife.63226 Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432. https://doi.org/10.1007/s11222-016-9696-4 Wang, X.-J. (2008). Decision Making in Recurrent Neuronal Circuits. Neuron, 60(2), 215–234. https://doi.org/10.1016/j.neuron.2008.09.034 Wilson, R. C., Takahashi, Y. K., Schoenbaum, G., & Niv, Y. (2014). Orbitofrontal Cortex as a Cognitive Map of Task Space. Neuron, 81(2), 267–279. https://doi.org/10.1016/j.neuron.2013.11.005 Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381–397. https://doi.org/10.1016/j.neuroimage.2014.01.060 Yang, T., & Shadlen, M. N. (2007). Probabilistic reasoning by neurons. Nature, 447(7148), 1075–1080. https://doi.org/10.1038/nature05852 Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using Stacking to Average Bayesian Predictive Distributions (with Discussion). Bayesian Analysis, 13(3), 917–1007. https://doi.org/10.1214/17-BA1091 Zhang, H., & Maloney, L. (2012). Ubiquitous Log Odds: A Common Representation of Probability and Frequency Distortion in Perception, Action, and Cognition. Frontiers in Neuroscience, 6, 1. https://doi.org/10.3389/fnins.2012.00001 | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83236 | - |
dc.description.abstract | 人類大腦反應外界訊息的過程可以貝氏推理的方法來呈現。這個方法是先利用既有的先驗信念來解讀資料,之後將概度整合以形塑成事後推論,而這兩個步驟是適應變化中環境的重要認知歷程。在這個研究中,我們假設年齡會改變大腦的神 經迴路,而這樣的變化使得年輕人與老年人以不同的能力來適應不斷變化的環境。 34 位年輕人(年齡 = 22.66 ± 2.58 歲,20 位女性)與 32 位老年人(年齡= 70.64 ± 5.60 歲, 17 位女性)參與我們的功能性磁振造影實驗。實驗任務會使用的三個箱子包含紅、藍、黃三種顏色的球,實驗參與者在實驗任務之前將會學習不同顏色的球的出現機率。在正式實驗任務中,三顆球將會從其中一個被選定的箱子中抽 出,而實驗參與者需要猜測待會抽出的三顆球是哪一個顏色比較多,之後會將抽出的三顆球呈現給參與者,並按照結果給予獎賞。參與者需結合自己的抽球前的先驗信念搭配抽球結果的概度來得出事後推論,才能準確判斷哪一個箱子才是被選定的箱子,而正確的推論能準確地預測抽球結果,並獲得更多的分數。實驗中,被選定的箱子將在一段時間之後更換為另一個箱子,所以參與者必須留心抽球結 果,並在準確地在正確的時間轉換先驗信念到另一個箱子。我們以貝氏修正 (Bayesian updating)的計算模型來適配參與者的行為資料,老年人相對於年輕人有比較低的機率保留原本的先驗信念(Cohen’s d = 0.86, 95%可靠區間 = [0.34, 1.38]),表示老年人的信念較不穩固,不顧抽球結果的概度就頻繁地更改心中對箱子的信念。而且,在整合抽球結果的概度上,並沒有發現統計上顯著的年齡差 異(Cohen’s d = -0.35, 95%可靠區間 = [-0.89, 0.20])。在功能性大腦影像資料中,我們因嚴重的頭動狀況排除一位年輕人與一位老年人,而剩餘的 33 位年輕人(年齡 = 22.72 ± 2.60 歲,19 位女性)與 31 位老年人(年齡 = 70.70 ± 5.68 歲,17 位 女性)以表徵相似分析(representational similarity analysis,RSA)與模型為基的單 變量廣義線性模型(model-based univariate generalized linear model)進行分析。 相對於年輕人,老年人功能性影像的右側頂下葉(inferior parietal lobe; 54, -42, 51)與楔前葉(precuneus; 0, -48, 45)的多變項神經模式(multivariate neural patterns)與後驗信念有比較差的關聯性,表示信念在大腦的神經表徵與老年人的 決策行為有不一致的狀況。另外,年輕人在腹內側前額葉(ventral medial prefrontal cortex; 0, 48, -15)與海馬迴(hippocampus; -33, -30, -15)的活化程度 與信念的信心有正相關,但在老年人的大腦上沒有這樣的正相關。總體來說,我們在行為上發現老年人的信念較不穩定,而這能反映在頂下葉與楔前葉有比較差 的神經表徵。相對應地,老年人減少腹內側前額葉與海馬迴的活化來維持信念的 信心。另外,在本論文中,我們結合預測性編碼(predictive coding)的理論架構來建立假說,我們預測大腦在低階層腦區或高階層腦區若遭受影響,將分別導致信念修正上比較頑固死板或比較反覆無常。我們的發現支持後者的假設,老年人後側頂葉的神經處理的失序使得在表徵高層次的抽象信念遇到問題,反映在決策行為上,與我們觀察到老年人有不穩定信念的結果一致。 | zh_TW |
dc.description.abstract | Human brains respond to incoming external information in a manner that can be formulated as Bayesian inference. Exploiting internal prior beliefs to make inferences about incoming information and then integrating likelihoods to form posterior inferences are two critical cognitive processes facilitating compliance with the changing environment. We postulated that age alters the neural circuits that reflect different abilities to adapt to changing environments in younger and older adults. In a functional magnetic resonance imaging (fMRI) experiment, we recruited 34 younger adults (age = 22.66 ± 2.58, 20 female) and 32 older adults (age = 70.64 ± 5.60, 17 female). Participants first learned about different proportions of red, blue, or yellow balls contained within three boxes. At test, participants were told that sets of three balls would be drawn from one of the boxes and they had to guess the majority color of the balls drawn with the source box hidden from participants. Outcomes of the drawn ball colors were then provided with correct guesses rewarded. Thus, participants had to update their posterior beliefs about which was the source box by integrating prior beliefs about the box and the likelihood of the drawn colors. Critically, source boxes were intermittently switched so that participants had to notice the outcome changes and transition to new beliefs. Computational model fitting with Bayesian updating showed older adults had lower probabilities to stay in the same belief (Cohen’s d = 0.86, 95% credible interval: [0.34, 1.38]), indicating less stable beliefs that frequently transitioned from trial to trial regardless of outcome likelihoods. However, we found no evidence of age-related difference in updating beliefs from outcome likelihoods (Cohen’s d = -0.35, 95% credible interval = [-0.89, 0.20]). We excluded one younger adult and one older adult for the functional brain data because of severe head motion during scanning. Functional brain imaging from the remaining 33 younger adults (age = 22.72 ± 2.60, 19 female) and 31 older adults (age = 70.70 ± 5.68, 17 female) were submitted to representational similarity analysis (RSA) and model-based univariate generalized linear modeling. Compared to younger adults, older adults showed reduced association between multivariate patterns and trial-wise posterior beliefs in the right inferior parietal lobe (IPL; 54, -42, 51) and precuneus (0, -48, 45), suggesting a behavioral disjoint with neural representations of belief in older adults. In younger adults, belief confidence also modulated activation in ventral medial prefrontal cortex (vmPFC; 0, 48, -15) and hippocampus (-33, -30, -15), which was not seen in older adults. Overall, we show that older adults have less stable beliefs. This is reflected by reduced neural representations of posterior beliefs in IPL and precuneus in older. Correspondingly, older adults showed reduced recruitment of VMPFC and hippocampus to maintain confident beliefs. Furthermore, in this dissertation, we incorporated a theoretical framework based on predictive coding and hypothesized that the disruption in brain regions at lower-level or higher-level hierarchies results in rigid or unstable beliefs, respectively. Our findings support the later speculation that older adults have disordered posterior parietal cortex (PPC) engagement in representing higher-level abstract beliefs which is associated with stochastic beliefs exemplified in decision behavior. