Abstract
The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.
References
Alais, D., & Burr, D. (2004). The ventriloquist effect results from near-optimal bimodal integration. Current Biology, 14(3), 257–262. https://doi.org/10.1016/j.cub.2004.01.029
Alilović, J., Timmermans, B., Reteig, L. C., van Gaal, S., & Slagter, H. A. (2019). No evidence that predictions and attention modulate the first feedforward sweep of cortical information processing. Cerebral Cortex, 29(5), 2261–2278. https://doi.org/10.1093/cercor/bhz038
Allen, M., Levy, A., Parr, T., & Friston, K. J. (2019). In the body’s eye: The computational anatomy of interoceptive inference. bioRxiv, 603928. https://doi.org/10.1101/603928
Apps, M. A. J., & Tsakiris, M. (2014). The free-energy self: A predictive coding account of self-recognition. Neuroscience & Biobehavioral Reviews, 41, 85–97. https://doi.org/10.1016/j.neubiorev.2013.01.029
Aru, J., Bachmann, T., Singer, W., & Melloni, L. (2012). Distilling the neural correlates of consciousness. Neuroscience & Biobehavioral Reviews, 36(2), 737–746. https://doi.org/10.1016/j.neubiorev.2011.12.003
Aru, J., Rutiku, R., Wibral, M., Singer, W., & Melloni, L. (2016). Early effects of previous experience on conscious perception. Neuroscience of Consciousness, 2016(1). https://doi.org/10.1093/nc/niw004
Aru, J., Suzuki, M., Rutiku, R., Larkum, M. E., & Bachmann, T. (2019). Coupling the state and contents of consciousness. Frontiers in Systems Neuroscience, 13. https://doi.org/10.3389/fnsys.2019.00043
Ashby, W. R. (1954). Design for a brain. Wiley.
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press.
Bachmann, T. (2012). How to begin to overcome the ambiguity present in differentiation between contents and levels of consciousness? Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00082
Baltieri, M., Buckley, C. L., & Bruineberg, J. (2020). Predictions in the eye of the beholder: An active inference account of Watt governors. Artificial Life Conference Proceedings, 32, 121–129. https://doi.org/10.1162/isal_a_00288
Barrett, A. B., Dienes, Z., & Seth, A. K. (2013). Measures of metacognition on signal-detection theoretic models. Psychological Methods, 18(4), 535–552. https://doi.org/10.1037/a0033268
Bastos, 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. https://doi.org/10.1016/j.neuron.2012.10.038
Bayne, T. (2007). Conscious states and conscious creatures: Explanation in the scientific study of consciousness. Philosophical Perspectives, 21(1), 1–22. https://doi.org/10.1111/j.1520-8583.2007.00118.x
Bayne, T., Cleeremans, A., & Wilken, P. (Eds.). (2009). Oxford companion to consciousness. Oxford University Press.
Bayne, T., Seth, A. K., & Massimini, M. (2020). Are there islands of awareness? Trends in Neurosciences, 43(1), 6–16. https://doi.org/10.1016/j.tins.2019.11.003
Bechtel, W. (2007). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. Taylor & Francis.
Block, N. (1995). On a confusion about a function of consciousness. Behavioral and Brain Sciences, 18(2), 227–247. https://doi.org/10.1017/S0140525X00038188
Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and learning. Journal of Mathematical Psychology, 76, 198–211. https://doi.org/10.1016/j.jmp.2015.11.003
Boly, M., Garrido, M. I., Gosseries, O., Bruno, M.-A., Boveroux, P., Schnakers, C., et al. (2011). Preserved feedforward but impaired top-down processes in the vegetative state. Science, 332(6031), 858–862. https://doi.org/10.1126/science.1202043
Boly, M., Moran, R., Murphy, M., Boveroux, P., Bruno, M.-A., Noirhomme, Q., et al. (2012). Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness. Journal of Neuroscience, 32(20), 7082–7090. https://doi.org/10.1523/JNEUROSCI.3769-11.2012
Brown, H., Adams, R. A., Parees, I., Edwards, M., & Friston, K. J. (2013). Active inference, sensory attenuation and illusions. Cognitive Processing, 14(4), 411–427. https://doi.org/10.1007/s10339-013-0571-3
Brown, R., Lau, H., & LeDoux, J. E. (2019). Understanding the higher-order approach to consciousness. Trends in Cognitive Sciences, 23(9), 754–768. https://doi.org/10.1016/j.tics.2019.06.009
Buckley, C. L., Kim, C. S., McGregor, S., & Seth, A. K. (2017). The free energy principle for action and perception: A mathematical review. Journal of Mathematical Psychology, 81, 55–79. https://doi.org/10.1016/j.jmp.2017.09.004
Cao, R. (2020). New labels for old ideas: Predictive processing and the interpretation of neural signals. Review of Philosophy and Psychology, 11(3), 517–546. https://doi.org/10.1007/s13164-020-00481-x
Carhart-Harris, R. L., Leech, R., Hellyer, P. J., Shanahan, M., Feilding, A., Tagliazucchi, E., et al. (2014). The entropic brain: A theory of conscious states informed by neuroimaging research with psychedelic drugs. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00020
Chalmers, D. J. (1996). The conscious mind: In search of a fundamental theory. Oxford University Press.
