{"paper_id":"28914eeb-eee2-42be-918d-96f6afb8d29c","body_text":"Post-saccadic scene category processing \n \n 1 \n \nPost-Saccadic Disruption of Semantic Category Information in Naturalistic Scenes \n \n \nYong Min Choi1*   choi.1696@osu.edu \nTzu-Yao Chiu1   chiu.315@osu.edu  \nJulie D. Golomb1   golomb.9@osu.edu  \n1Department of Psychology, The Ohio State University \n \n* Corresponding author information \nPostal address: M210E Lazenby Hall, 1827 Neil Avenue,  \nColumbus, OH 43210 \nEmail address: choi.1696@osu.edu \nPhone number: 614-390-7022 \n \nNumber of pages: 33 \nNumber of figures: 8 \nNumber of tables: 0 \nNumber of words for abstract: 241 words \nNumber of words for introduction: 649 words \nNumber of words for discussion: 1443 words \n \nConflict of Interest statement \nThe author(s) declares no conflicts of interest concerning the authorship or the publication of this article.  \n \nAcknowledgements \nThis work was supported by the National Institute of Health [NIH R01-EY025648 (JG)]; the National \nScience Foundation [NSF 1848939 (JG)]. \n \n \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 2 \nAbstract  \nDuring natural vision, people make saccades to efficiently sample visual information from \ncomplex scenes. However, a substantial body of evidence has shown impaired visual information \nprocessing around the time of a saccade. It remains unclear how saccades affect the processing of high-\nlevel visual attributes – such as semantic category information – which are essential for navigating \ndynamic environments and supporting complex behavioral goals. Here, we investigated whether/how the \nprocessing of semantic category information in naturalistic scenes is altered immediately after a saccade. \nThrough both human behavioral and neuroimaging studies, we compared semantic category judgments \n(Experiments 1A and 1B) and neural representations (Experiment 2) for scene images presented at \ndifferent time points following saccadic eye movements. In the behavioral experiments, we found a robust \nreduction in scene categorization accuracy when the scene image was presented within 50 ms after \nsaccade completion. In the neuroimaging experiment, we examined neural correlates of semantic category \ninformation in the visual system using fMRI multivoxel pattern analysis (MVPA). We found that scene \ncategory representations embedded in the neural activity patterns of the parahippocampal place area \n(PPA) were degraded for images presented with a short (0–100 ms) compared to a long post-saccadic \ndelay (400–600 ms), despite no corresponding reduction in overall activation levels. Together, these \nfindings reveal that post-saccadic disruption extends beyond basic visual features to high-level visual \nattributes of naturalistic scenes, highlighting a limitation of visual information processing in the short \npost-saccadic period before executing the next saccade. \n \nKeywords: eye movements, scene perception, scene category, spatial frequency, visual stability  \nSignificance Statement  \nDespite the seamless visual experience across saccadic eye movements, visual information \nprocessing is substantially impaired around the time of a saccade. While prior research has documented \nperi-saccadic disruptions in perception tasks involving basic visual features, it remains unclear whether \nimpairments extend to high-level visual attributes in natural scenes (e.g., semantic category) that are \nessential for everyday behavior. Using complementary behavioral and neuroimaging approaches, we \nshow that the processing of semantic category information is disrupted when a scene image is presented \nimmediately after a saccade. These findings highlight a fundamental tradeoff in natural scene perception: \nwhile saccades serve a functional benefit by projecting relevant information onto the retinal region with \nthe highest acuity, they can also incur brief consequences for perception.  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 3 \nIntroduction \nWhen viewing complex visual scenes, people make saccadic eye movements - rapid, ballistic \nshifts of fixation to different spatial locations – to efficiently sample visual information  (Najemnik & \nGeisler, 2005; Rayner, 2009; Renninger et al., 2007; Samonds et al., 2018; Yarbus, 1967). In many cases, \nsaccades are functionally beneficial, projecting relevant information onto the retinal region with the \nhighest spatial resolution, and maximizing information gain while reducing perceptual uncertainty \n(Renninger et al., 2007). However, each saccade also brings new visual input to the retina, requiring the \nvisual system to process novel visual information within a few hundred milliseconds before executing the \nnext saccade. Although people are often unaware of any instability, previous literature has documented \nreduced sensitivity around the time of saccadic eye movements when viewing simple visual stimuli \n(Dowd & Golomb, 2020; Burr et al., 1994; Ross et al., 1997), as well as basic-level visual features like \ncontrast (Dorr & Bex, 2013) or spatial frequency (Kwak et al., 2024) in naturalistic scene images. \nMeanwhile, in complex visual scenes, people must encode numerous high-level attributes - such as \nsemantic category, navigability, action affordance, etc. - to serve behavioral goals (see Malcolm et al., \n2016 for review). However, it remains unclear how saccadic eye movements impact the encoding of such \nvisual information.  \nHow might saccadic eye movements influence the subsequent encoding of semantic category \ninformation (e.g., mountain, city, highway, etc.) from naturalistic scene images? One possibility is that \nthe processing of semantic category information may be resilient to post-saccadic interference due to the \nredundant visual cues in natural scenes (Geisler, 2008; Kersten, 1987; Võ et al., 2019). Because semantic \ncategory information could be extracted from either basic-level (Castelhano & Henderson, 2008; Oliva & \nSchyns, 2000; Walther & Shen, 2014) or complex visual properties, such as spatial layout (Ross and \nOliva, 2011) or global summary statistics (Greene & Oliva, 2009; Oliva & Torralba, 2006), previous \nfindings on basic-level visual features may not be readily generalized to the semantic category \ninformation in naturalistic scene images. Alternatively, considering the linkage between processing of \nbasic visual features and semantic category information (Groen et al., 2013; 2017), semantic category \nrepresentations may be disrupted post-saccadically analogously to the processing of basic-level visual \nfeatures.  \nAnother intriguing alternative is that post-saccadic disruptions of semantic category \nrepresentations may be more nuanced, perhaps depending on the spatial frequency conveying the scene \ncontents. A prominent theory of rapid scene perception, the Coarse-to-Fine (CtF) model, suggests distinct \nroles of low and high spatial frequencies (Hegdé, 2008; Schyns & Oliva, 1994): The low spatial \nfrequency (LSF) information conveys an abstract and coarse summary of a scene image (e.g., global \nlayout) through the rapid magnocellular pathway, while the high spatial frequency (HSF) information \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 4 \ncarries finer details of a scene image (e.g., object details) through the relatively slower parvocellular \npathway (Kauffmann et al., 2014). Given a short post-saccadic period to process the full-spectrum of \nspatial frequency information, the visual system may preferentially use LSF to encode scene attributes, \nresulting in the processing of HSF visual information being more vulnerable to post-saccadic disruption.  \nThe goal of the current study is to answer whether and how the representation of semantic \ncategory information in naturalistic scenes is disrupted post-saccadically. To answer these questions, we \nconducted a series of behavioral and neuroimaging experiments integrated with eye-tracking. In the \nbehavioral experiments (Experiments 1A and 1B), we compared semantic categorization performance of \nnaturalistic scene images (sampled from beach, city, forest, highway, mountain, and office categories) \npresented at various time points after the completion of a saccadic eye movement. Experiment 2 used \nfunctional Magnetic Resonance Imaging (fMRI) and multi-voxel pattern analysis (MVPA; Haxby et al, \n2001) to examine post-saccadic neural representations of semantic category information. In both cases, to \nexamine whether the influence of post-saccadic delay is modulated by the spatial frequency conveying \nscene content, scene images were filtered with different spatial frequency filters to contain either low or \nhigh spatial frequency information. \n \nMaterials and Methods: Experiment 1 \nPre-registration Statement  \nExperiment 1A was not explicitly pre-registered; however, it was a modification of a similar \nexperiment we had pre-registered (https://osf.io/az9c7), retaining the core motivation, sample size, and \ndesign. Experiment 1B was pre-registered, including its rationale, design, and analysis plan \n(https://osf.io/h8dmu). Any additional analyses beyond the pre-registration are reported as exploratory.  \n \nParticipants \nAs pre-registered in our preliminary experiment, we set 18 subjects as a minimum sample size for \nExperiment 1A. This was based on a previous study (Perfetto et al., 2020) testing scene categorization \nperformance between LSF and HSF images. We performed a Bayesian analysis on their data (Experiment \n2, which reported no significant difference between conditions, t(17) = 0.034; p = 0.97) and found \nmoderate support for the null hypothesis (BF10<.228). Based on this, we planned to collect at least 18 \nparticipants and apply the Bayesian optional stopping rule (Rouder, 2014), continuing data collection by \nsets of three subjects to counterbalance spatial frequency condition order (see Stimuli section) until the \nBayes factor indicated sufficient evidence either for (BF₁₀ > 3) or against (BF10<0.333) our key effect of \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 5 \ninterest: the interaction between spatial frequency (HSF vs. LSF) and post-saccadic delay (Short vs. \nLong). The maximum sample size was set at 36.  \nUltimately, data from 21 participants (12 women, 9 men; M = 20.86 years, SD = 4.89) were \nincluded in the final analysis for Experiment 1A. Two additional participants completed the experiment \nbut were excluded for different reasons: one due to categorization accuracy for FS images lower than \npreregistered exclusion criterion of 50% (49.2%), and the other due to system error that caused a longer \npost-saccadic delay than intended. Experiment 1B followed the same sample size plan, and data from 18 \nparticipants (13 women, 5 men; M = 19.00 years, SD = 2.37) were collected, with no additional data \ncollection given sufficient Bayesian evidence. All participants had normal or corrected-to-normal vision \nand received either course credit or monetary compensation for participation ($15/hour). Experiments 1A \nand 1B were approved by the Ohio State University Behavioral and Social Sciences Institutional Review \nBoard.  \n \nExperiment design \nExperiment 1A. Subjects participated in a gaze-contingent behavioral experiment, where they were \ninstructed to follow a fixation dot with their eyes and perform a 6-AFC (i.e., beach, city, forest, highway, \nmountain, and office) scene categorization task on a briefly presented scene image. Each trial started with \nan initial fixation dot located at one corner of an imaginary 10° ×10° right square centered on the screen \n(Figure 1A). Once participants successfully fixated on the initial fixation for more than 1000 ms, the \nfixation dot disappeared and immediately reappeared at a different corner of the imaginary square \n(saccade cue). Subjects were instructed to make an eye movement toward the saccade cue as fast and \naccurately as possible. Eye position was monitored in real-time, and saccade completion was defined as \nwhen gaze position entered a 2° window around the saccade target (note that additional post-hoc analyses \nwere conducted with alternative methods of defining saccade completion). After a variable post-saccadic \ndelay, a large scene image (28° × 21°) was presented. In Experiment 1A, the scene image was presented \neither 5 ms or 500 ms after the recorded saccade offset, the 5 ms and 500 ms post-saccadic delay \ncondition, respectively. The scene image was always presented for 50 ms, followed by a noise mask (500 \nms). After the mask disappeared, subjects reported the category of scene image using a keyboard: S, D, F, \nJ, K, and L. Correspondence between the six keys and the six scene categories were randomly assigned \nfor each subject. Feedback for slow saccade reaction time was presented at the end of each trial if the \nsaccade reaction time for the current trial was longer than 500 ms (“Eye movement too slow!”). Feedback \nfor category reports was provided for 1000 ms only in practice trials (“Correct” or “Incorrect”). Subjects \npressed the spacebar to continue to the next trial. Note that in the gaze-contingent design, the current trial \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 6 \nwas aborted and restarted after calibration if the subject failed to fixate on the initial fixation after more \nthan 5 seconds after the onset of initial fixation, or failed to maintain fixation more than three times.  \n Experiment 1A included 24 practice trials using only full-spectrum (FS) scene images \n(see Stimuli section for details). In the first practice trial, images were presented for 150 ms, then linearly \nreduced to 29 ms over the course of 24 trials to familiarize participants with the task. During the main \nsession, scene images were always presented for 50 ms and belonged to one of three spatial frequency \nconditions: full-spectrum (FS), high-spatial frequency (HSF), or low-spatial frequency (LSF). The \nexperiment followed a 3 (Spatial Frequency: FS, HSF, LSF) × 2 (Post-Saccadic Delay: 5 ms, 500 ms) × \n6 (Scene Category) design, with each condition repeated 10 times, resulting in 360 trials presented in \nrandom order across six blocks. \n \nExperiment 1B. Experiment 1B was modified from Experiment 1A to examine a time course of post-\nsaccadic scene processing. To achieve this, intermediate post-saccadic delay conditions were added, \nresulting in five post-saccadic delay conditions logarithmically spaced between 5 and 500 ms (5, 16, 50, \n158, and 500 ms). Additionally, only LSF and HSF scene images, but not FS scene images, were used in \nboth practice and main sessions to maximize number of trials for conditions of interests in a single \nsession. Moreover, trials advanced automatically without requiring a spacebar press, and the saccade \ndirection was always either horizontal or vertical, instead of diagonal, to ensure consistent saccade \ndistance across trials. The main session followed a 2 (Spatial Frequency: LSF, HSF) × 5 (Post-Saccadic \nDelay: 5, 16, 50, 158, 500 ms) × 6 (Scene Category) design, with each condition repeated 10 times, \ntotaling 600 trials, presented in random order across 10 blocks.  \n \nStimuli \nScene images and MATLAB code to filter the spatial frequency of scene images were modified \nfrom Perfetto et al. (2020). To create LSF and HSF scene images, grayscaled scene images were \ndeconstructed using a two-dimensional Fast Fourier Transformation (FFT) and filtered with a low-pass (< \n1 cycles per degree; cpd) or high-pass (> 6 cpd) SF filter with a 2nd-order Butterworth shaped boundary. \nThe choice of the 2nd-order Butterworth filter and frequency cutoffs was based on Perfetto et al. (2020), \nwhere the scene categorization accuracy was comparable between HSF and LSF scene images \n(Experiment 1B; t(17) = 0.034; p = 0.97). The unfiltered full-spectrum image, HSF, and LSF version of a \nsingle image were jointly contrast-normalized (Figure 1B). The scene image was presented in size of \n28° ×21° in the behavioral experiments. \nWe created 50 noise images to be randomly presented as a mask in each trial. To create noise \nimages that contain low-level visual properties of scene images without identifiable category-specific \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 7 \nfeatures, we first calculated the average amplitude spectrum across all 432 scene images. Then, we \nperformed inverse FFT using average amplitude and 50 random phase matrices to create 50 mask images. \nThe 50 mask images were jointly contrast-normalized and rescaled to a range between 0.2 and 0.8.  \nThere were 72 exemplar scene images (800 × 600 pixels) for each of the six scene categories \n(e.g., beach, city, forest, highway, mountain, office; Figure 1B). In Experiment 1A, scene images for each \nscene category (72 scenes) were divided into 6 groups. 1 group of scene images (12 scenes) was presented \nduring the practice session, while the rest of the 5 groups (60 scenes) were used in the main session. \nDuring the main session, each image was shown only once. Potentially, scene images could be identified \nmore easily when filtered with either low or high spatial frequency bands. For example, forest scene \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 8 \nimages full of trees should be easier to recognize when filtered with high spatial frequency because of \nprevailing high spatial frequency information in vertical orientation (i.e., trees). To address such concern, \nwe counterbalanced three spatial frequency conditions across the scene images presented during the main \nsession between subjects. Specifically, for every group of three subjects, we used the same 12 scenes for \npractice trials, and the 60 scene images of the main session were divided into three sets of 20 images to be \nassigned to three spatial frequency conditions. In Experiment 1B, a scene image was randomly selected \nfor each trial out of 72 exemplars from a given scene category.  \n \nApparatus \nExperiments 1A and 1B were performed using MATLAB (The MathWorks, Natick, MA) with \nthe Psychophysics Toolbox (Version 3 extension; Brainard, 1997; Kleiner, 2007; Pelli, 1997). Either the \nleft or right eye position was monitored with the sampling rate of 1000 Hz using EyeLink 1000 eye-\ntracking system mounted on the desk, controlled by the Eyelink MATLAB Toolbox (Cornelissen et al., \n2002). The eye-tracking system was calibrated using a nine-point grid method, at the beginning of the \nexperiment and between trials if necessary.  \nExperiments were performed on a desk-top setting with 24.5-inch LCD monitor (ASUS ROG \nPG258Q) connected to NVIDIA GeForce RTX 2060, running at a 240 Hz refresh rate with a resolution of \n1920 ×1080 pixels, located 63 cm in front of the participants (39 pixels per degree visual angle). Stimuli \nwere presented above gray background (114 cd/m2) throughout the experiment.  \n \nBehavioral data analysis \nTo examine post-saccadic processing of semantic category information, it is critical to validate \nactual stimuli duration and stimuli onset latency relative to the detection of saccade offset through post-\nhoc analysis of eye-tracking data. We confirmed that errors in these temporal manipulations were \nnegligible. Experiment 1A showed scene duration close to 50 ms (mean = 48.47, sd = 1.81) and precise \npost-saccadic delay in 5 ms (mean = 3.73, sd = 1.76), and 500 ms post-saccadic delay trials (mean = \n500.15, sd = 1.50) for all subjects. Experiment 1B showed accurate scene image duration (mean = 48.51, \nsd = 1.56), post-saccadic delay in 5 ms (mean = 3.51, sd = 1.50), 16 ms (mean = 14.52, sd = 1.46), 50 ms \n(mean = 48.84, sd = 1.54), 158 ms (mean = 156.71, sd = 1.54), and 500 ms post-saccadic delay trials \n(mean = 498.57, sd = 1.51).  \nIn Experiment 1A, scene categorization accuracy for FS scene images was used to exclude \nsubjects. The main analysis used data from LSF and HSF condition trials; scene categorization accuracies \nwere compared by conducting 2 (Post-saccadic delay) × 2 (Spatial frequency) repeated measures \nANOVA to examine the effect of saccade on subsequent scene perception and the modulation effect of \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 9 \nspatial frequency. As pre-registered, Experiment 1B conducted a 5 (Post-saccadic delay) × 2 (Spatial \nfrequency) repeated measures ANOVA to test the main effect of post-saccadic delay, and how the spatial \nfrequency condition modulates the main effect of post-saccadic delay. The spatial frequency condition \nwas collapsed if the interaction term was not significant.  If we find a significant main effect of post-\nsaccadic delays on scene categorization accuracy, but not the interaction effect, we preregistered to \nperform post hoc t-tests after collapsing the spatial frequency condition. Specifically, scene categorization \naccuracy in the 500 ms post-saccadic delay condition (baseline) was compared with the remaining four \nshorter post-saccadic delay conditions (5, 16, 50, 158 ms). For each pairwise t-test, we used a critical \nalpha value of .0125, accounting for the number of t-tests performed (i.e., Bonferroni correction). Lower \ncategorization accuracy compared to 500 ms delay condition will indicate disrupted processing of \nsemantic scene category information. \n In addition to the pre-registered analysis, we tested whether decreased categorization accuracy in \nshorter post-saccadic delay conditions is attributed to residual eye movement after saccade offset. First, \nwe calculated eye movement velocity (°/sec) at each time point using a 10 ms sliding window for each \ntrial. Then, we excluded trials in which eye movement velocity at scene onset was faster than 25 °/sec. \nBecause of a small number of remaining trials with shorter post-saccadic delays, we compared scene \ncategorization accuracy between short post-saccadic delay trials (5 and 16 ms) and long post-saccadic \ndelay trials (50, 158, and 500 ms). Second, to generalize the result with different saccade detection \nalgorithms, we calculated post-saccadic delay for each trial based on the built-in online parsing system of \nEyelink 1000 that incorporates eye movement velocity and acceleration rate to define saccade onset and \noffset. Using re-calculated post-saccadic delays, we separated trials into three post-saccadic delay groups \n(0-16 ms, 16-250 ms, and 250-1000 ms) and compared mean categorization accuracy. \n \nResults: Experiment 1 \nExperiment 1A. Scene categorization accuracy exceeded chance level (0.16) for both HSF (mean = 0.59, \nsd = 0.12) and LSF (mean = 0.62 , sd = 0.12) conditions. We compared accuracies between the two post-\nsaccadic delay conditions (5 ms vs. 500 ms) and two SF conditions (HSF vs. LSF) by performing 2 × 2 \nrepeated-measures ANOVA (Figure 2A). We found a significant main effect of post-saccadic delay \n(F(1,20) = 17.11, p < .001, 𝜂!\t# = .46, BFincl = 189.06), with lower categorization accuracy in the 5 ms \ncompared to the 500 ms post-saccadic delay condition. However, we found no significant main effect of \nthe SF condition (F(1,20) = 2.11, p = .162, 𝜂!\t# = .09, BFincl = 0.76), nor significant interaction effect \nbetween the delay and SF condition (F(1,20) = 0.07, p = .790, 𝜂!\t# = .004, BFincl = 0.31). These findings \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 10 \nsuggest that the processing of semantic category information is disrupted when a scene image is presented \nbriefly following a saccadic eye movement, regardless of spatial frequency conveying the scene content.  \n \nExperiment 1B. Scene categorization accuracy pooled over delay condition exceeded chance level (0.16) \nfor both HSF (mean = 0.62, sd = 0.10) and LSF (mean = 0.59 , sd = 0.12) conditions. The scene \ncategorization accuracy across post-saccadic delay conditions and SF conditions is plotted in Figure 2B \n(faint gray lines). First, we performed the 5 (post-saccadic delay condition) × 2 (SF condition) repeated-\nmeasures ANOVA. Similar to the results of Experiment 1A, there was a significant main effect of the \npost-saccadic delay condition (F(4,68) = 7.15, p < .001, 𝜂!\t# = .30, BFincl = 75.54), and no significant \ninteraction effect with spatial frequence (F(4,68) = 0.53, p = .713, 𝜂!\t# = .03, BFincl = 0.07). While the \nBayesian evidence supported a main effect of spatial frequency condition (BFincl = 18.25), it did not reach \nsignificance with the frequentist approach (F(1,17) = 3.78, p = .069, 𝜂!\t# = .18).  \nAs pre-registered, we then conducted post-hoc t-tests after collapsing the spatial frequency \ncondition (Figure 2B, solid black line). Specifically, scene categorization accuracy in the 500 ms post-\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 11 \nsaccadic delay condition was considered as the baseline for recovered performance (Figure 2B, gray \nregion) and compared with the other shorter post-saccadic delay conditions (5, 16, 50, 158 ms). We found \nsignificantly lower categorization accuracy in the 5 ms (t(17) = -2.87, p = .011, d = -0.68, BF10 = 5.04) \ncompared to the 500ms baseline. Additionally, though it did not reach significance based on corrected \nalpha value (.0125), scene categorization accuracy was also lower in 16 ms post-saccadic delay conditions \ncompared to the baseline (t(17) = -2.71, p = .015, d = -0.64, BF10 = 3.80). However, the scene \ncategorization accuracy was not significantly different from the baseline in the 50 ms (t(17) = 0.73, p \n= .476, d = 0.17, BF10 = 0.31) and 158 ms post-saccadic delay conditions (t(17) = 1.29, p = .214, d = 0.30, \nBF10 = 0.50). Combined, these results demonstrated the time course of semantic category representation \nin post-saccadic period, characterized by a significant drop in scene categorization performance shortly \nfollowing the saccade offset and rapid recovery back to the baseline within 50 ms after the saccade offset.  \n \nExperiment 1B Exploratory analyses. For the above analyses we defined saccade offset in a real-time \ngaze-contingent manner, as the time when the distance between the current gaze location and the saccade \ntarget location becomes smaller than 2°. While this method is commonly used in literature, it likely \nunderestimates saccade offset time, such that the eye may still be moving for a brief period of time after \nthis marker. Indeed, when we performed post-hoc analyses calculating eye movement velocity at different \ntime points relative to the scene onset time, eye movement velocity at scene onset was higher with short \npost-saccadic delays (Figure 3A). Thus, the decreased scene categorization accuracy in shorter post-\nsaccadic delay trials could be attributed to the residual eye movement that can smear a visual image \nprojected to the retina.  \n To investigate whether retinal shifts of visual input are responsible for reduced scene \ncategorization performance, we excluded trials on which the eyes were still moving at scene onset (>25 \n°/sec), and compared categorization accuracy for short (5, 16 ms), intermediate (50, 158 ms)  and long \n(500 ms) post-saccadic delay trials (Figure 3B). One-way repeated-measures ANOVA revealed a \nsignificant main effect of post-saccadic delay (F(2,34) = 12.99, p < .001, 𝜂!\t# = .43, BFincl = 305.89), \ncharacterized by significantly lower categorization accuracy for short (5, 16 ms) post-saccadic delay trials \ncompared to intermediate (t(17) = -4.89, pbonf < .001, d = -1.15, BF10 = 6060.12) and long post-saccadic \ndelay trials (t(17) = -3.69, pbonf = .002, d = -0.87, BF10 = 7.09), without significant difference between \nintermediate and long post-saccadic delay trials (t(17) = 0.28, pbonf = .721, d = 0.28, BF10 = 0.44). These \nresults indicate that the observed post-saccadic drop in scene categorization accuracy was not due to the \nconfound of residual eye movement.  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 12 \n In addition, we also employed an alternative algorithm to detect saccade onset and offset. Using \nthe online parsing system built-in Eyelink 1000, we re-calculated trial-wise post-saccadic delay (Figure \n3C). The majority of re-calculated post-saccadic delays (histograms) were shorter than the intended post-\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 13 \nsaccadic delays (black vertical lines), suggesting that this method is more strict way of defining post-\nsaccadic delay for each stimuli onset. Then, we labeled each trial based on calculated post-saccadic delay \ninto four post-saccadic delay groups (0-16 ms, 16-50 ms, 50-158 ms, 250-1000 ms; Figure 3D). One-way \nrepeated-measures ANOVA again revealed a significant main effect of post-saccadic delay (F(3, 51) = \n4.38, p = .008, 𝜂!\t# = .205, BFincl = 5.42). Post-hoc analysis found lower scene categorization accuracy in 0-\n16 ms post-saccadic delay trials compared to the 16-50 ms (t(17) = -3.12, pbonf = .019, d = -0.73, BF10,U = \n3.34) and 50-158 ms (t(17) = -3.12, pbonf = .018, d = -0.74 , BF10,U =8.64) , with marginal difference \ncompared to the 158-1000 ms post-saccadic delay trials (t(17) = -2.47, pbonf = .10, d = -0.58, BF10,U = \n2.42). The exploratory analyses revealed impaired semantic category information for scene images \npresented immediately after saccadic eye movement, which is not attributed to smeared retinal image nor \nlimited to the saccade detection methods used in the main analysis.  \nMaterials and Method: Experiment 2 \nExperiment overview \nNext, we conducted a neuroimaging experiment (Experiment 2) using fMRI MVPA to assess \nwhether and how neural representations of semantic scene category information are altered by saccades. \nSpecifically, if scene content processing is disrupted post-saccade, this should be reflected in degraded \ndecoding of scene category information within scene-selective brain regions such as the parahippocampal \nplace area (PPA; Epstein & Kanwisher, 1998). Comparing neural representations of semantic scene \ncategory information in the absence of an explicit categorization task is particularly useful to rule out \nalternative explanations for the reduced scene category accuracy observed in Experiment 1. Specifically, \ndecreased behavioral categorization accuracy post-saccadically may not be due to perceptual disruption \nbut instead arise from non-perceptual factors such as interference with decision-making (Matsumiya & \nFurukawa, 2023) or motor planning and execution (Pashler et al., 1993; Richardson et al., 2013). These \nnon-perceptual factors could explain the diminished scene categorization accuracy observed in the short \ndelay conditions, and/or the absence of interaction with spatial frequency carrying the scene content. By \nexamining neural representation of scene category information without an explicit categorization task, \nExperiment 2 could more effectively eliminate the influence of non-perceptual processes, testing the post-\nsaccadic disruption of perceptual representations of scene content immediately following saccadic eye \nmovements.  \n \nParticipants \n17 subjects (14 women, 3 men, 0 nonbinary; agemean = 23.56, agestd = 3.84) with normal or \ncorrected-to-normal vision completed Experiment 2 (fMRI study). The sample size (N = 17) for \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 14 \nExperiment 2 was determined through a priori power analysis using G*Power version 3.1.9.6 (Faul et al., \n2009) based on a previous study (Berman et al., 2017), in which scene category decoding accuracy for \nhigh spatial frequency scene images in PPA was significantly above chance level (t(9) = 2.68, p = .025, d \n= 0.85). The power analysis estimated a required sample size of 17 for this effect size with a significance \ncriterion (𝛼) of 0.05 and a power of 0.9. All participants provided informed consent and were pre-\nscreened for MRI eligibility. The study protocol was approved by the Ohio State University Biomedical \nSciences institutional review board. \n \nExperiment design \n Subjects completed a 0.5-hour pre-scan session outside of the fMRI scanner and a 2-hour scan \nsession in an fMRI scanner on different days. In both sessions, subjects were asked to follow a fixation \ndot and perform a 1-back task on sequentially presented scene images (Figure 4A). In each trial, the initial \nfixation dot was presented at one corner of an imaginary square (7° ×\t7°) for 1000 ms. Then, the initial \nfixation dot disappeared, and a saccade cue was presented at a new fixation location displaced \nhorizontally or vertically by 7° , followed by the presentation of a large, full-field scene image for 100 \nms. The task was to compare the scene image on the current trial to the one seen on the immediately prior \ntrial (1-back task). Subjects were instructed to press a button only when a completely identical scene \nimage was repeated, based on both content and spatial frequency, and to not press the button otherwise. \nStimulus onset asynchrony (SOA) between trials was 4 seconds (50%), 6 seconds (33%), or 8 seconds \n(17%). \nAs a critical manipulation, we varied the timing of the saccade cue onset relative to scene onset \nacross trials (Post-saccadic delay condition), such that the scene was presented after either a short (0-100 \nmilliseconds) or long (400-600 milliseconds) post-saccadic delay. To achieve this, we used a different \napproach than the online gaze-contingent design employed in the behavioral experiments. In the fMRI \nexperiment, the scene onsets had to be pre-determined and time-locked to the scanner’s repetition time \n(TR; 1,800 ms). Thus, we employed an approach where we measured the average saccadic reaction time \nfor each subject in advance, and used this to individually adjust the time of saccade cue onset to maximize \nthe number of trials where the scene images would be presented at the intended post-saccadic delays. We \nthen performed post-hoc analyses of eye-tracking data for each subject to select trials where the scene \nimage was actually presented within the intended short or long post-saccadic delay windows.  \nSpecifically, we recorded saccade reaction times (SRT) - delay between saccade cue onset to \nsaccade offset - from the pre-scan session (Figure 4B). From the SRT distribution, we identified a 100 ms \ntime window encompassing most of the saccade reaction times (thick gray line in Figure 4B) and used the \nupper end of this window as the optimal saccade reaction time (optSRT; black arrow in Figure 4B). \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 15 \nDuring the scan session, the saccade cue was presented either optSRT or optSRT + 500 ms before the pre-\ndetermined time of scene onset, corresponding to the short and long post-saccadic delay conditions, \nrespectively. For example, if participant’s optSRT was 250 ms and the scene image is scheduled to be \npresented 4,000 ms after the trial onset, the saccade cue appeared at either 3,750 ms (short delay) or 3,250 \nms (long delay) after trial onset. With this approach, actual post-saccadic delays of individual subject \nfollowed a bimodal distribution, with peaks located between approximately 0–100 ms and 400–600 ms \n(Figure 4C). For each subject, trials which actually fell within the intended short or long post-saccadic \ndelay windows were selected through post-hoc analyses of eye-tracking data to be included in the main \nanalysis (Figure 4C colored portion of histograms; see Supplementary Figure 1 for individual subjects). \nNote that, due to individual variability of saccade onset latency, the number of included short and long \ndelay condition trials differed between subjects (Supplementary Figure 1). \nThe scan session included 8 runs, each consisting of 108 trials with 12 repeated trials (repetition \nrate of 11.11%) where subjects had to press ‘1’; those trials were removed from further analysis. The rest \nof the 96 non-repeated trials comprised of 3 repetitions for each combination of 2 post-saccadic delay \nconditions (short and long post-saccadic delay) × 4 scene categories (mountain, beach, highway, and city) \n× 4 SF conditions (LSF1, LSF2, HSF1, and HSF2). The pre-scan session included two practice runs \nidentical to that of the scan session, except that visual feedback (300 ms) was provided during the first run \nof the pre-scan (green: correct/red: incorrect). \n Additionally, the scan session included one functional localizer run to localize early visual cortex \nand scene-selective regions (see Region-of-Interest selection section for details). The functional localizer \nrun included 11 blocks: four object blocks, four scene blocks, and three fixation blocks. The order of \nblocks was counterbalanced across participants. In each block, 20 images were presented sequentially at \nthe screen center (17.42° ×17.42°) for 400 ms with 500 ms delay. Participants performed a 1-back task \nwith a repetition rate of 10%.  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 16 \n \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 17 \nStimuli \nThe set of scene images (Figure 4D) contained four subordinate scene categories (i.e. beach, \nmountain, city, and highway), affiliated with two superordinate scene categories (i.e. nature and urban). \nScene images were grayscaled and filtered to contain either low or high spatial frequency information. To \nmatch the hierarchical structure of the scene category manipulation, we used four spatial frequency \nranges, two low spatial frequency conditions and two high spatial frequency conditions: LSF1 (< 0.8 cpd \nlow-pass filter), LSF2 (< 1.6 cpd low-pass filter), HSF1 (4-5 cpd band-pass filter), and HSF2 (7-10 cpd \nband-pass filter). For HSF1 and HSF2 images, we computed the absolute values of filtered images to \nobtain more naturalistic and familiar images, similar to a line drawing (Perfetto et al., 2020; Experiment \n3). Four SF-filtered images from a single scene were jointly contrast-normalized and equated with mean \nluminance. The scene image was presented in size of 23.23° ×17.42° in both the pre-scan and the scan \nsession to fully cover the entire screen. \n The fixation dot was configured as an inner circle (0.3° diameter) with a thick outline (0.2° \nwidth). During an inter-trial interval, the white inner circle was surrounded by a black outline. The \nfixation dot changed to a black circle with a white outline (i.e., initial fixation onset; Figure 4A) 1 second \nprior to saccade cue onset to encourage subjects to fixate on the dot.  \n \nApparatus \nThe pre-scan and scan session of Experiment 2 were both performed using MATLAB (The \nMathWorks, Natick, MA) with the Psychophysics Toolbox (Version 3 extension; Brainard, 1997; \nKleiner, 2007; Pelli, 1997). The pre-scan session of Experiment 2 was performed at the same setting as \nExperiment 1.  \nThe scan session of Experiment 2 was carried out in a Siemens Prisma 3-T MRI scanner with an \nintegrated Total Imaging Matrix (TIM) system using a 32-channel phased array receiver head coil, \nlocated at the OSU Center for Cognitive and Behavioral Brain Imaging. Functional data were acquired \nusing a T2-weighted gradient-echo sequence (repetition time = 1,800 ms, echo time = 28 ms, flip angle = \n70°). We used multiband whole-brain coverage aligned to the AC–PC (72 slices, 2 × 2 × 2 mm voxel, \n10% gap, multiband factor = 3). Before the functional scan, a T1-weighted magnetization-prepared rapid \ngradient echo anatomic scan at 1-mm3 resolution was collected. Visual stimuli were presented using a \nrear-projection screen powered by a 3-chip DLP projector with a refresh rate of 60 Hz and a spatial \nresolution of 1280 × 1024 pixels. Participants lay down in the scanner and viewed stimuli distanced 74 \ncm via a mirror tilted 45° above the head coil. The EyeLink1000 eye-tracking system was positioned to \nmonitor the right eye through the mirror with the sampling rate of 1000 Hz. To prevent the head coil from \nblocking the view of the right eye, subjects were repositioned slightly to the right when necessary. The \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 18 \neye-tracking system was calibrated using a nine-point grid method at the beginning of the experiment and \nbetween runs if necessary. \n \nfMRI data analysis \nPreprocessing. fMRI data obtained from the functional localizer runs and main task runs were both \ncorrected for slice acquisition time and head motion, and registered into Talairach space (Talairach & \nTournoux, 1988) using Brain Voyager QX (Brain Innovation Maastricht, The Netherlands; Goebel et al., \n2006). Different pre-processing steps were applied for fMRI data from the functional localizer run and \nmain task runs.  \nThe fMRI data from the functional localizer run were pre-processed with temporal filtering \n(GLM Fourier, two cycles) and spatial smoothing using a 4-mm FWHM Gaussian kernel. A whole-brain \nrandom-effects GLM was then applied to estimate beta coefficients for fixation, scene, and object blocks.  