- Email: [email protected]
The influence of placing orientation on searching for food in a virtual restaurant
Food Quality and Preference 78 (2019) 103728
Contents lists available at ScienceDirect
Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual
The influence of placing orientation on searching for food in a virtual restaurant
T
Weiwei Zhanga,b, Zhihao Chena,c, Jianping Huanga, Xiaoang Wana,
⁎
a
Department of Psychology, Tsinghua University, Beijing, China The Future Lab, Tsinghua University, Beijing, China c Department of Physics, Tsinghua University, Beijing, China b
ARTICLE INFO
ABSTRACT
Keywords: Food Food-related attention Container Visual search Virtual reality
We conducted three experiments to investigate the influence of placing orientation on the visual search for foods. In the first two experiments, the participants were asked to identify whether one of the foods or containers with angular ends had a different orientation from the other foods or containers on the same table in a virtual restaurant. The results of Experiment 1 revealed that searching for a food was faster when its angular end pointed towards the observers than when the same food pointed away from the observers; whereas an opposite trend was found for containers. In Experiment 2, foods were presented in containers whose angular ends pointed to the same or opposite directions, and the results revealed a faster detection of inward-pointing foods served in outward-pointing containers. Then Experiment 3 was run with simple geometric figures, and the results revealed that searching for an inward-pointing triangular target was faster and more accurate than when the same target pointed outward. Collectively, these results demonstrate how the visual detection of foods is influenced by the incidental aspects of their visual appearances.
1. Introduction As a basic necessity of life, foods exert great influence on the orienting of visual attention (Higgs, Rutters, Thomas, Naish, & Humphreys, 2012; Kumar, Higgs, Rutters, & Humphreys, 2016), presumably because attentional allocation can be influenced by the relevance of something to a person’s needs and goals (Sander, Grandjean, & Scherer, 2005). Both the traits of an individual and the properties of a given food can influence the attentional resources that this person devotes to this food. On the one hand, numerous studies have investigated the role of attentional processing of food cues in obesity (Hendrikse et al., 2015), eating disorders (Wolz, Faqundo, Treasure, & FernandezAranda, 2015), and restrained eating (Hollitt, Kemps, Tiggemann, Smeets, & Mills, 2010; Werthmann, Jansen, & Roefs, 2016). An individual’s craving and frequency of food consumption can influence the attention he or she allocates to a given food (Hardman, Rogers, Etchells, Houstoun, & Munafò, 2013; Hollitt et al., 2010). Obese individuals often allocate more attentional resources to food stimuli than people with normal weight (Castellanos et al., 2009; Nijs, Muris, Euser, &
⁎
Franken, 2010), suggesting that attentional bias towards food stimuli may be considered as a predictor of obesity risk. On the other hand, the physical and sensory properties of foods, such as a food’s fat content, can also influence detection and discrimination of the food (Harrar, Toepel, Murray, & Spence, 2011; Sawada, Sato, Toichi, & Fushiki, 2017), while energy-dense foods can even elicit greater attentional bias than low energy foods (Seage & Lee, 2017). The visual appearances of a food not only modify how people perceive its odor, taste, and flavor (Michel, Velasco, Fraemohs, & Spence, 2015; Zellner et al., 2011), but also influence how people allocate attentional resources to it (Blechert, Meule, Busch, & Ohla, 2014; Hummel, Zerweck, Ehret, Winter, & Stroebele-Benschop, 2017). For instance, the image of triangular-shaped food shown on a computer monitor can be detected more quickly when it points down than when it points up (Shen, Wan, Mu, & Spence, 2015), a phenomenon referred to as the downward-pointing triangle superiority (DPTS) effect. However, it should be borne in mind that in real life, foods with angular ends are more often placed on tables or shelves so that their angular ends point towards the observers (i.e., inward-pointing) or away from the ob-
Corresponding author at: Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China. E-mail address: [email protected] (X. Wan).
https://doi.org/10.1016/j.foodqual.2019.103728 Received 19 March 2019; Received in revised form 17 June 2019; Accepted 17 June 2019 Available online 20 June 2019 0950-3293/ © 2019 Elsevier Ltd. All rights reserved.
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 1. Illustration of differently oriented foods in daily life. Specifically, slices of cakes on the higher shelf have their angular ends point towards potential customers, whereas another slice of cake on the lower shelf has its angular end point up.
modify people’s eating behavior in the real world by presenting food cues in VR (Spence, Okajima, Cheok, Petit, & Michel, 2016). VR systems can often be classified into immersive VR systems, such as VR cave (e.g., Gromer et al., 2018), VR cube (e.g., Wan, Wang, & Crowell, 2010), and head-mounted-display VR (e.g., Wan, Wang, & Crowell, 2009), or non-immersive VR which is run on the desktop computer and therefore referred to as desktop VR (e.g., Lee & Wong, 2014). We used desktop VR to conduct our first two experiments to investigate the visual detection of foods in a simulated restaurant, and then asked the participants to complete a computerized task with simple geometric figures in the third experiment.
servers (i.e., outward-pointing, see Fig. 1 for an illustration). To our best knowledge, it remains unclear how placing orientation influences the visual detection of the foods on the table or shelf. Moreover, very few studies have examined how the visual detection of foods can be affected by another influential factor in food preference and consumption, namely the context (Meiselman, Hirsh, & Popper, 1988; Rozin & Tuorila, 1993). As one type of contextual factors, the containers to serve foods play an important role in eating experience (Piqueras-Fiszman & Spence, 2014). For example, the color of the plateware or crockery to serve foods can influence consumer’s flavor/taste ratings of foods (Harrar, Piqueras-Fiszman, & Spence, 2011; PiquerasFiszman, Alcaide, Roura, & Spence, 2012; Stewart & Goss, 2013), whereas the size of the tableware or container can influence the amount of food consumed (e.g., Rolls, Morris, & Roe, 2002). By contrast, it remains unknown how contextual factors such as containers influence the visual detection of a given food. Similar to foods, containers can have an explicit angle pointing towards or away from the observers. Previous research has revealed the influence of context on the visual search for downward- or upward-pointing triangles (Zhao, Huang, Spence, & Wan, 2017; Zhao, Qi, Spence, & Wan, 2019). Therefore, it seems reasonable to expect that containers may also influence the visual search for inward- or outward-pointing foods. Considering the close relationship between the visual detection of object orientation and visual search (e.g., Marendaz, 1998), we examined the visual search for foods (with or without containers as contextual factors) to investigate the visual detection of food orientation. Compared to conducting visual search experiments in a real restaurant, virtual reality (VR) provides an efficient, flexible, and economical approach to simulate the restaurant scene. VR can effectively deliver realistic food cues (Huang, Huang, & Wan, 2019; Ledoux, Nguyen, Bakos-Block, & Bordnick, 2013), elicit natural attention-orienting and decision-making processes (Bigné, Llinares, & Torrecilla, 2016; Zhao et al., 2017), and provide a vivid simulation of the eating experience (Stelick, Penano, Riak, & Dando, 2018). Moreover, conducting the study in VR allows us to probe people’s responses to virtual foods and therefore provides important information regarding how to
2. Experiment 1 2.1. Methods 2.1.1. Participants Twenty participants (mean age = 21.9 ± 1.8 years old, ranging from 19 to 25 years old; 10 females) from mainland China took part in the present experiment. In the present and the following experiments, all participants reported to be right-handed, and to have normal or corrected-to-normal vision without color blindness. The present and following experiments were approved by the Ethics Committee of the Psychology Department at Tsinghua University, and conducted in accordance with the ethical standards laid out in the Declaration of Helsinki. All participants signed informed consent before the experiment started, and were monetarily compensated for their time and participation. 2.1.2. Apparatus and virtual displays The present experiment was conducted with desktop VR in the virtual reality lab of the Psychology Department of Tsinghua University, and the participants naturally viewed the virtual displays shown on the 17-inch monitor (set to 1024 × 768 pixels and a refresh rate of 60 Hz) of a Pentium-based computer. We used the Rhino software to create and render a virtual restaurant (see Fig. 2 for the floor
2
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 2. The floor plan of the virtual restaurant and the aisle where the participants completed the task in the present study.
plan), and used the Vizard 5 software to control the experiment and to record the data. The participants pressed different keys on the keyboard to virtually move in this virtual restaurant (without physically moving their bodies) and to interact with the virtual world. The whole virtual restaurant was 13.1 m long, 6.8 m wide, and 3.2 m high. During the whole experiment, the participants were only allowed to move along
one of the aisles with 4 red rectangular tables on the right side (also see Fig. 2 for an illustration of this area), with each table being surrounded by 4 white stools. As shown in Fig. 3, search displays were presented on tables in the virtual restaurant, and each display consisted of 8 objects of the same type (i.e., all of them were foods on some tables but containers on other
3
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 3. Screenshots for different types of target-present displays in Experiment 1.
tables). Specifically, each food stimulus was a piece of chocolate cake which was approximately one-sixth of a round cake, and each container stimulus was a triangular-shaped white plate. Both of these foods and containers had angular ends point towards or away from the observers. Based on the orientations of the foods or containers on each display, all search displays were classified into target-absent displays in which all objects had their angular ends point to the same direction, or targetpresent displays in which one object had it angular end point to a different orientation from the other objects.
(i.e., a time within this range was randomly determined for each trial). After that, a search display was presented on the table until the participants pressed one of the two keys on the keyboard. That is, they were instructed to press one key if all the objects on this table were in the same orientation, and to press another key if one of the objects had a different orientation from the other objects on the same table. Next, the participants pressed a third key to move to the next table before the next trial started. This procedure was repeated until the participants completed a block. Before the actual experiment started, the participants were allowed to freely explore the part of the restaurant where they would perform the visual search task for as long as they needed, and then finished a practice block of 8 trials to familiarize with the task.
2.1.3. Design and procedure The experimental task was to identify whether one food or container had its angular end point to a different direction from the other foods or containers on the same table. As for the target-present trials, we used a 2 (Target Orientation: inward- or outward-pointing) × 2 (Stimulus Type: food or container) within-participants design. We also used a 2 (Stimulus Orientation: inward- or outward-pointing) × 2 (Stimulus Type: food or container) within-participants design for the target-absent trials, but we only focused our data analyses on the target-present trials in the present and the following experiments. Each participant completed 16 blocks of 8 trials each. Equal numbers of different types of trials were mixed and presented in a random order within each block. At the beginning of a block, the participants pressed a key on the keyboard to proceed to the front of the first table (see Fig. 4 for an illustration of the trial sequence1), and then pressed another key to switch from the standing position to the sitting position (with eye height lowered from 1.6 m to 1.0 m). The first trial then started with a white fixation circle displayed on the table for 1–1.5 s
2.2. Results and discussion The participants showed a high level of accuracy of 96.7%. We excluded RTs shorter than 150 ms or longer than two standard deviations beyond the group mean from our data analyses, resulting in the discarding of 4.5% of the data. Mean accuracy and RTs calculated based on correct trials are shown in Fig. 5. We conducted 2 (Target Orientation: inward- or outwardpointing) × 2 (Stimulus Type: food or container) repeated-measures ANOVAs on the data of target-present trials. The results revealed a main effect of Stimulus Type on the RTs, F(1, 19) = 20.45, p < 0.001, ηp2 = 0.52, thus suggesting that responses to foods (740 ms) were faster than to containers (859 ms). The results also revealed a main effect of Target Orientation on the RTs, F(1, 19) = 6.54, p = 0.019, ηp2 = 0.26,
1 Two short videos of Experiments 1 and 2 are also available online at https:// youtu.be/-nXkfao9_Ik and https://youtu.be/vsz-QmOTmHo, respectively.