Chalmers, D. J. (2000). What is a neural correlate of consciousness? In T. Metzinger (Ed.), Neural correlates of consciousness (pp. 17–39). MIT Press.
Chalmers, D. J. (2018). The meta-problem of consciousness. Journal of Consciousness Studies, 25(9-10), 6–61.
Chang, A. Y. C., Biehl, M., Yu, Y., & Kanai, R. (2020). Information closure theory of consciousness. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01504
Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.
Clark, A. (2019). Consciousness as generative entanglement. Journal of Philosophy, 116(12), 645–662. https://doi.org/10.5840/jphil20191161241
Clark, A., Friston, K. J., & Wilkinson, S. (2019). Bayesing qualia: Consciousness as inference, not raw datum. Journal of Consciousness Studies, 26(9-10), 19–33.
Cleeremans, A. (2011). The radical plasticity thesis: How the brain learns to be conscious. Frontiers in Psychology, 2. https://doi.org/10.3389/fpsyg.2011.00086
Cohen, M. A., & Rubenstein, J. (2020). How much color do we see in the blink of an eye? Cognition, 200, 104268. https://doi.org/10.1016/j.cognition.2020.104268
Cole, D. M., Diaconescu, A. O., Pfeiffer, U. J., Brodersen, K. H., Mathys, C. D., Julkowski, D., et al. (2020). Atypical processing of uncertainty in individuals at risk for psychosis. NeuroImage: Clinical, 26, 102239. https://doi.org/10.1016/j.nicl.2020.102239
Corcoran, A. W., & Hohwy, J. (2018). Allostasis, interoception, and the free energy principle: Feeling our way forward. In M. Tsakiris & H. de Preester (Eds.), The interoceptive basis of the mind (pp. 272–292). Oxford University Press.
Corlett, 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. https://doi.org/10.1016/j.tics.2018.12.001
Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford University Press.
Crick, F., & Koch, C. (1990a). Some reflections on visual awareness. Sympos Quant Biol, 953–962. https://doi.org/10.1101/sqb.1990.055.01.089
Crick, F., & Koch, C. (1990b). Towards a neurobiological theory of consciousness. Seminars in the Neurosciences, 2, 263–275.
Damasio, A. (2000). The feeling of what happens: Body and emotion in the making of consciousness. Mariner Books.
Dehaene, S. (2011). Conscious and nonconscious processes: Distinct forms of evidence accumulation? Biological Physics. Progress in Mathematical Physics, 60. https://doi.org/10.1007/978-3-0346-0428-4_7
Dennett, D. C. (1991). Consciousness explained. Little, Brown & Company.
Descartes, R. (1641). Descartes: Meditations on first philosophy: With selections from the objections and replies (J. Cottingham, Ed.). Cambridge University Press.
Dijkstra, N., Ambrogioni, L., Vidaurre, D., & Gerven, M. van. (2020). Neural dynamics of perceptual inference and its reversal during imagery. eLife, 9, e53588. https://doi.org/10.7554/eLife.53588
Doerig, A., Schurger, A., & Herzog, M. H. (2020). Hard criteria for empirical theories of consciousness. Cognitive Neuroscience, 1–22. https://doi.org/10.1080/17588928.2020.1772214
Dołęga, K., & Dewhurst, J. E. (2020). Fame in the predictive brain: A deflationary approach to explaining consciousness in the prediction error minimization framework. Synthese. https://doi.org/10.1007/s11229-020-02548-9
Engel, A. K., Friston, K. J., & Kragic, D. (Eds.). (2016). The pragmatic turn: Toward action-oriented views in cognitive science. The MIT Press.
Engel, A. K., & Singer, W. (2001). Temporal binding and the neural correlates of sensory awareness. Trends in Cognitive Sciences, 5(1), 16–25. https://doi.org/10.1016/S1364-6613(00)01568-0
Feldman, H., & Friston, K. J. (2010). Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience, 4. https://doi.org/10.3389/fnhum.2010.00215
Felleman, D. J., & Essen, D. C. V. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex, 1–47. https://doi.org/10.1093/cercor/1.1.1
Fernández-Espejo, D., & Owen, A. M. (2013). Detecting awareness after severe brain injury. Nature Reviews Neuroscience, 14(11), 801–809. https://doi.org/10.1038/nrn3608
Fink, S. B. (2016). A deeper look at the “neural correlate of consciousness”. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01044
FitzGerald, T. H. B., Schwartenbeck, P., Moutoussis, M., Dolan, R. J., & Friston, K. J. (2014). Active inference, evidence accumulation, and the urn task. Neural Computation, 27(2), 306–328. https://doi.org/10.1162/NECO_a_00699
Fleming, S. M. (2020). Awareness as inference in a higher-order state space. Neuroscience of Consciousness, 2020(1). https: //doi.org/10.1093/nc/niz020
Fleming, S. M., Maniscalco, B., Ko, Y., Amendi, N., Ro, T., & Lau, H. (2015). Action-specific disruption of perceptual confidence: Psychological Science. https://doi.org/10.1177/0956797614557697
Fletcher, P. C., & Frith, C. D. (2009). Perceiving is believing: A Bayesian approach to explaining the positive symptoms of schizophrenia. Nature Reviews Neuroscience, 10(1), 48–58. https://doi.org/10.1038/nrn2536
