We internally represent the structure of our surroundings even when there is little layout information available in the visual image, such as when walking through fog or darkness. One way in which we disambiguate such scenes is through object cues; for example, seeing a boat supports the inference that the foggy scene is a lake. Recent studies have investigated the neural mechanisms by which object and scene processing interact to support object perception. The current study examines the reverse interaction by which objects facilitate the neural representation of scene layout. Photographs of indoor (closed) and outdoor (open) real-world scenes were blurred such that they were difficult to categorize on their own but easily disambiguated by the inclusion of an object. fMRI decoding was used to measure scene representations in scene-selective parahippocampal place area (PPA) and occipital place area (OPA). Classifiers were trained to distinguish response patterns to fully visible indoor and outdoor scenes, presented in an independent experiment. Testing these classifiers on blurred scenes revealed a strong improvement in classification in left PPA and OPA when objects were present, despite the reduced low-level visual feature overlap with the training set in this condition. These findings were specific to left PPA/OPA, with no evidence for object-driven facilitation in right PPA/OPA, object-selective areas, and early visual cortex. These findings demonstrate separate roles for left and right scene-selective cortex in scene representation, whereby left PPA/OPA represents inferred scene layout, influenced by contextual object cues, and right PPA/OPA represents a scene's visual features.
We recognize scenes at a glance, even though they contain rich and complex visual information (Potter, 1975). The ability to rapidly categorize scenes (e.g., as indoor or outdoor) has been shown to depend on scene-selective regions in visual cortex—regions defined by stronger neural responses to scenes and buildings than to isolated objects (Aguirre, Zarahn, & D'Esposito, 1998; Epstein & Kanwisher, 1998). For example, when activity in the scene-selective occipital place area (OPA) is disrupted by TMS, participants are less accurate in scene categorization, whereas object categorization remains unaffected (Dilks, Julian, Paunov, & Kanwisher, 2013; Ganaden, Mullin, & Steeves, 2013). This supports the distinction between object- and scene-selective pathways (Harel, Kravitz, & Baker, 2013; Mullin & Steeves, 2011; Park, Brady, Greene, & Oliva, 2011). In everyday life, however, scenes and objects are perceived together, and their processing heavily interacts, as observed in behavioral studies (Munneke, Brentari, & Peelen, 2013; Joubert, Rousselet, Fize, & Fabre-Thorpe, 2007; Oliva & Torralba, 2007; Davenport & Potter, 2004; Bar & Ullman, 1996; Biederman, Mezzanotte, & Rabinowitz, 1982). How are these interactions implemented in visual cortex?
In a recent study, we tested how scene processing in scene-selective cortex biases object processing in object-selective cortex (Brandman & Peelen, 2017). In that study, objects were hard to recognize on their own but easy to recognize when presented within their original scene context. This scene-based disambiguation of object processing was reflected in more distinct multivariate activity patterns in object-selective areas, with the strength of this effect being predicted by activity in scene-selective areas (Brandman & Peelen, 2017). In the current fMRI study, we investigated the reverse interaction, testing how object processing disambiguates scene perception. Similar to how scene context disambiguates object perception, the presence of objects allows us to interpret an otherwise ambiguous scene (e.g., Figure 1A), which often happens to us in darkness or fog, in which layout information is veiled. To our knowledge, this role of objects in scene perception has not been explored. Thus, to test for influences of object processing on scene representation, we examined the neural representation of scene category (indoor, outdoor) in blurred scenes, which were difficult to categorize on their own but easily disambiguated by inclusion of an intact object.
