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The effect of object type on building scene imagery – an MEG study

Previous studies have reported that some objects evoke a sense of local three-dimensional space (space-defining; SD), while others do not (space-ambiguous; SA), despite being imagined or viewed in isolation devoid of a background context. Moreover, people show a strong preference for SD objects when given a choice of objects with which to mentally construct scene imagery. When deconstructing scenes, people retain significantly more SD objects than SA objects. It therefore seems that SD objects might enjoy a privileged role in scene construction. In the current study, we leveraged the high temporal resolution of magnetoencephalography (MEG) to compare the neural responses to SD and SA objects while they were being used to build imagined scene representations, as this has not been examined before using neuroimaging. On each trial, participants gradually built a scene image from three successive auditorily-presented object descriptions and an imagined 3D space. We then examined the neural dynamics associated with the points during scene construction when either SD or SA objects were being imagined. We found that SD objects elicited theta changes relative to SA objects in two brain regions, the right ventromedial prefrontal cortex (vmPFC) and right superior temporal gyrus (STG). Furthermore, using dynamic causal modelling, we observed that the vmPFC drove STG activity. These findings may indicate that SD objects serve to activate schematic and conceptual knowledge in vmPFC and STG upon which scene representations are then built.


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Our lived experience of the world comprises a series of scenes that are perceived between the 31 interruptions imposed by eye blinks and saccades. Indeed, scene mental imagery has been shown to 32 dominate when people engage in critical cognitive functions such as recalling the past, imagining the 33 future and spatial navigation (Clark et  Baker, 2019), and yet they could influence how scene representations are constructed by the brain. 51 One object attribute that seems to play a role in scene construction was reported by Mullally 52 and Maguire (2011; see Kravitz et al., 2011 for related work). They observed that certain objects, when 53 viewed or imagined in isolation, evoked a strong sense of three-dimensional local space surrounding 54 them (space-defining (SD) objects), while others did not (space-ambiguous (SA) objects), and this was 55 associated with engagement of the PHC during functional MRI (fMRI). In other words, SD objects 56 seem to anchor or impose themselves on their surrounding space (e.g., the traffic light in Figure 1A) 57 and in doing so they define the space around them. By contrast, SA objects do not anchor themselves 58 in their surrounding space in the same way (e.g., a worn car tyre in Figure 1A). Participants in the 59 Mullally and Maguire study (2011) were asked to describe the difference between an SD and an SA 60 object, and a typical response included: "SD items conjure up a sense of space whereas SA items float 61 -they go anywhere." Many participants spontaneously used the word "float" when discussing SA 62 objects, emphasizing the detachment of these objects from an explicit sense of surrounding space. 63 Mullally and Maguire (2011) also found that SD objects tended to be less portable, maintaining a 64 permanent location, although a considerable number of SA objects had reasonably high permanence 65 also (e.g., a large hay bale in Figure 1A), suggesting that permanence, while certainly related to SD, is 66 not the sole basis of the SD effect. Indeed, object permanence has been linked to the RSC rather than and Maguire (2011) found that object size and contextual associations did not account for the SD effect. 69 In a subsequent behavioural study, participants showed a strong preference for SD objects when 70 given a choice of objects with which to mentally construct scenes, even when comparatively larger and 71 more permanent SA objects were available . Moreover, when 72 deconstructing scenes, participants retained significantly more SD objects than SA objects. It therefore 73 seems that SD objects might enjoy a privileged role in scene construction. 74 Mullally and Maguire (2011) examined SD and SA objects in isolation. However, given their 75 apparent influence during scene construction , in the current study we 76 compared neural responses to SD and SA objects while they were being used to build imagined scene 77 representations. We adapted a paradigm from Dalton et al. (2018) and Monk et al. (2020) where  78  participants gradually built a scene image from three successive auditorily-presented object  79 descriptions and an imagined 3D space. In order to capture the neural dynamics associated with the 80 points during scene construction when either SD or SA objects were being imagined, we leveraged the 81 high temporal resolution of magnetoencephalography (MEG). In previous MEG studies, changes in 82 vmPFC and anterior hippocampal theta were noted when participants imagined scenes in response to 83 scene-evoking cue words (Barry et al., 2019a(Barry et al., , 2019b, and when scene imagery was gradually built 84 , but the effect, if any, of SD and SA objects on brain responses remains unknown. 85 Here, we performed a whole brain analysis, and characterized the effective connectivity 86 between any brain regions that emerged from this analysis. While our main interest was in theta, we 87 also examined other frequencies. The obvious prediction, given the previous Mullally and Maguire 88 (2011) fMRI study, was that PHC would be engaged by SD objects. However, because all stimuli were 89 scenes, and the key manipulation of SD and SA objects within scenes was so subtle, we retained an 90 open mind about which brain areas might distinguish between the two object types. 91

