Age-related Changes to the Attentional Modulation of Temporal Binding
Ageing, attention, TBW, multisensory integration, stream-bounce illusion
In multisensory integration, the time range within which visual and auditory information can be perceived as synchronous and bound together is known as the temporal binding window (TBW). With increasing age, the TBW becomes wider, such that older adults erroneously, and often dangerously, integrate sensory inputs that are asynchronous. Recent research suggests that attentional cues can narrow the width of the TBW in younger adults, sharpening temporal perception and increasing the accuracy of integration. However, due to their age-related declines in attentional control, it is not yet known whether older adults can deploy attentional resources to narrow the TBW in the same way as younger adults. This study investigated the age-related changes to the attentional modulation of the TBW. 30 younger and 30 older adults completed a cued-spatial-attention version of the stream-bounce illusion, assessing the extent to which the visual and auditory stimuli were integrated when presented at three different stimulus onset asynchronies, and when attending to a validly-cued or invalidly-cued location. A 2x2x3 mixed ANOVA revealed that when participants attended to the validly-cued location (i.e. when attention was present), susceptibility to the stream-bounce illusion decreased. However, crucially, this attentional manipulation affected audiovisual integration in younger adults but not in older adults. Whilst no definitive conclusions could be drawn about the width of the TBW, the findings suggest that older adults have multisensory integration-related attentional deficits. Directions for future research and practical applications surrounding treatments to improve the safety of older adults’ perception and navigation through the environment are discussed.
Pre-screening tools Participants were asked to complete two pre-screening questionnaires using Qualtrics survey software (www.qualtrics.com), to assess their eligibility for the study. Speech, Spatial and Quality of Hearing Questionnaire (SSQ; Appendix A; Gatehouse & Noble, 2004). Participants rated their hearing ability in different acoustic scenarios using a sliding scale from 0-10 (0=“Not at all”, 10=“Perfectly”). Whilst, at present, no defined cut-off score on the SSQ is available as a parameter to inform decision-making, previous studies have indicated that a mean score of 5.5 is indicative of moderate hearing loss (Gatehouse & Noble, 2004). As a result, people whose average score on the SSQ was lower than 5.5 were not eligible to participate in the experiment. Informant Questionnaire on Cognitive Decline in the Elderly (IQ-CODE; Appendix B; Jorm, 2004). Participants rated how their performance in certain tasks now has changed compared to 10 years ago, answering on a 5-point Likert scale (1=“Much Improved”, 5=“Much worse”). An average score of approximately 3.3 is the usual cut-off point when evaluating cognitive impairment and dementia (Jorm, 2004), therefore people whose average score was higher than 3.3 were not eligible to participate in the experiment. The mean scores of each pre-screening questionnaire are displayed in Table 1. An independent t-test revealed that there was no significant difference between age groups on the SSQ questionnaire [t(58) = -1.15, p=.253]; however, there was a significant difference between age groups on the IQ-CODE questionnaire [t(58) = -13.29, p<.001]. Experimental Design This research implemented a 2(Age: Younger vs Older) x 2(Cue: Valid vs Invalid) x 4(Stimulus Onset Asynchrony [SOA]: Visual Only [VO] vs 0 milliseconds vs 150 milliseconds vs 300 milliseconds) mixed design, with Age as a between-subjects factor and Cue and SOA as within-subjects factors. The experiment consisted of 16 different trial conditions (Table 2), randomised across all participants. Replicating the paradigm used by Donohue et al. (2015), the experimental block contained 72 validly-cued trials and 24 invalidly-cued trials, which were equally distributed between each side of the screen (left/right) and SOA conditions; this means that each participant completed 144 valid trials and 48 invalid trials for each SOA. Stimuli and Materials Participants completed the experiment remotely, in a quiet room on a desktop or laptop computer with a standard keyboard. All participants were asked to wear headphones/earphones. A volume check was conducted at the beginning of the experiment; participants were presented with a constant tone and asked to adjust the volume of this tone to a clear and comfortable level. The stimuli used in the task were replicated from Donohue et al. (2015). Each trial started with an attentional cue in the centre of the screen – a letter “L” or a letter “R” instructing participants to focus on the left or the right side of the screen. In addition to this, 2 pairs of circles were positioned at the top of the screen, one pair in the left hemifield and one pair in the right hemifield. The attentional cue lasted for 1 second, and 650 milliseconds after this cue disappeared, the circles in each pair started to move towards each other downwards diagonally (i.e. the two left circles moving towards each other and the two right circles moving towards each other). In the trials, one pair of circles moved towards each other, intersected, and continued on the same trajectory (fully overlapping and moving away from each other). This full motion of the circles formed an “X” shape, with the circles appearing to “stream” or “pass through” each other. On the opposite side of the screen, the other pair of circles stopped moving before they intersected, forming half of this “X” motion. On 75% of the trials, the full “X”-shaped motion appeared on the side of the screen that the cue directed participants towards (validly-cued trials); on the other 25% of trials, the full motion occurred on opposite side of the screen to where the cue indicated, and the stopped motion occurred at the cued location (invalidly-cued trials). In addition to these visual stimuli, on 75% of the trials, an auditory stimulus was played binaurally (500Hz, 17 milliseconds), either at the same time as the circles intersected (0ms delay), 150ms after the intersection or 300ms after the intersection. The remaining 25% of the trials were visual-only (i.e. no sound was played). Participants were told that regardless of whether a sound was played, they must make their pass/bounce judgements based on the full motion of the circles (the “X” shape), even if the full motion occurred at the opposite side of the screen that they were attending to. The experiment ended after all 768 trials – participation lasted approximately 1 hour. The experiment was built in PsychoPy2 (Pierce et al., 2019) and hosted by Pavlovia (www.pavlovia.org). Procedure Prior to the experiment, a brief meeting was organised between the participant and the researcher via Microsoft Teams, to explain the task and answer any questions. Participants were emailed a link to a Qualtrics survey, which included the participant information sheet, consent form, demographic questions and pre-screening questionnaires. If the person was deemed eligible to take part in the experiment, Qualtrics redirected participants to the experiment in Pavlovia. Participants were then presented with instructions detailing the attentional cue elements of the task and asking them to base their judgements on the full X-shaped motion of the stimuli. Participants were asked to press M on the keyboard if they perceived the circles to “pass through” each other or press Z if they perceived the circles to “bounce off” each other, answering as quickly and as accurately as possible. Participants completed a practice block of 10 trials, then the test session commenced. After each set of 10 random trials, participants had the opportunity to take a break. Participants were provided with a full debrief upon completion of the experiment, and all participants could enter a prize draw to win one of two £50 Amazon vouchers. Statistical Analyses This study required two separate mixed ANOVAs to analyse main effects and interactions, investigating significant differences between groups and conditions. Reaction Times. For the first dependent variable of reaction times (RT), mean RTs were calculated for each participant in each Cue x SOA condition, representing the time taken, in milliseconds, for each participant to press M or Z on the keyboard at the end of each trial. A 2(Age: Younger vs Older) x 2(Cue: Valid vs Invalid) x 4(SOA: 0ms vs 150ms vs 300ms x Visual-Only) mixed ANOVA was then conducted on these mean RTs. Bounce/Pass Judgements. For the second dependent variable of the bounce/pass judgements, the percentage of “Bounce” responses provided in each Cue x SOA condition was calculated for each participant. A 2(Age: Younger vs Older) x 2(Cue: Valid vs Invalid) x 3(SOA: 0ms vs 150ms vs 300ms) mixed ANOVA was then conducted on these percentage data. Visual-Only (VO) trials were compared separately for valid and invalid conditions using a paired samples t-test. Post-hoc paired samples t-tests were also used to investigate significant differences between the 0ms, 150ms and 300ms SOA conditions. Bounce/Pass Judgements: Pairwise comparisons. To analyse pairwise comparisons in the significant interaction of Age and Cue, responses in each SOA condition were collapsed – that is, a grand mean percentage of “Bounce” responses was calculated by averaging the percentage of “Bounce” responses in the 0ms, 150ms and 300ms trials in the Valid condition and in the Invalid condition. This produced an overall Valid and an overall Invalid mean percentage of “Bounce” responses for each participant. A 2(Age: Younger vs Older) x 2(Collapsed Cue: Valid vs Invalid) mixed ANOVA was conducted on this collapsed data to investigate differences between the proportion of “Bounce” responses in the Valid and Invalid condition for younger adults, and in the Valid and Invalid condition for older adults. In addition, 2 separate one-way ANOVAs were conducted on this collapsed data (Age as the between-subjects factor, and Valid or Invalid as the within-subjects factor) to investigate differences between younger and older adults in the Valid condition, and differences between younger and older adults in the Invalid condition (Laerd, 2015). Significance. An alpha level of .05 was used for all statistical tests. Any responses (judgements or RTs) that were ±3 standard deviations from the mean were considered anomalous and were removed from the analyses. Mauchly’s test of sphericity was violated for the main effect of SOA, therefore Greenhouse Geisser adjusted p-values were used where appropriate. As an a-priori power analysis determined the desired sample size for this study, and this sample size was achieved, non-significant results will not be due to the study being underpowered. Statistical analyses were conducted using SPSS (version 25, IBM).