4
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 4. An illustration of the trial sequence in Experiment 1.
present foods in daily life and influence people’s perception of foods as contextual factors (Piqueras-Fiszman & Spence, 2014); whereas foods and containers were separately presented in the present experiment. Therefore, we examined the visual search for foods served in containers in Experiment 2 in order to examine the influence of containers (as contextual factors) on the visual detection of foods. Moreover, our results also revealed faster responses to chocolate cakes than to plates. However, the 3D models of the chocolate cakes used in the present experiment were more likely to have different color schemes when they pointed inward and outward, compared to the white plates. Therefore, we were not able to differentiate whether the RT difference between these two types of stimuli was due to the fundamental difference in the visual processing of food and non-food stimuli (also see Shen et al., 2015), or the influence of color schemes. In order to control for this confounding factor of color scheme, we chose a different type of food as experimental stimuli in Experiment 2.
but it was qualified by a significant interaction term on the RTs, F(1, 24) = 54.66, p < 0.001, ηp2 = 0.74. None of other main or interaction effects was significant, all Fs < 2.54, ps > 0.12. Planned pairwise comparisons revealed that searching for an inward-pointing food (707 ms, 96.3%) was faster than that for an outward-pointing food (774 ms, 95.7%), t(19) = 3.26, p = 0.004, Cohen’s d = 0.74, with comparable accuracy, t(19) = 0.38, p = 0.71. By contrast, searching for an inward-pointing plate (938 ms, 95.1%) was slower and less accurate than that for an outward-pointing plate (781 ms, 98.5%), RTs: t (24) = 6.10, p < 0.001, Cohen’s d = 1.41, accuracy: t(24) = 2.34, p = 0.04, Cohen’s d = 0.63. In summary, the results of the present experiment revealed a faster detection of inward-pointing foods compared to outward-pointing foods. By contrast, we observed a faster detection of outward-pointing containers compared to inward-pointing containers, thus suggesting a fundamental difference in the visual detection of food and non-food stimuli. However, it should be noted that containers are often used to
5
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
3. Experiment 2 3.1. Methods Twenty Chinese participants (mean age = 21.8 ± 1.8 years, ranging from 19 to 25 years; 10 females) took part in the present experiment, and none of them participated in the previous experiment. All of the aspects of the methods in the present experiment were the same those of Experiment 1 except for the following differences. In the present experiment, each search display consisted of eight pieces of pizza (approximately one-sixth of a round pizza) served in triangular-shaped plates (see Fig. 6 for illustrations). Both foods and containers have angular ends point towards or away from the observers. We used a 2 (Target Food Orientation: inward- or outward-pointing) × 2 (Congruency: orientations of the food and receptacle were congruent or incongruent) within-participants design for the target-present trials. That is, the orientation of each pizza can point to the same direction as the plate (i.e., an inward-pointing pizza presented in an inwardpointing plate, or an outward-pointing pizza presented in outwardpointing plate), or point to a different direction from the plate (i.e., an inward-pointing pizza served in an outward-pointing plate, or an outward-pointing pizza presented in an inward-pointing plate). We also used a 2 (Food Orientation: inward- or outward-pointing) × 2 (Congruency: orientations of the food and receptacle were congruent or incongruent) within-participants design for the target-absent trials, but once again, we only focused on the target-present trials. 3.2. Results and discussion The participants showed a high level of accuracy of 97.7%. We excluded RTs shorter than 150 ms or longer than two standard deviations beyond the group mean from our data analyses, resulting in 4.4% of the data being discarded. Mean accuracy and RTs calculated based on correct trials are shown in Fig. 7. We performed 2 (Target Food Orientation: inward- or outwardpointing) × 2 (Congruency: the orientations of the food and receptacle
Fig. 5. Mean RTs and accuracy for different types of target-present trials in Experiment 1. Note ** denotes p < 0.01, and *** denotes p < 0.001. Error bars show the standard errors of the means.
Fig. 6. Screenshots for different types of target-present displays in Experiment 2. 6
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
as a between-participants factor. The results revealed a significant main effect of Receptacle Presence on the RTs, F(1, 38) = 30.56, p < 0.001, ηp2 = 0.45, thus suggesting that responses to foods were faster when they were presented alone (740 ms) than when they were served in orientation-incongruent plates (1103 ms). The results also revealed a significant main effect of Target Food Orientation on the RTs, F(1, 38) = 19.49, p < 0.001, ηp2 = 0.34, thus suggesting that searching for an inward-pointing food target was faster than that for an outwardpointing food target (880 ms vs. 964 ms). None of other main or interaction effects was significant, all Fs < 0.80, ps > 0.37. In summary, the results of the present experiment revealed a faster detection of inward-pointing foods than outward-pointing foods presented in orientation-incongruent plates. And the absence of any significant interaction term in the combined data analysis of the two experiments suggests that the magnitude of this effect was comparable to what we observed with food alone in Experiment 1. By contrast, we did not observe such effect for foods served in orientation-congruent containers. That is, the facilitation effects on the detection of inwardpointing foods and outward-pointing containers do not add up to further facilitate the detection of an inward-pointing food served in an outward-pointing plate. However, the faster detection of an inwardpointing food can be undermined if it is presented in an inwardpointing container. These results, therefore, demonstrate how contextual factors can influence the visual search for triangles, which is consistent with Zhao et al. (2019) findings that the DPTS effect elicited by a triangular-shaped logo is influenced by the silhouette of bottle displaying this logo. In order to test whether the faster detection of inward-pointing foods or outward-pointing containers can be generalized to other stimuli, we ran a third experiment with simple geometric figures in a computerized task. We also compared the visual search for horizontally- and vertically- presented triangles by changing the position of the computer screen.
Fig. 7. Mean RTs and accuracy for different types of target-present trials in Experiment 2. Note that ** denotes p < 0.01, and error bars show the standard errors of the means.
4. Experiment 3 4.1. Methods
were congruent or incongruent) repeated-measures ANOVAs on the data of target-present trials. The main effect of Congruency was significant on the RTs, F(1, 19) = 36.83, p < 0.001, ηp2 = 0.66, and marginally significant on the accuracy data, F(1, 19) = 4.33, p = 0.051, ηp2 = 0.19. These results suggest that responses to a food were faster and slightly more accurate when its angular end pointed to the same direction as the plate to serve this food (942 ms, 98.2%), compared to when a food and its plate had their angular ends point to different directions (1103 ms, 96.8%). The results also revealed a marginally significant main effect of Target Food Orientation on the RTs, F(1, 19) = 4.03, p = 0.059, ηp2 = 0.18, but it was qualified by a significant interaction term on the RTs, F(1, 19) = 9.42, p = 0.006, ηp2 = 0.33. Neither of the two other effects was significant, both Fs < 1.55, ps > 0.23. We further performed pairwise comparisons for each type of stimuli. When a food target and its container had their angular ends point to the same orientation, there were no significant differences between the inward-pointing (956 ms, 98.7%) and outward-pointing food targets (928 ms, 97.8%) in terms of RTs or accuracy, both ts < 1.25, ps > 0.22. When a food target and its container had their angular ends point to opposite directions, searching for an inwardpointing food target (1053 ms, 97.5%) was faster than that for an outward-pointing food target (1253 ms, 96.0%), t(19) = 3.15, p = 0.005, Cohen’s d = 0.70, with comparable accuracy, t(19) = 0.90, p = 0.38. The results of the orientation-incongruent food-receptacle combinations in the present experiment are consistent with what we found with foods without containers in Experiment 1. Therefore, we combined these data and conducted 2 (Target Food Orientation: inward- or outward-pointing) × 2 (Receptacle Presence: absent or present with an incongruent orientation) mixed-design ANOVAs, with Target Food Orientation being a within-participants factor and Receptacle Presence
4.1.1. Participants Forty Chinese participants (mean age = 21.6 ± 2.1 years, ranging from 18 to 25; 21 females) took part in the present experiment, and none of them participated in previous experiments. 4.1.2. Apparatus and stimuli The present experiment was conducted on Pentium-based computers, and the MATLAB 2013b software installed with PsychToolbox 3.0 was used to present the stimuli and to record the data. A 17-inch LCD monitor (set to a resolution of 1024 × 768 pixels and a refresh rate of 60 Hz) was placed in an orientation perpendicular to the ground, so the triangular shapes shown on this monitor pointed either up or down (i.e., the condition of vertically-presented display). By contrast, a 15.6inch LCD laptop screen (set to a resolution of 1920 × 1080 pixels and a refresh rate of 60 Hz) was placed on a desk (parallel to the ground, see Fig. 8 for an illustration), so the triangular shapes shown on this screen pointed either inward or outward (i.e., the condition of horizontallypresented display). All participants were instructed to sit approximately 50 cm away from the center of the screen, and responded to the stimuli by keyboard presses. Similar to the design of the first experiment of Shen et al. (2015), each visual search display in the present experiment consisted of 16 triangles drawn in black lines against a white background. Each triangle (subtending 1.21° × 1.21°) was placed at the center of each cell of an invisible 4 × 4 matrix (4.81° × 4.81°) centered on the screen. The angular ends of these triangles pointed up or down in the vertically-presented displays, and pointed towards or away from the observers in the horizontally-presented displays. Similar to our Experiments 1 and 2, all displays were classified into target-absent displays in which all triangles 7
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 8. An illustration of the horizontally-presented display in Experiment 3.
pointed to the same direction, or target-present displays in which one triangle was in a different orientation from the rest. 4.1.3. Design and procedure As for the target-present trials, we used a 2 (Target Orientation: downward- or upward-pointing) × 2 (Display Orientation: vertical or horizontal) mixed-design, with Target Orientation as a within-participants factor and Display Orientation as a between-participants factor. Participants were randomly divided into two groups with the constraint of having equal numbers of male and female participants in each group, and completed the experiment when the monitor was in an upright or horizontal orientation, respectively. We also used an orthogonal design of 2 (Stimulus Orientation: downward- or upward-pointing) × 2 (Display Orientation: upright or horizontal) mixed-design for targetabsent trials, but once again, we mainly focused our data analyses on the target-present trials as we did in Experiments 1 and 2. The experimental task was to identify whether all triangles in each display pointed to the same direction, or one of the triangles had a different orientation from the others. After finishing a practice block of 20 trials, each participant finished 4 blocks of 64 trials each, with equal numbers of different types of trials being intermixed and presented in a random order. Each trial started with a black fixation cross centered at the white screen for 800 ms, followed by a search display for 2000 ms. The following trial started 2000 ms after a response was made by the participants. Participants were instructed to press two different keys to indicate the presence or absence of the target, and the stimulus-response mapping was counterbalanced across participants.
