Early spatiotemporal dynamics of navigational affordance coding in the dorsal visual cortex

Domain: biology
Published: 2026-01-08
Editorial outcome: major_revision

Abstract
Successful navigation requires extracting navigationally relevant signals from a dynamically changing visual environment. The process by which we identify navigable routes through the environment is termed navigational affordances. Here, using a combination of functional magnetic resonance imaging, magnetoencephalography and behavioural testing we report that the extraction of such navigational affordance information likely takes place rapidly within dorsal early visual cortex before higher-level scene selective regions. Whilst we replicate prior work showing the involvement of the occipital place area in navigational affordance coding, whole-brain analyses indicate the most likely cortical locus to be dorsal early visual cortex. Analyses comparing the spatiotemporal pattern of navigational affordances suggest such information is detectable within ~110 milliseconds post stimulus onset. Finally, through varying the presentation durations of scenes, we demonstrate that navigational affordance representations are emergent, but not strong with stimulus durations as short as 33-66 milliseconds but become robust with stimulus durations > 132 milliseconds. Taken together these data challenge previous views regarding the critical cortical locus for navigational affordance coding and suggest that such affordances can be extracted from very briefly presented stimuli.

Introduction
Successful navigation through the local environment is a consistent feature of life for mobile organisms1. In humans, this accomplishment requires the seamless processing and interpretation of dynamically shifting environments where changes occur rapidly (e.g. exiting the London underground during rush hour).

The human visual system is ideally tuned for such rapid scene processing2–4, enabling the extraction of relevant visual information to guide navigational and affordance behaviours5. Whilst basic-level scene characteristics (e.g. forest or lake) can be deciphered with an exposure of ~50ms, more global scene characterisations, such as whether a scene is navigable or not, can occur with even briefer presentation times ~34ms4. Cortically, scene processing is thought to rely upon the interactions of a set of scene-selective regions spanning the lateral, ventral and medial cortical surfaces, respectively6–12. Within this scene-selective network, prior neuroimaging work of lasting impact suggests the Occipital Place Area (OPA)7 on the lateral surface plays a special role in the coding of navigational affordances13. The lower visual field bias exhibited by OPA14–16 is consistent with computational modelling approaches quantifying that the majority of navigational affordance information is extracted from the lower visual field17.

Within the temporal domain, relatively recent work suggests that neural representations of visual features relevant to navigational affordances within highly simplified artificial environments can emerge as early as ~134ms post stimulus onset18. In contrast, computational work suggests a temporal order of feature extraction, such that the spatial structure of a scene (2D and 3D) is processed first, followed by semantic content and then navigational affordances19. Very recent work has extended this temporal framework to show that representations of locomotive action affordances (i.e. what type of action can be performed within a scene), which may be considered analogous to navigational affordances, are present within OPA ~200ms post stimulus onset20.

Taken together, current models suggest a specific role for OPA in the coding of navigational13,17 and locomotive action affordances20,21 that occur within the first few hundred milliseconds post stimulus onset. However, this picture is incomplete. For example, whether OPA should be considered a single functionally homogeneous scene-selective region is debated15,16,22. OPA spatially overlaps at least five separate maps of the visual field (Fig. 1A)15,16, which differ in their visual field representations and thus their potential for extracting navigationally relevant information13,17. Moreover, while the spatiotemporal pattern of responses in OPA has been linked to locomotive action affordances20,21, this operationalization of affordances differs substantially from previous navigational affordance tasks, which essentially require the identification of efficient routes of egress within scenes13,18,19. As such, it is currently unclear what the unique contribution of these maps is to the overall pattern of navigational affordance coding in OPA.

Here, we aim to bridge this gap by leveraging the ability of functional magnetic resonance imaging (fMRI) and Magnetoencephalography (MEG) to capture the brain’s spatial and temporal response properties at high-resolution. Participants (n=14) completed both fMRI and MEG experiments in which they passively viewed scene images taken from prior work on navigational affordances13. The spatial and temporal pattern of responses from the fMRI and MEG data were then compared to the navigational affordance representations of these same scenes. An independent group of participants (n=72) completed a behavioural experiment in which they were required to identify potential routes of egress (i.e. navigational affordances) from the same set of scenes as used in the fMRI and MEG experiments but under different presentation duration conditions (unrestricted, 33ms, 66ms, 132ms, 264ms and 528ms). To preview, our data suggest that a likely locus for navigational affordance computations is not OPA, but rather dorsal early visual cortex (V1d/V2d/V3d). Further, navigational affordance representations emerged early in time (~110ms post stimulus onset) and were relatively transient (~110–450ms post stimulus onset). This early processing of navigational affordances is consistent with our behavioural data showing that although navigational affordance representations can be extracted with presentation times as brief as 33–66ms, these representations only become robust with presentation times of 132ms and longer.

Results
In this results section, we first present our fMRI findings that are derived from largely replicating a previous study by Bonner et al.13 This is the necessary platform on which we address our approaches to investigating the role of OPA’s retinotopic subdivisions and the timing of navigational affordance coding, which are presented second. Third, through MEG-fMRI fusion23, representational similarity analysis (RSA)24 and commonality analysis, we bring together the spatial and temporal dynamics of navigational affordance coding and highlight both the early emergence of navigational affordances and their tendency to be strongest in dorsal early visual cortex. Finally, we ask when meaningful and robust behavioural signatures of navigational affordances emerge, and whether they align with the temporal characteristics of the neural responses we observed.

Replication of navigational affordance coding in OPA
Our initial analysis goal was to attempt to replicate prior work in which it was shown that the pattern of fMRI responses in OPA to different scene images correlated positively with the pattern of navigational affordance trajectories through those same scenes derived behaviourally13. To achieve this, we first computed the pairwise dissimilarity (1-r, where r is the Pearson correlation coefficient) in the fMRI response to all scenes within the left and right OPA, storing these in a representational dissimilarity matrix (RDM)24. Next, these participant-specific RDMs were correlated with the behavioural RDM from Bonner et al.13 to assess the degree of navigational affordance coding in OPA (Fig. 1B). The significance of the correlations with the navigational affordance model was determined via a series of two-tailed t-tests against a zero-correlation assumption. Assumptions were met in all cases.

