Do trustworthiness judgements help people to recognise synthetic faces?
StyleGAN, synthetic face, trustworthiness perception, facial trustworthiness
Recent advances in digital image generative models have allowed for artificial creation of fake imagery such as synthesising highly photorealistic human faces. Style-based Generative Adversarial Networks (StyleGAN) is one of the most state-of-the-art generative models in this field, and has been widely used on facial image generation. However, with the increasing ease of using such image generative models, the security in many domains, such as forensic, border control and mass media, is vulnerable in front of the potential threats resulted from the misuse of image generative technologies. To date there has only been limited empirical research into the facial characteristics of StyleGAN-generated faces to support the design of detection methods against such synthetic faces. This study used StyleGAN2 (an improved version of StyleGAN) to generate faces and invited people to complete two facial image evaluation tasks, 1) Discrimination task, 2) Trustworthiness rating task. The study results demonstrated that, in the discrimination task, subjects had trouble recognising synthetic faces by direct/explicit judgement; while in the trustworthiness rating task, subjects perceived the synthetic faces as significantly more trustworthy than real faces. The study further analysed gender bias and ethnicity bias on the perception of facial trustworthiness, with results showing some differences between different levels of gender and ethnicity. In conclusion, people’s ability to recognise synthetic faces is poor, but it is possible that people rely on the perception of facial trustworthiness to discriminate synthetic from real faces. The findings in this study have implications for the development of detection methods against digitally generated faces.
Subjects and design Three hundred and fifty-seven subjects (114 males, mean age = 25.2, SD = 5.8; 227 females, mean age = 25.0, SD = 6.3; 10 non-binary, mean age = 23.6, SD = 8.93) were recruited to complete an online survey test delivered on www.qualtrics.com. The responses of subjects who started but did not complete the online survey were eliminated to avoid distorting the research results. We used computer-synthesised facial images in this research as fake faces, mixed with real faces to examine people’s ability to detect fake faces and perceptual differences of trustworthiness between real/fake faces. Subjects did not get rewards for their participation, though they could see the test score of their performances at the end of the survey. The Qualtrics survey was based on a within-subjects design in which all subjects viewed the same two sets of adult facial images and completed each of the two tasks. To eliminate the effect of between-sets difference, the use of each image sets was counterbalanced in the individual test for each subject. Before the survey started, all subjects provided informed consent and completed a demographic questionnaire about their age, gender, ethnicity. In terms of the experimental power of 0.8 and significance level of 0.05, with a small effect, the power calculation indicated that the study needed at least 198 subjects. Stimuli A total of thirty-two human facial images (1024×1024 resolution), including 16 real and 16 synthetic faces, were used as stimuli in the survey. All real faces were taken from a publicly available dataset for high-quality human facial images, Flickr-Faces-HQ (FFHQ), which is created as a benchmark for GAN (see https://github.com/NVlabs/ffhq-dataset), and all synthetic faces were gained from the dataset of the generative image modeling, StyleGAN2 (see https://github.com/NVlabs/stylegan2). To ensure a diverse dataset, in each of the two sets of faces, there were 4 Black, 4 East Asian, 4 South Asian, 4 White, and 2 males and 2 females for each ethnicity. Among the sixteen faces of each set, half of them were real and half were synthetic, but this was unknown to subjects. Procedure First, subjects completed a short questionnaire for demographic information (age, gender, ethnicity), and subjects had to be 18 years of age or older to take part. Prior to the main body of test, there was an example of real and synthetic faces presented to provide subjects with a general impression of what real and synthetic faces look like. Subjects then were asked to complete two face evaluating tasks, 1) Discrimination Task, 2) Trustworthiness Rating Task. The two tasks were presented to subjects in a counterbalanced order to check for any possible order effects. Before the start of each task, participants were informed that they would see a series of 16 facial images, and that they had to carry out their evaluation following the instructions provided. In both tasks, only one image was presented at a time and individual images appeared in a random order. In the discrimination task, participants made their decision between two choices, “real” or “synthetic”, to classify the 16 faces according to whether they thought the presented faces were real or not. Subjects did not receive immediate feedback during the task on the correctness of their classifications. In this task, subjects relied on direct/explicit judgments. In the trustworthiness rating task, subjects were required to rate how trustworthy they thought each of 16 faces looked using a 7-point Likert scale (1 = extremely untrustworthy; 4 = neither untrustworthy nor trustworthy; 7 = extremely trustworthy). We instructed subjects that they did not need to consider face authenticity in this task, and they could just assume that the faces shown to them were all of real people. Although there was no time limit to respond for trustworthiness rating, we encouraged subjects to rely on their intuitions and provide their responses to work as quickly as possible. In this task, we expected to trigger a relatively indirect/implicit approach to evaluate faces as compared to direct/explicit judgement on face authenticity, specifically by trustworthiness perception. At the end of the survey, subjects saw a result report of their own mean trustworthiness rating scores for real and synthetic faces, and their mean accuracy in classifying real and synthetic faces in the discrimination task.
Does Advertising Truly Represent the LGBTQ+ Community? An Analysis of Intersectionality and Consumer Responses to LGBTQ+ Advertising
LGBTQ+ advertising, prosocial advertising, intersectionality, consumer attitudes
Depictions of sexual and gender minorities in advertising are becoming increasingly common and diverse. Yet, numerous intersections within these portrayals are still invisible. Previous research has found mixed results regarding consumer responses to LGBTQ+ identities in advertising. The current study aimed to obtain a further understanding into how a diverse range of consumers respond to heteronormative versus LGBTQ+ imagery in ads. This was assessed using semi-structured interviews to examine sexual and gender minority consumer (n = 13) and non-LGBTQ+ consumer (n = 6) reactions to three distinct IKEA ads. In addition to this, LGBTQ+ character depictions in 286 worldwide mainstream ads from 2016-2020 were analysed for measures of intersectionality across the dimensions of race, age and specific LGBTQ+ membership, extending the previous findings of Nölke (2018). Results indicated that non-LGBTQ+ participants showed similar responses and subsequent brand evaluation regardless of ad theme. Sexual and gender minority participants were found to show preference towards the ad featuring LGBTQ+ identities, though were often found to be sceptical of such portrayals. Intersectionality analysis uncovered that 47 out of a possible 96 intersections were completely invisible from 2016-2020, although representation of minorities within the community has increased substantially since the original findings. Results demonstrate the importance of character depictions in advertising, highlighting why intersectionality of such portrayals needs to increase in the future. Findings further denote how and why different consumers react to specific ad imagery, making recommendations to marketers regarding their inclusion of LGBTQ+ identities in advertising.
