How does metaphorical language affect individuals’ aesthetic perception in modern poetry: In the life span view
Beauty perception Metaphoricity Familiarity Imageability
This study examined the relationship between the degree of metaphoricity and beauty perception as well as between cognitive load and beauty perception, by controlling for other possibly confounding variables such as familiarity and imageability. While previous research has shown that the variables of metaphoricity, familiarity and imageability influence beauty perception, no study investigate how the degree of metaphoricity and cognitive load influence beauty perception in poetic sentences reading. Therefore, this study aimed to bridge this gap. Beauty rating scale and keypress experiment were conducted, involving 22 young adults and 18 elderly adults. Because of the collinearity among metaphoricity, familiarity and iamgeability, a new variable called interpretation of metaphors was used to explain the hypotheses in the present study. Rather than cognitive load, interpretability was the predictor of beauty perception in poetry sentences reading. Young adults’ beauty perception achieved to the highest point at novel metaphors, while elderly adults considered dead metaphors as the most beautiful stimuli. This study suggests that poetic sentences are generally perceived as more beautiful when its degree of interpretability is lower in young adults rather than elderly adults. These findings provide an initial implication for future longitudinal or neuroaesthetic studies to further the understanding between metaphorical language and beauty perception.
This study has been approved by the Psychology ethics committee at Lancaster University on 24/04/2018. Besides, this study were preregistered in ‘AsPredicted’ website, and the number was 11034. Participants. The participants were 22 young adults between the ages of 18 and 30, and 20 elderly adults between the ages of 55-75. They were recruited from SONA systems, social media (e.g., Facebook advert). All young participants have not suffered from any learning disability (i.e., dyslexia) and they were native English speakers. However, two elderly participants confirmed that they had a history of dyslexia, so they were excluded. Finally, there were 22 young adults with a mean age of 21.64 years (SD=3.05) and 18 elderly adults with a mean age of 63.22 years (SD=6.07) have participated. Participants were required to give informed consent via an online consent form before completing the online survey, and they would fill in a paper version consent form before the keypress experiment. All participant would receive four pounds after finishing all experiments. Materials. Stimuli. A bank of 92 stimuli, was generated by a previous student who was previously supervised by Dr Francesca Citron. Partially sentences are excerpted from modern poetry. The remaining sentences were created by this student, inspired by other poetic works. Creating novel sentences was to decrease the deviation caused by participants being familiar with some stimuli. All stimuli were divided into five categories, and the degree of metaphoricity of these categories was increasing. The first one is the literal expression which has concrete and pragmatic meaning and it usually equal to its literal meaning. It is not part of the metaphorical language. The following category is dead metaphors – a kind of metaphor that lose its imaginative space because of frequent use (Punter, 2007). The third one is the conventional metaphor that is commonly used in everyday life, and it is highly related to the specific culture. The fourth one is novel metaphors which is usually unusual in everyday life and challenging for the layperson to understand. The last category is extremely novel metaphors which are the most abstract and challenging. The semantic category overlap of subject and predicate in these sentences is less obvious than other categories. Considering the potential fatigue of the elderly participants, the researcher randomly selected 50 stimuli from the original stimuli bank as experimental materials (See Appendix A). There were ten sentences for each category. All stimuli were given a specific code for identification in the analysis procedure. The creator of the stimuli bank has invited 85 participants to rate the degree of metaphoricity for each stimulus via a 7-point Likert scale (1 for the minimum and 7 for the maximum). The result has shown that the degree of metaphoricity was increasing as the original design (See Figure 1). Figure 1. Scatterplot showing the trend of metaphoricity ratings of stimuli. The categories corresponding to the stimuli number as follows: literal sentences (1-14), dead metaphors (15-28), conventional metaphors (29-53), novel metaphors (54-75), and extremely novel metaphors (76-92). Apart from metaphoricity, these stimuli have been tested on multiple sentence-level characteristics, including familiarity and imageability in the same group of participants. Briefly, all ratings were collected by asking participants to rate "how familiar is this sentence to you?" and "how easy is it to imagine this sentence?" on two separate 7-point Likert scales. These raw data would be used for analysis in this study. Survey. Beauty rating scale was designed as a 7-point Likert scale via the online survey software ‘Qualtrics’. The scale included a digital version of the information sheet, consent form and debrief form, and it also investigated several basic information like age, biological sex and reading frequency (Appendix B). More importantly, the scale included the questions for checking whether the participants are British native speakers and whether they have had the history of learning disability (i.e., dyslexia) since these factors can influence the beauty ratings. In the formal test, 50 stimuli would be randomly presented to the participants through Qualtrics. Participants would see the poetic sentences, as well as the question ‘How beautiful is this sentence to you?" on the page. They need to give their responses by rating from 1 to 7(1 for not at all beautiful and 7 for extremely beautiful) for each sentence. Experiment. The researcher created a keypress experiment on ‘Presentation neurobehavioral system’ software. The material were identical to the online survey and included extra four filler sentences, five odd sentences, four questions related to the poetic stimuli. All new stimuli were generated by the researcher, but they would not be analysed eventually because of their functions (Appendix A). Filler sentences were used to let participants practice how to give their responses by the keypress. Odd sentences were unreasonable, and they were used to avoid the mechanically repeated responses. Similarly, some poetic stimuli would be followed by a question for checking whether participants have answered the question seriously. To ensure the randomness of the experimental materials, six versions of the experiment were created. Participants would be asked to read each sentence once at a time and to evaluate whether it was sensible for them by pressing a button (“F” for indicating “Yes” and “J” for indicating “No” via keyboard). Because wanting to avoid the habitual reaction caused by the participants being familiar with the traditional key press experiments, we also created six corresponding flipped version of the experiment. Overall, this experiment has 12 version, and they would be randomly allocated to the participant. Participants would take part in the experiment on the researcher’s computer, whereby the answer and the reaction time of each sentence would be collected by Presentation automatically and anonymously. Procedure. Questionnaire. When the participants decided to participate in the project, the researcher would send an anonymous questionnaire link to the participants by e-mail. The questionnaire can be completed on any electronic device, and the participants could pause the questionnaire at any time when they need a break. After clicking the link, the participants would read the information sheet and the electronic consent form orderly to ensure that they understood the necessary information of the questionnaire and gave their consents. They then need to answer ‘check questions’ to check whether they were native speakers and whether they have a previous or current learning disability. Knowing the answers to these questions was to confirm that the participants were suitable for the questionnaire. Subsequently, the demographic information would be asked, and all answers would be kept confidential. After, a brief instruction form would be presented to explain the basic operations of the questionnaire and some important terms (e.g., beauty) involved in the questionnaire. Then, 50 poetic stimuli which were varied in the degree of metaphoricity would be presented randomly, followed by a question after each stimulus: How beautiful is this sentence to you. Participants should give their responses by rating from the 7-point Likert scale (e.g., 1 for not at all beautiful and 7 for extremely beautiful). All answers would be automatically recorded by Qualtrics. After completing all, the participants would read the debrief sheet to understand the purpose and the design of this questionnaire. Also, the references about this questionnaire and the contact information of the experimenter would be given. When the participants completed the questionnaire, they would receive an e-mail from the experimenter to make an appointment for the keypress experiment. Time for the experiment was usually one or two days after completing the questionnaire. Keypress experiment. All participants were required to meet the experimenter personally to complete the keypress experiment. Before the experiment began, participants were required to sign on the paper version of the consent form. After that, the experimenter would verbally explain the operation of the experiment. Then, the experimenter would randomly select one of the twelve versions of the experiments and give the participant a unique code. Participants were asked to evaluate whether the sentences presented on the screen were reasonable at the time. When they think it was sensible, they need to press the button that represents ‘Yes’, and vice versa. When they need to answer the ‘Yes/No’ questions, the operation was the same. When the participants understood the operation, they would press F or J key to start the experiment. Before the poetic sentence was presented, there would be a white fixation cross in the center on the black screen, and the duration was 1000ms. Then, the stimulus would present on the screen and last for 8700ms. Usually, the participants need to give their responses during this period, and their reaction time was automatically recorded by the software. After the presentation of a stimulus, it would be followed by a blank screen that lasts for 300ms with a white jittered fixation cross before the next sentence/question was presented. If the subject answers the question at this time, their reaction time of this stimulus will be the reaction time during this period plus 8700ms. The font for all stimuli was 12, the font colour was white, and the background screen was black.
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
Implicit Hand Representations in Typical Ageing and in Parkinson's Disease
Having an internal representation of one’s own body is important for many interactions with the environment, and in making decisions about what actions we are capable of performing. However, even in healthy adults, these representations are known to be distorted. In the hand specifically, individuals are likely to underestimate the length of all fingers, but overestimate the distance between each adjacent pair of knuckles. Both healthy ageing and Parkinson’s Disease (PD) include apsects which are known to further distort body representations, including, but not limited to, diminished tactile sensitivity and impaired action capabilities. This study was designed to investigate the accuracy of hand representations in typical ageing and in PD. Fourteen participants with mild to moderate PD, 17 healthy age-matched controls and 20 younger controls made estimates about the location of hand landmarks when the hand was hidden from view. Estimations were compared with actual hand size. Older controls and individuals with PD both demonstrated more accurate representations of thumb length, and of distance between the index and middle knuckles than younger controls, with older controls also showing differences in their perception of distance between thumb and index knuckles. However, no differences were found between the PD group and older controls, suggesting that the formation of body representations is an ability which is preserved in PD. Possible explanations for, and implications of these results are discussed.