Friston, K. J. (2003). Learning and inference in the brain. Neural Networks, 16(9), 1325–1352. https://doi.org/10.1016/j.neunet.2003.06.005
Friston, K. J. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815–836. https://doi.org/10.1098/rstb.2005.1622
Friston, K. J. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787
Friston, K. J. (2017). The mathematics of mind-time. Aeon. Retrieved from https://aeon.co/essays/consciousness-is-not-athing-but-a-process-of-inference
Friston, K. J. (2018). Am I self-conscious? (Or does self-organization entail self-consciousness?). Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00579
Friston, K. J. (2019a). A free energy principle for a particular physics. arXiv. https://doi.org/arXiv:%201906.10184
Friston, K. J. (2019b). Waves of prediction. PLOS Biology, 17(10), e3000426. https://doi.org/10.1371/journal.pbio.3000426
Friston, K. J., Da Costa, L., Hafner, D., Hesp, C., & Parr, T. (2020a). Sophisticated inference. arXiv. https://doi.org/arXiv:%202006.04120
Friston, K. J., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience & Biobehavioral Reviews, 68, 862–879. https://doi.org/10.1016/j.neubiorev.2016.06.022
Friston, K. J., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2016). Active inference: A process theory. Neural Computation, 29(1), 1–49. https://doi.org/10.1162/NECO_a_00912
Friston, K. J., Parr, T., & Vries, B. de. (2017). The graphical brain: Belief propagation and active inference. Network Neuroscience, 1(4), 381–414. https://doi.org/10.1162/NETN_a_00018
Friston, K. J., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015). Active inference and epistemic value. Cognitive Neuroscience, 6(4), 187–214. https://doi.org/10.1080/17588928.2015.1020053
Friston, K. J., Schwartenbeck, P., Fitzgerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2013). The anatomy of choice: Active inference and agency. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00598
Friston, K. J., Thornton, C., & Clark, A. (2012). Free-energy minimization and the dark-room problem. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00130
Friston, K. J., Wiese, W., & Hobson, J. A. (2020b). Sentience and the origins of consciousness: From Cartesian duality to Markovian monism. Entropy, 22(5), 516. https://doi.org/10.3390/e22050516
Gold, I. (1999). Does 40-Hz oscillation play a role in visual consciousness? Consciousness and Cognition, 8(2), 186–195. https://doi.org/10.1006/ccog.1999.0399
Gordon, N., Tsuchiya, N., Koenig-Robert, R., & Hohwy, J. (2019). Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways. PLOS Biology, 17(4), e3000233. https://doi.org/10.1371/journal.pbio.3000233
Graaf, T. A. de, Hsieh, P.-J., & Sack, A. T. (2012). The “correlates” in neural correlates of consciousness. Neuroscience & Biobehavioral Reviews, 36(1), 191–197. https://doi.org/10.1016/j.neubiorev.2011.05.012
Graziano, M. S. A., Guterstam, A., Bio, B. J., & Wilterson, A. I. (2020). Toward a standard model of consciousness: Reconciling the attention schema, global workspace, higher-order thought, and illusionist theories. Cognitive Neuropsychology, 37(3-4), 155–172. https://doi.org/10.1080/02643294.2019.1670630
Graziano, M. S. A., & Webb, T. W. (2015). The attention schema theory: A mechanistic account of subjective awareness. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00500
Grush, R. (2006). How to, and how not to, bridge computational cognitive neuroscience and Husserlian phenomenology of time consciousness. Synthese, 153(3), 417–450. https://doi.org/10.1007/s11229-006-9100-6
de Haan, E. H. F., Corballis, P. M., Hillyard, S. A., Marzi, C. A., Seth, A., Lamme, V. A. F., et al. (2020). Split-brain: What we know now and why this is important for understanding consciousness. Neuropsychology Review, 30(2), 224–233. https://doi.org/10.1007/s11065-020-09439-3
Hameroff, S., & Penrose, R. (2014). Consciousness in the universe: A review of the “Orch OR” theory. Physics of Life Reviews, 11(1), 39–78. https://doi.org/10.1016/j.plrev.2013.08.002
Haun, A., & Tononi, G. (2019). Why does space feel the way it does? Towards a principled account of spatial experience. Entropy, 21(12), 1160. https://doi.org/10.3390/e21121160
Heeger, D. J. (2017). Theory of cortical function. Proceedings of the National Academy of Sciences, 114(8), 1773–1782. https://doi.org/10.1073/pnas.1619788114
Heilbron, M., Richter, D., Ekman, M., Hagoort, P., & Lange, F. P. de. (2020). Word contexts enhance the neural representation of individual letters in early visual cortex. Nature Communications, 11(1), 321. https://doi.org/10.1038/s41467-019-13996-4
Hobson, J. A., & Friston, K. J. (2012). Waking and dreaming consciousness: Neurobiological and functional considerations. Progress in Neurobiology, 98(1), 82–98. https://doi.org/10.1016/j.pneurobio.2012.05.003
Hohwy, J. (2012). Attention and conscious perception in the hypothesis testing brain. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00096
Hohwy, J. (2013). The predictive mind. Oxford University Press.