Participants The sample consisted of 19 participants aged between 18-53 at their time of interview; Mage = 23.5 years, SDage = 7.6. Of this sample, 13 participants stated that they identified as LGBTQ+ (2 White lesbian females, 1 Mixed-Race lesbian female, 2 White gay males, 1 Asian gay male, 2 White bisexual females, 1 Black bisexual female, 1 Asian bisexual male, 1 White transgender female, 1 White transgender male and 1 White transgender non-binary individual). A further 6 participants stated that they did not identify as LGBTQ+ (3 White males and 3 White females). Participants were recruited in a purposive manner through social media sites such as Instagram and WhatsApp and comprised mainly of acquaintances of the researcher. A high proportion of LGBTQ+ participants were utilised in an effort to ensure intersectionality of responses, which has been shown to provide a strong methodological framework within which to investigate underrepresented groups (Rodriguez, 2018). Design The study consisted of two distinct elements; semi-structured interviews and a content analysis of existing global advertisements that feature LGBTQ+ characters from 2016-2020. Semi-structured interviews were the chosen method of qualitative data gathering, as the style allows for analysis according to the basis of grounded theory (Glaser & Strauss, 1967) and gives scope for probing questions to supplement the richness of answers given. The combination of quantitative (quantified content analysis) and qualitative research employed in the study through separate investigations was undertaken in attempt to provide a rigorous understanding of LGBTQ+ diversity in advertising and its effects upon observers. Interviews All participants individually took part in an in-depth semi-structured interview with the researcher over Microsoft Teams. Due to the inductive nature of the exploration, no independent and dependent variables were implemented. The research broadly assessed the following measures across populations in the sample: the importance of character depictions, prosociality views, representation significance, brand attitudes, and purchasing likelihood succeeding exposure to three IKEA advertisements. The same brand was used for all ads in order to eliminate brand biases. The order in which advertisements were shown to participants was random in an effort to counterbalance order effects. Each interview lasted for approximately 45 minutes. Ad Intersectionality This additional component of the study involved conducting a content analysis of all global advertisements that feature LGBTQ+ identities from 2016-2020. This design mirrored that initially used by Nölke (2018), continuing their longitudinal analysis of intersectionality in LGBTQ+ advertising depictions which scoped the years 2009-2015. Identical to the original study, the source for these ads was AdRespect (http://adrespect.org), a website which comprehensively includes any advertisement featuring LGBTQ+ inclusion from around the world. The independent variable was time, as adverts which aired within each individual year were grouped together. Dependent measures included counts of different intersectionality measures, an approach first used by Gopaldas & DeRoy (2015) in their intersectional analysis of Gentlemen’s Quarterly covers, and were further investigated by Nölke (2018). In the present study these measures consisted of age, race and specific LGBTQ+ membership. Measures Interviews The semi-structured interview completed by each participant was devised entirely by the researcher and involved seven different sections which addressed questions surrounding the significance of character portrayals in advertising. The interview primarily consisted of open-ended questions, though some close-ended questions were also asked where definitive answers were required. Questions often had multiple sub-questions within them in order to probe more detailed responses from participants. In total the interview asked 31 unique questions, with nine of these questions repeated three times (in sections four, five and six). The first section was an overview which told participants what the interview would entail whilst it also asked general ad watching questions to prime the interviewee for more detailed questions to follow. An example question from section one includes “would you say in general that you watch many ads?”. Section two was focused on the participant’s views towards representation in advertising, particularly focusing on LGBTQ+ representation and its significance to them. Example questions include: “if you are to view an advertisement that openly features LGBTQ+ identities, how would it make you feel?” and “do the character depictions in adverts matter to you? What characteristic(s) are most significant to you? Why is this?”. The third section addressed identity formation, asking interviewees questions about advertising from when they were growing up in an attempt to investigate the impact of negligible LGBTQ+ depictions in the past. It asked questions including: “Do you ever remember seeing LGBTQ+ identities in advertising when you were younger? How did this make you feel?”. In addition to this, it attempted to gain an understanding of how characters in advertising impact the formation of identity from a retrospective viewpoint. The subsequent three sections all asked the same set of questions to participants after showing them three different IKEA adverts in a random order (https://bit.ly/2VkBCQs), (https://bit.ly/3yHXouV) and (https://bit.ly/2WVyElD). All ads were published to mainstream audiences on television by IKEA within the past two years and were matched closely in terms of length. The first ad (Ads of Brands, 2020) titled ‘next generation’ featured only heteronormative White characters, within a nuclear family unit. It was selected as it acted as a non-representative example which showcased very little intersectionality and no LGBTQ+ identities. The second ad ‘change a bit for good’ (IKEA UK, 2021) displayed identity neutral robots who attempt to tackle climate change. This ad acted as a control for participants, as it still addresses a prosocial topic whilst portraying no identifying elements of its characters. The final ad ‘be someone’s home’ (IKEA USA, 2020) showed a wide variety of diversity across intersections within the LGBTQ+ community, which functioned as an inclusive example to interviewees. Questions asked after exposure to each ad included items assessing the participant’s attitude towards the brand, their subsequent purchasing intentions and the believed importance of the identities portrayed. Example items include: “after watching this ad, would you feel more or less inclined to spend money with IKEA? Why is this?” and “do you believe the identities shown in the ad are important to others? Why do you think this?”. The last component of the interview asked participants about their general spending behaviour, brand evaluation and concluding questions about how LGBTQ+ visibility in advertising makes them feel. Sample items include: “would seeing an ad that positively depicts someone similar to you make you value the brand more? How come? Would this also make you more likely to buy?” and “is there anything that you would like to change in modern advertising? Less of something? More of something? Why?”