Fig. 9. Mean RTs and accuracy in Experiment 3, and the error bars show the standard errors of the means. Note that * denotes p < 0.05, and *** denotes p < 0.001.
accurate than that for an upward-pointing one (891 ms, 95.0%). The main effect of Display Orientation was marginally significant on the RTs, F(1, 38) = 3.48, p = 0.07, ηp2 = 0.08, but not significant on the accuracy data, F(1, 38) = 1.09, p = 0.30. These results suggest that visual search was slightly faster when a display was vertically presented (809 ms, 96.8%) than when it was horizontally presented (898 ms, 95.9%), with comparable accuracy. The interaction effect was not significant on the RTs or accuracy data, both Fs < 1.46, ps > 0.23. Planned pairwise comparisons revealed that searching for a downward-pointing triangle (771 ms, 97.9%) was faster and more accurate than that for an upward-pointing one (847 ms, 95.7%) when the display was vertically presented, RTs: t(19) = 6.10, p < 0.001, Cohen’s d = 1.59, accuracy: t(19) = 2.70, p = 0.014, Cohen’s d = 0.68. When the display was horizontally presented, searching for an inwardpointing triangle (862 ms, 97.7%) was faster and more accurate than that for an outward-pointing one (935 ms, 94.2%), RTs: t(19) = 5.03, p < 0.001, Cohen’s d = 1.17, accuracy: t(19) = 4.20, p < 0.001, Cohen’s d = 1.08. The magnitude of the RT difference between the downward- and upward-pointing targets (76 ms) was comparable to that between inward- and outward-pointing targets (73 ms), t (38) = 0.12, p = 0.90. The result pattern we found with simple geometric figures in the horizontally-presented condition is consistent with what we found with virtual cakes in Experiment 1. We thus combined these data and conducted 2 (Target Orientation: inward- or outward-pointing) × 2 (Stimulus Type: cakes or simple geometric figures) mixed-design ANOVAs, with Target Orientation being a within-participants factor and Stimulus Type as a between-participants factor. The results revealed a significant main effect of Stimulus Type on the RTs, F(1, 38) = 13.59, p = 0.001, ηp2 = 0.26, thus suggesting that responses to the virtual cakes (740 ms) were faster than those to the simple geometric figures (898 ms). The results also revealed a significant main effect of Target Orientation on the RTs, F(1, 38) = 31.02, p < 0.001, ηp2 = 0.45, and on the accuracy data, F(1, 38) = 4.80, p = 0.035, ηp2 = 0.11. These results suggest that searching for an inward-pointing
4.2. Results and discussion Overall, the participants had a high level of accuracy of 97.6% in the present experiment. We excluded RTs which were shorter than 150 ms or longer than two standard deviations beyond the group mean from the following data analyses, resulting in 1.7% of the data being discarded. Mean accuracy and RTs calculated based on correct trials are shown in Fig. 9. First, we performed 2 (Target Orientation: downward- or upwardpointing) × 2 (Display Orientation: vertical or horizontal) mixed-design ANOVAs on the data of all target-present trials. The results revealed a main effect of Target Orientation on the RTs, F(1, 38) = 60.63, p < 0.001, ηp2 = 0.63, and on the accuracy data, F(1, 38) = 24.14, p < 0.001, ηp2 = 0.39. These results suggest that searching for a downward-pointing target (816 ms, 97.8%) was faster and more 8
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
food or simple geometric figure (784 ms, 97.0%) were faster and more accurate than that for an outward-pointing one (854 ms, 94.9%). None of other main or interaction effects was significant, all Fs < 2.30, ps > 0.13. Taken together, these results revealed that searching for a triangular shape was faster and more accurate when it pointed towards the observers than when it pointed away from the observers, thus suggesting that the results we found with foods in Experiments 1 and 2 can be generalized to other stimuli. The absence of any significant interaction term in the combined data analysis revealed that these two types of stimuli elicited comparable effects in terms of the RT difference in the detection of inward- and outward-pointing targets. Moreover, the combined analysis of Experiments 1 and 3 revealed faster responses to virtual cakes than those to simple geometric shapes. However, it should be noted that the present experiment with simple geometrics was not run in VR, and the virtual chocolate cakes we used in Experiment 1 were more likely to have different color schemes when they pointed outward and inward, compared to the simple geometric shapes used in the present experiment.
inward-pointing food target can be undermined by skillfully placing the container to present this food. These results therefore suggest that contextual factors can modulate the visual detection of foods, which is in line with the literature about the influence of contextual factors on food perception (Spence, Harrar, & Piqueras-Fiszman, 2012; Zhao, An, Spence, & Wan, 2018). It should be noted that we observed a different result pattern for the containers from those for foods or simple geometric figures. The faster detection of outward-pointing containers might be attributed to the symbolic meanings of containers or receptacles (Ma, 2015). While using one’s own finger to point to another person is perceived as an inappropriate or offensive behavior under many cultures (Tallis, 2010), pointing to someone with the angular end of a container or receptacle (e.g., the spout of a teapot) is considered offensive in Chinese dining customs and etiquette. Therefore, one possibility is that the inward-pointing plates presented in the present study may be perceived as negative stimuli that our Chinese participant tend to avoid (Bradley, Codispoti, Cuthbert, & Lang, 2001; Lang & Bradley, 2010), resulting in a slower detection of inward-pointing containers compared to outward-pointing containers. The findings of the present study also demonstrate how VR can be a powerful tool to test people’s responses to different types of foods and sensitivity to contexts in studies of food perception or consumption, and have direct implications in the practice of presenting a food in VR while individuals are eating something else in the real world (see Huang et al., 2019; Spence et al., 2016). For instance, when an individual is going to choose food to eat (in the real world, of course), it is possible to have him or her to make this selection in VR where the perception of and attention allocated to certain foods can be easily influenced by varying the visual appearances of foods. The results of the present study revealed a faster detection of inward-pointing foods, but also showed that this effect can be eliminated if presenting a food in an orientationcongruent plate. Depending on an individual’s diet goal, it is possible to present foods (with explicit angular ends) in a certain orientation to elicit a faster detection if needed, or to present a food in an orientationcongruent container to diminish faster detection possibly elicited by the food’s fat content or energy level (see Harrar et al., 2011; Seage & Lee, 2017). As with any study, there are also limitations as far as the interpretation and generalizability of the present study are concerned. First, it will be interesting to test possible cross-cultural differences in future research, and cautions are needed if one tries to directly apply our findings to other populations. While the same arrangement of foods can have varied meanings to different populations (Zampollo, Wansink, Kniffin, Shimizu, & Omori, 2012), Chinese participants’ visual attention towards foods is more likely to be influenced by the containers than Westerners (Zhang & Seo, 2015). Second, similar to previous studies of the DPTS effect (Shen et al., 2015; Zhao et al., 2017, 2019), we controlled the set size of the visual search display in the present study; but it will be interesting to manipulate the set size in future studies, and/or use eye-tracking technique to measure the participants’ attention (e.g., Knoeferle, Knoeferle, Velasco, & Spence, 2016). It will also be interesting to add a control condition (such as foods in other shapes than the triangular shape) in future research to test whether the detection of outward-pointing foods is slowed down compared to the control condition. Third, it should be noted that the visual angles for objects shown on a horizontally-presented plane (i.e., parallel to the ground) vary depending on their location, and the angular end of a piece of pizza is not exactly a sharp angle (see also Shen et al., 2015). However, these artifacts may be inevitable when we present the search displays horizontally. In conclusion, the results of the present study revealed how the incidental aspects of the food appearances, such as the orientation of the foods, influence the cognitive processing of foods. Our results also demonstrate a fundamental difference between food and non-food stimuli in terms of visual detection. These findings may have direct implications in food presenting and serving in many places such as
5. General discussion In the present study, we first conducted two VR experiments to examine the visual search for foods with or without containers. The results of Experiment 1 revealed a faster detection for inward-pointing foods but outward-pointing containers, suggesting a fundamental difference in the visual detection of food and non-food stimuli. The results of Experiment 2 also revealed a faster detection of inward-pointing foods presented in outward-pointing containers, whereas no such pattern was found for an inward-pointing food target when it was presented in an inward-pointing container. Moreover, the results of the third experiment with simple geometric figures revealed a faster and more accurate search for an inward-pointing triangular shape among outward-pointing ones than vice versa. Most importantly, the results of the present study suggest that the visual detection of foods can be influenced by the incidental aspects of visual appearance of foods. On the one hand, our findings provide more empirical evidence regarding the asymmetry in the visual detection of different orientations (Foster & Ward, 1991), and are in line with the notion that visual detection of an orientation target among distractors occurs in the early stage of vision (Marendaz, 1998; Wolfe, 1994). On the other hand, our findings are also consistent with the literature about the influence of plating and ready-to-eat state on food perception (Hummel et al., 2017; Michel, Woods, Neuhäuser, Landgraf, & Spence, 2015; Zellner et al., 2011). There is surface similarity between our results with foods on the table (in Experiments 1 and 2) and simple geometric figures (in Experiment 3) and the DPTS effect observed with vertically-presented images of foods (Shen et al., 2015) or simple geometric figures (Larson, Aronoff, & Stearns, 2007). The search advantage for downward-pointing triangles has been attributed to the threat-related information conveyed by their resemblance of angry faces with muscles and eyes pulling down to form a ‘‘V’’ shape, as threat-related stimuli are more readily to attract attention than emotional-neutral stimuli (Larson, Aronoff, & Steuer, 2012; Larson, Aronoff, Sarinopoulos, & Zhu, 2009; LoBue & Larson, 2009; Salgado-Montejo, Salgado, Alvarado, & Spence, 2017; Watson, Blagrove, Evans, & Moore, 2012). Similarly, the faster detection of inward-pointing shapes or foods we found in the present study can also be attributed to the potential danger of having sharp angular ends point towards the observers, and consumers prefer that the angle of a food points away from themselves (Michel et al., 2015). Alternatively, it is possible to attribute the faster detection of inward-pointing foods on the table to the easiness of mental simulation of eating (i.e., biting the food), as mental simulation of eating plays an important role in food perception and consumption (Steinmetz, Tausen, & Risen, 2018; Xie, Minton, & Kahle, 2016). Moreover, our results also revealed that the faster detection of an 9
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
restaurants, stores, and schools. Importantly, our findings also demonstrate the promising future of using VR in influencing people’s eating behavior for better health management.