We observed strong evidence for the coding of navigational affordances in OPA bilaterally (LH: t(13)=6.14, p<0.001, Cohen’s d=1.70; RH: t(13)=2.77, p=0.015, Cohen’s d=0.77—two-tailed t-tests against zero), replicating prior work. One possibility is that this navigational affordance coding in OPA simply reflects responses to low-level visual features that are inherited from antecedent visual areas like V1. To account for this, we calculated the correlation between the OPA and navigational affordance RDMs in each hemisphere, partialling out the RDM computed from V1. The evidence for navigational affordance coding in OPA remained even after partialling out the V1 RDM in the left hemisphere (t(13)=4.83, p<0.001, Cohen’s d=1.33—two-tailed t-test against zero), but not the right hemisphere (t(13)=2.05, p=0.060, Cohen’s d=0.57—two-tailed t-tests against zero). Consistent with prior work13 we also tested whether the OPA RDMs correlated with low-level image statistics derived from the Gist model that describes the orientation and spatial frequency content of images25. Importantly, although the OPA RDMs also correlated significantly with one derived from Gist model descriptors25–27 in both hemispheres (Fig. 1C) (LH: t(13)=3.64, p=0.003, Cohen’s d=1.01; RH: t(13)=3.12, p=0.008, Cohen’s d=0.86 – two-tailed t-tests against zero), these were found not to survive when considering the contribution of V1 (LH: t(13)=0.42, p=0.678, Cohen’s d=0.11; RH: t(13)=1.22, p=0.241, Cohen’s d=0.34—two-tailed t-tests against zero). These data replicate evidence for the coding of navigational affordances in OPA, particularly in the left hemisphere13.

Navigational affordance coding within retinotopic subdivisions of OPA
OPA spatially overlaps at least five separate retinotopic maps (LO1, LO2, V3A, V3B and V7) to differing degrees15,16, which raises the question as to whether it should be considered functionally homogeneous. Recently, we showed that the retinotopic subdivisions of OPA do not represent scenes equally, but rather the similarity in their responses is driven largely by the similarity in their visual field representations. That is, maps that are more retinotopically similar (e.g. LO1 and LO2) respond to scenes more similarly16. Given this, we next assessed the unique contribution to navigational affordance coding across the retinotopic subdivisions of OPA (Fig. 1D). We were unable to define all five OPA subdivisions in each participant and hemisphere. To account for this, we made use of a probabilistic atlas28 to define retinotopic ROIs which were constrained to contribute an equal number of nodes (see ‘Methods’). Within each map, an RDM was computed based on the pairwise dissimilarity in responses (as above) and these map-specific RDMs were then correlated with the same navigational affordance RDM13. Given prior work suggesting that the majority of variance in navigational affordance coding comes from information in the lower visual field17 we predicted higher correlations in retinotopic maps with a more prominent lower visual field bias (i.e. LO1/LO2) rather than those with either hemifield (V3A/V3B) or more upper visual field representations (i.e. V7). To quantify this effect, the map-specific RSA correlations were submitted to a linear mixed model with Hemisphere (LH, RH) and ROI (LO1, LO2, V3A, V3B and V7) as fixed-effects. Participant was modelled as a random factor. We observed significant main effects of Hemisphere (F(1, 108)=30.95, p<0.001) and ROI (F(4, 108)=3.41, p=0.010) but no Hemisphere by ROI interaction (F(4, 108)=1.13, p=0.345). On average, correlations were higher in the left than right hemispheres (t(108)=5.56, p<0.001, Cohen’s d=2.25) and post-hoc comparisons (Bonferroni corrected) revealed only two significant differences with correlations higher in V3A and V3B compared to V7 (V3A versus V7: t(108)=2.93, p=0.040, Cohen’s d=0.11; V3B versus V7: t(108)=3.18, p=0.019, Cohen’s d=0.08; p>0.05, in all other cases). We next tested each map-specific RSA correlation against zero (i.e. no correlation assumption) via one-sample t-tests (Bonferroni corrected). A significant positive correlation was observed for LO1 (t(13)=4.83, p=0.002, Cohen’s d=1.39), LO2 (t(13)=3.08, p=0.040, Cohen’s d=0.89), V3A (t(13)=3.86, p=0.011, Cohen’s d=1.11), V3B (t(13)=4.36, p=0.004, Cohen’s d=1.26), but not V7 (t(13)=1.32, p=0.210, Cohen’s d=0.38).

Temporal coding of navigational affordances
We next sought to examine the temporal profile of navigational affordance coding using MEG. Here, RDMs were first constructed from the dissimilarity in the pairwise responses across all MEG sensors at each time-point (see ‘Methods’) before computing the correlation (Spearman’s rho) between (1) the MEG and navigational affordance RDMs and (2) the MEG and navigational affordance RDMs partialling out the contribution of the gist RDM (Fig. 2A, B). Time points with RSA correlations above zero were assessed using Bayesian t-tests29–32 (for comparison we also include significant time points identified via a frequentist approach on this and all subsequent time-dependent figures, see ‘Methods’). We note that overall, the two approaches identify largely equivalent time points (although there are certain cases in which Bayesian evidence for the alternative hypothesis is not identified via a frequentist approach, see ‘Methods’).

As shown in Fig. 2A, there were at least two distinct time-points with moderate to strong Bayesian evidence (BF10 > 3) for a correlation between the MEG and navigational affordance RDMs. An early and relatively transient period peaking ~137ms post stimulus onset and a later and more sustained period between ~269–381ms post stimulus onset. Interestingly, the Bayesian evidence for the early transient period weakens when partialling out the contribution of the Gist RDM, but the later more sustained period remains, albeit for a slightly later and narrower time window ~302–389ms post stimulus onset (Fig. 2B).

