If someone stops you in the street to ask for directions, you might find yourself picturing a particular crossing in your mind’s eye as you explain the route. This ability to mentally visualize things that we cannot see is known as visual imagery. Neuroscientists have shown that imagining an object activates some of the same brain regions as looking at that object. But do these regions also become active in the same order when we imagine rather than perceive?

Our ability to see the world around us depends on light bouncing off objects and entering the eye, which converts it into electrical signals. These signals travel to an area at the back of the brain that processes basic visual features, such as lines and angles. The electrical activity then spreads forward through the brain toward other visual areas, which perform more complex processing. Within a few hundred milliseconds of light entering the eye, the brain generates a percept of the object in front of us.

So, does the brain perform these same steps when we mentally visualize an object? Dijkstra et al. measured brain activity in healthy volunteers while they either imagined faces and houses, or looked at pictures of them. Electrical activity spread from visual areas at the back of the brain to visual areas nearer the front as the volunteers looked at the pictures. But this did not happen when the volunteers imagined the faces and houses. Contrary to perception, the different brain areas did not seem to become activated in any apparent order. Instead, the brain areas active during imagining were those that only became active during perception after 130 milliseconds. This is the time at which brain areas responsible for complex visual processing become active when we look at objects.

These findings shed new light on how we see with our mind’s eye. They suggest that when we imagine an object, the brain activates the entire representation of that object at once rather than building it up in steps. Understanding how the brain forms a mental image in real time could help us develop new technologies, such as brain-computer interfaces. These devices aim to interpret patterns of brain activity and display the output on a computer. Such equipment could help people with paralysis to communicate.

Visual imagery is the ability to generate visual experience in the absence of the associated sensory input. This ability plays an important role in various cognitive processes such as (working) memory, spatial navigation, mental rotation, and reasoning about future events (Kosslyn et al., 2001). When we engage in visual imagery, a large network covering parietal, frontal and occipital areas becomes active (Ganis et al., 2004; Ishai et al., 2000). Multivariate fMRI studies have shown that imagery activates similar distributed representations in the visual cortex as perception for the same content (Reddy et al., 2010; Lee et al., 2012; Albers et al., 2013). There is a gradient in this representational overlap, in which higher, anterior visual areas show more overlap between imagery and perception than lower, posterior visual areas (Lee et al., 2012; Horikawa and Kamitani, 2017). The overlap in low-level visual areas furthermore depends on the amount of visual detail required by the task (Kosslyn and Thompson, 2003; Bergmann et al., 2016) and the experienced imagery vividness (Albers et al., 2013; Dijkstra et al., 2017a). Thus, much is known about the spatial features of neural representations underlying imagery. However, the temporal features of these representations remain unclear.

In contrast, the temporal properties of perceptual processing are well studied. Perception is a highly dynamic process during which representations change rapidly over time before arriving at a stable percept. After signals from the retina reach the cortex, activation progresses up the visual hierarchy starting at primary, posterior visual areas and then spreading towards secondary, more anterior visual areas over time (Serre et al., 2007; Lerner et al., 2001; Van Essen et al., 1992). First, simple features such as orientation and spatial frequency are processed in posterior visual areas (Hubel and Wiesel, 1968) after which more complex features such as shape and eventually semantic category are processed more anteriorly (Maunsell and Newsome, 1987; Vogels and Orban, 1996; Seeliger et al., 2017). After this initial feedforward sweep, feedback from anterior to posterior areas is believed to further sharpen the visual representation over time until a stable percept is achieved (Cauchoix et al., 2014; Bastos et al., 2015; Bastos et al., 2012).

However, the temporal dynamics of visual imagery remain unclear. During imagery, there is no bottom-up sensory input. Instead, visual areas are assumed to be activated by top-down connections from fronto-parietal areas (Mechelli et al., 2004; Dijkstra et al., 2017b). Given the absence of bottom-up input, it might be that during imagery, high-level representations in anterior visual areas are activated first, after which activity is propagated down to more low-level areas to fill in the visual details. This would be in line with the reverse hierarchy theory (Ahissar and Hochstein, 2004; Hochstein and Ahissar, 2002). Alternatively, there may be no ordering such that during imagery, perceptual representations at different levels of the hierarchy are reinstated simultaneously. Furthermore, the temporal profile of the overlap between imagery and perception remains unclear. The finding that imagery relies on similar representations in low-level visual cortex as perception (Albers et al., 2013) suggests that imagery might already show overlap with the earliest stages of perceptual processing. On the other hand, it is also possible that more processing is necessary before the perceptual representation has a format that can be accessed by imagery, which would be reflected by overlap arising later in time.

