Neural dynamics of perceptual inference and its reversal during imagery
Open accessPublished version of a preprint
Abstract
After the presentation of a visual stimulus, neural processing cascades from low-level sensory areas to increasingly abstract representations in higher-level areas. It is often hypothesised that a reversal in neural processing underlies the generation of mental images as abstract representations are used to construct sensory representations in the absence of sensory input. According to predictive processing theories, such reversed processing also plays a central role in later stages of perception. Direct experimental evidence of reversals in neural information flow has been missing. Here, we used a combination of machine learning and magnetoencephalography to characterise neural dynamics in humans. We provide direct evidence for a reversal of the perceptual feed-forward cascade during imagery and show that, during perception, such reversals alternate with feed-forward processing in an 11 Hz oscillatory pattern. Together, these results show how common feedback processes support both veridical perception and mental imagery.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Visual cortices are activated during visual imagery, with a reversal of the dynamics of the perceptual feed-forward cascade.
“visual cortices are activated during visual imagery, with a reversal of the dynamics of the perceptual feed-forward cascade”
From Aphantasia and hyperphantasia: exploring imagery vividness extremes (2024) · cites this paper as [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707 · see citing works
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323 · see citing works
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526 · see citing works
Receptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,520 · see citing works
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538 · see citing works
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956 · see citing works
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588 · see citing works
Van Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.623056 · Cited by 2,561 · see citing works
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · see citing works
The distinct modes of vision offered by feedforward and recurrent processing
Lamme, V. A. F., & Roelfsema, P. R. (2000) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01657-x · Cited by 2,338 · see citing works
MNE software for processing MEG and EEG data
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C. J., Parkkonen, L. T., & Hämäläinen, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.027 · Cited by 1,924 · see citing works
Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001) · Nature Reviews Neuroscience · doi:10.1038/35090055 · Cited by 1,894 · see citing works
On empirical mode decomposition and its algorithms
Rilling, G., Flandrin, P., & Gonçalvès, P. (2003) · Cited by 1,402 · see citing works
Hochstein (2002) · doi:10.1016/s0896-6273(02)01091-7 · Cited by 1,397 · see citing works
The emulation theory of representation: Motor control, imagery, and perception
Grush, R. (2004) · Behavioral and Brain Sciences · doi:10.1017/s0140525x04000093 · Cited by 1,387 · cited 1× · see citing works
“DOI: https://doi.org/10.7554/eLife.53588 10 of 19 Grush, 2004; Hobson and Friston, 2012; Moulton and Kosslyn, 2009).”
InfluentialOn the interpretation of weight vectors of linear models in multivariate neuroimaging
Haufe, S., Meinecke, F. C., Görgen, K., Dähne, S., Haynes, J., Blankertz, B., & Bießmann, F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.067 · Cited by 1,367 · see citing works
Decoding reveals the contents of visual working memory in early visual areas
Harrison, S. A., & Tong, F. (2009) · Nature · doi:10.1038/nature07832 · Cited by 1,347 · see citing works
Object perception as Bayesian inference
Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269 · see citing works