Next, we examined the temporal coding of responses within OPA specifically. To achieve this, we employed MEG-fMRI fusion analyses23. The aim of this analysis was to identify when in time the representational structure of responses in OPA best matched the representational structure in the MEG time course (see ‘Methods’). We calculated the correlation (Spearman’s rho) between each participant’s OPA RDM (derived from fMRI data) with that participant’s MEG RDM at each time point, before averaging across participants (Fig. 2C, D). As above, we assessed time points with fusion correlations above zero using Bayesian t-tests, but also identify significant time points via a frequentist approach for the interested reader. In the left hemisphere, we observed moderate to strong Bayesian evidence (BF10 > 3) for MEG-fMRI fusion during two early and relatively transient time-windows, peaking ~118ms and ~246ms post-stimulus onset. A later and more sustained period of fMRI-MEG fusion was also present between ~754–980ms post-stimulus onset (Fig. 2C). In general, evidence for MEG-fMRI fusion was weaker in the right hemisphere, but was nevertheless present for both an early (~113ms post stimulus onset) and a late (~806–863ms post stimulus onset) time window (Fig. 2D).

Whole-brain searchlight analyses
Thus far, our analyses have largely considered the spatiotemporal pattern of responses in OPA and its retinotopic subdivisions. To ascertain whether regions outside of OPA also contribute to the coding of navigational affordances we implemented a searchlight approach33. For each sphere (6mm radius around each voxel) we computed the correlation between the fMRI RDM and navigational affordance RDM whilst partialling out the contribution of the Gist RDM (see ‘Methods’). Prior work implementing a searchlight approach on fMRI data in response to the same stimuli used here reported evidence for navigational affordance coding in the vicinity of OPA in the right hemisphere, but not the left hemisphere13.

In our data, whilst we do observe evidence for navigational affordance coding within the left hemisphere OPA, the peak of navigational affordance coding was shown to correspond to dorsal early visual cortex (V1d/V2d/V3d), respectively in both hemispheres (Fig. 3A). Indeed, the spatial extent of significant navigational affordance coding in the right hemisphere approached but did not overlap with our group-based OPA ROI. Importantly, a defining feature of dorsal early visual cortex is an explicit representation of the lower visual field34, where the vast majority of variance in navigational affordance coding reportedly comes from17.

We conducted two further analyses to rule out the possibility that this lateralisation was due to systematic leftward biases in path trajectories. First, we have calculated the mean leftward frequency for all 50 scenes in the Bonner and Edinburgh replication datasets. The difference in mean leftward angular frequency across scenes was statistically non-significant (t(49)=2.00, p=0.056). This result suggest that unequal sampling of path trajectories is unlikely to explain the differences in lateralisation of the fMRI searchlight data between the two studies. Next, we compared the left versus right trajectories in the Edinburgh replication dataset to rule out a potential trajectory bias in this dataset alone. We calculated how many of the 50 scenes contained trajectories in only one half of the image (i.e. either all left or all right). None of the 50 scenes met this criterion. This indicates that in all 50 scenes path trajectories were in both left and right halves of the image. Given this, we next calculated the mean angular frequency in the left half or right halves of each scene. Such an analysis provides a means to test for a leftward or rightward bias in path trajectories. A paired t-test indicated the difference in mean angular frequency between left and right halves of the scenes to be statistically non-significant (t(49)=1.10, p=0.274).

To better visualise the magnitude of navigational affordance coding as a function of visual field position we combined the searchlight analyses with population receptive field (pRF) data (Fig. 3A middle). We first sampled all cortical nodes with significant evidence for navigational affordance coding (t-value>3.75) and extracted the x, y centre positions of those nodes from the group-average pRF data. The visual field positions of these nodes were then visualised and colour-coded according to the searchlight strength (t-value). In line with prior computational modelling17, these data indicate that more of the variance in navigational affordance coding is explained by nodes whose pRFs represent the lower rather than upper visual field. To quantify this effect further, we computed an elevation bias in each participant, by taking the average searchlight t-value for pRFs with x, y centre positions in the upper visual field (UVF) minus the lower visual field (LVF), respectively (Fig. 3B). These biases were then tested against zero (no bias assumption) using paired t-tests. In both hemispheres, significantly more of the variance in navigational affordance coding was present in pRFs with centres in the LVF (LH: t(13)=12.01, p<0.001, Cohen’s d=3.46; RH: t(13)=6.71, p<0.001, Cohen’s d=1.93).

Temporal pattern of MEG-fMRI fusion for dorsal early visual cortex
The searchlight peaks within dorsal early visual cortex suggest that a likely cortical locus for navigational affordance coding might be earlier in the visual hierarchy than the OPA, despite evidence for OPA’s involvement. If responses in dorsal early visual cortex code navigational affordances, then one might predict to see strong and early MEG-fMRI fusion when considering these regions specifically. As a confirmatory step, we applied the same MEG-fMRI fusion analyses above, but now using the fMRI-derived RDMs from V1, V2d and V3d defined using participant-specific pRF mapping data (Fig. 4A–C). It is worth noting that this was not a pre-specified hypothesis, but more a confirmatory check. As above, we primarily assessed the strength of MEG-fMRI fusion using Bayesian t-tests, but also include a frequentist analysis for comparison. In general, a similar pattern of MEG-fMRI fusion was evident across all three ROIs. In V1, we observed moderate to strong Bayesian evidence (BF10 > 3) for MEG-fMRI fusion during an early and sustained period in the left (~100–480ms, Fig. 4A) and right hemispheres (~100–450ms, Fig. 4B). A later period of MEG-fMRI fusion was also present between ~780–980ms post-stimulus onset in the left hemisphere that was not present in the right. In V2d, we observed moderate to strong Bayesian evidence (BF10 > 3) for MEG-fMRI fusion during an early and sustained period (~100–400ms) in both the left (Fig. 4C) and right hemispheres (Fig. 4D). Similar to V1, a later period of MEG-fMRI fusion was also present between ~780–980ms post-stimulus onset in the left hemisphere that was not present in the right (Fig. 4D). A similar pattern was observed in V3d, with moderate to strong Bayesian evidence for an early and sustained period of MEG-fMRI fusion (~100–400ms) in both the left (Fig. 4E) and right hemispheres (Fig. 4F). A later period of MEG-fMRI fusion (~880–970ms) was present in both the left and right hemispheres (Fig. 4F), The MEG-fMRI fusion timecourses are not themselves directly related to the behavioural operationalisation of navigational affordances. To make such a link, we also conducted a commonality analysis between the MEG-fMRI data and the navigational affordance RDM (Fig. 5A, B).