Hohwy, J. (2015). Prediction error minimization, mental and developmental disorder, and statistical theories of consciousness. In Disturbed consciousness: New essays on psychopathology and theories of consciousness (pp. 293–324). MIT Press.
Hohwy, J. (2020a). New directions in predictive processing. Mind & Language, 35(2), 209–223. https://doi.org/10.1111/mila.12281
Hohwy, J. (2020b). Self-supervision, normativity and the free energy principle. Synthese. https://doi.org/10.1007/s11229-020-02622-2
Hohwy, J. (2009). The neural correlates of consciousness: New experimental approaches needed? Consciousness and Cognition, 18(2), 428–438. https://doi.org/10.1016/j.concog.2009.02.006
Hohwy, J., & Frith, C. (2004). Can neuroscience explain consciousness? Journal of Consciousness Studies, 11(7-8), 180–198.
Hohwy, J., & Michael, J. (2017). Why does any body have a self? In F. de Vignemont & A. J. T. Alsmith (Eds.), The body and the self, revisited (pp. 363–391). MIT Press.
Hohwy, J., Paton, B., & Palmer, C. (2016). Distrusting the present. Phenomenology and the Cognitive Sciences, 15(3), 315–335. https://doi.org/10.1007/s11097-015-9439-6
Hohwy, J., Roepstorff, A., & Friston, K. J. (2008). Predictive coding explains binocular rivalry: An epistemological review. Cognition, 108(3), 687–701. https://doi.org/10.1016/j.cognition.2008.05.010
Hurley, S. L. (1998). Consciousness in action. Harvard University Press.
Hutto, D. D., & Myin, E. (2013). Radicalizing enactivism: Basic minds without content. Cambridge, Mass.: MIT Press.
Jackson, F. (1982). Epiphenomenal qualia. The Philosophical Quarterly 32, 32(127), 127–136. https://doi.org/10.2307/2960077
Kanai, R., Chang, A., Yu, Y., Magrans de Abril, I., Biehl, M., & Guttenberg, N. (2019). Information generation as a functional basis of consciousness. Neuroscience of Consciousness, 2019(1). https://doi.org/10.1093/nc/niz016
Kiefer, A. B. (2017). Literal perceptual inference. In T. K. Metzinger & W. Wiese (Eds.), Philosophy and predictive processing. MIND Group.
Klein, C., & Barron, A. B. (2020). How experimental neuroscientists can fix the hard problem of consciousness. Neuroscience of Consciousness, 2020(1). https://doi.org/10.1093/nc/niaa009
Koch, C., Massimini, M., Boly, M., & Tononi, G. (2016). Neural correlates of consciousness: Progress and problems. Nature Reviews Neuroscience, 17(5), 307–321. https://doi.org/10.1038/nrn.2016.22
Koch, C., & Tsuchiya, N. (2007). Attention and consciousness: Two distinct brain processes. Trends in Cognitive Sciences, 11(1), 16–22. https://doi.org/10.1016/j.tics.2006.10.012
Kok, P., Rahnev, D., Jehee, J. F. M., Lau, H. C., & Lange, F. P. de. (2012). Attention reverses the effect of prediction in silencing sensory signals. Cerebral Cortex, 22(9), 2197–2206. https://doi.org/10.1093/cercor/bhr310
Kok, P., Rait, L. I., & Turk-Browne, N. B. (2019). Content-based dissociation of hippocampal involvement in prediction. Journal of Cognitive Neuroscience, 32(3), 527–545. https://doi.org/10.1162/jocn_a_01509
Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., & Poeppel, D. (2017). Neuroscience needs behavior: Correcting a reductionist bias. Neuron, 93(3), 480–490. https://doi.org/10.1016/j.neuron.2016.12.041
Lamme, V. A. F. (2020). Visual functions generating conscious seeing. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.00083
Lamme, V. A. F. (2010). How neuroscience will change our view on consciousness. Cognitive Neuroscience, 1(3), 204–220. https://doi.org/10.1080/17588921003731586
Lange, F. P. de, Heilbron, M., & Kok, P. (2018). How do expectations shape perception? Trends in Cognitive Sciences, 22(9), 764–779. https://doi.org/10.1016/j.tics.2018.06.002
Lau, H. C. (2008). A higher order Bayesian decision theory of consciousness. Elsevier, 168, 35–48. https://doi.org/10.1016/S0079-6123(07)68004-2
Lau, H., & Rosenthal, D. (2011). Empirical support for higher-order theories of conscious awareness. Trends in Cognitive Sciences, 15(8), 365–373. https://doi.org/10.1016/j.tics.2011.05.009
Laureys, S. (2005). The neural correlate of (un)awareness: Lessons from the vegetative state. Trends in Cognitive Sciences, 9(12), 556–559. https://doi.org/10.1016/j.tics.2005.10.010
Lawson, R. P., Mathys, C., & Rees, G. (2017). Adults with autism overestimate the volatility of the sensory environment. Nature Neuroscience, 20(9), 1293–1299. https://doi.org/10.1038/nn.4615
Lettvin, J. Y. (1976). On seeing sidelong. The Sciences, 16(4), 10–20. https://doi.org/10.1002/j.23261951.1976.tb01231.x
Levine, J. (1983). Materialism and qualia: The explanatory gap. Pacific Philosophical Quarterly, 64(4), 354–361. https://doi.org/10.1111/j.1468-0114.1983.tb00207.x