. Ad Intersectionality Coding Scheme. The present study followed the coding scheme of Nölke (2018), but chose to exclude class as a coding dimension, due to an absence of representation in this area. The coding dimensions analysed within the study were LGBTQ+ membership, age and race. Each portrayal was coded across all three dimensions. LGBTQ+ Membership. Items within this dimension were coded accordingly: ‘lesbian female’, ‘gay male’, ‘bisexual’, ‘trans-female’ (MtF) which included drag queens, ‘trans-male’ (FtM) and ‘gender neutral/non-binary’. Nölke (2018) did not code gender neutral or non-binary identities due to the absence of such portrayals. The current study implemented this additional measure as it saw the need to recognise the additional membership which is becoming increasingly prevalent in modern depictions. Transgender depictions were either explicitly labelled as such within the ad, overtly presented (for example, in terms of top-surgery scarring) or for celebrity depictions, publicly accessible data on their identity was used. Gender neutral/non-binary coded characters were either stated as such within the ad, their gender was indiscernible, or in celebrity cases, publicly available information on their identity was again utilised. Age. Based upon Gopaldas & DeRoy’s (2015) scheme, age was determined by estimations to the nearest multiple of five based upon observation. The following codes were used: “teen” (aged 13+), “young adult” (20+), “middle-aged” (35+) and “mature” (50+). Race. The race of characters was coded according to visual appearance, language and ad text. Codes included “White”, “Black”, “Asian” and “Latinx”. It is important to note that these terms differ from those used by Nölke (2018), in accordance to APA’s guidance on inclusive language regarding racial and ethnic identity (American Psychological Association, 2019). Procedure Ethical approval for this study was acquired through the project supervisor and ethics partner at Lancaster University, as the proposed research was deemed low risk. Interviews Participants were each given an electronic information sheet, consent form and short demographic questionnaire which included LGBTQ+ membership status questions to complete through Qualtrics (https://www.qualtrics.com). To ensure participants were comfortable, all questions in this form were optional to answer. After consent was obtained, participants were contacted to arrange a suitable interview date and time, which was conducted via Microsoft Teams. During each interview, the researcher asked questions according to the interview schedule in a semi-structured manner. These interviews were recorded and transcribed for analysis. Throughout the interviews, participants were reminded that they did not need to answer any questions that they did not want to and that they were free to leave at any point should they wish. Any identifying data was removed during transcription to maintain participant confidentiality. After interviews had finished, all participants were sent a debriefing form via email. Ad Intersectionality Ad Selection. Ads published between 2016-2020 on AdRespect were selected according to the same principles utilised by Nölke (2018). To begin, the 531 ads submitted to AdRespect during the years 2016-2020 were evaluated. AdRespect states the audience in which each ad was published to and those that were exclusively published to LGBTQ+ audiences were excluded from analysis. Additionally rejected from analysis were ads where the character’s LGBTQ+ status was not evident, ads that showed no explicit depiction of people and ads for non-profit organisations. This exclusion criteria left 284 ads. As AdRespect is a crowdsourced platform, a further search for ads that met the inclusion criteria was conducted across the internet in case any were left out by the online database. This search found a further two ads, producing a total of 286 ads within the final dataset. These ads were then coded according to the dimensions of age, race and LGBTQ+ membership. Ads were coded for every LGBTQ+ portrayal shown, thus often multiple characters were displayed within each ad and were analysed per individual depiction. Analysis Qualitative Analysis of Interviews After transcription, all interviews were analysed through inductive thematic analysis due to the exploratory nature of the research (Braun & Clarke, 2006). This process adhered to their six phases of analysis: familiarization of the data, initial code generation, theme search, theme review, defining and naming themes and report production, which allowed the researcher to identify the themes that underpin consumer responses and attitudes towards LGBTQ+ portrayals. This analysis was conducted through NVivo 12 qualitative data analysis software. Quantitative Analysis of ad Intersectionality Quantitative analyses of the dataset were conducted through collation of codes ascribed to portrayals across time. The depictions were summarised across intersectional and unidimensional measures according to which year they belonged to. This was analysed as a singular project as well as comparatively against the original findings from Nölke (2018), which allowed to researcher to demonstrate how portrayals of the LGBTQ+ community in advertising have transformed from 2009-2020. In addition to the researcher, a secondary coder was randomly assigned 25 ads from the dataset in order to test inter-rater reliability, which stood at 100% across all coding dimensions.
Qualitative analysis has no relation. Content analysis extends the work of Nölke (2018). Nölke, A. I. (2018). Making diversity conform? An intersectional, longitudinal analysis of LGBT-specific mainstream media advertisements. Journal of Homosexuality, 65(2), 224-255.
Qualitative analysis has no relation. Content analysis extends the work of Nölke (2018)
Does implicit mentalising involve the representation of others’ mental state content?
implicit mentalising, co-representation, joint action, domain specificity
Implicit mentalising involves the automatic awareness of the perspectives of those around oneself. Its development is crucial to successful social functioning and joint action. However, the domain specificity of implicit mentalising is debated. The individual/joint Simon task is often used to demonstrate implicit mentalising in the form of a Joint Simon Effect (JSE), in which a spatial compatibility effect is elicited more strongly in a joint versus an individual condition. Some have proposed that the JSE stems from the automatic action co-representation of a social partner’s frame-of-reference, which creates a spatial overlap between stimulus-response location in the joint (but not individual) condition. However, others have argued that any sufficiently salient entity (not necessarily a social partner) can induce the JSE. To provide a fresh perspective, the present study attempted to investigate the content of co-representation (n = 65). We employed a novel variant of the individual/joint Simon task where typical geometric stimuli were replaced with a unique set of animal silhouettes. Half of the set were each surreptitiously assigned to either the participant themselves or their partner. Critically, to examine the content of co-representation, participants were presented with a surprise image recognition task afterwards. Image memory accuracy was analysed to identify any partner-driven effects exclusive to the joint condition. However, the current experiment failed to replicate the key JSE in the Simon task, as only a cross-condition spatial compatibility effect was found. This severely limited our ability to interpret the results of the recognition memory task and its implications on the contents of co-representation. Potential design-related reasons for these inconclusive results were discussed. Possible methodological remedies for future studies were suggested.
