References
- 1Adams, R. A., Brown, H. R., & Friston, K. J. (2014). Bayesian inference, predictive coding and delusions. AVANT. The Journal of the Philosophical-Interdisciplinary Vanguard, 5(3), 51–88. 10.26913/50302014.0112.0004
- 2Adams, R. A., Pinotsis, D., Tsirlis, K., Unruh, L., Mahajan, A., Horas, A. M., Convertino, L., Summerfelt, A., Sampath, H., Du, X. M., Kochunov, P., Ji, J. L., Repovs, G., Murray, J. D., Friston, K. J., Hong, L. E., & Anticevic, A. (2022). Computational modeling of electroencephalography and functional magnetic resonance imaging paradigms indicates a consistent loss of pyramidal cell synaptic gain in schizophrenia. Biological Psychiatry, 91(2), 202–215. 10.1016/j.biopsych.2021.07.024
- 3Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in Psychiatry, 4. 10.3389/fpsyt.2013.00047
- 4Aitken, F., & Kok, P. (2022). Hippocampal representations switch from errors to predictions during acquisition of predictive associations. Nature Communications, 13(1),
3294 . 10.1038/s41467-022-31040-w - 5Aitken, F., Menelaou, G., Warrington, O., Koolschijn, R. S., Corbin, N., Callaghan, M. F., & Kok, P. (2020). Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex. PLOS Biology, 18(12),
e3001023 . 10.1371/journal.pbio.3001023 - 6Alexander, W. H., & Brown, J. W. (2011). Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14(10), 1338–1344. 10.1038/nn.2921
- 7Alexander, W. H., & Brown, J. W. (2015). Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27(11), 2354–2410. 10.1162/NECO_a_00779
- 8Alexander, W. H., & Brown, J. W. (2018). Frontal cortex function as derived from hierarchical predictive coding. Scientific Reports, 8(1),
3843 . 10.1038/s41598-018-21407-9 - 9Alexander, W. H., & Brown, J. W. (2019). The role of the anterior cingulate cortex in prediction error and signaling surprise. Topics in Cognitive Science, 11(1), 119–135. 10.1111/tops.12307
- 10Allen, S. J., Bharadwaj, R., Hyde, T. M., & Kleinman, J. E. (2020). Genetic neuropathology revisited: Gene expression in psychosis. Case Studies in Clinical Psychological Science: Bridging the Gap from Science to Practice, 1–7. 10.1093/MED/9780190653279.003.0019
- 11Altmann, G. T. M., & Kamide, Y. (1999). Incremental interpretation at verbs: Restricting the domain of subsequent reference. Cognition, 73(3), 247–264. 10.1016/S0010-0277(99)00059-1
- 12American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders. 10.1176/appi.books.9780890425787
- 13Angeletos Chrysaitis, N., & Seriès, P. (2023). 10 years of bayesian theories of autism: A comprehensive review. Neuroscience & Biobehavioral Reviews, 145,
105022 . 10.1016/j.neubiorev.2022.105022 - 14Arnal, L. H., Wyart, V., & Giraud, A.-L. (2011). Transitions in neural oscillations reflect prediction errors generated in audiovisual speech. Nature Neuroscience, 14(6), 797–801. 10.1038/nn.2810
- 15Attinger, A., Wang, B., & Keller, G. B. (2017). Visuomotor coupling shapes the functional development of mouse visual cortex. Cell, 169(7), 1291–1302.e14. 10.1016/j.cell.2017.05.023
- 16Auksztulewicz, R., Barascud, N., Cooray, G., Nobre, A. C., Chait, M., & Friston, K. (2017). The cumulative effects of predictability on synaptic gain in the auditory processing stream. The Journal of Neuroscience, 37(28), 6751–6760. 10.1523/JNEUROSCI.0291-17.2017
- 17Auksztulewicz, R., & Friston, K. (2016). Repetition suppression and its contextual determinants in predictive coding. Cortex, 80, 125–140. 10.1016/j.cortex.2015.11.024
- 18Badcock, P. B., Davey, C. G., Whittle, S., Allen, N. B., & Friston, K. J. (2017). The depressed brain: An evolutionary systems theory. Trends in Cognitive Sciences, 21(3), 182–194. 10.1016/j.tics.2017.01.005
- 19Baddeley, A. (2011). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1–29. 10.1146/annurev-psych-120710-100422
- 20Balota, D. A., Pollatsek, A., & Rayner, K. (1985). The interaction of contextual constraints and parafoveal visual information in reading. Cognitive Psychology, 17(3), 364–390. 10.1016/0010-0285(85)90013-1
- 21Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016). Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns. Proceedings of the National Academy of Sciences, 113(5). 10.1073/pnas.1508523113
- 22Barron, H. C., Auksztulewicz, R., & Friston, K. (2020). Prediction and memory: A predictive coding account. Progress in Neurobiology, 192,
101821 . 10.1016/j.pneurobio.2020.101821 - 23Bastos, A. M., Lundqvist, M., Waite, A. S., Kopell, N., & Miller, E. K. (2020). Layer and rhythm specificity for predictive routing. Proceedings of the National Academy of Sciences, 117(49), 31459–31469. 10.1073/pnas.2014868117
- 24Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76(4), 695–711. 10.1016/j.neuron.2012.10.038
- 25Bein, O., Duncan, K., & Davachi, L. (2020). Mnemonic prediction errors bias hippocampal states. Nature Communications, 11(1),
3451 . 10.1038/s41467-020-17287-1 - 26Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011). Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science, 331(6013), 83–87. 10.1126/science.1195870