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 19 \nFor fMRI data from main task, we calculated single-trial bata estimates for each onset of scene \nimages (i.e., 864 trials) using GLMsingle toolbox (Prince et al., 2022), which was developed to optimize \nthe estimation of single-trial fMRI responses using advanced denoising techniques (Kay et al., 2013; \nRokem & Kay, 2020). Spatial smoothing was not performed because we planned to run MVPA analysis. \nMoreover, no temporal filtering was applied before running GLMsingle because GLMsingle accounts for \nbaseline signal drift within runs by incorporating polynomial regressors into the model (Kay et al., 2013; \nPrince et al., 2022). The design matrices used for the GLMsingle included 16 conditions (4 spatial \nfrequency conditions × 4 scene category conditions), without the post-saccadic delay condition to avoid \nsystematic differences in estimated trial-wise beta coefficients between short and long post-saccadic delay \ntrials. \n \nRegion-of-Interest selection. We defined functional regions of interest (ROIs) for individual subjects \nusing GLM contrasts between scene, object, and fixation blocks during the functional localizer run. As a \nprimary scene-selective ROI, we localized the parahippocampal place area (PPA; R. Epstein & \nKanwisher, 1998), 𝑠𝑐𝑒𝑛𝑒𝑠\t > 𝑜𝑏𝑗𝑒𝑐𝑡𝑠 contrast. Clusters of voxels showing significant activation were \nselected in volume space, with thresholds adjusted individually across subjects (most subjects: p < 0.01, a \nfew subjects: up to p < 0.035; Supplementary Figure 2).  \n For exploratory analysis motivated by the functional distinction of PPA along the anterior-\nposterior axis (Baldassano et al., 2016; Berman et al., 2017; Epstein & Baker, 2019; Steel et al., 2024), \nwe further divided PPA into anterior PPA (aPPA) and posterior PPA (pPPA) for each participant to have \nan equal number of voxels between the two PPA subregions. Additionally, we defined the retrosplenial \ncomplex (RSC; R. A. Epstein, 2008; O’Craven & Kanwisher, 2000) and the occipital place area (OPA; \nDilks et al., 2013; Nakamura, 2000) for supplemental analyses. Finally, the early visual cortex (EVC) was \nlocalized using the 𝑠𝑐𝑒𝑛𝑒𝑠\t&\t𝑜𝑏𝑗𝑒𝑐𝑡𝑠 > 𝑓𝑖𝑥𝑎𝑡𝑖𝑜𝑛 contrast.  \n \nMVPA analysis. We used multi-voxel pattern analysis (MVPA) to quantify semantic scene category \ninformation in PPA using representational similarity calculated from correlation matrices (Haxby et al., \n2001; Golomb & Kanwisher, 2012). Trials that met the eye-tracking inclusion criteria for one of the two \ndelays were coded into 32 conditions (Figure 5A): 2 post-saccadic delay conditions (short and long) × 4 \nscene category conditions (beach, mountain, city, and highway) × 4 spatial frequency conditions (LSF1, \nLSF2, HSF1, and HSF2). We then calculated a 32 × 32 representational similarity matrix (RSM) using \nthe split-half correlation method. First, we split the 8 runs into two groups of runs. Single-trial beta \nestimates were averaged across all trials with the same condition label within each group of runs. Then, \nfor each group of runs separately, we normalized each voxel’s response by subtracting the beta coefficient \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 20 \naveraged across conditions from the beta estimates of each condition. Lastly, the voxel-wise beta \ncoefficients for each of the 32 conditions in one group of runs were correlated with each of the 32 \nconditions in the other group of runs, and Pearson’s r values were converted to z-scores using Fisher’s z \ntransformation, generating a 32 × 32 correlation matrix for PPA (Figure 5B). All subsequent analyses \nwere performed on the z-scored data. \nNotably, when splitting data into two groups of runs, we first followed the conventional odd \nversus even runs split-half method (Haxby et al., 2001). However, because we excluded 1-back repeat \ntrials and those with a post-saccadic delay outside the range of either the short or long post-saccadic \ndelay, the number of trials remaining for each condition in each group of runs was not only different but \nalso sometimes zero. If there was a condition in either group of runs with no included trials, we randomly \nre-divided the eight runs into two groups until there was at least one trial for every condition \n(Supplementary Figure 3). There were typically more trials in the long delay conditions compared to the \nshort delay conditions. To account for the different number of trials between the short and long delay \nconditions, we performed a control analysis where we down-sampled the long delay trials to match the \nnumber of short delay trials for each condition per each group of run (e.g., equal number of trials between \ncondition 1 and 17, or 2 and 18, etc. in Supplementary Figure 3). Nevertheless, the pattern of results \nremained the same (Supplementary Figure 4), and therefore, we focus on the results without down-\nsampling. \nFrom the RSM, we quantified the amount of scene category information (Nature vs. Urban) in the short or \nlong-delay trials, separately for LSF and HSF trials (Figure 5C). First, we divided the 32 × 32 correlation \nmatrix into four 8 × 8 subsets, each corresponding to different post-saccadic delay conditions (Short vs. \nLong) and spatial frequency conditions (LSF vs. HSF). Then, we calculated the average representational \nsimilarity (average z-score) for the cells corresponding to the same and the different scene category pairs, \nand took their difference as an index for scene category representation. For example, to calculate scene \ncategory information (Nature vs. Urban) in the short post-saccadic delay HSF trials, we selected the \nsubset of RSM cells corresponding to the short post-saccadic delay and HSF conditions (Figure 5C, third \nfrom the left). Then, we subtracted the average similarity between the different scene category pairs \n(Figure 5C; black cells) from the same scene category pairs (Figure 5C; white cells). If the voxel-wise \nresponse pattern is more similar (i.e., higher correlation) between conditions sharing the same scene \ncategory than for those with different scene categories (significantly positive value after subtraction), then \nthat indicates the neural activity pattern in PPA contains a representation of scene category information \n(Haxby et al., 2001). For scene category information, we focus primarily on superordinate category \nrepresentations (Nature vs. Urban), but we observed similar results for subordinate categories (Beach vs  \nMountain; City vs Highway). We also report analyses separating the RSM only by delay (16 x 16 cell \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 21 \nRSMs) to explore effects of post-saccadic delay on scene category information regardless of spatial \nfrequency. In the main text we report the results of our primary analyses on the PPA, but in the \nsupplement include exploratory analyses investigating scene category information in other scene-selective \nbrain regions (i.e., RSC, OPA; Supplementary Figure 5).  \nWe also examined how the representation of a low-level visual feature, spatial frequency, is \ninfluenced post-saccadically, by performing analogous MVPA analysis quantifying spatial frequency \ninformation, focusing on early visual cortex (EVC).  \nFor all statistical testing of results, we used both frequentist and Bayesian approaches using JASP \nsoftware (Version 0.14; JASP, 2017).  \n \nResults: Experiment 2 \nPost-saccadic disruption of scene category information in PPA.  \nTo examine the neural correlates of disrupted semantic category representation after saccadic eye \nmovements, we used MVPA to quantify scene category information from neural activity patterns and \ncompared scene category representations in the PPA for images presented with short versus long post-\nsaccadic delays.  \nFirst, we found that activity patterns were more similar for images of the same scene category \nthan for different categories, across all spatial frequency and post-saccadic delay conditions (ps<.021, \nBF10s >2.97; Figure 6A). To assess how scene category information (indexed by the same-minus-different \ncategory difference scores) was influenced by post-saccadic delay and its interaction with spatial \nfrequency (Figure 6B), we conducted a 2 (Post-saccadic delay condition) × 2 (Spatial frequency \ncondition) repeated measures ANOVA. The ANOVA showed no significant interaction (F(1,16) = 1.622, \np = .221, 𝜂!\t# = .09, BFincl = 0.61) or main effect of spatial frequency (F(1,16) = 3.38, p = .085, 𝜂!