An exploration of how young adults engage with charities
Research exploring how individuals choose to engage with charities has been limited to studies and interpretations from the 20th Century. In addition to this, research into how young adults choose to interact with charities has not been explored frequently. The present study aims to tackle both of these issues by exploring how young adults choose to interact with charities. Using Sargeant’s (1990) donor decision model as a base, this investigation explores what motivates and deters potential donors from engaging with charity and exploring how they choose to engage. It was found that income was a major barrier towards donation and that the role of others was an important motivator. Lastly participants registered that social media is a prevalent part of how people choose to interact with charities, however donation and volunteering are more valued.
Participants This investigation consisted of 15 participants based in Lancaster between the ages of 19-25 years, all of which studied at Lancaster University. The sample consisted of eight male participants with an age range of 19-25 and seven female participants with an age range of 19-22. Participants were recruited via opportunistic methods on social media. Advertisement for participation was published on various social media platforms relevant to the University. Each recruited participant was asked to invite a friend to their focus group discussion. Participants were provided with refreshments as an incentive for participation. Due to the method of online recruitment, it was assumed that all of the participants were frequent users of social media and therefore understood its utility. Participants were not filtered for their donation history as it was assumed that individuals would have donated at some point in the past. Procedure Each focus group consisted of up to four participants which, as a result of the recruitment method, ensured that each group would be consist of two pairs who were not familiar with each other. The intention of this conflicting paired discussion was to encourage a more open and honest discussion. As well as this, the design of having a paired discussion ensures that statements made by an individual can be verified or rejected by the paired member as they are familiar with the activities of the speaker. As such, the paired member can act as a moderator for the contributions. The focus groups were segmented by gender. One group consisted of all male participants, another consisted of all female participants. The remaining two groups were mixed gender groups. The purpose behind this segmentation was to explore if there was a difference in responses between male and female participants. The focus group discussions took place in a quiet and comfortable room within Lancaster University to encourage a free-flowing discussion without interruption. Upon arrival, each participant was provided with a participant information sheet to read, and a consent form to complete outlining the nature of the study and the confidentiality of the data recorded. After any questions were addressed the discussions began and were audio recorded. The topics for discussion centred on the areas of exploration mentioned above. The discussion was structured (see Appendix C for Discussion Guide) but was open allowing the discussion to migrate to a number of areas that were pertinent to the participants. The researcher terminated the discussion upon satisfaction that participants had nothing further to add. Participants were then provided with debrief sheets outlining the purpose of the study and its aims. Each focus group discussion was transcribed onto a word document and subsequently added to NVivo 12 for qualitative analysis. Analysis The transcripts from each group were exported for analysis to NVivo 12 qualitative analysis software (QSR International Pty Ltd. Version 12, 2018). These were then analysed using the framework for thematic analysis derived from Braun and Clarke (2006). Transcripts were read multiple times to ensure familiarity with the content of the discussions. Areas of the discussion that were deemed interesting were subsequently coded within the software according to both the semantic and latent quality. These codes were informed by pre-existing psychological literature in addition to code generation in vivo. This data was then organised into several themes from which conclusions could be generated. These themes were re- analysed to ensure that they were an accurate and valid representation of the content of the discussions. The final themes were then solidified.
An exploration of the psychological mechanism and effectiveness behind the co-creation process in advertising, based on the ‘Co-create by Sharp’ method.
Keywords: Co-creation, advertising, psychology, behaviour
Scholars have referred to co-creation as the interaction between firms and stakeholders to create value. Co-creation for product innovation and campaign delivery is a growing trend in today’s competitive market due to a demand for consumer-centric communication and product development strategies. Even when traditional research techniques are relevant for evaluating brand messaging, they are considered backwards looking. Traditional research techniques (e.g., interviews, questionnaires, focus groups) rely on companies’ terms rather than the customer’s domain, limiting stakeholders to only react to market offers instead of cooperating to build them. The application of co-creation techniques does not replace reactive research but is the new next step to building brand and campaign strategies. The Sharp Agency developed ‘Co-create by Sharp’, a co-creation methodology that aims to build campaign and brand strategies with a higher value of insight than other approaches. According to The Sharp Agency, their unique approach to co-creating ideas with stakeholders has demonstrated effectiveness in their clients’ performance (i.e., 400% of revenue increase, 33% growth speed, and 19% spending increase). However, the method lacks information that supports its efficacy, more specifically, an exploration of the perceptions of people involved in their co-creation methodology (i.e., co-creation participants, Sharp team members, and Sharp’s commissioning clients). This report aims to identify the presence of plausible psychological theories that could support the ‘Co-create by Sharp’ methodology. Accordingly, this study intends to explore the dynamics, perceived effectiveness, and limitations of the ‘Co-create by Sharp’ methodology through a series of individual interviews with the people involved in the process. The researcher worked as an intern in the Sharp Agency, and a qualitative experimental design was used to investigate the research objective. Three types of interviews were conducted to understand the ‘Co-create by Sharp’ process from its main perspectives: Co-creation participants, Sharp team members, and Sharp’s commissioning clients. Findings indicated that the ‘Co-create by Sharp’ method is supported by a specific psychological mechanism explained by Self-Determination and Implicit Self-esteem theories. Based on these theories, interviewees’ perceptions of co-creation suggest that the ‘Co-create by Sharp’ methodology generates participant engagement in brand co-creation. According to the literature reviewed, participant engagement increases the level of insight in co-creation outcomes. As a result, this report has determined that the ‘Co-create by Sharp’ methodology produces a chain of benefits that begins with psychological benefits and brand-self connection, resulting in higher campaign delivery effectiveness. In conclusion, the ‘Co-create by Sharp’ methodology appears to be supported by a psychological mechanism that motivates participants to co-create in developing campaign strategies and brand building. Moreover, co-creating with stakeholders as a next step to gathering data with market research techniques could increase customer value in campaign delivery.