- 27Bhat, A., Irizar, H., Thygesen, J. H., Kuchenbaecker, K., Pain, O., Adams, R. A., Zartaloudi, E., Harju-Seppänen, J., Austin-Zimmerman, I., Wang, B., Muir, R., Summerfelt, A., Du, X. M., Bruce, H., O’Donnell, P., Srivastava, D. P., Friston, K., Hong, L. E., Hall, M.-H., & Bramon, E. (2021). Transcriptome-wide association study reveals two genes that influence mismatch negativity. Cell Reports, 34(11),
108868 . 10.1016/j.celrep.2021.108868 - 28Cacciaglia, R., Escera, C., Slabu, L., Grimm, S., Sanjuán, A., Ventura-Campos, N., & Ávila, C. (2015). Involvement of the human midbrain and thalamus in auditory deviance detection. Neuropsychologia, 68, 51–58. 10.1016/j.neuropsychologia.2015.01.001
- 29Calabrò, M., Porcelli, S., Crisafulli, C., Albani, D., Kasper, S., Zohar, J., Souery, D., Montgomery, S., Mantovani, V., Mendlewicz, J., Bonassi, S., Vieta, E., Frustaci, A., Ducci, G., Landi, S., Boccia, S., Bellomo, A., Di Nicola, M., Janiri, L., & Serretti, A. (2020). Genetic variants associated with psychotic symptoms across psychiatric disorders. Neuroscience Letters, 720,
134754 . 10.1016/j.neulet.2020.134754 - 30Cannon, J., O’Brien, A. M., Bungert, L., & Sinha, P. (2021). Prediction in autism spectrum disorder: A systematic review of empirical evidence. Autism Research, 14(4), 604–630. 10.1002/aur.2482
- 31Caucheteux, C., Gramfort, A., & King, J.-R. (2023). Evidence of a predictive coding hierarchy in the human brain listening to speech. Nature Human Behaviour. 10.1038/s41562-022-01516-2
- 32Chao, Z. C., Huang, Y. T., & Wu, C.-T. (2022). A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain. Communications Biology, 5(1),
1076 . 10.1038/s42003-022-04049-6 - 33Chu, Q., Ma, O., Hang, Y., & Tian, X. (2023). Dual-stream cortical pathways mediate sensory prediction. Cerebral Cortex, 33(14), 8890–8903. 10.1093/cercor/bhad168
- 34Clementz, B. A., Parker, D. A., Trotti, R. L., McDowell, J. E., Keedy, S. K., Keshavan, M. S., Pearlson, G. D., Gershon, E. S., Ivleva, E. I., Huang, L.-Y., Hill, S. K., Sweeney, J. A., Thomas, O., Hudgens-Haney, M., Gibbons, R. D., & Tamminga, C. A. (2022). Psychosis biotypes: Replication and validation from the b-snip consortium. Schizophrenia Bulletin, 48(1), 56–68. 10.1093/schbul/sbab090
- 35Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., Keshavan, M. S., & Tamminga, C. A. (2016). Identification of distinct psychosis biotypes using brain-based biomarkers. American Journal of Psychiatry, 173(4), 373–384. 10.1176/appi.ajp.2015.14091200
- 36Cohen, B. M., & Öngür, D. (2023). The need for evidence-based updating of icd and dsm models of psychotic and mood disorders. Molecular Psychiatry. 10.1038/s41380-023-01967-7
- 37Corlett, P. R., Bansal, S., & Gold, J. M. (2023). Studying healthy psychosislike experiences to improve illness prediction. JAMA Psychiatry, 80(5),
515 . 10.1001/jamapsychiatry.2023.0059 - 38Corlett, P. R., Horga, G., Fletcher, P. C., Alderson-Day, B., Schmack, K., & Powers, A. R. (2019). Hallucinations and strong priors. Trends in Cognitive Sciences, 23(2), 114–127. 10.1016/j.tics.2018.12.001
- 39Cuthbert, B. N. (2020). The role of rdoc in future classification of mental disorders. Dialogues in Clinical Neuroscience, 22(1), 81–85. 10.31887/DCNS.2020.22.1/bcuthbert
- 40Cuthbert, B. N. (2022). Research domain criteria (rdoc): Progress and potential. Current Directions in Psychological Science, 31(2), 107–114. 10.1177/09637214211051363
- 41Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of rdoc. BMC Medicine, 11(1),
126 . 10.1186/1741-7015-11-126 - 42Cuthbert, B. N., & Morris, S. E. (2021). Evolving concepts of the schizophrenia spectrum: A research domain criteria perspective. Frontiers in Psychiatry, 12. 10.3389/fpsyt.2021.641319
- 43Donaldson, K. R., Novak, K. D., Foti, D., Marder, M., Perlman, G., Kotov, R., & Mohanty, A. (2020). Associations of mismatch negativity with psychotic symptoms and functioning transdiagnostically across psychotic disorders. Journal of Abnormal Psychology, 129(6), 570–580. 10.1037/abn0000506
- 44Dürschmid, S., Edwards, E., Reichert, C., Dewar, C., Hinrichs, H., Heinze, H.-J., Kirsch, H. E., Dalal, S. S., Deouell, L. Y., & Knight, R. T. (2016). Hierarchy of prediction errors for auditory events in human temporal and frontal cortex. Proceedings of the National Academy of Sciences, 113(24), 6755–6760. 10.1073/pnas.1525030113
- 45Dzafic, I., Larsen, K. M., Darke, H., Pertile, H., Carter, O., Sundram, S., & Garrido, M. I. (2021). Stronger top-down and weaker bottom-up frontotemporal connections during sensory learning are associated with severity of psychotic phenomena. Schizophrenia Bulletin, 47(4), 1039–1047. 10.1093/schbul/sbaa188
- 46Dzafic, I., Randeniya, R., Harris, C. D., Bammel, M., & Garrido, M. I. (2020). Statistical learning and inference is impaired in the nonclinical continuum of psychosis. The Journal of Neuroscience, 40(35), 6759–6769. 10.1523/JNEUROSCI.0315-20.2020