Prior studies have investigated the contribution of objects to scene representations both in behavior and neuroimaging. Behavioral studies have shown that scenes are perceived more accurately and more quickly when presented with a semantically congruent object than with an incongruent object (Joubert et al., 2007; Davenport & Potter, 2004). Furthermore, neuroimaging findings suggest that, although scene-selective areas more accurately decode spatial property-based judgments than object-based judgments (Linsley & MacEvoy, 2014), the scene-selective OPA and parahippocampal place area (PPA) are sensitive not only to scene layout but also to object information (Bainbridge & Oliva, 2015; Linsley & MacEvoy, 2015; Harel et al., 2013). Finally, PPA and the scene-selective retrosplenial complex (RSC) have been implicated in object-based contextual processing (Kveraga et al., 2011; Bar, 2004). These findings show that scene-selective areas may also carry object information. However, it remains unclear what role objects play in scene-selective representations. Here, examining the net effect of an object on the representation of an ambiguous scene allowed us to specifically test how object and scene information interact to support scene-selective representations.
In addition to revealing interactions between object- and scene-selective pathways, the current study addresses a recent debate about the representational content of scene-selective areas. With their discovery, the critical factor in scene-selective responses was found to be the presence of spatial layout information. It was therefore suggested that scene-selective areas represent place information by encoding the geometry of the local environment (Epstein & Kanwisher, 1998) as part of a network involved in spatial navigation (Epstein, 2008). One source of information about the spatial layout of scenes (e.g., open vs. closed) is provided by global visual features, with second-order image statistics being informative for scene category or scene “gist” (Torralba & Oliva, 2003). This raises the possibility that spatial layout information in scene-selective areas (Kravitz, Peng, & Baker, 2011; Park et al., 2011; Walther, Caddigan, Fei-Fei, & Beck, 2009) reflects the feedforward processing of such visual features. This view is in line with recent studies showing that scene selectivity itself can be (partly) explained by sensitivity to relatively low-level visual features, such as cardinal orientations and rectilinearity (Nasr, Echavarria, & Tootell, 2014; Nasr & Tootell, 2012; Zeidman, Mullally, Schwarzkopf, & Maguire, 2012; Rajimehr, Devaney, Bilenko, Young, & Tootell, 2011). Alternatively, scene representations in scene-selective areas may be more abstract, representing the layout of a scene as inferred based on all cues available to the individual (Peelen & Downing, 2017; Wolbers, Klatzky, Loomis, Wutte, & Giudice, 2011; Epstein, 2008).
Here we distinguish between accounts of abstracted versus visually driven scene-selective representations of layout by measuring the level of scene disambiguation gained by object cues (Figure 1C). These objects add contextual cues without adding visual features associated with spatial layout. We therefore predicted that regions representing global visual scene features should carry most information about scenes without objects, because objects are likely to attract attention, thereby engaging local content processing rather than global layout processing (Park et al., 2011). By contrast, regions representing inferred spatial layout should carry most information about scenes in which the layout information is disambiguated by the objects.
To measure the amount of scene category information gained by the inclusion of object cues, photographs of real-world indoor (closed) and outdoor (open) scenes were blurred, such that they were difficult to categorize on their own but easily categorized with an intact object overlaid on the scene in its original position. In this way, scene category (or scene layout), processed in scene-selective areas (Kravitz et al., 2011; Park et al., 2011; Walther et al., 2009), was disambiguated by object cues. The objects were also presented alone to assess the baseline level of scene category information carried by the objects themselves (Harel et al., 2013). We then measured scene category information using multivariate pattern analysis (MVPA; Haxby et al., 2001), with classifiers trained on the response patterns evoked by intact scenes in a separate experiment, and tested on responses evoked by degraded scenes, degraded scenes with objects, and objects alone (Figure 1B). Within each tested brain region, the effect of objects on contextual disambiguation of scenes was measured by the difference in decoding accuracy for degraded scenes with and without objects. Thereby, we were able to distinguish between areas representing inferred scene layout and areas representing visual scene features.
For degraded scenes, degraded scenes with objects, and for objects alone, we measured the multivariate representations of scene category in the fMRI signal while participants performed a 1-back task (see Procedure). In a separate pattern localizer used for classifier training, participants viewed intact indoor and outdoor scenes, fully visible and in high resolution, presented without objects. In addition, an ROI localizer served to localize scene- and object-selective ROIs in visual cortex. All procedures were approved by the ethics committee of the University of Trento.
