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Article  |   June 2025
Effects of object-scene congruency with and without awareness
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Journal of Vision June 2025, Vol.25, 3. doi:https://doi.org/10.1167/jov.25.7.3
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      Weina Zhu, Jan Drewes; Effects of object-scene congruency with and without awareness. Journal of Vision 2025;25(7):3. https://doi.org/10.1167/jov.25.7.3.

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Abstract

Scene context has been shown to influence object recognition; it is not clear what level of visual processing is required for this effect to manifest. Specifically, it is unclear if such object/context interactions may exist in the absence of conscious awareness. By conducting experiments with and without the use of continuous flash suppression (CFS), we examined how context (background) congruency affects target recognition and response time. We used animal and vehicle images in natural or man-made scenes, which formed congruent/non-congruent image groups (100 images each). By comparing among three experimental designs (b-CFS, plain 2AFC, and 2AFC-CFS), we found the response time in the congruent scenes was significantly faster than in the incongruent scenes in plain 2AFC (without suppression). This congruency effect persisted only in the vehicle group when under b-CFS suppression. When combining the two paradigms (2AFC-CFS), the results replicated the congruency effect from the plain 2AFC condition. This indicates that the congruency effect does not emerge at the lowest levels of perception, but requires additional processing, necessitating a degree of conscious access.