Pre-test: Selection of Suitable Stimuli Participants Twenty-five undergraduate students at Lancaster University were recruited via SONA systems (a University-managed research participation system) and gave informed consent to participate in an online pre-test that aided in the selection of suitable experimental stimuli for the main experiment. Ethical considerations were reviewed and approved by a member of the University Psychology department. Stimuli and Materials Pavlovia, the online counterpart to the experiment building software package PsychoPy (version 2022.2.0; Peirce et al., 2019), was used to remotely run the stimuli selection pre-test. One hundred images of common black-and-white animal silhouettes were initially selected and downloaded from PhyloPic (Palomo-Munoz, n.d.), an online database of taxonomic organism images, freely reusable under a Creative Commons Attribution 3.0 Unported license . All images were resized and standardised to fit within an 854 x 480-pixel rectangle. Design and Procedure An online pre-test was conducted to identify the recognisability of possible animal stimuli and to select the most recognisable set of 32 animal silhouettes to use in the main experiment. Recognisability was an important consideration because participants would only briefly glimpse at the animals; therefore, the ability to recognise the silhouettes quickly and subconsciously was paramount. The 100 chosen animal silhouettes (as outlined in the Stimuli and Materials section) were randomised and sequentially presented. Each image was displayed for 1000ms to match the duration of stimuli exposure in the final experimental design. The participant then rated each animal’s recognisability on a 7-point Likert scale (1 = Extremely Unrecognisable to 7 = Extremely Recognisable). Additionally, they were asked to guess each animal’s name by typing it in a text box, and to provide a confidence rating corresponding to each naming attempt (again, on a 7-point Likert scale, from 1 = Extremely Unconfident to 7 = Extremely Confident). To choose which 32 animals were included, the recognisability scores for each animal were summed, averaged, and sorted in descending order. Duplicate animal species were excluded by removing all but the highest-scoring animal of the same species. Because the 32nd place was tied between two animals which achieved the same recognisability scores, the animal with the highest name-guessing confidence rating was selected. Main Experiment Participants Sixty-five participants who have not previously participated in the pre-test gave informed consent to participate in the main experiment (M¬age = 23.93 years, SDage = 8.06; 49 females), 51 of whom were students/staff/members of the public at Lancaster University recruited via SONA systems or through opportunistic recruitment around the University campus (e.g., on University Open Days). The remaining 14 participants were A-level students around Lancashire, recruited as part of a Psychology taster event at the University. All participants had normal or corrected-to-normal vision and had normal colour vision. Past studies of the JSE obtained medium-to-large effect sizes (e.g., Shafaei et al., 2020; Stenzel et al., 2014). An a priori power analysis was performed using G*Power (Version 3.1.9.6; Faul et al., 2009) to estimate the participant sample size required to detect a similar interaction. Due to the novel adaptation made to the Simon task (thus possibly attenuating the strength of previously found effects) and the additional memory/recognition task, a conservative-leaning effect size estimate was used. With power set to 0.8 and effect size f set to 0.2, the projected sample size needed to detect a medium-small effect size, repeated measures, within-between interaction was approximately 52. Stimuli and Materials The online survey software Qualtrics (Qualtrics, 2022) was used to provide participants in the main experiment with information and consent forms, plus obtain demographic information and (for participants in the joint condition) interpersonal relationship scores (see Appendix A for a list of the presented questions). The Simon and Recognition Tasks were run using the PsychoPy on three iMac desktop computers with screen sizes of 60 cm by 34 cm and screen resolutions of 5120 x 2880 @ 60 Hz. Responses to the Simon task were recorded using custom pushbuttons (see Appendix B for images) assembled and provided by Departmental technicians. The 32 animals chosen via the pre-test to be used in the main experiment (Simon/Recognition task) were recoloured to be entirely in either blue (hexadecimal colour code: #00FFFF) or orange (#FFA500). Varying by trial, the animals were displayed either 1440 pixels on the left or the right from the centre of the screen (for an example, see Figure 1). Figure 1 Example of Stimuli Used in Simon Task Note. Diagram (a) contains a screenshot of the Simon Task in which the orange stimulus appeared on left, whilst diagram (b) depicts a blue stimulus appearing on the right. Design and Procedure Simon Task. For the Simon task, a 2 x 2 mixed design was employed, with Compatibility (compatible vs. incompatible) as a within-subject variable and Condition (individual vs. joint) as a between-subject variable. Participants were first individually directed to computers running Qualtrics to read and sign information and consent forms, and to provide demographic information. Afterwards, participants were guided to sit at a third computer, where they sat approximately 60 cm (diagonally, approximately 45° from the centre of the screen) away from the computer either on the left or right side, with a custom pushbutton set directly in front of them. They were instructed to use their dominant hand on the pushbutton. In the joint condition, each pair of participants sat side-by-side, approximately 75 cm beside their partner. In the individual condition, an empty chair was placed in an equivalent location next to the participant. In both conditions, participants were individually assigned a colour (either blue or orange) to pay attention to. Participants were instructed to “catch” the animals by pressing their pushbutton when an animal silhouette of their assigned colour appeared on the computer screen . Participants were not otherwise instructed to pay specific attention to any of the animal species, nor the location (left/right) that it appears in; the focus was solely on the animals’ colour. Crucially, participants were unaware of the recognition task which came afterwards. Sixteen out of the 32 animal silhouettes selected during the pre-test were chosen to be displayed to them during the Simon task. The 16 animals were further divided in half and matched to each of the two colours, such that each participant was assigned eight animals in their respective colours. The remaining unchosen 16 animals were used as foils in the Recognition Task. Participant sitting location (left/right), stimuli colour (blue/orange), and animals presented (as stimuli in the Simon task/ as foils in the Recognition task) were counterbalanced between participants. Additionally, stimuli presentation position (left/right, and by extension, compatibility/incompatibility) was pseudorandomised on a within-subject, per-block basis. After reading brief instructions, participants completed a practice section. When participants achieved eight more cumulative correct trials than incorrect/time-out trials, they were allowed to proceed to the main experiment. This consisted of eight experimental blocks, where each block contained 16 trials (which corresponded to the 16 chosen animals), totalling 128 trials. Half of the trials in each block (i.e., 8) were spatially compatible, while the remaining half were incompatible. Furthermore, each block contained the same number of (in)compatible trials for each participant (i.e., four of each compatible/incompatible trials per participant). Trials in which the coloured stimulus and its correct corresponding response pushbutton were spatially congruent were coded as compatible, whilst spatially incongruent trials were coded as incompatible trials. A mandatory 10-second break was included at the half-way point of the experiment (i.e., after block four, 64 trials). Each trial began with a fixation cross in the centre of the screen for 250 ms. Following this, colour stimuli (circles in the practice trials, animal silhouettes in the main experiment) appeared on either the left or right of the screen for 1000 ms. A 250 ms intertrial interval (blank screen) was implemented. If a participant correctly pressed their pushbutton when stimuli of their assigned colour appeared, they were met with the feedback “well done”. Incorrect responses (i.e., when a participant pressed their pushbutton when a stimulus not of their assigned colour appeared) or timeouts (i.e., failing to respond within 1000 ms) were met with the feedback “incorrect, sorry” or “timeout exceeded” respectively. In addition to recording accuracy (correct/incorrect responses), each trial’s reaction time (time elapsed between stimulus display and pushbutton response) was also recorded and coded as response variables. Regardless of participants’ response time, each stimulus appeared for the full 1000 ms, and feedback was only provided after a full second has elapsed. This deviated from the design of previously used Simon tasks—in some studies, each trial (and thus stimuli presentation) immediately terminated upon any type of response (e.g., Dudarev et al., 2021); in other studies, each stimulus was only displayed for a fraction of a second (e.g., 150 ms; Dittrich et al., 2012), after which was a response window during which the stimulus was not displayed at all. The design choice of fixing the stimuli presentation duration to 1000 ms irrespective of participant response was to ensure that each animal colour/species were displayed for an equal duration of time. This was important so as not to bias the incidental memory of participants towards trials wherein one participant was slower to respond (and would have therefore kept the stimulus on screen for longer, disproportionally encouraging encoding). Surprise Recognition Task. For the recognition task, a 2 x 2 mixed design was employed, with Colour Assignment (self-assigned vs. other-assigned) as a within-subject variable and Condition (individual vs. joint) as a between-subject variable. Colour Assignment refers to whether the animal was previously assigned to, and presented in the Simon task as, the participant’s personal colour (i.e., self-assigned) or their partner’s colour (in individual condition’s case, this simply refers to the not-self-assigned colour, i.e., other-assigned). After completing the Simon task, participants were each guided back to their individual computers which they had initially used to give consent and demographic information, so as to minimize bias from familiarity effects on memory. Using a PsychoPy programme, participants were shown 32 black-and-white animal silhouettes one-by-one and were asked two questions: (1) “Do you recall seeing this animal in the task before?”, with binary “yes” or “no” response options; and (2) “How confident are you in your answer above?”, with a 7-point Likert scale between 1 = Extremely Unconfident to 7 = Extremely Confident as response options. For both questions, participants used a mouse to click on their desired response. Participants were additionally instructed that it did not matter what colour the animals appeared as during the previous (Simon) task—so long as they remember having seen the silhouette at all, they were asked to select “yes”. There was no time limit on this task. Thirty-two animal silhouettes were presented, of which 16 were seen in the Simon task, while the remaining 16 yet-to-be-seen animal images were added in as foils in this recognition task. The participants’ responses to the two aforementioned questions were recorded as key response variables. Check Questions and Interpersonal Closeness Ratings. At the end of the study, participants were asked several check questions which, depending on their answers, would lead to further questions. For example, they were asked about whether they had any suspicions of what the study was testing, or whether they paid specific attention to, and/or memorised the animal species shown in the Simon task on purpose (see Appendix A for a full list of questions and associated branching paths). The latter questions served to identify whether participants had intentionally memorised the animals, which may undermine the usefulness of the data collected in the object recognition task. Additionally, participants in the joint condition were also asked to individually rate their feelings of interpersonal closeness with their task partner with two questions. The first was a text-based question which asks how well the participant knows their partner (Shafaei et al., 2020), with four possible responses between “I have never seen him/her before: s/he is a stranger to me.”, and “I know him/her very well and I have a familial/friendly/spousal relationship with him/her.” The next was question contained the Inclusion of the Other in the Self (IOS) scale (Aron et al., 1992), which consisted of pictographic representations of the degree of interpersonal relationships. Specifically, as can be seen in Figure 2, the scale contained six diagrams, each of which consisted of two Venn diagram-esque labelled circles which represented the “self” (i.e., the participant) and the “other” (i.e., the participant’s partner) respectively. The six diagrams depicted the circles at varying levels of overlap, as a proxy measure of increasing interconnectedness. Participants were asked to rate which diagram best described the relationship with their partner during the study. In following the steps of Shafaei et al. (2020), the first text-based question was included, and was used as a confirmatory measure for the IOS scale, the latter of which was the primary measure for interpersonal closeness. Figure 2 Inclusion of Other in the Self (IOS) scale
Malcolm Wong Aubrey Covill Elisha Moreton
Does Noise Affect How Children Learn Grammar in the Classroom?
In a classroom environment noise can be a significant impediment, obstructing and distorting essential information being taught. Extensive prior research consistently indicates that noise has a detrimental impact on learning, those who learn in noise retain and comprehend far less information than their counterparts who learn in quiet. To date there are no studies that investigate the effect of noise on learning grammar specifically -the primary aim of the current study is the address this research gap. This paper details our recruitment of 16 children aged 7– 12 through the Babylab database at Lancaster university. This study employed a between participants design, where children completed a three-part audio evaluation, engaged in an artificial grammar paradigm, and a undertook a working memory task. The artificial grammar paradigm was employed as our primary assessment tool, participants were exposed to the grammar either in noise or in quiet. Results were analysed using a multiple regression with total grammar score as the dependent variable and age, gender, condition, and working memory as the independent variables. In contrast the prior research, our results revealed that the effect of the independent variables on the dependent variable was statistically nonsignificant, proving our null hypotheses to be true. These findings suggest that background noise does not affect how children learn grammar in the classroom challenging the existing understanding that noise negatively impacts learning.
