Instagram Use, Interest and Recognition of Images
by Catherine Pope, University of Massachusetts Dartmouth
Abstract: To test the effect individual differences in interest and Instagram use have on recognition of images, a study was conducted consisting of 35 participants, (4 males and 31 females) from the University of Massachusetts Dartmouth. In this study “interest” is used to describe content specific concepts, specifically how interest as a directive force ties into individual differences of knowledge acquisition. The participants were tested on their memory of images in three different popular categories from the app Instagram. Food, fashion, and nature, were the categories of images used in the study. Participants then answered questions to determine individual interest for each category. The number of hours they spent daily on Instagram was measured to determine exposure level and individual interest in using Instagram. We found no significant effect of participant total interest on the recognition of images. There was a marginally significant effect found for the amount of Instagram use on the recognition of images. These results provide partial support for previous research results that experience should predict image recognition, while they refute previous research showing interest predicts image recognition.
Keywords: individual interest, Instagram, recognition, images, individual differences, social media
Previously steady growth of social media platforms has slowed, Instagram is the only platform that showed an increase in users from 2016 to 2019 (Perrin & Anderson, 2019). Through Instagram, users can view massive amounts of media, and images revolving around their interests. The popularity of this platform shows a shift towards image based social media. The present study seeks to investigate how the daily viewing of these images affects users' image recognition, especially for images that are related to their interests. Undergraduate students at the University of Massachusetts Dartmouth were recruited, as they fit the demographic which uses Instagram the most. According to PEW Research Center (Perrin & Anderson, 2019), those ages 18 to 24 are substantially more likely than those ages 25 to 29 to use Instagram, at 75% vs 57% respectively.
The present study uses the following definition of interest which is based on the research of Schiefele (1991):
Interest is a content-specific concept, i.e., it is always related to specific topics, tasks or activities. Our study looks at interest in three general topics (food, fashion, and nature) and one main activity (Instagram use). Prior relevant literature has studied interest in terms of Pokemon (Xie & Zhang, 2017), sports media (Hahn & Cummins, 2019), aerial photograph analyzation (Sikl, Svatonova, Dechterenko, & Urbanek, 2019), and letters versus pictures (Sorenson & Kyllingsbaek, 2012)
Interest is a directive force. It can explain an individual's choice of area to which they strive for high levels of performance, often exhibiting an intrinsic motivation to do so. The present study looks at Instagram use, measuring participant motivation to view images through the amount of time they spend on the app. Previous literature has defined this as having gained expertise in a subject (Sikl et al., 2019), acquired knowledge and extent to which they enact a passion (Hahn & Cummins 2019), familiarity with a stimulus (Xie & Zhang 2017), and learning to read and write (Sorenson & Kyllingsbaek 2012).
When understanding content-specific content, interest fits well with cognitive theories of knowledge acquisition and will be closely associated with knowledge acquisition in the current study. Schiefele (1991) states this is due to new information being acquired in particular domains, with interest playing a moderating factor for motivation in which domain an individual gains knowledge. Again, prior literature (Xie & Zhang, 2017; Hahn & Cummins, 2019; Sikl, Svatonova, Dechterenko, Urbanek, 2019) looks at individual differences in interest alongside individual differences in knowledge due to their interest.
Previous research has shown that interest and knowledge of a topic enhances memory for that topic. These studies are relevant to our research as they show that individual differences in interest play a moderating role in recall. Previous research shows that interest in distinct categories, as well as experience or expertise can impact memory. Individuals who gain expertise in an area of their interest have been shown to recall more information of that kind, such as sports fans (Hahn & Cummins, 2019) and Aerial image experts (Sikl et al., 2019). Experts at recognizing aerial images showed a higher accuracy at recognizing both vertical and oblique aerial photos, with their accuracy remaining stable across both conditions despite the oblique aerial photos being more difficult stimuli. Despite the level of homogeneity in the photos, the expert participants were able to recognize the correct image, due to their experience, which led them to be more interested in certain aspects of the images, such as shape, size, spatial arrangement, shadows, texture, and brightness, to identify ground objects. This ability to pick out key differences comes from the amount of time they spent viewing these images.
Exposure level has also been shown to affect memory, as demonstrated by Xie and Zhang (2017) who showed that more interaction with Pokémon led to higher recognition. Xie and Zhang (2017) predicted that strong familiarity with first-generation Pokemon in long term memory (LTM), would increase short term memory (STM) for the first-generation Pokemon over the less familiar second-generation Pokemon. The results of their study supported this prediction; participants remembered more Pokemon from the generation they were familiar with. Interestingly, the participants who had familiarity with Pokemon showed an increase in STM storage capacity.