The researcher worked as an intern in the Sharp Agency to better understand the company’s way of work and the ‘Co-create by Sharp’ method. The internship allowed the researcher to be involved in various steps of the co-creation method: 1) Attend co-create sessions and observe participant behaviour (see Appendix K and Appendix L). 2) Develop post-co-create decks of information, including sessions’ outputs. 3) Participate in strategic brainstorming sessions. 4) Collate evidence of the final results of messaging and visuals for campaign delivery. A qualitative experimental design was used to investigate the research objectives and provide answers to research questions. Three types of interviews were conducted with different participant profiles, including co-creation participants, Sharp team members and Sharp clients. Interview sessions lasted between twenty and thirty minutes, using a pre-determined discussion guide for each interview and received ethical approval. Interviews were designed to gather insights about co-creation perceptions from every person involved in the process. A qualitative design allowed interviewees to express freely their co-creation experience with The Sharp Agency. Considering the research aimed to explore people’s attitudes, it would not have been appropriate to use a quantitative method. Instead, a qualitative design allowed for gathering a spectrum of people’s observations and feedback. Sampling Representative sampling was used to obtain results that reflect each participant’s profile perspective. Interviewing involved five participants from the latest co-creation sessions moderated by Sharp, seven Sharp team members with roles involved in different stages of the co-creation process (including founders of the ‘Co-create by Sharp’ method), and three company commissioning clients that represent market leader companies (i.e., Medical Protection Service, Barbour ABI, and Lonza). Considering that Medical Protection Service (MPS) and Lonza are part of the healthcare industry and Barbour ABI provides data and intelligence to the construction industry, these companies manage technical language and require higher accuracy of message delivery. (Ekiyor & Altan, 2021; Mokhtariani et al., 2017). This project received ethical approval under the auspices of the Lancaster University Psychology Department (see Appendix M). Participants gave informed consent using a consent form sent and signed via e-mail (see Appendix B). Participants were additionally provided with a debrief sheet, including contact details, should they have further questions (see Appendix C). Materials Interviews were regulated using three discussion guides (see Appendix E, Appendix F, and Appendix G). These were devised based on the objectives of the investigation set collectively with Sharp. Each discussion guide responded to a specific question based on participants’ profiles (co-creation participants, Sharp team members, and commissioning clients. Participants were encouraged to elaborate on their answers as much as possible. When conducted virtually, interviews were recorded using the current version of Microsoft Teams, and in person, interviews were recorded using Apple’s Voice Memo app. Research Procedure Participants were introduced to the researcher by The Sharp Agency and invited to participate in a scheduled interview via Microsoft Teams or in Sharp’s headquarters in the case of Sharp team member participants. The interviewer followed a discussion guide that began with questions that allowed participants to introduce themselves and warm up to the conversation. It concluded with questions that aimed to obtain the most robust responses. For further analysis purposes, interviews were transcribed using the Otter.ai software.
An Exploration of the Use and Effectiveness of Nature Imagery, Metaphor, and Symbolism in Advertising.
Nature imagery in advertising, symbolism in advertising, metaphors in advertising
Core participation of nature imagery, indoor scenery, visual metaphor, and literal image in the construction of commercials and advertising industry has been established through repeated research. The current study aims to deeper investigate regarding the role of two specific components of aesthetic communication (nature imagery, poetry) in advertising. Results suggested that between nature imagery and indoor scenery there was a significant preference to nature imagery. However, results from the comparison between visual metaphor and literal image indicated a more divided outcome with participants suggesting that both presented as equally appealing. Overall, our results suggest that nature imagery was established to be the most significant component towards forming an appealing advertisement. We indicated that further research could investigate and highlight the effectiveness of other mediator components of aesthetics (verbal language, humour, music, etc) in advertising.
In all focus groups, a digital voice recorder was used for further analysis. The first selected pair of ads, with indoor and outdoor imageries, was about Coca-Cola brand. At the first Coca Cola’s ad film, which was broadcasted for the first time in 2010, participants had the chance to watch two young people inside an overcrowded bus. Even if these two passengers were completely strangers, they finally broke the ice between them, thanks to an invisible Coca-Cola bottle. At the second Coca-Cola’s commercial, diversity in terms of gender, religion and race, within the United States of America, was presented. At the same time, the viewers were given the opportunity of admiring some of the most breathtaking landscapes in USA. The second selected pair of ads, in terms of connotative and denotative imageries, was about Smirnoff brand. At the Smirnoff’s connotative commercial, there were clear signs that its creators intended to show temptation and seduction. From the beginning it was clear that the starring couple was meant to represent a modern day Adam and Eve. As the music picked up, snakes appeared from the bartender’s sleeves to help make an Apple Bite and the customers got up to dance in a fast-paced song. The bartender was leading ‘Adam and Eve’ to the apple flavour cocktail and the fast-paced music suggested that something big would happen if the drink was taken. This also insinuated that the drink was so desirable that they would not be able to resist the apple drink. At the denotative one, there was a stylish, classy man that was just listing the values of Smirnoff vodka. The initial 40 advertisements were selected randomly from Coloribus.com (See Appendix H for full links) an online databases for commercials and advertisements. Data analysis Responses to the focus groups’ questions were thematically analyzed. The current research followed the six step thematic analysis approach as described by Braun and Clarke (2006). Notes of detailed observation were used to generate and apply codes to the qualitative data and to identify potential themes, as the small sample gave us that opportunity.
An Exploratory Analysis of Cortical Hyperexcitability, Anxious Tendencies, and Sleep
Cortical hyperexcitability, Anxiety, Problematic Sleep
Cortical hyperexcitability reflects abnormal or aberrant neural processes and has been associated with visual distortions, discomfort, and hallucinations. Abnormal visual behaviors have previously been found to exist within non-clinical populations. The present study explored the previously implied relationship between anxiety, sleep patterns, and cortical hyperexcitability. Three inventories were used to gather data; the General Anxiety Disorder inventory (GAD-7; Löwe et al., 2008) measured anxiety, the Pittsburgh Sleep Quality Index (PSQI: Buysse, Reynolds, Monk, Berman, & Kupfer, 1989) measured problematic sleep behaviors, and the Cortical Hyperexcitability index version two (CHi-II; Braithwaite, Marchant, Takahashi, Dewe, & Watson, 2015; Fong, Takahashi, & Braithwaite, 2019) measured cortical hyperexcitability, which is composed of three separate dimensions, or ‘factors’. In order to analyse the three factors, this study utilised three separate multiple regression models (n = 97), and a correlation analysis was used to analyse the relationship between anxiety symptoms and problematic sleep behaviors. Some significant results were found in support of the relationship between anxiety and cortical hyperexcitability. No significant results were found for the relationship between sleep and cortical hyperexcitability. A significant correlation analysis found that there is a significant positive relationship between anxiety and sleep. Collectively, these findings provide additional external validation for the CHi-II as an indirect proxy measure for symptoms of cortical hyperexcitability.