Participants To determine the number of participants necessary, a priori power analysis was conducted in G*Power (Faul et al., 2009), using α= 0.05, β= .08 and effect size = 0.32. This effect size was calculated from Longo (2014), which employed a similar methodology. The analysis determined that 10 participants in each condition were required to yield sufficient power. Previous studies using this methodology have included sample sizes ranging from 12-22 participants (Longo & Haggard, 2010, 2012; Peviani & Bottani, 2020). The intended sample size, therefore, was 20 participants per condition. Due to the time constraints of the study, this number was not reached for all conditions, but all conditions included more than the 10 participants needed as suggested by the priori analysis. 20 younger controls were tested (15 female). Their ages ranged from 19 to 30 (M = 22.40 yrs, SD = 2.21 yrs). 17 were right-handed, and 3 were left-handed, with handedness ranging from -89.5 to 100 (M = 64.52, SD = 61.84) on the Edinburgh Handedness Inventory (EHI; Oldfield, 1971). 17 healthy older controls were tested (11 female). Their ages ranged from 52 to 79 (M = 66.12 yrs, SD = 9.16 yrs). 14 were right-handed, and 3 were left-handed, with handedness scores ranging from -100 to 100 (M = 65.29, SD = 77.31). 14 individuals with PD were tested (4 female). Their ages ranged from 54 to 78 (M = 65.93 yrs, SD = 8.43 yrs). All PD participants were right-handed, with handedness scores ranging from 33.5 to 100 (M = 88.31, SD = 21.20). There was no significant difference between the ages of the participants in the typically ageing and the PD condition, t(29) = 0.06, p = .95. For the PD participants, the most recent onset of PD was 3 years ago, with the longest diagnosis of 20 years (M = 7.75 yrs, SD = 4.81 yrs). All presented with a Hoehn and Yahr Stage of 3 or below. This indicated that all participants were physically independent. All participants had been prescribed antiparkinsonian medication, and they were all tested under their normal medication regime. Younger controls were recruited through use of social media and personal connections of the researcher. PD participants were recruited through a Parkinson’s Research Interest Database which was developed by the researcher’s supervisor (Dr Megan Readman), and by contacting a local branch of Parkinson’s UK. Older controls were primarily friends and family of PD participants. Materials 24 hours before testing, participants were asked to submit demographic information in a questionnaire created using the design software Qualtrics (Qualtrics, Provo, UT). Participants’ hand movements were recorded by an Xbox Kinect camera, mounted on the ceiling directly above the hand. The camera had a resolution of 640x480 pixels, and a frame rate of 30 captures per second. The recording was made using the Kinect Studio application. Within the frame of the recording, a 30cm ruler was placed, to allow for conversion of pixels to centimetres during analysis. During the experiment, the board used to hide the participants’ hands from view was a piece of black cardboard, approximately 85x60cm. The board was 2mm thick and completely opaque. The board was positioned approximately 10cm above the hand, and was supported in this position by 5 cylindrical weights (one under each corner of the board, and one placed centrally). At each side of the board was a small mark of duct tape. This was to indicate where the participants should point between each trial. A mark was placed on each side of the board, as the handedness of the participant determined which hand they used during testing, and therefore determined which side of the board was easier to point to. Participants were asked to point using a red straw, approximately 10cm long. All participants completed the EHI (Oldfield, 1971). This includes a list of tasks (for example, writing or striking a match), for which the participant must indicate which hand they prefer to use. The response options include a strong or slight preference for the right or left hand, or no preference. A score of 100 indicates pure right-handedness, while a score of 100 indicates pure left-handedness. Participants in all conditions were screened for cognitive impairments using the Addenbrookes Cognitive Examination (ACE-III; Hodges & Larner, 2017). This assessment included 19 tasks which examine cognitive function on 5 separate domains; attention (e.g. ‘count down from 100 in 7’s’), memory (e.g. ‘remember this name and address’), fluency (e.g. ‘name as many animals as you can in one minute’), language (e.g. ‘write two full sentences’) and visuospatial reasoning (e.g. ‘draw a clock which reads 10 past 5). Typically, a score of less than 87 out of 100 would be considered abnormal, however, as some aspects of the ACE-III require participants to perform motor tasks, it is accepted that the best cut-off score to identify cognitive impairment in Parkinson’s is 80 points (Kaszás et al., 2012). Using this assessment as an exclusion criterion, only 1 PD participant’s data was removed from further analysis. There was no significant difference in the ACE-III scores of the remaining participants between the three conditions, F(2, 48) = 2.10, p = .13. Participants in the PD condition were also assessed using the Movement Disorder Society- Unified Parkinson’s Disease Rating Scale (MDS-UPDRS; Goetz et al., 2008), to determine the severity of PD symptoms at the time of testing. The UPDRS assesses both the motor and the non-motor symptoms of PD. The non-motor assessment involves questions about the individual’s experience of symptoms during the past week, for example how well they are sleeping, and if they are experiencing tremors regularly. A motor assessment is also conducted, with the participants performing tasks such as opening and closing their hand as quickly as possible, and walking from one side of the room to the other. The researcher was also required to make judgements about the severity of typical PD symptoms such as tremors and rigidity present throughout the examination. All questions and tasks are scored on a scale of 0 to 4, with 0 indicating no impairment, and 4 indicating severe impairment. This assessment has previously been validated and determined to be a reliable indicator of the severity of PD symptoms at the time of testing (Gallagher et al., 2012; Martinez-Martin et al., 2013). Testing occurred in the action and perception lab in the Whewell building at Lancaster University. This study received ethical approval from the Ethics Department of Lancaster University. Procedure Participants were emailed an information sheet 24 hours in advance to inform them of the requirements of the study. This email also directed them to a Qualtrics survey, where they were asked to submit their demographic information (age and sex). Here, they also completed the EHI, and were asked to confirm that they had normal or corrected-to-normal vision. On the day of testing, participants were first screened for cognitive impairment using the ACE-III. At this point PD participants also completed the full MDS-UPDRS. After the recording had started, participants were asked to place their dominant hand (as determined by the EHI) on the table in front of them. They were asked to move their chair so that their hand was aligned with the middle of their body. The participants were instructed to not move their hand throughout the experiment, before an occluding board was placed so that the participants could no longer see their hand. They were asked verbally to confirm that this was the case. Participants were given a straw to use as a baton with which to point. They were then directed to use the straw to point on the board, directly above where they believed specific locations of the hand to be. 10 different locations were used: the tips of each finger, and the knuckle where each finger meets the palm of the hand. Small duct tape marks were placed on the knuckles of each finger. This was done both to ensure that the participants were clear about which knuckles were intended, and also so that location of the knuckle would be clearer on the recording. The location for each trial was read aloud by the experimenter. Between each trial, participants were asked to move the straw to point at a duct tape mark on the side of the board. This was to ensure that all estimations were made where participants believed their hand to be, instead of them using alternative methods such as measuring where they believe one location to be based on the previous location. One block of testing consisted of 10 trials (one trial for each hand landmark). For the younger control condition, participants were directed to each landmark 10 times, meaning that data were obtained over 10 blocks. However, testing of the first PD participant determined that asking participants in this condition to complete all 10 blocks was not a viable option. Individuals with PD suffer from motor fatigue ability (Fabbrini et al., 2013) and multiple repetitive tasks led to an increased severity of PD symptoms such as tremors. For these reasons, all subsequent participants only completed 5 blocks of 10 trials each. This ensured we still had 5 estimations for each landmark, without causing distress to participants. Two different random orders were created for the presentation of the locations, and these were randomly assigned to participants. After testing, the occluding board was removed so that the recording could be used to ensure that the hand had not significantly moved throughout the testing period, before the recording was ended. Data Analysis To determine both the actual and estimated locations of the hands, the recordings were replayed using the Kinect Studio software. For each trial, the footage was paused when the participant had the straw pointed at the estimated location. The cursor was then moved to this point, and the x and y coordinates of the cursor was manually inputted into a spreadsheet. The same method was used to determine the actual position of each hand while the occluding board was not in place. The beginning and end of each recording was examined to confirm that the hand had not moved between the start and the end of the experiment. It was often the case that although the hand had not moved in any significant way, there was a couple of pixels difference in the position of a few landmarks. For this reason, the x and y coordinates of the hand position was recorded both before the board was placed, and after it was removed, and the average of these locations was used. For analysis, we were interested in the overestimation of the length of each finger and of the distance between each pair of adjacent knuckles. To calculate the length of each finger, the difference between the x coordinates of the tip and knuckle of the finger was calculated, and the same was done for the y coordinates. Pythagoras’s theorem was then employed to determine the distance, leading to the following formula: The same formula was adapted to determine the distance between each pair of knuckles. These distances were calculated for each block of 10 trials, and then the average was taken for each participant, before being compared to the actual measurements to calculate the percentage overestimation of each distance. For the detection of outliers, all estimations were plotted using RStudio. Code was adapted from Helbing (2020) to plot an ellipse for each hand location per participant, which encompassed at least 80% of all data points. Estimations outside these ellipses were treated as outliers and removed from further analysis. Setting the inclusion of data points to 80% meant that even for older participants, who only performed 5 trials per location, it was still possible for outliers to be seen outside of the ellipse. RStudio did not have the capacity to plot 10 separate ellipses at once, therefore 2 separate plots had to be made per participant. Before analysis, hand maps were also created using RStudio. Although these plots were not used for analysis, they helped to visualise the data. All hand maps can be found in the Appendices.