We used a cross-decoding approach to compare intact scene representations in the pattern localizer to the representations evoked by degraded scenes with and without objects in the main experiment. The difference in response patterns to indoor and outdoor scenes was used as a test for scene category sensitivity, comparable across contextual conditions. This allowed us to measure the effect of objects on scene representations without directly comparing response patterns to degraded scenes with and without objects, which would have been confounded by their low-level visual difference (i.e., the addition of the object).
Nineteen healthy participants (eight women, mean = 24 years, SD = 2.98 years) were included. All participants had normal or corrected-to-normal vision and gave informed consent. Sample size was chosen to match that of previous work examining contextual effects of object and scene integration with similar fMRI decoding methods (Brandman & Peelen, 2017). Six additional participants were excluded from data analysis due to excessive head motion during scanning (n = 4), widespread anatomical artifact (n = 1), or failure to follow task instructions (n = 1).
The stimulus set consisted of degraded scenes that were perceived as ambiguous on their own but that were easily categorized when presented with an object. Both indoor and outdoor scenes included a mixture of animate and inanimate objects of various categories. We ensured that the scenes did not contain other objects contextually associated with the foreground objects. The main experiment included photographs of 30 indoor and 30 outdoor scenes. Sixty photographs of scenes from Unsplash (unsplash.com) and Pixabay (pixabay.com) were cropped and cleaned to include one dominant foreground object. Across scenes, the foreground objects had equal likelihood to appear to the left or right of scene center (binomial test of noncentralized objects; 25 left, 28 right; p = .39). The scene excluding the object was degraded (blurred), and contrast was reduced for the entire image. Scenes were saved with and without the object (object was edited out). The object was also saved in isolation on a uniform gray background of mean luminance of the original background. The final images (180 in total) included the degraded scenes, degraded scenes with objects, and objects alone (see samples in Figure 1A and the full set in Supplementary Figure 1, https://s3.amazonaws.com/science.taliabrandman.com/SuppFig1_allStim.jpg). To avoid familiarity effects passing from scenes with objects to scenes alone, the stimulus set was split into two, such that different scenes were presented for degraded scenes with objects and for degraded scenes alone within a given subject (e.g., for two stimuli, Beach1 and Beach2, a given participant would either view Beach1 alone and Beach2 with a boat, or vice versa). The objects alone matched the degraded scenes alone (participants who viewed Beach1 alone would also view its boat separately, but not embedded). The two sets were counterbalanced across subjects. The pattern localizer used included the 60 scenes from the main experiment (with different cropping), and an additional set of 60 new scenes that were matched for category and subcategory of the main experiment set in high resolution. All stimuli and fixation points were presented centrally at a visual angle of 5.99 × 5.99 degrees (400 × 400 pixels).
Stimulus Optimization and Selection
The stimulus set was optimized and validated in an online behavioral pilot experiment in Amazon Mechanical Turk, in which participants rated the degraded scenes' category on a scale of indoor–outdoor to compare the level of scene ambiguity with and without objects. Participants were asked to rate each degraded scene, presented either with its original embedded object or alone, on a scale from 1 = indoor to 8 = outdoor. Participants' ratings were normalized to a mean of 0 and standard deviation of 1. The final stimulus set, presented in the MRI, consisted only of scenes that were perceived as more ambiguous on their own than with objects (n = 29; indoor: t = 10.83, p < .001; outdoor: t = 15.69, p < .001), excluding scenes that were less or similarly ambiguous on their own than with objects, with a mean difference of 1.02 between normalized scores of scenes with and without objects. Forty-five additional scenes, beyond the 60 included in the main experiment, showing smaller differences in ratings (with vs. without an object), were tested in the piloting stages but were excluded from the final stimulus set.