Temporal coding of navigational affordances in V2d, V3d and OPA
Broadly speaking, the MEG-fMRI fusion data reported above identified two time-windows where the representational structure of the fMRI responses in each ROI best matched the representational structure of the MEG responses; an early time-window (~100–450ms) and a later time-window (~800–1000ms). Importantly, the MEG-fMRI fusion analyses do not relate specifically to the representation of navigational affordances. To quantify the unique variance attributable to navigational affordances a commonality analysis was implemented as in ref. 35 (see ‘Methods’). Figure 6 depict the commonality time series for V2d, V3d and OPA in both hemispheres, respectively. A similar pattern is evident across hemispheres with unique variance attributable to the navigational affordance model emerging in two stages within the first 500ms post stimulus onset. An early stage emerging ~75–100ms, followed by a slightly later stage ~200–400ms post stimulus onset. Notably, unlike the MEG-fMRI fusion analyses, which indicated both early and late time-windows, the unique variance attributable to the navigational affordances model appears to be restricted to the stimulus presentation window (i.e. 0–500ms).

Speed of navigational affordance coding
Taken at face value, the MEG-fMRI fusion and commonality analyses would suggest that the time needed to extract navigationally relevant information from scenes in order to determine efficient routes of egress may, in fact, be surprisingly short. To test this prediction, we ran a behavioural experiment in which we systematically varied the presentation time of each scene across six timing conditions (unrestricted, 33ms, 66ms, 132ms, 264ms and 528ms). In each condition, separate groups of participants (n=12) viewed each scene before indicating the available navigational routes. These path trajectories were analysed according to the approach described previously13 (see ‘Methods’). In brief, path trajectories were summed across participants before being converted into angular histograms (Fig. 6A). A pairwise dissimilarity RDM of angular histograms was then computed. Initially, we sought to compare the original navigational affordance RDM13 with our baseline replication RDM (unrestricted condition). A strong positive correlation was observed between the two RDMs (r=0.63, Fig. 6A). This result provides reassurances that our unrestricted condition captures navigational affordance behaviour in a similar way to that reported previously13.

Next, RDMs summed across participants were computed for each timing conditon (Fig. 6B). To assess the relationship between conditions we computed the pairwise dissimilarity (1-Pearson’s r) between these time-dependent RDMs (Fig. 6C). From the resulting matrix, three general patterns emerged. First, the representation of navigational affordances elicited at 33ms was very different from all other conditions with the difference increasing as a function of presentation time. Second, the representation of navigational affordances elicited at 66ms remained different from all other conditions, albeit to a lesser extent than the 33ms condition. Third, and most crucially, the representations elicited at 132ms, 264ms and 528ms were highly similar to each other. This pattern suggests that robust navigational affordance representations can emerge from presentation times of 132ms and beyond.

Next, we asked how these time-dependent navigational affordance representations related to both our unrestricted baseline RDM and, for completeness, the original RDM from Bonner et al.13 To assess the similarity between each time-dependent RDM and our baseline RDM we implemented a bootstrapping analysis. For each timing condition, we randomly shuffled the RDM values before computing the correlation between the baseline RDM and shuffled RDM (repeated 10k times). The resulting distributions represent the relationship between the baseline and time-dependent RDMs one might expect by chance. The observed correlation between the baseline and each time-dependent RDM was then compared against the 95th percentile of the null distributions. The observed correlation fell to the right of the 95th percentile of the null distribution in each timing condition (Fig. 6D). In accord with the relationship between time-dependent conditions mentioned above (Fig. 6C) the correlation with our baseline RDM increased rapidly from weak at 33ms (r=0.06), to strong and robust at 132ms and beyond (132ms: r=0.55, 264ms: r=0.56; 528ms: r=0.56).

For completeness, we also performed the same analysis, but now comparing the observed correlation between the original navigational affordance RDM of Bonner et al.13 against the time-dependent shuffled distributions (Fig. 6D). Only at the earliest time point (33ms) did the observed correlation fall to the left of the 95th percentile of the null distribution. In all other conditions, the observed correlation falls to the right of the 95th percentile of the null distribution and becomes strong and robust at 132ms and beyond. Taken together, we observed strong evidence that the visual information relevant for navigational affordances can be extracted robustly with as little as 132ms of stimulus presentation. The significant correlations present with briefer stimulus durations (33ms and 66ms) should be treated with caution as these navigational affordance representations were shown to be less robust than those elicited at longer durations.

Methods
The ethics committee at York Neuroimaging centre at the University of York approved the fMRI and MEG experiments. Ethical approval for the behavioural experiment was provided by the School of Philosophy, Psychology and Language Sciences ethics committee at the University of Edinburgh.

Participants
14 (10 female) participants with normal or corrected-to-normal vision and a mean age of 24.43 (±4.85) years were recruited for the fMRI and MEG experiments. All participants took part in ~2h of fMRI sessions and 1h of MEG at the York Neuroimaging Centre. The ethics committee at York Neuroimaging centre at the University of York approved this experiment. 72 (52 female) participants with normal or corrected-to-normal vision and a mean age of 28.51 (±4.10) were recruited for the navigational affordance behavioural experiment. All participants completed the task in one condition only (n=12 per condition). Ethical approval was provided by the School of Philosophy, Psychology and Language Sciences ethics committee at the University of Edinburgh. All participants provided informed consent and were compensated monetarily for their participation.

Visual stimuli and tasks
Across all experiments (fMRI, MEG and Behaviour) visual stimuli were presented using PsychoPy. The delivery system used for the visual stimulus in fMRI scans was a ViewPixx projector which projected the stimulus onto a custom-made acrylic screen. The participant viewed the screen with a mirror fixed to the head coil in the scanner. In MEG the stimuli were presented using a ViewPixx Projector which projected the stimulus onto a screen in the MEG suite. Visual stimuli for the behavioural experiment were presented via a HP EliteDesk monitor (resolution: 1920×1080 pixels; refresh rate: 60Hz; visible display size: 531.36mm×298.89mm). We did not receive reports of dropped frames during the experiment. Post data collection we nevertheless tested for dropped frames using the in-built timeByFramesEx.py module within PsychoPy. Out of a sample of 500 frames none were detected as dropped (mean refresh rate=16.67ms, standard deviation=0.03ms).