Similarly, Sorenson and Kyllingsbaek (2012) showed that training and expertise affected visual short-term memory through their study on learning the symbols or letters of a language. This study compared visual short term memory (VSTM) capacity for simple pictures or line drawings with VSTM for letters across different age groups. This study found a significant difference in VSTM due to prolonged training in reading and writing, this difference in expertise led to a marked increase in VSTM capacity across age groups for letters. Sorenson and Kyllingsbaek (2012) suggested that this improvement is due to the cognitive training, and establishment of mental categories due to training in reading and writing. This training in reading and writing is a result of constant exposure, and interaction with the material. Similarly, in the present study, we examine how the amount of time an individual interacts with Instagram affects their ability to recognize images.
Despite this broad consensus, there are studies in contradiction to these results, such as Blake et al. (2015) who showed that individuals who were exclusively Apple device users, or had more exposure to Apple products, did not recognize the Apple logo at a higher rate than non-Apple users. Most relevant to the current study is the recognition task in which participants were asked to identify the correct Apple logo from an array of eight similar Apple logos with altered features. While Apple users had a numerically higher score for the recognition task, this was not statistically significant. The study also had more participants who were Apple users (52 strict Apple users) over PC (10 strict PC users), and mixed users (23 used a combination of Apple and PC). This might explain why numerically Apple users identified the symbol more, but the differences were insignificant. Apple users did not recognize the Apple logo at a significantly higher rate, despite their increased interest in Apple products. This contradiction with previous findings is interesting, and we predicted that our current study would support the conclusions of (Xie & Zhang, 2017; Hahn & Cummins, 2019; Sikl et al., 2019; Sorenson & Kyllingsbaek, 2012) which demonstrated the effect of interest on memory and recognition.
In a society where individuals are constantly viewing media, there is an inundation of images. For those who choose to view a higher amount of images than others, i.e. spend more time on image viewing platforms, it is important to study the cognitive affect this has on the user. The present study will add to the body of knowledge on individual interest and its interaction with memory, while adding to the emerging literature on social media and its cognitive effects. Social media is the key difference between our study and past literature. Previous studies have examined expertise in individual interest for a variety of subjects. The present study is the first to analyze how expertise in Instagram use affects recognition for images. Instagram is a relatively new social media app, and this study contributes to the emerging literature regarding its effects.
The quasi-independent variables studied were Instagram use, and individual interest in topics while the dependent variable was total image recognition. We predicted that those who spend the most time on the app Instagram would have the greatest recognition for images. Our secondary prediction was individual total interest would increase image recognition.
These hypotheses are supported by prior literature showing a combination of interest and exposure predicts improved recognition (Xie & Zhang, 2017; Hahn & Cummins, 2019; Sikl et al., 2019; Sorenson & Kyllingsbaek, 2012). Xie and Zhang (2017) confirm the idea that familiarity in LTM leads to a boost in STM, which lends support to our study’s hypothesis, that the amount of time one spends on the image viewing site Instagram, or familiarity with Instagram, will improve overall recognition of images. Sikl et al. (2019) show that exposure level moderated by interest, increases ability to distinguish minor details resulting in increased image recognition regardless of orientation. These studies support our hypothesis that those who spend more time viewing images on Instagram will be able to recognize more overall images. This prior literature supports our prediction that experience using the app Instagram, measured as time spent on Instagram daily, will affect the overall number of images recognized positively. It also supports the idea individual interest will produce an improved recognition.
A total of 35 Undergraduates from the University of Massachusetts Dartmouth took part in the study. The mean age of participants was 22.6 years (SD = 3.95). Participants were 4 men and 31 women aged 18 to 39 years, all of whom were enrolled in PSY 308 and other Methods classes at the time of the study. Participants volunteered to complete this study in exchange for extra credit and were sent the study survey via email.
Participants viewed 24 target images from Instagram: eight images for each of the three image categories. The three categories of images were chosen due to their popularity on Instagram: food, fashion, and nature. Images were chosen from the app Instagram using the search feature, posted by Instagram users. Each image was subjectively paired by the researchers with another image with similar format, composition, colors, and imagery. During the recognition task participants were shown an original image, and a mirrored version of the image, for a total of 48 images, 24 target images, and 24 distractor images. Mirrored images were used due to results from the pilot study, which used related images. In the pilot study the distractor image and target image were two different images of the same subject matter, and composition. Based on ceiling effects shown in this pilot, distractors were created using a mirrored version of the target image. Images were shown in the initial task, and the recognition task via a Qualtrics online survey (see Appendix A).