Li, H.-H., & Ma, W. J. (2020). Confidence reports in decision-making with multiple alternatives violate the Bayesian confidence hypothesis. Nature Communications, 11(1), 2004. https://doi.org/10.1038/s41467-020-15581-6
Limanowski, J., & Blankenburg, F. (2013). Minimal self-models and the free energy principle. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00547
Litwin, P., & Miłkowski, M. (2020). Unification by fiat: Arrested development of predictive processing. Cognitive Science, 44(7), e12867. https://doi.org/10.1111/cogs.12867
Lush, P., Botan, V., Scott, R. B., Seth, A. K., Ward, J., & Dienes, Z. (2020). Trait phenomenological control predicts experience of mirror synaesthesia and the rubber hand illusion. Nature Communications, 11(1), 4853. https://doi.org/10.1038/s41467-020-18591-6
Marchi, F., & Hohwy, J. (2020). The intermediate scope of consciousness in the predictive mind. Erkenntnis. https://doi.org/10.1007/s10670-020-00222-7
Markov, N. T., Ercsey-Ravasz, M., Essen, D. C. V., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013). Cortical high-density counterstream architectures. Science, 342(6158). https://doi.org/10.1126/science.1238406
Marshel, J. H., Kim, Y. S., Machado, T. A., Quirin, S., Benson, B., Kadmon, J., Raja, C., Chibukhchyan, A., Ramakrishnan, C., Inoue, M., Shane, J. C., McKnight, D. J., Yoshizawa, S., Kato, H. E., Ganguli, S., & Deisseroth, K. (2019). Cortical layer–specific critical dynamics triggering perception. Science, 365(6453). https://doi.org/10.1126/science.aaw5202
Martin, J.-R., & Pacherie, E. (2019). Alterations of agency in hypnosis: A new predictive coding model. Psychological Review, 126(1), 133–152. https://doi.org/10.1037/rev0000134
Mashour, G. A., Roelfsema, P., Changeux, J.-P., & Dehaene, S. (2020). Conscious processing and the global neuronal workspace hypothesis. Neuron, 105(5), 776–798. https://doi.org/10.1016/j.neuron.2020.01.026
Mathys, C., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A Bayesian foundation for individual learning under uncertainty. Frontiers in Human Neuroscience, 5. https://doi.org/10.3389/fnhum.2011.00039
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014). Uncertainty in perception and the hierarchical Gaussian filter. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00825
McFadden, J. (2020). Integrating information in the brain’s EM field: The cemi field theory of consciousness. Neuroscience of Consciousness, 2020(1). https://doi.org/10.1093/nc/niaa016
McFadden, J. (2002). The conscious electromagnetic information (Cemi) field theory: The hard problem made easy? Journal of Consciousness Studies, 9(8), 45–60.
Meijs, E. L., Slagter, H. A., Lange, F. P. de, & Gaal, S. van. (2018). Dynamic interactions between top–down expectations and conscious awareness. Journal of Neuroscience, 38(9), 2318–2327. https://doi.org/10.1523/JNEUROSCI.1952-17.2017
Melloni, L., Schwiedrzik, C. M., Müller, N., Rodriguez, E., & Singer, W. (2011). Expectations change the signatures and timing of electrophysiological correlates of perceptual awareness. Journal of Neuroscience, 31(4), 1386–1396. https://doi.org/10.1523/JNEUROSCI.4570-10.2011
Mendonça, D., Curado, M., & Gouveia, S. (2020). The philosophy and science of predictive processing. Bloomsbury Publishing.
Metzinger, T. (2004). Being no one. MIT Press.
Metzinger, T. (Ed.). (2000). Neural correlates of consciousness: Empirical and conceptual questions. MIT Press.
Metzinger, T., & Wiese, W. (Eds.). (2017). Philosophy and predictive processing. MIND Group. https://predictive-mind.net/papers
Miller, S. M. (2015). The constitution of phenomenal consciousness. John Benjamins.
Miller, S. M. (2007). On the correlation/constitution distinction problem (and other hard problems) in the scientific study of consciousness. Acta Neuropsychiatrica, 19(3), 159–176. https://doi.org/10.1111/j.1601-5215.2007.00207.x
Millidge, B., Tschantz, A., & Buckley, C. L. (2020). Whence the expected free energy? https://doi.org/arXiv:2004.08128v3
Milliere, R., & Metzinger, T. (2020). Radical disruptions of self-consciousness: Philosophy and the Mind Sciences, 1(I), 1–13. https://doi.org/10.33735/phimisci.2020.I.50
Muckli, 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. https://doi.org/10.1016/j.cub.2015.08.057
Neisser, J. (2012). Neural correlates of consciousness reconsidered. Consciousness and Cognition, 21(2), 681–690. https://doi.org/10.1016/j.concog.2011.03.012
Noë, A. (2004). Action in perception. MIT Press.
Noë, A., & O’Regan, J. K. (2001). A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences, 24(5), 939–973.