A novel source of arterial valve cells linked to bicuspid aortic valve without raphe in mice
Eley, L., Alqahtani, A., MacGrogan, D., Richardson, R. V., Murphy, L. B., Salguero-Jiménez, A., Pedro, M. S. R. S., Tiurma, S., McCutcheon, L., Gilmore, A., et al. (2018) · eLife · doi:10.7554/elife.34110 · Cited by 1,146 · see citing works
Visual Processing in Monkey Extrastriate Cortex
Maunsell, J. H. R., & Newsome, W. T. (1987) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.10.030187.002051 · Cited by 1,135 · see citing works
Integrated model of visual processing
Bullier, J. (2001) · Brain Research Reviews · doi:10.1016/s0165-0173(01)00085-6 · Cited by 1,016 · see citing works
The reverse hierarchy theory of visual perceptual learning
Ahissar, M., & Hochstein, S. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.08.011 · Cited by 1,005 · see citing works
Characterizing the dynamics of mental representations: the temporal generalization method
King, J.-R., & Dehaene, S. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.01.002 · Cited by 896 · see citing works
Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex
Kok, P., Jehee, J. F. M., & de Lange, F. P. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.034 · Cited by 798 · see citing works
Unveiling the Biometric Potential of Finger-Based ECG Signals
Lourenço, A., Silva, H., & Fred, A. L. N. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/720971 · Cited by 769 · see citing works
Specificity of monosynaptic connections from thalamus to visual cortex
Reid, R. C., & Alonso, J. (1995) · Nature · doi:10.1038/378281a0 · Cited by 732 · see citing works
Michalareas, G., Vezoli, J., van Pelt, S., Schoffelen, J.-M., Kennedy, H., & Fries, P. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.018 · Cited by 696 · see citing works
Distributed Neural Systems for the Generation of Visual Images
Ishai, A., Ungerleider, L. G., & Haxby, J. V. (2000) · Neuron · doi:10.1016/s0896-6273(00)00168-9 · Cited by 579 · see citing works
Shared representations for working memory and mental imagery in early visual cortex
Albers, A. M., Kok, P., Toni, I., Dijkerman, H. C., & de Lange, F. P. (2013) · Current Biology · doi:10.1016/j.cub.2013.05.065 · Cited by 528 · see citing works
When is early visual cortex activated during visual mental imagery?
Kosslyn, S. M., & Thompson, W. L. (2003) · Psychological Bulletin · doi:10.1037/0033-2909.129.5.723 · Cited by 515 · see citing works
Shared Neural Mechanisms of Visual Perception and Imagery
Dijkstra, N., Bosch, S., & van Gerven, M. A. J. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.004 · Cited by 445 · see citing works
Self-citationImagining predictions: mental imagery as mental emulation
Moulton, S. T., & Kosslyn, S. M. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0314 · Cited by 421 · see citing works
The functional impact of mental imagery on conscious perception
Pearson, J., Clifford, C. W. G., & Tong, F. (2008) · Current Biology · doi:10.1016/j.cub.2008.05.048 · Cited by 409 · see citing works
Vividness of Visual Imagery Depends on the Neural Overlap with Perception in Visual Areas
Dijkstra, N., Bosch, S. E., & van Gerven, M. A. J. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3022-16.2016 · Cited by 315 · see citing works
Self-citationSeeking Categories in the Brain
Thorpe, S. J., & Fabre-Thorpe, M. (2001) · Science · doi:10.1126/science.1058249 · Cited by 300 · see citing works
Organization of lateral geniculate‐hypothalamic connections in the rat
Card, J. P., & Moore, R. Y. (1989) · The Journal of Comparative Neurology · doi:10.1002/cne.902840110 · Cited by 299 · see citing works
Reading the mind's eye: Decoding category information during mental imagery
Reddy, L., Tsuchiya, N., & Serre, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.084 · Cited by 296 · see citing works
Waking and dreaming consciousness: neurobiological and functional considerations.