On each trial, participants viewed a single briefly presented (80 msec) stimulus. Throughout all runs, participants performed a 1-back task in which they pressed a button each time an image appeared twice in a row. The main experiment consisted of five scanner runs of 352-sec duration, each composed of four fixation blocks and three blocks for each of the six conditions: indoor/outdoor × scene/scene with object/object (total 18). The main experiment was followed by a pattern localizer, which consisted of three scanner runs of 336-sec duration, each composed of five fixation blocks and four blocks per condition: indoor/outdoor × old/new (total 16). Each block consisted of 16 trials of the 15 exemplars and one target trial, in random order. Each stimulus was presented for 50 msec followed by a 950-msec fixation. This resulted in 240 trials (15 16-sec blocks, 120 2-sec volumes) per condition in the main experiment and 192 trials (12 16-sec blocks, 96 2-sec volumes) per condition in the pattern localizer. A category-selective localizer ended the scanning session, designed to identify scene- and object-selective areas. The localizer included 80 grayscale images per category of objects, scenes, bodies, and scrambled objects (i.e., a random mixture of pixels of each of the object images). It consisted of two scanner runs of 336-sec duration, each composed of five fixation blocks and four blocks per condition: scene/object/body/scrambled object (total 16). Each block consisted of 20 trials, in which a stimulus was presented for 350 msec followed by a 450-msec fixation.
Data Acquisition and Preprocessing
Whole-brain scanning was performed with a 4-T Bruker MedSpec MRI scanner using an eight-channel head coil. T2*-weighted EPIs were collected (repetition time = 2.0 sec, echo time = 33 msec, 73° flip angle, 3 × 3 × 3 mm voxel size, 1-mm gap, 30 slices, 192-mm field of view). A high-resolution T1-weighted image (magnetization-prepared rapid gradient echo; 1 × 1 × 1 mm voxel size) was obtained as an anatomical reference. The data were analyzed using MATLAB (The MathWorks, Natick, MA) with statistical parametric mapping (SPM). Each run was preceded by 12-sec fixation discarded from the analysis. Preprocessing included slice-timing correction, realignment, and spatial smoothing with a 6-mm FWHM Gaussian kernel. A General Linear Model (GLM) Hemodynamic Response Pattern (HRP) model was estimated for each participant for the univariate analyses.
Functional ROIs were defined using a two-step group-constrained subject-specific method (e.g., Julian, Fedorenko, Webster, & Kanwisher, 2012). The first selection step was based on results of a group analysis in Montreal Neurological Institute (MNI) space. Scene-selective areas were defined by contrasting activity evoked by scenes against objects, against scrambled objects, and against baseline activity. The PPA and OPA ROIs were generated by identifying temporal and occipital voxels in the ventral visual stream where all three contrasts garnered uncorrected p values of less than .01 at group level (random effects). The scene-selective RSC was too small to perform MVPA (also because of its variable dimensionality, with an average of 10 voxels in the right hemisphere and 2 voxels in the left hemisphere). It was therefore excluded from the main analysis. An alternative selection method was used to enable classification in RSC and surrounding voxels (see below). Using the same approach as with PPA and OPA, object-selective areas were defined by contrasting activity evoked by objects against scenes, against scrambled objects, and against baseline activity. The posterior fusiform gyrus (pFs) and lateral occipital (LO) ROIs were generated by identifying temporal and occipital voxels in the ventral visual stream where all three contrasts garnered uncorrected p values of less than .01 at group level (random effects). The second ROI selection step was performed for each participant separately, where group-selected ROIs were used as inclusion masks for individual ROI selection. Only the most significant 50 voxels of each ROI, as measured by individually estimated t values, were selected for multivariate analysis.