Noë, A., & Thompson, E. (2004). Are there neural correlates of consciousness? Journal of Consciousness Studies, 11(1), 3–28.
Noreika, V., Canales-Johnson, A., Harrison, W. J., Johnson, A., Arnatkevičiūtė, A., Koh, J., Chennu, S., & Bekinschtein, T. A. (2017). Wakefulness state modulates conscious access: Suppression of auditory detection in the transition to sleep. bioRxiv, 155705. https://doi.org/10.1101/155705
O’Brien, G., & Opie, J. (1999). A connectionist theory of phenomenal experience. Behavioral and Brain Sciences, 22, 127–148.
Oizumi, M., Albantakis, L., & Tononi, G. (2014). From the phenomenology to the mechanisms of consciousness: Integrated information theory 3.0. PLOS Computational Biology, 10(5), e1003588. https://doi.org/10.1371/journal.pcbi.1003588
Pacherie, E. (2008). The phenomenology of action: A conceptual framework. Cognition, 107(1), 179–217. https://doi.org/10.1016/j.cognition.2007.09.003
Pal, D., Li, D., Dean, J. G., Brito, M. A., Liu, T., Fryzel, A. M., Hudetz, A. G., & Mashour, G. A. (2020). Level of consciousness is dissociable from electroencephalographic measures of cortical connectivity, slow oscillations, and complexity. Journal of Neuroscience, 40(3), 605–618. https://doi.org/10.1523/JNEUROSCI.1910-19.2019
Parr, T., Corcoran, A. W., Friston, K. J., & Hohwy, J. (2019). Perceptual awareness and active inference. Neuroscience of Consciousness, 2019(1). https://doi.org/10.1093/nc/niz012
Parr, T., & Friston, K. J. (2018). The anatomy of inference: Generative models and brain structure. Frontiers in Computational Neuroscience, 12. https://doi.org/10.3389/fncom.2018.00090
Pascual-Leone, A., & Walsh, V. (2001). Fast backprojections from the motion to the primary visual area necessary for visual awareness. Science, 292(5516), 510–512. https://doi.org/10.1126/science.1057099
Pearl, J. (2000). Causality: Models, reasoning and inference. Cambridge University Press.
Perrykkad, K., & Hohwy, J. (2020). Fidgeting as self-evidencing: A predictive processing account of non-goal-directed action. New Ideas in Psychology, 56, 100750. https://doi.org/10.1016/j.newideapsych.2019.100750
Petzschner, F. H., Weber, L. A., Wellstein, K. V., Paolini, G., Do, C. T., & Stephan, K. E. (2019). Focus of attention modulates the heartbeat evoked potential. NeuroImage, 186, 595–606. https://doi.org/10.1016/j.neuroimage.2018.11.037
Pinto, Y., Gaal, S. van, Lange, F. P. de, Lamme, V. A. F., & Seth, A. K. (2015). Expectations accelerate entry of visual stimuli into awareness. Journal of Vision, 15(8), 13–13. https://doi.org/10.1167/15.8.13
Podvalny, E., Yeagle, E., Mégevand, P., Sarid, N., Harel, M., Chechik, G., Mehta, A. D., & Malach, R. (2017). Invariant temporal dynamics underlie perceptual stability in human visual cortex. Current Biology, 27(2), 155–165. https://doi.org/10.1016/j.cub.2016.11.024
Powers, A. R., Mathys, C., & Corlett, P. R. (2017). Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science, 357(6351), 596–600. https://doi.org/10.1126/science.aan3458
Prinz, J. J. (2012). The conscious brain: How attention engenders experience. Oxford University Press.
Rahnev, D., & Denison, R. N. (2018). Behavior is sensible but not globally optimal: Seeking common ground in the optimality debate. Behavioral and Brain Sciences, 41. https://doi.org/10.1017/S0140525X18002121
Reardon, S. (2019). “Outlandish” competition seeks the brain’s source of consciousness. Science. https://www.sciencemag.org/news/2019/10/outlandish-competition-seeks-brain-s-source-consciousness
Revonsuo, A. (2006). Inner presence: Consciousness as a biological phenomenon. MIT Press.
Rosenthal, D. M. (1986). Two concepts of consciousness. Philosophical Studies, 49(3), 329–359.
Rosenthal, D. M. (1997). A theory of consciousness. In N. Block, O. J. Flanagan, & G. Guzeldere (Eds.), The nature of consciousness (pp. 729–753). MIT Press.