Hobson, J. A., & Friston, K. J. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.05.003 · Cited by 287 · see citing works
Rhodes, L. E., de Rie, M. A., Leifsdottir, R., Yu, R. C., Bachmann, I., Goulden, V., Wong, G. A. E., Richard, M.-A., Anstey, A., & Wolf, P. (2007) · Archives of Dermatology · doi:10.1001/archderm.143.9.1131 · Cited by 275 · see citing works
The dynamics of invariant object recognition in the human visual system
Işık, L., Meyers, E. M., Leibo, J. Z., & Poggio, T. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00394.2013 · Cited by 266 · see citing works
Disentangling visual imagery and perception of real-world objects
Lee, S.-H., Kravitz, D. J., & Baker, C. I. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.055 · Cited by 263 · see citing works
Stable and Dynamic Coding for Working Memory in Primate Prefrontal Cortex
Spaak, E., Watanabe, K., Funahashi, S., & Stokes, M. G. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3364-16.2017 · Cited by 229 · see citing works
Online and offline tools for head movement compensation in MEG
Stolk, A., Todorović, A., Schoffelen, J., & Oostenveld, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.047 · Cited by 209 · see citing works
Wang, G., CHEN, X.-Y., Qiao, F., Wu, Z., & Huang, N. E. (2010) · Advances in Adaptive Data Analysis · doi:10.1142/s1793536910000549 · Cited by 201 · see citing works
Linde-Domingo, J., Treder, M. S., Kerrén, C., & Wimber, M. (2019) · Nature Communications · doi:10.1038/s41467-018-08080-2 · Cited by 152 · see citing works
Seeliger, K., Fritsche, M., Güçlü, U., Schoenmakers, S., Schoffelen, J., Bosch, S., & van Gerven, M. A. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.018 · Cited by 118 · see citing works
Differential temporal dynamics during visual imagery and perception
Dijkstra, N., Mostert, P., de Lange, F. P., Bosch, S., & van Gerven, M. A. J. (2018) · eLife · doi:10.7554/elife.33904 · Cited by 118 · see citing works
Self-citationThe Neural Dynamics of Face Detection in the Wild Revealed by MVPA
Cauchoix, M., Barragan‐Jason, G., Serre, T., & Barbeau, E. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3030-13.2014 · Cited by 111 · see citing works
An Optimal Oscillatory Phase for Pattern Reactivation during Memory Retrieval
Kerrén, C., Domingo, J. L., Hanslmayr, S., & Wimber, M. (2018) · Current Biology · doi:10.1016/j.cub.2018.08.065 · Cited by 108 · see citing works
Lozano‐Soldevilla, D., & Rufin Vanrullen, A. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.058 · Cited by 106 · see citing works
Dissociating sensory from decision processes in human perceptual decision making
Mostert, P., Kok, P., & de Lange, F. P. (2015) · Scientific Reports · doi:10.1038/srep18253 · Cited by 90 · see citing works
Redefining Visual Working Memory: A Cognitive-Strategy, Brain-Region Approach
Pearson, J., & Keogh, R. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419835210 · Cited by 90 · see citing works
The Generative Adversarial Brain
Gershman, S. J. (2019) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2019.00018 · Cited by 89 · see citing works
Alilović, J., Timmermans, B., Reteig, L. C., van Gaal, S., & Slagter, H. A. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz038 · Cited by 77 · see citing works
Towards a Unified View on Pathways and Functions of Neural Recurrent Processing
Pennartz, C. M. A., Dora, S., Muckli, L., & Lorteije, J. A. M. (2019) · Trends in Neurosciences · doi:10.1016/j.tins.2019.07.005 · Cited by 73 · see citing works
Feature-Specific Information Processing Precedes Concerted Activation in Human Visual Cortex
Ramkumar, P., Jas, M., Pannasch, S., Hari, R., & Parkkonen, L. T. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3905-12.2013 · Cited by 70 · see citing works
Chapter 14 Coding of stimulus invariances by inferior temporal neurons
Vogels, R., & Orban, G. A. (1996) · Progress in brain research · doi:10.1016/s0079-6123(08)63330-0 · Cited by 67 · see citing works
Finding decodable information that can be read out in behaviour
Grootswagers, T., Cichy, R. M., & Carlson, T. A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.022 · Cited by 60 · see citing works
Temporally Unconstrained Decoding Reveals Consistent but Time-Varying Stages of Stimulus Processing