Foods, 2, 195–199. Marendaz, C. (1998). Nature and dynamics of reference frames in visual search for orientation: Implications for early visual processing. Psychological Science, 9(1), 27–32. Meiselman, H. L., Hirsh, E. S., & Popper, R. D. (1988). Sensory, hedonic and situational factors in food acceptance and consumption. In D. M. H. Thomson (Ed.). Food acceptability (pp. 77–87). London: Elsevier. Michel, C., Velasco, C., Fraemohs, P., & Spence, C. (2015). Studying the impact of plating on ratings of the food served in a naturalistic dining context. Appetite, 90, 45–50. Michel, C., Woods, A. T., Neuhäuser, M., Landgraf, A., & Spence, C. (2015). Rotating plates: Online study demonstrates the importance of orientation in the plating of food. Food Quality and Preference, 44, 194–202. Nijs, I. M., Muris, P., Euser, A. S., & Franken, I. H. A. (2010). Differences in attention to food and food intake between overweight/obese and normal-weight females under conditions of hunger and satiety. Appetite, 54, 243–254. Piqueras-Fiszman, B., Alcaide, J., Roura, E., & Spence, C. (2012). Is it the plate or is it the food? Assessing the influence of the color (black or white) and shape of the plate on the perception of the food placed on it. Food Quality and Preference, 24, 205–208. Piqueras-Fiszman, B., & Spence, C. (2014). Colour, pleasantness, and consumption behaviour within a meal. Appetite, 75, 165–172. Rolls, B. J., Morris, E. L., & Roe, L. S. (2002). Portion size of food affects energy intake in normal-weight and overweight men and women. American Journal of Clinical Nutrition, 76, 1207–1213. Rozin, P., & Tuorila, H. (1993). Simultaneous and temporal contextual influences on food acceptance. Food Quality and Preference, 4, 11–20. Salgado-Montejo, A., Salgado, C., Alvarado, J., & Spence, C. (2017). Simple lines and shapes are associated with, and communicate, distinct emotions. Cognition & Emotion, 31, 511–525. Sander, D., Grandjean, D., & Scherer, K. (2005). A systems approach to appraisal mechanisms in emotion. Neural Networks, 18, 317–352. Sawada, R., Sato, W., Toichi, M., & Fushiki, T. (2017). Fat content modulates rapid detection of food: A visual search study using fast food and Japanese diet. Frontiers in Psychology, 8, 1033. Seage, C. H., & Lee, M. (2017). Do disinhibited eaters pay increased attention to food cues? Appetite, 108, 151–155. Shen, X., Wan, X., Mu, B., & Spence, C. (2015). Searching for triangles: An extension to food & packaging. Food Quality and Preference, 44, 26–35. Spence, C., Harrar, V., & Piqueras-Fiszman, B. (2012). Assessing the impact of tableware and other contextual variables on multisensory flavour perception. Flavour, 1, 7. Spence, C., Okajima, K., Cheok, A. D., Petit, O., & Michel, C. (2016). Eating with our eyes: From visual hunger to digital satiation. Brain and Cognition, 110, 53–63. Stelick, A., Penano, A. G., Riak, A. C., & Dando, R. (2018). Dynamic context sensory testing – A proof of concept study bringing virtual reality to the sensory booth. Journal of Food Science, 83, 2047–2051. Steinmetz, J., Tausen, B. M., & Risen, J. L. (2018). Mental simulation of visceral states affects preferences and behavior. Personality and Social Psychology Bulletin, 44(3), 406–417. Stewart, P. C., & Goss, E. (2013). Plate shape and colour interact to influence taste and quality judgments. Flavour, 2(1), 27. Tallis, R. (2010). Michelangelo’s Finger. An exploration of everyday transcendence. New Have: Yale University Press. Wan, X., Wang, R. F., & Crowell, J. A. (2009). Spatial updating in superimposed real and virtual environments. Attention, Perception & Psychophysics, 71, 42–51. Wan, X., Wang, R. F., & Crowell, J. A. (2010). The effect of active selection in human path integration. Journal of Vision, 10(11), 25. Watson, D. G., Blagrove, E., Evans, C., & Moore, L. (2012). Negative triangles: Simple geometric shapes convey emotional valence. Emotion, 12, 18–22. Werthmann, J., Jansen, A., & Roefs, A. (2016). Make up your mind about food: A healthy mindset attenuates attention for high-calorie food in restrained eaters. Appetite, 105, 53–59. Wolfe, J. M. (1994). Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1, 202–238. Wolz, I., Faqundo, A. B., Treasure, J., & Fernandez-Aranda, F. (2015). The processing of food stimuli in abnormal eating: A systematic review of electrophysiology. European Eating Disorders Review, 23(4), 251–261. Xie, H., Minton, E. A., & Kahle, L. R. (2016). Cake or fruit? Influencing healthy food choice through the interaction of automatic and instructed mental simulation. Marketing Letters, 27, 627–644. Zampollo, F., Wansink, B., Kniffin, K. M., Shimizu, M., & Omori, A. (2012). Looks good enough to eat: How food plating preferences differ across cultures and continents. Cross-Cultural Research, 46(1), 31–49. Zellner, D. A., Siemers, E., Teran, V., Conroy, R., Lankford, M., Agrafiotis, A., et al. (2011). Neatness counts. How plating affects liking for the taste of food. Appetite, 57, 642–648. Zhang, B., & Seo, H. (2015). Visual attention toward food-item images can vary as a function of background saliency and culture: An eye-tracking study. Food Quality and Preference, 41, 172–179. Zhao, H., An, J., Spence, C., & Wan, X. (2018). Influence of the color and size of the plate on the subjective ratings of, taste expectations concerning, and willingness-to-pay for, Asian noodles. Journal of Sensory Studies, 33, e12443. Zhao, H., Huang, F., Spence, C., & Wan, X. (2017). Visual search for wines with a triangle on the label in a virtual store. Frontiers in Psychology, 8, 2173. Zhao, H., Qi, Y., Spence, C., & Wan, X. (2019). On the costs and benefits of using triangles in packaging design. Food Quality & Preference. https://doi.org/10.1016/j.foodqual. 2019.103719.
Declaration of Competing Interest The authors declare no conflict of interest. Acknowledgements The first and second authors contributed equally to this work. This research was supported by the National Natural Science Foundation of China (Grant No. 71472106) awarded to Xiaoang Wan. The authors would like to thank Kaung Myat for his technical assistance. Comments concerning this article should be sent to Dr. Xiaoang Wan at wanxa@ mail.tsinghua.edu.cn. References Bigné, E., Llinares, C., & Torrecilla, C. (2016). Elapsed time on first buying triggers brand choices within a category: A virtual reality-based study. Journal of Business Research, 69(4), 1423–1427. Blechert, J., Meule, A., Busch, N. A., & Ohla, K. (2014). Food-pics: An image database for experimental research on eating and appetite. Frontiers in Psychology, 5, 1–10. Bradley, M., Codispoti, M., Cuthbert, B. N., & Lang, P. J. (2001). Emotion and motivation I: Defensive and appetitive reactions in picture processing. Emotion, 1(3), 276–298. Castellanos, E. H., Charboneau, E., Dietrich, M. S., Park, S., Bradley, B. P., Mogg, K., et al. (2009). Obese adults have visual attention bias for food cue images: Evidence for altered reward system function. International Journal of Obesity, 33(9), 1063–1073. Foster, D. H., & Ward, P. A. (1991). Asymmetries in oriented line detection indicate two orthogonal filters in early vision. Proceedings of the Royal Society of London, 243, 75–81. Gromer, D., Madeira, O., Gast, P., Nehfischer, M., Jost, M., & Müller, M. (2018). Height simulation in a virtual reality CAVE system: Validity of fear responses and effects of an immersion manipulation. Frontiers in Human Neuroscience, 12, 372. Harrar, V., Piqueras-Fiszman, B., & Spence, C. (2011). There’s more to taste in a coloured bowl. Perception, 40, 880–882. Harrar, V., Toepel, U., Murray, M., & Spence, C. (2011). Food’s visually-perceived fat content affects discrimination speed in an orthogonal spatial task. Experimental Brain Research, 214, 351–356. Higgs, S., Rutters, F., Thomas, J. M., Naish, K., & Humphreys, G. W. (2012). Top-down modulation of attention to food cues via working memory. Appetite, 59, 71–75. Hardman, C. A., Rogers, P. J., Etchells, K. A., Houstoun, K. V. E., & Munafò, M. R. (2013). The effects of food-related attentional bias training on appetite and food intake. Appetite, 71, 295–300. Hendrikse, J. J., Cachia, R. L., Kothe, E. J., McPhie, S., Skouteris, H., & Hayden, M. J. (2015). Attentional biases for food cues in overweight and individuals with obesity: A systematic review of the literature. Obesity Reviews, 16(5), 424–432. Hollitt, S., Kemps, E., Tiggemann, M., Smeets, E., & Mills, J. S. (2010). Components of attentional bias for food cues among restrained eaters. Appetite, 54, 309–313. Huang, F., Huang, J., & Wan, X. (2019). Influence of virtual color on the perceived taste of tea: Multisensory integration between virtual and real worlds. Computers in Human Behavior, 95, 168–174. Hummel, G., Zerweck, I., Ehret, J., Winter, S. S., & Stroebele-Benschop, N. (2017). The influence of the arrangement of different food images on participants’ attention: An experimental eye-tracking study. Food Quality and Preference, 62, 111–119. Knoeferle, K., Knoeferle, P., Velasco, C., & Spence, C. (2016). Multisensory brand search: How the meaning of sounds guides consumers' visual attention. Journal of Experimental Psychology: Applied, 22, 196–210. Kumar, S., Higgs, S., Rutters, F., & Humphreys, G. W. (2016). Biased towards food: Electrophysiological evidence for biased attention to food stimuli. Brain and Cognition, 110, 85–93. Lang, P. J., & Bradley, M. M. (2010). Emotion and the motivational brain. Biological Psychology, 84, 437–450. Larson, C. L., Aronoff, J., Sarinopoulos, I. C., & Zhu, D. C. (2009). Recognizing threat: Simple geometric shapes activate neural circuitry underlying threat detection. Journal of Cognitive Neuroscience, 21, 1523–1535. Larson, C. L., Aronoff, J., & Stearns, J. J. (2007). The shape of threat: Simple geometric forms evoke rapid and sustained capture of attention. Emotion, 7, 526–534. Larson, C. L., Aronoff, J., & Steuer, E. L. (2012). Simple geometric shapes are implicitly associated with affective value. Motivation and Emotion, 36, 404–413. Ledoux, T., Nguyen, A. S., Bakos-Block, C., & Bordnick, P. (2013). Using virtual reality to study food cravings. Appetite, 71, 396–402. Lee, E. A., & Wong, K. W. (2014). Learning with desktop virtual reality: Low spatial ability learners are more positively affected. Computers & Education, 79, 49–58. LoBue, V., & Larson, C. L. (2009). What makes an angry face look so angry? Examining visual attention to the shape of threat in children and adults. Visual Cognition, 18, 1165–1178. Ma, G. (2015). Food, eating behavior, and culture in Chinese society. Journal of Ethnic
10
Contents lists available at ScienceDirect
Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual
The influence of placing orientation on searching for food in a virtual restaurant
T
Weiwei Zhanga,b, Zhihao Chena,c, Jianping Huanga, Xiaoang Wana,
⁎
a
Department of Psychology, Tsinghua University, Beijing, China The Future Lab, Tsinghua University, Beijing, China c Department of Physics, Tsinghua University, Beijing, China b
ARTICLE INFO
ABSTRACT
Keywords: Food Food-related attention Container Visual search Virtual reality
We conducted three experiments to investigate the influence of placing orientation on the visual search for foods. In the first two experiments, the participants were asked to identify whether one of the foods or containers with angular ends had a different orientation from the other foods or containers on the same table in a virtual restaurant. The results of Experiment 1 revealed that searching for a food was faster when its angular end pointed towards the observers than when the same food pointed away from the observers; whereas an opposite trend was found for containers. In Experiment 2, foods were presented in containers whose angular ends pointed to the same or opposite directions, and the results revealed a faster detection of inward-pointing foods served in outward-pointing containers. Then Experiment 3 was run with simple geometric figures, and the results revealed that searching for an inward-pointing triangular target was faster and more accurate than when the same target pointed outward. Collectively, these results demonstrate how the visual detection of foods is influenced by the incidental aspects of their visual appearances.