- 47Edwards, E., Soltani, M., Deouell, L. Y., Berger, M. S., & Knight, R. T. (2005). High gamma activity in response to deviant auditory stimuli recorded directly from human cortex. Journal of Neurophysiology, 94(6), 4269–4280. 10.1152/jn.00324.2005
- 48Egner, T., Monti, J. M., & Summerfield, C. (2010). Expectation and surprise determine neural population responses in the ventral visual stream. The Journal of Neuroscience, 30(49), 16601–16608. 10.1523/JNEUROSCI.2770-10.2010
- 49El Karoui, I., King, J.-R., Sitt, J., Meyniel, F., Van Gaal, S., Hasboun, D., Adam, C., Navarro, V., Baulac, M., Dehaene, S., Cohen, L., & Naccache, L. (2015). Event-related potential, time-frequency, and functional connectivity facets of local and global auditory novelty processing: An intracranial study in humans. Cerebral Cortex, 25(11), 4203–4212. 10.1093/cercor/bhu143
- 50Escera, C. (2023). Contributions of the subcortical auditory system to predictive coding and the neural encoding of speech. Current Opinion in Behavioral Sciences, 54,
101324 . 10.1016/j.cobeha.2023.101324 - 51Feldman, H., & Friston, K. J. (2010). Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience, 4. 10.3389/fnhum.2010.00215
- 52Ferrante, M., Redish, A. D., Oquendo, M. A., Averbeck, B. B., Kinnane, M. E., & Gordon, J. A. (2019). Computational psychiatry: A report from the 2017 nimh workshop on opportunities and challenges. Molecular Psychiatry, 24(4), 479–483. 10.1038/s41380-018-0063-z
- 53Ferreira, F., & Chantavarin, S. (2018). Integration and prediction in language processing: A synthesis of old and new. Current Directions in Psychological Science, 27(6), 443–448. 10.1177/0963721418794491
- 54Ferreira, F., & Qiu, Z. (2021). Predicting syntactic structure. Brain Research, 1770,
147632 . 10.1016/j.brainres.2021.147632 - 55Ficco, L., Mancuso, L., Manuello, J., Teneggi, A., Liloia, D., Duca, S., Costa, T., Kovacs, G. Z., & Cauda, F. (2021). Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network. Scientific Reports, 11(1),
16258 . 10.1038/s41598-021-95603-5 - 56Fiser, A., Mahringer, D., Oyibo, H. K., Petersen, A. V., Leinweber, M., & Keller, G. B. (2016). Experience-dependent spatial expectations in mouse visual cortex. Nature Neuroscience, 19(12), 1658–1664. 10.1038/nn.4385
- 57Fontolan, L., Morillon, B., Liegeois-Chauvel, C., & Giraud, A.-L. (2014). The contribution of frequency-specific activity to hierarchical information processing in the human auditory cortex. Nature Communications, 5(1),
4694 . 10.1038/ncomms5694 - 58Ford, J. M., Hamilton, H. K., Llerena, K., Roach, B. J., & Mathalon, D. H. (2020). Neurophysiologic biomarkers of psychosis: Event-related potential biomarkers. Case Studies in Clinical Psychological Science: Bridging the Gap from Science to Practice, 1–7. 10.1093/MED/9780190653279.003.0026
- 59Forseth, K. J., Hickok, G., Rollo, P. S., & Tandon, N. (2020). Language prediction mechanisms in human auditory cortex. Nature Communications, 11(1),
5240 . 10.1038/s41467-020-19010-6 - 60Friston, K. (2018). Does predictive coding have a future? Nature Neuroscience, 21(8), 1019–1021. 10.1038/s41593-018-0200-7
- 61Friston, K. (2023). Computational psychiatry: From synapses to sentience. Molecular Psychiatry, 28(1), 256–268. 10.1038/s41380-022-01743-z
- 62Friston, K. J. (2017). Precision psychiatry. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(8), 640–643. 10.1016/j.bpsc.2017.08.007
- 63Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. 10.1016/S1053-8119(03)00202-7
- 64Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: The brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148–158. 10.1016/S2215-0366(14)70275-5
- 65Gagnepain, P., Henson, R. N., & Davis, M. H. (2012). Temporal predictive codes for spoken words in auditory cortex. Current Biology, 22(7), 615–621. 10.1016/j.cub.2012.02.015
- 66Garrido, M. I., Barnes, G. R., Kumaran, D., Maguire, E. A., & Dolan, R. J. (2015). Ventromedial prefrontal cortex drives hippocampal theta oscillations induced by mismatch computations. NeuroImage, 120, 362–370. 10.1016/j.neuroimage.2015.07.016
- 67Garrido, M. I., Friston, K. J., Kiebel, S. J., Stephan, K. E., Baldeweg, T., & Kilner, J. M. (2008). The functional anatomy of the mmn: A dcm study of the roving paradigm. NeuroImage, 42(2), 936–944. 10.1016/j.neuroimage.2008.05.018
- 68Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology, 120(3), 453–463. 10.1016/j.clinph.2008.11.029
- 69Garrido, M. I., Rowe, E. G., Halász, V., & Mattingley, J. B. (2018). Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli. Cerebral Cortex, 28(5), 1771–1782. 10.1093/cercor/bhx087
- 70Gavornik, J. P., & Bear, M. F. (2014). Learned spatiotemporal sequence recognition and prediction in primary visual cortex. Nature Neuroscience, 17(5), 732–737. 10.1038/nn.3683
- 71Gazzaniga, M., Ivary, R., & Mangun, G. (2019). Cognitive neuroscience: The biology of the mind (fifth). W.W. Norton & Company.
