Investigating Change Blindness: Psychological Approaches

Abstract

This document presents the findings of an experiment investigating change blindness among a group of 250 respondents. The participants viewed 16 everyday scenes and stated whether they detected a change or not. A change was recorded when they clicked on the associated image. Sixteen scenes were presented to the informants and each one of them represented an experimental trial with two nearly identical images.

An intervening blank screen separated the images and it appeared for the same number of time (0.25 seconds) it took to show the other images. While undertaking the experiment, three different types of change (within-category, congruent and incongruent changes) were introduced to the stimuli and only one was made per trial. Two major hypotheses helped to assess their occurrence. The first one suggested that gender had a significant effect on how people detected change blindness and the second one stated that age does not influence change blindness. Both hypotheses were confirmed to be true.

Introduction

According to Tomonaga and Imura (2015), change blindness refers to the failure of a person to detect changes in visual stimuli. Several researchers have explored the concept of change blindness and come up with varied findings. For example, Rensink, O’Regan and Clark (1997) used the flicker paradigm and attention blink model to find out how fast people could detect changes. Their findings showed that full attention is needed to detect changes in the environment.

The “mud splash” study by O’Regan, Rensink and Clark (1999) also used the flicker paradigm to investigate environmental changes but excluded attention blink as part of the analytical model. It was discovered that human memory could not be overwritten but change blindness could occur at any circumstance. Similar to the findings of Rensink, O’Regan and Clark (1997), O’Regan, Rensink and Clark (1999) also observed that central changes were easier to spot compared to fringe alterations in images.

The door study by Simons and Levin (1998) paints a different picture of the occurrence of change blindness by pointing out that the inconsistencies observed in most studies investigating change blindness were caused by the use of minimal details in the pictures or images involved. These findings were developed after a group of participants were shown two images of one person initiating a conversation with an experimenter and another that had the same setting but with the person initiating the conversation replaced.

Only one-half of the respondents detected the change. The findings of the study suggested that social group dynamics affected people’s attention if the meaning of the change remained constant. The social group dynamics highlighted by Simons and Levin (1998) also draw attention to other models explaining human attention, such as the one advanced by Lavie (1995), which suggests that people can filter out irrelevant material when under the effects of perceptual overload. Relative to this assertion, Nisbett (2003), Miyamoto, Nisbett and Masuda (2006) also highlight the role of the cultural environment in detecting changes.

Their findings were developed after analysing changes observed among a group of American and Japanese respondents about different images lifted from their cities. Collectively, their findings suggested that individuals deploy their attention at group-levels. The findings highlighted above show that perception is a function of attention. Therefore, people who lack attention may not easily spot changes in their primary environment, thereby limiting their perception of the same changes (Brace & Byford 2012).

The purpose of this study is to investigate whether change blindness is a function of the type of change involved. This analysis is important because it helps to understand aspects of perception that are not inherently known to the conscious mind (Herbranson 2015). This study varies the type of change presented: congruent, incongruent or within category. The following two hypotheses were tested in the study:

  • H1: Gender had a significant effect on how people detected change blindness
  • H2: Age does not influence change blindness.

The independent and dependent variables are also outlined below

Dependent Variables (IV): Type of Change (within-category, congruent and incongruent changes

Independent Variable (DV): Demographic characteristics (age and gender)

Method

As highlighted in this report, the purpose of this study is to investigate whether change blindness is a function of the type of change involved. Different techniques were used to address this research issue. This section of the report shows the design, nature of participants, materials used and procedures followed to test the research hypotheses.

Design

The experiment was designed to explore the concept of change blindness using the flicker paradigm. Participants viewed 16 everyday scenes and stated whether they detected a change, or not. Their response time was computed to know how fast they detected the changes. Each of the 16 scenes presented to the participants represented an experimental trial with two nearly identical images. An intervening blank screen separated the first and second images and it was shown for the same number of seconds (0.25) it took to show both images.

Three different types of change (within-category, congruent and incongruent changes) were introduced to the stimuli and only one was made per trial. Within-category change involved the replacement of one object with another that was within the category of the original object. Alternatively, congruent change was associated with maintaining the scenery after the replacement of an object and, lastly, an incongruent change involved the replacement of one object with another that does not fall in the same category.

Participants

Data was collected from 250 DE200 students who experimented in 2019 and information was categorised into a data pool. The research respondents were selected to participate in the study after considering the ethical implications of their recruitment, as proposed by McNabb (2015) and Coe et al. (2017). Ethical provisions, such as confidentiality and anonymity, which are described by Marchiori (2018) and the British Psychological Society (2019) were also observed in the study.

Furthermore, before participating in the study, all the participants were required to sign an informed consent form, which stated their voluntary participation in the research (see appendix 3). The resources used in the experiment were also identified with this principle in mind. The respondents were made aware of the purpose of the study before they participated in it (see debriefing report on appendix 4). Therefore, they could withdraw or continue with it voluntarily.