In the current study, we investigated these questions by tracking the neural representations of imagined and perceived stimuli over time. We combined magnetoencephalography (MEG) with multivariate decoding. First, we investigated the temporal dynamics within perception and imagery by exploring the stability and recurrence of activation patterns over time (King and Dehaene, 2014; Astrand et al., 2015). Second, we investigated the temporal overlap by exploring which time points during perception generalized to imagery.

Results

Behavioral results

Twenty-five participants executed a retro-cue task in which they perceived and imagined faces and houses and rated their experienced imagery vividness on each trial using a sliding bar (see Figure 1). Prior to scanning, participants filled in the Vividness of Visual Imagery Questionnaire, which is a measure of people’s imagery ability (Marks, 1973). There was a significant correlation between VVIQ and averaged vividness ratings (r = −0.45, p=0.02), which indicates that people with a higher imagery vividness as measured by the VVIQ also rated their imagery as more vivid on average during the experiment. Participants reported relatively high vividness on average (49.6 ± 26.6 on a scale between −150 and +150, where 0 is the starting point of the slider and positive numbers indicate high vividness and negative numbers indicate low vividness). There was no significant difference in vividness ratings between faces (54.0 ± 29.7) and houses (48.7 ± 26.7; t(24) = 1.46, p=0.16). To ensure that participants were imagining the correct images, on 7% of the trials participants had to indicate which of four exemplars they imagined. The imagined exemplar was correctly identified in 89.8% (±5.4%) of the catch trials, indicating that participants performed the task correctly. There was also no significant difference between the two stimulus categories in the percentage of correct catch trials (faces: 90.9 ± 6.6, houses: 88.8 ± 7.1; t(24) = −1.25, p=0.22). Furthermore, correctly identified catch trials were experienced as more vivid (52.06 ± 33.1) than incorrectly identified catch trials (24.31 ± 33.84; t(23) = 3.11, p=0.0049), giving support to the criterion validity of the identification task. We had to drop one subject for this comparison because this person did not have any incorrect trials.

Figure 1

Download asset Open asset

Representational dynamics during perception and imagery

To uncover the temporal dynamics of category representations during perception and imagery, we decoded the category from the MEG signal over time. The results are shown in Figure 2. Testing and training on the same time points revealed that during perception, significantly different patterns of activity for faces and houses were present from 73 ms after stimulus onset with the peak accuracy at 153 ms (Figure 2A, left). During imagery, category information could be decoded significantly from 540 ms after retro-cue onset, with the peak at 1073 ms (Figure 2A, right, Figure 2—figure supplement 1B). The generation of a visual representation from a cue thus seems to take longer than the activation via bottom-up sensory input. Furthermore, imagery decoding resulted in a much lower accuracy than perception decoding (peak perception is at ~90%, peak imagery at ~60%). This is also observed in fMRI decoding studies (Reddy et al., 2010; Lee et al., 2012) and is probably due to the fact that imagery is a less automatic process than perception, which leads to higher between trial variation in neural activations. Note that, to allow better comparison between perception and imagery, we only showed the first 1000 ms after cue onset during imagery (see Figure 2—figure supplement 1 for the results throughout the entire imagery period).

Figure 2 with 3 supplements see all

Download asset Open asset

Figure 2—source data 1

Temporal dynamics within perception and imagery.

The files '...Acc.mat' contain the subjects x time x time decoding accuracy within perception and imagery. The files '...Sig.mat' contain the time x time cluster p values of the comparison of these accuracies with chance level.

https://doi.org/10.7554/eLife.33904.008

Download elife-33904-fig2-data1-v1.zip

Figure 2—source data 2

Decoding from eye tracker data. 

The file '..eyeAcc.mat' contains the subject x time decoding accuracies from the x and y eye tracker channels within perception and imagery. The file '.. EyeSig.mat' contains the cluster based p values of these accuracies compared to chance level. 

https://doi.org/10.7554/eLife.33904.009

Download elife-33904-fig2-data2-v1.zip

Figure 2—source data 3

Vividness median split.