Participants 16 children aged 7-12 years old participated in this study, unfortunately due to technical issues 5 participants’ data were excluded leaving 11 children’s data to be included in the analysis (M=8.64, SD=1.63, female=7, male=4). Children were recruited through the Lancaster University Babylab database and by flyers posted on social media and local community. A requirement of the current study was that children be English speaking monolinguals, this is because an abundance of research has indicated that those who can speak two or more languages are at a far greater advantage when it comes to new language acquisition (Antoniou et al., 2015). Therefore, in order to control the likelihood of extraneous variables such as this we ensured all participants were English speaking monolinguals only. Furthermore, children were also required to have normal vision or corrected to normal vision. To rule out hearing loss all children had to pass an otoscope inspection, a tympanometry test, and a pure tone hearing screening at 20dB in the standard frequencies (250Hz-8kHZ). The current study employed a between participant design whereby subjects were allocated to a condition based on their age and gender -age was categorised into 7-9 and 10-12- in order to ensure that there were as equal an amount of males and females in each condition over all ages. It is crucial for the validity of the study that children are only exposed to the artificial grammar paradigm once or data will be rendered unreliable as they will have an unfair advantage over the other participants. Ethics for the current study have been obtained from the Departmental Ethics Committee (DEC), Psychology Department at Lancaster University. Materials This study was conducted within a double walled soundproof chamber at Lancaster University’s PELiCAN lab where the participant sat at a desk with a monitor placed in front of them. A secondary researcher was present in the lab for health and safety purposes. Consent and assent forms, a background questionnaire on the child’s hearing, audio evaluation results, and task data were all recorded on REDCap (Harris et al., 2009; Harris et al., 2019): a GDPR compliant application for data capture. Travel compensation was provided: £5 within 40 minutes and £10 for over 40 minutes. Furthermore, children received a certificate and book of their choosing from the PELiCAN lab. The audio evaluation This study was comprised of three sections: an audio evaluation whereby an otoscope examination, tympanometry test, and audiogram using Affinity Suite were conducted. During the audiogram participants wore headphones and had a handheld button that they pressed when they heard the pure tone sounds. The Artificial Grammar Paradigm After passing the hearing evaluation the children completed an artificial grammar paradigm previously used by Torkildsen et al. (2013) consisting of two grammatical forms: aX and Yb. The paradigm was presented in the form of an alien game whereby the children helped an alien learn a new language. We presented the paradigm in this format in order to increase engagement; children are motivated by the colourful and curious nature of a game (Blumberg et al., 2019) and therefore we are far more likely to obtain more data (less drop outs due to fatigue and boredom). This task was created in PsychoPy and hosted by Pavlovia. The background noise In order to imitate the background noise of a classroom speech shaped noise (SSN) (e.g. Leibold et al., 2013) was emitted through a speaker on the back wall of the booth behind the child. The background noise speaker was 180 degrees on the azimuth, and the target speaker was 0 degrees on the azimuth. Background stimuli was calibrated so that for the quiet condition the stimulus was emitted at 35dB and for the noisy condition it was played at 65dB. The n-back Test of Working Memory Lastly, we conducted the 1-back test of working memory (Owen et al., 2005) which was also created on PsychoPy and hosted by Pavlovia Procedure Prior to the commencement of the study guardians gave informed consent (See Appendix C), if the child was 11 or older they gave informed assent in addition to this (See Appendix D). Guardians were then asked to complete a short background questionnaire pertaining to their child’s hearing (See Appendix H). Whilst they completed these forms the researcher began the study inside the booth; using Affinity suite it was ensured that the microphone inside the booth was turned on in order for the guardian to be able to hear what was going on inside the booth by using the headphones places outside the booth. As aforementioned, the audio evaluation consisted of three tests, these were administered in the booth by the researcher and took up to 15 minutes. Firstly, an ear inspection was conducted using an otoscope, participants were required to have clear ears free of perforations and/or any infection. Secondly, a tympanometry test was conducted whereby participants must have passed with type A (normal) results. Lastly a pure tone hearing screening was conducted at 20dB in the standard frequencies (250Hz-8kHZ). The researcher left the booth for the audiogram in order to run the program on the desktop outside the booth while the child remained inside the booth. The task consisted of 11 blocks comprised of 4 exposure items and 2 test items, before the test portion children were exposed to 4 examples of what is expected of them, they had to get these right in order for the software to move onto the test phase. If children did not get these right the researcher explained and promoted them to pick the correct answer. Children were required to press ‘x’ on the keyboard for right and ‘n’ on the keyboard for wrong, answers were saved and recorded automatically on Pavlovia. The software was run by the researcher from outside the booth and was mirrored onto the desktop inside the booth. Lastly, we conducted the 1-back test of working memory (Owen et al., 2005), where children were exposed to a number of animal sounds and were required to record weather the stimuli was a new sound or one they had heard before, ‘x’ represented repeated sound and ‘n’ represented a new sound, participants had to ensure they made a button press after each noise. Once all tasks were completed the researcher collected the child from inside the booth and a short verbal and written debrief was given to the child and guardian. Guardians were given and signed for their travel compensation, and children received a certificate from the PELiCAN lab and were able to choose a book of their liking. Participants were walked back to their car or bus to bring a close to the visit. Analysis In order to answer our research questions we will carry out a multiple linear regression using IBM SPSS Statistics (version 28). We will be employing a between participants design where we will examine the effect of background noise (noisy and quiet) on total grammar score. Our additional independent variables will be working memory, gender and age. If we find a statistically significant result with regard to grammar score then we will be conducting a post hoc test on grammar score breaking them down into aX and Yb in order to determine the difference between the two types of grammar.
