The effects of screen exposure on developmental skills among children at two and three years of age.
Previous research into the topic of children’s development has tended to take place in Western nations (Kuta, 2017; Martinot, 2021). One aspect of development is language development, and one aspect of research on that matter is the use of electronic devices, with the potential for consequent effects on children’s language abilities. This paper reviews and builds upon the scope of the available research, with its disparate findings, by offering research from the context of Kuwait, a non-western nation where parents tend to be in favour of their children having access to new technologies regardless of their age (Dashti & Yateem, 2018). The increasing number of children being exposed to electronic devices of various descriptions raises concerns regarding the possible adverse effects of screen exposure on their development, particularly through displacement of educationally enriching activities, which provides the motivation here (Haughton, Aiken & Cheevers 2015). Based on a review of the existing literature, the present research starts from the hypothesis that language development will be negatively correlated with media exposure. Valid data relating to 96 children of 24 to 36 months of age were collected using two questionnaires, one relating to the child’s knowledge of Arabic words on various topics (voices of animals, names of animals, vehicles, toys, food and drink, etc.) and the other quantifying the child’s daily screen time. Ordinary least squares analysis was performed using SPSS, version 26. While a statistically significant positive moderate correlation between language expression score and age was found – an increase in age was associated with an increase in language expression or the number of words understood and expressed – no significant effect of screen time on language expression was found after adjusting for age. This indicates, therefore, the value of employing non-western populations in research into cognitive development, and suggests the need for further research in order to attain generalisable findings.
The parents of a total of 100 participant children) took part in a questionnaire survey. The reports of 4 parents were excluded because their child’s age exceeded 36 months and the inclusion criteria for the study were set at 24 to 36 months. Participants were selected by means of opportunity sampling. An announcement was sent via WhatsApp to those of my contacts who had children of an age appropriate for inclusion in the study. Parents were recruited by sending a link to the survey through WhatsApp. Family and friends were then asked to deliver the WhatsApp number to those who they knew who had children within the set age range. Parents read information about the study and their informed consent to participate in the questionnaire survey was obtained via Qualtrics. The Lancaster University Psychology Department gave ethical approval for the present study. Procedure The data for the present work were gathered by means of an online questionnaire via Qualtrics between 7 June 2021 and 22 June 2021. During this time, participants submitted answers to two questionnaires: a) the Arabic CDI, which measures Arabic words arranged according to groups (for example voices of animals, names of animals, vehicles, toys, food and drink, etc.) to measure the child’s knowledge of the Arabic language (Abdel Wahab, 2020) and b) a questionnaire related to the number of hours the child spent in front of the screen , and their opinion of the appropriate amount of screen time which children can spend at their screens, as well as their control over their children’s viewing of the screens, and whether or not they are allowed to watch while sleeping and eating. The survey instruments were designed to measure the extent to which screen viewing is related to the language development of Kuwaiti children aged between two and three years. Materials CDI: The Arabic CDI language scale developed by Abdel Wahab (2020) is a questionnaire comprising a set of categories containing checklists for identifying variety and number of words. In front of each word there are three options (‘knows it’, ‘knows it and says it’, ‘does not know it’) and parents are asked to respond to each item according to their children’s knowledge of these words. The Arabic CDI questionnaire contains 100 words divided into the following categories: voices of animals, names of animals, transport, toys, food and drink, clothes, parts of body, home furniture, little things inside the house, things and places outside the home, people, games and daily routine, actions, time-related words, adjectives, pronouns, question words, prepositions, and number formulas. Media exposure questionnaire: Following the language questionnaire, parents completed a second survey measuring their children’s screen viewing, stating how many hours per day they spent watching a screen. Parents were asked to report frequency of screen use by choosing among the following six options: None, 0 to 1 hour, 1 to 2 hours, 3 to 4 hours, 5 to 6 hours, and > 6 hours. Participating parents were then asked to state what length of time they would consider it appropriate for their children to watch a screen, with the same set of responses available to them. There was then an item asking the parents whether they were making any efforts to reduce their children’s screen time, such as setting specific days or times for viewing or preventing them from viewing their screens while eating or in the bedroom, for example.
Film language affecting behaviour: A psycholinguistic approach
Films are a popular form of art and entertainment that enable people to enjoy a story through multiple stimuli perception and stimulation of emotions. Plenty are the film elements that impact the audience’s attitude towards the film, yet language style has rarely been taken in consideration for research. This study focused on examining whether there exists a relationship between the audience’s favouritism for films and the linguistic style present in them, predominantly concentrating on emotional factors of language in films. A dataset containing the widest public ratings of films was obtained from the Internet Movie Database platform and paired with respective transcribed film dialogues provided by OpenSubtitles.org. The corpora’s transcripts (n=88,573) were analysed using the Linguistic Inquiry and Word Count software and all the variables produced were then correlated with IMDb’s weighted film ratings. The project found that all types of emotions present in transcripts of film language were significantly, negatively associated with the IMDb rating outcomes, while the effect sizes were small. This finding suggests there might be an inclination for emotions to be felt in other areas of stimuli perception, rather than verbal language, when it comes to films. Additional exploratory analyses showed how other variables correlated with film rating scores and practical application of study findings within the advertising industry were identified.
Dataset The dataset used for the study is purely secondary and consists of transcribed film dialogues (N=88,573) complemented with each film’s respective Internet Movie Database (IMDb) rating, which at the time of collection had a minimum of 100 user ratings per film. IMDb is an online film rating platform where the wider audience must register for an account and is then able to rate and review the films they have watched. Registered IMDb members rate films on a 10-point scale, with 1 indicating “terrible” and 10 indicating “excellent” (Boyd et al., 2020). IMDb’s rating algorithms produce ratings that are weighted by metrics associated with users, rather than average ratings. Although the algorithms are unavailable to the public, IMDb’s rating system has shown consistency across all films because the weighted ratings constantly provide reliability by reducing the possibilities of a small group of users to take advantage of the rating system (IMDb, 2021). IMDb is one of the most popular and authoritative film rating websites, where the total ratings of a film are anonymous and voluntarily provided (Sawers, 2015). The transcribed film dialogues data was provided by OpenSubtitles.org and the corpora was previously organised and used in a study by Boyd et al. (2020); it was generally provided by the authors for the purpose of this project. OpenSubtitles.org is an online website that provides transcribed and translated captions of motion pictures, audio files and various other audio-visual files (OpenSubtitles.org, 2021). The corpora used by Boyd et al. (2020) contains purely English-language film subtitles, corresponding to films originally released in English, or foreign films whose dialogues have been translated to English. Boyd et al. (2020) combined the transcribed film dialogues provided by OpenSubtitles.org with the IMDb ratings, along with other IMDb categories such as film genre, year of release, country of production, et cetera. Almost 90% of the IMDb categories linked to the films’ ratings are irrelevant for the purpose of this project, thus solely the film ratings will be taken in consideration for analysis. Automated Textual Analysis Software (LIWC) To conduct the automated textual analysis, this research project will use the Linguistic Inquiry and Word Count (LIWC) tool; also called “Luke”. LIWC is a textual analysis program that measures the degree to which various dimensions of words are used in a text (Tausczik & Pennebaker, 2010). LIWC program has two central features – the processing component and the dictionaries. The processing feature takes a text file and analyses it word by word, comparing each word with the dictionary files, sorting the word out as, for example, verb or second person pronoun (Boyd, 2017). Once the program finishes running, it produces an output where all the LIWC categories used in the text are listed, as well as the rates and percentages that each category was used in the given text. The dictionaries are at the heart of the LIWC program and they identify the group of words that belong to each category (Pennebaker et al., 2015). When the program was being created, the authors aimed at developing measures to define emotions present in words, cognitive processes, signs of self-reflection, et cetera, and in order to assign a psychological component to words, human judges contributed in developing the categories LIWC possesses today (Boyd, 2017). Across approximately 80 dimensions (see Appendix A), LIWC analyses the text in relation to various parts of speech, thinking styles, social concerns and emotions (Pennebaker et al., 2001). For example, the “positive emotion” category contains words such as “love”, “happy” and “nice”, while the “cognitive processes” category comprises words like “examine”, “think” and “understand”. Over the years, LIWC has been able to uncover psychological patters and personalities purely from textual analysis; Petrie et al. (2008) used LIWC to investigate the Beatles’ lyrics and found out that it was possible to distinguish each songwriter’s unique language style, and also to discover whose Beatle’s style was predominant in collaboratively written songs. Researches have shown LIWC to be one of the most reliable automated textual analysis tools that is able to uncover and predict psychological implications residing in written sources, thus this study will employ this tool to test its hypothesis. Data Preparation and Analysis The initial corpora was subjected to cleaning procedures, where data which did not meet all inclusion criteria was removed from the dataset. The inclusion criteria consisted of film ratings having at least 100 user votes, transcribed dialogues having at least 100 words and corpora variables containing all data values. The cleared dataset (N=85,130) is going to be tested in the LIWC program, where each word within the transcripts will be counted and sorted among the LIWC dictionary categories it belongs to. For the main hypothesis, the program will analyse the dataset for LIWC variables that have been shown to be correlated with positive and negative evaluations in the past. This way, the quantified rates of positive and negative emotion words in each dialogue will be identified. Once the rates have been extracted, a bivariate Pearson’s correlation will be conducted to assess whether there exists a significant relationship between positive and negative emotion words in film dialogues and their IMDb ratings. Additionally, exploratory analyses will be run to search for significant relationships between the dataset variables and the film ratings, again by conducting Pearson’s correlation tests between the ratings and all LIWC variables produced.