Infant Gesture and Parent Knowledge of Development
Background: Before children can communicate verbally, they use gesture to tell us what they want. Our understanding of the importance of gesture in language development has expanded greatly over the past few decades. Furthermore, the methods used to measure gesture and language development have also progressed. Gesture and language assessment rely heavily on parent reports. It has been suggested that what parents know about development has also consequences for their child’s developmental outcomes. Aims: To validate the gesture section of the UK-CDI Words and Gestures (Alcock, Meints, & Rowland, 2017). And to explore parent knowledge of language and gesture milestones Methods & Procedure: Twenty-seven children and their parents participated in the first experiment. The parents completed the UK-CDI W&G and the children participated in an in- person gesture validation task. Thirty parents with a child 8-18 months participated in the second experiment. They completed the UK-CDI W&G as well as our new parent knowledge questionnaire. Results: In Experiment one, children’s score from the gesture task correlated significantly with parent-reported scores on the UK-CDI W&G. In experiment two, parents were more accurate at ordering and estimating the age of language milestones than they were gesture milestones. Conclusions: The findings for experiment one provides more support and confidence for the UK-CDI W&G as a language assessment tool. This will provide and benefit researchers and clinicians with a standardised tool and method for assessing language norms and delays. The findings for experiment two inform us that parents are not that knowledgeable within the developmental domain of gesture. This provides us with information on where parents need to be educated to benefit the developmental outcomes of their children.
Experiment 1 Method In this experiment we attempted to validate the gesture section of the UK-CDI Words and Gestures questionnaire through responses to the questionnaire and with an in-person gesture task procedure. Participants Twenty-seven children and their parent participated in this study. Participants included 10 girls and 17 boys between eight and eighteen months (M= 12.5 months, SD= 2.3 months) who were recruited from the Lancaster University Babylab and through social media (e.g. Facebook). The parents who participated in this study were 26 mothers and one father. To be eligible for this study all participants had to be native British English speakers. All participants were self-selected and received a children’s book for participant payment. Apparatus and Materials UK-CDI Words and Gestures The UK-CDI Words and Gestures (Alcock et al. 2013) is a parent-report questionnaire used to assess the language development of children aged eight to 18 months old. This questionnaire offers a checklist of words from several different categories (e.g., animals, toys, household items), with a total of 395 words. Parents are asked to indicate whether their child can say and understand, just understand, or does not know a word. The child obtains a score for total comprehension (sum of the words they understand) and a total score for production (sum of the words they say and understand). There is also a gesture section consisting of 57 gestures. The gesture section is divided into subsections (e.g., first communicative gestures, games, actions, pretending to be a parent, and imitating other adult actions). In the First Communicative Gesture section, parents are asked to indicate whether their child does a gesture often (two points), sometimes (one point), or not yet (zero points). For the remaining sections parents are asked to tick yes or no if their child does a gesture. A total gesture score is calculated by taking 0.5* the First Communicative Gesture section score and is then summed with the total number of Yes scores from the remaining sections. See Appendix D for full UK-CDI W&G questionnaire. Gesture Task The gesture task used in this study was constructed by (Alcock et al. 2013) to establish content validity of the gesture scale on the UK-CDI W&G. The gesture task consists of 10 gesture items taken from the gesture section of the UK-CDI Words and gestures. The items range from low frequency items (e.g., ‘can you give me a high five?’), medium frequency items (e.g., ‘can you put on a hat?’), and high frequency items, (e.g., ‘Can you feed the teddy/dolly?’). The stimuli were nine children’s toys required for the items on the gesture task. See Appendix B. Procedure Participants were asked to complete the UK-CDI Words and Gestures (Alcock et al. 2016) prior to the home visit. Participants were sent the UK-CDI Words and Gestures via an electronic link. Upon completion of the UK-CDI a home visit was scheduled and took place in each participants home. The task was administered by the researcher in a quiet room with the child and parent. Prior to the gesture task being administered, parents were pre-warned of the procedure and were told to not repeat instructions during the gesture task until cued by the researcher. Participants were asked each item first without any demonstration or cueing. If there was no response the researcher would demonstrate the gesture and say, ‘Can you show me the (x)?’. If there was still no response the parent was asked to demonstrate the gesture. (See Appendix B and C for gesture task procedure and list of stimuli). Each participant was recorded for approximately 45 minutes. Scoring For the gesture task, participants were scored for 30 minutes. Any time the participant was out of the cameras view or was not cooperating was not included in the video analysis. For each item on the gesture task participants scored two points for completing a gesture on their own, one point for completing a gesture after a demonstration, or zero points for not completing the gesture. Participants were also observed and scored for any spontaneous gestures exhibited during the scored time. Spontaneous gestures included any gestures exhibited by the participant that are on the UK-CDI W&G questionnaire but weren’t on the gesture task. Spontaneous gestures observed during the home-visit were given a score of one if they did it or zero if they did not. Inter-rater Reliability Each video was scored twice by the researcher and scored a third time by another masters student at Lancaster University. The second scorer was briefed on the nature of the videos, the UK-CDI W&G questionnaire, the gesture task, and was familiar with the content of the study. The agreement level was calculated using, Percent agreement= (agreements/ (agreements + disagreements)) x100. The two scorers reached an agreement level of 94%. Experiment 2 Methods This experiment was investigating what parents know about language and gesture development using two online questionnaires. Participants Thirty parents with a child between the ages of eight and 18 months participated in this study. All participants who participated were mothers. Participants were recruited through the Lancaster University Babylab and through social media advertisements for the study. To be eligible for this study participants had to be native British English speakers. All participants who completed the study were entered in a draw to win a £20 Amazon gift voucher. Apparatus and Materials UK-CDI Words and Gestures The same version of the UK-CDI Words and Gestures (Alcock et al. 2016) was used in the second experiment. Parent Knowledge Questionnaire The researcher constructed a questionnaire to investigate what parents know about language and gesture development. The format of the questionnaire was based on a previous study investigating what mothers know about play and language development (Tamis- LeMonda et al. 1998). The questionnaire consisted of 11 language items and 11 gesture items. The researcher used a paired-comparisons procedure to match each item in the respective domain (language or gesture) with the remaining items, resulting in 55 pairs for language and 55 pairs for gesture. All pairs were randomized and presented in a left-right alignment. Participants were asked to select the item they believed to be more difficult and to occur at a later age. Following the paired-comparisons task, the same 11 language and gesture items were used on an age checklist and randomized. Participants were then asked to estimate the age each milestone emerged. See Appendix E for full questionnaire. Language and Gesture Scales The language and gesture items were chosen based on empirical findings about language and gesture development in the literature, and the previous work of Tamis-Lemonda et al. (1998). The language items gradually increased in sophistication from level one to level 11. Levels one through four represented prelinguistic communication from nondiscriminant cooing to requesting a target object. Level five through seven represented single-word utterances, from imitation to expressing possession. Levels eight to 11 represented multi-word utterances, from expressing concrete desires to then expressing memories and emotions. The gesture items were taken from the UK-CDI W&G (Alcock et al. 2016) gesture section. Items were selected to ensure the full age range of eight to 18 months was represented. Procedure Participants were sent two links to complete the UK-CDI W&G questionnaire and the Parent Knowledge questionnaire. For the UK-CDI W&G questionnaire, participants were instructed to indicate whether their child could understand and say, just understand, or could not understand a word. Participants were also instructed to indicate if their child could complete a gesture or not. Upon completion of the UK-CDI W&G participants were then instructed to complete the Parent Knowledge Questionnaire. The first task on the parent knowledge questionnaire included 11 language and 11 gesture items which rendered 55 paired comparisons (in each domain). Participants were asked to select the item in each pair they believed to be more difficult, that is, to occur later in development. Following the paired comparisons task, participants were then given the 11 language and 11 gesture items individually (and randomized) and were asked to estimate the age they believe each milestone first occurs. From these procedures, the researcher calculated the parents’ accuracy at judging the difficulty of language and gesture items by correlating their ordering of items with the empirical scales using Spearman rho. Four accuracy scores were calculated for each participant: those obtained from paired-comparisons tasks for language and gesture separately and two age estimation accuracy scores from the language and gesture age checklists. The researcher also calculated two discrepancy scores for each participant, one for language and one for gesture. Each score estimated how discrepant parents’ judgements of age onsets were; These values were computed by summing the absolute differences between parents age estimates and the empirical ages of onsets as stated in the literature. Ethics After reading information about the study, parents ticked a box to give their consent to participate in this study. Ethical approval for the study was obtained from the Lancaster University Research Ethics Committee.