In a classroom environment noise can be a significant impediment, obstructing and distorting essential information being taught. Extensive prior research consistently indicates that noise has a detrimental impact on learning, those who learn in noise retain and comprehend far less information than their counterparts who learn in quiet. To date there are no studies that investigate the effect of noise on learning grammar specifically -the primary aim of the current study is the address this research gap. This paper details our recruitment of 16 children aged 7– 12 through the Babylab database at Lancaster university. This study employed a between participants design, where children completed a three-part audio evaluation, engaged in an artificial grammar paradigm, and a undertook a working memory task. The artificial grammar paradigm was employed as our primary assessment tool, participants were exposed to the grammar either in noise or in quiet. Results were analysed using a multiple regression with total grammar score as the dependent variable and age, gender, condition, and working memory as the independent variables. In contrast the prior research, our results revealed that the effect of the independent variables on the dependent variable was statistically nonsignificant, proving our null hypotheses to be true. These findings suggest that background noise does not affect how children learn grammar in the classroom challenging the existing understanding that noise negatively impacts learning. Analysis In order to answer our research questions we will carry out a multiple linear regression using IBM SPSS Statistics (version 28). We will be employing a between participants design where we will examine the effect of background noise (noisy and quiet) on total grammar score. Our additional independent variables will be working memory, gender and age. If we find a statistically significant result with regard to grammar score then we will be conducting a post hoc test on grammar score breaking them down into aX and Yb in order to determine the difference between the two types of grammar.
Participants 16 children aged 7-12 years old participated in this study, unfortunately due to technical issues 5 participants’ data were excluded leaving 11 children’s data to be included in the analysis (M=8.64, SD=1.63, female=7, male=4). Children were recruited through the Lancaster University Babylab database and by flyers posted on social media and local community. A requirement of the current study was that children be English speaking monolinguals, this is because an abundance of research has indicated that those who can speak two or more languages are at a far greater advantage when it comes to new language acquisition (Antoniou et al., 2015). Therefore, in order to control the likelihood of extraneous variables such as this we ensured all participants were English speaking monolinguals only. Furthermore, children were also required to have normal vision or corrected to normal vision. To rule out hearing loss all children had to pass an otoscope inspection, a tympanometry test, and a pure tone hearing screening at 20dB in the standard frequencies (250Hz-8kHZ). The current study employed a between participant design whereby subjects were allocated to a condition based on their age and gender -age was categorised into 7-9 and 10-12 in order to ensure that there were as equal an amount of males and females in each condition over all ages. It is crucial for the validity of the study that children are only exposed to the artificial grammar paradigm once or data will be rendered unreliable as they will have an unfair advantage over the other participants. Ethics for the current study have been obtained from the Departmental Ethics Committee (DEC), Psychology Department at Lancaster University. Materials This study was conducted within a double walled soundproof chamber at Lancaster University’s PELiCAN lab where the participant sat at a desk with a monitor placed in front of them. A secondary researcher was present in the lab for health and safety purposes. Consent and assent forms, a background questionnaire on the child’s hearing, audio evaluation results, and task data were all recorded on REDCap (Harris et al., 2009; Harris et al., 2019): a GDPR compliant application for data capture. Travel compensation was provided: £5 within 40 minutes and £10 for over 40 minutes. Furthermore, children received a certificate and book of their choosing from the PELiCAN lab. The audio evaluation This study was comprised of three sections: an audio evaluation whereby an otoscope examination, tympanometry test, and audiogram using Affinity Suite were conducted. During the audiogram participants wore headphones and had a handheld button that they pressed when they heard the pure tone sounds. The Artificial Grammar Paradigm After passing the hearing evaluation the children completed an artificial grammar paradigm previously used by Torkildsen et al. (2013) consisting of two grammatical forms: aX and Yb. The paradigm was presented in the form of an alien game whereby the children helped an alien learn a new language. We presented the paradigm in this format in order to increase engagement; children are motivated by the colourful and curious nature of a game (Blumberg et al., 2019) and therefore we are far more likely to obtain more data (less drop outs due to fatigue and boredom). This task was created in PsychoPy and hosted by Pavlovia. The background noise In order to imitate the background noise of a classroom speech shaped noise (SSN) (e.g. Leibold et al., 2013) was emitted through a speaker on the back wall of the booth behind the child. The background noise speaker was 180 degrees on the azimuth, and the target speaker was 0 degrees on the azimuth. Background stimuli was calibrated so that for the quiet condition the stimulus was emitted at 35dB and for the noisy condition it was played at 65dB. The n-back Test of Working Memory Lastly, we conducted the 1-back test of working memory (Owen et al., 2005) which was also created on PsychoPy and hosted by Pavlovia Procedure Prior to the commencement of the study guardians gave informed consent (See Appendix C), if the child was 11 or older they gave informed assent in addition to this (See Appendix D). Guardians were then asked to complete a short background questionnaire pertaining to their child’s hearing (See Appendix H). Whilst they completed these forms the researcher began the study inside the booth; using Affinity suite it was ensured that the microphone inside the booth was turned on in order for the guardian to be able to hear what was going on inside the booth by using the headphones places outside the booth. As aforementioned, the audio evaluation consisted of three tests, these were administered in the booth by the researcher and took up to 15 minutes. Firstly, an ear inspection was conducted using an otoscope, participants were required to have clear ears free of perforations and/or any infection. Secondly, a tympanometry test was conducted whereby participants must have passed with type A (normal) results. Lastly a pure tone hearing screening was conducted at 20dB in the standard frequencies (250Hz-8kHZ). The researcher left the booth for the audiogram in order to run the program on the desktop outside the booth while the child remained inside the booth. The task consisted of 11 blocks comprised of 4 exposure items and 2 test items, before the test portion children were exposed to 4 examples of what is expected of them, they had to get these right in order for the software to move onto the test phase. If children did not get these right the researcher explained and promoted them to pick the correct answer. Children were required to press ‘x’ on the keyboard for right and ‘n’ on the keyboard for wrong, answers were saved and recorded automatically on Pavlovia. The software was run by the researcher from outside the booth and was mirrored onto the desktop inside the booth. Lastly, we conducted the 1-back test of working memory (Owen et al., 2005), where children were exposed to a number of animal sounds and were required to record weather the stimuli was a new sound or one they had heard before, ‘x’ represented repeated sound and ‘n’ represented a new sound, participants had to ensure they made a button press after each noise. Once all tasks were completed the researcher collected the child from inside the booth and a short verbal and written debrief was given to the child and guardian. Guardians were given and signed for their travel compensation, and children received a certificate from the PELiCAN lab and were able to choose a book of their liking. Participants were walked back to their car or bus to bring a close to the visit.
Does the use of prompts in shared reading facilitate the quantity and quality of language in Down Syndrome children?
Down syndrome, linguistic abilities, dialogic reading, prompted reading.