\t# = .17, \nBFincl = 2.11). Nevertheless, there was significant main effect of post-saccadic delay condition (F(1,16) = \n9.99, p = .006, 𝜂!\t# = .38, BFincl = 1.07), indicated by reduced scene category information in the short \ncompared to the long post-saccadic delay trials. A post-hoc analysis of simple main effects revealed \nreduced scene category information in short compared to long post-saccadic delay trials in the HSF \ncondition  (F(1) = 6.94, p = .018, d = -0.64, BF10 = 3.31), but not in the LSF condition (F(1) = 0.26, p \n= .615, d = -0.12, BF10 = 0.28). \nThe analysis above was conducted on the separate 8 × 8 RSMs (correlations within each delay x \nSF condition). To better capture the broader effect of post-saccadic delay, we performed another MVPA \nanalysis separating the RSM only by delay (16 × 16 cell RSMs) to calculate scene category information \nat each delay regardless of spatial frequency. A paired-samples t-test confirmed significantly lower scene \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 22 \ncategory information in the short post-saccadic delay trials (M = 0.053) compared to that of long post-\nsaccadic delay trials (M = 0.078; t(16) = -3.84, p = .001, d = -0.93, BF10 = 27.64).  \n \nDisrupted neural activity pattern without reduced activation.  \nIs the reduction in semantic category representation in neural activity pattern driven by an overall \nreduction in PPA activation to scene images? We conducted a standard univariate analysis averaging beta \nestimates in PPA (Figure 6C). A 2 (Post-saccadic delay condition) × 2 (Spatial frequency condition) \nrepeated measures ANOVA revealed no significant main effect of post-saccadic delay (F(1,16) = 0.86, p \n= .366, 𝜂!\t# = .051, BFincl = 0.41), nor a significant interaction (F(1,16) = .001, p = .974, 𝜂!\t# = .00, BFincl = \n0.31) or main effect of spatial frequency (F(1,16) = 0.023, p = .881, 𝜂!\t# = .001, BFincl = 0.25). The absence \nof post-saccadic delay effect on univariate activation suggests that overall activation to visual scene \nstimuli remains intact after a saccade, even when the neural activation pattern encoding the semantic \nscene content was disrupted.  \n \nConsistent patterns in PPA subregions along anterior-posterior axis \nMotivated by the functional distinction of PPA along the anterior-posterior axis (Baldassano et al., 2016; \nBerman et al., 2017), we further investigated if sub-regions of PPA along the anterior-posterior axis are \ndifferently influenced post-saccade. We performed a 2 (Post-saccadic delay condition) × 2 (Spatial \nfrequency condition) × 2 (PPA subregions) repeated measures ANOVA (Figure 7A). Consistent with \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 23 \npost-saccadic disruption of scene category information in PPA as a whole, we found a main effect of post-\nsaccadic delay on scene category information (F(1,16) = 7.17, p = .016, 𝜂!\t# = .31, BFincl = 1.37), without \nsignificant interaction between post-saccadic delay and spatial frequency (F(1,16) = 1.50, p = .238, \n𝜂!\t# = .09, BFincl = 1.35). Moreover, there was a significant higher scene category information in HSF, \ncompared to the LSF condition (F(1,16) = 4.772, p = .044, 𝜂!\t# = .23, BFincl = 32.05). Importantly, we did \nnot find any 2-way nor 3-way interaction effects involving PPA subregions (ps>0.67, BF10s < .24), \nsuggesting no functional distinction between anterior and posterior PPA concerning the post-saccadic \nprocessing of semantic category information. Consistent with the univariate results for the PPA overall, a \n2 × 2 × 2 repeated measures ANOVA on univariate activation (Figure 7B) did not find any significant \nmain effects nor interactions with subregion (ps > .40, BFincls < 0.46).  \n \nSpatial frequency information in early visual cortex \nFinally, while our primary focus is on post-saccadic representations of semantic scene content (scene \ncategory information), the spatial frequency manipulation also allowed us to examine post-saccadic \nprocessing of basic-level visual features in complex scene images (i.e., spatial frequency information). \nSimilar to above, we conducted both MVPA and univariate analyses, now examining the amount of \nspatial frequency information (LSF vs. HSF) in the early visual cortex (EVC). As shown in Figure 8A, \nthe MVPA analysis tested whether there was significant information in the pattern of EVC response to \ndifferentiate whether a scene contained high vs low spatial frequency content. EVC exhibited significant \nscene frequency information in both short (t(16) = 3.69, p = .002, d = 0.90, BF10 = 21.126) and long post-\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 24 \nsaccadic delay trials (t(16) = 2.58, p = .02, d = 0.63, BF10 = 3.04). Although the magnitude was \nnumerically higher in the long delay, a paired-samples t-test revealed no significant effect of post-\nsaccadic delay on spatial frequency representation (t(16) = -0.98, p = .34, d = -0.24, BF10 = 0.38). The \nunivariate analysis also found no significant difference between the short and long post-saccadic delay \ncondition in EVC (Figure 8B; t(16) = -0.92, p = .370, d = -0.224, BF10 = 0.361). Additional analyses \ncalculating spatial frequency information within low (LSF1 vs. LSF2) or high (HSF1 vs. HSF2) spatial \nfrequency bands were not significant in EVC (Supplementary Figure 6).  \n \nGeneral Discussion \nThe current study used a combination of behavioral and neuroimaging approaches to investigate an \nunderstudied aspect of naturalistic visual scene perception: whether representations of semantic scene \ncategory information are briefly altered in the time period immediately following a saccadic eye \nmovement. Our behavioral experiments revealed significantly diminished scene categorization accuracy \nwhen the scene image was presented following the shortest post-saccadic delays (<50 ms), compared to \nafter longer delays. Moreover, in the fMRI experiment, we assessed neural representations of semantic \ncategory in scene-selective brain region PPA using MVPA, and found analogously disrupted semantic \ncategory representations for scene images presented with short (0-100 ms) compared to longer post-\nsaccadic delays (400-600 ms). The degraded neural representation even in absence of an explicit semantic \ntask rules out non-perceptual explanation such as decision-making interference (Matsumiya & Furukawa, \n2023) or motor planning (Pashler et al., 1993; Richardson et al., 2013), underscoring genuine disruption \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 25 \nof semantic category representation in post-saccadic period. Together, these results add to prior literature \nsuggesting that in addition to the functional benefits of saccadic eye movements during active exploration \nof visual scenes, saccades may also carry brief costs for visual information processing. Our findings \nreveal that high-level visual attributes of naturalistic scene are vulnerable to disruption following \nsaccades, despite the redundancy and regularity of naturalistic scene images (Geisler, 2008; Kersten, \n1987; Malcolm et al., 2016; Võ et al., 2019).  \nAdditionally, fMRI data revealed no effect of post-saccadic delay on univariate activation in \nPPA, suggesting that saccades interfere with representations of scene content (neural pattern encoding), \nrather than reducing overall activity. The lack of activation difference argues against the possibility that \nresidual eye movements restrict the amount of visual information reaching the system at early processing \nstages. Moreover, it may indicate that PPA still recognized the visual input as a ‘scene’, while the detailed \nsemantic content is not fully processed post-saccadically. This proposes interesting correspondence with \nprior findings, where people are often surprisingly insensitive to trans-saccadic changes in scene details \n(Choi et al., 2025; Henderson & Hollingworth, 2003; Kwak et al., 2024), whilst maintaining a coherent \nconscious percept of the visual scene. \n \nThe effect of spatial frequency conveying semantic category information.  \nIn all experiments, we manipulated the spatial frequency content of scene stimuli to examine its influence \non semantic category processing in the post-saccadic period. Particularly, inspired by the Coarse-to-Fine \n(CtF) model (Hegdé, 2008; Schyns & Oliva, 1994), we hypothesized that the rapid processing of high-\nlevel scene attributes may rely more on the LSF information, making HSF images more susceptible to \npost-saccadic disruption. On the other hand, some studies of saccadic suppression have found stronger \nsuppression (i.e., reduced sensitivity) for LSF compared to HSF stimuli (Burr et al., 1994; Idrees et al., \n2020; Kleiser et al., 2004), which would predict the opposite pattern in our study.  \nOur fMRI study revealed interesting effects of spatial frequency. First, we found overall stronger \nsemantic category representations for HSF compared to LSF scene images, especially in long-post-\nsaccadic delay trials, consistent with prior work suggesting that scene content may be predominantly \nconveyed by HSF information (Berman et al., 2017; Kauffmann et al., 2015; Rajimehr et al., 2011). \nInterestingly, it was HSF scene images - not LSF scene images - that exhibited a significant reduction in \nsemantic category representation in short post-saccadic delay trials, although the interaction effect was \nnot significant. While the relative preservation of semantic information for LSF scenes under short delays \nis consistent with the prediction grounded on CtF model, the current findings alone are insufficient to \nconclude if the visual system preferentially relies on LSF information in post-saccadic scene perception. \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 26 \nFuture research could clarify how the visual system differentially processes spatial frequency information \nduring the immediate post-saccadic period. \n The greater post-saccadic impairment for HSF scene images does seem inconsistent with a \nstronger saccadic suppression (i.e., reduced sensitivity) for LSF compared to HSF stimuli (Burr et al., \n1994; Idrees et al., 2020; Kleiser et al., 2004). This discrepancy may reflect distinctive processing of \nlocalized objects versus naturalistic scenes (Boucart et al., 2013; Hasson et al., 2002; Levy et al., 2001; \nMalach et al., 2002). While object recognition relies on central vision with high spatial resolution, scene \nprocessing remains robust in peripheral vision (Boucart et al., 2013) and even with low-pass filtered \nimages (Nuthmann, 2013, 2014). Indeed, scene-selective voxels are clustered medially in the ventral \ntemporal cortex and exhibit a preference for peripheral visual input (Grill-Spector & Weiner, 2014; \nHasson et al., 2002; Levy et al., 2001; Malach et al., 2002). Taken together, the distinct patterns of post-\nsaccadic visual perception, modulated by spatial frequency, may reflect an optimized use of different \nspatial frequencies around the time of saccadic eye movements for more efficient scene processing. \nUnlike the fMRI experiment, the behavioral experiments did not find a corresponding effect of \nspatial frequency information, possibly due to insufficient sensitivity of the categorization task to capture \nsubtle effects of low-level image statistics. The visual environment is highly complex and redundant \n(Geisler, 2008; Kersten, 1987; Võ et al., 2019). When explicitly categorizing scenes, observers may rely \non a variety of cues - including basic features (Castelhano & Henderson, 2008; Oliva & Schyns, 2000; \nWalther & Shen, 2014), spatial layout (Ross & Oliva, 2011), or global summary statistics (Greene & \nOliva, 2009; Oliva & Torralba, 2006) – potentially obscuring subtle effects of spatial frequency. \n \nThe absence of disrupted spatial frequency information  \nInterestingly, in contrast to prior behavioral findings showing impaired sensitivity to basic-level visual \nfeatures like contrast (Dorr & Bex, 2013) or spatial frequency (Kwak et al., 2024) in naturalistic scenes, \nour fMRI results revealed no significant effect of post-saccadic delay on the neural representation of \nspatial frequency information in early visual cortex. One possible explanation is that the duration of the \nscene image in our fMRI study (100 ms) was sufficiently long to allow adequate processing of basic-level \nvisual information even when accounting for post-saccadic disruption. Using neuroimaging techniques \nwith superior temporal resolution (e.g., EEG, MEG), previous studies have examined the time course of \nnaturalistic visual stimuli processing for different attributes (Dima et al., 2018; Fakche et al., 2024). \nSpecifically, a recent MEG experiment showed the neural representation of object color emerging around \n100 ms after saccade offset, followed by category-level information around 145 ms (Fakche et al., 2024). \nThe more rapid processing of basic-level visual features may mean that it could have escaped from post-\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 27 \nsaccadic interference in our experiment design, particularly on trials where the scene image was presented \nat the later end of the short-delay window. \n \nOther high-level visual attributes during naturalistic scene processing \nWhile we focused on semantic category information in naturalistic scenes, it does not capture the full \nrange of high-level visual attributes necessary for interacting with the environment, such as action \naffordance and navigability (Epstein & Baker, 2019; Malcolm et al., 2016). For example, the stronger \ndegradation of semantic category information when viewing HSF scene images may not generalize to \nother attributes (e.g., action affordance, navigability), considering literature suggesting distinct, flexible \nusage of spatial frequency information depending on task demands (Wiesman et al., 2021). Moreover, \ncompared to some of these other attributes, semantic category is a more stable attribute over time. While \nthe semantic category of the current visual scene generally does not change across eye movements, \nnavigable paths—defined in egocentric coordinates—change with each fixation and must be continuously \nupdated across saccades (Wang & Spelke, 2000; Bonner & Epstein, 2017), as do the action affordances of \nobjects (Medendorp et al., 2008; Henrique et al., 1998; Batista et al., 1999). Future research could explore \nhow saccades affect these more dynamic scene attributes and how the visual system interacts with motor \nnetworks to enable seamless perception and action in naturalistic environments (Goodale, 2011; \nTagliabue & McIntyre, 2012). \nLastly, our findings raise a fundamental question: how do individuals navigate complex visual \nenvironments effortlessly despite disruptions in high-level visual processing after saccades? Decades of \nresearch have identified multiple mechanisms supporting trans-saccadic perceptual stability, spanning \nneural (Duhamel et al., 1992; Wurtz, 2008), cognitive (MacKay, 1973), and visual (Binda & Morrone, \n2018) levels. While majority of these theories were built upon the stability of basic visual properties, such \nas spatial displacement (Deubel et al., 1996) or changes in surface features of isolated objects (Weiß et \nal., 2015), there is increasing recognition on testing stability mechanisms in a more ecologically valid \ncontext (Choi et al., 2025). Here, we leveraged complementary use of behavioral and neural evidence and \ndemonstrated disrupted processing of a high-level visual attribute – semantic category information – \nwhen viewing naturalistic scene images. Our results further underscore the need for future research to \nexplore trans-saccadic perception in naturalistic settings with dynamic task demands to fully understand \nhow the brain achieves coherent visual experience in real-world contexts. \n \nCode availability \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted June 10, 2025. ; https://doi.org/10.1101/2025.06.06.658316doi: bioRxiv preprint \n\nPost-saccadic scene category processing \n \n 28 \nAll code for the experiments and analyses, as well as behavioral and raw fMRI data to obtain the \nresults reported in this manuscript are made publicly available on OSF \n(https://doi.org/10.17605/OSF.IO/X3D6N).  \n \nReferences \nBaldassano, C., Esteva, A., Fei-Fei, L., & Beck, D. M. (2016). 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