Participants Due to the ongoing pandemic, 34 participants were gathered from Lancaster University student-based Facebook groups. This was an attempt to gather students in a similar manner to common recruitment without the use of in-person interaction. In addition to the gathered 34, 63 additional participants were used from previously collected data, gathered in-person also from Lancaster University students. Overall, 97 participants were gathered for this study. The mean age of participants was 21 (age range 18-33 years), of which 65 (67%) were female. Informed consent was obtained from all participants. Materials Due to the remote nature of the study, each of the surveys used was a digital variant made on Qualtrics. The Cortical Hyperexcitability II (CHi-II) is one of the first verified measures of cortical hyperexcitability. The CHi-II consists of 30 items and takes 20 to 25 minutes to complete. Each item focuses on a specific experience followed by two 7-point Likert scales, to measure participant’s frequency and intensity of each experience (Fong et al., 2019). The CHi-II can be broken down into three separate factors. Factor one, “Heightened Visual Sensitivity and Discomfort”, which consists of 11 items. Factor two, “Aura-Like Visual Hallucinatory Experiences”, which consists of nine items. Factor three, “Distorted Visual Perception,” which consists of six items (see Fong et al., 2019). For each of the three factors, intensity and frequency scores were added for a global score of each factor, for each participant. These global factor scores were then divided by the number of items in each factor, respectively, to provide an average for each participant for each factor. There are four items that are not part of any factor. Although these items were recorded in data collection, they are not used in this analysis. The General Anxiety Disorder assessment (GAD-7) measures anxious tendencies and has been established as conceptually valid and reliable in measuring anxiety in non-clinical populations (Löwe et al., 2008). The GAD7 consists of seven items with a 4-point Likert scale of frequency for responses. All items will be scored (never – 0, almost always – 3) and be added together to form a global anxiety score. A higher score indicates a greater level of anxiety. Total scoring will have a range of 0-21, with set cut offs for mild (5-10), moderate (10-15), and severe anxiety (15+). This inventory should only take about 5 to 10 minutes to complete. The Pittsburgh Sleep Quality Index (PSQI) is a measure of sleep quality and sleep disturbances over the past month, and has demonstrated good psychometric properties with various populations, including non-clinical subjects (Buysse et al., 1989; Grandner et al., 2006; Mollayeva et al., 2015). The PSQI has been found to have high internal validity, high test-retest consistency, and is one of the most direct methods of measuring sleep quality (Mollayeva et al., 2015). The PSQI consists of 19 items measuring: subjective sleep quality, sleep latency, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. All 19 items are scored and added to form a conclusory global score (range 0 to 21), a higher score indicating an increased presence of sleep disturbances (Buysse et al., 1989). This inventory should only take about 10 to 15 minutes. Overall, these three surveys should take 35 to 50 minutes to complete. Once each score is coded from the digital surveys, researchers used R Studio to find various predicted relationships or significance between scores. Each participant was given an anonymous participant ID, and a randomised order to complete the three surveys, in order to avoid order effects. Procedure Participants were contacted through email, where they were given participant IDs, in order to anonymise their results, and the order in which they were to complete the three surveys. One link was sent to each participant which contained separate links for each survey. The main link also contained a participant information form, which informed participants about what the study consisted of and what was expected of them, and a consent form. Participants were advised to complete the surveys in an isolated area, free from distractions, and all in one sitting. After the three main surveys were completed the participants received a debrief form. Analysis To uncover significant positive relationships between the two variables, sleep and anxiety, and cortical excitability, three separate multiple regression models were conducted. Three regression models were conducted separately by the “visreg,” “tidyverse,” “pwr,” and “gvlma” packages installed under the R statistical program (version 3.6.1, R Development Core Team, 2019; see Champely, 2020; Breheny & Burchett, 2017; Pena & Slate, 2019; Wickham et al., 2019). For the multiple regressions, the independent variables, or ‘predictor variables’, used for each were the universal sleep (PSQI) and anxiety (GAD-7) scores. For the dependent variables, each of the three factors of the CHi-II were used, respectively. In addition, a correlation analysis was used to determine if there was a significant relationship between the GAD-7 and PSQI scores in order to validate the use of this particular sample. Particularly, this relationship should be significant as found by previous studies, a non-significant result would show that this particular sample is problematic. Finally, to ensure no major deviations occurred between the two separately-collected groups of participants, descriptive statistics of both groups were gathered and a T-test analysis of all variables were conducted.
An investigation into automatic imitation: Comparing live and video setups, the effect of prior training and the influence on affective empathy
automatic imitation, empathy, imitation training, inhibition training, mirror neuron system, self-other distinction
If decreased Automatic Imitation(AI) improves empathetic abilities, then selfother distinction processes are probably the mediating factor between imitation and empathy. But if increased AI improves empathy, then probably imitation is at the core of the socio-cognitive functions. Until now, it was shown that decreased AI improved visual perspective taking, corticospinal empathy and self-reported empathy. Also, the studies until now focus on video AI stimuli. But to understand whether AI has a more direct relation to mimicry, I developed also live paradigm. My research questions were firstly, what effect will imitation training and inhibition training have on AI. Secondly, whether live stimuli AI will have the same effects on AI testing (inhibition versus imitation) and arousal empathy testing. Thirdly, whether the effects are transferable on arousal empathy. As expected, there was a significant decrease of AI in the video inhibition condition in comparison to the video imitation condition. Unexpectedly, a significant, but weak increase in arousal empathy was observed in the video imitation condition and not in the video inhibition group. The difference in AI and arousal empathy between the life imitation group and the life inhibition group were not significant. The results give a new perspective on the topic of AI. If the results can be reproduced by more studies, then probably imitation is more important than self-other distinction processes or maybe arousal empathy is different from other forms of empathy. Finally, the insignificant results in the life imitation versus life inhibition training indicate that there are maybe confounding factors in live AI research or that the video AI designs are more artificial than it is assumed.
Participants Sixty(N=60) participants were recruited in a two-by-two factorial design, and divided equally among two between-subject factors. The first factor is Stimulus, whereby AI will be measured in response hand actions performed by an experimenter sat across a table from the participant (Live) or to the actor’s pre-recorded hand-action stimuli presented on a monitor. The second factor is Training, whereby participants will undertake a brief period of imitating the actions of the live or videoed handaction stimuli (IMI) or performing the opposite actions (IMI-IN). The participants were recruited from students of the University of Lancaster. Random selection could not be used, because of practical and logistical difficulties. Hence, most of the participants were Masters students and some were PhD students. The participants were either friends and acquaintances or they had the motivation to win a 10 pounds amazon voucher. In many cases they had the motivation to participate in my study, because they also wanted me to participate in their study. Firstly, we conducted the experiments with the video paradigm (15 participants in the imitation training condition and 15 participants in the inhibition training condition) and then we conducted the experiments of the live paradigm (15 participants in the imitation training condition and 15 participants in the inhibition training condition). We use random assignment for the recruitment in the training condition, thusly, every participant was randomly assigned in either the imitation training or in the inhibition training condition. For instance, we did not conduct first 15 experiments in the imitation training condition and then 15 experiments in the inhibition training condition, but every participant was in a different training condition. Nevertheless, one possible limitation may be that we did not do the same for the stimulus condition, as we conducted first the experiments of the video condition and then the experiments of the real condition. Materials The experiment was conducted on the personal laptop of the researcher. No specific room was needed for the experiment to have more flexibility with the data collection. The software Mathlab and the program Cogent were used to code and make the script. In order to measure affective empathy, we used the Multi-faceted empathy test (MET). It consisted of 40 images, but it was split in two METS to include also a pre-test approach. For imitation training and for inhibition training we used three images of the hand of the researcher. In one image, the hand of the participant was in the neutral position. In the second image, the