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Participants 93 Twenty-three healthy, right-handed people (13 females; mean age = 25.35 years; standard deviation = 94 3.69) participated in the experiment. Participants were either monolingual native English speakers, or 95 bilingual native English speakers (i.e., they had English as their first language, but were also but fluent 96 in a second language). They were reimbursed £10 per hour for taking part which was paid at study 97 completion. The study was approved by the University College London Research Ethics Committee 98 (project ID: 1825/005). All participants gave written informed consent in accordance with the 99 Declaration of Helsinki. 100

Stimuli 101
Each stimulus comprised auditorily-presented SD and SA object descriptions (see examples in Figure  102 1A) and a 3D space. We used the same auditory object descriptions as Dalton et al. (2018). As part of 103 this previous study, the objects were rated as SD or SA. Using these stimuli, we found that SD and SA 104 objects were matched on utterance length (Z = 1.643, p < 0.1) and number of syllables (Z = 1.788, p < 105 0.074). Unsurprisingly, as outlined above, SD objects were rated as more permanent than SA objects 106 (Z = 5.431, p < 0.001). All objects were rated as highly imageable, obtaining a score of at least 4 on a 107 scale from 1 (not imageable) to 5 (extremely imageable). Objects in each triplet were not contextually 108 related to each other. Participants in the current MEG study were unaware of the SD-SA distinction. blocks. We emphasized the importance of following the cue configurations as precisely as possible. 118 This ensured matched eye movements between the scene and array conditions, consistency across 119 participants, and that objects were imagined as separate and non-overlapping. Participants then fixated 120 on the screen center (1 sec). During the scene construction task (~9 sec) ( Figure 1B), keeping their 121 eyes open whilst looking at a blank screen, participants first imagined a 3D grid covering 122 approximately the bottom two-thirds of the blank screen. Upon hearing each of three auditory 123 descriptions, one at a time, they imagined the objects in the separate, cue-specified positions on the 3D 124 grid. They were instructed to move their eyes to where they were imagining each object on the screen, 125 but also to maintain imagery of previous objects and the grid in their fixed positions. Each construction 126 stage consisted of a ~2 sec object description and a silent 1 sec gap before the presentation of the next 127 object. An additional 1 sec at the end of scene construction avoided an abrupt end to the task. By the 128 end of a trial, participants had created a mental image of a simple scene composed of a 3D grid and 129 three objects. Vividness of the entire scene was then rated on a scale of 1 (not vivid at all) to 5 130 (extremely vivid). An inter-trial interval (2 sec) preceded the next trial. 131 Participants imagined a total of 66 scenes (composed of 99 SD, 99 SA objects). Each object 132 description was heard only once. The order of presentation of SD and SA objects within triplets was 133 balanced across scenes with an equal number of SD and SA objects in the first, second and third 134 construction stages (33 in each). A control task (33 trials) involved participants attending to a backward 135 series of auditorily-presented numbers, and was designed to provide relief from the effortful 136 imagination task; it was not subject to analysis. Seven additional catch trials (5 scenes, 2 counting) 137 were also included and pseudorandomly presented across blocks to ensure that participants sustained 138 attention -participants pressed a button upon hearing a repeated object description or number within a 139 triplet. 140 The visit of each participant took 2.5 hours in total (including training, set up in the MEG scanner 141 and eye tracker calibration, the experimental task (~50 minutes) and the post-scan memory test). 142

Eye Tracking 143
Eye movements were recorded during the MEG scan using an Eyelink 1000 Plus (SR Research) eye 144 tracking system with a sampling rate of 2000 Hz. The right eye was used for calibration and data 145 acquisition. For some participants the calibration was insufficiently accurate, leaving 19 data sets for 146 the eye tracking analyses. 147

Surprise Post-Scan Memory Test 148
If we found a neural difference between SD and SA objects, it could be argued that this was due to a 149 difference in encoding success. For example, perhaps SD objects were more readily encoded into 150 memory than SA objects, even though there was no explicit memory encoding requirement in this task. 151 By conducting a surprise memory test for SD and SA objects immediately post-scan, we could examine 152 this issue. Participants were presented with a randomized order of all previously heard auditory object 153 descriptions and an additional 33 SD and 33 SA new object description (lures). After hearing each 154 item, they indicated whether or not they had heard the object description during the scan, and their 155 confidence in their decision (1 = low, to 5 = high). 156