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-11T17:02:18Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-01-11T17:02:18Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | PhD Dissertation Acceptance Certificate ................................................................................................. i
Acknowledgements ....................................................................................................................... ii Abstract .............................................................................................................................. iii 中文摘要 ................................................................................................................................. v Table of Contents ..................................................................................................................... vii Introduction .............................................................................................................................1 Predictive processing as a fundamental brain operation ...................................................................................1 Cognitive aging under the predictive processing perspective ..............................................................................3 Sequential decision-making and Bayesian belief updating ..................................................................................6 Rational of experimental design and hypothesis ...........................................................................................9 Methods .................................................................................................................................11 Participants ............................................................................................................................11 Procedures and experimental task ........................................................................................................12 Functional imaging acquisition and preprocessing ........................................................................................17 General statistical testing and inference ...............................................................................................18 Modeling belief trajectories ............................................................................................................19 Computational modeling for choice strategies ............................................................................................21 Representational similarity analysis ....................................................................................................32 Multivariate pattern decoding analysis ..................................................................................................34 Model-based univariate analysis .........................................................................................................35 Results .................................................................................................................................37 Both younger adults and older adults were able to make latent state inference followed by the evidence ..................................37 Older adults equivalently increased responses to the previous and the irrelevant latent state ...........................................38 Older adults show volatile beliefs and increased choice switches ........................................................................40 Comparison of computational models suggests participants were implementing Bayesian strategies ..........................................42 Parameters from the computation model consistent with more unstable beliefs and stochastic choices in older than younger adults .........45 Latent beliefs can be estimated from individual parameters ..............................................................................47 The relationship between model-free metrics and parameters from model-fitting ...........................................................48 RSA analysis reveals disrupted neural representation of latent beliefs in parietal regions in older adults ..............................49 Decoding performance using temporal windows consistent with disrupted latent beliefs in older adult brain ...............................51 Neural modulation associated with maintaining latent beliefs absent in older adults .....................................................53 Discussion ..............................................................................................................................56 Posterior parietal cortex represents belief space to encode relative position for beliefs ...............................................58 Older adult PPC filled with noise........................................................................................................61 Limitation and future direction .........................................................................................................65 References ..............................................................................................................................69 Figures and Tables ......................................................................................................................87 Figure 1. ...............................................................................................................................88 Figure 2. ...............................................................................................................................89 Figure 3. ...............................................................................................................................90 Figure 4. ...............................................................................................................................92 Figure 5. ...............................................................................................................................93 Figure 6. ...............................................................................................................................95 Figure 7. ...............................................................................................................................97 Figure 8. ...............................................................................................................................99 Figure 9. ..............................................................................................................................101 Figure 10. .............................................................................................................................103 Figure 11. .............................................................................................................................104 Figure 12. .............................................................................................................................105 Figure 13. .............................................................................................................................106 Figure 14. .............................................................................................................................108 Figure 15. .............................................................................................................................109 Figure 16. .............................................................................................................................111 Figure 17. .............................................................................................................................113 Figure 18. .............................................................................................................................115 Figure 19. .............................................................................................................................117 Figure 20. .............................................................................................................................119 Figure 21. .............................................................................................................................120 Figure 22. .............................................................................................................................122 Figure 23. .............................................................................................................................123 Figure 24. .............................................................................................................................124 Figure 25. .............................................................................................................................126 Figure 26. .............................................................................................................................127 Figure 27. .............................................................................................................................129 Table 1.................................................................................................................................130 Table 2.................................................................................................................................131 | - |
dc.language.iso | en | - |
dc.title | 預測處理功能於年輕與老年神經迴路之研究 | zh_TW |
dc.title | Evaluation of predictive processing in younger and older neural circuits | en |
dc.title.alternative | Evaluation of predictive processing in younger and older neural circuits | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 博士 | - |
dc.contributor.coadvisor | 李佳頴 | zh_TW |
dc.contributor.coadvisor | Chia-Ying Lee | en |
dc.contributor.oralexamcommittee | 謝淑蘭;謝伯讓;黃植懋 | zh_TW |
dc.contributor.oralexamcommittee | Shulan Hsieh;Po-Jang Hsieh;Chih-Mao Huang | en |
dc.subject.keyword | 貝氏推論,信念修正,老化,預測編碼,功能性磁振造影,表徵相似分析,計算模型, | zh_TW |
dc.subject.keyword | Bayesian inference,belief updating,aging,predictive coding,fMRI,RSA,computational model, | en |
dc.relation.page | 131 | - |
dc.identifier.doi | 10.6342/NTU202210190 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2022-12-30 | - |
dc.contributor.author-college | 生命科學院 | - |
dc.contributor.author-dept | 跨領域神經科學國際研究生博士學位學程 | - |
dc.date.embargo-lift | 2025-06-30 | - |
顯示於系所單位: | 跨領域神經科學國際研究生博士學位學程 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-0208221228369002.pdf 此日期後於網路公開 2025-06-30 | 18.16 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。