Comparison of Ethical Decision-Making in Emergency Service Workers and Laypeople
Trolley Problem, ethical dilemmas, time pressure, emergency service workers, decision-making.
The Trolley Problem is a theoretical ethical dilemma in which it is asked whether it is morally acceptable to actively kill one person to save five (Thomson, 1976). Emergency service workers (ESW) are often presented with ethical dilemmas, such as whether to resuscitate someone who does not want to be resuscitated (Guru et al., 1999). The present study investigated the differences in decisions made when faced with variations of the Trolley Problem between laypeople (non-ESW) and ESW. The effect of time pressure on making these decisions was also investigated, measured through response time. 99 participants were tested, 47 laypeople and 52 ESW. Participants were presented with five different Trolley Problem dilemmas wherein they could passively allow five people to die, or to make an active decision to sacrifice one person to save the others. These dilemmas had distinct variations, such as the one person being a co-worker, or where participants had to physically push and kill a large man. Half the participants were placed into a time pressure condition, and were told that they had a time limit in which to respond, when no time limit existed. Results showed that neither occupation nor time pressure significantly affected response time or participant choice. Further analysis suggested some interaction effects between occupation, time pressure, and specific dilemma types. Implications such as suggested training practices for ESW will be discussed. Criticisms of the methodology and recommendations for future research will also be discussed.
Method Sample This project aimed to use a total of 112 participants, with 56 of these being ESW, and 56 being laypeople. This number was calculated using the G*Power software, using an alpha of .05, power of .8, and a medium expected effect size of .35, using five levels of measurement. In total, 99 participants were gathered for the present study. 47 of the sample were laypeople, whilst the other 52 were ESW. Of these, 22 were police officers, and 30 were ambulance crewmembers. Overall, ESW had an average of 7.7 years of experience (SD = 8.29), with ambulance staff having an average of 10.14 years (SD = 9.89), and police having an average of 4.52 years (SD = 4.17). Unfortunately, no other emergency service branches such as coast guard or firefighters completed the study. A gender split of 47 males to 48 females was gathered, along with an average age of 35.65 years old (SD = 12.98). Three participants declined to disclose their gender, and one participant identified as agender. Ethical Approval and Pre-Registration This study gained ethical approval on 13/04/2021, from members of the Psychology department at Lancaster University. This study was also pre-registered on the Open Science Frameworks website on 17/05/2021. This can be found at the following link: https://osf.io/4ecjg/?view_only=95615bd16f2c4a9db88dd77543780ec2 Materials Survey The present study was delivered through a Qualtrics survey file, created fully by the researcher. The survey contains standard psychological research documents, such as an information page, consent form, demographic information page, and debriefing. The survey also contains two sets of five vignettes describing ethical dilemmas for each condition of the experiment. Demographics Participants are asked to provide some demographic information: age, gender, and occupation. Participants are given options for occupation, including police, fire, or ambulance, as well as an option for ‘other’ emergency services, where a free typing box is presented. This is to cover occupations outside of the main three emergency services, such as coastguard or mountain rescue. If participants are not ESW, they have the option to say they are not a member of the emergency services. Ethical Dilemmas The present study tests a set of five ethical dilemma vignettes. To read each dilemma, see Appendix A. Each vignette describes a version of the Trolley Problem, where there is an out-of-control trolley (the word “tram” is used to make it clearer to British participants) speeding down the tracks towards a group of five people. For each dilemma, there is an active choice, or a passive choice, which entails sacrificing one life to save five, or allowing five people to die to avoid killing one person. Each dilemma presents a different single person who could be placed in danger, these are: a non-descript person, an elderly person, a co-worker, a large man, and the “culprit”. Non-Descript Person. This dilemma is a traditional retelling of the Trolley Problem. Participants are told that there is an out-of-control trolley speeding down the tracks, towards five people who are stranded. Participants are told that they have the choice to pull a lever and divert the trolley onto a different track, however there is one person stranded on those tracks. The decision participants are faced with here is whether to make an active choice or a passive choice. The active choice is to pull the lever, diverting the trolley and saving the five, whilst sacrificing the individual. The passive choice is to not pull the lever, allowing the trolley to hit the five people, whilst saving the individual. It is often found that people sacrifice one person to save five in this dilemma (Thomson, 1976; Greene, 2016). Responses to this condition demonstrate how people weigh up lives on a strictly numerical basis, knowing nothing about the traits of the person. By having a condition in which participants know nothing about the person on the tracks, this can be compared to responses when it is an elderly person or a co-worker on the tracks. Elderly Person. This dilemma is the same as the non-descript person dilemma, however participants are told that the person on the tracks is elderly. This condition has been found to affect how people respond to the Trolley Problem, with people being more likely to sacrifice the elderly person over any other ages (Kawai et al., 2014). This is interesting in the study of moral psychology, as it shows how people weigh up the worth of lives based on certain attributes, such as age. This can also be compared to how people respond when they know nothing about the person on the tracks. This is also important to investigate in an ESW context, as elderly people are more likely to be admitted to hospital (Burns, 2001), leading ambulance crews to encounter them more often. Co-Worker. This dilemma is the same as the non-descript person dilemma, however participants are told that the person on the tracks is one of their co-workers. This dilemma was chosen based on past research suggesting that participants are less likely to sacrifice people they perceive to be part of their identity in-group (Swann Jr et al., 2010). This is a relevant factor to investigate as part of a study into ESW, a group who develop strong in-group feelings, including having better self-care and social support (Shakespeare-Finch et al., 2002). This is also interesting when investigating ESW populations such as firefighters or police, who may be placed into situations where a co-worker is in danger whilst trying to save members of the public. This dilemma demonstrates how ESW weigh up the lives of their co-workers compared to strangers. Large Man. In this dilemma, participants are told that there are five people on the tracks, and stood next to them is a large man. Participants are told that if they push the large man into the tracks, that would stop the trolley and the five people would be saved. The decision participants are faced with here is whether to make an active choice and push the large man onto the tracks, stopping the trolley and saving the five, or to make a passive choice and allow the trolley to hit the five people. This is a version of the “Footbridge Dilemma”, in which it is found participants are typically less willing to make the active decision and push the man (Nichols & Mallon, 2006). It is an interesting take on the Trolley Problem