1. Introduction As a basic necessity of life, foods exert great influence on the orienting of visual attention (Higgs, Rutters, Thomas, Naish, & Humphreys, 2012; Kumar, Higgs, Rutters, & Humphreys, 2016), presumably because attentional allocation can be influenced by the relevance of something to a person’s needs and goals (Sander, Grandjean, & Scherer, 2005). Both the traits of an individual and the properties of a given food can influence the attentional resources that this person devotes to this food. On the one hand, numerous studies have investigated the role of attentional processing of food cues in obesity (Hendrikse et al., 2015), eating disorders (Wolz, Faqundo, Treasure, & FernandezAranda, 2015), and restrained eating (Hollitt, Kemps, Tiggemann, Smeets, & Mills, 2010; Werthmann, Jansen, & Roefs, 2016). An individual’s craving and frequency of food consumption can influence the attention he or she allocates to a given food (Hardman, Rogers, Etchells, Houstoun, & Munafò, 2013; Hollitt et al., 2010). Obese individuals often allocate more attentional resources to food stimuli than people with normal weight (Castellanos et al., 2009; Nijs, Muris, Euser, &
⁎
Franken, 2010), suggesting that attentional bias towards food stimuli may be considered as a predictor of obesity risk. On the other hand, the physical and sensory properties of foods, such as a food’s fat content, can also influence detection and discrimination of the food (Harrar, Toepel, Murray, & Spence, 2011; Sawada, Sato, Toichi, & Fushiki, 2017), while energy-dense foods can even elicit greater attentional bias than low energy foods (Seage & Lee, 2017). The visual appearances of a food not only modify how people perceive its odor, taste, and flavor (Michel, Velasco, Fraemohs, & Spence, 2015; Zellner et al., 2011), but also influence how people allocate attentional resources to it (Blechert, Meule, Busch, & Ohla, 2014; Hummel, Zerweck, Ehret, Winter, & Stroebele-Benschop, 2017). For instance, the image of triangular-shaped food shown on a computer monitor can be detected more quickly when it points down than when it points up (Shen, Wan, Mu, & Spence, 2015), a phenomenon referred to as the downward-pointing triangle superiority (DPTS) effect. However, it should be borne in mind that in real life, foods with angular ends are more often placed on tables or shelves so that their angular ends point towards the observers (i.e., inward-pointing) or away from the ob-
Corresponding author at: Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China. E-mail address: [email protected] (X. Wan).
https://doi.org/10.1016/j.foodqual.2019.103728 Received 19 March 2019; Received in revised form 17 June 2019; Accepted 17 June 2019 Available online 20 June 2019 0950-3293/ © 2019 Elsevier Ltd. All rights reserved.
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 1. Illustration of differently oriented foods in daily life. Specifically, slices of cakes on the higher shelf have their angular ends point towards potential customers, whereas another slice of cake on the lower shelf has its angular end point up.
modify people’s eating behavior in the real world by presenting food cues in VR (Spence, Okajima, Cheok, Petit, & Michel, 2016). VR systems can often be classified into immersive VR systems, such as VR cave (e.g., Gromer et al., 2018), VR cube (e.g., Wan, Wang, & Crowell, 2010), and head-mounted-display VR (e.g., Wan, Wang, & Crowell, 2009), or non-immersive VR which is run on the desktop computer and therefore referred to as desktop VR (e.g., Lee & Wong, 2014). We used desktop VR to conduct our first two experiments to investigate the visual detection of foods in a simulated restaurant, and then asked the participants to complete a computerized task with simple geometric figures in the third experiment.
servers (i.e., outward-pointing, see Fig. 1 for an illustration). To our best knowledge, it remains unclear how placing orientation influences the visual detection of the foods on the table or shelf. Moreover, very few studies have examined how the visual detection of foods can be affected by another influential factor in food preference and consumption, namely the context (Meiselman, Hirsh, & Popper, 1988; Rozin & Tuorila, 1993). As one type of contextual factors, the containers to serve foods play an important role in eating experience (Piqueras-Fiszman & Spence, 2014). For example, the color of the plateware or crockery to serve foods can influence consumer’s flavor/taste ratings of foods (Harrar, Piqueras-Fiszman, & Spence, 2011; PiquerasFiszman, Alcaide, Roura, & Spence, 2012; Stewart & Goss, 2013), whereas the size of the tableware or container can influence the amount of food consumed (e.g., Rolls, Morris, & Roe, 2002). By contrast, it remains unknown how contextual factors such as containers influence the visual detection of a given food. Similar to foods, containers can have an explicit angle pointing towards or away from the observers. Previous research has revealed the influence of context on the visual search for downward- or upward-pointing triangles (Zhao, Huang, Spence, & Wan, 2017; Zhao, Qi, Spence, & Wan, 2019). Therefore, it seems reasonable to expect that containers may also influence the visual search for inward- or outward-pointing foods. Considering the close relationship between the visual detection of object orientation and visual search (e.g., Marendaz, 1998), we examined the visual search for foods (with or without containers as contextual factors) to investigate the visual detection of food orientation. Compared to conducting visual search experiments in a real restaurant, virtual reality (VR) provides an efficient, flexible, and economical approach to simulate the restaurant scene. VR can effectively deliver realistic food cues (Huang, Huang, & Wan, 2019; Ledoux, Nguyen, Bakos-Block, & Bordnick, 2013), elicit natural attention-orienting and decision-making processes (Bigné, Llinares, & Torrecilla, 2016; Zhao et al., 2017), and provide a vivid simulation of the eating experience (Stelick, Penano, Riak, & Dando, 2018). Moreover, conducting the study in VR allows us to probe people’s responses to virtual foods and therefore provides important information regarding how to
2. Experiment 1 2.1. Methods 2.1.1. Participants Twenty participants (mean age = 21.9 ± 1.8 years old, ranging from 19 to 25 years old; 10 females) from mainland China took part in the present experiment. In the present and the following experiments, all participants reported to be right-handed, and to have normal or corrected-to-normal vision without color blindness. The present and following experiments were approved by the Ethics Committee of the Psychology Department at Tsinghua University, and conducted in accordance with the ethical standards laid out in the Declaration of Helsinki. All participants signed informed consent before the experiment started, and were monetarily compensated for their time and participation. 2.1.2. Apparatus and virtual displays The present experiment was conducted with desktop VR in the virtual reality lab of the Psychology Department of Tsinghua University, and the participants naturally viewed the virtual displays shown on the 17-inch monitor (set to 1024 × 768 pixels and a refresh rate of 60 Hz) of a Pentium-based computer. We used the Rhino software to create and render a virtual restaurant (see Fig. 2 for the floor
2
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 2. The floor plan of the virtual restaurant and the aisle where the participants completed the task in the present study.
plan), and used the Vizard 5 software to control the experiment and to record the data. The participants pressed different keys on the keyboard to virtually move in this virtual restaurant (without physically moving their bodies) and to interact with the virtual world. The whole virtual restaurant was 13.1 m long, 6.8 m wide, and 3.2 m high. During the whole experiment, the participants were only allowed to move along
one of the aisles with 4 red rectangular tables on the right side (also see Fig. 2 for an illustration of this area), with each table being surrounded by 4 white stools. As shown in Fig. 3, search displays were presented on tables in the virtual restaurant, and each display consisted of 8 objects of the same type (i.e., all of them were foods on some tables but containers on other
3
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 3. Screenshots for different types of target-present displays in Experiment 1.
tables). Specifically, each food stimulus was a piece of chocolate cake which was approximately one-sixth of a round cake, and each container stimulus was a triangular-shaped white plate. Both of these foods and containers had angular ends point towards or away from the observers. Based on the orientations of the foods or containers on each display, all search displays were classified into target-absent displays in which all objects had their angular ends point to the same direction, or targetpresent displays in which one object had it angular end point to a different orientation from the other objects.
(i.e., a time within this range was randomly determined for each trial). After that, a search display was presented on the table until the participants pressed one of the two keys on the keyboard. That is, they were instructed to press one key if all the objects on this table were in the same orientation, and to press another key if one of the objects had a different orientation from the other objects on the same table. Next, the participants pressed a third key to move to the next table before the next trial started. This procedure was repeated until the participants completed a block. Before the actual experiment started, the participants were allowed to freely explore the part of the restaurant where they would perform the visual search task for as long as they needed, and then finished a practice block of 8 trials to familiarize with the task.
2.1.3. Design and procedure The experimental task was to identify whether one food or container had its angular end point to a different direction from the other foods or containers on the same table. As for the target-present trials, we used a 2 (Target Orientation: inward- or outward-pointing) × 2 (Stimulus Type: food or container) within-participants design. We also used a 2 (Stimulus Orientation: inward- or outward-pointing) × 2 (Stimulus Type: food or container) within-participants design for the target-absent trials, but we only focused our data analyses on the target-present trials in the present and the following experiments. Each participant completed 16 blocks of 8 trials each. Equal numbers of different types of trials were mixed and presented in a random order within each block. At the beginning of a block, the participants pressed a key on the keyboard to proceed to the front of the first table (see Fig. 4 for an illustration of the trial sequence1), and then pressed another key to switch from the standing position to the sitting position (with eye height lowered from 1.6 m to 1.0 m). The first trial then started with a white fixation circle displayed on the table for 1–1.5 s
2.2. Results and discussion The participants showed a high level of accuracy of 96.7%. We excluded RTs shorter than 150 ms or longer than two standard deviations beyond the group mean from our data analyses, resulting in the discarding of 4.5% of the data. Mean accuracy and RTs calculated based on correct trials are shown in Fig. 5. We conducted 2 (Target Orientation: inward- or outwardpointing) × 2 (Stimulus Type: food or container) repeated-measures ANOVAs on the data of target-present trials. The results revealed a main effect of Stimulus Type on the RTs, F(1, 19) = 20.45, p < 0.001, ηp2 = 0.52, thus suggesting that responses to foods (740 ms) were faster than to containers (859 ms). The results also revealed a main effect of Target Orientation on the RTs, F(1, 19) = 6.54, p = 0.019, ηp2 = 0.26,
1 Two short videos of Experiments 1 and 2 are also available online at https:// youtu.be/-nXkfao9_Ik and https://youtu.be/vsz-QmOTmHo, respectively.