index finger was lifted and in the third image the middle finger was lifted. Design and Procedure First, we conducted the experiments of the video paradigm (30 participants) and then we conducted the experiments of the live paradigm. The experimental procedure was divided into four phases: First, participants will do the MET (Multifaceted empathy test). The first Met had 22 images. The MET tests affective empathy. Participants must choose from a scale from 1 until 4 how strong is their affective arousal when they see the image. The MET took approximately 5-10 minutes, depending on the participants. After the first MET, participants did either imitation training or imitation inhibition training. The default position for the participants in this task was to press two buttons all the time with their right-hand index and middle finger. In the video condition, they pressed button A and button Z -with their right-hand index and middle finger, and in the live paradigm they pressed the left and right arrow button -with their right-hand index and middle finger respectively. In imitation testing, they had to lift their index finger when they saw a lifted index finger (video or live) and to lift their middle finger when they saw a lifted middle finger (video or live). Both actions should be done as quickly as possible. In the inhibition training the participants did the opposite actions of the observed movements. Thus, when they saw a lifted index finger, they lifted their middle finger as quickly as possible. When they saw a lifted middle finger, then they lifted their index finger, again, as quickly as possible. The training phase consisted of two tasks and a small break. Every task had a duration of 6 minutes approximately. After the training, there was the testing phase. Here we tested the effects of training on Automatic Imitation. The training phase consisted of two 6 minutes tasks with a break between the two tasks. In the first task, the participants had to lift only their index finger as quickly as possible, irrespective of the lifted finger they saw (either in video or in the live condition). In the second testing task, they had to lift their middle finger as quickly as possible, again irrespective of the lifted fingers that they saw. Automatic imitation is measured as the difference in their latency to lift the pre-defined finger when the observed action is the same in relation to when the observed action is the opposite finger movement. For instance, when the participant lifts his index finger, we measure the reaction time of his movement, when he sees a lifted index finger and when he sees a lifted middle finger. Automatic imitation is the difference of those two reaction times. This testing phase lasted 10 minutes, comprising 100 trials divided among two blocks. After the lifting of the finger, the participants pressed the button again (default position). Thusly, the reaction times were measured by how fast the participant would lift his finger. To ensure that the training and the testing really focused on Automatic Imitation and to exclude the spatial compatibility confounds, the participants were perpendicular to the stimuli (in both the video and the live condition). Sadly, we could not have the same perpendicular angle for both conditions, but the difference of the degrees was very small. In the video condition, the angle was approximately 45 degrees (the fingers of the participants were at the buttons A and Z and the stimuli were on the laptop screen) and on the live condition, the stimuli were approximately 90 degrees perpendicular (the fingers of the participants were on the right and left arrow and the real stimulus of the experimenter was at the buttons “tab” and “shift”). In the final phase, the participants did a second MET test. It was exactly like the first, only with different images. The order of the MET tests was changed with every participant. In other words, one participant did first the MET.1 and in the end the MET.2, while the other participants did first MET.1 and in the end, they did the MET.2. Both MET tests different parts of the same MET test, but we splitted the test arbitrarily in the middle to have also a pretest empathy base. I changed the order of the MET tests with every participant to exclude the factor that some pictures of the Test are less difficult than the others. Thus, if we find a large and statistical significant difference in the final MET between the imitation and the inhibition training group, then we can say that in both training conditions we changed equally the order of the MET tests, so the observed change in empathy performance does not have to do with some images being easier or more difficult than the others. In the IMI condition, the participants were required to lift their index finger when they see the stimulus hand (live or videoed) perform an index-finger action, or lift their middle finger when they observe a middle-finger action; in the IMI-IN condition they will do the opposite - they will lift their index finger when they observe a middle-finger action or lift their middle finger when they see an indexfinger action. In the second phase, the participants performed AI testing, during which they will be required to make a pre-defined finger-lifting movement (index- or middlefinger lifting action) as soon as the stimulus hand (live or videoed) moves, regardless of whether the observed movement is an index- or middle-finger lifting action. In the third phase, participants will perform the Multi-Faceted Empathy Test, during which they will be presented with 30 images of individuals expressing emotions and asked to judge which emotion is being expressed. The accuracy of their responses will be recorded. This final phase takes 10 minutes.
An investigation into the effect of climatic, ambient temperature on societal-level income inequality
Previous research has revealed contradictory findings concerning the relationship between temperature and behaviour. Some studies have found a warmer-is-better effect; warmer temperatures are associated with enhanced interpersonal interactions, including pro-social behaviours. Whereas other studies have found a warmer-is-worse effect; warmer temperatures are associated with negative social behaviours such as conflict, societal instability, crime and aggressive behaviours. The present study investigated the relationship between climatic, ambient temperature and societal income inequality. Climatic temperatures and Gini ratios (a measure of income inequality) were sourced from online databases for 29 countries across a range of time periods that fell between 1961 and 2015. A panel linear model analysis revealed that climatic temperature had no direct effect, nor lagged effect on income inequality. Therefore, the findings are not congruent with the warmer-is-better literature or the warmer-is-worse literature. Despite the null effect, the present study provides a further data point towards the debate concerning the effect of temperature on behaviour.
Firstly, the study required Gini ratios of disposable, equivilsed income. The Gini ratio is a measure of income inequality whereby a ratio of 1 reflects perfect inequality (i.e. one household receives all of the income) and a ratio of 0 is indicative of perfect equality (i.e. income is equally shared across households). The ratio was calculated from disposable income, which is income after the deduction of taxes and social security charges. Additionally the ratio was equivilised which means that the ratio was adjusted to account for different sizes and compositions of households. Secondly, the study required mean climatic temperatures in degrees celsius. Procedure Gini ratios for 29 countries belonging to the organisation for economic co-operation and development (OECD) were sourced from several online databases that had calculated the ratios. The countries and years used in the present analysis were somewhat dictated by the availability of Gini ratios online and as a result the OECD countries Australia, Chile, Israel, Japan, Korea and Mexico could not be included in the present analysis and the year ranges included fell between 1961-2015. See table 1 for the sources of Gini ratios, and the countries and years for which Gini ratios were available. It is important to note that the surveys from which the Gini ratios were calculated were slightly different, for example, some had different definitions of a ‘household’. Additionally, not all of the sources provided the exact Gini ratio calculation used. Table 1: Online sources from which Gini ratios were obtained from several countries across several, differing, time periods Country Time period Source of Gini ratios Austria (AUT) 1995-2001, 2003-2015 Eurostat, European Union Statistics on Income and Living Conditions (2017). Belgium (BEL) 1995-2001, 2003-2015 See Austria. Canada (CAN) 1976-2015 Statistics Canada (2017). Czechoslovakia (CZE) 2001, 2005-2015 See Austria. Denmark (DEN) 1987-2015 Statistics Denmark (2017). Estonia (EST) 2000-2002, 2004-2015 See Austria. Finland (FIN) 1987-2014 OECD Data (2017) France (FRA) 1995-2002, 2004-2015 See Austria. Germany (GER) 1984-2013 German Socio-economic Panel Study (2015) Greece (GRE) 1995-2001, 2003-2015 See Austria. Hungary (HUN) 2000-2002, 2005-2015 See Austria. Iceland (ISL) 2004-2015 See Austria. Ireland (IRL) 1995-2001, 2003-2015 See Austria. Italy (ITA) 1995-2001, 2004-2015 See Austria. Latvia (LVA) 2000, 2005-2015 See Austria. Luxembourg (LUX) 1995-2001, 2003-2015 See Austria. Netherlands (NED) 2000-2014 Netherlands Central Bureau of Statistics (2017) New Zealand (NZL) 1984, 1988, 1990, 1992, 1994, 1996, 1998, 2001, 2004, 2007, 2009-2014 Perry (2016) Norway (NOR) 1986-2015 Statistics Norway (2017). Poland (POL) 2001, 2005-2015 See Austria. Portugal (POR) 1995-2001, 2004-2015 See Austria. Slovakia (SVK) 2005-2015 See Austria. Slovenia (SVN) 2000-2002, 2005-2015 See Austria. Spain (ESP) 1995-2002, 2004-2015 See Austria. Sweden (SWE) 1975, 1978-2013 Statistics Sweden (2017). Switzerland (SWI) 2007-2015 See Austria. Turkey (TUR) 2002, 2006-2013 See Austria. United Kingdom (UK) 1961-2014 Institute for fiscal studies (2016) United States (USA) 1967-2013 Proctor, Semega & Kollar, M. A. (2016). Temperatures were