dilemma, as it forces participants to make a more physical decision through pushing and directly causing a person’s death, as opposed to pulling a switch which then indirectly leads to someone’s death. This is also relevant in the study of ESW, who tend to work directly and physically with people as opposed to making indirect decisions. Culprit. This dilemma is the same as the Large Man dilemma, however rather than a large man, participants are told that stood next to them is the “culprit”. The “culprit” is explained to participants as the person who stranded the other five people on the tracks. This dilemma was chosen as it tests how people respond to the same physical pushing decision as the Large Man condition, however when the person they can push is not an innocent bystander, and instead is someone who is trying to end the lives of others. This allows for the investigation of how people weigh the lives of criminals compared to innocent people. This is also interesting in the study of ESW, especially when regarding police, since their occupation involves apprehending criminals so they can then be sentenced, not choosing the punishment based on their own moral reasoning. Time Pressure Participants who are assigned to the Time Pressure condition are told both during instructions and above each dilemma that they only have a limited amount of time to make their decision. They are told that after that time has passed, they may not be able to provide a response. This is not true, there are no time limits on any question. This is to attempt to simulate time pressure, by making participants feel they have limited time to react. Overall, 52 participants were assigned to the Time Pressure condition, and 47 were assigned to No Time Pressure. A more equal split was aimed for, however was not possible due to the number of incomplete responses interfering with the equal randomisation of conditions. Response Time The decision-making speed is automatically recorded by Qualtrics, determining how long it took participants to finalise their decision. This is taken as the time from when participants opened a vignette, until they submitted their response. It was decided that the response time would be taken at the point the choice is submitted, as opposed to the last button press participants made. This is as it cannot be certain at what point participants have finished considering their response. They may still be thinking about their answer after selecting the option, but before submitting. Therefore, it cannot be assumed that the final button press was the end of their decision-making. Justification After each decision, participants are asked to briefly explain why they made the decision they did, imagining they are speaking to a close friend. This ensures participants think deeper into the decision they make, as they know they will have to defend it. This is presented to participants as a free entry text box, shown after each dilemma they respond to. Pilot Study The present study was first piloted on an ESW member, in this case a senior paramedic, to test for validity of the ethical dilemmas as well as any other issues with the survey. The only negative feedback received was that some of the dilemmas looked visually similar on the page, and could be mistaken for being the same as the dilemma before. To resolve this, a section reminding participants to read carefully since every dilemma was different was added, as well as formatting changes such as boldening the critical sections of text to make them more obviously different. Procedure Participants were recruited via social media, ESW were gathered via the Our Blue Light ESW charity’s social media pages, as well as being sent around stations via the researcher’s contacts. Laypeople were also gathered through social media, with some being recruited from the Our Blue Light pages, as well as through friends and family of the researcher. Participants had access to the study through a link, which took them to the introduction page of the present study. After reading this and giving consent, the study began. Participants were randomly assigned by the Qualtrics software to either the Time Pressure or No Time Pressure condition. This affected which set of instructions they saw. Participants were all shown each of the five dilemmas, presented one by one on their screen. The dilemmas were presented in a randomised order for each participant, to avoid any order effects. Following each dilemma, participants were presented with the justification question and free entry text box. After repeating this for each dilemma, participants were presented with a debrief page, and the study concluded. Data Analysis To examine the choices ESW made compared to laypeople, a 2x2 chi square test will be conducted. A 2x2 chi square test will also be conducted to examine the choices made by those in the time pressure condition against those who were not. Descriptive statistics will also be presented, including the counts of each choice made separated into groups, along with means and standard deviations of response time. In order to analyse the impact of Occupation, Time Pressure, and Type of Ethical Dilemma on the decisions participants make, a generalised linear mixed-effects model will be used (Baayen et al., 2008). The statistical family used for this model will be binomial. This test was chosen as the dependent variable here, participant choice, is a categorical variable with two options (push or no push). There are also three categorical independent variables, two of which are between-subjects factors (ESW v Layperson, Time Pressure v No Time Pressure), and one within-subjects factor (Type of Ethical Dilemma). The only random effect to be used in the model is individual subjects, as each independent variable is critical to the present study, and so will be treated as fixed effects. To compare the response time between ESW and laypeople, as well as time pressured participants and participants with no time pressure, two one-way ANOVAs will be conducted. This was chosen as the intention here is to compare performance between two independent groups. A 2x2 ANOVA on sum scores was considered, however was not possible due to participants having simultaneous membership of two groups (e.g. ESW + Time Pressure, ESW + No time pressure). To further analyse participant response times to the ethical dilemmas, a 2x2x5 Mixed ANOVA will be conducted. This was chosen as the method of analysis as one aim of the present study is to compare variance between ESW and laypeople, as well as participants being under time pressure or not. There is also the factor of ethical dilemma, which has five levels due to there being five different dilemmas.
Main Data_35645845/Excel.csv , 35645845 Occupation Response Time Sum Scores/Excel.csv , 35645845 Time Pressure Response Time Sum Scores/Excel.csv, 35645845_RStudio Code/RStudio.R
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).
How the correlation of Istagram tweets readability and brand hedoism affect audiecne engagment
Readability, Brand hedonism, readbility formula, audience engagment
Social media marketing is increasing in importance and more and more brands are embracing social media to increase their brand reach and communicate with their audience. However, there is still little empirical research on how brand message features affect consumer engagement. This study focuses on the impact of readability as an influence on consumer engagement while also noting that the effect of hedonic value of a brand may potentially moderate the level of audience engagement. An experiment based on a sample of 20 of the 100 brands covered by Forbes Media was conducted for this study. In total, a sample of 400 Instagram tweets were collected and analysed for their text readability and audience engagement. Still, the results did not indicate a significant interaction between readability and engagement. A careful analysis of the difficulties and shortcomings encountered in this experiment provides some insights for any subsequent research on the readability of short-form communication by brands.