4
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 4. An illustration of the trial sequence in Experiment 1.
present foods in daily life and influence people’s perception of foods as contextual factors (Piqueras-Fiszman & Spence, 2014); whereas foods and containers were separately presented in the present experiment. Therefore, we examined the visual search for foods served in containers in Experiment 2 in order to examine the influence of containers (as contextual factors) on the visual detection of foods. Moreover, our results also revealed faster responses to chocolate cakes than to plates. However, the 3D models of the chocolate cakes used in the present experiment were more likely to have different color schemes when they pointed inward and outward, compared to the white plates. Therefore, we were not able to differentiate whether the RT difference between these two types of stimuli was due to the fundamental difference in the visual processing of food and non-food stimuli (also see Shen et al., 2015), or the influence of color schemes. In order to control for this confounding factor of color scheme, we chose a different type of food as experimental stimuli in Experiment 2.
but it was qualified by a significant interaction term on the RTs, F(1, 24) = 54.66, p < 0.001, ηp2 = 0.74. None of other main or interaction effects was significant, all Fs < 2.54, ps > 0.12. Planned pairwise comparisons revealed that searching for an inward-pointing food (707 ms, 96.3%) was faster than that for an outward-pointing food (774 ms, 95.7%), t(19) = 3.26, p = 0.004, Cohen’s d = 0.74, with comparable accuracy, t(19) = 0.38, p = 0.71. By contrast, searching for an inward-pointing plate (938 ms, 95.1%) was slower and less accurate than that for an outward-pointing plate (781 ms, 98.5%), RTs: t (24) = 6.10, p < 0.001, Cohen’s d = 1.41, accuracy: t(24) = 2.34, p = 0.04, Cohen’s d = 0.63. In summary, the results of the present experiment revealed a faster detection of inward-pointing foods compared to outward-pointing foods. By contrast, we observed a faster detection of outward-pointing containers compared to inward-pointing containers, thus suggesting a fundamental difference in the visual detection of food and non-food stimuli. However, it should be noted that containers are often used to
5
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
3. Experiment 2 3.1. Methods Twenty Chinese participants (mean age = 21.8 ± 1.8 years, ranging from 19 to 25 years; 10 females) took part in the present experiment, and none of them participated in the previous experiment. All of the aspects of the methods in the present experiment were the same those of Experiment 1 except for the following differences. In the present experiment, each search display consisted of eight pieces of pizza (approximately one-sixth of a round pizza) served in triangular-shaped plates (see Fig. 6 for illustrations). Both foods and containers have angular ends point towards or away from the observers. We used a 2 (Target Food Orientation: inward- or outward-pointing) × 2 (Congruency: orientations of the food and receptacle were congruent or incongruent) within-participants design for the target-present trials. That is, the orientation of each pizza can point to the same direction as the plate (i.e., an inward-pointing pizza presented in an inwardpointing plate, or an outward-pointing pizza presented in outwardpointing plate), or point to a different direction from the plate (i.e., an inward-pointing pizza served in an outward-pointing plate, or an outward-pointing pizza presented in an inward-pointing plate). We also used a 2 (Food Orientation: inward- or outward-pointing) × 2 (Congruency: orientations of the food and receptacle were congruent or incongruent) within-participants design for the target-absent trials, but once again, we only focused on the target-present trials. 3.2. Results and discussion The participants showed a high level of accuracy of 97.7%. We excluded RTs shorter than 150 ms or longer than two standard deviations beyond the group mean from our data analyses, resulting in 4.4% of the data being discarded. Mean accuracy and RTs calculated based on correct trials are shown in Fig. 7. We performed 2 (Target Food Orientation: inward- or outwardpointing) × 2 (Congruency: the orientations of the food and receptacle
Fig. 5. Mean RTs and accuracy for different types of target-present trials in Experiment 1. Note ** denotes p < 0.01, and *** denotes p < 0.001. Error bars show the standard errors of the means.
Fig. 6. Screenshots for different types of target-present displays in Experiment 2. 6
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
as a between-participants factor. The results revealed a significant main effect of Receptacle Presence on the RTs, F(1, 38) = 30.56, p < 0.001, ηp2 = 0.45, thus suggesting that responses to foods were faster when they were presented alone (740 ms) than when they were served in orientation-incongruent plates (1103 ms). The results also revealed a significant main effect of Target Food Orientation on the RTs, F(1, 38) = 19.49, p < 0.001, ηp2 = 0.34, thus suggesting that searching for an inward-pointing food target was faster than that for an outwardpointing food target (880 ms vs. 964 ms). None of other main or interaction effects was significant, all Fs < 0.80, ps > 0.37. In summary, the results of the present experiment revealed a faster detection of inward-pointing foods than outward-pointing foods presented in orientation-incongruent plates. And the absence of any significant interaction term in the combined data analysis of the two experiments suggests that the magnitude of this effect was comparable to what we observed with food alone in Experiment 1. By contrast, we did not observe such effect for foods served in orientation-congruent containers. That is, the facilitation effects on the detection of inwardpointing foods and outward-pointing containers do not add up to further facilitate the detection of an inward-pointing food served in an outward-pointing plate. However, the faster detection of an inwardpointing food can be undermined if it is presented in an inwardpointing container. These results, therefore, demonstrate how contextual factors can influence the visual search for triangles, which is consistent with Zhao et al. (2019) findings that the DPTS effect elicited by a triangular-shaped logo is influenced by the silhouette of bottle displaying this logo. In order to test whether the faster detection of inward-pointing foods or outward-pointing containers can be generalized to other stimuli, we ran a third experiment with simple geometric figures in a computerized task. We also compared the visual search for horizontally- and vertically- presented triangles by changing the position of the computer screen.
Fig. 7. Mean RTs and accuracy for different types of target-present trials in Experiment 2. Note that ** denotes p < 0.01, and error bars show the standard errors of the means.
4. Experiment 3 4.1. Methods
were congruent or incongruent) repeated-measures ANOVAs on the data of target-present trials. The main effect of Congruency was significant on the RTs, F(1, 19) = 36.83, p < 0.001, ηp2 = 0.66, and marginally significant on the accuracy data, F(1, 19) = 4.33, p = 0.051, ηp2 = 0.19. These results suggest that responses to a food were faster and slightly more accurate when its angular end pointed to the same direction as the plate to serve this food (942 ms, 98.2%), compared to when a food and its plate had their angular ends point to different directions (1103 ms, 96.8%). The results also revealed a marginally significant main effect of Target Food Orientation on the RTs, F(1, 19) = 4.03, p = 0.059, ηp2 = 0.18, but it was qualified by a significant interaction term on the RTs, F(1, 19) = 9.42, p = 0.006, ηp2 = 0.33. Neither of the two other effects was significant, both Fs < 1.55, ps > 0.23. We further performed pairwise comparisons for each type of stimuli. When a food target and its container had their angular ends point to the same orientation, there were no significant differences between the inward-pointing (956 ms, 98.7%) and outward-pointing food targets (928 ms, 97.8%) in terms of RTs or accuracy, both ts < 1.25, ps > 0.22. When a food target and its container had their angular ends point to opposite directions, searching for an inwardpointing food target (1053 ms, 97.5%) was faster than that for an outward-pointing food target (1253 ms, 96.0%), t(19) = 3.15, p = 0.005, Cohen’s d = 0.70, with comparable accuracy, t(19) = 0.90, p = 0.38. The results of the orientation-incongruent food-receptacle combinations in the present experiment are consistent with what we found with foods without containers in Experiment 1. Therefore, we combined these data and conducted 2 (Target Food Orientation: inward- or outward-pointing) × 2 (Receptacle Presence: absent or present with an incongruent orientation) mixed-design ANOVAs, with Target Food Orientation being a within-participants factor and Receptacle Presence
4.1.1. Participants Forty Chinese participants (mean age = 21.6 ± 2.1 years, ranging from 18 to 25; 21 females) took part in the present experiment, and none of them participated in previous experiments. 4.1.2. Apparatus and stimuli The present experiment was conducted on Pentium-based computers, and the MATLAB 2013b software installed with PsychToolbox 3.0 was used to present the stimuli and to record the data. A 17-inch LCD monitor (set to a resolution of 1024 × 768 pixels and a refresh rate of 60 Hz) was placed in an orientation perpendicular to the ground, so the triangular shapes shown on this monitor pointed either up or down (i.e., the condition of vertically-presented display). By contrast, a 15.6inch LCD laptop screen (set to a resolution of 1920 × 1080 pixels and a refresh rate of 60 Hz) was placed on a desk (parallel to the ground, see Fig. 8 for an illustration), so the triangular shapes shown on this screen pointed either inward or outward (i.e., the condition of horizontallypresented display). All participants were instructed to sit approximately 50 cm away from the center of the screen, and responded to the stimuli by keyboard presses. Similar to the design of the first experiment of Shen et al. (2015), each visual search display in the present experiment consisted of 16 triangles drawn in black lines against a white background. Each triangle (subtending 1.21° × 1.21°) was placed at the center of each cell of an invisible 4 × 4 matrix (4.81° × 4.81°) centered on the screen. The angular ends of these triangles pointed up or down in the vertically-presented displays, and pointed towards or away from the observers in the horizontally-presented displays. Similar to our Experiments 1 and 2, all displays were classified into target-absent displays in which all triangles 7
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
Fig. 8. An illustration of the horizontally-presented display in Experiment 3.
pointed to the same direction, or target-present displays in which one triangle was in a different orientation from the rest. 4.1.3. Design and procedure As for the target-present trials, we used a 2 (Target Orientation: downward- or upward-pointing) × 2 (Display Orientation: vertical or horizontal) mixed-design, with Target Orientation as a within-participants factor and Display Orientation as a between-participants factor. Participants were randomly divided into two groups with the constraint of having equal numbers of male and female participants in each group, and completed the experiment when the monitor was in an upright or horizontal orientation, respectively. We also used an orthogonal design of 2 (Stimulus Orientation: downward- or upward-pointing) × 2 (Display Orientation: upright or horizontal) mixed-design for targetabsent trials, but once again, we mainly focused our data analyses on the target-present trials as we did in Experiments 1 and 2. The experimental task was to identify whether all triangles in each display pointed to the same direction, or one of the triangles had a different orientation from the others. After finishing a practice block of 20 trials, each participant finished 4 blocks of 64 trials each, with equal numbers of different types of trials being intermixed and presented in a random order. Each trial started with a black fixation cross centered at the white screen for 800 ms, followed by a search display for 2000 ms. The following trial started 2000 ms after a response was made by the participants. Participants were instructed to press two different keys to indicate the presence or absence of the target, and the stimulus-response mapping was counterbalanced across participants.