Introduction
Every day, we are facing a rich and complex surrounding world, yet the processing required for the recognition of objects and scenes seems to be instantaneous and effortless for the human visual system. When we view an image, we can usually understand what is contained in it very quickly and easily. A very well-known example of this ability is animal categorization tasks in which participants are asked to decide whether there is an animal in the scene (Thorpe, Fize, & Marlot, 1996; Kirchner & Thorpe, 2006; Drewes, Trommershauser, & Gegenfurtner, 2011; Zhu, Drewes, & Gegenfurtner, 2013). In a go/nogo manual reaction task with very briefly presented targets (20 ms), accuracy is approximately 92% whereas simultaneously, response times can be as short as 390 ms (Thorpe et al., 1996). Saccadic latencies in a comparable 2AFC task achieved even faster responses (228 ms) when participants were asked to indicate which one of two images contained an animal (Kirchner & Thorpe, 2006), and the shortest reaction times in which participants could identify the target image with above-chance performance were on the order of 120 ms. This has given rise to the theory that a feed-forward pass through the ventral stream might be the underlying neural mechanism for this phenomenon (Thorpe et al., 1996; Kirchner & Thorpe 2006; Crouzet & Serre 2011). 
Like the animals contained in the natural scenes of the above studies, in real life, objects (either animate or inanimate) are rarely isolated but always embedded in an often complex visual scene. Many studies examined how visual categorization improves when objects and their surrounding context are semantically consistent (Palmer 1975; Bar 2004; Davenport & Potter, 2004; Oliva & Torralba, 2006; Joubert, Rousselet, Fize, & Fabre-Thorpe, 2007; Joubert, Fize, Rousselet, & Fabre-Thorpe, 2008; Mack & Palmeri, 2010; Munneke, Brentari, & Peelen, 2013; Leroy, Faure, & Spotorno, 2020). It is assumed that because our previous experience taught us the frequent occurrence of a given object in a specific context, our brains generate expectations, and contextual information can thus facilitate object recognition within scenes (Bar, 2004; Gronau, Neta, & Bar, 2008). The accuracy of object recognition may be higher in the appropriate context condition and lower in an inappropriate context condition (for example, an animal in a natural vs. a man-made environment) (Palmer, 1975; Bar, 2004; Davenport & Potter, 2004; Joubert et al., 2008; Munneke, et al. 2013; Leroy et al. 2020). This semantic consistency advantage has been found also for categorization of the scene's context in terms of its gist (for example, beach, forest, office) (Davenport & Potter 2004; Oliva & Torralba, 2006; Joubert et al. 2007; Mack & Palmeri 2010; Leroy et al., 2020). By comparing scene and object categorizations, either with isolated or mixed stimuli, Davenport and Potter (2004) found better performance when foreground (object) and background were congruent, whether participants had to categorize only the foreground object, only the background, or both (Davenport & Potter, 2004). By manipulating context, it is even possible to predispose participants to false recognition of objects (Palmer 1975) or to create false memories (Miller & Gazzaniga, 1998). 
Furthermore, the congruency effect between a scene and an object appeared very early in an ultra-rapid categorization task, in which context incongruence induced a 10% drop of correct hits and a 16-ms increase in median reaction times (406 ms in the congruent context and 421 ms in the non-congruent context), affecting even the earliest behavioral responses. Thus Joubert et al. suggested a parallel processing of both context and object information (Joubert et al., 2008). It has been also shown in monkeys that object categorization can be either enhanced or impaired by contextual background, depending on object/context congruency (Fize, Cauchoix, & Fabre-Thorpe, 2011; Collet, Fize, & VanRullen, 2015). 
There are several studies using even-related potentials to examine the time course of the congruency effects on object categorization (Ganis & Kutas, 2003; Mudrik, Lamy, & Deouell, 2010; Vo & Wolfe 2013; Mudrik, Shalgi, Lamy, & Deouell, 2014; Truman & Mudrik, 2018). Mudrik and et al. found that a frontocentral negativity for incongruent scenes started as early as ∼210 ms after stimulus presentation; thus they concluded that contextual information affects object model selection processes prior to full object identification (Mudrik et al., 2010; Mudrik et al., 2014). This negativity lasted for about 330 ms and a greater N300/N400 evoked by inconsistent objects, which resembles the N400 previously associated with semantic integration. Therefore it was interpreted as reflecting difficulties in semantic object-context integration, similarly to what happens with semantic violations during language processing (Ganis & Kutas, 2003; Bar, 2004). A P600 can be elicited by a spatially incongruent scene (such as a kettle on the kitchen floor), which is typically associated with processing syntactic anomalies in language. Thus Vo and collogues suggested that the P600 reflects the difficulty in integrating an incongruent object into its surrounding scene, similar to how the brain might revise an initial interpretation in language comprehension when faced with an unexpected or grammatically incorrect phrase (Vo & Wolfe, 2013). Note though that the P600 did not emerge, however, when the spatial incongruency violated physical rules (e.g., a hovering beer bottle). 
To further understand the neural substrates behind congruency effects, it would be interesting to know to what degree an effect of congruency on object categorization requires conscious access (or awareness, here to be used synonymously). Li and colleagues demonstrated that detecting target objects (e.g. animals) in natural scenes only requires little attention in a dual-task paradigm. They conclude that some visual tasks associated with “high-level” cortical areas may proceed in the near absence of attention (Li, VanRullen, Koch, & Perona, 2002; Li, VanRullen, Koch, & Perona, 2005). 