Research Question & Hypotheses The research question for this study is: can the readability of tweets influence the level of audience engagement? As readability increases the perception associated with processing fluency (Rennekamp, 2012), the ability to process information fluently makes the target message more appealing to the audience, and visual fluency in processing information can also increase people's perception of the processing target (Novemsky et al., 2007). Language that can be processed fluently also enhances consumer perceptions (Lee & Aaker, 2004; Lee & Labroo, 2004). Thus, for most brands with low levels of hedonism, higher tweet readability means higher processing fluency which can reduce audience metacognitive difficulties and thus increase tweet engagement levels. At the same time, for products with high hedonic demand, lower familiarity and uniqueness may provide consumers with greater signals of value, with metacognitive difficulties increasing the appeal of the product by making it appear unique or unusual. More easily processed messages reduce the appeal of the product, possibly because they appear too familiar and therefore less consistent with the perception of uniqueness (Pocheptsova, Labroo, 2004；Pocheptsova, Labroo, and Dhar 2010). It is therefore hypothesised that text features associated with greater readability will be positively associated with consumer engagement with the message. However, given the presence of brand hedonistic features, it can be argued that low readability of messages may increase consumer engagement in brand tweets with higher levels of hedonism instead. Data collection for the experiment From the above, whether the readability of the tweet text and the level of brand hedonism of the brand to which the tweet belongs combine to influence consumer engagement with the brand's social tweets must be determined. Instagram was chosen because it is one of the world's most popular social networks, with around one billion active users per month, and over two-thirds of the Instagram audience is under the age of 34, making the platform particularly attractive to marketers. At the same time, Instagram is an open public platform and information on experiments can be easily accessed by searching for the brand name to use for experiments. This included the number of followers of the brand, the history and content of the tweets, the number of comments and the number of likes. To make the experiment practical, 20 tweets from each of the 20 brands (see Step 1 below) were selected for the experiment. The process of collecting information was as follows. Step 1 involved the selection of the experimental subject brands. The results of a hedonistic study of the TOP 100 most valuable brands in the world on the Forbes list (Davis et al., 2019) were used to rank the brands from the highest to lowest level of hedonism using the hedonism index (from Davis et al 2019 survey, for detail see Degree of brand hedonism) as the key indicator. A computer generated a random series of 20 numbers from 1 to 100, and the numbers in this series were used to correspond to the serial numbers of the brands in the hedonism table. The following 20 brands for this experiment were selected: Goldman, Sachs, HSBC, Walmart, Thomson Reuters, IBM, Subway, Verizon, HP, Hyundai USA, Boeing, Chanel, Coach, ESPN, Starbuck coffee, Nike, Gucci, amazon, Mercedes-Benz, Google, Porsche（For the logic behind the selection of these brands, please see Degree of brand hedonism）. In Step 2, text samples and audience engagement data were collected. In order to control the variables of the experiment as much as possible, text samples of tweets were collected from August 12 to August 13, 2021, and only tweets with 30-150 words were selected to control the discrete nature of the sample. To avoid the influence of rich media such as video/audio on audience engagement, tweets in the form of rich media were also excluded from the sample, ensuring that all samples contained only images and textual content. The number of likes and comments on each tweet was also recorded. To ensure that the selected sample of tweets accumulated enough likes and comments, all samples were posted before 7 August, ensuring that they had five days to accumulate interaction data with the audience. According to the official Twitter report (Twitter，2016), due to the instantaneous nature of the social media platform, in general tweets were largely ignored by audiences a week after they were posted and they therefore found it difficult to accumulate further feedback data. Step 3 was the readability analysis of the text samples. Considering that some of the tweet samples were less than 100 words, and that The Flesch Reading Ease formula recommends a text count of 100 words or more, and considering the validity of the formula, this experiment combined two or more samples for tweets with a text count of fewer than 100 words to obtain at least 100 words before using the formula for analysis , so as to the average readability score for this group of samples was calculated (See Message readability for details of the Flesch Reading Ease formula) Variables and measures Message readability Readability formulas have evolved to the point where there are now over 40 readability formulas (Heydari, 2012). The most widely known of these is Rudolph Flesch's formula, created in 1948 and published in the Journal of Applied Psychology in his article ' A New Readability Yardstick'. This formula is considered to be one of the oldest and most accurate formulas for readability, and has made Flesch an authority on readability scholarship. It was originally created to assess the readability of readers at grade level and is widely regarded as an accurate measure without much scrutiny. The formula is best suited to school texts, but it is also widely used by US government agencies (including the US Department of Defense) to assess the readability of their published documents and forms, and some states even require insurance policies to achieve a Flesch reading-ease score of 45 or higher. The Readability Formula is even installed in Microsoft Office Word, where the program checks the spelling and grammar of a text as well as its readability level (Heydari, 2012). The specific mathematical formula is as follows: RE = 206.835 – (1.015 x ASL) – (84.6 x ASW) RE = Readability Ease the output is a number ranging from 0 to 100. The higher the number, the easier the text is to read ASL = Average Sentence Length (i.e., the number of words divided by the number of sentences) ASW = Average number of syllables per word (i.e., the number of syllables divided by the number of words) Table1: Description and predicted reading grade for Flesch Reading Ease Score (Stein, 1984) Score School level (US) Notes 100.00–90.00 5th grade Very easy to read. Easily understood by an average 11-year-old student. 90.0–80.0 6th grade Easy to read. Conversational English for consumers. 80.0–70.0 7th grade Fairly easy to read. 70.0–60.0 8th & 9th grade Plain English. Easily understood by 13- to 15-year-old students. 60.0–50.0 10th to 12th grade Fairly difficult to read. 50.0–30.0 College Difficult to read. 30.0–10.0 College graduate Very difficult to read. Best understood by university graduates. 10.0–0.0 Professional Extremely difficult to read. Best understood by university graduates. As can be deduced, the text samples should ideally contain short sentences and words. As most texts on social media are short sentences or words, the Flesch Reading Ease Score was considered to be the most suitable tool for measuring the readability of tweets in this experiment. The Flesch Reading Ease readability formula in the online automatic readability checker was used in this study (https://readabilityformulas.com/free-readability-formula-tests.php). Consumer engagement with brands As Instagram retweets can only be sent to friends or groups of friends and not to the user's public page, this experiment only measured the number of "likes" (users click on the red love button below the tweet or double click on the tweet to like it) and comments on the tweet, as retweet data is difficult to collect. As described in the data collection process, the collected tweets were given at least 5 days to accumulate comments and likes. These two numbers (comments+likes) were then added together and divided by the number of brand trackers and multiplied by 10,000 to obtain the final audience engagement level score. Degree of brand hedonism As this experiment was limited by resources and practicability, the results of the Davis et al 2019 survey on the level of brand hedonism were used directly here. The following is an introduction to the process of Davis et al.'s 2019 survey on levels of brand hedonism which measured the level of hedonism of 100 brands primarily by human judges on a rating scale (four non-social media active brands were finally excluded, giving a final total of 96 brands). In the Davis et al. experiment, a total of 200 human judges participated in scoring the level of brand hedonism. Each judge was randomly assigned to 10 brands and they scored each brand on four hedonism-related indicators: fun, excitement, thrill and pleasure, on a scale of 1 'not at all' to 7 'very much'. The final brand hedonism index was derived from these four indicators and then averaged across the 10 judges. The judges who participated in the experiment were recruited from the Amazon Mechanical Turk online panel. A total of 200 judges participated in the experiment, 61% of whom were male and the remainder female, all aged 35 years and of unknown ethnic background, but all participants were US residents. Detailed results of the original experiment can be found in Appendix A. In this particular experiment, the brands were ranked from the highest to lowest hedonism level using the hedonism index of Davis et al. A computer generated a random series of 20 numbers from 1-96, and the numbers in this series were used to correspond to the serial numbers of the brands in the hedonism table as the experimental subjects. Table 2 shows the average hedonism scores of the 20 brands selected. Figure 1 shows the conceptual model for this experiment, the relevant experimental variables and the control variables. Table 2 the brand hedonism scores NO Brands Mean SD 1 Porsche 6.05 1.26 2 Google 5.92 0.95 3 Mercedes-Benz 5.68 1.13 4 Amazon 5.41 1.59 5 Gucci 5.29 1.13 6 Nike 5.05 1.40 7 Starbucks Coffee 4.89 1.19 8 ESPN 4.75 1.93 9 Coach. Inc 4.53 1.60 10 Chanel 4.40 1.26 11 Boeing 4.27 1.77 12 Hyundai USA 4.12 1.45 13 HP 3.86 1.75 14 Subway 3.75 1.67 15 Verizon 3.75 1.36 16 IBM 3.45 1.47 17 Walmart 3.15 1.39 18 Walmart 3.15 1.39 19 HSBC 2.89 1.35 20 Goldman Sachs 2.14 1.23
Effect of Attention and Noise on Echoic Memory as Indexed by the N1-Adaptation.