A questionnaire was given to participants via an online survey to define their individual interests. Each category of interest relevant to the study (food, fashion, and nature) was given a five point Likert scale which participants used to rank interest. For each category there was a single Likert scale on which participants rated their interest from least to most interested (see Appendix B1).
Participants answered questions about their media use via an online survey. They were asked the amount of time spent on the app Instagram. To quantify how long participants spent on Instagram they were asked a multiple choice question, “How many hours daily do you spend on Instagram?” with the answer options of: 0 hours, 1-2 hours, 2-4 hours or 5+ hours (see Appendix B2).
For a distractor task participants completed 13 math problems: 6 division, 7 multiplication. Participants were able to advance after 45 seconds, and were automatically advanced after 90 seconds. They were then shown three different meme images, and asked to identify them from a multiple choice list (see Appendix C). Meme images were chosen as they are picture images, and popular across various social media platforms, including Instagram.
Participants began the study by clicking on a link to the online survey which had been sent to them via email. For informed consent they were informed of the purpose of the study, as well as the risks and benefits. They were also reminded of their right to withdraw at any point, and given the information of the study supervisor if they had any questions or concerns. Afterwards, participants were asked two demographic questions: their age and their gender. Then they were prompted with “Next you are going to view a series of images, please view each image in preparation for a memory test that will follow” before being shown the 24 target images. Each image was presented individually for one second with a page break in between. After all images were shown, participants completed the distractor tasks.
Then participants completed the recognition task, in which they were shown two images, the target image, and its mirrored version as a distractor image. Participants were asked to select the image they saw in the first portion of the study or the image that seemed most familiar to them, and to select an answer even if they were unsure. After choosing the most familiar image for all 24 image pairs, participants were asked to report their social media use, and their interest in the three categories of images presented (food, fashion and nature). They were then thanked for their time and asked to provide their participant number.
To investigate the effects of Instagram use and individual interest on the total number of images recalled, a hierarchical multiple linear regression was calculated to predict total recall of images based on Instagram use, and average interest. The first block contained our two primary predictors Instagram use and average interest. An interaction between Instagram use and average interest was entered in the second block along with our two primary predictors. Descriptive statistics and correlations for all variables are in Table 1.
Note. * p < .05. n = 34.
Block 1 was significant overall R2 = .18, F (2, 31) = 3.38, p=.05, where Instagram use (β = .33, p=.05) was found to be a marginally significant predictor of total recall. Those who used the app for more hours recalled more images then those who reported using the app less. This association is shown in Figure 1. While average interest had a negative effect on total recall, it was not a significant predictor (β = -.30, p = .07) . Block 2 was non-significant, R2 = .19, F (3, 30) = 2.38, p = .09, meaning there was no significant effect of our interaction term, Instagram use x average interest. These results are shown in Table 2.
Note. * p < .05. n = 34.
As society moves more and more online, it is important to analyze how this change affects cognitive processes. The current study sought to determine whether Instagram use and individual interest would affect recall of images. Instagram use directly corresponds with image viewing, as it is an image sharing and viewing app. Understanding how extended periods of time on this app affects users cognitively is important so users can make more informed decisions. The current study predicted that experience using the app Instagram, measured as time spent on Instagram daily, would affect the overall number of images recognized correctly by participants. This hypothesis was supported, although the finding was barely significant. Participants with higher reported Instagram use had a greater number of images correctly recalled, although this only explained 18% of the total variation in data. Our second hypothesis that individual interest would affect the number of images recalled in the category of interest was not supported. Average individual interest did not significantly affect participant recognition of images.
During the recognition portion of the current study, participants looked at images that were nearly identical to one another. One image was in the original orientation, while the other image was mirrored. Participants with a greater reported Instagram use correctly recognized more images then those with a lesser Instagram use. Sikl et al.’s (2019) results support these findings, in that Sikl et al. found that individuals who looked at aerial images professionally had a greater recognition of aerial images compared to a control group. Distractor images used by Sikl et al. (2019) were in the same orientation with small variation in details, while the current study used mirror images. Future research might use near identical Instagram images, with only small details changed to see if this would increase the significance of the present study’s results.