CFS is a variant of binocular rivalry in which a series of different masking patterns are continuously flashed to one eye, causing a static low contrast image presented to the other eye to be reliably suppressed for prolonged periods of time (Tsuchiya & Koch, 2005; Tsuchiya, Koch, Gilroy, & Blake, 2006). Using CFS as methodology, a growing number of studies found that some stimuli that do not reach conscious awareness and are thus “invisible” are still processed to some degree by the visual system. Although the exact functioning and underlying neural substrate of the technique is still under investigation (Sterzer, Stein, Ludwig, Rothkirch, & Hesselmann, 2014; Han, Lunghi, & Alais, 2016; Zhu, Drewes, & Melcher, 2016; Moors, Hesselmann, Wagemans, J., & van Ee, 2017; Han, Lukaszewski, & Alais, 2019; Stein & Peelen 2021; Drewes, Witzel, & Zhu, 2023), numerous studies have delivered evidence that the unconscious (or unaware) processing of stimuli under CFS includes low-level visual features, such as orientation (Montaser-Kouhsari, Moradi, Zandvakili, & Esteky, 2004; Rajimehr 2004; Bahrami, Carmel, Walsh, Rees, & Lavie, 2008), spatial information (van Boxtel, Tsuchiya, & Koch, 2010), motion (Kaunitz, Fracasso, & Melcher, 2011) (see the review [Lin & He, 2009]), and the binding of low-level visual features based on Gestalt grouping cues, such as good continuation and proximity (Mitroff & Scholl, 2005). It has also been reported that “high-level” stages of visual processing may be possible without being aware of the percept, for example in face inversion (Jiang, Costello, & He, 2007; Zhou, Zhang, Liu, Yang, & Qu, 2010; Stein, Hebart, & Sterzer, 2011), face expressions (Jiang et al. 2009; Smith 2012), semantic information (Hebrew and Chinese writing) (Jiang, et al. 2007; Costello, Jiang, Baartman, McGlennen, & He, 2009; Kang, Blake, & Woodman, 2011) but see (Moors, Boelens, van Overwalle, & Wagemans, 2016). As part of higher-level visual processing, can object-scene integration happen without conscious awareness? Mudrik and colleges, using scenes that were manually edited to be either congruent or incongruent in a breaking CFS paradigm with two alternative horizontal target positions, showed that complex scenes including incongruent objects broke perceptual suppression faster than congruent scenes. These results provided evidence of integration between an object and its background without conscious awareness (Mudrik, Breska, Lamy, & Deouell, 2011). 
However, Moors and colleges, using a comparable experimental paradigm with the same stimuli, failed to replicate Mudrik's results (Moors, Boelens, van Overwalle, & Wagemans, 2016). In their experiments, they did not find evidence for a scene-congruency effect. These findings created a dispute of whether visual awareness is necessary in the semantic processing of object-background relationships in scenes. In Mudrik's following study (Biderman & Mudrik, 2018), using a forward/backward masking paradigm instead of CFS, they failed to find evidence for integration of objects and scenes without awareness as well. But in the same study, they concluded that the processing of congruency may yet be possible independent of the participant's awareness. Until now, there still has been no general consent regarding the need of conscious awareness for object-scene integration or the processing of scene congruency/incongruency. Therefore the aim of this study is to investigate whether such object/context interactions exist in the absence of awareness to further understand what kind of visual processing is possible in the absence of conscious awareness. 
Repeatedly, animal stimuli have been shown to differ from non-animal stimuli in aspects like response time and the attraction of attentional resources (New, Cosmides, & Tooby, 2007; Ohman 2007, Mormann et al., 2008; Mahon, Anzellotti, Schwarzbach, Zampini, & Caramazza, 2009; Crouzet, Joubert, Thorpe, & Fabre-Thorpe, 2012; Yang et al., 2012; Drewes, Zhu, Wutz, & Melcher, 2015). Crouzet and colleges found that participants could detect animal stimuli as quickly as 120 ms after presentation in an ultra-rapid categorization task. This detection time was significantly faster compared to non-animal stimuli (180ms) (Crouzet et al., 2012). By using EEG, it was demonstrated that human can start distinguishing animal images from non-animal images in about 150 ms after stimulus onset. The decision to press a button indicating animal presence took place within 300 to 350 ms, showing how quickly animals are processed (Thorpe et al., 1996). In a change-detection paradigm of complex natural scenes, subjects are substantially faster and more accurate at detecting changes in animals relative to changes in inanimate objects, even vehicles, New and colleagues suggested it is an evolutionary bias toward detecting biologically relevant stimuli (New et al., 2007). However, in Li and colleagues' study, by comparing the “animal” and “vehicle” category tasks in a dual-task paradigm where attention was nearly absent, they suggested that categorization of natural scenes in the near absence of attention might be a general phenomenon, not limited to evolutionarily relevant object categories (e.g., animals) (Li et al., 2002; Li et al., 2005). 
This provides evidence for a fast mode of visual processing that functions when there is awareness of the stimulus, even if not attended. It is thus interesting to investigate whether this kind of processing can function in the absence of awareness and whether interactions between object and background still exist in this state. 
Experiment 1: b-CFS (with suppression)
Ethics statement
All experiments were approved by the local Ethics Committee of Yunnan University and performed according to the principles expressed in the Declaration of Helsinki. 
Apparatus and stimuli
Visual stimuli were displayed on a 20-inch SUN CRT monitor (1024 × 768 pixel resolution, 120Hz frame rate). Participants’ heads were stabilized by a chin-and-head rest, while viewing the presentation through a mirror stereoscope. The physical viewing distance was 57 cm, although the optical distance was increased by approximately 9.5 cm because of the detour through the mirror stereoscope. The spatial distance between the left and right presentation areas, as well as the angle of the mirrors, were adjusted for each observer to achieve good fusion of the display. Visual stimuli were presented in MATLAB (The MathWorks Inc., 2012) using the Psychophysics Toolbox (Brainard, 1997). 
Color photographs of real-world scenes were gathered from various sources, including Google image search and the LabelMe online database (Russell, Torralba, Murphy, & Freeman, 2008). The animal target scenes (N = 200) included mammals and birds, and the vehicle target scenes (N = 200) included cars, trucks, trains, airplanes, and boats. Half of the target images within each category were “congruent,” meaning that the target object was shown in a typical scene background (e.g., a deer in the grassland, a car in a street scene), and half of the target images within each category were “incongruent,” meaning that the target object was shown in a less-common scene background (e.g., a bear on the highway, an airplane on a field). The images were used as found and not composed from separate objects and backgrounds. The physical resolution of each stimulus was 320 × 320 pixels, corresponding to approximately 10.9°of visual field as seen by the participants. Examples of the stimulus images are shown in Figure 1
Figure 1.
 