Attention, Noise, N1-adaptation, auditory sensory memory
There are numerous studies that support the notion that echoic memory is indexed by the adaptation of the N1 peak in auditory event related potentials (ERPs). Although the number research on the effects of parameters like noise and attention on the amplitude of the N1 is immense, to date there are no studies on the effect of these parameters on the adaptation of the N1. Here, I investigated the effect of noise and attention on the adaptation of N1, P2 and N1-P2. Secondary analysis was conducted on data collected from 33 participants in three conditions: passive recording condition (participant listen passively to stimulus while staring at a fixation cross); attention/oddball conditions (participant were task with counting the deviating tones); and noise condition where the tones are presented in white noise. Within each condition, two Stimulus onset intervals (SOI): 1.7 s and 3.5 were used in separate stimulus blocks and the ratio R = M1.7s / M3.5s was used as a dimensionless measure of adaptation. My results found no significant effect of noise an attention on the amplitudes and adaption of the N1, P2 and N1-P2. I propose that the lack of effect on the adaption of the ERPS might be due to noise and attention having a scaling effect on all of the amplitudes equally so that adaption lifetime is not affected. As this is the first study of its kind, further research will be needed to gain a better understanding of how adaptation is affected by these two factors.
Participants This project carries out secondary analysis on data from an EEG experiment with 33 human participants. The data was received from supervisor, Patrick May. The participants were all adult undergraduate and post graduate students at Lancaster University, with no self-reported hearing loss or neurological disorder. The experiment was approved by the research ethics procedures of the Department of Psychology, Lancaster University, and the participants provided written consent before the experiment began. Equipment and Procedure for EEG measurements Three dry electrodes were attached at locations: Fpz, Fz, Cz. Reference and ground electrodes were attached to the right ear lobe. For this report, only the data acquired from the Fz location was used as this is the channel that recorded the best ERPs for all the participants. The participants were directed to passively listen to stimuli while staring at a fixation cross and moving and blinking as little as possible. The stimuli comprised of 500-Hz pure tones with a duration of 100ms, including 10-ms linear onset and offset ramps. The stimuli were presented in blocks of 100 isochronous stimuli. The stimuli were presented binaurally via Sennheiser headphones using laboratory laptop and MATLAB interfaced with the Enobio EEG device in a soundproof chamber. Data was collected in three conditions: baseline passive recording condition (participant listen passively to stimulus while staring at a fixation cross); attention/oddball conditions (participant were task with counting the deviating tones); and noise condition where the tones are presented in white noise. Withing each condition, two Stimulus onset interval (SOI): 1.7 s and 3.5 were used in separate stimulus blocks. The order of experiments were randomised across the participants. Data Analysis The data was passband filtered at 1-30 Hz and sectioned into epochs of single trial data. To remove artefacts (e.g., due to blinking) 15% of epochs with the largest absolute amplitudes were removed. Single trial epochs was then averaged to reveal the ERP. The average ERP in a 100ms time window immediately preceding stimulus onset was calculated and subtracted from the whole ERP (baseline correction). The N1 is not the only peak that shows adaptation in auditory ERPS. Although many of the research on adaption is focused on the N1 peak, different researchers have looked at other auditory ERP peaks in relation to adaptations such as the P2 and P3 peaks. In fact, Lanting et al. (2013) found that the P2 was more very strongly affected by adaption than the N1. In addition, the peak-to-peak difference between the N1 and the P2 has been previously used to estimate adaptation in several studies as it provides a more reliable measure of activity in auditory cortex because as it has the advantage of not being dependent on the baseline activity which can be noisy (Lanting et al., 2013; Lavoie et al., 2008; Muller-Gass et al., 2008). Because of this, both the N1 and the P2 peaks were identified - the N1 was identified as the peak negativity at around 100ms and P2 peak positivity at around 200ms. The peak-to-peak difference between the N1 and the P2 was calculated and the N1 and P2 amplitude as well as the difference between the N1 and P2 amplitude was used to estimate the lifetime of adaptation. Statistical data analysis was conducted using Analysis of Variance (ANOVA). Specifically, three one-way (condition) and three two-way (SOI x condition) repeated measures ANOVAs was conducted of the N1, P2 and the difference between the N1 and P2 amplitudes and amplitude ratios respectively. Calculating the lifetime of adaptation (τ) The recovery time constant for adaptation is usually calculated by fitting an exponentially saturating function to peak amplitudes plotted across SOIs (Lu et al., 1992). This curve is characterized by  as well as by two other fitting parameters: asymptotic magnitude and crossing point on SOI axis. The parameter  determines the steepness of the magnitude curve: the smaller its value, the quicker the curve approaches the asymptote (i.e., levels out) as SOI is increased. The SOIs where this levelling out has occurred represent stimulation where the silent period between two consecutive stimuli is large enough for adaptation to have died away. Therefore,  expresses the lifetime of adaptation: with low values, the curve levels out to its maximum value quicker; with high values, the amplitude rises slower as a function of SOI, meaning that adaptation is strongly present in a larger range of SOIs. For fitting the exponential function reliably, a large number of SOIs should be employed, and the largest SOI should measure approximately 10s to ensure that adaptation has died away. Coupled with the requirements of data quality (large number of stimulus repetitions), this means long measurement times. In this experiment, this was bypassed by noting that the ratio between the magnitudes measured at two different SOIs is proportional to . Expressing the magnitudes of the brain responses measured at SOIs 1.7 s and 3.5 s by M1.7s, and M3.5s, respectively, the ratio R = M1.7s / M3.5s was used as a dimensionless measure of  and adaptation lifetime. The smaller R is, the shorter adaptation lifetime is. R was calculated separately for each participant for each of the experimental conditions and for each SOI. In addition, R was also calculated separately for the N1 and P2 peaks as well as the difference between these peaks. Note that the actual adaptation lifetime cannot be estimated by the use of this method. Results 18 participants’ data did not show identifiable ERP responses and were thus discarded from analysis. The ERPs obtained from the final sample of 15 were plotted as shown in Figure 1 for each participant. The means and standard deviations were then calculated for the identified N1, P2 and the difference between the N1 and P2 for each SOI and condition as shown in Table 1. Seeing as there is such a large variability across the conditions, it is predictable that no statistical differences were found by the ANOVA.
Infants' Awareness of Number: Innate Ability or Perceptual Bias?