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.
Influence of an autobiographical memory recollection on moral decision making.
The language effect emotional decision making guilt empathy pro-social behaviour
Research shows that emotional states are involved in moral reasoning and may affect people’s decision-making processes (Achar, So, Agrawal, & Duhachek, 2016). However, in recent research emotional states were shown to be easily influenced by such factors as the language type. It was found that a stimulus presented in the native language was perceived more emotionally when compared to stimuli presented in the second language (Pavlenko, 2005). This difference in emotionality was called the language effect (Puntoni, S., Langhe, & Van Osselaer, 2009). The relationship between used language (native vs. second) and emotionality level is important as it may provide potential applications in promoting beneficial decision making and consequent behaviour. Many advertising campaigns already target emotions (i.e. empathy, guilt, regret) in order to persuade people to act by their request (Lee, Andrade, Palmer, 2013). Thus the focus of this research was to analyse the relationship between emotional language processing (native vs. second language) targeting guilt, empathy levels and how they influence the consequent behaviour (i.e. helping). A multicultural sample of 126 bilingual adults, who all speak English, as a second language, completed an online questionnaire, assessing self-reported guilt, pro-social behaviour inclination, empathy and pro-social behaviour. Results showed that no significant differences were found between two language groups, indicating the lack of language effect in the present sample. Also, the results showed that high levels of self-reported guilt were significantly and positively associated with high levels of pro-social inclination and pro-social behaviour. Empathy was shown to have the same association – high levels of empathy being associated with high levels of pro-social inclination and pro-social behaviour. Lastly, further analysis found selfreported guilt as a predictor of pro-social behaviour and pro-social behaviour inclination. Overall, this study contradicted the previous research on the language effect, but at the same time, it supported the relationship between guilt and pro-social behaviour. Based on current findings and consideration of potential limitations, future research could examine the emotional language processing and its’ influence on behaviour by targeting specific two languages and presenting text adverts as an emotional stimulus in order to control more variables and to increase applicability.
Participants Participants of this study were adults, who spoke English as a second language (N= 126, 58 males, 68 females) and ranged in age from 18 to 46. Originally there were 138 participants, but 12 were excluded from further analysis due to being the native speakers of the English language and therefore not fitting the core requirement – speaking English as a second language. Also 10 participants wrote the memory in English instead of their native language, so they were diverted to the second language condition before proceeding with the analysis. The whole sample of participants was very diverse, which consisted of 25 different nationalities and 24 different native languages (see Appendix D), the top four languages being - Lithuanian (40), Spanish (14), German (13) and Polish (13). All participants signed an online consent form and answered questions about their nationality, native language, country of residence, and English language proficiency before proceeding with the questionnaire. Materials Self-reported guilt Self-reported guilt was measured by asking participants to first – recall and describe a memory in either their native or second language (i.e. English) and then to evaluate how bad they feel about their recalled actions, how guilty they feel about those actions and how much they regret them (Nelissen, 2012). All of the three questions testing self-reported guilt were assessed by 6-point forced choice Likert scale from 1 (‘Very much’) to 6 (‘Not at all’). Before the start of analyses, the scale was reverse scored from 1 (‘Not at all’) to 6 (‘Very much’) to ensure consistency with other measures. Pro-social behaviour The pro-social behaviour was measured by asking participants, how many additional questions they would be willing to answer after completing the survey. Participants were informed that they are almost done with the survey. However, it was stated, that it would be a great help to the researchers if participants could answer some additional questions from a different survey. Participants were provided with a choice to answer from zero to 10 questions, after completing the original survey. Consequently, willingness to answer the higher number of questions was perceived as an indication of higher pro-social behaviour. Pro-social behaviour inclinations Pro-social behaviour inclination was measured using a set of five moral dilemmas from the research done by Zhang, Chen, Jiang, Xu, Wang, and Zhao (2017). This measure tested how much a person is inclined to display helping behaviour. The answers to these moral dilemmas were assessed by a forced choice Likert scale from 1 (‘Strongly disagree’) to 6 (‘Strongly agree’), which was changed from the original 7-point Likert scale to ensure consistency with the measures of the present study. Also, some moral dilemmas were adapted by changing mentioned currency from yens to pounds in order to make dilemmas more relatable for mostly UK based participants. A sample of the item measuring pro-social behaviour inclination is: ‘Your school’s foundation is raising money for children from poor areas. The money will be used to buy textbooks and writing materials for the children. You have 100 pounds to spare. Are you willing to donate the money to the student?’ Empathy Empathy was assessed by using The Short 3 Factor Version of Empathy Quotient (Muncer & Ling, 2006). The empathy measure consisted of 15 items, with a choice of answers assessed by a forced choice 6 point - Likert scale from 1 (‘Strongly disagree’) to 6 (‘Strongly agree’). The original measure was provided with a 4 point Likert scale (1-strongly disagree, 2- disagree, 3- agree, 4- strongly agree), but it was changed for the current research into 6 points Likert scale in order to ensure consistency with other measures and provide a wider range of answers. Seven items (6, 7, 8, 9, 12, 13, 14) of this empathy measure were reverse scored before the start of analysis to ensure its’ reliability and validity. A sample of the item measuring empathy is: ‘I am quick to spot when someone in a group is feeling awkward or uncomfortable.’ Procedure This study received the ethical approval from the Psychology Department Research Ethics Committee of Lancaster University. The hypotheses, method and analyses of the current study were preregistered before the collection of participants has started. The whole information about the study will be available on the Open Science Framework page. Participants were recruited using social media platforms like Facebook and Instagram and in person, inviting people to complete the survey online. The survey was distributed using an anonymous link, which directed to a Qualtrics page of the survey. Participants were presented with an information sheet and the consent form and only after signing it, they were allowed to proceed with the questionnaire. At first, participants were asked to provide some general information about themselves such as age, gender, nationality, English language proficiency and country of residence. Participants were then randomly allocated to an experimental condition (native language, second language). Then they were requested to recall a memory, when they caused someone harm and felt bad about it. They were asked to recall and describe this memory either in English or in their native language at random. Participants in the native language condition were asked to recall the memory in their native language. Participants in the second language condition were asked to recall the memory in English. After providing the memory, participants were asked to evaluate how bad, guilty and regretful they feel about their recalled actions. Afterwards, they completed the measure of pro-social behavior, by indicating how many additional questions they would be willing to answer after the current survey ends. The last part of the survey consisted of five moral dilemmas assessing pro-social behaviour inclination (see Appendix A) and the measure of empathy (see Appendix B). Overall, the survey took approximately 15 minutes to complete. At the end of the survey, participants were presented with a debrief sheet, explaining the aims and the importance of this research (see Appendix C).
Inner Speech and Grit: Do Positive Inner Speech and Evaluative Inner Speech Lead to Grit Behaviour
Inner speech, grit, articulatory suppression
Grit, defined as perseverance and passion for long-term goals, is a reliable predictor of success metrics, surpassing even IQ. While the exploration of grit has been conducted extensively, studies on the mechanisms of grit are still lacking. Inner speech, the silent production of words in one’s mind, plays a pivotal role in managing thoughts. This includes cognitive reframing, which is essential for enhancing perseverance. Theoretically, inner speech can predict grit. This study, employing a survey and experimental design, aims to investigate whether positive inner speech and evaluative inner speech can predict grit behaviour. The data for this study (n=56) were collected in two ways: (1) using the grit scale and inner speech VISQ-R via a Qualtrics survey, and (2) using participants’ task retention decisions and a qualitative classification approach. The data were analysed using R Studio. The survey data were analysed via a linear model, while the qualitative data were analysed using a generalised linear mixed-effects model. The survey results showed that only evaluative inner speech can positively predict grit. However, there were imbalanced results regarding the participants’ task retention decisions. Collectively, these findings underscore that grit can be predicted by evaluative inner speech. This prompts further research to explore its multifaceted role in shaping grit across various domains.