- 72Gold, J. M., Corlett, P. R., Erickson, M., Waltz, J. A., August, S., Dutterer, J., & Bansal, S. (2023). Phenomenological and cognitive features associated with auditory hallucinations in clinical and nonclinical voice hearers. Schizophrenia Bulletin, 49(6), 1591–1601. 10.1093/schbul/sbad083
- 73Haarsma, J., Kok, P., & Browning, M. (2022). The promise of layer-specific neuroimaging for testing predictive coding theories of psychosis. Schizophrenia Research, 245, 68–76. 10.1016/j.schres.2020.10.009
- 74Hainmueller, T., & Bartos, M. (2020). Dentate gyrus circuits for encoding, retrieval and discrimination of episodic memories. Nature Reviews Neuroscience, 21(3), 153–168. 10.1038/s41583-019-0260-z
- 75Hartwigsen, G., Golombek, T., & Obleser, J. (2015). Repetitive transcranial magnetic stimulation over left angular gyrus modulates the predictability gain in degraded speech comprehension. Cortex, 68, 100–110. 10.1016/j.cortex.2014.08.027
- 76Heilbron, M., & Chait, M. (2018). Great expectations: Is there evidence for predictive coding in auditory cortex? Neuroscience, 389, 54–73. 10.1016/j.neuroscience.2017.07.061
- 77Hein, T. P., Gong, Z., Ivanova, M., Fedele, T., Nikulin, V., & Herrojo Ruiz, M. (2023). Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans. Communications Biology, 6(1),
271 . 10.1038/s42003-023-04628-1 - 78Henson, R. N., & Gagnepain, P. (2010). Predictive, interactive multiple memory systems. Hippocampus, 20(11), 1315–1326. 10.1002/hipo.20857
- 79Herzog, L. E., Wang, L., Yu, E., Choi, S., Farsi, Z., Song, B. J., Pan, J. Q., & Sheng, M. (2023). Mouse mutants in schizophrenia risk genes grin2a and akap11 show eeg abnormalities in common with schizophrenia patients. Translational Psychiatry, 13(1),
92 . 10.1038/s41398-023-02393-7 - 80Hickok, G. (2009). The functional neuroanatomy of language. Physics of Life Reviews, 6(3), 121–143. 10.1016/j.plrev.2009.06.001
- 81Hill, S. K., Keefe, R. S. E., & Sweeney, J. A. (2020).
Cognitive biomarkers of psychosis . In Psychotic disorders (pp. 195–203). Oxford University Press. 10.1093/med/9780190653279.003.0023 - 82Hodson, R., Mehta, M., & Smith, R. (2024). The empirical status of predictive coding and active inference. Neuroscience & Biobehavioral Reviews, 157,
105473 . 10.1016/j.neubiorev.2023.105473 - 83Homan, P., Levy, I., Feltham, E., Gordon, C., Hu, J., Li, J., Pietrzak, R. H., Southwick, S., Krystal, J. H., Harpaz-Rotem, I., & Schiller, D. (2019). Neural computations of threat in the aftermath of combat trauma. Nature Neuroscience, 22(3), 470–476. 10.1038/s41593-018-0315-x
- 84Hsu, Y.-F., Hämäläinen, J. A., & Waszak, F. (2014). Both attention and prediction are necessary for adaptive neuronal tuning in sensory processing. Frontiers in Human Neuroscience, 8. 10.3389/fnhum.2014.00152
- 85Huettig, F. (2015). Four central questions about prediction in language processing. Brain Research, 1626, 118–135. 10.1016/j.brainres.2015.02.014
- 86Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), 404–413. 10.1038/nn.4238
- 87Katsumi, Y., Zhang, J., Chen, D., Kamona, N., Bunce, J. G., Hutchinson, J. B., Yarossi, M., Tunik, E., Dickerson, B. C., Quigley, K. S., & Barrett, L. F. (2023). Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus. Communications Biology, 6(1),
401 . 10.1038/s42003-023-04796-0 - 88Keller, G. B., & Mrsic-Flogel, T. D. (2018). Predictive processing: A canonical cortical computation. Neuron, 100(2), 424–435. 10.1016/j.neuron.2018.10.003
- 89Kirihara, K., Tada, M., Koshiyama, D., Fujioka, M., Usui, K., Araki, T., & Kasai, K. (2020). A predictive coding perspective on mismatch negativity impairment in schizophrenia. Frontiers in Psychiatry, 11. 10.3389/fpsyt.2020.00660
- 90Kok, P., Bains, L. J., van Mourik, T., Norris, D. G., & de Lange, F. P. (2016). Selective activation of the deep layers of the human primary visual cortex by top-down feedback. Current Biology, 26(3), 371–376. 10.1016/j.cub.2015.12.038
- 91Kok, P., Rahnev, D., Jehee, J. F. M., Lau, H. C., & de Lange, F. P. (2012). Attention reverses the effect of prediction in silencing sensory signals. Cerebral Cortex, 22(9), 2197–2206. 10.1093/cercor/bhr310
- 92Köster, M., Kayhan, E., Langeloh, M., & Hoehl, S. (2020). Making sense of the world: Infant learning from a predictive processing perspective. Perspectives on Psychological Science, 15(3), 562–571. 10.1177/1745691619895071
- 93Lahti, A. C., & Kraguljac, N. V. (2020). Mr spectroscopy. Case Studies in Clinical Psychological Science: Bridging the Gap from Science to Practice, 1–7. 10.1093/MED/9780190653279.003.0030