Behavioral Data Analysis 159
In-scanner vividness was compared between SD-majority (where 2 out of the 3 objects were SD) and 160 SA-majority scenes (where 2 out of the 3 objects were SA) using a paired-samples t-test. Eye tracking 161 data were analyzed using two-way repeated measures ANOVAs. Memory performance was assessed 162 using the sensitivity index d', based on Signal Detection Theory (Macmillan and Creelman, 1990). We 163 also quantified the bias of participants, to control for any tendency participants might have to give one 164 type of response more than another. This response bias 'c' was calculated using the formula: c = -165 0.5(Hit Rate + False Alarm Rate). Differences in d' and c as a function of object type (SD, SA) and 166 construction stage (first, second, third) were each analyzed using a two-way repeated measures 167 ANOVA. Statistical analyses were performed in SPSS25 using a significance threshold of p < 0. 05

Source Reconstruction 181
Epochs corresponding to each construction period were defined as 0-3 sec relative to the onset of the 182 SD or SA object description, and concatenated across scanning blocks. Source reconstruction was 183 performed using a linearly constrained minimum variance (LCMV) beamformer (Van Veen et al.,  184 1997). This approach allowed us to estimate power differences between SD and SA objects in source 185 space within selected frequency bands: theta (4-8 Hz), alpha (9-12 Hz), beta (13-30 Hz), and gamma 186 (30-100 Hz). 187 For each participant, a set of filter weights was built based on data from the SD and SA conditions 188 within each frequency band, and a 0-3 sec time window encapsulating a construction period. 189 Coregistration to MNI space was performed using a 5 mm volumetric grid and was based on nasion, 190 left and right preauricular fiducials. The forward model was computed using a single-shell head model 191 (Nolte, 2003). At the first level, power in a particular frequency band was estimated to create one image 192 per object type (SD or SA) per participant. Images were spatially smoothed using a 12 mm Gaussian 193 kernel and entered into a second-level random effects paired t-test to determine power differences 194 between SD objects and SA objects across the whole brain. An uncorrected threshold of p < 0.001 with 195 a cluster extent threshold of >100 voxels was applied to each contrast, in line with numerous other 196 MEG studies (e.g., Barry  The contrast of primary interest was the direct comparison between SD and SA objects. As a 202 secondary issue, we were mindful that it was also of interest to know whether an effect represented an 203 increase or a decrease in power from baseline. Including baseline correction in the original beamformer 204 would be the usual way to examine this. However, this is challenging to implement in the case of the 205 current design because an SD or SA object was not necessarily preceded by a clean baseline. On each 206 trial, three object descriptions were heard one after the other. While the first object in a triplet was 207 preceded by a clean 1 sec fixation baseline, objects 2 and 3 had an object imagination stage preceding 208 them. Objects at different construction stages would therefore be corrected against different types of 209 baseline (i.e., object 1 vs preceding fixation, object 2 vs preceding object 1, and object 3 vs preceding 210 object 2). Conducting this analysis could have introduced spurious effects. Consequently, having 211 established that a difference was apparent between SD and SA objects when they were compared 212 directly, we then sought to ascertain the direction of power change. By using a separate beamformer 213 where the 1 sec pre-stimulus fixation period was the only baseline for all objects (whether a SD/SA or 214 first/second/third object), and hence did not overlap with any stimulus period, we were able to establish 215 the direction of power change for SD and SA objects in a straightforward way. 216

Dynamic Causal Modelling (DCM) 217
Brain areas identified in the whole brain SD versus SA beamformer provided the seed regions for the 218 subsequent effective connectivity analysis, which was conducted using DCM for cross spectral 219 densities ). This approach permitted us to compare different biologically plausible 220 models of how one brain region influences another, as well as mutual entrainment between regions 221 (Friston, 2009

Behavioral Data 228
There was no significant difference in the vividness of mental imagery between SD-majority (M = adherence to cue configuration instructions across participants (Figure 2). 239 In terms of recognition memory (see Table 1

Power Changes 246
A whole brain beamforming analysis revealed significant theta power attenuation for SD compared to 247 SA objects in only two regions: the right vmPFC (peak MNI = 12, 60, -8; t-value = 3.66; cluster size 248 = 1960) and right superior temporal gyrus (STG; peak MNI = 66, -6, -12; t-value = 3.76; cluster size 249 = 1197) ( Figure 3A). We did not observe any significant differences in theta power between permanent and non-255 permanent objects across the whole brain. 256 Analysis of alpha, beta and gamma showed no significant power differences across the whole 257 brain when SD and SA objects were compared. 258