Navigational affordance stimuli fMRI
50 colour images of indoor scenes identical to those used in experiment two of Bonner and Epstein13. The navigational affordance of each scene was determined by the previous researchers, who recruited 11 participants to draw the potential routes of egress through each scene. The navigable paths of each scene were summed across participants into a heatmap of responses. The 50 scenes used in both the current and the previous study were selected so that the correlations between navigational affordance models and visual models of gist were not reliably greater than chance. For more information on stimuli generation and selection see ref. 13. Each scene consisted of 1024×768 pixels and subtended 17×13 degrees of visual angle. During fMRI scans, each image was shown for 1.5s with a 2.5s inter stimulus interval. Participant took part in 10 runs which each lasted 4min and 16s. Each of the 50 scene stimuli were shown in every run. Five catch trials (bathroom images) were also randomly presented in each run and the participant was instructed to press a button when a bathroom was presented.

Navigational affordance stimuli MEG
Participants were presented with the same 50 scenes as used in the fMRI navigational affordance experiment. Each scene was presented for 500ms followed by a 800–1000ms ISI. Every stimulus was presented eight times in a run, for five runs, leading to 40 presentations per stimulus. Each run lasted 10min 16s. 13 participants took part in the experiment as described, one participant took part in ten runs of half the length per run, leading to the same number of presentations per image.

Behaviour
Participants across all conditions were presented with the same 50 scenes as used in the fMRI and MEG experiments. Following a variable inter-trial-interval (1–1.5s), a scene was presented briefly (either 33, 66 or 132ms) followed by a dynamic mask (5×50ms). Post mask, the outline of the image remained in red and participants were asked to draw the available routes of egress. The unrestricted condition was similar apart from the lack of a dynamic mask as the scene remained visible throughout the path drawing phase. The start position of the mouse was fixed on each trial to be the centre and bottom of the scene (unrestricted condition) or scene outline (time-dependent conditions). Mask stimuli were created by first dividing each scene (768×1024 pixels) into 64 equal sized rectangles (96×128 pixels) before shuffling rectangles both within and across scenes.

Model comparisons
Our initial analyses relied upon the navigational affordance RDM reported for experiment two of Bonner and colleagues13. Additionally, we also computed image statistics using the gist descriptor (http://people.csail.mit.edu/torralba/code/spatialenvelope/) to capture the low-level features of each scene. For each scene, a vector composed of 512 values was created by passing each scene through a series of Gabor filters across four spatial frequencies and eight orientations. The resulting vector characterises the image in terms of the spatial frequencies and orientations at different spatial locations within the image. We computed the pairwise Euclidean distance between vectors to produce an RDM across stimuli.

Scene localiser fMRI
In a block design, participants were presented with images of faces and scenes (different from those used in the navigational affordance experiment), with blocks lasting 16s. Each block consisted of 20 images presented for 300ms followed by 500ms fixation. There were a total of 18 blocks per run. Participants performed a one-back task (button press when same image presented twice in a row). Each run lasted for 5min and 20s and each participant completed two runs.

pRF mapping fMRI
Sweeping bars moved in 8 bar aperture directions which revealed random scene fragments. Each sweep took 36s to traverse a 17 degree diameter circular aperture in 18 steps. For each step, 5 images were rapidly displayed (2.5Hz) out of a possible 90 without repeating, so each possible image was displayed once per sweep. Participants performed a colour change detection task at fixation, indicated with a button press. Each run lasted for 5min 20s and was repeated four times per participant. This procedure is described in detail in other work14.

Data acquisition
MRI/fMRI. A single, high resolution, anatomical, T1-weighted scan (TR, 2500ms; TE, 2.26ms; TI, 900ms; voxel size, 1×1×1mm3; flip angle, 7; matrix size, 256, 256, 176, total acquisition time, 306s) was acquired for each participant. All functional scans consisted of 48 multiband-multiecho EPI interleaved slices (acceleration factor=2, TEs=14.6, 32.83, 51.06ms) within a FOV of 240×240mm with 2.7mm isotropic voxels (TR=2000ms, flip angle=150°).

MRI/fMRI preprocessing. MRI scans were preprocessed using AFNI41, freesurfer42 and SUMA43. During preprocessing, dummy volumes were first removed from the start of each run (AFNI 3dTcat). Large deviations in signal were removed (3dDespike) followed by slice time correction (3dTshift) aligning each slice with a time offset of zero. The skull was removed from the first echo 1 scan (TE=14.6ms) and used to create a brain mask (3dSkullStrip and 3dAutomask), as this echo contained the most signal. The first echo 2 scan (TE=35.79ms) was used as a base for motion correction and registration with the T1 structural scan (3dbucket), as this echo was the most similar to standard EPI acquisition. Motion parameters were estimated for the echo 2 scans (3dVolreg) and applied to the other echos (3dAllineate). After completing the standard preprocessing, the data were processed using Tedana44–47 to denoise the multi-echo scans (version 0.0.12, using default options). Tedana optimally combined and denoised output was then scaled. To do this, we divided the signal in each voxel by its mean value and multiplied the signal by 100 (3dTstat and 3dcalc). This means that the fMRI values can be interpreted as a percentage of the mean signal, and effect estimates can be viewed as percentage change from baseline48. For the pRF scans, an average was then calculated across runs to leave a single time series for further analysis. Freesurfer reconstructions were estimated from the T1 anatomical scans (recon-all), and the output used to create surfaces readable in SUMA (SUMA_Make_Spec_FS). The SUMA structural was then aligned to the Session 1 experimental structural to ensure alignment with the functional images (SUMA_AlignToExperiment). Surface-based analyses were conducted using the SUMA standard cortical surface (std.141).