Retrosplenial Complex Selection
To preserve fixed dimensionality and allow for a reasonable number of features for classification, RSC was defined as the peak scene-selective voxel of the region for each participant separately, together with its surrounding sphere with a radius of 2 voxels. This resulted in an average of 37 voxels in the right hemisphere and 36 in the left hemisphere (after excluding null voxels).
Early Visual Areas
Early visual ROIs were defined separately for each participant, in each hemisphere, by selecting the most significant 50 voxels as measured by an individually estimated t contrast of scrambled objects against baseline activity, within Brodmann's area 17.
The data within each voxel were detrended and normalized (mean and standard deviation) across the time course of each run and shifted two volumes (4 sec) to account for the hemodynamic lag. The data were then averaged across blocks within each run, resulting in one block of eight volumes per condition per run. The sample matrix for each pairwise classification included both indoor and outdoor volumes, thereby resulting in 16 samples per run per condition. Multivariate analysis was performed using the CoSMoMVPA toolbox (Oosterhof, Connolly, & Haxby, 2016). A support vector machine (SVM) classifier discriminated between response patterns to indoor versus outdoor scenes. The decoding approach is illustrated in Figure 1B. First, decoding of intact scene category was measured within the pattern localizer by training on old scene trials (i.e., scenes included in the main experiment set; 48 samples) and testing on new scene trials (48 samples). Next, cross-decoding was achieved by training on all conditions of the pattern localizer (96 samples) and testing on each of the main experiment conditions (scene, scene with object, object; 80 samples each). For each participant, cross-decoding was performed across the voxels of each ROI, resulting in an overall accuracy score for the ROI for each of the three conditions.
To confirm there were no differences in classifier performance trained on old versus new scene trials, we compared cross-decoding trained on each of these sets separately, finding no differences between them: Repeated-measures ANOVA with Training set (old, new), ROI (PPA, OPA), Hemisphere (right, left), and Context (scene, scene with object, object) revealed no main effect of Data set, F(1, 18) = 2.38, p = .140, and no interactions between Data set and any other factor, F < 1.62, p > .219.
Controlling for Multiple Comparisons
All significant t tests and correlations reported remained significant when correcting for multiple comparisons within each section of the Results, using false discovery rate at a significance level of .05. We therefore report only the raw (uncorrected) p values of all tests.
Decoding Intact Scene Category in Scene-selective Cortex
We first assessed the representation of intact scene category (indoor vs. outdoor scenes) in scene-selective areas for the pattern localizer data. Results showed that scene category was strongly represented in four scene-selective areas, right PPA (decoding accuracy M = 72.37%), left PPA (M = 72.48%), right OPA (M = 64.80%), and left OPA (M = 68.86%), and was decoded significantly above chance, t(18) > 4.69, p < .001, for all regions. These results are in line with previous findings of scene category decoding in visual cortex (Park et al., 2011; Walther et al., 2009). Repeated-measures ANOVA of intact scene decoding with Hemisphere (right, left) and Region (PPA, OPA) revealed no main effects of Region, F(1, 18) = 4.12, p = .057, and Hemisphere, F(1, 18) = 0.88, p = .360, and no interaction, F(1, 18) = 1.22, p = .284.
Decoding Degraded Scene Category in Scene-selective Cortex
Next, we examined the representation of scene category in scene-selective areas, the left and right PPA and OPA, for each of the three main experiment conditions (Figure 2). Classifiers were trained on data from the pattern localizer and tested on the conditions in the main experiment using a cross-decoding approach (Figure 1B). Importantly, the effect of our context manipulation varied across hemispheres. Particularly, repeated-measures ANOVA with Region (PPA, OPA), Hemisphere (right, left), and Context (scene, scene with object, object) revealed a two-way interaction, F(2, 36) = 10.35, p < .001, between hemisphere and context, no interaction between region and context, F(2, 36) = 1.65, p = .205, and no three-way interaction, F(2, 36) = 0.30, p = .741. Considering this pattern of results, we examined the average decoding across PPA and OPA, separately for each hemisphere.