Rudrauf, D., Bennequin, D., Granic, I., Landini, G., Friston, K. J., & Williford, K. (2017). A mathematical model of embodied consciousness. Journal of Theoretical Biology, 428, 106–131. https://doi.org/10.1016/j.jtbi.2017.05.032
Sandved Smith, L., Hesp, C., Lutz, A., Mattout, J., Friston, K. J., & Ramstead, M. (2020). Towards a formal neurophenomenology of metacognition: Modelling meta-awareness, mental action, and attentional control with deep active inference. In PsyArXiv. https://doi.org/10.31234/osf.io/5jh3c
Schartner, M. M., Carhart-Harris, R. L., Barrett, A. B., Seth, A. K., & Muthukumaraswamy, S. D. (2017). Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin. Scientific Reports, 7(1), 46421. https://doi.org/10.1038/srep46421
Schroeder, C. E., Wilson, D. A., Radman, T., Scharfman, H., & Lakatos, P. (2010). Dynamics of active sensing and perceptual selection. Current Opinion in Neurobiology, 20(2), 172–176. https://doi.org/10.1016/j.conb.2010.02.010
Searle, J. R. (2000). Consciousness. Annual Review of Neuroscience, 23(1), 557–578. https://doi.org/10.1146/annurev.neuro.23.1.557
Seth, A. (2009). Explanatory correlates of consciousness: Theoretical and computational challenges. Cognitive Computation, 1(1), 50–63. https://doi.org/10.1007/s12559-009-9007-x
Seth, A. K. (2016). The real problem. Aeon. https://aeon.co/essays/the-hard-problem-of-consciousness-is-a-distraction-from-the-real-one
Seth, A. K. (2021). Being you. Faber & Faber.
Seth, A. K. (2015a). Inference to the best prediction. In T. K. Metzinger & J. M. Windt (Eds.), Open mind. MIND Group.
Seth, A. K. (2015b). The cybernetic Bayesian brain: From interoceptive inference to sensorimotor contingencies. In T. K. Metzinger & J. M. Windt (Eds.), Open mind. MIND Group.
Seth, A. K. (2014). A predictive processing theory of sensorimotor contingencies: Explaining the puzzle of perceptual presence and its absence in synesthesia. Cognitive Neuroscience, 5(2), 97–118. https://doi.org/10.1080/17588928.2013.877880
Seth, A. K. (2019). From unconscious inference to the beholder’s share: Predictive perception and human experience. European Review, 27(3), 378–410. https://doi.org/10.1017/S1062798719000061
Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565–573. https://doi.org/10.1016/j.tics.2013.09.007
Seth, A. K., Millidge, B., Buckley, C. L., & Tschantz, A. (2020). Curious inferences: Reply to sun and firestone on the dark room problem. Trends in Cognitive Sciences, 24(9), 681–683. https://doi.org/10.1016/j.tics.2020.05.011
Seth, A. K., & Tsakiris, M. (2018). Being a beast machine: The somatic basis of selfhood. Trends in Cognitive Sciences, 22(11), 969–981. https://doi.org/10.1016/j.tics.2018.08.008
Sherman, M. T., Fountas, Z., Seth, A. K., & Roseboom, W. (2020). Accumulation of salient perceptual events predicts subjective time. bioRxiv, 2020.01.09.900423. https://doi.org/10.1101/2020.01.09.900423
Sherman, M. T., Seth, A. K., Barrett, A. B., & Kanai, R. (2015). Prior expectations facilitate metacognition for perceptual decision. Consciousness and Cognition, 35, 53–65. https://doi.org/10.1016/j.concog.2015.04.015
Skewes, J. C., Jegindø, E.-M., & Gebauer, L. (2015). Perceptual inference and autistic traits. Autism, 19(11). https://doi.org/10.1177/1362361313519872
Skora, L., Seth, A., & Scott, R. B. (2020). Sensorimotor predictions shape reported conscious visual experience in a breaking continuous flash suppression task. In PsyArXiv. https://doi.org/10.31234/osf.io/hwkdj
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., & Petzschner, F. H. (2016). Allostatic self-efficacy: A metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00550
Stuke, H., Weilnhammer, V. A., Sterzer, P., & Schmack, K. (2019). Delusion proneness is linked to a reduced usage of prior beliefs in perceptual decisions. Schizophrenia Bulletin, 45(1), 80–86. https://doi.org/10.1093/schbul/sbx189
Summerfield, C., & Egner, T. (2009). Expectation (and attention) in visual cognition. Trends in Cognitive Sciences, 13(9), 403–409. https://doi.org/10.1016/j.tics.2009.06.003
Sun, Z., & Firestone, C. (2020a). The dark room problem. Trends in Cognitive Sciences, 24(5), 346–348. https://doi.org/10.1016/j.tics.2020.02.006
Sun, Z., & Firestone, C. (2020b). Optimism and pessimism in the predictive brain. Trends in Cognitive Sciences, 24(9), 683–685. https://doi.org/10.1016/j.tics.2020.06.001
Suzuki, K., Schwartzman, D. J., Augusto, R., & Seth, A. K. (2019). Sensorimotor contingency modulates breakthrough of virtual 3D objects during a breaking continuous flash suppression paradigm. Cognition, 187, 95–107. https://doi.org/10.1016/j.cognition.2019.03.003
Teufel, C., & Fletcher, P. C. (2020). Forms of prediction in the nervous system. Nature Reviews Neuroscience, 21(4), 231–242. https://doi.org/10.1038/s41583-020-0275-5
Tong, F., Nakayama, K., Vaughan, J. T., & Kanwisher, N. (1998). Binocular rivalry and visual awareness in human extrastriate cortex. Neuron, 21(4), 753–759. https://doi.org/10.1016/S0896-6273(00)80592-9
Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461. https://doi.org/10.1038/nrn.2016.44
Tononi, G., & Edelman, G. M. (1998). Consciousness and complexity. Science, 282(5395), 1846–1851. https://doi.org/10.1126/science.282.5395.1846
Tononi, G., & Koch, C. (2008). The neural correlates of consciousness. Annals of the New York Academy of Sciences, 1124(1), 239–261. https://doi.org/10.1196/annals.1440.004
Tsakiris, M., & Preester, H. D. (2018). The interoceptive mind: From homeostasis to awareness. Oxford University Press.