Sample stimulus images (congruent and incongruent). Up: animals. Bottom: vehicles.
Figure 1.
 
Sample stimulus images (congruent and incongruent). Up: animals. Bottom: vehicles.
It is difficult to generate perfectly balanced, yet natural-looking congruent/incongruent image pairs, even when using artificially composed images (pasting an object in front of a background). Two different objects pasted on the same background will typically not cover the exact same area of the image, leaving different parts of the background visible. Also, when pasting objects into scenery, differences in lighting, camera distance and settings will possibly leave artefacts (e.g., different highlight angles or unnatural looking boarders between object and background), which may affect the visual appearance of the stimuli (and thus participant performance). Here, we decided to use entire images rather than scenes composed from separate background/object pairs to avoid any distracting effects resulting from cut-and-paste. This approach ensures that all images are always physically possible (because they are real) and do not appear “photo-shopped” (i.e., synthetic or forged). We took care, however, that in most cases, the type of background found in incongruent scenes from one target category was also found as a scene background of congruent scenes from the other target category: Most incongruent animal targets were shown in urban backgrounds, the same as most congruent vehicles, and most incongruent vehicle targets were shown in nature-related scenes, the same as most congruent animals. However, because images were used in complete form, backgrounds between congruent and incongruent scenes were only similar and not identical. To minimize global differences between target categories, images were averaged for brightness and contrast by grayscale histogram equalization using the SHINE toolbox (Willenbockel et al., 2010). 
All the processed pictures were tested in a separate post-experimental session after the main session, in which each participant viewed each scene again for one second and then answered the question, “How congruent was the picture, in your opinion?” Responses were made on a scale ranging from 1 (incongruent) to 5 (congruent). Pictures from the congruent set received predominantly congruent ratings (3.69 ± 0.21 for all; 3.54 ± 0.34 for animals and 3.84 ± 0.15 for vehicles), whereas incongruent pictures got predominantly incongruent scores (2.31 ± 0.29 for all, 1.93 ± 0.40 for animals and 2.68 ± 0.27 for vehicles). The rating difference showed the congruent pictures were rated significantly more congruent than the incongruent pictures (t(23) = 22.9, p < 0.001), also separately for animals (t(23) = 18.2, p < 0.001) and vehicles (t(23) = 19, p < 0.001), validating the collected image sets for use in our experiments. We note, however, that there were significant differences between the congruent (t(23) = 4.7, p < 0.001) and incongruent (t(23) = 10, p < 0.001) ratings of animals compared to vehicles. All above t-test results are robust against the Bonferroni correction. 
Participants
Participants were 24 (12 females and 12 males) students from Yunnan University (mean age 22 years, range 19–25 years, SD = 1.98). Sample size was chosen similar to other studies in the field e. g. (Mudrik et al., 2011; Leroy et al., 2020; Drewes et al., 2023). 
All participants were naive with respect to the purposes of the study and reported normal or corrected-to-normal sight and no psychiatric or neurological history. The right eye was found to be the dominant one in 20 participants. All participants took part in all three experiments, in the same order, with an interval of one week in between. 
Procedure
In this experiment we used a breaking CFS paradigm. Using a mirror stereoscope, the masks were flashed to the dominant eye at a predefined frequency (10Hz), while the target stimulus image was presented to the other eye, as illustrated in Figure 2. The experiment started with a stereoscope calibration phase to ensure optimal fusion between both eyes. Each trial began with a 300∼500-ms presentation of the fixation cross. Subsequently, Mondrian-style masks were presented to the participant's dominant eye at full contrast, and the target stimulus was presented to the other eye. The contrast of the target stimulus was ramped up gradually from 0% to 100%. The contrast values were computed for each frame by rounding a precise (stepless) contrast ramp to the precision of the display system (256 steps, 8-bit). After six seconds, when the target stimulus had reached full contrast, the target stimulus remained on screen at full contrast for one second further. The trial ended when the observer pressed a key to indicate the detection of any part of a target stimulus or when the full seven seconds of target-on time expired. 
Figure 2.
 
Experimental paradigm of Experiment 1: breaking-CFS.
Figure 2.
 
Experimental paradigm of Experiment 1: breaking-CFS.
The experimental session included 500 CFS trials (400 + 100 catch trials without target), divided into four blocks. In each block, congruent scenes were presented in half of the trials, and incongruent scenes were presented in the other half. The two scene types (congruent vs. incongruent) were intermixed and their sequence of appearance was random. 
Before the experiment, participants performed a training session to ensure that they were able to perform the task for the experiment and understood the requirements. In this session, the stimuli were picked from a training set, separate from the experimental set. The procedure of the training sessions was the same as in the experimental sessions and contained 100 trials. 
Results
Participants responded on average to only 5.5% ± 3.6% of the catch trials, suggesting good following of the experimental instructions. The median response times were analyzed by an ANOVA design for repeated measures with the target type (animal vs. vehicle) and the scene type (congruent vs. incongruent) as within-subject factors. 
There was a significant main effect of target type, F(1,23) = 7.69, p = 0.011. The response time with animal scenes (2642 ± 143 ms) was longer than with vehicle scenes (2577 ± 141 ms) (see Figure 3A). The response time of congruent scenes (2588 ± 141 ms) was smaller than incongruent scenes (2631 ± 144 ms), but the difference (23 ms) was only marginally significant, F(1,23) = 3.53, p = 0.073, (see Figure 3B). 
Figure 3.
 
Results from Experiment 1 (b-CFS). (A) Response times of animals versus vehicles. (B) Response times of congruent versus incongruent stimuli. (C) Four-way response times.
Figure 3.
 