This study applied a mixed-method and correlational research design that aims to examine whether evaluative inner speech and positive inner speech lead to grit behaviour. The data for this study were collected using two methods: (1) questionnaires through a Qualtrics survey, and (2) an experimental task where the participants were asked to complete two sets of puzzles under different conditions (baseline and with articulatory suppression) and provide their retrospective experience after each puzzle task. Participants’ task retention decisions (decision to quit) were also recorded in the study. Three different analyses were applied in the research. For the first analysis, the positive inner speech and evaluative inner speech scores from VISQ-R acted as the predictors, and grit from the Short Grit Scale as the outcome. For the second analysis, the participant’s grit score acted as the predictor, and the participant’s task retention decision acted as the outcome. Lastly, the third analyses the types of inner speech based on the participant’s retrospective experience (positive inner speech and evaluative inner speech) acted as the predictors, and the participant’s decision to quit or not to quit was the output. In this study, the participants were students from Lancaster University, ranging from undergraduate degree students to master’s degree students and doctorate students. Participants were recruited using social networks, direct emails, and posters around the campus and/or on social media. The session took approximately 30 minutes for the data collection process, including the briefing, and each participant was reimbursed with five GBP for participating. Ethical approval for this study was submitted and approved by the ethics committees at Lancaster University. The number of participants involved in the study was 56 people in total. This number was determined by using G power. The test family was set at the t-test because this research will use a comparison between the control approach (baseline) and the experimental approach (with articulatory suppression). The effect size f2 was set at 0.15, while the α-error probability was set to 0.05 (5%) and the power 1−β error of probability at 0.8 (80%), with the number of predictors set at five. In total, 56 participants took part in the study, where the number of male and female participants was 23 (41%) and 33 (59%), respectively, and the number of native English participants in the study was 15 (27%), while non-native speakers were 41(73%). Demographic Information: The demographic information collected pertained to each participant’s attributes. This included sex (male, female, non-binary/third gender, and prefer not to say) and English native background (yes or no). Although the study has no biases towards the participant’s native language, the word used in the study ‘aluminium’, a word that is suggested by Gathercole and Baddeley (2014) for the research, may or may not influence the fluidity of pronunciation, making the articulatory suppression more challenging for non-native speakers. Varieties of Inner Speech Questionnaires Revised (VISQ-R): The VISQ-R was developed to link the everyday phenomenology of inner speech, including any psychopathological traits and inner dialogue (Alderson-Day et al., 2018). There are two versions of the Varieties of Inner Speech Questionnaire, where the original one consisted of 18 items and the revised version VISQ-R consisted of 26 items (see Appendix D) that took approximately 5-8 minutes to be completed via a Qualtrics survey. In this study, VISQ-R has been presented as internal experience questions as a dummy to the real name. This is to eliminate any possible biases by the respondents. Responses from VISQ-R can be subdivided into five dimensions and into seven scales (Not like me at all – Very much like me) for scoring: dialogical, evaluative, condensation, other people, and positive. A higher score in dialogical indicates that the person often uses inner speech to exchange ideas with oneself and vice-versa. A higher evaluative score means that the person often uses inner speech to evaluate their thoughts, actions, and decisions. For condensation, a higher score indicates that a person talks to themselves in a concise or short words manner to encapsulate complex thoughts or ideas. Meanwhile, a higher ‘other people’ score indicates that a person often imagines other people’s voices or opinions when engaging in inner speech. Lastly, a high positive score indicates that the person often uses inner speech to encourage oneself in a supportive and comforting manner. Subscale totals for each dimension were acquired by adding the scores for each subscale and dividing it by the total number of items answered across the respective subscale. The Varieties of Inner Speech Questionnaire has been supported for its reliability and validity in measuring inner speech. Racy et al. (2022) have studied the reliability of VISQ-R and compared it to six other instruments relating to inner speech. VISQ-R has moderate to strong concurrent validity with other instruments with self-evaluation showing a strong correlation with other measures. The internal consistencies and reliabilities were excellent (Cronbach’s α > .80) for each of the dimensions with only a positive dimension that is slightly lower with moderate to high test-retest reliability (>.60) (Alderson-Day et al., 2018). Short Grit Scale (Grit-S): The questionnaire of Grit-S was developed by Angela Duckworth to measure the trait level of perseverance and passion for long-term goals (Duckworth & Quinn, 2009). The Grit-S consisted of eight items of questions (See Appendix D) with four fewer items in comparison to the original version, retaining the factor structure and improving on the psychometric properties. The questionnaire needs an approximation of 3- 5 minutes to be completed in the Qualtrics survey. Similar to VISQ-R, the Grit-S questionnaire has been presented as a personality instead of a grit scale to avoid any possible biases. There are two dimensions included in the Grit-S for scoring: Consistency of Interest, where a higher scale subscale score indicates that the individual is able to maintain their interest for and focus on their long-term goal, and Perseverance of Effort, where a higher subscale score represents sustained effort towards a long-term goal despite the presence of setbacks (Van Doren et al., 2019). The subscale for the dimension of Consistency of Interest is acquired by adding the scores for all the subscale items (item-1, item-3, item-5, and item-6), while for Perseverance of Effort (item-2, item-4, item-7, and item-8). There are a few items that have been coded inversely and have been recoded before running the analysis. Several research studies have confirmed the validity and reliability of the Short-Grit Scale Instrument. Eskreis-Winkler et al. (2014) conducted a study involving predicting retention in the military where the grit instrument was used to measure the grit level of cadets. The instrument has been proven to be reliable as grittier soldiers were more likely to complete the Army Special Operation Forces (ARSOF), likely to get a job, and likely to stay married. In a more recent study by Priyohadi et al. (2019), the Grit-S again proved its validity and consistency. The internal consistencies between items in a dimension were moderate to high (>.60) for both persistence of effort and consistency of interest and have high consistencies between studies. Active Task: The jigsaw puzzle was used as the active task for this research. Two jigsaw puzzles from Livewire Puzzles were predetermined by the website as expert-level with 70 puzzle pieces (10 X 7) with an 8-minute time limitation. The puzzle can be accessed through the games.puzzle.ca website. The puzzles have been created by Arkadium, a company that is well-recognised in making online games. New puzzles have been uploaded daily, but to avoid any possible advantage or disadvantage, the puzzles used are from the 22nd of June 2023 and 21st of June 2023. Marks will also be provided at the end of each puzzle. There are two ways of measuring participants’ performance: (1) Quitting - participants were allowed to quit the task at any time during the 8-minute time limit by telling the researcher present that they want to stop, and (2) Puzzle performance - marks will be given at the end of the puzzle (marks will be given even if participants quit halfway) by the source website. The marks will be calculated based on the number of puzzles fixed correctly and then divided by the total number of unfixed puzzles and will be multiplied by the amount of time left in the puzzle. The maximum score of the puzzle is 5,000 and the minimum score is zero. All calculations will be automatically measured by the source website. The puzzle from Livewire Puzzle has also been used by other studies that focus on measuring grit using an active task. Kalia et al. (2019), similar to this study, used puzzles from Livewire Puzzle as an active task to measure perseverance in participants. Instead of using a jigsaw puzzle, Kalia opted to use sudoku to measure the role of grit and cognitive flexibility 2.4 Procedure The research took place in one-on-one sessions at the Lancaster University library. Data collection sessions were administered in the following order: demographic information, the first puzzle task, the difficulty level question, the subjective inner speech question, the second puzzle task, the second puzzle difficulty level question, and finally, the second subjective inner speech question. Each participant undertook the puzzle task in both control (baseline) and experimental conditions (with articulatory suppression). The sequence of which puzzle task they had to complete first was decided based on the participant’s subject ID assigned by the researcher. Participants with odd Subject ID numbers were assigned the control puzzle task first, while participants with even Subject ID numbers were assigned the experimental puzzle task first. Before starting the experimental puzzle task, the researcher spent a few minutes helping the participants practice performing the articulatory suppression by saying the word ‘aluminium’ repeatedly at 90 BPM using an online metronome. Throughout the experimental task, if the participants mispronounced the word too obviously or consistently missed or skipped a beat, the researcher aided them by correcting their pronunciation or assisting them to meet the 90 BPM until they matched the rhythm again. During data collection, the researcher offered participants an opportunity for a break between puzzles if they began to get tired to prevent their answers from being expedited. The participants were also allowed to ask any questions while they were completing the questionnaire to clarify their understanding of the items presented. At the end of each data collection session, the researcher thanked the participants for their participation and answered any questions that they had. The researcher also explained that participants would be emailed a participant debrief sheet and could request a summary of the study’s findings once data analyses had been completed. For participants who were eligible for reimbursement of travel expenses, they were asked to fill out a participant payment form as a receipt of confirmation that they had been paid. Three different models of analysis were carried out in the study. To measure the first prediction, a linear model was used by entering the positive inner speech and evaluative inner speech scores from the VISQ as the predictors and the grit score from the short grit scale as the output. For the second prediction, a linear model was used with the outcome set at the participant’s decision to quit or not to quit and the predictor set as the interaction between different experimental conditions and grit. To measure the third prediction, a generalised linear mixed-effect model was explored by entering the interaction of different experimental conditions and dimensions of inner speech (evaluative inner speech and positive inner speech) recorded from the participant’s retrospective experience as the predictor and participant’s decision to quit the task as the outcome. In this model, a random effect of differences between the conditions (baseline and with articulatory suppression) in slope and participants in the intercept were also included.