MEG acquisition and preprocessing. MEG data was recorded on a 248-channel 4D Neuroimaging Magnes 3600 MEG with electronics upgraded by York Instruments Ltd. Data was acquired at a sampling rate of 1001Hz with reference channels used to reduce contributions from external noise. Data pre-processing was performed using MNE-Python and consisted of band-pass filtering between 0.05 and 500Hz, and notch filtering power source noise at 50Hz and harmonics up to 250Hz. The time-series data was divided into epochs from 200ms prior to stimulus onset through to 1200ms post-onset, and the epochs were demeaned with respect to the 200ms pre-onset period. Bad channels were automatically detected using the Maxwell filtering utility implemented in MNE-Python, this resulted in a mean exclusion of 5.4±1.2 channels across all recordings.

MRI analysis
Regions of interest. To localise the OPA, a general linear model was estimated for the scene localiser scans using a block design with a 16-s Gamma basis function (GAM: 8.6, 0.547, 16, 3dDeconvolve and 3dREMLfit). The output of the model was then projected onto the SUMA cortical surface (3dVol2Surf), and smoothed with a FWHM of 2mm (SurfSmooth). OPA ROIs were drawn manually for each subject on the surface (SUMA draw ROI) after thresholding the contrast of scenes versus faces at t>3.5 (height defining threshold, P<0.0001, uncorrected). These participant-specific data were applied to a group-level analysis (3dttest++) in order to define group-level ROIs in both hemispheres. Population receptive fields were estimated using AFNI’s non-linear fitting algorithm (3dNLfim) and the Gamma (GAM) basis function14. The outputs were used to delineate subject-specific V1 ROIs which were drawn manually on the SUMA standard (std.141) surface using the polar angle and eccentricity estimates. Retinotopic maps subdividing OPA (LO1, LO2, V3A, V3B and V7)15,16 were taken from a probabilistic atlas28. To equate ROI size across maps we selected the top 100 nodes in all ROIs. All ROIs (OPA and retinotopic maps) were converted into 1D files (ROI2dataset) to facilitate future node selection.

Navigational affordance coding. The activity associated with each stimulus in the navigational affordance scans was deconvolved using a GAM basis function aligned to the onset of each stimulus. All runs were modelled together, and each stimulus regressor included ten onsets. The data within each participant-specific ROI was then extracted for analysis in MATLAB (ConvertDset) using an RSA framework24. The t-values for each stimulus at each node in the ROI were taken from the event-related general linear model (GLM) output. The pairwise dissimilarities between stimulus t-values were then calculated in MATLAB (1—corr, Pearson, v.2021a MathWorks). To quantify the degree of navigational affordance coding in each ROI, we computed the Spearman’s correlation (rho) between the navigational affordance RDM and each ROI RDM. To account for the influence of V1 on the observed correlations we employed partial correlation, partialling out the V1 RDM.

Searchlight analyses were performed in Matlab using CoSMoMVPA33 and the regress_dsms function. For each sphere (6mm radius around each voxel) we computed the correlation between the fMRI RDM and navigational affordance RDM whilst partialling out the contribution of the Gist RDM. We used centreing of the RDM and converted the correlation values to Fisher’s z scores for group level analysis. Volumetric searchlight analyses were submitted to whole-brain voxel-wise t-tests of random effects across subjects before being projected into surface space in SUMA (std.141).

MEG analysis. Correlation matrices were generated for each MEG recording run by calculating the Pearson correlation between the pre-processed sensor activity vectors from all pairs of stimuli, at each time sample. Relative dissimilarity matrices were generated by subtracting the resulting correlation values from 1.0, to yield an N-stimuli square matrix for each time sample and each run. The RDMs were averaged across runs by calculating the element-wise mean. This approach was adopted to minimise the impact of head movement between runs.

MEG-fMRI fusion. We implemented MEG-fMRI fusion using a representational dissimilarity analysis framework23. The aim of this approach is to identify when in time the representational structure across a set of stimuli in a given fMRI ROI is matched in the MEG time course. One benefit of this RSA fusion approach is that the signal is extracted away from the primary MEG and fMRI data formats, which vary considerably in their respective spatial and temporal resolutions49. It is worth noting a downside to this approach; if two fMRI regions represent a stimulus set in a very similar way then they will have correlated similarity matrices and therefore similar fusion time series, even if this representation emerged at different points in time. However, it is generally accepted that information travelling across regions is transformed non-linearly, meaning that the representational format will likely differ across the brain23. To calculate the similarity between the stimulus representation across time and space, we then correlated (Spearman’s rho) the MEG RDMs at each time point with the corresponding fMRI RDM for each ROI in each hemisphere. Time points with fusion correlations above zero were assessed using Bayesian t-tests. This was implemented using a half-Cauchy prior with a default width of 0.707 and a range of 0.5 to increase the detection of small effects under the null hypothesis50.

Permutation testing of time-resolved data. Our primary approach for assessing the strength of evidence of time-resolved effects used a Bayesian approach. We note, however, that frequentist approaches are also commonly used and so for completeness we also implemented a permutation-based approach for all time-resolved analyses. Correction for multiple comparisons was implemented in CoSMoMVPA (cosmo motecarlo cluster stat33) using threshold-free cluster-enhancement51–53. For each analysis we ran 10,000 permutations. For a one-sample t-test and an alpha of 0.05, z-score values greater than 1.65 indicate that the statistic is significant. As we allow for clustering over time, we can make inferences at the cluster level rather than at individual time points. A similar approach was adopted for the MEG-RSA analysis taking into account the gist model, for the MEG-fMRI fusion analyses and the commonality analysis. We emphasise here that our approach for assessing the strength of evidence of time-resolved effects used a Bayesian approach. Comparing the results of Bayesian and frequentist approaches can be difficult. For example, it is not clear to us how one should interpret a result if an effect is present in one framework (e.g. Bayesian) but not another (e.g. frequentist). As such, our results are discussed with reference to the Bayesian statistics, but frequentists statistics are also provided for the interested reader.

Commonality analysis. We implemented a commonality analysis following the approach described previously35. Under this framework, for each ROI, we compared the coefficients of determination (R2) at each time point between (1) the group-averaged MEG and fMRI RDMs and (2) the group-averaged MEG and fMRI RDMs when partialing out the navigational affordance RDM. In order to determine significant time-points we implemented the cluster based analysis described above. For every time-point we randomly shuffled the MEG RDM (1000 times) before calculating the correlation (Spearman’s rho). We computed the difference between these two coefficients of determination (for the observed and permuted data) and for each timepoint we assessed whether the observed correlation was greater than the 95th percentile of that time points permuted null distribution. We considered windows with more than 3 contiguous time points to be significant.