Results from Experiment 1 (b-CFS). (A) Response times of animals versus vehicles. (B) Response times of congruent versus incongruent stimuli. (C) Four-way response times.
There was an interaction between target type (animal, vehicle) and scene type (congruent, incongruent), F(1, 23) = 9.39, p = 0.005. It showed significant difference between congruent and incongruent scenes with vehicle objects (2516 ms vs. 2639 ms, p = 0.007), but not with animal objects (2660 ms vs. 2624 ms, p = 0.182). With congruent scenes, vehicles had significantly faster RTs than animals (2660 ms vs. 2516 ms, p = 0.001), but not with incongruent ones, where animals were slightly faster by trend (2624 ms vs. 2639 ms, p = 0.658, see Figure 3C). 
Discussion
The goal of Experiment 1 was to investigate whether interactions between an object and the surrounding scene exist in the absence of awareness, and whether the privilege of animal stimuli exists in the interactions between object and background without awareness. The results of Experiment 1 showed that congruent scenes had shorter response times than incongruent scenes with vehicle objects but not with animal objects. On the other hand, we found longer response time for the stimuli with animals, which is inconsistent with the results found in previous studies in the conscious condition (New et al., 2007; Ohman 2007; Mormann et al., 2008; Crouzet et al., 2012; Yang et al., 2012; Drewes et al., 2015). This effect was carried by the congruent stimuli only, with no significant effect found in the incongruent ones. Although the significant congruency effect within the vehicle images corresponds well with the high congruency ratings of the congruent vehicle images, the actual difference of the ratings between congruent and incongruent images is larger for the animal images—yet there was no significant effect found within these, making it seem unlikely that the results seen in Figure 3C are driven solely by the differences in congruency ratings between animals and vehicles. Most classic studies showing a response time advantage for animal stimuli do however use 2AFC-style paradigms for object recognition (Kirchner & Thorpe, 2006; Crouzet et al., 2012; Drewes et al., 2015). To better compare our b-CFS results with previous studies in the conscious condition, we designed Experiment 2, using a 2AFC paradigm (without suppression). 
Experiment 2: 2AFC (without suppression)
The participants, setup and stimuli were exactly the same as in Experiment 1. To improve comparability with the CFS conditions, a monocular display modality using the same stereoscopic setup as before was used, even though no CFS suppression was used. 
Procedure
The experimental session included 400 2AFC trials, divided into four blocks. In each block, congruent scenes were presented in half of the trials, and incongruent scenes were presented in the other half. The two scene types (congruent vs. incongruent) were intermixed and the sequence of appearance was random. 
The experimental paradigm is depicted in Figure 4. Each trial began with a randomized 300∼500 ms presentation of a fixation cross. Next, the target scene was presented to the participant's non dominant eye at full contrast for 16.67 ms. Then Mondrian-style masks were presented to both of the participant's eyes to avoid possible effects of after images. Participants were requested to press the “←” or “→” keys to choose either animal or vehicle, resting their hands on or near the keys between trials. The trial ended when the observer pressed a key to complete the classification of a scene or after five seconds, whichever happened first. 
Figure 4.
 
Experimental paradigm of Experiment 2 (2AFC)
Figure 4.
 
Experimental paradigm of Experiment 2 (2AFC)
Before the experimental session, participants performed training trials (N = 100) to ensure that they were able to perform the task and understood the requirements of the experiment. The stimuli for the training trials were picked from the same training set as in Experiment 1. The procedure of the training session was identical to the experimental session. 
Results
Similar to Experiment 1, median response times and accuracy were analyzed by an ANOVA design for repeated measures with target type (animal vs. vehicle) and scene type (target-background relationship: congruent vs. incongruent) as within-subject factors. As Experiment 2 used a 2AFC paradigm, we limited our analysis to the correct answers; however, as accuracy was very high (see below), the resulting number of excluded trials was small. 
Both main effects of response time were significant. The response time of animal scenes (607 ± 15 ms) was shorter than vehicle scenes (635 ± 18 ms; F(1,23) = 13.92, p = 0.001). The response time of congruent scenes (610 ± 15 ms) was smaller than incongruent scenes (632 ± 17 ms; F(1,23) = 31.25, p < 0.001; Figures 5A and 5B). There was no interaction between target type and scene type (F(1,23) = 0.14, p = 0.71). For both animal and vehicle, congruent scenes were faster than incongruent scenes (animal: 597 ms vs. 618 ms, 20.83 ms faster, p < 0.001; vehicle: 623 ms vs. 647 ms, 23.28 ms faster, p < 0.001; Figure 5C). 
Figure 5.
 
Results from Experiment 2 (2AFC without suppression). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
Figure 5.
 