The file '...ImaVivSplit.mat contains the subjects x time x time decoding accuracy within imagery for the high vividness and low vividness group and the time x time p values of the comparison between the two groups. The file '...PercVivSplit.mat' contains the same for the decoding within perception. 

https://doi.org/10.7554/eLife.33904.010

Download elife-33904-fig2-data3-v1.zip

To reveal the generalization of representations over time, classifiers were trained on one time point and tested on all other time points (King and Dehaene, 2014) (Figure 2B). Furthermore, to investigate the temporal specificity of the representations at each time point, we calculated the proportion of off-diagonal classifiers that had a significantly lower accuracy than the diagonal classifier of that time point (Astrand et al., 2015) (Figure 2C; see Materials and methods).

During perception, distinct processing stages can be distinguished (Figure 2B–C, left). During the first stage, between 70 and 120 ms, diagonal decoding was significant and there was very high temporal specificity. This indicates sequential processing with rapidly changing representations (King and Dehaene, 2014). During this time period, the classifier mostly relied on activity in posterior visual areas (Figure 2D, left). Therefore, these results are consistent with initial feedforward stimulus processing. In the second stage, around 160 ms, the classifier generalized to neighboring points as well as testing points after 250 ms. The associated sources are spread out over the ventral visual stream (Figure 2D, left), indicating that high-level representations are activated at this time. In the third stage, around 210 ms, we again observed high temporal specificity (Figure 2C, left) and a gap in generalization to 160 ms (Figure 2B, left). This pattern could reflect feedback to low-level visual areas. Finally, from 300 ms onwards there is a broad off-diagonal generalization pattern that also generalizes to time points around 160 ms and an associated drop in temporal specificity (Figure 2B–C, left). This broad off-diagonal pattern likely reflects stabilization of the visual representation.

In contrast, during imagery, we did not observe any clear distinct processing stages. Instead, there was a broad off-diagonal generalization throughout the entire imagery period (Figure 2B, right; Figure 2—figure supplement 1A). Already at the onset of imagery decoding, there was high generalization and low specificity (Figure 2B–C, right). This indicates that the neural representation during imagery remains highly stable (King and Dehaene, 2014). The only change seems to be in decoding strength, which first increases and then decreases over time (Fig. S1B), indicating that either representations at those times are weaker or that they are more variable over trials. The sources that contributed to classification were mostly located in the ventral visual stream and there was also some evidence for frontal and parietal contributions (Figure 2D, right).

Even though we attempted to remove eye movements from our data as well as possible (see Materials and methods), it is theoretically possible that eye movements which systematically differed between the conditions caused part of the neural signal that was picked up by the decoding analyses (Mostert et al., 2017). In order to investigate this possibility, we tried to decode the stimulus category from the X and Y position of the eyes as measured with an eye tracker. The results for this analysis are shown in Figure 2—figure supplement 2. During imagery, eye tracker decoding was at chance level for all time points, indicating that there were no condition-specific eye movements during imagery (Figure 2—figure supplement 2B). However, during perception, eye tracker decoding was significant from 316 ms onwards (Figure 2—figure supplement 2A), indicating that differences in eye movements between the conditions may have driven (part of) the brain decoding. If this were the case, there would be a high, positive correlation between eye tracker decoding and brain decoding. Figure 2—figure supplement 2C however shows that there was no such correlation, suggesting that our perception decoding results for that time window were not driven by eye movements.

Temporal overlap between perception and imagery

To investigate when perceptual processing generalizes to imagery, we trained a classifier on one data segment and tested it on the other segment. We first trained a classifier during perception and then used this classifier to decode the neural signal during imagery (Figure 3A–B). Already around 350 ms after imagery cue onset, classifiers trained on perception data from 160, 700 and 960 ms after stimulus onset could significantly decode the imagined stimulus category (Figure 3A). This is earlier than classification within imagery, which started at 540 ms after cue onset (Figure 2A, right). Considering the increased decoding accuracy during perception compared to imagery (Figure 2A, left versus right), this difference might reflect an increase in signal-to-noise ratio (SNR) by training on perception compared to imagery.