After completing the study, the respondents were informed about the researcher would use the data gathered and at this point of the investigation, any questions posed by the participants were answered. The data gathered from the participants had three major dependent variables: congruent, incongruent and within-category changes. These changes were analysed to explore whether they affected the likelihood of the respondents to detect changes in repetitively occurring images using age and gender as moderating variables for analysing their behaviours and response times.

Materials

Data used in this study came from class materials (week materials) and course books. These pieces of information were central in providing background data regarding change blindness and in explaining different models of attention. Data from relevant peer-reviewed psychology journals and books were also included in this study for purposes of comparing new findings with existing data. The search terms used were: change, blindness and attention.

Procedure

The order of presentation of the trial was randomised across the respondent group. To make sure that the participants observed a change, an in-built control system was included in the study by showing identical images. The results of participants who reported having seen changes in the identical images were excluded from the study. Therefore, the findings that remained were for participants who detected actual changes. Lastly, data were analysed using the statistical packages for social sciences (SPSS) software.

Results

As highlighted in the methodology section of this report, while undertaking the experiment, three different types of changes (within-category, congruent and incongruent changes) were introduced to the stimuli and only one was made per trial. The main variables were gender, age, and types of change (incongruent, congruent, and within-category changes). Based on the nature of these variables, the one-way ANOVA test was undertaken to test the two major hypotheses highlighted in the introduction section.

To recap, the first one suggested that gender had a significant effect on how people detected change blindness and the second one stated that age did not influence change blindness. Both hypotheses were confirmed to be true. According to appendix 1, it was established that there were no statistically significant differences in the changes observed across the different age groups of the respondents because the significance level was higher than 0.05 for all category of changes. Therefore, the respondents’ ages were not a significant determinant of the kind of changes observed.

Gender was another variable investigated in the study. Appendix 2 shows its associated findings. According to the ANOVA data output, it was established that there was a statistically significant difference in the manner males and female respondents viewed incongruent changes. This view stems from the 0.26 significance level posted for this category of change, which is less than the statistically significant level of P>0.05 for this test. There was also a difference in the manner male and female respondents detected within-category and congruent changes but it was not statistically significant.

Discussion

According to the ANOVA findings highlighted in appendix 1, it was established that there was no statistically significant difference in the changes observed across the different ages of the respondents because the significance levels for the three types of changes observed were higher than 0.05. Therefore, the respondents’ ages were not a significant determinant of the kind of changes observed. This finding is inconsistent with existing studies, which suggest that age is an important moderating variable in analysing change blindness using the flicker paradigm (Hagen & Laeng 2016). This is because older respondents often have a longer response time compared to their younger counterparts in detecting the changes (Hagen & Laeng 2016).

The findings reported in this study are also inconsistent with those of XYZ, which show that age, substance abuse and order of presentation were significant moderating variables for analysing change blindness using the flicker paradigm. It is reported that these conditions create change blindness because of the appearance of blank fields (Hagen & Laeng 2016). Gunnell et al. (2019) support this view by saying that blank screens produce motion signals throughout a display, which makes it difficult for respondents to detect changes.

Based on this view, bigger signals overwhelm smaller ones, thereby making it difficult to detect changes. This explanation mostly relates to the order of presenting images, which can be analysed with age as a moderating variable for analysing change blindness. The findings highlighted here suggests that hypothesis 1 (H1) is true because age did not influence the respondents’ ability to detect change.

According to the ANOVA findings for gender as an independent variable highlighted in appendix 2, it was established that there was a statistically significant difference in the manner males and female respondents viewed incongruent changes. This view stems from the 0.26 significance level posted for this category of change, which is less than the statistically significant level of 0.05 for this test.

There was also a difference in the manner male and female respondents detected within-category and congruent changes but it was not statistically significant. The findings of this study are consistent with those of other researchers, such as Gunnell et al. (2019) and Harris (2019) who have noted the effects of gender on change blindness. The researchers suggest that the inability of men to multitask in the same manner as women do could lower their predisposition to change blindness.

The findings of this study are consistent with those of other researchers who have observed a significant delay in detecting changes in the physical environment even if participants are instructed to look for it (Hagen & Laeng 2016; Gunnell et al. 2019). In line with this assertion, it has been demonstrated that some people could take up to one minute to detect a change amongst a series of flickering images. The nature of the findings identified in this study is also consistent with those of other researchers who have pointed out that changes in the middle of a picture are more likely to be detected than those that are on its fringes (Lupyan 2017; Ammawat et al. 2019). This is why most of the changes observed in the experiment were for incongruent changes.

The findings of this study show the interconnection between perception and attention, as described by Gordon et al. (2019), Philbeck and Witt (2015). The use of a few outside sources for comparing the current findings with existing literature means that future research should compare the findings highlighted in this paper with those of other researchers to identify further areas of investigation that need attention. Nonetheless, for purposes of this study, important skills about data collection and analysis have been provided. Overall, based on the findings presented in this report, change blindness is a function of the type of change involved.