Tschantz, A., Millidge, B., Seth, A. K., & Buckley, C. L. (2020). Reinforcement learning through active inference. https://arxiv.org/abs/2002.12636
Tschantz, A., Seth, A. K., & Buckley, C. L. (2020). Learning action-oriented models through active inference. PLOS Computational Biology, 16(4), e1007805. https://doi.org/10.1371/journal.pcbi.1007805
Tsuchiya, N., Wilke, M., Frässle, S., & Lamme, V. A. F. (2015). No-report paradigms: Extracting the true neural correlates of consciousness. Trends in Cognitive Sciences, 19(12), 757–770. https://doi.org/10.1016/j.tics.2015.10.002
Van de Cruys, S., Friston, K. J., & Clark, A. (2020). Controlled optimism: Reply to Sun and Firestone on the dark room problem. Trends in Cognitive Sciences, 24(9), 680–681. https://doi.org/10.1016/j.tics.2020.05.012
Van der Helm, P. A. (2016). Structural coding versus free-energy predictive coding. Psychonomic Bulletin & Review, 23(3), 663–677. https://doi.org/10.3758/s13423-015-0938-9
Van Doorn, G., Paton, B., Howell, J., & Hohwy, J. (2015). Attenuated self-tickle sensation even under trajectory perturbation. Consciousness and Cognition, 36, 147–153. https://doi.org/10.1016/j.concog.2015.06.016
Van Es, T. (2020). Living models or life modelled? On the use of models in the free energy principle. Adaptive Behavior. https://doi.org/10.1177/1059712320918678
Van Gelder, T. (1995). What might cognition be, if not computation? The Journal of Philosophy, 92(7), 345–381. https://doi.org/10.2307/2941061
Vasser, M., Vuillaume, L., Cleeremans, A., & Aru, J. (2019). Waving goodbye to contrast: Self-generated hand movements attenuate visual sensitivity. Neuroscience of Consciousness, 2019(1). https://doi.org/10.1093/nc/niy013
Vogel, D. H. V., Beeker, T., Haidl, T., Kupke, C., Heinze, M., & Vogeley, K. (2019). Disturbed time experience during and after psychosis. Schizophrenia Research: Cognition, 17, 100136. https://doi.org/10.1016/j.scog.2019.100136
Vogel, D. H. V., Falter-Wagner, C. M., Schoofs, T., Krämer, K., Kupke, C., & Vogeley, K. (2020). Flow and structure of time experience – concept, empirical validation and implications for psychopathology. Phenomenology and the Cognitive Sciences, 19(2), 235–258. https://doi.org/10.1007/s11097-018-9573-z
Walsh, 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. https://doi.org/10.1111/nyas.14321
Weilnhammer, V., Fritsch, M., Chikermane, M., Eckert, A.-L., Kanthak, K., Stuke, H., Kaminski, J., & Sterzer, P. (2020). Evidence for an active role of inferior frontal cortex in conscious experience. bioRxiv. https://doi.org/10.1101/2020.05.28.114645
Weilnhammer, V., Stuke, H., Hesselmann, G., Sterzer, P., & Schmack, K. (2017). A predictive coding account of bistable perception – a model-based fMRI study. PLOS Computational Biology, 13(5), e1005536. https://doi.org/10.1371/journal.pcbi.1005536
Whyte, C. J. (2019). Integrating the global neuronal workspace into the framework of predictive processing: Towards a working hypothesis. Consciousness and Cognition, 73, 102763. https://doi.org/10.1016/j.concog.2019.102763
Whyte, C. J., & Smith, R. (2020). The predictive global neuronal workspace: A formal active inference model of visual consciousness. bioRxiv. https://doi.org/10.1101/2020.02.11.944611
Wiese, W. (2018). Toward a mature science of consciousness. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00693
Wiese, W. (2015). Perceptual presence in the Kuhnian-Popperian Bayesian brain. In T. K. Metzinger & J. M. Windt (Eds.), Open mind. MIND Group.
Wiese, W. (2020). The science of consciousness does not need another theory, it needs a minimal unifying model. Neuroscience of Consciousness, 2020(1). https://doi.org/10.1093/nc/niaa013
Williford, K., Bennequin, D., Friston, K. J., & Rudrauf, D. (2018). The projective consciousness model and phenomenal selfhood. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.02571
Woodward, J. (2003). Making things happen. Oxford University Press.
Yon, D., Gilbert, S. J., Lange, F. P. de, & Press, C. (2018). Action sharpens sensory representations of expected outcomes. Nature Communications, 9(1), 4288. https://doi.org/10.1038/s41467-018-06752-7
Yon, D., Lange, F. P. de, & Press, C. (2019). The predictive brain as a stubborn scientist. Trends in Cognitive Sciences, 23(1), 6–8. https://doi.org/10.1016/j.tics.2018.10.003
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2020 Jakob Hohwy, Anil Seth