Figure 3 with 1 supplement see all

Download asset Open asset

Figure 3—source data 1

https://doi.org/10.7554/eLife.33904.013

Download elife-33904-fig3-data1-v1.zip

Figure 3—source data 2

https://doi.org/10.7554/eLife.33904.014

Download elife-33904-fig3-data2-v1.zip

Furthermore, the distinct processing stages found during perception (Figure 2B, left) were also reflected in the generalization to imagery (Figure 3A–B). Perceptual processes around 160 ms and after 300 ms significantly overlapped with imagery (Figure 3B, right plots). In contrast, processing at 90 ms did not generalize to any time point during imagery (Figure 3B, top left). Perceptual processing at 210 ms showed intermittent generalization to imagery, with generalization at some time points and no generalization at other times (Figure 3B, bottom left). Significant generalization at this time could also reflect the effects of smoothing over neighboring time points which are significant (see Materials and methods). This would mean that there is no real overlap at 210 ms but that this overlap is caused by overlap from earlier or later time points.

To further pinpoint when perception started to overlap with imagery, we performed an additional analysis in which we reversed the generalization: we trained classifiers on different time points during imagery and used these to classify perception data. This analysis revealed a similar pattern of high overlap with perception around 160 and after 300 ms and low overlap before 100 ms and around 210 ms (Figure 3C–D). Note that this profile is stable throughout imagery and is already present at the start of imagery, albeit with lower accuracies (Figure 3-D, bottom panel). Furthermore, the onset of perceptual overlap is highly consistent over the course of imagery: overlap starts around 130 ms, with the first peak at approximately 160 ms (Figure 3C). In general, cross-classification accuracy was higher when training on imagery than when training on perception (Figure 3C vs. Figure 3A). This is surprising, because training on high SNR data (in our case, perception) is reported to lead to higher classification accuracy than training on low SNR data (King and Dehaene, 2014) (imagery). This difference may reflect the fact that the perceptual representation contained more unique features than the imagery representation, leading to a lower generalization performance when training on perception.

We also investigated whether the temporal dynamics were influenced by imagery vividness by investigating whether the results of previous analyses were different for participants with high or low vividness (see Figure 2—figure supplement 3 for within perception and imagery decoding and Figure 3—figure supplement 1 for cross-decoding). Decoding accuracy seemed to be higher in the high vividness group, however, none of the differences were significant after correction for multiple comparisons.

The data discussed in the manuscript are available at the Donders Repository (https://data.donders.ru.nl/collections/shared/di.dcc.DSC_2017.00072_245?2). To download the data, the user has to agree with the accompanying Data Use Agreement (DUA), which states, a.o. that the user will not attempt to identify participants from the data and will acknowledge the origin of the data in any resulting publication. The DUA can be signed and the data can be downloaded by clicking on 'Request access' and logging in using either an institutional or a social account (e.g. Google+, Twitter, LinkedIn).

1. 1. Nadine Dijkstra
   2. Pim Mostert
   3. Sander Bosch
   4. Floris P de Lange
   5. Marcel AJ van Gerven

   (2018) Temporal dynamics of visual imagery and perception

   Publicly available at Donders Repository.

   https://data.donders.ru.nl/collections/shared/di.dcc.DSC_2017.00072_245?2

1. 1. Ahissar M
   2. Hochstein S

   (2004) The reverse hierarchy theory of visual perceptual learning

   Trends in Cognitive Sciences 8:457–464.

   https://doi.org/10.1016/j.tics.2004.08.011

   • PubMed
   • Google Scholar
2. 1. Albers AM
   2. Kok P
   3. Toni I
   4. Dijkerman HC
   5. de Lange FP

   (2013) Shared representations for working memory and mental imagery in early visual cortex

   Current Biology 23:1427–1431.

   https://doi.org/10.1016/j.cub.2013.05.065

   • PubMed
   • Google Scholar
3. 1. Astrand E
   2. Ibos G
   3. Duhamel JR
   4. Ben Hamed S

   (2015) Differential dynamics of spatial attention, position, and color coding within the parietofrontal network

   Journal of Neuroscience 35:3174–3189.

   https://doi.org/10.1523/JNEUROSCI.2370-14.2015

   • PubMed
   • Google Scholar
4. Book

   1. Bachmann T

   (2006)

   Microgenesis of perception: Conceptual, psychophysical, and neurobiological aspects

   In: Ögmen H, Breitmeyer B. G, editors. The First Half Second: The Microgenesis and Temporal Dynamics of Unconscious and Conscious Visual Processes. MIT Press. ISBN 978-0-262-05114-9. pp. 11–33.