Results from Experiment 2 (2AFC without suppression). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
The accuracy of animal scenes (96.1%) was higher than vehicle scenes (93.2%; F(1,23) = 14.39, p = 0.001; Figures 5D, 5E). The accuracy of congruent scenes (96.1%) was higher than incongruent scenes (93.2%; F(1,23) = 18.59, p < 0.001; Figure 5F). There was only a marginally significant interaction between target type and scene type (F(1,23) = 4.09, p = 0.055). The effect of congruency was significant for both animal stimuli (F(1,23) = 7.80, p = 0.01) and vehicle stimuli (F(1,23) = 18.71, p < 0.001). 
Discussion
The goal of Experiment 2 was to replicate the response time advantage for animal stimuli observed in earlier studies by using 2AFC-style paradigms for object recognition (Kirchner & Thorpe, 2006; Crouzet et al., 2012; Drewes et al., 2015), as well as to compare these results with the previous studies in consciousness and our result in the unconscious condition in Experiment 1. The results of Experiment 2 showed that animal scenes had shorter response times than vehicle scenes, and the response times of congruent scenes was faster than incongruent scenes too. In both cases, no speed-accuracy trade-off was found. These results are consistent with the previous findings in the context of object recognition and congruency effects (Palmer, 1975; Biederman, Mezzanotte, & Rabinowitz, 1982; De Graef, Christiaens, & d'Ydewalle, 1990; Boyce & Pollatsek; 1992, De Graef, De Troy, & D'Ydewalle, 1992). 
In the aware condition, we found a main congruency effect with both target categories. In the unaware (suppressed) condition, the congruency effect only reached significance in the vehicle category. When comparing the paradigms we used in Experiment 1 and 2, we noticed that in the conscious condition (2AFC) participants were required to press separate buttons for animals and vehicles, which is a decision involving categorization, requiring recognition of the objects. In the unconscious condition (b-CFS), however, participants were requested to press a single button once they saw the images or any part of the images, which requires object detection rather than recognition. The task difference (detection vs. recognition) between conscious and unconscious conditions may thus have affected the outcome. We endeavor to resolve this in Experiment 3
Experiment 3: 2AFC-CFS (with suppression)
In this experiment, to exclude the task difference between conscious and unconscious conditions, we changed the task of the b-CFS into a 2AFC task. Participants were asked to press different buttons for animals and vehicles during a CFS paradigm (named here 2AFC-CFS). In this case, participants were required to perform object recognition in a CFS paradigm, to improve comparability between conscious and unconscious conditions. 
Procedure
The participants, setup and stimuli were again the same as in Experiments 1 and 2. The training and experimental session were the same as in the Experiment 1, except in this experiment participants were requested to press the “←” or “→” keys to decide between animal and vehicle targets instead of the single-key response modality from Experiment 1
Results
The analysis was similar to Experiment 2. With the median response times of Experiment 3, both main effects were significant (target type F(1,23) = 13.76, p = 0.001, and scene type F(1,23) = 30.43, p < 0.001). The response times to animal stimuli (2787 ms) were on average 139ms longer than those to vehicle stimuli (2648 ms; see Figures 6A, 6B). The response times of congruent scenes (2654ms) were on average 128ms shorter than those of incongruent scenes (2782 ms). This trend existed in both animal and vehicle stimuli (animals: 2716 ms vs. 2859 ms, 143 ms shorter, p = 0.003; vehicles: 2591 ms vs. 2705 ms, 1114 ms shorter, p < 0.001; see Figure 6C). There was no significant interaction between target type and scene type (F(1,23) = 0.30, p = 0.587). 
Figure 6.
 
Experimental paradigm of experiment 3 (2AFC-CFS).
Figure 6.
 
Experimental paradigm of experiment 3 (2AFC-CFS).
The accuracy of animal scenes (98.2%) was similar to that of vehicle scenes (98.7%); F(1,23) = 1.75, p = 0.199, while the accuracy of congruent scenes (98.8%) was higher than that of incongruent scenes (98.1%); F(1,23) = 4.52, p = 0.001 (see Figure 7). There was no significant interaction between target type and scene type (F(1,23) = 0.26, p = 0.617), and no speed/accuracy tradeoff was found. 
Figure 7.
 
Results of Experiment 3 (2AFC-CFS). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
Figure 7.
 