Behaviour. Analysis procedure was identical for all conditions and followed the approach outlined previously13. Analyses were conducted in Matlab and R (RStudio Version 2024.12.0). The x, y pixel locations of path trajectories for each scene were extracted for each participant. Next, trajectories were spatially blurred (imfilter) before being summed across participants for each scene. Summed heat maps were converted into angular histograms by calculating the summed value in each of 180 angular bins. Angular histograms were then normalised (zscore) and smoothed (smoothn). Finally, RDMs were created by calculating the pairwise dissimilarity in the squared euclidean distance. To create the null distributions for each timing condition, we first randomly shuffled the time-dependent RDM before computing the correlation (Pearson’s r) between the shuffled RDM and our replication RDM. This procedure was repeated 10,000 times creating a null distribution of expected correlations by chance. The same procedure was performed to compare the time-dependent RDMs with the original navigational affordance RDM from Bonner and colleagues13.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript uses fMRI/MEG fusion plus a behavioral experiment to examine the neural representation of navigational affordances. The fMRI work replicates the results of Bonner & Epstein (2018), showing OPA involvement in coding navigational affordances. The MEG work shows sustained navigational affordances after 270 ms. The fusion analysis shows V2d/V3d and early (33-66 ms) coding of navigational affordances. A behavioral student presented duration-limited scenes (33.3 ms-132 ms) and asked participants to trace a navigational path. Observers were able to do this with brief presentation times for the most part.\nThis is a timely paper that examines this problem from multiple perspectives. The involvement of V2d/V3d is novel, and I appreciate the authors' explicit replication of previous work. I am not an expert in the fMRI methods, so I will not comment on this aspect of the paper.\nWhile I feel positively towards this paper, I have two major concerns with the behavioral experiment. The first is logical. As described in the paragraph that starts on line 277, the authors' motivation for this experiment is to verify the rapid coding of navigational affordances revealed by the MEG data. This risks conflating presentation duration with precessing time. Presenting a scene for 33.3 ms and finding that an observer can assess the navigational paths is not the same as the navigational paths being coded in 33.3 ms as the MEG shows. I recommend exampling the paper from Van Rullen (2011) on this fallacy. This interpretation also shows up in lines 309-310 (\"Taken together, we observed strong evidence that the visual information relevant for navigational affordances can be extracted in the briefest of glances (~33-66ms)\" I would also urge the authors to be more careful in this statement, given the lack of statistical robustness in the earliest presentation time.\nMy second issue with the behavioral experiment is technical. On lines 568-569 the authors note that the display was \"HP EliteDesk monitor (resolution: 1920 × 1080 pixels; refresh rate: 60 Hz; visible display size: 531.36 mm × 298.89 mm).\" This means that in the fastest condition, images were on screen for two monitor refreshes. Dropped frames can be very common on PsychoPy (especially on Mac computers, but I have observed it across Linux and Windows machines, too). A dropped frame during a scene presentation would create 50% error and could be a driver of the 33.3 ms condition not showing statistical significance.\nAs neat as this behavioral paradigm is, the logical and technical issues lead me to believe that this would be a stronger paper without this experiment.\nReferences:\nVanRullen, R. (2011). Four Common Conceptual Fallacies in Mapping the Time Course of Recognition. Frontiers in Psychology, 2. https://doi.org/10.3389/fpsyg.2011.00365", "reviewer_2": "This paper investigates a concept that has not been extensively studied despite it being very relevant for all motile animals and decades old: affordances. The paper does an in depth study on where affordances are processed in the brain, and the Authors present a clear manuscript with the use of cool approaches and analyses. I have a couple questions/comments (in order they came up while reading the manuscript):\n1. Would you predict that species that may be more tuned into upper visual field (eg: flying/underwater animals, especially eg: killer whales or other marine hunters who find prey above them) may have different affordances? Different representations in the brain?\n2. Is action without locomotion the same as affordances?\n3. Why left hemisphere effects only? Could this be related to the Figure 5A middle plot of behavioural trajectories which seem to show few turns to the left in your dataset?\n4. Why would LO12 effect not be stronger if the argument being made is about lower visual fields' importance in affordance coding?\n5. Why is the reasoning behind using all MEG sensors?\n6. Why not check significant time periods with cluster based stats (which in my opinion are more common for M/EEG analyses) instead of the Bayesian factor?\n7. Please include an image of OPA sub-areas tested (LO1, LO2, V3A, V3B & V7) to compare to Figure 3.\n8. Figure 5b timing in the image is different from what is in text?\n9. How was the accuracy of extraction of path trajectories established?\n10. How do the differences in timing in Bonner (2sec) change the comparisons?\n11. Because the earliest time of the effect in MEG is ~100ms and not not <66ms, I don't think it's fair to say that the neural effects match the behavioural speed of extraction (and see point 9 above - what does effect at 33ms really mean)?\n12. This is just personal but I don't really like the abbreviation \"NA\" for navigation affordance coding (Figure 1), it's just a little confusing because NA is commonly \"not applicable\"\n13. I am not sure I agree the differences found between Bonner and there are from the fMRI preprocessing, but more likely to be related to performance and/or timing (point 10 too). Also just to be clear, when it is stated that the \"behavioral RDM from Bonner et al [11]\" you are referring to Fig 2 in the paper.", "reviewer_3": "Zamboni et al. conducted a study on the coding of navigational affordances in the human brain. For this they combine fMRI, MEG and behavioral data. Their two main claims are that cortical regions V2d/V3d prior to OPA already code for navigational affordances, and that navigational affordances can be extracted from images even with brief glances lasting as little as 66ms. This is an interesting and thought-provoking study, which adds substantially to the literature on navigational coding. It builds a strong foundation by first replicating previous work before moving on to novel analyses.\nI have several questions the answers to which should enable me to better determine the strengths of the empirical evidence. I also have suggestions for further analyses and clarifications that may strengthen the manuscript even further.\n1) I am uncertain what the exact claims of the authors are about the relationship between the two major claims they make (see above). The abstract reads ”…we demonstrate that navigational affordances can be extracted within 33-66ms, consistent with navigationally relevant signals emerging early in visual processing.”