The category of degraded scenes could be reliably decoded in both hemispheres of scene-selective areas (against chance; t(18) > 7.98, p < .001, for both hemispheres). However, the role of objects in the representation of scene category varied across hemispheres, corresponding to the two hypothesized response profiles (Figure 1C). Particularly, in left scene-selective areas, objects significantly facilitated the decoding of degraded scenes compared with when the scenes were presented without objects, paired t(18) = 2.25, p = .037. This was not observed in right scene-selective areas, which showed a trend in the opposite direction, paired t(18) = 1.80, p = .089.
The scene-selective RSC was not included in the main analysis due to its small and variable dimensionality. Nevertheless, given the strong interest in RSC as an area involved in contextual object–scene interactions (Brandman & Peelen, 2017; Bar, 2004), we examined decoding in the RSC and its surrounding voxels using an alternative selection method (see Methods). Average decoding accuracy in these areas was 52.4 ± 1.6%, with no significant differences between conditions or hemispheres. Particularly, repeated-measures ANOVA with Hemisphere (right, left) and Context (scene, scene with object, object) revealed no main effects of Hemisphere, F(1, 18) = 0.16, p = .689, or Context, F(2, 36) = 2.90, p = .068, and no interaction between them, F(2, 36) = 1.73, p = .192. The effects of objects on scene decoding in RSC should be further examined in future studies using high-resolution fMRI or additional localizer data.
Univariate Differences in Scene-selective Cortex
To test whether hemispheric differences found in scene-selective areas were related to differences in overall activation in these regions, we examined their indoor and outdoor univariate BOLD responses. Data were processed similarly as for multivariate analysis, excluding the normalization step. Repeated-measures ANOVA with Region (PPA, OPA), Hemisphere (right, left), Context (scene, scene with object, object), and Scene category (indoor, outdoor) revealed a main effect of Scene category, F(1, 18) = 19.46, p < .001, with higher activity for indoor than outdoor scenes, replicating previous reports (Henderson, Larson, & Zhu, 2007). Importantly, scene category did not interact with context, region, or hemisphere (F < 2.52, p > .094, for all tests). Thus, the differences found in multivariate representations of scene category across experimental conditions and hemispheres cannot be explained by regional response-magnitude differences.
Decoding Scene Category from Objects in Scene-selective Cortex
Interestingly, results showed that objects presented in their original position on a gray background with the mean luminance of the original scene were sufficient predictors of scene category in left scene-selective areas (against chance; t(18) = 3.07, p = .007), but not in right scene-selective areas (against chance; t(18) = 1.60, p = .127). We hypothesized that this may reflect a similar (though reduced) perceptual inference as observed in the degraded scene with object condition, with the mean luminance background acting as a more extremely degraded scene. We therefore asked whether object-driven facilitation of blurred scenes was associated, across participants, with object-based decoding of mean luminance backgrounds. Results revealed that the level of object-driven facilitation, as measured by the difference in decoding accuracies between scenes with and without objects, was indeed highly correlated with decoding of objects on mean luminance backgrounds in left scene-selective areas, r(17) = 0.73, p < .001, but not in right scene-selective areas, r(17) = 0.39, p = .100 (Figure 3). These results provide further evidence for the hemispheric specificity of object-based facilitation and suggest a common origin for the effects observed in the scene-with-object and gray background-with-object conditions.