Reference List

Ammawat, W, Attanak, A, Kornpetpanee, S & Wongupparaj, P 2019, ‘Pre-schoolers’ visual perception and attention networks influencing naming speed: an individual difference perspective’, Heliyon, vol. 5, no. 10, pp. 1-10.

Brace, N & Byford, J 2012, Investigating psychology: key concepts, key studies, key approaches, OUP, Oxford.

British Psychological Society 2019, Standards and guidelines. Web.

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Gordon, N, Tsuchiya, N, Koenig-Robert, R & Hohwy, J 2019, ‘Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways’, PLoS Biology, vol. 17, no. 4, pp. 1-10.

Gunnell, D, Kunar, MA, Norman, DG & Watson, DG 2019, ‘The hazards of perception: evaluating a change blindness demonstration within a real-world driver education course’, Cognitive Research: Principles and Implications, vol. 4, no. 1, pp. 15-22.

Hagen, T & Laeng, B 2016, ‘The change detection advantage for animals: an effect of ancestral priorities or progeny of experimental design?’, I-Perception, vol. 7, no. 3, pp. 1-10.

Harris, D (ed.) 2019, Engineering psychology and cognitive ergonomics, Springer, New York, NY.

Herbranson, W 2015, ‘Change blindness in pigeons (Columba livia): the effects of change salience and timing’, Frontiers in Psychology, vol. 6, no. 1109, pp. 1-10.

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Miyamoto, Y, Nisbett, RE & Masuda, T 2006, ‘Culture and the physical environment: holistic versus analytic perceptual affordances’, Psychological Science, vol. 17, no. 1, pp. 113-119.

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O’Regan, JK, Rensink, RA & Clark, JJ 1999, ‘Change-blindness as a result of mudsplashes’, Nature, vol. 398, no. 6722, pp. 34-46.

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Rensink, R, O’Regan, J & Clark, J 1997, ‘To see or not to see: the need for attention to perceive changes in scenes’, Psychological Science, vol. 8, no. 5, pp. 368-373.

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Appendices

Appendix 1. ANOVA Findings for Age and Type of Change

ANOVA
Sum of Squares df Mean Square F Sig.
Average RT for Congruent Changes Between Groups 86.552 45 1.923 .972 .528
Within Groups 403.615 204 1.979
Total 490.167 249
Average RT for Within Category Changes Between Groups 174.432 45 3.876 .919 .621
Within Groups 860.402 204 4.218
Total 1034.834 249
Average RT for Incongruent Changes Between Groups 118.451 45 2.632 .784 .833
Within Groups 684.696 204 3.356
Total 803.147 249

Appendix 2: ANOVA Findings for Gender and Type of Change

ANOVA
Sum of Squares df Mean Square F Sig.
Average RT for Congruent Changes Between Groups 1.820 1 1.820 .924 .337
Within Groups 488.348 248 1.969
Total 490.167 249
Average RT for Within Category Changes Between Groups 8.505 1 8.505 2.055 .153
Within Groups 1026.329 248 4.138
Total 1034.834 249
Average RT for Incongruent Changes Between Groups 15.973 1 15.973 5.032 .026
Within Groups 787.174 248 3.174
Total 803.147 249

Appendix 3: Informed Consent Form

By signing this form you are agreeing to the following:

 I understand that my participation in this project will involve me viewing a series of images and pressing a button if I detect a change in the images. This will require approximately 15 minutes of my time.

 I understand that participation in this study is entirely voluntary and that I can withdraw from the study at any time, without giving a reason.

 I understand that I am free to ask any questions at any time as well as discuss any concerns with the experimenter.

 I understand that the information provided by me will be held anonymously.

 I understand that at the end of the experiment I will be provided with additional information about the purpose of the study.

 I have had an opportunity to discuss with the experimenter any questions or concerns I have about the study at this stage.

I consent to participate in this study:

………………………..

Appendix 4: Debriefing

Thank you for taking the time to participate in this experiment.

This study was investigating the change blindness effect, which is the inability to notice changes in a scene even when you are actively searching for them. This inability to detect changes is a normal and common phenomenon – around 30% of any given population will fail to see the changes in scenes such as those you have just viewed.

You will have noticed that the images you saw were either taken in an office, a kitchen, a dining room or a bedroom. The changes that you may, or may not, have detected fell into different categories: they were either congruent changes (e.g. a phone in an office scene changed to a stapler); incongruent changes (e.g. a phone in an office scene changed to a banana); or they showed within category changes (e.g. a landline phone in an office scene changed to a mobile phone). We also included some trials where no changes were present.

We were interested in whether the type of change in a scene affects the likelihood of it being detected by a viewer. This is why the different categories were presented.

Should you have any further questions about the experiment, please feel free to ask now, or you can contact the researchers by email [[email protected]].