   • Google Scholar
5. 1. Bastos AM
   2. Usrey WM
   3. Adams RA
   4. Mangun GR
   5. Fries P
   6. Friston KJ

   (2012) Canonical microcircuits for predictive coding

   Neuron 76:695–711.

   https://doi.org/10.1016/j.neuron.2012.10.038

   • PubMed
   • Google Scholar
6. 1. Bastos AM
   2. Vezoli J
   3. Bosman CA
   4. Schoffelen JM
   5. Oostenveld R
   6. Dowdall JR
   7. De Weerd P
   8. Kennedy H
   9. Fries P

   (2015) Visual areas exert feedforward and feedback influences through distinct frequency channels

   Neuron 85:390–401.

   https://doi.org/10.1016/j.neuron.2014.12.018

   • PubMed
   • Google Scholar
7. 1. Bentin S
   2. Allison T
   3. Puce A
   4. Perez E
   5. McCarthy G

   (1996) Electrophysiological studies of face perception in humans

   Journal of Cognitive Neuroscience 8:551–565.

   https://doi.org/10.1162/jocn.1996.8.6.551

   • PubMed
   • Google Scholar
8. 1. Bergmann J
   2. Genç E
   3. Kohler A
   4. Singer W
   5. Pearson J

   (2016) Smaller primary visual cortex is associated with stronger, but less precise mental imagery

   Cerebral Cortex 26:3838–3850.

   https://doi.org/10.1093/cercor/bhv186

   • Google Scholar
9. 1. Carlson T
   2. Tovar DA
   3. Alink A
   4. Kriegeskorte N

   (2013) Representational dynamics of object vision: the first 1000 ms

   Journal of Vision 13:1–19.

   https://doi.org/10.1167/13.10.1

   • PubMed
   • Google Scholar
10. 1. Carlson TA
    2. Hogendoorn H
    3. Kanai R
    4. Mesik J
    5. Turret J

    (2011) High temporal resolution decoding of object position and category

    Journal of Vision 11:9–17.

    https://doi.org/10.1167/11.10.9

    • PubMed
    • Google Scholar
11. 1. Cauchoix M
    2. Barragan-Jason G
    3. Serre T
    4. Barbeau EJ

    (2014) The neural dynamics of face detection in the wild revealed by MVPA

    The Journal of Neuroscience 34:846–854.

    https://doi.org/10.1523/JNEUROSCI.3030-13.2014

    • PubMed
    • Google Scholar
12. 1. Cui X
    2. Jeter CB
    3. Yang D
    4. Montague PR
    5. Eagleman DM

    (2007) Vividness of mental imagery: individual variability can be measured objectively

    Vision Research 47:474–478.

    https://doi.org/10.1016/j.visres.2006.11.013

    • PubMed
    • Google Scholar
13. 1. Deffke I
    2. Sander T
    3. Heidenreich J
    4. Sommer W
    5. Curio G
    6. Trahms L
    7. Lueschow A

    (2007) MEG/EEG sources of the 170-ms response to faces are co-localized in the fusiform gyrus

    NeuroImage 35:1495–1501.

    https://doi.org/10.1016/j.neuroimage.2007.01.034

    • PubMed
    • Google Scholar
14. 1. Destrieux C
    2. Fischl B
    3. Dale A
    4. Halgren E

    (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature

    NeuroImage 53:1–15.

    https://doi.org/10.1016/j.neuroimage.2010.06.010

    • PubMed
    • Google Scholar
15. 1. Dijkstra N
    2. Bosch SE
    3. van Gerven MA

    (2017a) Vividness of visual imagery depends on the neural overlap with perception in visual Areas

    The Journal of Neuroscience 37:1367–1373.

    https://doi.org/10.1523/JNEUROSCI.3022-16.2016

    • PubMed
    • Google Scholar
16. 1. Dijkstra N
    2. Zeidman P
    3. Ondobaka S
    4. van Gerven MAJ
    5. Friston K