sourced from the Climate Change and Knowledge Portal (2017) which contained the mean temperatures in degrees celsius for every country that was included in the present analysis for each month from years 1901-2015. Because we obtained mean Gini ratios for each year, we calculated mean climatic temperatures by calculating the average of the 12 months for each year, and country, that a Gini ratio was obtained. All Gini ratios and temperatures were accessed on 28th June 2017. Design and analysis In the present study the predictor variable was temperature and the outcome variable was Gini ratios. Data was collected for 29 countries across differing time periods ranging from 8-53 years resulting in a dataset with 594 observations. The dataset was a panel dataset whereby the data was cross-sectional (i.e. across countries) and longitudinal (i.e. across time periods) and unbalanced because of the differing time periods for each country. Therefore, to analyse the effect of temperature on Gini ratios, the plm package (Croissant & Millo, 2008) in R (R development core team, 2012) was used because this analysis has been designed to account for panel, unbalanced datasets. Additionally this package could determine whether country and time had an effect on Gini ratios and how these effects should be accounted for. The general linear model for the data set was (Croissant & Millo, 2008): yit = α + Txit + µi + t + it i = country t = time yit = Gini ratios α = intercept Txit the coefficient of the effect of temperature on Gini ratios µi = the unobserved error as a result of the effect of country on Gini ratios t = the unobserved error as a result of the effect of time on Gini ratios it = residual/idiosyncratic error, independent of the predictor and individual error components The specific model that was used in the present analysis was dependent on the existence of country effects (i.e. µi) and time effects (i.e.t) and the nature of these effects. There are three potential ways to model the panel datasets when estimating the effect of temperature on Gini ratios (Croissant & Millo, 2008): 1 – Pooled model; where time and country have no effect on Gini ratios (i.e. µi =0, t =0). Thus, the pooled models estimation is consistent and efficient, and applies across countries and time. 2 – Fixed effects model; where there are effects of country and/or time on Gini ratios and these effect(s) are correlated with the predictor variable, temperature. These correlated effect(s) result in the pooled models’ estimation being inconsistent because the estimates differ across countries and/or across time. Therefore, the fixed effects model accounts for the heterogeneity between countries and/or time by treating country and/or time as parameters to be estimated in the model and consequently the model gives consistent estimates of the effect of temperature on Gini ratios. This model can be one-way (i.e. the effect of country or time are taken into account) or two-way (i.e. the effects of country and time are taken into account). 3 – Random effects model; where there are effects of country and/or time on Gini ratios and these effect(s) are uncorrelated with the predictor variable, temperature. As a consequence of these uncorrelated effects, although the pooled models estimation is consistent, this estimation is inefficient. Thus, the random effects model accounts for the heterogeneity between countries and/or time by treating country and/or time as a separate error component(s) in the model and consequently the model gives consistent and efficient estimates of the effect of temperature on Gini ratios. Similar to the fixed effects model, the random effects model can be one-way or two-way. To determine which model was appropriate for the dataset, and thus to determine the nature of the effects of country and time, we performed exploratory (i.e. graphical representations) and confirmatory (i.e. hypothesis testing) analyses. Firstly, we used graphs to visualise whether the intercepts were heterogeneous across countries and time as heterogeneity would suggest that the pooled model (i.e. model 1) was not appropriate. Following this, to determine whether the pooled model was appropriate for the dataset, we used the F test of stability, which by default tests whether the same coefficients applied to each country. Following this F test there were two potential routes. (i) If the analysis revealed that the same coefficients applied across countries, we would then implement an F test of stability to test whether the same coefficients applied across time. If the second F test revealed that the same coefficients applied across time, a pooled model (i.e. model 1) would be used as this would provide a consistent and efficient estimation. Whereas if the second F test revealed that the coefficients did not apply across time, this would suggest that the pooled model was not appropriate and thus a Hausman test would be required to determine whether time should be modelled as a fixed effect (i.e. one-way fixed effects model; model 2) or random effect (i.e. a one-way random effects model; model 3). (ii) If the analysis revealed that coefficients did not apply across countries this would suggest that a pooled model (i.e. model 1) was inappropriate for the dataset. Consequently a langrage multiplier test would be required to determine whether a one-way or two-way effects model should be used i.e. whether country alone had an effect on Gini ratios (i.e. one-way) or whether country and time had independent significant effects on Gini ratios (i.e. two-way). Secondly, a Hausman test would be necessary to determine whether the effect(s) should be modelled as fixed (i.e. fixed effects model; model 2) or random (i.e. random effects model; model 3). Once a model had been specified, we estimated the direct and lagged effect of temperature on Gini ratios. Finally, we carried out diagnostic testing to analyse whether there was serial correlation or cross-sectional independence in the idiosyncratic errors of the model that would need to be dealt with.
An Investigation into the Effects of Temporary Visual Deprivation on Cortical Hyperexcitability, and Links with Multisensory Integration
Cortical hyperexcitability, pattern glare, sensory hallucinations, temporary visual deprivation, migraine with aura
Cortical hyperexcitability is a state of highly increased neuronal activity in the brain. The current research is a novel investigation into the effects of short term temporary visual deprivation on cortical hyperexcitability and resultant aberrant visual experiences in non-migraineurs, migraine with aura, and migraine only participants. This research also assesses the link between cortical hyperexcitability and its effects on aberrant experiences across all senses; vision, audition, gustation, olfaction, and bodily sensations. Forty-three participants, including three migraine aura sufferers and three migraine only sufferers, completed the pattern glare test to induce and measure state-based cortical hyperexcitability under normal and temporary visual deprivation conditions, along with two questionnaire measures; the Cortical Hyperexcitability Index (version II; CHi-II), measuring trait-based cortical hyperexcitability; and the Multi-Modality Unusual Sensory Experiences Questionnaire, assessing aberrant experiences across senses. Results indicated no effect of temporary visual deprivation on cortical hyperexcitability, although migraine aura participants reported higher cortical hyperexcitability levels overall compared to migraine only and non-migraineurs. State-based pattern glare was not associated with unusual experiences in senses aside from olfactory, however the trait-based CHi-II was strongly correlated with unusual auditory, gustatory, and bodily sensations. Potential methodological and theoretical reasons for these results are discussed, alongside improvements and new directions for future research.
Participants Forty-three participants took part in this study, consisting of 28 females and 15 males. All participants were students at Lancaster University with a mean age of 22.5 years, ranging from 19 to 36 years (SD=2.92, SE=0.45). Twenty-two participants were native English speakers, and 21 spoke English as their second language. Of these participants, three self-reported suffering with migraine only (MO) and three with migraine with aura (MA). Participants were recruited using opportunity sampling, and all gave fully informed consent before completing the experiment. Prior to participation, all participants were screened to ensure they did not suffer with any form of epilepsy, seizures of an unknown origin, and that they had not recently undergone brain or eye surgery. As no subjects reported these experiences, no participants were excluded on this basis. One participant reported suffering with micropsia; a visual impairment causing distortion of object size, so was removed from future analyses. All remaining participants reported normal or corrected to normal vision (i.e. through aid of glasses or contact lenses). Two participants were later removed from analysis for unusual scores on the baseline pattern glare task measure, explained in the results section. As such, the final sample size was 40 (age: M=22.53, SD=3.02, SE=0.48). Materials and Procedure Pattern glare task. Participants completed the pattern glare task under two conditions; blindfold or non-blindfold, creating a within-subjects design. Half completed the blindfold condition first, followed by the non-blindfold condition, with the other half completing the non-blindfold condition followed by the blindfold condition. The pattern glare task utilised three black and white striped grating patterns. The low frequency grating, calculated to have a spatial frequency of 0.5 cycles per degree (cpd; Figure 1), and the high frequency grating of 5.8cpd (Figure 2) acted as baseline measures. The medium frequency was the critical triggering stimuli, with a grating of 2.5cpd (Figure 3). Stimuli measured 17.5cm by 13.5cm each and were presented on paper. They were placed on the wall at eye level 50cm from the participant, resulting in a visual angle of 15.4°. Participants completed two trials; blindfold and non-blindfold. In the non-blindfold