Fig. 9. Mean RTs and accuracy in Experiment 3, and the error bars show the standard errors of the means. Note that * denotes p < 0.05, and *** denotes p < 0.001.
accurate than that for an upward-pointing one (891 ms, 95.0%). The main effect of Display Orientation was marginally significant on the RTs, F(1, 38) = 3.48, p = 0.07, ηp2 = 0.08, but not significant on the accuracy data, F(1, 38) = 1.09, p = 0.30. These results suggest that visual search was slightly faster when a display was vertically presented (809 ms, 96.8%) than when it was horizontally presented (898 ms, 95.9%), with comparable accuracy. The interaction effect was not significant on the RTs or accuracy data, both Fs < 1.46, ps > 0.23. Planned pairwise comparisons revealed that searching for a downward-pointing triangle (771 ms, 97.9%) was faster and more accurate than that for an upward-pointing one (847 ms, 95.7%) when the display was vertically presented, RTs: t(19) = 6.10, p < 0.001, Cohen’s d = 1.59, accuracy: t(19) = 2.70, p = 0.014, Cohen’s d = 0.68. When the display was horizontally presented, searching for an inwardpointing triangle (862 ms, 97.7%) was faster and more accurate than that for an outward-pointing one (935 ms, 94.2%), RTs: t(19) = 5.03, p < 0.001, Cohen’s d = 1.17, accuracy: t(19) = 4.20, p < 0.001, Cohen’s d = 1.08. The magnitude of the RT difference between the downward- and upward-pointing targets (76 ms) was comparable to that between inward- and outward-pointing targets (73 ms), t (38) = 0.12, p = 0.90. The result pattern we found with simple geometric figures in the horizontally-presented condition is consistent with what we found with virtual cakes in Experiment 1. We thus combined these data and conducted 2 (Target Orientation: inward- or outward-pointing) × 2 (Stimulus Type: cakes or simple geometric figures) mixed-design ANOVAs, with Target Orientation being a within-participants factor and Stimulus Type as a between-participants factor. The results revealed a significant main effect of Stimulus Type on the RTs, F(1, 38) = 13.59, p = 0.001, ηp2 = 0.26, thus suggesting that responses to the virtual cakes (740 ms) were faster than those to the simple geometric figures (898 ms). The results also revealed a significant main effect of Target Orientation on the RTs, F(1, 38) = 31.02, p < 0.001, ηp2 = 0.45, and on the accuracy data, F(1, 38) = 4.80, p = 0.035, ηp2 = 0.11. These results suggest that searching for an inward-pointing
4.2. Results and discussion Overall, the participants had a high level of accuracy of 97.6% in the present experiment. We excluded RTs which were shorter than 150 ms or longer than two standard deviations beyond the group mean from the following data analyses, resulting in 1.7% of the data being discarded. Mean accuracy and RTs calculated based on correct trials are shown in Fig. 9. First, we performed 2 (Target Orientation: downward- or upwardpointing) × 2 (Display Orientation: vertical or horizontal) mixed-design ANOVAs on the data of all target-present trials. The results revealed a main effect of Target Orientation on the RTs, F(1, 38) = 60.63, p < 0.001, ηp2 = 0.63, and on the accuracy data, F(1, 38) = 24.14, p < 0.001, ηp2 = 0.39. These results suggest that searching for a downward-pointing target (816 ms, 97.8%) was faster and more 8
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
food or simple geometric figure (784 ms, 97.0%) were faster and more accurate than that for an outward-pointing one (854 ms, 94.9%). None of other main or interaction effects was significant, all Fs < 2.30, ps > 0.13. Taken together, these results revealed that searching for a triangular shape was faster and more accurate when it pointed towards the observers than when it pointed away from the observers, thus suggesting that the results we found with foods in Experiments 1 and 2 can be generalized to other stimuli. The absence of any significant interaction term in the combined data analysis revealed that these two types of stimuli elicited comparable effects in terms of the RT difference in the detection of inward- and outward-pointing targets. Moreover, the combined analysis of Experiments 1 and 3 revealed faster responses to virtual cakes than those to simple geometric shapes. However, it should be noted that the present experiment with simple geometrics was not run in VR, and the virtual chocolate cakes we used in Experiment 1 were more likely to have different color schemes when they pointed outward and inward, compared to the simple geometric shapes used in the present experiment.
inward-pointing food target can be undermined by skillfully placing the container to present this food. These results therefore suggest that contextual factors can modulate the visual detection of foods, which is in line with the literature about the influence of contextual factors on food perception (Spence, Harrar, & Piqueras-Fiszman, 2012; Zhao, An, Spence, & Wan, 2018). It should be noted that we observed a different result pattern for the containers from those for foods or simple geometric figures. The faster detection of outward-pointing containers might be attributed to the symbolic meanings of containers or receptacles (Ma, 2015). While using one’s own finger to point to another person is perceived as an inappropriate or offensive behavior under many cultures (Tallis, 2010), pointing to someone with the angular end of a container or receptacle (e.g., the spout of a teapot) is considered offensive in Chinese dining customs and etiquette. Therefore, one possibility is that the inward-pointing plates presented in the present study may be perceived as negative stimuli that our Chinese participant tend to avoid (Bradley, Codispoti, Cuthbert, & Lang, 2001; Lang & Bradley, 2010), resulting in a slower detection of inward-pointing containers compared to outward-pointing containers. The findings of the present study also demonstrate how VR can be a powerful tool to test people’s responses to different types of foods and sensitivity to contexts in studies of food perception or consumption, and have direct implications in the practice of presenting a food in VR while individuals are eating something else in the real world (see Huang et al., 2019; Spence et al., 2016). For instance, when an individual is going to choose food to eat (in the real world, of course), it is possible to have him or her to make this selection in VR where the perception of and attention allocated to certain foods can be easily influenced by varying the visual appearances of foods. The results of the present study revealed a faster detection of inward-pointing foods, but also showed that this effect can be eliminated if presenting a food in an orientationcongruent plate. Depending on an individual’s diet goal, it is possible to present foods (with explicit angular ends) in a certain orientation to elicit a faster detection if needed, or to present a food in an orientationcongruent container to diminish faster detection possibly elicited by the food’s fat content or energy level (see Harrar et al., 2011; Seage & Lee, 2017). As with any study, there are also limitations as far as the interpretation and generalizability of the present study are concerned. First, it will be interesting to test possible cross-cultural differences in future research, and cautions are needed if one tries to directly apply our findings to other populations. While the same arrangement of foods can have varied meanings to different populations (Zampollo, Wansink, Kniffin, Shimizu, & Omori, 2012), Chinese participants’ visual attention towards foods is more likely to be influenced by the containers than Westerners (Zhang & Seo, 2015). Second, similar to previous studies of the DPTS effect (Shen et al., 2015; Zhao et al., 2017, 2019), we controlled the set size of the visual search display in the present study; but it will be interesting to manipulate the set size in future studies, and/or use eye-tracking technique to measure the participants’ attention (e.g., Knoeferle, Knoeferle, Velasco, & Spence, 2016). It will also be interesting to add a control condition (such as foods in other shapes than the triangular shape) in future research to test whether the detection of outward-pointing foods is slowed down compared to the control condition. Third, it should be noted that the visual angles for objects shown on a horizontally-presented plane (i.e., parallel to the ground) vary depending on their location, and the angular end of a piece of pizza is not exactly a sharp angle (see also Shen et al., 2015). However, these artifacts may be inevitable when we present the search displays horizontally. In conclusion, the results of the present study revealed how the incidental aspects of the food appearances, such as the orientation of the foods, influence the cognitive processing of foods. Our results also demonstrate a fundamental difference between food and non-food stimuli in terms of visual detection. These findings may have direct implications in food presenting and serving in many places such as
5. General discussion In the present study, we first conducted two VR experiments to examine the visual search for foods with or without containers. The results of Experiment 1 revealed a faster detection for inward-pointing foods but outward-pointing containers, suggesting a fundamental difference in the visual detection of food and non-food stimuli. The results of Experiment 2 also revealed a faster detection of inward-pointing foods presented in outward-pointing containers, whereas no such pattern was found for an inward-pointing food target when it was presented in an inward-pointing container. Moreover, the results of the third experiment with simple geometric figures revealed a faster and more accurate search for an inward-pointing triangular shape among outward-pointing ones than vice versa. Most importantly, the results of the present study suggest that the visual detection of foods can be influenced by the incidental aspects of visual appearance of foods. On the one hand, our findings provide more empirical evidence regarding the asymmetry in the visual detection of different orientations (Foster & Ward, 1991), and are in line with the notion that visual detection of an orientation target among distractors occurs in the early stage of vision (Marendaz, 1998; Wolfe, 1994). On the other hand, our findings are also consistent with the literature about the influence of plating and ready-to-eat state on food perception (Hummel et al., 2017; Michel, Woods, Neuhäuser, Landgraf, & Spence, 2015; Zellner et al., 2011). There is surface similarity between our results with foods on the table (in Experiments 1 and 2) and simple geometric figures (in Experiment 3) and the DPTS effect observed with vertically-presented images of foods (Shen et al., 2015) or simple geometric figures (Larson, Aronoff, & Stearns, 2007). The search advantage for downward-pointing triangles has been attributed to the threat-related information conveyed by their resemblance of angry faces with muscles and eyes pulling down to form a ‘‘V’’ shape, as threat-related stimuli are more readily to attract attention than emotional-neutral stimuli (Larson, Aronoff, & Steuer, 2012; Larson, Aronoff, Sarinopoulos, & Zhu, 2009; LoBue & Larson, 2009; Salgado-Montejo, Salgado, Alvarado, & Spence, 2017; Watson, Blagrove, Evans, & Moore, 2012). Similarly, the faster detection of inward-pointing shapes or foods we found in the present study can also be attributed to the potential danger of having sharp angular ends point towards the observers, and consumers prefer that the angle of a food points away from themselves (Michel et al., 2015). Alternatively, it is possible to attribute the faster detection of inward-pointing foods on the table to the easiness of mental simulation of eating (i.e., biting the food), as mental simulation of eating plays an important role in food perception and consumption (Steinmetz, Tausen, & Risen, 2018; Xie, Minton, & Kahle, 2016). Moreover, our results also revealed that the faster detection of an 9
Food Quality and Preference 78 (2019) 103728
W. Zhang, et al.
restaurants, stores, and schools. Importantly, our findings also demonstrate the promising future of using VR in influencing people’s eating behavior for better health management.