- 94Lange, I., Papalini, S., & Vervliet, B. (2021). Experimental models in psychopathology research: The relation between research domain criteria and experimental psychopathology. Current Opinion in Psychology, 41, 118–123. 10.1016/j.copsyc.2021.07.004
- 95Larsen, K. M., Dzafic, I., Darke, H., Pertile, H., Carter, O., Sundram, S., & Garrido, M. I. (2020). Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences. Schizophrenia Research, 222, 185–194. 10.1016/j.schres.2020.05.061
- 96Larsen, K. M., Madsen, K. S., Ver Loren van Themaat, A. H., Thorup, A. A. E., Plessen, K. J., Mors, O., Nordentoft, M., & Siebner, H. R. (2024). Children at familial high risk of schizophrenia and bipolar disorder exhibit altered connectivity patterns during pre-attentive processing of an auditory prediction error. Schizophrenia Bulletin, 50(1), 166–176. 10.1093/schbul/sbad092
- 97Larsen, K. M., Mørup, M., Birknow, M. R., Fischer, E., Hulme, O., Vangkilde, A., Schmock, H., Baaré, W. F. C., Didriksen, M., Olsen, L., Werge, T., Siebner, H. R., & Garrido, M. I. (2018). Altered auditory processing and effective connectivity in 22q11.2 deletion syndrome. Schizophrenia Research, 197, 328–336. 10.1016/j.schres.2018.01.026
- 98Lawson, R. P., Mathys, C., & Rees, G. (2017). Adults with autism overestimate the volatility of the sensory environment. Nature Neuroscience, 20(9), 1293–1299. 10.1038/nn.4615
- 99Lecaignard, F., Bertrand, O., Caclin, A., & Mattout, J. (2022). Neurocomputational underpinnings of expected surprise. The Journal of Neuroscience, 42(3), 474–486. 10.1523/JNEUROSCI.0601-21.2021
- 100Lee, M., Sehatpour, P., Hoptman, M. J., Lakatos, P., Dias, E. C., Kantrowitz, J. T., Martinez, A. M., & Javitt, D. C. (2017). Neural mechanisms of mismatch negativity dysfunction in schizophrenia. Molecular Psychiatry, 22(11), 1585–1593. 10.1038/mp.2017.3
- 101Leptourgos, P., Bansal, S., Dutterer, J., Culbreth, A., Powers, A., Suthaharan, P., Kenney, J., Erickson, M., Waltz, J., Wijtenburg, S. A., Gaston, F., Rowland, L. M., Gold, J., & Corlett, P. (2022). Relating glutamate, conditioned, and clinical hallucinations via 1h-mr spectroscopy. Schizophrenia Bulletin, 48(4), 912–920. 10.1093/schbul/sbac006
- 102Liu, Z., Shu, S., Lu, L., Ge, J., & Gao, J.-H. (2020). Spatiotemporal dynamics of predictive brain mechanisms during speech processing: An meg study. Brain and Language, 203,
104755 . 10.1016/j.bandl.2020.104755 - 103Lyall, A. E., Seitz, J., & Kubicki, M. (2020). Structural connectivity in psychosis. Case Studies in Clinical Psychological Science: Bridging the Gap from Science to Practice, 1–7. 10.1093/MED/9780190653279.003.0028
- 104Lyndon, S., & Corlett, P. R. (2020). Hallucinations in posttraumatic stress disorder: Insights from predictive coding. Journal of Abnormal Psychology, 129(6), 534–543. 10.1037/abn0000531
- 105McDonald, S. A., & Shillcock, R. C. (2003). Eye movements reveal the on-line computation of lexical probabilities during reading. Psychological Science, 14(6), 648–652. 10.1046/j.0956-7976.2003.psci_1480.x
- 106Mendoza-Halliday, D., Major, A. J., Lee, N., Lichtenfeld, M. J., Carlson, B., Mitchell, B., Meng, P. D., Xiong, Y. S., Westerberg, J. A., Jia, X., Johnston, K. D., Selvanayagam, J., Everling, S., Maier, A., Desimone, R., Miller, E. K., & Bastos, A. M. (2024). A ubiquitous spectrolaminar motif of local field potential power across the primate cortex. Nature Neuroscience, 27(3), 547–560. 10.1038/s41593-023-01554-7
- 107Menon, V., & D’Esposito, M. (2022). The role of pfc networks in cognitive control and executive function. Neuropsychopharmacology, 47(1), 90–103. 10.1038/s41386-021-01152-w
- 108Miller, E. K. (2000). The prefontral cortex and cognitive control. Nature Reviews Neuroscience, 1(1), 59–65. 10.1038/35036228
- 109Mohanta, S., Afrasiabi, M., Casey, C. P., Tanabe, S., Redinbaugh, M. J., Kambi, N. A., Phillips, J. M., Polyakov, D., Filbey, W., Austerweil, J. L., Sanders, R. D., & Saalmann, Y. B. (2021). Predictive feedback, early sensory representations, and fast responses to predicted stimuli depend on nmda receptors. The Journal of Neuroscience, 41(49), 10130–10147. 10.1523/JNEUROSCI.1311-21.2021
- 110Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2013). Free energy, precision and learning: The role of cholinergic neuromodulation. Journal of Neuroscience, 33(19), 8227–8236. 10.1523/JNEUROSCI.4255-12.2013
- 111Morris, R. K. (1994). Lexical and message-level sentence context effects on fixation times in reading. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(1), 92–103. 10.1037/0278-7393.20.1.92
- 112Morris, S. E., Pacheco, J., & Sanislow, C. A. (2020).