Results of Experiment 3 (2AFC-CFS). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
Discussion
In Experiment 3, the response time to animal stimuli was longer than that to vehicle stimuli. This is consistent with the results in Experiment 1 but opposite with the results in Experiment 2. The differences between animals and vehicles in response time in the unaware (suppressed) condition appear to be opposite of those found in the aware condition and are thus not caused by the difference in task (recognition vs. detection). 
In Experiment 3, the response time of congruent scenes was shorter than that of incongruent scenes, similar as in Experiment 1 and Experiment 2. This means the congruency effects found in the unaware and aware conditions are consistent. 
Discussion and conclusions
The goal of this study was to investigate whether the processing of scene congruency/incongruency (or object/context interactions) exist in the absence of awareness. Furthermore, does the privilege of animal stimuli exist in the interactions between object and background without awareness. In Experiment 1, using a b-CFS paradigm, we observed that congruent scenes had shorter response times than incongruent scenes, but this difference was only marginally significant. Further analysis showed that the difference between congruent and incongruent scenes was significant with vehicle targets but not with animal targets. On the other hand, in Experiment 1, the response time for animal stimuli was longer than for vehicle stimuli. This was opposite to the results found in previous studies in the conscious condition (New et al., 2007; Ohman, 2007; Mormann et al., 2008; Crouzet et al., 2012; Yang et al., 2012; Drewes et al., 2015). A possible explanation may be found in the search/detection strategy used by our participants; although instructed to respond as soon as they saw any part of any target (and thus without instructed preference of one or the other target category), they may have been actively looking for one of the two target types more than the other, resulting in shorter response times for that category (here: vehicles). Such behavior as a proxy strategy has been postulated before (Drewes et al., 2015). When expecting (or actively looking for) one type of target, response times to the appearance of this stimulus category may be slightly faster (Stein & Peelen, 2015; Stein 7 Peelen, 2017; Stein, Utz, & van Opstal, 2020). The same expectation may have facilitated the appearance of congruency effects for the expected target category: together with the expectation of, for example, with a vehicle one might expect a road in the background but not a forest or ocean. 
Experiment 1 was a breaking-CFS paradigm, and differences in response (or break-through) time are generally considered indications of differences in unconscious/unaware processing (before breakthrough) rather than in aware processing after breakthrough. The results ran contrary to findings in previous studies in the aware condition. To replicate the previous studies (in the conscious/aware condition) with the same stimulus material and the same participants and to compare with our result in unconsciousness in the b-CFS experiment, we ran Experiment 2. Considering the response time advantage for animal stimuli observed in the most classic studies by using 2AFC-style paradigms for object recognition (Kirchner & Thorpe 2006; Crouzet et al., 2012; Drewes et al., 2015), we used a 2AFC paradigm in Experiment 2, which was designed to be comparable to previous studies, except for the use of monocular target presentation. In the aware condition in Experiment 2, we did observe the same results with previous studies: animal stimuli had shorter response times than vehicles and the response time of congruent scenes was faster than incongruent scenes (Palmer, 1975; Biederman et al., 1982; De Graef et al. 1990; Boyce & Pollatsek, 1992; De Graef et al., 1992). 
In the unaware (suppressed) condition in Experiment 1, the congruency effect was overall only marginally significant, possibly because of existing (as far as statistical significance is concerned) only within the vehicle images (and even there only within the congruent ones). Although the congruency ratings (stronger in congruent vehicle images) might explain the partial effect in Experiment 1 found only with these, we can still exclude that the unexpected overall results we found in Experiment 1 originated from either our choice of stimuli or an unusual batch of participants, because both were the same throughout the entire study. 
However, it would be hasty to conclude from these results that congruency effects with animal images do not exist without awareness: in the unconscious condition (b-CFS), participants were requested to press a single button once they saw any part of the stimulus images, which required them to perform (indiscriminate) object (or stimulus) detection. In the conscious condition (2AFC) however, participants were required to press separate buttons for animals and vehicles, which required object discrimination (recognition). Differences between the experiments may therefore originate either from difference between conscious and unconscious conditions, or from the difference in perceptual task (detection/discrimination). To address this, we designed Experiment 3. Participants were asked to press different buttons for animals and vehicles during a CFS paradigm (named here 2AFC-CFS). In Experiment 3, participants were required to perform object recognition in a CFS paradigm. This was intended to avoid the task difference between conscious and unconscious conditions, therefore improving comparability between conscious and unconscious conditions. 
The results from Experiment 3 showed longer response times to animal stimuli than that to vehicle stimuli. This is consistent with the results in Experiment 1 (with suppression) but opposite with the results in in Experiment 2 (without suppression) and earlier studies in the conscious condition. This suggests that the difference between animals and vehicles on response time in the unaware (suppressed) condition is opposite from that found in the aware condition and is not caused by the difference in task (recognition vs. detection). 
When we compared the response times between Experiment 1 and Experiment 3, we found the response time for detection (Experiment 1) was generally shorter than recognition (Experiment 3; see Table 1). This could be explained by the 2AFC-CFS task actually including two processing steps: detection and recognition, with recognition requiring additional time after the initial stimulus detection. We do note, however, that the time difference between the b-CFS and the 2AFC-CFS condition is on average in the order of 80 ms, which is faster than the results obtained in previous CFS studies (see introduction, at least 120ms but typically more than 200 ms). However, the processing requirements between the b-CFS and 2AFC-CFS most likely overlap substantially, so we do not believe that a simple addition of the response times of the b-CFS paradigm and those of a conventional 2AFC-paradigm would be a good model of our 2AFC-CFS Experiment. Although further investigation would be required to disentangle the contributions of CFS and 2AFC to the outcome of Experiment 3, we interpret the overall longer response times in the 2AFC-CFS experiment to be an indication that the congruency effects found likely do not emerge at the lowest levels of perception but emerge later in the processing chain, when the observer is at least partially aware of the stimulus. Effects of congruency might then emerge in the unconscious condition to a weaker extent, in our case only surfacing with the vehicle, but not the animal stimuli in Experiment 1—perhaps comparable to the earlier stages considered by Leroy et al. (2020) (early processing or memory matching)—whereas a certain degree of conscious access would be required for the effect to develop fully, comparable to the later stages of (Leroy et al., 2020). It would also indicate that the processing involved in our CFS experiments is different than what would be expected from (Grill-Spector & Kanwisher, 2005): In their study, Grill-Spector and Kanwisher used forward/backward masking rather than monocular masking (CFS), which is likely to introduce a different kind of suppression. 
Table 1.
 
Comparison of response times between Experiments 1 and 3
Table 1.
 