Vidaurre, D., Myers, N. E., Stokes, M. G., Nobre, A. C., & Woolrich, M. W. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy290 · Cited by 60 · see citing works
Self-citationSenden, M., Emmerling, T. C., van Hoof, R., Frost, M. A., & Goebel, R. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01828-6 · Cited by 59 · see citing works
Stimulus Familiarity and Expectation Jointly Modulate Neural Activity in the Visual Ventral Stream
Manahova, M. E., Mostert, P., Kok, P., Schoffelen, J., & de Lange, F. P. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01281 · Cited by 50 · see citing works
Spatiotemporal Dynamics of Cortical Representations during and after Stimulus Presentation
van de Nieuwenhuijzen, M. E., van den Borne, E. W. P., Jensen, O. B., & van Gerven, M. A. J. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00042 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The prevalence of aphantasia (imagery weakness) in the general population
Dance, C. J., Ipser, A., & Simner, J. (2022) · Consciousness and Cognition · doi:10.1016/j.concog.2021.103243 · Cited by 167 · see citing works
Predictive processing as a systematic basis for identifying the neural correlates of consciousness
Hohwy, J., & Seth, A. (2020) · Philosophy and the Mind Sciences · doi:10.33735/phimisci.2020.ii.64 · Cited by 170 · see citing works
Aphantasia and hyperphantasia: exploring imagery vividness extremes
Zeman, A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.02.007 · Cited by 148 · see citing works
Visual working memory in aphantasia: Retained accuracy and capacity with a different strategy
Keogh, R., Wicken, M., & Pearson, J. (2021) · Cortex · doi:10.1016/j.cortex.2021.07.012 · Cited by 119 · see citing works
Perceptual reality monitoring: Neural mechanisms dissociating imagination from reality
Dijkstra, N., Kok, P., & Fleming, S. M. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104557 · Cited by 107 · see citing works
Self-citationPrior Expectations of Motion Direction Modulate Early Sensory Processing
Aitken, F., Turner, G., & Kok, P. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0537-20.2020 · Cited by 76 · see citing works
Perception and memory have distinct spatial tuning properties in human visual cortex
Favila, S. E., Kuhl, B. A., & Winawer, J. A. (2022) · Nature Communications · doi:10.1038/s41467-022-33161-8 · Cited by 81 · see citing works
Predictive coding is a consequence of energy efficiency in recurrent neural networks
Ali, A. G., Ahmad, N., de Groot, E., van Gerven, M. A. J., & Kietzmann, T. C. (2022) · Patterns · doi:10.1016/j.patter.2022.100639 · Cited by 83 · see citing works
Visual mental imagery: Evidence for a heterarchical neural architecture
Spagna, A., Heidenry, Z., Miselevich, M., Lambert, C. J., Eisenstadt, B. E., Tremblay, L., Liu, Z., Liu, J., & Bartolomeo, P. (2023) · Physics of Life Reviews · doi:10.1016/j.plrev.2023.12.012 · Cited by 66 · see citing works
The Architect Who Lost the Ability to Imagine: The Cerebral Basis of Visual Imagery
Þórudóttir, S., Sigurdardottir, H. M., Rice, G. E., Kerry, S. J., Robotham, R. J., Leff, A. P., & Starrfelt, R. (2020) · Brain Sciences · doi:10.3390/brainsci10020059 · Cited by 65 · see citing works
Untying the knot: imagination, perception and their neural substrates
Cavedon‐Taylor, D. (2021) · Synthese · doi:10.1007/s11229-021-03110-x · Cited by 56 · see citing works
Social-affective features drive human representations of observed actions
Dima, D. C., Tomita, T. M., Honey, C. J., & Işık, L. (2022) · eLife · doi:10.7554/elife.75027 · Cited by 47 · see citing works
Visual Representations: Insights from Neural Decoding
Robinson, A. K., Quek, G. L., & Carlson, T. A. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100120-025301 · Cited by 48 · see citing works
Königsmark, V., Bergmann, J., & Reeder, R. (2021) · Cortex · doi:10.1016/j.cortex.2021.05.007 · Cited by 50 · see citing works
Aphantasia: The science of visual imagery extremes
Keogh, R., Pearson, J., & Zeman, A. Z. (2021) · Handbook of clinical neurology · doi:10.1016/b978-0-12-821377-3.00012-x · Cited by 52 · see citing works
Multisensory subtypes of aphantasia: Mental imagery as supramodal perception in reverse
Dawes, A. J., Keogh, R., & Pearson, J. (2023) · Neuroscience Research · doi:10.1016/j.neures.2023.11.009 · Cited by 46 · see citing works