    (2017b) Distinct top-down and bottom-up brain connectivity during visual perception and imagery

    Scientific Reports 7:5677.

    https://doi.org/10.1038/s41598-017-05888-8

    • PubMed
    • Google Scholar
17. 1. Epstein R
    2. Graham KS
    3. Downing PE

    (2003) Viewpoint-specific scene representations in human parahippocampal cortex

    Neuron 37:865–876.

    https://doi.org/10.1016/S0896-6273(03)00117-X

    • PubMed
    • Google Scholar
18. 1. Ganis G
    2. Schendan HE

    (2008) Visual mental imagery and perception produce opposite adaptation effects on early brain potentials

    NeuroImage 42:1714–1727.

    https://doi.org/10.1016/j.neuroimage.2008.07.004

    • PubMed
    • Google Scholar
19. 1. Ganis G
    2. Thompson WL
    3. Kosslyn SM

    (2004) Brain areas underlying visual mental imagery and visual perception: an fMRI study

    Cognitive Brain Research 20:226–241.

    https://doi.org/10.1016/j.cogbrainres.2004.02.012

    • PubMed
    • Google Scholar
20. 1. Halgren E
    2. Raij T
    3. Marinkovic K
    4. Jousmäki V
    5. Hari R

    (2000) Cognitive response profile of the human fusiform face area as determined by MEG

    Cerebral Cortex 10:69–81.

    https://doi.org/10.1093/cercor/10.1.69

    • PubMed
    • Google Scholar
21. 1. Harrison SA
    2. Tong F

    (2009) Decoding reveals the contents of visual working memory in early visual areas

    Nature 458:632–635.

    https://doi.org/10.1038/nature07832

    • PubMed
    • Google Scholar
22. 1. Haufe S
    2. Meinecke F
    3. Görgen K
    4. Dähne S
    5. Haynes JD
    6. Blankertz B
    7. Bießmann F

    (2014) On the interpretation of weight vectors of linear models in multivariate neuroimaging

    NeuroImage 87:96–110.

    https://doi.org/10.1016/j.neuroimage.2013.10.067

    • PubMed
    • Google Scholar
23. 1. Henson RN
    2. Mouchlianitis E
    3. Friston KJ

    (2009) MEG and EEG data fusion: simultaneous localisation of face-evoked responses

    NeuroImage 47:581–589.

    https://doi.org/10.1016/j.neuroimage.2009.04.063

    • PubMed
    • Google Scholar
24. 1. Hochstein S
    2. Ahissar M

    (2002) View from the top: hierarchies and reverse hierarchies in the visual system

    Neuron 36:791–804.

    https://doi.org/10.1016/S0896-6273(02)01091-7

    • PubMed
    • Google Scholar
25. 1. Horikawa T
    2. Kamitani Y

    (2017) Generic decoding of seen and imagined objects using hierarchical visual features

    Nature Communications 8:15037.

    https://doi.org/10.1038/ncomms15037

    • PubMed
    • Google Scholar
26. 1. Hubel DH
    2. Wiesel TN

    (1968) Receptive fields and functional architecture of monkey striate cortex

    The Journal of Physiology 195:215–243.

    https://doi.org/10.1113/jphysiol.1968.sp008455

    • PubMed
    • Google Scholar
27. 1. Ishai A
    2. Haxby JV
    3. Ungerleider LG

    (2002) Visual imagery of famous faces: effects of memory and attention revealed by fMRI

    NeuroImage 17:1729–1741.

    https://doi.org/10.1006/nimg.2002.1330

    • PubMed
    • Google Scholar
28. 1. Ishai A
    2. Ungerleider LG
    3. Haxby JV

    (2000) Distributed neural systems for the generation of visual images

    Neuron 28:979–990.

    https://doi.org/10.1016/S0896-6273(00)00168-9

    • PubMed
    • Google Scholar
29. 1. Isik L
    2. Meyers EM
    3. Leibo JZ
    4. Poggio T

    (2014) The dynamics of invariant object recognition in the human visual system

    Journal of Neurophysiology 111:91–102.

    https://doi.org/10.1152/jn.00394.2013

    • PubMed
    • Google Scholar
30. 1. Kaiser D
    2. Oosterhof NN
    3. Peelen MV