Applying research domain criteria (rdoc) dimensions to psychosis . In Psychotic disorders (pp. 29–37). Oxford University Press. 10.1093/med/9780190653279.003.0004 - 113Morris, S. E., Sanislow, C. A., Pacheco, J., Vaidyanathan, U., Gordon, J. A., & Cuthbert, B. N. (2022). Revisiting the seven pillars of rdoc. BMC Medicine, 20(1),
220 . 10.1186/s12916-022-02414-0 - 114Moutoussis, M., Fearon, P., El-Deredy, W., Dolan, R. J., & Friston, K. J. (2014). Bayesian inferences about the self (and others): A review. Consciousness and Cognition, 25, 67–76. 10.1016/j.concog.2014.01.009
- 115Muckli, L., De Martino, F., Vizioli, L., Petro, L. S., Smith, F. W., Ugurbil, K., Goebel, R., & Yacoub, E. (2015). Contextual feedback to superficial layers of v1. Current Biology, 25(20), 2690–2695. 10.1016/j.cub.2015.08.057
- 116National Institute of Mental Health (NIMH). (2024, April 20). Research Domain Criteria (RDoC).
https://grants.nih.gov/grants/guide/rfa-files/RFA-MH-19-242.html - 117Nieuwland, M. S. (2019). Do ‘early’ brain responses reveal word form prediction during language comprehension? a critical review. Neuroscience & Biobehavioral Reviews, 96, 367–400. 10.1016/j.neubiorev.2018.11.019
- 118Obleser, J., & Kotz, S. A. (2010). Expectancy constraints in degraded speech modulate the language comprehension network. Cerebral Cortex, 20(3), 633–640. 10.1093/cercor/bhp128
- 119Okada, K., Matchin, W., & Hickok, G. (2018). Neural evidence for predictive coding in auditory cortex during speech production. Psychonomic Bulletin & Review, 25(1), 423–430. 10.3758/s13423-017-1284-x
- 120Ortiz-Tudela, J., Bergmann, J., Bennett, M., Ehrlich, I., Muckli, L., & Shing, Y. L. (2023). Concurrent contextual and time-distant mnemonic information co-exist as feedback in the human visual cortex. NeuroImage, 265,
119778 . 10.1016/j.neuroimage.2022.119778 - 121O’Toole, S. M., Oyibo, H. K., & Keller, G. B. (2023). Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses. Neuron, 111(18), 2918–2928.e8. 10.1016/j.neuron.2023.08.015
- 122Parr, T., & Friston, K. J. (2017). Working memory, attention, and salience in active inference. Scientific Reports, 7(1),
14678 . 10.1038/s41598-017-15249-0 - 123Parr, T., & Friston, K. J. (2018). The anatomy of inference: Generative models and brain structure. Frontiers in Computational Neuroscience, 12. 10.3389/fncom.2018.00090
- 124Parr, T., & Friston, K. J. (2019). Attention or salience? Current Opinion in Psychology, 29, 1–5. 10.1016/j.copsyc.2018.10.006
- 125Parr, T., Rikhye, R. V., Halassa, M. M., & Friston, K. J. (2020). Prefrontal computation as active inference. Cerebral Cortex, 30(2), 682–695. 10.1093/cercor/bhz118
- 126Paulus, M. P., Feinstein, J. S., & Khalsa, S. S. (2019). An active inference approach to interoceptive psychopathology. Annual Review of Clinical Psychology, 15(1), 97–122. 10.1146/annurev-clinpsy-050718-095617
- 127Pearlson, G., & Stevens, M. (2020). Functional connectivity biomarkers of psychosis. Case Studies in Clinical Psychological Science: Bridging the Gap from Science to Practice, 1–7. 10.1093/MED/9780190653279.003.0029
- 128Pereira, I., Frässle, S., Heinzle, J., Schöbi, D., Do, C. T., Gruber, M., & Stephan, K. E. (2021). Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities. NeuroImage, 245,
118662 . 10.1016/j.neuroimage.2021.118662 - 129Pezzulo, G., Kemere, C., & van der Meer, M. A. A. (2017). Internally generated hippocampal sequences as a vantage point to probe future-oriented cognition. Annals of the New York Academy of Sciences, 1396(1), 144–165. 10.1111/nyas.13329
- 130Pezzulo, G., Parr, T., & Friston, K. (2024). Active inference as a theory of sentient behavior. Biological Psychology, 186,
108741 . 10.1016/j.biopsycho.2023.108741 - 131Pezzulo, G., Rigoli, F., & Friston, K. (2015). Active inference, homeostatic regulation and adaptive behavioural control. Progress in Neurobiology, 134, 17–35. 10.1016/j.pneurobio.2015.09.001
- 132Pezzulo, G., Rigoli, F., & Friston, K. J. (2018). Hierarchical active inference: A theory of motivated control. Trends in Cognitive Sciences, 22(4), 294–306. 10.1016/j.tics.2018.01.009