Inner Speech and Its Role in Purchasing Decision-Making Process: Analysis of Within-Subjects Experiment and Questionnaires
Inner speech, memory, decision-making, purchasing behaviour.
Inner speech is a cognitive function related to language processes. Based on its functions reflecting information processing and memorising, it may link to the purchasing process, which includes searching and evaluating product information. Inner speech may also help people think and imagine using the product in the future during their purchasing process. This study discussed and investigated the role of inner speech in the purchasing process and how it might affect the decision-making time. This study also mentioned how inner speech may be identified and suppressed. Participants’ data was collected through experiments and several questionnaires. The findings indicated that inner speech might help people in Information Search and Alternative evaluation and affect decision time. The findings also suggested what people may consider and how they use inner speech. By uncovering the potential relationship between the purchasing process and inner speech, this research provided valuable information for marketing and psychology research fields. It gave companies some suggestions for practical use, reflecting how people may use inner speech during the purchasing process.
This study was approved by ethics committees at Lancaster University. There were no ethical issues for researchers managing the personal information. The participants’ information remained anonymous and were assigned subject ID (P01, P02, P03…, P30 in Experiment 1 and PCT01, PCT02, PCT03…, PCT30 in Experiment 2). All data were stored anonymously with no identifiable information. Participants were given the Participant Information Sheet (PIS) before participating in the experiments. On the day of testing, they asked any questions they might have, then consented to attend the experiment in person or via online platforms like Microsoft Teams, Zoom, or Google Meet to ensure that the suppression was active when needed. The experiment took approximately 30 minutes, including answering all questionnaires. The experiment was held in the participant’s home or a place where no one spoke so that the participant would not be disturbed by any chance. Experiment 1 Participants G*power suggested 52 participants within groups using t-tests and multiple mixed linear regression models, with a .4 effect size and .05 (5%) a-error probability in 80% power (1-b error of probability) (Brysbaert, 2019). Thirty participants were recruited in this experiment with no record or history of neurophysiological disorders, such as dyslexia or aphasia, to ensure that no conditions influence the result and affect the participant to complete the tasks in the experiment. The recruitment process included in-person invitations around campus and social media messages to reach diverse participants. Although only 30 participants were recruited in this experiment, the results of the t-tests suggest that the effect size (see Experiment 1 result section) may be enough for testing the hypothesis. Design This study was an experimental within-subjects design. Participants simulated purchase experience in the suppression task and the control task without interference assigned to them. The independent variables were self-rating agreements on information search and alternative evaluation and participants’ average decision time in the suppression and control tasks. The dependent variables were inner speech frequency in five dimensions measured by the Inner Speech Frequency Questionnaire (VISQ). Quantitative data were analysed using R to conduct t-tests, GLMM and CLMM. Secondly, qualitative data were collected through questionnaires and categorised into different variables to identify why participants made the decisions and their inner speech content during the purchasing process. Overall, the experiment aims to investigate how people use inner speech during purchasing and whether Articulate Suppression task and task without interference influenced decision time and agreement score on information search and alternative evaluation. Materials Stimuli Participants viewed six product sets (stimuli), which information was copied from the official website. To prevent participants from focusing on the effect of the products’ brands and prices (Albari & Safitri, 2020), the products in each set were the same brand with similar or the same price, unisex, and recognisable, although these products might not exist or remain the latest information on the market. Two-item Statement Questions (see Appendix B) Participants rated the two statements on a seven-point Likert score from strongly disagree to strongly agree (Maity & Dass, 2014) to identify the Information Search and Alternative evaluation agreement level between tasks. Then, participants were asked: “Which product did you choose? Why?” after each purchasing decision. Variety of Inner Speech Frequency Questionnaire (VISQ, see Appendix C) The Inner Speech Frequency Questionnaire (Alderson-Day et al., 2018) included twenty questions asking participants to generally rate their inner speech frequency after the mock e-commerce purchasing tasks with a 7-point Likert scale ranging from "Never" to "All the time". Questions 7 and 15 were reversely coded; the value should be reversely calculated when doing analysis. Experiment 1 Qualitative Questions (ExpQ1, see Appendix D) After participants finished all the tasks (six decisions), they were asked to answer three questions at the end of the experiment. These questions gathered qualitative data about the participants’ experiences during the mock e-commerce purchasing tasks and what they had in mind. Procedure Figure 2 illustrates the diagram of Experiment 1. Participants were invited and consented to join the research to do Suppression and Control (without interference) tasks. Each task contained three product sets; participants were asked to imagine and choose a product for themselves or a friend according to the provided information on the mock e-commerce channel (Maity & Dass, 2014). The screen of the researcher or participants presented the information, including the price and details of the product set. Since these two tasks are counterbalanced and randomly ordered, participants repeated the decision-making process three times in the control task and the other three in the suppression task. After each decision, participants answered the two-statement questionnaire and explained which products they chose and why they chose them. According to different tasks, they started with the control task by themselves. However, they were asked to practise counting out loud from 1 to 4 following 160 bpm metronome sounds until the researcher ensured they remained suppressed before starting the suppression task. Then, they answered VISQ, which measured their inner speech frequency and qualitative questionnaires (ExpQ1) to understand how they used inner speech when viewing the products in the last part of the study. Analysis R was used to analyse the quantitative data to identify the task differences via t-tests and the relationship between variables in two tasks via Generalised Linear Mixed Effect Models (GLMM) and Cumulative Link Mixed Model (CLMM). When conducting the GLMM with family gamma, the quantitative data will follow the standard procedure of data trimming and keep the trimmed data within 5% or 2.5 standard deviations (Berger & Kiefer, 2021). The qualitative coding scheme (See Appendix F) was created to identify what participants considered and what they said to themselves using inner speech during the experiment. The coding process involved re-reading the data to identify and assign relevant contexts to the appropriate categories. For example, if participants mention that they have used the product before, the value of the variable “Memory” increases by one unit. These variables were then calculated to identify what factors influenced participants’ purchasing decision-making more. Following the same coding scheme, what kind of inner speech was used when viewing the products could also be found. For example, people may ask themselves questions or repeat the product in mind. In summary, Quantitative and qualitative data were analysed to report the results for different purposes and test the hypothesis in this research. Figure 2 The Diagram of Experiment 1 Procedure Note: Participants were required to do suppression and control tasks, the order was randomised and counterbalanced. The products presented during the tasks were also randomised. Experiment Optimising The task without interference in Experiment 1 may not be a reasonable control task since it might include the secretary task effect, as participants were asked to do both tasks and be influenced after they did the suppression task when they were doing the control task. As a secretary task, the finger-tapping task, which has been used in inner speech experiments, could be the better control task in Experiment 2 (Emerson & Miyake, 2003; Wallace et al., 2009). Although Finger-tapping might influence working memory’s function and influence people to memorise (Armson et al., 2019; Kane & Engle, 2000; Moscovitch, 1994; Rose et al., 2009), Rogalsky et al. (2008) also mentioned that the performance of people’s understanding of complex sentences might decrease but not as much as suppression occur. Therefore, doing the second experiment was motivated to replicate the results with a better control condition involving Finger-tapping. Experiment 2 Participants Based on the findings of Experiment 1, another 30 participants were recruited with the duplicate requirements as the first experiment. The recruitment requirement and process were the same as in the previous experiment. Design The independent variables were similar to Experiment 1, while the only difference was that the control task here had been changed into the Finger-tapping task. The goal of the whole design is to replicate the results of Experiment 1 to investigate the role of inner speech in the purchasing process. Materials Experiment 2 applied the same materials used in Experiment 1. The only difference was the qualitative questions after tasks. In Experiment 1, participants answered “Experiment 1 Qualitative Questions” at the end of the experiment. However, to better understand the difference between tasks, they were asked to answer a similar questionnaire (see below) after each task to discover the inner speech used in the two tasks. Experiment 2 Qualitative Questions (ExpQ2, see Appendix E) Participants were asked to answer three questions about their experiences during the mock e-commerce purchasing tasks and what they had in mind for the Suppression and Finger-tapping tasks separately. Procedure The procedure was the same as the first experiment, except for adjusting the control task and the order of the qualitative questionnaire (ExpQ2). Figure 3 illustrates that participants were invited to the experiment using the same stimuli, similar questionnaires, and the same method of presenting stimuli (participants joined in person or via online platforms) with Suppression and Finger-tapping tasks. Participants were asked to practice counting 1,2,3,4 out loud or tapping their index, middle, ring, and little fingers in order (see which task came first) following metronome beats at 160 bpm before the researchers decided to move on. They were asked to view the product set by imagining choosing one for a friend or themselves three times in each task. Participants answered two statements and answered what product was chosen and why after each decision they made. Then, they were asked to answer three Qualitative questions (Appendix E) after each task. They repeated another task in the same process afterwards with a 2-minute break between tasks. After they finished the Finger-tapping and Suppression tasks, they answered VISQ questions at the end of the experiment. Analysis R was also used to analyse the quantitative data for the same purposes and followed the same data-trimming procedure if needed. The same coding scheme was followed to generate the result that could replicate and optimise the clarity of the Experiment 1 results. Overall, the second experiment is to generate the same or more evident results as Experiment 1 and to find more valuable information for the different inner speech used between tasks. In conclusion, these two experiments and the analysis might give this research a deeper understanding of inner speech and its role and provide more precise information on how inner speech may related to the purchasing process. Figure 3 The Diagram of Experiment 2 Procedure Note: Participants were required to do suppression and control tasks, the order was randomised and counterbalanced. The products presented during the tasks were also randomised.
inner speech, purchasing behaviour, memory, decision-making.