Decoding Degraded Scene Category in Early Visual Cortex
To test whether the hypothesized profile of visual scene representation (Figure 1C) correctly predicted low-level visual processing, we examined the decoding of scene category also in early visual cortex (EVC; Figure 4), using the same cross-decoding approach as for scene-selective areas (Figure 1B). Indeed, in EVC, objects did not carry scene category information nor did they facilitate scene decoding. Specifically, scene category was best decoded for scenes alone (against chance; t(18) = 5.82, p < .001) and also decoded above chance for scenes with objects (against chance; t(18) = 2.89, p = .010), but not for objects alone (against chance; t(18) = −0.74, p = .468). Decoding accuracy for scenes with objects was significantly higher than for object alone, paired t(18) = 3.29, p = .004, and similar to scenes alone, paired t(18) = 1.72, p = .102. Decoding did not vary between hemispheres, as shown by repeated-measures ANOVA with Hemisphere (right, left) and Context (scene, scene with object, object), revealing a main effect of Context, F(2, 36) = 11.75, p < .001, no effect of Hemisphere, F(1, 18) = 1.12, p = .303, and no interaction, F(2, 36) = 0.84, p = .438.
Decoding Degraded Scene Category in Object-selective Cortex
Finally, we examined representations of scene category in object-selective areas (Figure 5) using the same cross-decoding approach as for scene-selective areas (Figure 1B). Repeated-measures ANOVA with Region (LO, pFs), Context (scene, scene with object, object), and Hemisphere (right, left) revealed no main effects of Context, F(2, 36) = 2.40, p = .105, Region, F(1, 18) = 3.27, p = .087, and Hemisphere, F(1, 18) = 0.53, p = .477, and no interactions between them (F < 1.36, p > .259). Thus, in contrast to scene-selective areas in the left hemisphere, representations of scene category in object-selective areas were not facilitated by objects. This was further confirmed by a significant interaction between scene- and object-selective areas in the left hemisphere. Specifically, repeated-measures ANOVA of decoding in the left hemisphere, with Category selectivity (scene-selective, object-selective), Region (dorsal, ventral), and Context (scene, scene with object, object), revealed an interaction between Category selectivity and Context, F(2, 36) = 6.29, p = .004.
In the current study, we examined the contribution of contextual object cues to the neural representation of real-world scenes. The key finding was that objects affected scene representation in left, but not in right, scene-selective areas, with no difference between effects in OPA and PPA. The magnitude of contextual facilitation in left scene-selective areas was significantly correlated with scene category information carried by objects presented on gray background of mean luminance of the original scene. In addition, objects did not facilitate scene representations in EVC and object-selective areas. These results provide neural evidence for interactions between object and scene processing, with object processing facilitating scene representations in left scene-selective areas.
The basis for interpreting the current findings is that the dependent measure in all conditions was scene layout classification (indoor/outdoor). Within this framework, we show that objects, which are not an integral feature of scene layout, provide information sufficient to facilitate the representation of scenes in left scene-selective areas, but not in right scene-selective areas. This suggests an interaction between object and scene processing, with object processing informing scene representations in left PPA/OPA. This raises the question of whether object information was processed within the scene-selective cortex or fed to it by external regions. Taking into account the well-established functional dissociation between object- and scene-selective processing (Dilks et al., 2013; Ganaden et al., 2013; Harel et al., 2013; Mullin & Steeves, 2011; Park et al., 2011), we suggest that object information is most likely processed in object-selective cortex and thereafter relayed to scene-selective cortex for contextual facilitation. This is in line with our previous study, which provided evidence for the reverse interaction, with scene processing in scene-selective areas informing object representation in object-selective areas (Brandman & Peelen, 2017).
What appears to speak against this interpretation is the finding that the response to objects alone allowed for above-chance decoding of the layout of the scene from which the objects were taken (Figure 2). This raises the possibility that both objects and scenes are processed in (left) scene-selective areas. It should be noted, however, that the objects in the isolated-objects condition were still presented in their original scene position, were presented in an experimental context of scenes, and were overlaid on a background frame that had the mean luminance of the corresponding scene. In some of these cases, the object subjectively still evokes the percept of a scene or, at least, its coarse layout with the gray background simply being a more extremely degraded scene (e.g., traffic light and painting in Figure 1A). As such, we believe that the above-chance scene decoding in the isolated-objects condition may reflect the same interactive process as in the scenes-with-objects condition, though to a lesser extent. The strong positive correlation between these two effects is in line with this interpretation.