trial, participants were presented the three striped gratings, one at a time. Participants were asked to look at the grating for fifteen seconds, focusing on a central fixation point. If they found stimuli too aversive to view for the full time, they could inform the researcher, who would promptly remove the stimuli. There were 10 second intervals between presentations of gratings to allow the researcher to prepare the next stimulus. All stimuli were presented in a randomised order, to avoid order and carryover effects confounding results. After viewing each grating, participants completed a questionnaire consisting of seventeen items (Appendix A) asking about any visual distortions and discomforts experienced whilst viewing the stimuli, such as “shadowy shapes”, “colour distortions”, and “illusory stripes”. These are termed Associated Visual Distortions (AVDs). Each question was answered using a 7-point Likert scale assessing the intensity of each AVD experienced (0 = “not at all”, 6 = “extremely”). Responses were used to calculate a pattern glare score; a measure of state-based cortical hyperexcitability triggered by the stimuli. The blindfold condition followed a similar procedure, the only difference being that participants were required to wear a blackout blindfold for five minutes at the start of the trial before viewing only the medium and high frequency stimuli and answering the questionnaire as in the non-blindfold condition. Whilst conducting the experiment, laboratory light conditions were controlled with blackout blinds covering all windows and relying on internal lighting controlled by the researcher. This prevented differences in intensity of light affecting how participants responded to the stimuli, particularly after removing the blindfold. Each pattern glare trial took approximately 10-15 minutes to complete. Additional questionnaire measures were carried out between the two pattern glare task trials, allowing a washout period for participants’ eyes to recover between viewings of uncomfortable stimuli, and excitability levels to return to normal. The full experiment took approximately 40 minutes to complete. Figure 1. Stimuli with low frequency grating (0.5 cycles per degree) for pattern glare task. Figure 2. Stimuli with high frequency grating (5.8 cycles per degree) for pattern glare task. Figure 3. Stimuli with medium frequency grating (2.5 cycles per degree) for pattern glare task. Questionnaire measures. Participants were asked to complete two different questionnaire measures; the Cortical Hyperexcitability Index version II (CHi-II; Fong et al., under review), and the Multi-Modality Unusual Sensory Experiences Questionnaire (MUSEQ; Mitchell et al., 2017). Cortical Hyperexcitability Index version II (CHi-II). The CHi-II (Appendix B) is a trait-based proxy measure for assessing experiences thought to reflect cortical hyperexcitability. Measurements from the original CHi questionnaire (Braithwaite, Marchant, Takahashi, Dewe, & Watson, 2015) correlate with neurological measures of cortical hyperexcitability (Braithwaite, Mevorach, & Takahashi, 2015), suggesting CHi accurately and reliably measures cortical hyperexcitability. The updated version (CHi-II) consists of 30 questions. Each item used a seven-point Likert response scale to rate participants’ unusual visual experiences in terms of frequency (0 = “never”, 6 = “all the time”) and intensity (0 = “not at all”, 6 = “extremely intense”). Experiences examined fall under three factors; Heightened Visual Sensitivity and Discomfort (HVSD), for example “irritation from indoor lights”; Aura-Like Hallucinatory Experiences (AHE), such as “flashes of moving shapes”; and Distorted Visual Perception, including “everyday objects look different”. Frequency and intensity scores for each question were added, making a maximum of twelve. The totals for each of the 30 items were summed to give a score of cortical hyperexcitability for each participant, with a maximum score of 360. Multi-Modality Unusual Sensory Experiences Questionnaire (MUSEQ). The recently devised MUSEQ (Appendix C) measures unusual sensory experiences across six human senses: auditory, visual, olfactory, gustatory, bodily sensations, and sensed presence of others. Within each factor, questions range from broad sensory tricks (i.e. “my eyes have played tricks on me”) to hallucinatory experiences (i.e. “I have heard a person’s voice and found that no-one was there”), encompassing a range of more common to more unusual perceptual experiences. Questions used five-point Likert scales (0 = “never; never happened”, 4 = “frequently; at least monthly”). As one item in the original MUSEQ was highly similar to an item in CHi-II, this was removed from the present version of MUSEQ used in the current study, in order to avoid conflation of results when comparing the two questionnaires. Ethics statement This research was ethically approved by the Departmental Ethics Committee at Lancaster University’s Department of Psychology on 11/05/2018.
An investigation of the influence of individual differences on susceptibility to product placement
Marketing, Product placement, Individual differences, Cognitive capabilities, Inhibitory control, Age, Familiarity, Gender, Purchase intentions.
Product placement increased in popularity in 1982 when Reese’s Pieces Chocolate was included in E.T. the film, which led to a 65% increase in sales. Still to this day product placement is omnipresent within our cultural climate and research has supported that it enhances our purchase intentions. However, what remains unknown is how individual differences may influence product placement susceptibility. To address this gap, the current study investigated whether individual differences in cognitive capabilities, inhibitory control, age, familiarity, gender and timepoint enhance/reduce the likelihood of individuals' purchasing intentions being influenced by product placement. To do this, 55 participants (23 younger adults (Mage = 61.62(8.70)) and 22 older adults (Mage = 21.75(0.68)) were presented with images of four cups of coffee and asked to rank their purchase intentions/familiarity with the products. Following this, participants watched three scenes from Coronation Street, with the second clip including a product placement (Costa Coffee). Approximately 48 hours later, participants completed another purchase intentions questionnaire on the same four cups of coffee. The results highlighted that purchase intentions increased immediately post-clip; however they decreased 48 hours post-clip. Therefore, advertisers may use this information to discover ways in which the consumer can easily purchase the product immediately post-clip e.g. through QR codes. In regard to all other variables, no other significant relationships were found. Thus, it cannot be suggested to advertising agencies that product placement targeted to individuals who fulfil a given criteria (e.g. older adults, etc) will achieve optimal results when compared to non-targeted product placement.
Method Design The present quantitative study adopted a repeated measures design. There were several predictor variables: overall cognitive capabilities (including executive functioning; as assessed by the ACE-III; Hsieh et al., 2013), inhibitory control (as assessed by the Stroop effect), age, familiarity, gender, and timepoint. The dependent variable was susceptibility to product placement as measured by change in purchase intention. Participants At the time of the current studies design no published studies had investigated the influence of individual differences on product placement susceptibility, therefore the required sample size was modelled on the most comparable study the authors could source. Specifically, Hoek et al. (2022) investigated the influence of inhibitory control on advertising literacy activation and advertising susceptibility. Hoek et al. (2022) recruited 57 participants. Given the time restraints of data collection, the authors elected to recruit 55 participants. A total of 55 participants volunteered to participate in part one of the study. All participants were recruited via opportunity sampling through word of mouth and through advertisements placed on various Lancaster University Facebook pages (e.g. the Perception and Action Lab group). Participants were either aged between 18-25 (younger adults) or aged 50 and over (older adults). Out of the 55 participants, there were 27 younger adults (19 women; Mage = 60.93; SDage = 8.26) and 28 older adults (18 women; Mage = 21.78; SDage = 0.85). No participant had a known diagnosis of a psychiatric, neurological, or visual impairment, thus psychiatric, neurological, and visual impairments were not included in the analysis. All participants were White British/Irish. Therefore, there was no variation between ethnicities, thus ethnicity was not included in the analysis either. Given that cognitive capabilities was a key predictor variable within this study, it was necessary to ensure that participants with a known cognitive impairment or probable indication of cognitive impairment were removed from the study. Subsequently, all participants were screened for the probable presence of mild cognitive impairment through the Addenbrookes Cognitive Examination (ACE-III; Hsieh et al., 2013). After applying the pre-validated cut off point, 10 participants were excluded. Therefore, 45 participants were included in the analysis. Participants were either aged between 18-25 (younger adults) or aged 50 and over (older adults). Out of the 45 participants, there were 23 younger adults (16 women; Mage = 61.62; SDage = 8.70) and 22 older adults (17 women; Mage = 21.75; SDage = 0.68). Materials Inhibitory Control Inhibitory control was measured through an online Stroop task developed and run through Psytoolkit (Stoet, 2010, 2017). Completion of this task required participants to ignore the meaning of the colour word and indicate the print colour. Participants were generally presented with a colour word and a print word that were incongruent to one another. Thus, participants needed to use their ability to inhibit a pertinent response (i.e. the print colour) and indicate the print colour, which would be done more efficiently by competent readers (von Hippel & Gonsalkorale, 