Addition, subtraction, Number, Object Tracking, object files, Infant perception
In order to identify the origin of our understanding of numerosity and arithmetic abilities, it is essential that such abilities are measured in infants. In Wynn’s (1992) study, a case was made for an innate ability to perform arithmetic operation on small number sets as it was demonstrated that infants would look longer at displays that violated their expectations of number. However, research in the years following this seminal study cast doubt on this interpretation of infants’ behaviour. Other research has suggested that perceptual biases are at play, rather than infants possessing a symbolic understanding of number. To address the contrasting finding in this area of developmental research, this study set out to analyse preexisting data to investigate the factors that influence infants’ abilities to track objects over occlusion and to identify the most appropriate level of interpretation of this ability The present study recruited a sample of 32 infants across two experiments. Adapting the methodology from Wynn (1992), Experiment 1 measured looking time when an object was revealed to be missing from the display, violating infants’ expectation of presence. Experiment 2 measured looking time when an object was revealed to be in the incorrect position on the stage, violating infants’ expectation of position. It was found that infants violation trial had a significant effect on looking time and whether the object missing was the first or last to be placed had a significant effect on looking time in violation of presence conditions
Participants: In this study, participants were 32 infants aged 5- to 7-months, (M = 188.38 days, SD = 10.51, range = 175 – 218). Infants were 15 males and 17 females. 16 participants were used in each experiment. In Experiment 1, participants were 7 males and 9. In Experiment 2, participants were 8 males and 8 females. Participants in each experiment were matched based on age. Apparatus & Stimuli: The experiment took place in a dimly lit test room, with displays presented on a grey stage measuring 64cm wide by 40cm high and 31cm deep. An 8.5cm high black screen located 31.5cm behind the front of the stage was used to occlude the display by being rotated upwards. The display also consisted of a 30cm rotating platform that allowed different configurations of objects to be rotated rapidly. The objects used in this study were two 12.5cm high by 9.5cm wide toy hedgehogs that squeaked when squeezed. These toys were magnetic at the bottom. Procedure: Infants were sat in either a high seat or on a caregiver’s lap, 60cm from the front edge of the stage. In cases where infants were sat on a caregiver’s lap, the caregiver’s eyes were above the stage as to avoid them seeing the display and possibly influencing the infant’s behaviour. After gaze calibration to ensure the accuracy of eye-tracking measures, the procedure closely followed that of Wynn (1992) and Bremner et al (2017). Three pre-test (baseline) trials were presented initially. These resulted in the correct outcome of the operation as well as the two incorrect outcomes in counterbalanced order. The screen was lowered to reveal either one or two toys, depending on the trial, and the observer recorded where the infant looked on the stage. In terms of the location of the toys in trials, when one was presented, it was placed 7.5cm to the right of the stage’s centre. When two toys were presented, the second toy was placed 7.5cm to the left of the stage’s centre. Pre-test trials continued until the infant accumulated at least 2 seconds of looking time and looked away from the display for seconds or more. When this was achieved, the screen was raised and the same procedure was repeated for the displays for the other two outcomes. Test trials were administered in two blocks of four trials. The experimenter’s hand emerged at one side above the screen. The side at which the toy first appears was counterbalanced across participants. The toy squeaked to capture the infant’s attention and continued to squeak to maintain this attention as it was placed on one of the locations used during the correct outcome familiarisation trial. The experimenter then slowly withdrew their hand, clasping and unclasping the hand to show the infant that it was empty, and the screen was then raised to occlude the toy from the infant’s view. The time taken from the appearance of the toy to the withdrawal of the hand took approximately 5 seconds. The experimenter’s hand then reappeared above the screen from the opposite side of the display, holding an identical squeaking toy. Once the infant’s attention had been captures, the toy was placed in the other location used during correct outcome familiarisation trials. The hand was then raised and, again, clasped and unclasped to show the infant the hand was empty. The hand as then slowly withdrawn from the display. The screen was then lowered to reveal either the correct or incorrect outcome. In Experiment 1, conditions involved violation of object presence. In ‘added object absent’ trials, the screen was lowered to reveal the last object to be placed was missing from the display. In ‘original object absent’ trials, the screen was lowered to reveal the first object to be placed, present before the screen was raised, was missing from the display. In Experiment 2, conditions involved violation of object position. In ‘added object in wrong location’ trials, the screen was lowered to reveal the last object to be placed appeared in the centre of the stage rather than on the side of the stage in which it was placed. In ‘original object in wrong location’ trials, the screen was lowered to reveal the original object in the display appeared in the centre of the stage rather than on the side it was in before the screen was raised. These test trials continued until the infant had accumulated at least 2 seconds of looking tie and looked away from the display for 2 seconds or more.
Examining the Effect of Anxiety on the Development of False Memory
memory, generalized anxiety disorder, confidence-accuracy calibration
Up till the late 70s, people believed their memory worked in similar to a video-recorder, accurately collecting and storing every information seen and heard. This belief was brought to question after researchers started thorough investigation on memory, and found that in actuality memory is highly impressionable and prone to numerous errors such as the formation of false memories. There now appears to have been found many causes for the formation of false memories. However, limited to no research exists on the effect of generalized anxiety disorder (GAD) on formation of false memories. The present study aimed to investigate the effect of GAD on the development of false memories by using the misinformation effect paradigm. Confidence-accuracy calibration (CAC) was assessed as a secondary analysis. Participants (N = 100) were recruited through online means and took part in a 15-45-minute-long experiment involving neutral stimuli. The experiment consisted of a video of an event and were subsequently asked to read a text description with misinformation after partaking in filler tasks. Afterwards their memory of the original event was tested. Results demonstrate that GAD and false memory are not significantly associated. CAC analysis revealed that participants were relatively aware of when their memory had been distorted by providing low confidence ratings to more inaccurate items and higher confidence ratings to accurately recalled answers. Additionally, false memories created due to misinformation was significantly observed, though GAD did have any influence over this. To conclude, GAD does not contribute to the formation of false memories.