What is it about the object that facilitates scene category? We propose two plausible explanations. The first, following from the idea that left scene-selective representations are more semantically abstracted, is that the object provides semantic context. By this account, an object more likely to appear in an outdoor scene would bias the representation to open scene layouts, whereas an object likely to appear indoors would imply a closed layout. The second mechanism by which objects may facilitate scene representation is via a fill-in effect. As such, the objects may assist in disambiguating the 3-D spatial layout by providing relative cues of dimension and distance, thereby facilitating not just semantic interpretation but rather the visual percept of scene layout. Further research is needed to dissociate the contributions of each of these proposed mechanisms, for example, by examining effects for individual stimuli, which could not be examined within the current blocked presentation.
Regarding the debate about the representational content of scene-selective areas, we hypothesized that areas encoding high-level information about scene layout would exhibit higher discriminability of degraded scenes presented with objects than without, whereas areas that rely exclusively on information provided by scene-typical visual features would not benefit from contextual object cues (Figure 1C). The current findings provide evidence for both high-level views (Aminoff, Kveraga, & Bar, 2013; Kravitz et al., 2011; Park et al., 2011; Wolbers et al., 2011; Epstein, 2008) and low-level views (Nasr et al., 2014; Nasr & Tootell, 2012; Zeidman et al., 2012; Rajimehr et al., 2011) of scene-selective areas. Particularly, representations of scene category in the left PPA and OPA were facilitated by the presence of an object. This suggests that contextual cues increase the amount of information used to disambiguate open and closed scenes, even when low-level typical scene features are similar. Thus, scene-selective areas in the left hemisphere carry high-level representations of scene layout, beyond information carried by global visual scene features. Such representations may contribute to proposed roles of scene-selective areas in high-level functions, such as navigation (Epstein, 2008) and semantic context processing (Aminoff et al., 2013). In contrast, in the right hemisphere, the PPA and OPA did not appear to represent inferred scene layout but rather represented scene category based only on global visual scene features present in the image. Together, these findings demonstrate different roles for left and right PPA/OPA in the representation of scene layout.
Why are high-level representations of scene layout left lateralized? One possibility is that this lateralization reflects the more general role of the left hemisphere in semantic processing (Price, 2010), following the traditional left verbal/right visuospatial model. As discussed earlier, one interpretation of our findings is that scene representations in the left PPA and OPA are more semantically abstracted than scene representations in the right PPA and OPA. Alternatively, the observed lateralization may reflect hemispheric differences in spatial frequency sensitivity, with the left hemisphere being relatively more sensitive to high-frequency information and the right hemisphere to low-frequency information, for example, due to hemispheric differences in receptive field sizes (Sergent, 1983). Following this idea, left scene-selective areas may be more sensitive to high-frequency object cues, whereas right scene-selective areas would be better tuned to global low-frequency features. Both these hypotheses should be tested in future studies examining the lateralization of semantic representation and sensitivity to objects in the PPA and OPA.
To conclude, we found that objects play an important role in the processing of real-world scenes. Specifically, our results show that the representation of scene layout in PPA/OPA was facilitated by contextual object cues. Intriguingly, this effect was strongly left lateralized, demonstrating separate roles for left and right PPA/OPA in the representation of visual scenes, whereby left PPA/OPA represents inferred scene layout, influenced by contextual object cues, and right PPA/OPA represents a scene's global visual features.
The project was funded by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 659778 and by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 725970). This manuscript reflects only the authors' view, and the Agency is not responsible for any use that may be made of the information it contains.
Reprint requests should be sent to Talia Brandman, Corso Bettini, 31, 38068 Rovereto, Trento, Italy, or via e-mail: firstname.lastname@example.org.
This paper appeared as part of a Special Focus deriving from a symposium at the 2017 annual meeting of Cognitive Neuroscience Society, entitled, “Real World Neuroscience.”