Methods Section: This study was approved by ethics committees at Lancaster University. There were no ethical issues for researchers managing the personal information. The participants’ information remained anonymous and were assigned subject ID (P01, P02, P03…, P30 in Experiment 1 and PCT01, PCT02, PCT03…, PCT30 in Experiment 2). All data were stored anonymously with no identifiable information. Participants were given the Participant Information Sheet (PIS) before participating in the experiments. On the day of testing, they asked any questions they might have, then consented to attend the experiment in person or via online platforms like Microsoft Teams, Zoom, or Google Meet to ensure that the suppression was active when needed. The experiment took approximately 30 minutes, including answering all questionnaires. The experiment was held in the participant’s home or a place where no one spoke so that the participant would not be disturbed by any chance. Experiment 1 Participants G*power suggested 52 participants within groups using t-tests and multiple mixed linear regression models, with a .4 effect size and .05 (5%) a-error probability in 80% power (1-b error of probability) (Brysbaert, 2019). Thirty participants were recruited in this experiment with no record or history of neurophysiological disorders, such as dyslexia or aphasia, to ensure that no conditions influence the result and affect the participant to complete the tasks in the experiment. The recruitment process included in-person invitations around campus and social media messages to reach diverse participants. Although only 30 participants were recruited in this experiment, the results of the t-tests suggest that the effect size (see Experiment 1 result section) may be enough for testing the hypothesis. Design This study was an experimental within-subjects design. Participants simulated purchase experience in the suppression task and the control task without interference assigned to them. The independent variables were self-rating agreements on information search and alternative evaluation and participants’ average decision time in the suppression and control tasks. The dependent variables were inner speech frequency in five dimensions measured by the Inner Speech Frequency Questionnaire (VISQ). Quantitative data were analysed using R to conduct t-tests, GLMM and CLMM. Secondly, qualitative data were collected through questionnaires and categorised into different variables to identify why participants made the decisions and their inner speech content during the purchasing process. Overall, the experiment aims to investigate how people use inner speech during purchasing and whether Articulate Suppression task and task without interference influenced decision time and agreement score on information search and alternative evaluation. Materials Stimuli Participants viewed six product sets (stimuli), which information was copied from the official website. To prevent participants from focusing on the effect of the products’ brands and prices (Albari & Safitri, 2020), the products in each set were the same brand with similar or the same price, unisex, and recognisable, although these products might not exist or remain the latest information on the market. Two-item Statement Questions (see Appendix B) Participants rated the two statements on a seven-point Likert score from strongly disagree to strongly agree (Maity & Dass, 2014) to identify the Information Search and Alternative evaluation agreement level between tasks. Then, participants were asked: “Which product did you choose? Why?” after each purchasing decision. Variety of Inner Speech Frequency Questionnaire (VISQ, see Appendix C) The Inner Speech Frequency Questionnaire (Alderson-Day et al., 2018) included twenty questions asking participants to generally rate their inner speech frequency after the mock e-commerce purchasing tasks with a 7-point Likert scale ranging from "Never" to "All the time". Questions 7 and 15 were reversely coded; the value should be reversely calculated when doing analysis. Experiment 1 Qualitative Questions (ExpQ1, see Appendix D) After participants finished all the tasks (six decisions), they were asked to answer three questions at the end of the experiment. These questions gathered qualitative data about the participants’ experiences during the mock e-commerce purchasing tasks and what they had in mind. Procedure Figure 2 illustrates the diagram of Experiment 1. Participants were invited and consented to join the research to do Suppression and Control (without interference) tasks. Each task contained three product sets; participants were asked to imagine and choose a product for themselves or a friend according to the provided information on the mock e-commerce channel (Maity & Dass, 2014). The screen of the researcher or participants presented the information, including the price and details of the product set. Since these two tasks are counterbalanced and randomly ordered, participants repeated the decision-making process three times in the control task and the other three in the suppression task. After each decision, participants answered the two-statement questionnaire and explained which products they chose and why they chose them. According to different tasks, they started with the control task by themselves. However, they were asked to practise counting out loud from 1 to 4 following 160 bpm metronome sounds until the researcher ensured they remained suppressed before starting the suppression task. Then, they answered VISQ, which measured their inner speech frequency and qualitative questionnaires (ExpQ1) to understand how they used inner speech when viewing the products in the last part of the study. Analysis R was used to analyse the quantitative data to identify the task differences via t-tests and the relationship between variables in two tasks via Generalised Linear Mixed Effect Models (GLMM) and Cumulative Link Mixed Model (CLMM). When conducting the GLMM with family gamma, the quantitative data will follow the standard procedure of data trimming and keep the trimmed data within 5% or 2.5 standard deviations (Berger & Kiefer, 2021). The qualitative coding scheme (See Appendix F) was created to identify what participants considered and what they said to themselves using inner speech during the experiment. The coding process involved re-reading the data to identify and assign relevant contexts to the appropriate categories. For example, if participants mention that they have used the product before, the value of the variable “Memory” increases by one unit. These variables were then calculated to identify what factors influenced participants’ purchasing decision-making more. Following the same coding scheme, what kind of inner speech was used when viewing the products could also be found. For example, people may ask themselves questions or repeat the product in mind. In summary, Quantitative and qualitative data were analysed to report the results for different purposes and test the hypothesis in this research. Experiment Optimising The task without interference in Experiment 1 may not be a reasonable control task since it might include the secretary task effect, as participants were asked to do both tasks and be influenced after they did the suppression task when they were doing the control task. As a secretary task, the finger-tapping task, which has been used in inner speech experiments, could be the better control task in Experiment 2 (Emerson & Miyake, 2003; Wallace et al., 2009). Although Finger-tapping might influence working memory’s function and influence people to memorise (Armson et al., 2019; Kane & Engle, 2000; Moscovitch, 1994; Rose et al., 2009), Rogalsky et al. (2008) also mentioned that the performance of people’s understanding of complex sentences might decrease but not as much as suppression occur. Therefore, doing the second experiment was motivated to replicate the results with a better control condition involving Finger-tapping. Experiment 2 Participants Based on the findings of Experiment 1, another 30 participants were recruited with the duplicate requirements as the first experiment. The recruitment requirement and process were the same as in the previous experiment. Design The independent variables were similar to Experiment 1, while the only difference was that the control task here had been changed into the Finger-tapping task. The goal of the whole design is to replicate the results of Experiment 1 to investigate the role of inner speech in the purchasing process. Materials Experiment 2 applied the same materials used in Experiment 1. The only difference was the qualitative questions after tasks. In Experiment 1, participants answered “Experiment 1 Qualitative Questions” at the end of the experiment. However, to better understand the difference between tasks, they were asked to answer a similar questionnaire (see below) after each task to discover the inner speech used in the two tasks. Experiment 2 Qualitative Questions (ExpQ2, see Appendix E) Participants were asked to answer three questions about their experiences during the mock e-commerce purchasing tasks and what they had in mind for the Suppression and Finger-tapping tasks separately. Procedure The procedure was the same as the first experiment, except for adjusting the control task and the order of the qualitative questionnaire (ExpQ2). Figure 3 illustrates that participants were invited to the experiment using the same stimuli, similar questionnaires, and the same method of presenting stimuli (participants joined in person or via online platforms) with Suppression and Finger-tapping tasks. Participants were asked to practice counting 1,2,3,4 out loud or tapping their index, middle, ring, and little fingers in order (see which task came first) following metronome beats at 160 bpm before the researchers decided to move on. They were asked to view the product set by imagining choosing one for a friend or themselves three times in each task. Participants answered two statements and answered what product was chosen and why after each decision they made. Then, they were asked to answer three Qualitative questions (Appendix E) after each task. They repeated another task in the same process afterwards with a 2-minute break between tasks. After they finished the Finger-tapping and Suppression tasks, they answered VISQ questions at the end of the experiment. Analysis R was also used to analyse the quantitative data for the same purposes and followed the same data-trimming procedure if needed. The same coding scheme was followed to generate the result that could replicate and optimise the clarity of the Experiment 1 results. Overall, the second experiment is to generate the same or more evident results as Experiment 1 and to find more valuable information for the different inner speech used between tasks. In conclusion, these two experiments and the analysis might give this research a deeper understanding of inner speech and its role and provide more precise information on how inner speech may related to the purchasing process.