    (2016) The neural dynamics of attentional selection in natural scenes

    Journal of Neuroscience 36:10522–10528.

    https://doi.org/10.1523/JNEUROSCI.1385-16.2016

    • PubMed
    • Google Scholar
31. 1. Kanwisher N
    2. McDermott J
    3. Chun MM

    (1997) The fusiform face area: a module in human extrastriate cortex specialized for face perception

    The Journal of Neuroscience 17:4302–4311.

    https://doi.org/10.1523/JNEUROSCI.17-11-04302.1997

    • PubMed
    • Google Scholar
32. 1. King JR
    2. Dehaene S

    (2014) Characterizing the dynamics of mental representations: the temporal generalization method

    Trends in Cognitive Sciences 18:203–210.

    https://doi.org/10.1016/j.tics.2014.01.002

    • PubMed
    • Google Scholar
33. 1. King JR
    2. Gramfort A
    3. Schurger A
    4. Naccache L
    5. Dehaene S

    (2014) Two distinct dynamic modes subtend the detection of unexpected sounds

    PLoS ONE 9:e85791.

    https://doi.org/10.1371/journal.pone.0085791

    • PubMed
    • Google Scholar
34. 1. Koivisto M
    2. Railo H
    3. Revonsuo A
    4. Vanni S
    5. Salminen-Vaparanta N

    (2011) Recurrent processing in V1/V2 contributes to categorization of natural scenes

    Journal of Neuroscience 31:2488–2492.

    https://doi.org/10.1523/JNEUROSCI.3074-10.2011

    • PubMed
    • Google Scholar
35. 1. Kok P
    2. Jehee JF
    3. de Lange FP

    (2012) Less is more: expectation sharpens representations in the primary visual cortex

    Neuron 75:265–270.

    https://doi.org/10.1016/j.neuron.2012.04.034

    • PubMed
    • Google Scholar
36. 1. Kosslyn SM
    2. Ganis G
    3. Thompson WL

    (2001) Neural foundations of imagery

    Nature Reviews Neuroscience 2:635–642.

    https://doi.org/10.1038/35090055

    • PubMed
    • Google Scholar
37. 1. Kosslyn SM
    2. Thompson WL

    (2003) When is early visual cortex activated during visual mental imagery?

    Psychological Bulletin 129:723–746.

    https://doi.org/10.1037/0033-2909.129.5.723

    • PubMed
    • Google Scholar
38. 1. Lee SH
    2. Kravitz DJ
    3. Baker CI

    (2012) Disentangling visual imagery and perception of real-world objects

    NeuroImage 59:4064–4073.

    https://doi.org/10.1016/j.neuroimage.2011.10.055

    • PubMed
    • Google Scholar
39. 1. Lerner Y
    2. Hendler T
    3. Ben-Bashat D
    4. Harel M
    5. Malach R

    (2001) A hierarchical axis of object processing stages in the human visual cortex

    Cerebral Cortex 11:287–297.

    https://doi.org/10.1093/cercor/11.4.287

    • PubMed
    • Google Scholar
40. Preprint

    1. Manahova ME
    2. Mostert P
    3. Kok P
    4. Schoffelen JM
    5. de Lange FP

    (2017) Stimulus familiarity and expectation jointly modulate neural activity in the visual ventral stream

    bioRxiv.

    https://doi.org/10.1101/192518

    • Google Scholar
41. 1. Maris E
    2. Oostenveld R

    (2007) Nonparametric statistical testing of EEG- and MEG-data

    Journal of Neuroscience Methods 164:177–190.

    https://doi.org/10.1016/j.jneumeth.2007.03.024

    • PubMed
    • Google Scholar
42. 1. Marks DF

    (1973) Visual imagery differences in the recall of pictures

    British Journal of Psychology 64:17–24.

    https://doi.org/10.1111/j.2044-8295.1973.tb01322.x

    • PubMed
    • Google Scholar
43. 1. Maunsell JH
    2. Newsome WT

    (1987) Visual processing in monkey extrastriate cortex

    Annual Review of Neuroscience 10:363–401.

    https://doi.org/10.1146/annurev.ne.10.030187.002051

    • PubMed
    • Google Scholar
44. 1. Mechelli A
    2. Price CJ
    3. Friston KJ
    4. Ishai A

    (2004) Where bottom-up meets top-down: neuronal interactions during perception and imagery