Foods, 2, 195–199. Marendaz, C. (1998). Nature and dynamics of reference frames in visual search for orientation: Implications for early visual processing. Psychological Science, 9(1), 27–32. Meiselman, H. L., Hirsh, E. S., & Popper, R. D. (1988). Sensory, hedonic and situational factors in food acceptance and consumption. In D. M. H. Thomson (Ed.). Food acceptability (pp. 77–87). London: Elsevier. Michel, C., Velasco, C., Fraemohs, P., & Spence, C. (2015). Studying the impact of plating on ratings of the food served in a naturalistic dining context. Appetite, 90, 45–50. Michel, C., Woods, A. T., Neuhäuser, M., Landgraf, A., & Spence, C. (2015). Rotating plates: Online study demonstrates the importance of orientation in the plating of food. Food Quality and Preference, 44, 194–202. Nijs, I. M., Muris, P., Euser, A. S., & Franken, I. H. A. (2010). Differences in attention to food and food intake between overweight/obese and normal-weight females under conditions of hunger and satiety. Appetite, 54, 243–254. Piqueras-Fiszman, B., Alcaide, J., Roura, E., & Spence, C. (2012). Is it the plate or is it the food? Assessing the influence of the color (black or white) and shape of the plate on the perception of the food placed on it. Food Quality and Preference, 24, 205–208. Piqueras-Fiszman, B., & Spence, C. (2014). Colour, pleasantness, and consumption behaviour within a meal. Appetite, 75, 165–172. Rolls, B. J., Morris, E. L., & Roe, L. S. (2002). Portion size of food affects energy intake in normal-weight and overweight men and women. American Journal of Clinical Nutrition, 76, 1207–1213. Rozin, P., & Tuorila, H. (1993). Simultaneous and temporal contextual influences on food acceptance. Food Quality and Preference, 4, 11–20. Salgado-Montejo, A., Salgado, C., Alvarado, J., & Spence, C. (2017). Simple lines and shapes are associated with, and communicate, distinct emotions. Cognition & Emotion, 31, 511–525. Sander, D., Grandjean, D., & Scherer, K. (2005). A systems approach to appraisal mechanisms in emotion. Neural Networks, 18, 317–352. Sawada, R., Sato, W., Toichi, M., & Fushiki, T. (2017). Fat content modulates rapid detection of food: A visual search study using fast food and Japanese diet. Frontiers in Psychology, 8, 1033. Seage, C. H., & Lee, M. (2017). Do disinhibited eaters pay increased attention to food cues? Appetite, 108, 151–155. Shen, X., Wan, X., Mu, B., & Spence, C. (2015). Searching for triangles: An extension to food & packaging. Food Quality and Preference, 44, 26–35. Spence, C., Harrar, V., & Piqueras-Fiszman, B. (2012). Assessing the impact of tableware and other contextual variables on multisensory flavour perception. Flavour, 1, 7. Spence, C., Okajima, K., Cheok, A. D., Petit, O., & Michel, C. (2016). Eating with our eyes: From visual hunger to digital satiation. Brain and Cognition, 110, 53–63. Stelick, A., Penano, A. G., Riak, A. C., & Dando, R. (2018). Dynamic context sensory testing – A proof of concept study bringing virtual reality to the sensory booth. Journal of Food Science, 83, 2047–2051. Steinmetz, J., Tausen, B. M., & Risen, J. L. (2018). Mental simulation of visceral states affects preferences and behavior. Personality and Social Psychology Bulletin, 44(3), 406–417. Stewart, P. C., & Goss, E. (2013). Plate shape and colour interact to influence taste and quality judgments. Flavour, 2(1), 27. Tallis, R. (2010). Michelangelo’s Finger. An exploration of everyday transcendence. New Have: Yale University Press. Wan, X., Wang, R. F., & Crowell, J. A. (2009). Spatial updating in superimposed real and virtual environments. Attention, Perception & Psychophysics, 71, 42–51. Wan, X., Wang, R. F., & Crowell, J. A. (2010). The effect of active selection in human path integration. Journal of Vision, 10(11), 25. Watson, D. G., Blagrove, E., Evans, C., & Moore, L. (2012). Negative triangles: Simple geometric shapes convey emotional valence. Emotion, 12, 18–22. Werthmann, J., Jansen, A., & Roefs, A. (2016). Make up your mind about food: A healthy mindset attenuates attention for high-calorie food in restrained eaters. Appetite, 105, 53–59. Wolfe, J. M. (1994). Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1, 202–238. Wolz, I., Faqundo, A. B., Treasure, J., & Fernandez-Aranda, F. (2015). The processing of food stimuli in abnormal eating: A systematic review of electrophysiology. European Eating Disorders Review, 23(4), 251–261. Xie, H., Minton, E. A., & Kahle, L. R. (2016). Cake or fruit? Influencing healthy food choice through the interaction of automatic and instructed mental simulation. Marketing Letters, 27, 627–644. Zampollo, F., Wansink, B., Kniffin, K. M., Shimizu, M., & Omori, A. (2012). Looks good enough to eat: How food plating preferences differ across cultures and continents. Cross-Cultural Research, 46(1), 31–49. Zellner, D. A., Siemers, E., Teran, V., Conroy, R., Lankford, M., Agrafiotis, A., et al. (2011). Neatness counts. How plating affects liking for the taste of food. Appetite, 57, 642–648. Zhang, B., & Seo, H. (2015). Visual attention toward food-item images can vary as a function of background saliency and culture: An eye-tracking study. Food Quality and Preference, 41, 172–179. Zhao, H., An, J., Spence, C., & Wan, X. (2018). Influence of the color and size of the plate on the subjective ratings of, taste expectations concerning, and willingness-to-pay for, Asian noodles. Journal of Sensory Studies, 33, e12443. Zhao, H., Huang, F., Spence, C., & Wan, X. (2017). Visual search for wines with a triangle on the label in a virtual store. Frontiers in Psychology, 8, 2173. Zhao, H., Qi, Y., Spence, C., & Wan, X. (2019). On the costs and benefits of using triangles in packaging design. Food Quality & Preference. https://doi.org/10.1016/j.foodqual. 2019.103719.
Declaration of Competing Interest The authors declare no conflict of interest. Acknowledgements The first and second authors contributed equally to this work. This research was supported by the National Natural Science Foundation of China (Grant No. 71472106) awarded to Xiaoang Wan. The authors would like to thank Kaung Myat for his technical assistance. Comments concerning this article should be sent to Dr. Xiaoang Wan at wanxa@ mail.tsinghua.edu.cn. References Bigné, E., Llinares, C., & Torrecilla, C. (2016). Elapsed time on first buying triggers brand choices within a category: A virtual reality-based study. Journal of Business Research, 69(4), 1423–1427. Blechert, J., Meule, A., Busch, N. A., & Ohla, K. (2014). Food-pics: An image database for experimental research on eating and appetite. Frontiers in Psychology, 5, 1–10. Bradley, M., Codispoti, M., Cuthbert, B. N., & Lang, P. J. (2001). Emotion and motivation I: Defensive and appetitive reactions in picture processing. Emotion, 1(3), 276–298. Castellanos, E. H., Charboneau, E., Dietrich, M. S., Park, S., Bradley, B. P., Mogg, K., et al. (2009). Obese adults have visual attention bias for food cue images: Evidence for altered reward system function. International Journal of Obesity, 33(9), 1063–1073. Foster, D. H., & Ward, P. A. (1991). Asymmetries in oriented line detection indicate two orthogonal filters in early vision. Proceedings of the Royal Society of London, 243, 75–81. Gromer, D., Madeira, O., Gast, P., Nehfischer, M., Jost, M., & Müller, M. (2018). Height simulation in a virtual reality CAVE system: Validity of fear responses and effects of an immersion manipulation. Frontiers in Human Neuroscience, 12, 372. Harrar, V., Piqueras-Fiszman, B., & Spence, C. (2011). There’s more to taste in a coloured bowl. Perception, 40, 880–882. Harrar, V., Toepel, U., Murray, M., & Spence, C. (2011). Food’s visually-perceived fat content affects discrimination speed in an orthogonal spatial task. Experimental Brain Research, 214, 351–356. Higgs, S., Rutters, F., Thomas, J. M., Naish, K., & Humphreys, G. W. (2012). Top-down modulation of attention to food cues via working memory. Appetite, 59, 71–75. Hardman, C. A., Rogers, P. J., Etchells, K. A., Houstoun, K. V. E., & Munafò, M. R. (2013). The effects of food-related attentional bias training on appetite and food intake. Appetite, 71, 295–300. Hendrikse, J. J., Cachia, R. L., Kothe, E. J., McPhie, S., Skouteris, H., & Hayden, M. J. (2015). Attentional biases for food cues in overweight and individuals with obesity: A systematic review of the literature. Obesity Reviews, 16(5), 424–432. Hollitt, S., Kemps, E., Tiggemann, M., Smeets, E., & Mills, J. S. (2010). Components of attentional bias for food cues among restrained eaters. Appetite, 54, 309–313. Huang, F., Huang, J., & Wan, X. (2019). Influence of virtual color on the perceived taste of tea: Multisensory integration between virtual and real worlds. Computers in Human Behavior, 95, 168–174. Hummel, G., Zerweck, I., Ehret, J., Winter, S. S., & Stroebele-Benschop, N. (2017). The influence of the arrangement of different food images on participants’ attention: An experimental eye-tracking study. Food Quality and Preference, 62, 111–119. Knoeferle, K., Knoeferle, P., Velasco, C., & Spence, C. (2016). Multisensory brand search: How the meaning of sounds guides consumers' visual attention. Journal of Experimental Psychology: Applied, 22, 196–210. Kumar, S., Higgs, S., Rutters, F., & Humphreys, G. W. (2016). Biased towards food: Electrophysiological evidence for biased attention to food stimuli. Brain and Cognition, 110, 85–93. Lang, P. J., & Bradley, M. M. (2010). Emotion and the motivational brain. Biological Psychology, 84, 437–450. Larson, C. L., Aronoff, J., Sarinopoulos, I. C., & Zhu, D. C. (2009). Recognizing threat: Simple geometric shapes activate neural circuitry underlying threat detection. Journal of Cognitive Neuroscience, 21, 1523–1535. Larson, C. L., Aronoff, J., & Stearns, J. J. (2007). The shape of threat: Simple geometric forms evoke rapid and sustained capture of attention. Emotion, 7, 526–534. Larson, C. L., Aronoff, J., & Steuer, E. L. (2012). Simple geometric shapes are implicitly associated with affective value. Motivation and Emotion, 36, 404–413. Ledoux, T., Nguyen, A. S., Bakos-Block, C., & Bordnick, P. (2013). Using virtual reality to study food cravings. Appetite, 71, 396–402. Lee, E. A., & Wong, K. W. (2014). Learning with desktop virtual reality: Low spatial ability learners are more positively affected. Computers & Education, 79, 49–58. LoBue, V., & Larson, C. L. (2009). What makes an angry face look so angry? Examining visual attention to the shape of threat in children and adults. Visual Cognition, 18, 1165–1178. Ma, G. (2015). Food, eating behavior, and culture in Chinese society. Journal of Ethnic
10