- 133Pomerantz, J. R. (2006). Perception: Overview. Encyclopedia of Cognitive Science. 10.1002/0470018860.s00589
- 134Posner, M. I. (2023). The evolution and future development of attention networks. Journal of Intelligence, 11(6),
98 . 10.3390/jintelligence11060098 - 135Posner, M. I., & Rothbart, M. K. (2007). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology, 58(1), 1–23. 10.1146/annurev.psych.58.110405.085516
- 136Posner, M. I., & Rothbart, M. K. (2023). Fifty years integrating neurobiology and psychology to study attention. Biological Psychology, 180,
108574 . 10.1016/j.biopsycho.2023.108574 - 137Powers, A. R., Mathys, C., & Corlett, P. R. (2017). Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science, 357(6351), 596–600. 10.1126/science.aan3458
- 138Prabhakaran, V., Narayanan, K., Zhao, Z., & Gabrieli, J. D. E. (2000). Integration of diverse information in working memory within the frontal lobe. Nature Neuroscience, 3(1), 85–90. 10.1038/71156
- 139Radošević, T., Malaia, E. A., & Milković, M. (2022). Predictive processing in sign languages: A systematic review. Frontiers in Psychology, 13. 10.3389/fpsyg.2022.805792
- 140Randeniya, R., Oestreich, L. K. L., & Garrido, M. I. (2018). Sensory prediction errors in the continuum of psychosis. Schizophrenia Research, 191, 109–122. 10.1016/j.schres.2017.04.019
- 141Richards, K. L., Karvelis, P., Lawrie, S. M., & Seriès, P. (2020). Visual statistical learning and integration of perceptual priors are intact in attention deficit hyperactivity disorder. PLOS ONE, 15(12),
e0243100 . 10.1371/journal.pone.0243100 - 142Rosch, R. E., Auksztulewicz, R., Leung, P. D., Friston, K. J., & Baldeweg, T. (2019). Selective prefrontal disinhibition in a roving auditory oddball paradigm under n-methyl-d-aspartate receptor blockade. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4(2), 140–150. 10.1016/j.bpsc.2018.07.003
- 143Ross, C. A., & Margolis, R. L. (2019). Research domain criteria: Strengths, weaknesses, and potential alternatives for future psychiatric research. Complex Psychiatry, 5(4), 218–236. 10.1159/000501797
- 144Rowe, E. G., Harris, C. D., Dzafic, I., & Garrido, M. I. (2023). Anxiety attenuates learning advantages conferred by statistical stability and induces loss of volatility-attuning in brain activity. Human Brain Mapping, 44(6), 2557–2571. 10.1002/hbm.26230
- 145Sanislow, C. A., Ferrante, M., Pacheco, J., Rudorfer, M. V., & Morris, S. E. (2019). Advancing translational research using nimh research domain criteria and computational methods. Neuron, 101(5), 779–782. 10.1016/j.neuron.2019.02.024
- 146Scangos, K. W., State, M. W., Miller, A. H., Baker, J. T., & Williams, L. M. (2023). New and emerging approaches to treat psychiatric disorders. Nature Medicine, 29(2), 317–333. 10.1038/s41591-022-02197-0
- 147Schall, U., Johnston, P., Todd, J., Ward, P. B., & Michie, P. T. (2003). Functional neuroanatomy of auditory mismatch processing: An event-related fmri study of duration-deviant oddballs. NeuroImage, 20(2), 729–736. 10.1016/S1053-8119(03)00398-7
- 148Schroën, J. A. M., Gunter, T. C., Numssen, O., Kroczek, L. O. H., Hartwigsen, G., & Friederici, A. D. (2023). Causal evidence for a coordinated temporal interplay within the language network. Proceedings of the National Academy of Sciences, 120(47). 10.1073/pnas.2306279120
- 149Sedley, W., Gander, P. E., Kumar, S., Kovach, C. K., Oya, H., Kawasaki, H., Howard, M. A., & Griffiths, T. D. (2016). Neural signatures of perceptual inference. ELife, 5. 10.7554/eLife.11476
- 150Shine, J. M., Müller, E. J., Munn, B., Cabral, J., Moran, R. J., & Breakspear, M. (2021). Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics. Nature Neuroscience, 24(6), 765–776. 10.1038/s41593-021-00824-6
- 151Shipp, S. (2016). Neural elements for predictive coding. Frontiers in Psychology, 7. 10.3389/fpsyg.2016.01792
- 152Simmons, J. M., Cuthbert, B., Gordon, J. A., & Ferrante, M. (2020).