Our findings are also supported by Sorenson and Kyllingsbaek (2012), who found that consistent exposure and interaction with a material led to an improvement in visual short-term memory for that material. Their study looked at the acquisition of language, using letters to test cognitive differences in image recognition for letters. Individual differences in expertise and experience was measured in years, by age groups. Participants in this study had a much more extensive interaction with the material, due to daily interaction with language across many years. Whereas the current study measured experience with the material (i.e. Instagram images) in hours spent viewing daily. This difference in measuring daily consumption over cumulative consumption may explain differences in results.
Blake et al. (2015) found that despite Apple users’ increased interest in Apple products, they did not have a significantly higher recognition of the Apple logo compared to PC users. The current study supports this finding, as individuals did not show greater recall of images in categories they had a greater interest in. Both studies used image orientation in distraction images, the present study used mirror images, while Blake et al. (2015) manipulated bite mark, and leaf placement in the apple logo. This similarity in distractor images may explain why both studies found no difference in individual recall. Image orientation may play a factor in individuals recognizing the target image over distractor images.
Findings by Hahn and Cummings (2019) and Xie and Zhang (2017) were not supported by the current study, individual interest did not affect recognition in either direction. This may be due to differences in methodology, and subject of individual interest. Both studies focused on differences in recall of information due to individual differences in interest, however the information was not transformed in either study. Hahn and Cummings (2019) used sports footage to show participants sports statistics, they were then asked to recall the statistics shown. Participants answered open ended questions about the statistics shown, and while the order of footage and statistics varied, there were no distractor statistics. Similarly, Xie and Zhang (2017) measured participants' recognition of first generation Pokemon compared to recent generation Pokemon. Participants were shown a single image of a Pokemon and asked if it was a new image or old image. Whereas in the current study participants were presented two orientations of the same originally shown image and asked which was the most familiar image. This difference in methodology of data presentation, and manipulation may account for differences in results.
Limitations to the current study may come from natural variations in individuals’ interest. There may be outside variables that may interact with our predictors and the outcomes. These may include time spent viewing images, or the length of time an individual has had an Instagram account. Expertise in Instagram may be linked to the length of time an individual has had an account. The current study defined expertise as daily use of Instagram, however total length of time using the app should be investigated further. A limitation of the current study is that participants were not asked the amount of time they spent viewing each category of interest. This might help to explain why total interest was unrelated to recognition of images. Individuals may have reported having interest in a category presented to them by the researchers that they did not spend time looking at on the app Instagram. Image orientation may be a limitation of the current study, there could be a spatial aspect to the recognition of images that is acting as a confound. While Sikl et al. (2012) found that aerial image experts had a higher rate of identifying target images across both orientations, this was done in groups. Participants were tested on vertical and oblique variations, the target image and distractor image were of the same orientation for each image pairing. Whereas the present study used a target image and a distractor image of a mirrored orientation. Future research may use the same image, with slight variation in detail to account for this possible confound. Another limitation of the current study may be the operational definitions of expertise, and interest. The relevant literature we reviewed varied in the definitions and measurements of expertise and interest. Although these definitions fit broadly, a more uniform definition may produce more uniform results. There may also be an interaction between participants' real life experience or lifestyle with interest, and their online interest. A passive interest in a subject may affect image recognition differently than individuals who actively engage regularly in an interest online and offline. There may be a difference in online knowledge compared to real life expertise, and this is an area future research may measure.
Future research could expand upon individual interest through measuring the amount of time spent viewing each category of interest shown. This would further explore the differences in memory related to individual interest and viewing frequency. Participants might also receive a pre-survey, in which they would report their interests. Researchers could then use these lists of interests to provide categories of images for the study. This would ensure that the participants involved would be interacting with content they spent time outside the experiment viewing. Future research may also examine an individual's expertise in interests in terms of real life or online interaction with the interest. Previous research shows conflicting results regarding interest, recognition of images, and experience with stimuli. Future research is needed in these areas to gain a better understanding of memory, as well as how individual interest acts as a moderator of memory.
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Note: Each dot represents an individual participant.
Instagram use is measured in number of hours spent on the app daily such that: 1= 0 hours, 2= 1-2 hours, 3= 2-4 hours, and 4= 5+ hours.
There was a marginally significant relationship between total recall and Instagram use.
Images shown 24 control, 24 distractor
Categories food, fashion and nature taken from https://www.instagram.com/?hl=en