How do the blind ‘see’? The role of spontaneous brain activity in self-generated perception
Hahamy, A., Wilf, M., Rosin, B., Behrmann, M., & Malach, R. (2020) · Brain · doi:10.1093/brain/awaa384 · Cited by 40 · see citing works
Johnson, P. A., Blom, T., van Gaal, S., Feuerriegel, D., Bode, S., & Hogendoorn, H. (2022) · eLife · doi:10.7554/elife.82424 · Cited by 31 · see citing works
Properties of imagined experience across visual, auditory, and other sensory modalities
Sulfaro, A. A., Robinson, A. K., & Carlson, T. A. (2023) · Consciousness and Cognition · doi:10.1016/j.concog.2023.103598 · Cited by 33 · see citing works
Suppression weakens unwanted memories via a sustained reduction of neural reactivation
Meyer, A.-K., & Benoit, R. G. (2022) · eLife · doi:10.7554/elife.71309 · Cited by 27 · see citing works
Mograbi, D. C., Hall, S., Arantes, B., & Huntley, J. D. (2023) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1670 · Cited by 25 · see citing works
Li, S., Zeng, X., Shao, Z., & Yu, Q. (2023) · The Journal of Neuroscience · doi:10.1523/jneurosci.0592-23.2023 · Cited by 23 · see citing works
Overlapping neural representations for the position of visible and imagined objects
Robinson, A. K., Grootswagers, T., Shatek, S. M., Gerboni, J., Holcombe, A. O., & Carlson, T. A. (2021) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.19129 · Cited by 22 · see citing works
Hu, Y., & Yu, Q. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113242 · Cited by 24 · see citing works
No Evidence for Neural Overlap between Unconsciously Processed and Imagined Stimuli
Dijkstra, N., van Gaal, S., Geerligs, L., Bosch, S., & van Gerven, M. A. J. (2021) · eNeuro · doi:10.1523/eneuro.0228-21.2021 · Cited by 19 · see citing works
Self-citationPredictive coding is a consequence of energy efficiency in recurrent neural networksPreprint
Ali, A. G., Ahmad, N., de Groot, E., van Gerven, M. A. J., & Kietzmann, T. C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.16.430904 · Cited by 18 · see citing works
Dissociable Components of Information Encoding in Human Perception
Vidaurre, D., Cichy, R. M., & Woolrich, M. W. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab189 · Cited by 15 · see citing works
Self-citationRepresentations of imaginary scenes and their properties in cortical alpha activity
Stecher, R., & KAISER, D. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-63320-4 · Cited by 13 · see citing works
Warrington, O., Graedel, N. N., Callaghan, M. F., & Kok, P. (2025) · Science Advances · doi:10.1126/sciadv.ads4970 · Cited by 13 · see citing works
Higgins, C., van Es, M. W. J., Quinn, A. J., Vidaurre, D., & Woolrich, M. W. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119462 · Cited by 9 · see citing works
Self-citationSulfaro, A. A., Robinson, A. K., & Carlson, T. A. (2023) · Neuroscience of Consciousness · doi:10.1093/nc/niad018 · Cited by 12 · see citing works
Perception and memory have distinct spatial tuning properties in human visual cortex
Favila, S. E., Kuhl, B. A., & Winawer, J. A. (2022) · RePEc: Research Papers in Economics · Cited by 12 · see citing works
Testing covariance models for MEG source reconstruction of hippocampal activity
O’Neill, G. C., Barry, D. N., Tierney, T. M., Mellor, S., Maguire, E. A., & Barnes, G. R. (2021) · Scientific Reports · doi:10.1038/s41598-021-96933-0 · Cited by 11 · see citing works
Damiani, S., Cavicchioli, M., Guiot, C., Donadeo, A., Scalabrini, A., Grecuzzo, V., Bergamaschini, I., Provenzani, U., Politi, P., & Fusar-Poli, P. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2023.11.036 · Cited by 9 · see citing works
Woodry, R., Curtis, C. E., & Winawer, J. A. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0681-24.2025 · Cited by 10 · see citing works
Overlapping neural representations for the position of visible and imagined objectsPreprint
Robinson, A. K., Grootswagers, T., Shatek, S. M., Gerboni, J., Holcombe, A. O., & Carlson, T. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.02.974162 · Cited by 10 · see citing works
Strength of predicted information content in the brain biases decision behavior
Yan, Y., Zhan, J., Garrod, O., Cui, X., Ince, R. A. A., & Schyns, P. G. (2023) · Current Biology · doi:10.1016/j.cub.2023.10.042 · Cited by 10 · see citing works
Predictive processing and perception: What does imagining have to do with it?