. How is that consistent or inconsistent? A strong interpretation would be that being extractable in a brief glance is a counterpart to signals emerging early in visual processing – but are these not unrelated measures? In the discussion instead those two topics seems to be discussed in a fashion not indicating a close connection. So what is the stance of the authors?\n2) The results section jumps right in with the first analysis without describing the experimental design and task in any detail. The authors should add a paragraph on what actually happened: what was the paradigm, what was the task, what kind of data was measured to guide the reader.\n3) It is confusing that some of the region definitions that are possible based on retinotopic mapping are based on participant-specific pRF estimates, and some are not, instead relying on anatomical atlases. Those atlases can only be less precise than participant-specific mapping. This is important when for example differentiating OPA from more posterior regions. For the claims of the manuscript this is not a major downside as arguably, even with better localization, there would be no other regions posterior to OPA than V2/3d. However, the limitation should be acknowledged and possible implications discussed.\n4) l. 134 and before. The emerging results pattern is complex. What do the authors finally conclude from the results? Please clarify.\n5) Assessing the temporal coding of navigational affordances the authors make a point differentiating early from late responses. I suggest a temporal generalization analysis to determine the relationship between the representations at earlier versus later time points, across analyses in the manuscript.\n6) Based on the results reported in Fig. 2D the authors claim that navigational affordances are already coded at the border of V23d. For one, I am uncertain about whether the expression “at the border” is justified, given that we are dealing with group-average anatomically determined borders. Second, there are also strong effects posterior to V2, in V1, or, using the authors’ terms, at the border of V1 and V2d (especially in the right hemisphere. How do the authors interpret this? I also suggest that the authors add a MEG-fMRI fusion analysis for V1.\n7) Figure 4 reports strong effects after stimulus offset. How is this to be interpreted?\n8) The authors conduct fusion analysis and RSA to navigational affordance RDM separately. I suggest combining those two approaches, using commonality analysis.\n9) The authors have behavioral data that allows the construction of navigational affordance RDMs for the different image presentation durations. Interestingly, the images were followed by a dynamics mask, making this a backward masking paradigm that has been associated with blocking recurrent processing. As the authors speculate that the early versus late observed correlations to the original navigational affordance RDM to depend on feedforward versus recurrent processing, these additional behavioral RDMs allow testing predictions of that hypothesis (e.g. short duration RDMs should correlate with neural responses early, not late, etc.).\n10) There is an inconsistency across analyses, and with respect to previous results, as to whether experimental effects occur in left OPA, right OPA, or both. The authors speculate in the discussion that this might be due to SNR differences related to single versus multishot EPI sequences used. I do not understand this. How would a difference in SNR affect lateralization. Is it not simply that the power of both the previous and the current study is simply not sufficient to exclude likely false negatives?"}, "author_response": "We thank all three reviewers for their positive and insightful comments. We have taken these comments on board and made substantial changes to the manuscript. Below we provide a point-point response and all changes to the revised manuscript are highlighted in blue. [Author response continues with detailed point-by-point replies to Reviewers 1, 2, and 3]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the care the authors have put into the revision. They have adequately addressed my concerns.", "reviewer_2": "Thank you for the detailed responses. The manuscript is much improved.\nI am still not sure I understand, or agree, about the laterlization issue. If the trajectories are indeed biased to go more to the right than left (Fig 6A), and if the accuracy of the trajectories is not checked (whether they match Bonner or not is not equivalent to participants accurately labelling possible routes of egress, moreover Bonner data shows similar amount to the left and right, unless I am not understanding the plot), I cannot see how the neural effects aren't just biased by unequal sampling.", "reviewer_3": "The manuscript has improved considerably. The authors have provided additional reader guidance, acknowledged limitations (e.g. ROI definition styles), and clarified results patterns. They provided additional empirical data with presentation times of 264 and 628ms, which provided relevant context, and led the authors to a more balanced interpretation of their results.\nConcerning the previous point 5, I suggest a commonality analysis with 3 variables: MEG at time point 1, MEG at time point 2, and navigational affordance. This requires splitting the MEG data in two to avoid spurious correlations (otherwise if time point 1 = time point 2, the correlation is 1 due to shared signal and noise). I do not insist on this point, but I would encourage the authors to see whether the analysis further supports their claims.\nConcerning the previous point 8, I wonder why the authors set the condition of 3 contiguous time points as a statistical criterion. Why not use cluster-level correction, or FDR, or FWE? The 3 time points do not appropriately control for multiple comparisons."}, "author_response": "We are grateful again to all three reviewers for their constructive comments. We have taken these comments on board and provide below a point-point response. All edits to the manuscript are highlighted in blue. [Author response continues with detailed point-by-point replies]"}, {"round": 3, "reviewer_comments": {"reviewer_2": "Thank you for the clarification. I would like to request that the analysis included on the left trajectories is added to the main paper, and the wording changed to \"trend\" rather than \"not significant\". Additionally, it would help if rather than just comparing Bonner with Edinburgh on the leftward tendency, there could be a test of whether left vs right trajectories, in the current dataset, were more frequent (Chi square?) and if the Authors are correct, there should be no significant difference and then my concerns about neural lateralization would be very much assuaged.", "reviewer_3": "The authors have sufficiently addressed my concerns. I wish they had conducted the additional analysis, but as indicated, I will not insist. More importantly, the frequentist statistics are now on a firmer footing. I have no further comments."}, "author_response": "We are grateful again to all two reviewers for their constructive comments. Below we provide a point-point response. All edits to the manuscript are highlighted in blue. [Author response continues with detailed point-by-point replies]"}, {"round": 4, "reviewer_comments": {"reviewer_2": "Thank you for the addition into the text. The manuscript is now fantastic and perhaps it's just me, but I think this new analysis strengthens your original claims."}, "author_response": ""}]

Decision letter