Interacting in a Virtual Environment, the role of visual perception, the human hand and the recognition of rescaling.
Visual Perception Rescaling effects Virtual Reality Hand-size Constancy Body size effects.
A common assumption from perception research is that we can estimate the size of the environment by using our own hand as a perceptual metric by comparing the size of our hand to the environment. Further research aimed to explore this effect by manipulating the size of the hand to see if it could accurately estimate the size of objects and found that even when the hand was magnified or minimized people perceived their hand to stay around the same size. The effect that the hand is perceived as a constant size is called the hand-size constancy effect and the current research has aimed to expand on previous research by examining if hand-size constancy still occurs even when hand size increases whilst in the presence of the participant. This research was done using a new method which eliminates more demand characteristics than previous hand-size constancy research. Participants took part in a virtual scenario using virtual reality in which each time a participant attempted the task, their hand or non-corporeal hand gradually increased in size, until a total of 38% size increase. Results from this research found that participants did recognise their hand size increase in the non-corporeal condition and did not notice hand size change in the real hand condition. These results support previous research by finding that hand size constancy can still occur even when eliminating demand characteristics that may have occurred in previous research using a more discrete method.
Participants. The participants were 30 typically developing adults between the ages of 19 and 50 (N = 30, 12 male and 18 female, M = 24.39 years old, SD = 7.76 years). Participants were mainly recruited from a major university in the North West of England using posters on the university campus and online advertisements. Participants received £5 in return for their participation. Materials The current research used the Oculus Rift with leap motion to detect hand movement. The experiment was created using the Unity game engine software to create a programme called the virtual bowling alley. The virtual bowling alley was created to mimic a real table top bowling alley in which all the items in the game were created for this experiment including the bowling ball, the pins and the virtual hand used in the experiment. Two questionnaires were essential to this study, “The Embodiment Questionnaire” and “The Virtual Presence Questionnaire”. The Embodiment questionnaire was an adaptation from Sanchez-Vives’ research which explored visual hand illusions (Sanchez-Vives, Spanlang, Frisoli, Bergamasco, & Slater, 2010). The embodiment questionnaire was used to test the extent of different variables the participant may exhibit whilst within virtual reality. Variables related to ownership of limbs in virtual reality e.g. “I sometimes felt as if my hand was located where my virtual hand was to be” the illusion of movement which looked at how much your virtual arm impacted on the movement of your real arm, validity which looked at how your movements impacted on your virtual arm and control regarding how much control you had of your virtual arm. The Embodiment Questionnaire uses a 7-point Likert scale in which you rate how much you agree with the statement (Appendix 1). The other questionnaire required for this study was “The Virtual Presence Questionnaire” which was an adapted version from Usoh’s paper looking at presence questionnaires (Usho, Catena, Arman, & Slater, 2000). The virtual presence questionnaire was used to examine how much the participant rated their immersion within the virtual scenario. Rating of virtual immersion was done through questions which examined whether their sense of immersion whilst being within the virtual scenario was stronger than their sense of place in their actual location within the virtual reality lab. For example, questions like “To what extent were there times the virtual bowling scenario was reality for you” was used to examine immersion and presence within virtual reality. The virtual presence questionnaire also used a 7-point Likert scale (1 = disagree, 7 = agree) (Appendix 2). Other materials required were a calculator to count the amount of bowling pins knocked down each attempt and to total the amount of bowling pins knocked down per participant. All the appropriate ethical documentation was also required (Information sheet, Virtual Reality health and safety sheet, consent document and debriefing sheet). Procedure After the 30 participants required for the study were obtained, the participants were asked to sign a digital calendar in which they selected which day they were free to take part in the study with the promise of a 30-minute experiment and £5 reward for taking part. Participants were advised to arrive to the lab 10 minutes prior to the study and when they did arrive they were greeted by the researcher at the door of the lab. After a short introduction, the participant was then sat down at a table with some documents and writing equipment. The participant was asked to look at the study information sheet first, this sheet contained contextual knowledge about the study regarding the task that they would get involved in. After the participant stated that they understood all the information on the information sheet then they were given the ethics consent form to sign. The ethics consent form contained all the participants ethical rights (right to withdraw, anonymisation of the data etc…), the participants were advised to carefully read through the sheet to make sure they understood their ethic rights and asked to sign their name, age and date on the ethic sheet. Also, on a separate piece of paper, the researcher noted the participants participant number which was used to code the data anonymously. When the participant completed the ethics consent form, they were told that the experiment would now begin. The participant was escorted to a different desk with a computer set up on the desk. Noticeably, the computer was set up in a way in which the chair was at a set distance from the oculus rift sensor to allow for full immersion. The participant was sat down on the chair that was at the set distance away from the computer, in which the computer was set to the home screen and the researcher assisted the participant in putting on the oculus rift head mounted display (HMD), the HMD had a hand sensor attached to the front of it to detect hand movement. When the participant was sat down, they were asked to confirm that they were comfortable with the HMD on and that they could see clearly. When the participant gave consent, they were told that they were going to enter the virtual bowling alley now, the virtual bowling alley was an in-house created virtual scene used for this experiment. The virtual bowling alley was created in the unity engine using C++ to create virtual objects such as the pins and ensuring they had an interaction engine script attached to them to give them physics. The virtual bowling alley was a table top bowling simulation, created with the intention that there would be a lot of hand exposure during the experiment as the participants would have to use their hands to push the ball and knock over the pins Participants were assigned to 1 of 2 groups; the hand group or the non-corporeal hand group. The group the participant was assigned to impacted on what type of hands they would have during the virtual bowling scenario, for example, when entering the virtual bowling alley in the hand group your hands would be regular virtual hands that are created to mimic real hands. (Figure 2). Participants who were assigned to group 2 (the non-corporeal hand group) when entering the virtual bowling alley, they would see blocks in place of their hands, these block hands were created to replace their hands in virtual reality with objects that could complete the same tasks that a hand could, but did not represent the hand in any way, a non-corporeal hand. When participants entered the virtual bowling scenario and confirmed that they were calibrated to the bowling scenario (their visual view point was correct, and they could move their hands around accurately) then they were told they had 20 attempts to knock down as many pins as they could, with 10 pins an attempt this means there was a total of 200 pins. Each time an attempt was completed by the participant, the experiment would press a key on the keyboard which reset the pins and the bowling ball for the participant. Each time the virtual bowling alley attempt was reset the participants hand (group 1) or cubes (group 2) increased in size by approximately 2% per bowling task attempt until they completed their 20 attempts in which their hand/ non-corporeal hand would have increased in size by 38%. Also, it is worth mentioning that each time the bowling ball attempt was completed, and the alley was reset the bowling ball would randomly change from bigger to smaller sizes (10 different sizes per experiment between 50% increase in size and 50% decrease in size, twice per size). The changes in the ball size were required so that participants did not use the bowling ball as a reference of scale to compare to their change of size in hands or cube hands (non-corporeal hands). When the participant completed the 20 attempts of the bowling task, the virtual bowling programme would automatically exit, and the participant was asked to take off the HMD and escorted back to their first seat which was the table they completed their consent form. The experimenter also made note on a separate sheet of the participants total bowling pins knocked down out of 200. When the participant was sat down at the table the experimenter would then hand the participant a sheet with 2 questions on it. Question 1: “Did you detect any changes whilst in the virtual environment?” this is a yes or no response. After the participant answered question 1 they were then asked question 2 “If hand size was manipulated would you estimate your hand changed in size or not?”. The response for question 2 would also be a yes or no response, it is worth noting that if the participant did respond with “yes” to question 2, then the researcher asked them if they estimated if hand size increased or decreased in which the experimenter would ask the participant to note this response underneath question 2. After they answered the 2 questions regarding the virtual bowling alley the participant would then be handed 2 more documents both being questionnaires. The participant would be asked to firstly fill out the virtual presence questionnaire and then the virtual embodiment questionnaires, they were also told if they had any questions regarding the questionnaires they could ask at any time. After the participant confirmed that they were happy with their responses to the questions and completed all the questions then the experimenter passed the participant a debrief sheet which gave more context to the experiment and was very explicit about the participants hand changing in size over time. The participant was asked if they had any questions regarding the experiment, if they did the researcher happily answered them, if not, then the researcher would thank the participant for their time. When all the results were collected from the 30 participants, the data was stored on a locked private computer in which only the experimenters had access to. All documents regarding the experiment were also locked in a storage cabinet which was under lock and key. The independent variable in this study was hand type (hands vs non-corporal hands) and the dependent variable in this study was the response to the questions regarding the virtual bowling scenario (question 1 and 2). Due to the nature of the dependent variables data a Chi-Square was used as nominal data was collected on 2 independent groups. Other data regarding age, gender, handedness, virtual presence scores and virtual embodiment scores were also analysed using independent t-tests.