A total of 100 participants were recruited and provided with an online link through social media sites, ages ranging from 18-50. Out of the recruited participants, 66 identified as females, 31 as males, two as non-binary and, one preferred not to say. The link begins with the consent sheet, and once the participants click to agree, they were then redirected to the start of the experiment. Participant’s anxiety was tested by administering a standardized and validated tool, the Generalized Anxiety Disorder Questionnaire (GAD-7) (Spitzer et al., 2006), (see Appendix B). GAD7 has seven rating scale questions, and the participant’s anxiety was calculated by assigning scores of zero (not at all), one (several days), two (more than half the days), and three (nearly every day). Samples questions include “worrying too much about different things?” and “becoming easily annoyed or irritable?”. For scores ten and above, GAD-7 has a specificity of 82% and sensitivity of 89% (Kroenke et al., 2007). Cut-off points for the scores are a score of five for mild anxiety, ten for moderate anxiety, and 15 for severe anxiety. For the present study, participants who scored nine and below were grouped under “low” anxiety, and participants who scored ten and above were grouped under “high” anxiety. The stimulus set developed by Okado and Start (2005) were used for this study. Two neutral stimuli were obtained, and each stimulus consisted of 50 coloured digital images. These were compiled into a short video, with each image displayed for 300ms, and the whole video lasting 150s. Out of the 50 slides, 12 of them were critical, meaning these slides consisted of an item that would later be altered in the text description of the event, hence providing the misinformation. The two stimuli are summarized below. Stimulus One is about a female named Rachel who was doing her work at home, then feels hungry and checks her refrigerator for food, sees that there is not much at hand, and so goes grocery shopping. She was seen viewing different aisles for grocery and sees a friend in there as well. She then pays the bill and takes the elevator back home and stores the food away. (See Appendix C for the critical images) Stimulus Two is about a male student named Nicholas who was just seen leaving his classroom to go sit on a bench in the hallway, studying between classes and runs into three friends: a male (Henry) who displays his new shirt, another male (Frank) who wanted to know when an exam was scheduled, and a female (Stephanie) whose conversation was interrupted by a phone call. (See Appendix F for the critical images) Text descriptions derived from Okado and Stark’s (2005) stimulus set were used for the present study. For both Stimulus One and Stimulus Two, 12 critical details from the original event were altered in the text description, with every other detail remaining true to the original event. To give an example of a critical detail, in stimulus One’s original event a woman was seen picking up two bananas, whereas in the text description it was written, “She started with the healthy items and picked up five bananas.” (See Appendix D and G). Recognition test involving three choice options derived from Okado and Stark (2005) were used for the present study. The test was composed of 18 detailed questions concerning the video presented at the beginning (the original event phase). Out of the 18 questions, 12 were critical questions (i.e., regarding the events that were changed in the text description), and six were control questions (i.e., regarding events that were consistent throughout the video and text description). After each question participants reported their confidence in their response on a scale of 0-100, where zero indicated not at all confident and 100 indicated extremely confident. A sample critical question was, “In the fruits section, how many bananas did Rachel pick up?” Participants were required to choose one answer out of the three: (1) one banana (filler option), (2) two bananas (as seen from the original event’s video), and (3) five bananas (altered detail presented in the text description). Control questions were also akin to critical questions, e.g., “Where does Rachel put her shopping bags in the kitchen?” For answers: (1) on the counter (as seen from the original event’s video), (2) on the floor (filler option), (3) on the table (filler option). (See Appendix E and H). The current research was designed as a 2x2x2 mixed factorial study. All participants had to complete all aspects of the experiment; henceforth, the memory accuracy for control and critical items were within-subject factors. The levels of anxiety (high and low) and stimulus (one and two), were between-subject factors. Participants were tested individually online and were informed they are partaking in a study concerning memory and mood. The experiment was created online in Qualtrics, and upon viewing, participants are first required to consent. The consent sheet had also explained that the study is completely voluntary and participants can withdraw at any point. Subsequently, participants were to either watch stimulus One or Two (the two videos were set to view randomly), and a timer was set to ensure no skipping was allowed. Immediately afterwards, participants had to fill in few demographic questions pertaining to their age, education, and employment (see Appendix A). Afterwards, they were required to complete the GAD-7. These two questionnaires served as a filler task to ensure sufficient time to allow some memory decay between watching the video of the event and reading the text description of the event. Next, participants read the altered text descriptions of the original event shown in the video. Participants were unaware of the changes brought and were told to read the text descriptions which had described the events from the original video. Akin to the video, a two-minute timer was set to ensure participants do not skip the text descriptions. Thereupon, participants were diverted to a game of sudoku, where they would spend at least five minutes playing it. They were instructed that we were interested in knowing how individuals play games and so were not aware of the true nature of the game, which was to serve as a second filler task. Lastly, participants completed the recognition memory test, where they had to choose the correct answer out of the three response options and to indicate their confidence for each answer to assess the C-A relationship. CAC layout is relatively simple by computing the accuracy for each level of confidence. When perfect calibration occurs, it is a straight line with the decisions being made at each level of confidence are all correct. Once completed, participants were thanked for their time spent on the experiment and presented with the debrief sheet explaining the true nature of the study The debrief sheet was provided with international and local numbers for people from different continents should they need to seek immediate assistance. Participants spent around an estimate of 15-45 minutes to complete the experiment.
Do trustworthiness judgements help people to recognise synthetic faces?
StyleGAN, synthetic face, trustworthiness perception, facial trustworthiness
Recent advances in digital image generative models have allowed for artificial creation of fake imagery such as synthesising highly photorealistic human faces. Style-based Generative Adversarial Networks (StyleGAN) is one of the most state-of-the-art generative models in this field, and has been widely used on facial image generation. However, with the increasing ease of using such image generative models, the security in many domains, such as forensic, border control and mass media, is vulnerable in front of the potential threats resulted from the misuse of image generative technologies. To date there has only been limited empirical research into the facial characteristics of StyleGAN-generated faces to support the design of detection methods against such synthetic faces. This study used StyleGAN2 (an improved version of StyleGAN) to generate faces and invited people to complete two facial image evaluation tasks, 1) Discrimination task, 2) Trustworthiness rating task. The study results demonstrated that, in the discrimination task, subjects had trouble recognising synthetic faces by direct/explicit judgement; while in the trustworthiness rating task, subjects perceived the synthetic faces as significantly more trustworthy than real faces. The study further analysed gender bias and ethnicity bias on the perception of facial trustworthiness, with results showing some differences between different levels of gender and ethnicity. In conclusion, people’s ability to recognise synthetic faces is poor, but it is possible that people rely on the perception of facial trustworthiness to discriminate synthetic from real faces. The findings in this study have implications for the development of detection methods against digitally generated faces.
Three hundred and fifty-seven subjects (114 males, mean age = 25.2, SD = 5.8; 227 females, mean age = 25.0, SD = 6.3; 10 non-binary, mean age = 23.6, SD = 8.93) were recruited to complete an online survey test delivered on www.qualtrics.com. The responses of subjects who started but did not complete the online survey were eliminated to avoid distorting the research results. We used computer-synthesised facial images in this research as fake faces, mixed with real faces to examine people’s ability to detect fake faces and perceptual differences of trustworthiness between real/fake faces. Subjects did not get rewards for their participation, though they could see the test score of their performances at the end of the survey. The Qualtrics survey was based on a within-subjects design in which all subjects viewed the same two sets of adult facial images and completed each of the two tasks. To eliminate the effect of between-sets difference, the use of each image sets was counterbalanced in the individual test for each subject. Before the survey started, all subjects provided informed consent and completed a demographic questionnaire about their age, gender, ethnicity. In terms of the experimental power of 0.8 and significance level of 0.05, with a small effect, the power calculation indicated that the study needed at least 198 subjects. Stimuli A total of thirty-two human facial images (1024×1024 resolution), including 16 real and 16 synthetic faces, were used as stimuli in the survey. All real faces were taken from a publicly available dataset for high-quality human facial images, Flickr-Faces-HQ (FFHQ), which is created as a benchmark for GAN (see https://github.com/NVlabs/ffhq-dataset), and all synthetic faces were gained from the dataset of the generative image modeling, StyleGAN2 (see https://github.com/NVlabs/stylegan2). To ensure a diverse dataset, in each of the two sets of faces, there were 4 Black, 4 East Asian, 4 South Asian, 4 White, and 2 males and 2 females for each ethnicity. Among the sixteen faces of each set, half of them were real and half were synthetic, but this was unknown to subjects.
The Paradox of Choice in fictitious COVID-19 vaccination scenario: the role of the number of options and the amount of information in decision-making.
Previous research evidence showed that when people face abundance of choices or too much information, they tend to experience the paradox of choice. This study investigates the role of the number of options and amount of information in decision-making, respectively, the paradox of choice in the fictitious COVID-19 vaccination scenario. Participants (N = 128) were randomly allocated to one of four experimental conditions. The conditions differed in the number of options (high – six options; low – two options) and the information (high – six pieces of information per option; low – two pieces of information per option). As a result, the four experimental conditions were: low options, low information; low options, high information; high options, low information; high options, high information. Participants were asked to choose one of the vaccines from a list presented separately from the experimental stimuli. The reaction time of choosing a vaccine was measured. Participants were asked to evaluate how satisfied they were with their choice, how confident they were about their choice and their anticipated regret. Participants were also asked to write the reason why they chose a particular option. The study did not find a significant effect of the number of options and the amount of information on the decision-making. Participants identified five main themes why they chose a particular option: features of the vaccine, scientific evidence, information, lawfulness, and personal preference. The study revealed positive relationships between choice satisfaction, confidence, and anticipated regret.