Cavedon‐Taylor, D. (2022) · Consciousness and Cognition · doi:10.1016/j.concog.2022.103419 · Cited by 9 · see citing works
Prioritization of semantic over visuo-perceptual aspects in multi-item working memoryPreprint
Kerrén, C., Domingo, J. L., & Spitzer, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.29.498168 · Cited by 10 · see citing works
Perception and memory have distinct spatial tuning properties in human visual cortexPreprint
Favila, S. E., Kuhl, B. A., & Winawer, J. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/811331 · Cited by 8 · see citing works
Decoding Remapped Spatial Information in the Peri-Saccadic Period
Moran, C., Johnson, P. A., Landau, A. N., & Hogendoorn, H. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.2134-23.2024 · Cited by 7 · see citing works
Reconstructing Spatiotemporal Trajectories of Visual Object Memories in the Human Brain
Lifanov, J., Griffiths, B. J., Domingo, J. L., Ferreira, C. S., Wilson, M., Mayhew, S. D., Charest, I., & Wimber, M. (2024) · eNeuro · doi:10.1523/eneuro.0091-24.2024 · Cited by 7 · see citing works
Top-down generation of low-resolution representations improves visual perception and imagination
Bi, Z., Li, H., & Tian, L. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.12.030 · Cited by 7 · see citing works
Nuancing the heterarchical theory of visual mental imagery
Dijkstra, N. (2024) · Physics of Life Reviews · doi:10.1016/j.plrev.2024.02.008 · Cited by 7 · see citing works
Self-citationVividness as the similarity between generated imagery and an internal model
Riley, S. N., & Davies, J. (2023) · Brain and Cognition · doi:10.1016/j.bandc.2023.105988 · Cited by 6 · see citing works
Parrotta, E., McDonough, K. L., & Bach, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.07.544005 · Cited by 6 · see citing works
Marre, Q., Huet, N., & Labeye, É. (2024) · Memory · doi:10.1080/09658211.2024.2336161 · Cited by 5 · see citing works
Mantegna, F., Olivetti, E., Schwedhelm, P., & Baldauf, D. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121171 · Cited by 6 · see citing works
Sustained neural representations of personally familiar people and places during cued recall
Corriveau, A., Kidder, A., Teichmann, L., Wardle, S. G., & Baker, C. I. (2022) · Cortex · doi:10.1016/j.cortex.2022.08.014 · Cited by 5 · see citing works
Scrivener, C. L., & Silson, E. H. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00507 · Cited by 4 · see citing works
Visual category-driven differences in memory
Steel, A., & Silson, E. H. (2022) · Routledge eBooks · doi:10.4324/9781003158134-14 · Cited by 4 · see citing works
Gaze Restriction and Reactivation of Place-bound Content Drive Eye Movements During Mental Imagery
Gurtner, L. M., Bischof, W. F., & Mast, F. W. (2023) · Journal of Cognition · doi:10.5334/joc.316 · Cited by 4 · see citing works
Dissociable components of oscillatory activity underly information encoding in human perceptionPreprint
Vidaurre, D., Cichy, R. M., & Woolrich, M. W. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.10.291294 · Cited by 4 · see citing works
Self-citationLiu, Z., Song, T., Lambert, C. J., Chang, K., Chuaqui, K., Baer, N., Frank, R., Liu, J., Bartolomeo, P., & Spagna, A. (2025) · Imagination Cognition and Personality · doi:10.1177/02762366251382836 · Cited by 3 · see citing works
Towards naturalized phenomenology: Dynamics of space–time clouds and power law of working memory
Lubashevsky, I., & Lubashevskiy, V. (2025) · Cognitive Systems Research · doi:10.1016/j.cogsys.2025.101374 · Cited by 3 · see citing works
Visual working memory representations in visual and parietal cortex do not remap after eye movementsPreprint
He, T., Ekman, M., Vandenbroucke, A. R. E., & de Lange, F. P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/747329 · Cited by 3 · see citing works