    Cerebral Cortex 14:1256–1265.

    https://doi.org/10.1093/cercor/bhh087

    • PubMed
    • Google Scholar
45. Preprint

    1. Mostert P
    2. Albers A
    3. Brinkman L
    4. Todorova L
    5. Kok P
    6. de Lange F

    (2017) Eye movement-related confounds in neural decoding of visual working memory representations

    bioRxiv.

    https://doi.org/10.1101/215509

    • Google Scholar
46. 1. Mostert P
    2. Kok P
    3. de Lange FP

    (2015) Dissociating sensory from decision processes in human perceptual decision making

    Scientific Reports 5:18253.

    https://doi.org/10.1038/srep18253

    • PubMed
    • Google Scholar
47. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:1–9.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
48. 1. Reddy L
    2. Tsuchiya N
    3. Serre T

    (2010) Reading the mind's eye: decoding category information during mental imagery

    NeuroImage 50:818–825.

    https://doi.org/10.1016/j.neuroimage.2009.11.084

    • PubMed
    • Google Scholar
49. 1. Roelfsema PR
    2. Lamme VA
    3. Spekreijse H

    (1998) Object-based attention in the primary visual cortex of the macaque monkey

    Nature 395:376–381.

    https://doi.org/10.1038/26475

    • PubMed
    • Google Scholar
50. 1. Seeliger K
    2. Fritsche M
    3. Güçlü U
    4. Schoenmakers S
    5. Schoffelen J-M
    6. Bosch SE
    7. van Gerven MAJ

    (2017) Convolutional neural network-based encoding and decoding of visual object recognition in space and time

    NeuroImage 17:30586–30584.

    https://doi.org/10.1016/j.neuroimage.2017.07.018

    • Google Scholar
51. 1. Serre T
    2. Oliva A
    3. Poggio T

    (2007) A feedforward architecture accounts for rapid categorization

    PNAS 104:6424–6429.

    https://doi.org/10.1073/pnas.0700622104

    • PubMed
    • Google Scholar
52. 1. Stolk A
    2. Todorovic A
    3. Schoffelen JM
    4. Oostenveld R

    (2013) Online and offline tools for head movement compensation in MEG

    NeuroImage 68:39–48.

    https://doi.org/10.1016/j.neuroimage.2012.11.047

    • PubMed
    • Google Scholar
53. 1. Thorpe S
    2. Fize D
    3. Marlot C

    (1996) Speed of processing in the human visual system

    Nature 381:520–522.

    https://doi.org/10.1038/381520a0

    • PubMed
    • Google Scholar
54. 1. van de Nieuwenhuijzen ME
    2. van den Borne EW
    3. Jensen O
    4. van Gerven MA

    (2016) Spatiotemporal dynamics of cortical representations during and after stimulus presentation

    Frontiers in Systems Neuroscience 10:42.

    https://doi.org/10.3389/fnsys.2016.00042

    • PubMed
    • Google Scholar
55. 1. Van Essen DC
    2. Anderson CH
    3. Felleman DJ

    (1992) Information processing in the primate visual system: an integrated systems perspective

    Science 255:419–423.

    https://doi.org/10.1126/science.1734518

    • PubMed
    • Google Scholar
56. 1. Van Veen BD
    2. van Drongelen W
    3. Yuchtman M
    4. Suzuki A

    (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

    IEEE Transactions on Biomedical Engineering 44:867–880.

    https://doi.org/10.1109/10.623056

    • PubMed
    • Google Scholar
57. 1. Vogels R
    2. Orban GA

    (1996) Coding of stimulus invariances by inferior temporal neurons

    Progress in Brain Research 112:195–211.

    https://doi.org/10.1016/S0079-6123(08)63330-0

    • PubMed
    • Google Scholar

Author details

1. Nadine Dijkstra

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   n.dijkstra@donders.ru.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1423-9277

2. Pim Mostert

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Floris P de Lange

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   Conceptualization, Investigation, Writing—review and editing

   Competing interests

   Reviewing Editor, eLife

4. Sander Bosch

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   Conceptualization, Data curation, Supervision, Investigation, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. Marcel AJ van Gerven

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   Conceptualization, Supervision, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

• 4,728

  views

• 538

  downloads

• 93

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.