Participants The participants’ pool was collected from the general public. In total, 191 participants took part in the study. However, 62 participants were excluded due to missing values. Another participant was further excluded because of stating being 0 years of age. Thus, the data of 128 participants (36 males, 90 females, 1 non-binary/third gender, and 1 prefer not to say) were used for the analysis. The participants were in the age group between 18 and 51 years of age (M = 23.1, SD = 6.02). Based on power analysis when effect size f = .25 (medium effect), p = .05, power (1 – β error probability) = .80, and the number of groups = 4, it was indicated that the sample size of 128 participants is necessary to ensure the study results have high statistical power. In terms of age, one participant was stated to be 22,5 years old. For the analysis, this was taken as 22 years of age. The participants were invited to the research via Facebook post, Instagram story, and direct messaging friends and family circles. From the overall sample, 103 participants (29 males, 72 females, 1 non-binary/third gender, 1 prefer not to say) also filled an additional qualitative question investigating the reasoning behind the participants’ choice. As drawn from the overall sample, the age of participants responding to the qualitative question ranged from 18 to 47 years of age (M = 22, SD = 5.76). Design Participants were presented with information about fictitious COVID-19 vaccines. The current study applied a 2x2 between-subject design. Participants were randomly split according to the number of options (high – six options; low – two options) and the amount of information they received (high – six pieces of information per option; low – two pieces of information per option). Consequently, the four experimental conditions were: a) Low options, low information b) Low options, high information c) High options, low information d) High options, high information The six vaccines in the high options scenario represented the first six COVID-19 vaccines used in the world in more than two countries (Forbes, 2021). Two vaccines in the low option scenario were chosen as it is the smallest number of options participants can compare and choose from. The amount of information then copied the design of the number of options. The number of options and the amount of information was then counterbalanced, enabling testing the effect of the number of options versus the amount of information and their interaction on decision-making. Materials As mentioned above, the data was collected using an online questionnaire. Participants were randomly allocated to one of the experimental conditions using the Qualtrics.com question randomiser function. Thus, there was no control of the researcher regarding the experimental condition allocation. The experimental stimuli consisted of pictures containing the information about vaccines varying in the number of information and the number of vaccines, as can be seen in Figure 1 to Figure 4. The information about each vaccine in the experimental stimuli was inspired by the real-world COVID-19 vaccines in use. For collecting the information, official sources were reviewed, news articles and videos, and other websites. Although the information was modified, it does not directly correspond with any real-world vaccine. The sources also do not directly match with real-world sources of information. All people, social media accounts, and websites are fictitious. The information was counterbalanced, so each of the fictitious vaccines has a similar amount of information from official sources (CDC, NHS, WHO, Government) and unofficial sources (made up websites and social media profiles). Further, to ensure there is no dominant option, the number of people in fictitious vaccine trials was similar. Likewise, the efficiency levels were kept similar across the options, and positive and negative information was also balanced. Figure 1 Experimental stimulus – Low information, low options Note. This picture represents the low information, low options experimental condition presenting two pieces of information and two vaccines options. Figure 2 Experimental stimulus – Low information, low options Note. In this picture, the low option high information experimental condition can be seen. Two options of vaccines and six pieces of information for each vaccine were presented in this condition. Figure 3 Experimental stimulus – Low information, high options Note. This figure illustrates the high options and low information experimental condition. In this experimental condition, six vaccines and two pieces of information for each vaccine were presented. Figure 4 Experimental stimulus – High information, high options Note. The high options, high information condition stimulus was split into two pictures to assure that the font of the text is sufficiently large for the participants to read the information. Six options of vaccines and six pieces of information to each vaccine were presented in this condition. After viewing the stimuli, participants were asked to select one vaccine from the list on a separate page. The lists of vaccines varied depending on the experimental conditions—the number and type of vaccines corresponded with the experimental stimulus. The page with vaccines options was timed to measure how long participants spend deciding between the vaccines. The time was measured from when the page came up until submitting the page. For the open-ended question about the rationale behind the choice, a larger text box was provided so the participants could type in a short paragraph about why they decided on that vaccine. 5-point Likert scales were used to measure satisfaction (unsatisfied to satisfied), confidence (unconfident to confident), and regret (regret to not regret at all). Procedure In the beginning, participants were informed about the nature of the experimental task; however, they were not told that the study measures the paradox of choice. Participants could continue the study after completing a consent form. Then, participants were informed that they would view lists of information about fictitious COVID-19 vaccines. They were recommended to take notes to maximise their attention to the information. Then participants proceeded to one of the experimental conditions and were asked to read through the information presented. Then they continued to another page and were asked to choose one of the vaccines from the list based on the information from the previous page. The questionnaire continued with the open-ended question. The following page contained the evaluation of the choice satisfaction, confidence, and regret. Participants were disclosed that the paradox of choice was measured in the debriefing, followed by its definition and links to the actual COVID-19 vaccines information. The participants were given the option to withdraw by closing the browser window without saving their data if they no longer wished to participate in the study. The experimental design was reviewed and approved by the Lancaster University Department of Psychology ethical committee. Data analysis methods This study investigates the effect of the number of options and the amount of information on the paradox of choice across the four experimental conditions. The dependent variables measured were the reaction time, satisfaction levels, choice confidence, and anticipated regret measured using a 5-point Likert scale. The data gathered consists of independent observations as everyone went through one experimental condition at the time. Convenient sampling was used to collect data as the participants were mainly the researcher’s family, friends, and acquaintances. However, the participants come from different countries, age categories and educational backgrounds; thus, it can be assumed that the observations are independent of each other. The effect of two factors (information, options) with two levels (low and high) on dependent variables are observed. Hence, a 2x2 analysis of variance (ANOVA) was chosen as an appropriate analysis for testing the research hypotheses. The relationships between choice satisfaction, confidence, and anticipated regret were also investigated. The data were checked for the assumption of linearity. The data on satisfaction seems to be positively skewed similarly to the data on confidence and regret. However, the data appear not to be linear nor homoscedastic. Therefore, Spearman’s correlation was chosen as an adequate analysis for this type of data. The short responses to the qualitative question “Why did you decide on that option?” was analysed using template analysis (King & Brooks, 2017). Template analysis is a flexible type of thematic analysis that can be used to analyse written responses to an open-ended question on a questionnaire (Brooks et al., 2015; King & Brooks, 2017). The question about the participants’ rationale behind their choice used in the current study was open-ended. Participants were asked to give a short written answer. Because the data result from an open-ended question and the flexibility of template analysis, template analysis was chosen to analyse the quantitative data. The final template is presented below in Figure 5 in the results section. In the beginning, all data was put together in one document. Next, the participants’ answers were coded line by line. Then the line coding was used to identify themes. The second level of themes was generated from the first level themes. Because the answers consist of short sentences with a maximum of short paragraphs, two levels of themes were used in the analysis. The template was developed from the two levels of codes. The template checked whether it fits all the recorded answers ensuring the template accuracy. Then the template was reviewed and concluded, referring to the sixth research question.