No overlap between unconscious and imagined representationsPreprint
Dijkstra, N., van Gaal, S., Geerligs, L., Bosch, S., & van Gerven, M. A. J. (2021) · doi:10.31234/osf.io/ctdmk · Cited by 3 · see citing works
Self-citationNoble, C., Taylor, N. L., Milton, F., Fulford, J., Tan, J. B., O’Callaghan, C., Zeman, A. Z., & Shine, J. M. (2025) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2025.109322 · Cited by 8 · see citing works
The contribution of default mode network areas to visual perception
Ùjhelyi, B., Korda, Ž., & Zaretskaya, N. (2026) · Trends in Neurosciences · doi:10.1016/j.tins.2026.03.009 · Cited by 3 · see citing works
Testing covariance models for MEG source reconstruction of hippocampal activityPreprint
O’Neill, G. C., Barry, D. N., Tierney, T. M., Mellor, S., Maguire, E. A., & Barnes, G. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.441929 · Cited by 2 · see citing works
Predictive Coding is a Consequence of Energy Efficiency in Recurrent Neural NetworksPreprint
Ali, A. G., Ahmad, N., de Groot, E., van Gerven, M. A. J., & Kietzmann, T. C. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3976481 · Cited by 3 · see citing works
The neural dynamics of feedforward and feedback interactions in predictive processingPreprint
Vinck, M. A., Uran, C., & Canales‐Johnson, A. (2022) · doi:10.31234/osf.io/n3afb · Cited by 2 · see citing works
Imaginary scenes are represented in cortical alpha activityPreprint
Stecher, R., & KAISER, D. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.23.563249 · Cited by 2 · see citing works
Bates, K. E., Smith, M. L., Farran, E. K., & Machizawa, M. G. (2023) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02085 · Cited by 1 · see citing works
How are opposite neurofeedback tasks represented at cortical and corticospinal tract levels?
Luca, I. S., & Vučković, A. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adbcdb · Cited by 1 · see citing works
Vivid imagery is reported faster than weak imagery
Barnett, B., Mazor, M., Cabbai, G., & Dijkstra, N. (2025) · Neuroscience of Consciousness · doi:10.1093/nc/niaf054 · Cited by 1 · see citing works
Self-citationTaylor, R., Sumner, P., Singh, K. D., & Jones, C. R. G. (2026) · Scientific Reports · doi:10.1038/s41598-026-38574-9 · Cited by 2 · see citing works
Madeira, R. M., & dos Santos, R. A. T. (2024) · Psychology of Music · doi:10.1177/03057356241270940 · Cited by 1 · see citing works
Johnson, P. A., Blom, T., van Gaal, S., Feuerriegel, D., Bode, S., & Hogendoorn, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.26.496535 · Cited by 1 · see citing works
Bates, K. E., Smith, M. L., Farran, E. K., & Machizawa, M. G. (2023) · doi:10.31234/osf.io/kfh3a · Cited by 1 · see citing works
Neural representation strength of predicted category features biases decision behaviorPreprint
Yan, Y., Zhan, J., Garrod, O., Cui, X., Ince, R. A. A., & Schyns, P. G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.05.539587 · Cited by 1 · see citing works
Learning Equivariant Object Recognition and its Reverse Application to ImageryPreprint
Klepel, F., & Goebel, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.20.541553 · Cited by 1 · see citing works
Area V1 Development, a Model System to Link Corticogenesis to Adult Cortex Structure and Function
Dehay, C., & Kennedy, H. (2023) · Wiley eBooks · doi:10.1002/9781119860914.ch4 · Cited by 1 · see citing works
Decoding Remapped Spatial Information in the Peri-Saccadic PeriodPreprint
Moran, C., Johnson, P. A., Landau, A. N., & Hogendoorn, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.07.565952 · Cited by 1 · see citing works
Neural interference between real and imagined visual stimuliPreprint