2005). Previous scholars have chosen to use the Stroop task, as it offers a good measure of individual variation in inhibition (e.g., Long & Prat, 2002). As this study was conducted remotely, via Microsoft Teams share screen function, participants were asked to verbally indicate the print colour and the researcher pressed the related keys (e.g. r for red, g for green, b for blue, and y for yellow). Participants first completed four practice trials followed by 40 test trials. Cognitive Functioning Cognitive capabilities were measured using an adaptation of the Addenbrooke’s Cognitive Examination (ACE-III; Hsieh et al., 2013). The original version assesses the participants’ attention, memory, fluency, language, and visuospatial abilities and has a combined score of 100. Although the adapted version examines the same five cognitive domains, it has a combined score of 77, the reason being that some questions were removed, as they were not deemed suitable for an online study – the first two questions on attention, the first two questions on language, and the first three questions on visuospatial abilities. The original version's pre-validated cut off point was 88 (88%) and therefore the adapted version's was 68 (88.31%). The participants who scored below the pre-validated cut off point were removed prior to analysis to ensure that the presence of cognitive impairment would not confound the subsequent analysis. Demographic and Health Characteristics Demographic information, including age, ethnicity, and gender, and background health information, including whether the participant had a current or history of a diagnosis of any cognitive, neurological, visual, or psychiatric impairments, was collected through an online Qualtrics Questionnaire. Purchase Intentions Questionnaire Prior to the questionnaire, participants were presented with the name and an image of each of the four cups of coffee. Purchase intentions of the four cups of coffee were then measured using a 7-point Likert scale. Participants were asked to rate on a scale of 1-7, 1 being ‘Extremely unlikely’ to 7 being ‘Extremely likely’, how likely they were to purchase a cup of coffee from: Caffè Nero, Costa Coffee, Greggs, and Starbucks. Comparably, familiarity was also measured using a 7-point Likert scale. Participants were asked to rate on a scale of 1-7, 1 being ‘Extremely unfamiliar’ to 7 being ‘Extremely familiar’ with how familiar they were with each cup of coffee from Caffè Nero, Costa Coffee, Greggs, and Starbucks. Purchase intentions and familiarity were measured using a 7-point Likert scale, rather than the commonly used 5-point Likert scale, as the inclusion of several options enhances the likelihood of acquiring a more accurate response (Joshi et al., 2015). It was important that purchase intention and familiarity of Costa Coffee was assessed alongside alternative brands, so that it was not made apparent that the study was focusing upon the participants' purchase intention ranking of Costa Coffee only. Therefore, Caffè Nero, Greggs, and Starbucks were chosen alongside Costa Coffee, because according to a survey conducted by Lock (2022), they are the UK’s top four leading coffee shop chains. The images were provided by Adobe Stock (2019) and Dreams Time (2019a, 2019b, 2019c). Product Placement Video The British TV Soap Coronation Street was selected, as prior research (e.g. Armstrong, 2018) suggests that it is popular amongst both younger and older adults (YouGov, 2011). The first clip chosen was a scene from 8th January 2018 Part 1, lasting 1 minute 16 seconds. The second clip chosen was a scene from 29th January 2018 Part 1, lasting 1 minute 15 seconds. The third clip chosen was a scene from 7th February 2018 Part 2, lasting 1 minute 23 seconds. It was the second scene shown that included the product placement (Costa Coffee). The researcher screen recorded each clip from https://www.dailymotion.com/gb and saved them into an encrypted file on a password-protected computer. Procedure A member of the psychology department research ethics committee approved the study before it was undertaken. Participants were invited to attend a 40–50-minute online Microsoft Teams meeting on a set date and time agreed on by the participant and the researcher. To commence, the researcher shared their screen and aided the participant in reading the participant information sheet and consent form via an online Qualtrics Questionnaire. At this time, participants were informed of their right to withdraw up to 2 weeks after participating without giving any reason and they were told their personal information would remain confidential and would be stored in encrypted files (that only myself and my supervisor have access to) on password-protected computers. The participants were only able to progress into the study on attainment of verbal consent. Participants were then asked to disclose various demographic characteristics (e.g., age and gender) and details relating to their current health status (e.g., any cognitive or visual impairments). The participants were then presented with four images of cups of coffee from Caffè Nero, Costa Coffee, Greggs, and Starbucks. They were then asked to rank their purchase intentions and familiarity, on a seven-point Likert scale, with these products via an online Qualtrics Questionnaire. Following this, participants were asked to watch three short scenes from Coronation Street. The second clip shown included a product placement of Costa Coffee. The researcher then implemented an online Stroop task using Psytoolkit (Stoet, 2010, 2017). The participants were also screened for the presence of mild cognitive impairments through the ACE. After this, the participants were presented with the same four images and asked to rank their purchase intentions of these products via the online Qualtrics Questionnaire (see Figure 1). Approximately 48 hours after completing the first part of the study, participants were sent an email invitation to complete another online Qualtrics Questionnaire. Participants were first asked to provide their participation number, which could be found in the email. They were then shown the same four images of cups of coffee and asked to rank their purchase intentions. Finally, the participants were provided with a debrief form at the end of the online Qualtrics Questionnaire (see Figure 2). This debrief disclosed the small degree of deception involved. Specifically, it was explained that participants were not informed at the start that the study considered product placement, as this might have influenced the subsequent data. Participants were reminded that they had the right to withdraw up to 2 weeks after participating and were provided with contact details in case they had any questions. The participants' purchase intentions of the four cups of coffee were measured three times throughout the course of the two studies: pre-clip, immediately post-clip, and 48 hours post-clip. This was to see whether the participants' purchase intentions for the four cups of coffee, specifically Costa Coffee, had increased or decreased following the product placement clip and whether their ranking would withstand the test of time (48 hours post-clip). This is why the participant were asked to include their participant number in part two, so that the participants prolonged purchase intention (48 hours post-clip) could be traced back to their earlier purchase intention rankings (pre-clip and immediately post-clip). Figure 1. A flowchart of part one tasks. Figure 2. A flowchart of part two tasks. Data Processing Inhibitory Control Participants raw Stroop data were downloaded from Psytoolkit into a Microsoft Excel file and saved in an encrypted files on a password-protected computer. From this raw data Stroop effect (the average incompatible conditions response time (ms) - compatible conditions response time (ms)) and percentage error rate (which involved adding the total of incorrect and timed out responses and dividing it by 40 (number of trials)) were calculated. Stroop effect and percentage error rate were used as an indicator of the participants inhibitory control capabilities. Specifically, a high Stroop effect would suggest less difficulty in inhibiting interference and a higher error rate would suggest reduced inhibitory capabilities. Cognitive Functioning The scores of the ACE-III were added and entered into the Microsoft Excel file, which was saved in an encrypted files on a password-protected computer. A higher score was indicative of superior cognitive functioning. Demographic and Health Characteristics To ensure all demographic and health data was readable by R-Studio all variables were dummy coded using numerical values. So, for instance, to determine the participants' gender, they were asked ‘What gender do you identify’ and given the option to choose from one of several responses. Each response was allocated a number, for example, 1 = Man, 2 = Woman, etc, and this was entered into the Microsoft Excel document. Susceptibility to product Placement (change in Purchase Intentions) To investigate the susceptibility to product placement, two difference in purchasing behaviour score were calculated (one for short duration, one for prolonged duration). To calculate these values, the likelihood of purchasing the product value prior to watching the clip was subtracted from likelihood of purchasing the product value after watching the clip (either immediately post-clip or 48 hours after). A positive difference meant that purchase intentions had increased following placement clip. A negative difference meant that purchase intentions had decreased following placement clip. A difference of zero meant that the placement clip had failed to alter purchase intentions Familiarity The familiarity ratings of Costa Coffee were entered into the Microsoft Excel file, which was saved in an encrypted files on a password-protected computer. The higher the score, the more familiar the participant was with the product. Data Analysis To analyse the data, a linear mixed effects model was chosen. The reason being that the current study employs a repeated measures design, and a linear mixed effects model permits an analysis of hierarchically structured data (Baayen et al., 2008).