Effective Connectivity 259
Having established a response to object type in the vmPFC and STG, we next sought to examine the 260 effective connectivity between these regions. We tested three simple hypotheses: (1) vmPFC and STG 261 are mutually entrained, (2) STG drives vmPFC, or (3) vmPFC drives STG. We embodied each 262 hypothesis as a DCM where models differed in which connection could be modulated by SD relative 263 to SA objects. BMS identified the winning model to be vmPFC driving STG during SD more so than 264 SA objects, with an exceedance probability of 91.62% ( Figure 3B, left panel). This model was also 265 the most consistent across participants ( Figure 3B, right panel). 266

267
In this study we focused on an object property, SD-SA, that has been shown to influence how scene 268 imagery is constructed . We found that while these object types were 269 being imagined during scene construction, SD objects elicited significant theta changes relative to SA 270 objects in two brain regions, the right vmPFC and right STG. Furthermore, the vmPFC drove STG 271 theta activity. 272 SD and SA objects were matched in terms of the vividness of mental imagery, eye movements 273 and incidental memory encoding. All objects were incorporated into the same three-object scene 274 structures within which the order of SD or SA object presentation and object locations were carefully 275 controlled. We also examined object permanence, and found that this property did not engage the 276 vmPFC or STG. Our findings are therefore unlikely to be explained by these factors. 277 Most of the brain regions typically associated with scenes did not respond to SD objects during 278 scene construction. This is likely because scene processing was constant throughout the experiment, 279 and so there was no variation required in the activity of these areas. It is notable that the PHC, which 280 was active during fMRI in response to SD objects when they were viewed or imagined in isolation and 281 devoid of a scene context (Mullally and Maguire, 2011), did not exhibit power changes during scene 282 construction. It may be that examining objects in isolation afforded a "purer" expression of SD 283 whereas, once these objects were included in scene building, higher-order areas then came online to 284 direct their use in constructing scene representations, a possibility that we discuss next. 285 Considering first the right STG, while this region has been linked to speech processing (e.g., 286 Hullett et al., 2016), the close matching of auditory stimuli and the absence of activity changes in other 287 auditory areas suggests this factor does not account for its responsivity to SD objects. Perhaps more 288 germane is the location of the STG within the anterior temporal lobe, a key neural substrate of semantic 289 and conceptual knowledge that supports object recognition ( this might indicate the scene is from a park. Although in the current study the scenes were deliberately 303 composed of semantically unrelated objects, this may not have impeded the STG in nevertheless 304 registering SD objects more so than SA objects because SD objects would normally offer useful 305 conceptual information to help anchor a scene. 306 The operation of the right STG might be facilitated by the right vmPFC. Converging evidence 307 across multiple studies has shown that the part of the vmPFC that was active in response to SD objects 308 plays a role in the abstraction of key features across multiple episodes (Roy et al., 2012). These 309 contribute to the formation of schemas, which are internal models of the world representing elements 310 that likely exist in a prototypical scene, based on previous exposure to such scenes (Tse et al., 2007;311 van Kesteren et al., 2013;Gilboa and Marlatte, 2017 influence over the right STG, indicating it may be engaging in top-down modulation of conceptual 320 object processing by the STG, specifically during the processing of SD objects. Our results may 321 therefore indicate that SD objects help to define a scene by priming relevant schemas in the vmPFC 322 which then guide conceptual processing in areas such as the STG. 323 There is another possible explanation for our findings. In the current study, the scenes were 324 deliberately composed of semantically unrelated objects, and this could have introduced ambiguity 325 about a scene's identity. vmPFC and STG engagement may therefore be evidence of additional neural 326 processing that was required to resolve incongruences inherent to acontextual scenes (Chiou and  The structure and timings of an example trial. Note that participants never saw visual objects. During 558 the task the participants imagined the simple scenes while looking at a blank screen. to SA objects revealed attenuation in the right ventromedial prefrontal cortex (R_vmPFC) and right 574 superior temporal gyrus (R_STG). Images are superimposed on the MNI 152 template and thresholded 575 at uncorrected p < 0.001. (B) Effective connectivity between R_vmPFC and R_STG was examined 576 using DCM. Three models were compared, with R_vmPFC driving R_STG theta activity during SD 577 compared to SA objects being the model that best explained the data (left panel). Log Bayes factors 578 per participant (right panel) showed positive to strong evidence for this model in most participants. 579 Participants for whom there was no conclusive evidence for either model are represented by black bars. 580 Where log Bayes factors exceeded five, bars are truncated and the exact values are adjacently 581 displayed. 582