Comparison of response times between Experiments 1 and 3
An alternative explanation for the results, particularly in Experiment 1, would be differences in the overall congruency of the stimuli. Vehicle images were indeed judged as being more congruent overall, both for the congruent and for the incongruent samples (yet with a significant difference between), which may have led to a stronger effect with the congruent images. On the other hand, the difference between the ratings of the congruent and incongruent stimuli was larger with the animal images, which one would have expected to lead to a stronger effect. Furthermore, this effect did not re-appear in Experiments 2 and 3, which would also suggest it to be tied to the novelty of the images. In sum, it is unclear why the higher congruency rating with the vehicle images would have led to an effect, while the larger difference between congruency ratings with the animal images did not. 
During the 2AFC experiments, accuracy ratings are very high throughout. This indicates that our participants had no trouble performing the recognition task. Values near the ceiling can in principle obscure differences between conditions; in our case, however, all main effects were significant despite the high overall values. 
It should be noted that the assignment of the response keys in the 2AFC conditions was fixed (animals on the left arrow), and because most participants were right-dominant, this would have put stimulus images in the view of the left eye. This may in principle have biased responses in favor of animals. However, only in Experiment 2 did we find an animal advantage; more importantly, this advantage should have affected all three experiments in the same direction and certainly cannot explain the opposite results (vehicles faster than animals) in Experiments 1 and 3
Stimuli were selected from natural scenes, without editing the images by pasting objects on backgrounds. This eliminated possible artefacts from cut and paste or similar but prevented us from creating a perfectly balanced stimulus set. It is therefore possible that certain differences between the animal and vehicle images influenced the results reported here. However, because the images were the same in all three experiments, this cannot be the cause for the main differences found between the CFS, 2AFC, and CFS/2AFC paradigms. 
Finally, considering the order of the experiments, because all participants completed the experiments in the same order, a certain amount of learning- or training-based improvement is to be expected—both from training in the experimental condition (e.g., in CFS) but also from the fact that the images are no longer novel to the participants after they passed Experiment 1. This may have improved overall response times and hit ratios in Experiments 2 and 3, potentially affecting direct comparisons of experiments. However, because of the task differences between the experiments, results were not intended to be compared across experiments directly, by design. The analysis was based on the relative differences within experiments. Thus keeping the same order of experiments for all participants did not endanger the validity of the analysis and reduced within-experiment result variability compared to a randomized approach. The remaining concern would be the fact that animal stimuli in Experiment 1 resulted in slower response times, yet in faster response times in Experiment 2. Seen alone, we would not immediately be able to exclude this to be the result of a training effect. However, if this was the case, then the RTs to animal stimuli should have remained faster in Experiment 3, as there appears to be no obvious reason why a training effect such as this, if it existed, would reverse itself with even more training. Ultimately, we thus conclude that any training effect that may exist in our study did not affect the reported results in a significant way. 
Acknowledgments
The authors thank Zhao Zhang for helping with data collection. 
WZ was supported by the National Natural Science Foundation of China (61263042, 61563056). 
Commercial relationships: none. 
Corresponding authors: Weina Zhu, Jan Drewes. 
Addresses: School of Information Science, Yunnan University, Kunming 650106, China; Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610068, China. 
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Figure 1.
 
Sample stimulus images (congruent and incongruent). Up: animals. Bottom: vehicles.
Figure 1.
 
Sample stimulus images (congruent and incongruent). Up: animals. Bottom: vehicles.
Figure 2.
 
Experimental paradigm of Experiment 1: breaking-CFS.
Figure 2.
 
Experimental paradigm of Experiment 1: breaking-CFS.
Figure 3.
 
Results from Experiment 1 (b-CFS). (A) Response times of animals versus vehicles. (B) Response times of congruent versus incongruent stimuli. (C) Four-way response times.
Figure 3.
 
Results from Experiment 1 (b-CFS). (A) Response times of animals versus vehicles. (B) Response times of congruent versus incongruent stimuli. (C) Four-way response times.
Figure 4.
 
Experimental paradigm of Experiment 2 (2AFC)
Figure 4.
 
Experimental paradigm of Experiment 2 (2AFC)
Figure 5.
 
Results from Experiment 2 (2AFC without suppression). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
Figure 5.
 
Results from Experiment 2 (2AFC without suppression). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
Figure 6.
 
Experimental paradigm of experiment 3 (2AFC-CFS).
Figure 6.
 
Experimental paradigm of experiment 3 (2AFC-CFS).
Figure 7.
 
Results of Experiment 3 (2AFC-CFS). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
Figure 7.
 
Results of Experiment 3 (2AFC-CFS). (A) Response times of animal versus vehicle targets. (B) Response times of congruent versus incongruent targets. (C) Four-way response time results. (D) Accuracy of animal versus vehicle targets. (E) Accuracy of congruent versus incongruent targets. (F) Four-way accuracy results.
Table 1.
 
Comparison of response times between Experiments 1 and 3
Table 1.
 
Comparison of response times between Experiments 1 and 3
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