Introduction: Toward a computational approach to psychiatry . In P. Seriès (Ed.), Computational psychiatry (pp. 10–13). The MIT Press. 10.1234/56789 - 153Smith, R., Badcock, P., & Friston, K. J. (2021). Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry and Clinical Neurosciences, 75(1), 3–13. 10.1111/pcn.13138
- 154Southwell, R., & Chait, M. (2018). Enhanced deviant responses in patterned relative to random sound sequences. Cortex, 109, 92–103. 10.1016/j.cortex.2018.08.032
- 155Sprevak, M., & Smith, R. (2023). An introduction to predictive processing models of perception and decision-making. Topics in Cognitive Science. 10.1111/tops.12704
- 156Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L., Petrovic, P., Uhlhaas, P., Voss, M., & Corlett, P. R. (2018). The predictive coding account of psychosis. Biological Psychiatry, 84, 634–643. 10.1016/j.biopsych.2018.05.015
- 157Talsma, D. (2015). Predictive coding and multisensory integration: An attentional account of the multisensory mind. Frontiers in Integrative Neuroscience, 9. 10.3389/fnint.2015.00019
- 158Tarasi, L., Trajkovic, J., Diciotti, S., di Pellegrino, G., Ferri, F., Ursino, M., & Romei, V. (2022). Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model. Neuroscience and Biobehavioral Reviews, 132, 1–22. 10.1016/j.neubiorev.2021.11.006
- 159Tavano, A., & Scharinger, M. (2015). Prediction in speech and language processing. Cortex, 68, 1–7. 10.1016/j.cortex.2015.05.001
- 160Taylor, J. A., Larsen, K. M., & Garrido, M. I. (2020). Multi-dimensional predictions of psychotic symptoms via machine learning. Human Brain Mapping, 41(18), 5151–5163. 10.1002/hbm.25181
- 161Thomas, E. R., Haarsma, J., Nicholson, J., Yon, D., Kok, P., & Press, C. (2024). Predictions and errors are distinctly represented across v1 layers. Current Biology, 34(10), 2265–2271.e4. 10.1016/j.cub.2024.04.036
- 162Topolnik, L., & Tamboli, S. (2022). The role of inhibitory circuits in hippocampal memory processing. Nature Reviews Neuroscience, 23(8), 476–492. 10.1038/s41583-022-00599-0
- 163Tremblay, S., Shiller, D. M., & Ostry, D. J. (2003). Somatosensory basis of speech production. Nature, 423(6942), 866–869. 10.1038/nature01710
- 164Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014). Precise minds in uncertain worlds: Predictive coding in autism. Psychological Review, 121(4), 649–675. 10.1037/a0037665
- 165Verguts, T. (2017).
Computational models of cognitive control . In The wiley handbook of cognitive control (pp. 125–142). John Wiley & Sons, Ltd. 10.1002/9781118920497.ch8 - 166Walsh, K. S., McGovern, D. P., Clark, A., & O’Connell, R. G. (2020). Evaluating the neurophysiological evidence for predictive processing as a model of perception. Annals of the New York Academy of Sciences, 1464(1), 242–268. 10.1111/nyas.14321
- 167Wang, B., Zartaloudi, E., Linden, J. F., & Bramon, E. (2022). Neurophysiology in psychosis: The quest for disease biomarkers. Translational Psychiatry, 12(1),
100 . 10.1038/s41398-022-01860-x - 168Warrington, O., Graedel, N. N., Callaghan, M. F., & Kok, P. (2024). Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex. BioRxiv, 2024.03.28.587186. 10.1101/2024.03.28.587186
- 169Weber, L. A., Diaconescu, A. O., Mathys, C., Schmidt, A., Kometer, M., Vollenweider, F., & Stephan, K. E. (2020). Ketamine affects prediction errors about statistical regularities: A computational single-trial analysis of the mismatch negativity. The Journal of Neuroscience, 40(29), 5658–5668. 10.1523/JNEUROSCI.3069-19.2020
- 170Wienholz, A., & Lieberman, A. M. (2019). Semantic processing of adjectives and nouns in american sign language: Effects of reference ambiguity and word order across development. Journal of Cultural Cognitive Science, 3(2), 217–234. 10.1007/s41809-019-00024-6
- 171Willsey, A. J., Morris, M. T., Wang, S., Willsey, H. R., Sun, N., Teerikorpi, N., Baum, T. B., Cagney, G., Bender, K. J., Desai, T. A., Srivastava, D., Davis, G. W., Doudna, J., Chang, E., Sohal, V., Lowenstein, D. H., Li, H., Agard, D., Keiser, M. J., & Krogan, N. J. (2018). The psychiatric cell map initiative: A convergent systems biological approach to illuminating key molecular pathways in neuropsychiatric disorders. Cell, 174(3), 505–520. 10.1016/j.cell.2018.06.016
- 172Wood, J., Meyer, A., & Nee, D. E. (2024). Causal evidence for hierarchical predictive coding among cingulo-opercular and frontoparietal networks supporting cognitive control [Paper presented at the Florida State University, Florida, United State].
https://neelab.wixsite.com/neelab/presentations - 173World Health Organization. (2019). International statistical classification of diseases and related health problems (11th ed.)
https://icd.who.int/ - 174Yu, Y., Huber, L., Yang, J., Jangraw, D. C., Handwerker, D. A., Molfese, P. J., Chen, G., Ejima, Y., Wu, J., & Bandettini, P. A. (2019). Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex. Science Advances, 5(5). 10.1126/sciadv.aav9053
- 175Zelano, C., Mohanty, A., & Gottfried, J. A. (2011). Olfactory predictive codes and stimulus templates in piriform cortex. Neuron, 72(1), 178–187. 10.1016/j.neuron.2011.08.010