Sulfaro, A. A., Robinson, A. K., & Carlson, T. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.05.574285 · Cited by 1 · see citing works
He, C., Lamarle, D., Bartolomeo, P., Liu, J., & Spagna, A. (2024) · doi:10.31234/osf.io/t3enh · Cited by 1 · see citing works
Cuenca‐Martínez, F., Bocos-Corredor, E., Espinosa-Giménez, Á., Barrero-Santiago, L., Nefa-Díaz, N., Canchal-Crespo, D., Varangot‐Reille, C., Herranz‐Gómez, A., Suso‐Martí, L., Sempere‐Rubio, N., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191911878
Dissociating Hippocampal and Cortical Contributions to Predictive Processing
Mynick, A., & Steel, A. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1840-22.2022
Uemura, M., Katagiri, Y., Imai, E., Kawahara, Y., Otani, Y., Ichinose, T., Kondo, K., & Kowa, H. (2024) · Brain Science · doi:10.3390/brainsci14080757 · Cited by 1 · see citing works
Evolving Engrams Demand Changes in Effective Cues
Domingo, J. L., & Kerrén, C. (2025) · Hippocampus · doi:10.1002/hipo.70015 · Cited by 1 · see citing works
ADA: A decoding algorithm for temporally-variable brain responses
Oyarzo, P., Cichy, R. M., & Vidaurre, D. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.10.044
Self-citationReverse Spatiotemporal Hierarchy during Cross-modal Memory Recall and ImageryPreprint
Hu, Y., Diedrichsen, J., & Mohsenzadeh, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.26.690759
Using EEG to detect lapses in sustained attention to moving stimuli
Lowe, B. G., Woolgar, A., Smit, S., & Rich, A. N. (2025) · Cortex · doi:10.1016/j.cortex.2025.12.002
Characterising semantic prioritisation in visual working memoryPreprint
Kerrén, C., González‐García, C., & Domingo, J. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.20.706943
Decoupling sensory from attentional processes in conscious perception of speech
Garnier-Allain, A. (2022) · theses.fr (ABES) · doi:10.70675/0e9945a4zf9c7z49d6z991az674158fdf237
Does mental imagery support temporally extended agency? Rethinking the experimental approach
Pedersen, R. (2026) · Synthese · doi:10.1007/s11229-026-05637-3
Characterising semantic prioritisation in visual working memoryPreprint
Kerrén, C., González‐García, C., & Domingo, J. L. (2026) · eLife · doi:10.7554/elife.111859
Characterising semantic prioritisation in visual working memoryPreprint
Kerrén, C., González‐García, C., & Domingo, J. L. (2026) · eLife · doi:10.7554/elife.111859.1
Medial temporal default mode network selectively encodes autobiographical visual imagery
Anderson, A. J., Turnbull, A. G., & Lin, F. V. (2026) · Science Advances · doi:10.1126/sciadv.aee4232
- Authors
- Dijkstra, N., Ambrogioni, L., Vidaurre, D., & van Gerven, M.
- Affiliations
- Radboud University Nijmegen (NL)
- Aarhus University (DK)
- Donders Centre for Cognition, Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
- Corresponding author
- Dijkstra, Nadine
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2020-07-20
- Volume
- 9
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC7371419
- OpenAlex
- W3044887000
- Cited by
- 89
- References
- 63
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 10 · 2023: 22 · 2022: 16 · 2021: 13 · 2020: 6 · 2019: 2
- FWCI
- 7.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Netherlands Organisation for Scientific Research (639.072.51)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- perceptual inference, mental imagery, neural dynamics, predictive processing, feedback processing, feedforward processing, neural oscillations, visual perception, magnetoencephalography, machine learning decoding
- MeSH terms
- Machine Learning, Adult, Humans, Mental Processes, Neural Pathways, Perception, Magnetoencephalography, Young Adult, Spatio-Temporal Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