Children with Down syndrome typically present with specific linguistic and communicative difficulties. The present study aims to explore how dialogic prompted reading facilitates better quality and quantity of language production in pre-school aged Down syndrome children. Research has demonstrated how reading interventions enhance typically developing children’s linguistic qualities, yet few studies have investigated the beneficial effects of dialogic prompted reading among Down syndrome children. Eight Down syndrome and 8 typically developing children completed two shared reading tasks with their mothers. One task involved reading a book containing a series of prompted questions, the other book contained no prompts. As predicted, prompted reading resulted in the development of more complex syntax, better vocabulary production and facilitated better responses accuracy to literal and inferential concepts, in Down syndrome children. In addition, the inclusion of prompts also increased parental scaffolding techniques for both diagnostic groups. The results from this study indicate that dialogic prompted reading does improve Down syndrome children’s qualitative and quantitative linguistic abilities and promotes better communication with parents during shared reading tasks. These findings highlight the educational significance of prompted dialogic reading as a highly beneficial intervention for developing an array of linguistic qualities in children with Down syndrome.
Participants A total of 16 children and their mothers took part in this study. Eight children with Down syndrome (DS: 4 female, 4 male, age range = 4.58 years to 6.75 years, Mage = 5.3 years) and 8 typically developing children (TD: 2 female, 6 male, age range = 3.9 years to 6.66 years, Mage = 5.1 years). This is a secondary data analysis study and all participants were previously recruited by the principle investigator and supervisor, Kate Cain. Video recordings of all child-parent reading dyads were made and were transcribed into written from. The Departmental Ethics Committee approved this study prior to the author receiving any video or transcribed data. Stimuli In this study, mothers were given two books to read with their children, ‘Mooncake’ and ‘Skyfire’ (Asch, 2014; 2014). Parents were asked to read both books as they normally would read at home with their child. One version of each book contained a series of 12 prompts which were inserted at specific points and parents where asked read them aloud as they went through the book (based on Van Kleek et al, 2006). Between both books there were a total of 24 prompts. For each book, prompts were evenly split between 4 sub-categories: picture labelling prompts “What is that? (pointing to Bear)”, vocabulary prompts “What does ‘hollow’ mean?”, inference prompts “ Why did Bear fall asleep?”, and general knowledge prompts “What else could Bear have used to stick the spoon to the arrow?”. The aim of the prompts was to encourage communication and scaffolding interactions between mothers and children when reading together. These books where specifically selected for multiple reasons: first, they have been successfully used in previous studies investigating linguistic impairments in pre-school aged children with language difficulties (Van Kleek et al 1997; 2006; Hammet, Van Kleek & Huberty, 2003). Second, the classic story-line of each book provides opportunities for children to follow a written and pictorial narrative, enhancing their visual perception skills, as well as being age suitable and cognitively stimulating for DS and TD children (Gibson 1996; Engevik et al 2016). Procedure and Design Parent/child reading dyads were separated into groups based on diagnosis, where DS children and their mothers formed the experimental group, and TD children and their mothers formed the control group. There were two conditions; typical ‘unprompted’ reading and prompted reading. All participants took part in all conditions. In the unprompted reading condition, parents were given one of the books, selected at random (i.e ‘mooncake’) and asked to read with their child as they would normally read at home. In the prompted reading condition, parents were given the other book (i.e ‘skyefire’) and asked to read with their child as they normally would at home, but to additionally ask the twelve prompts that were inserted into the book. Within each condition, the order in which each child/parent dyad read each book was counterbalanced, as well as the order of books being presented between diagnostic groups was counterbalanced. The experimental sessions were conducted either in a university lab or at the participants home, and was a record session. The researcher did not take part in reading sessions, and was there for recording purposes only. Each reading session was audio and video recorded, which was later transcribed in to written format using Microsoft Excel. The specific areas of language where coded for using the Excel written transcript and then inputted into SPSS for statistical analysis. Coding Categories Child and parent speech were coded for under the following categories: children’s production of language (length and syntax), children’s production of specific vocabulary types (nouns, verbs, adjectives, adverbs, affirmatives and fillers), parents use of questions (literal and inferential questioning styles), children’s language abilities in response to questions (literal and inferential), accuracy of children’s response to questions (literal and inferential), parental scaffolding techniques and children linguistic abilities in response to scaffolding techniques. This was done so the direct effects of prompted reading on a variety children’s language abilities could be primarily investigated, as well as assessing the effect prompted reading has on parental scaffolding techniques. Length and Syntax: total number of utterances, total number of words and mean length of utterances produced by children The total number of utterances produced by DS and TD children was coded for using a simple counting strategy, from the written transcripts in Microsoft Excel. Each sentence spoken by both groups of children, including singular words which posed as a sentence, were tallied to create the total number of utterances, between reading conditions. The total number of words was calculated by totalling every word in each utterance across both reading conditions, and the mean length of utterance was calculated by dividing by the total number of words by the total number of utterances each child spoke. Inaudible speech and vocalisations were not included in the coding, neither where onomatopoeic noises children made, such as ‘Zzzzz’ when pretending to be a bee, as they are representations of sound not speech. Onomatopoeic speech, for example ‘splash’ or ‘bang’ was included in the coding process as they are representations of speech. Additionally, speech where children were reading sections of the book alongside their mothers was excluded from the coding process, for the sole reason that reading alongside a parent does not represent language ability but reflects their reading ability. Each child had a score for the total number of utterances, total number of words and mean length of utterances produced for prompted and unprompted reading conditions, which were then inputted into statistical software SPSS. These factors represent the quantity element of language. Vocabulary Production: nouns, verbs, adjectives, adverbs, affirmatives and fillers Children’s vocabulary production was coded under six sub-categories: nouns, verbs, adjectives, adverbs, affirmatives and fillers. These specific categories were chosen as previous research investigating vocabulary within DS has demonstrated that children present difficulties producing complex vocabulary categories, therefore two tiers of vocabulary were created: ‘basic’ vocabulary (nouns and verbs) and ‘complex’ vocabulary (adjectives and adverbs), to assess the effect of prompted reading on a large selection of vocabulary categories, rather than focusing on one particular type of vocabulary. These specific vocabulary categories are also applicable to the age range of children used in the study. Affirmatives (‘yes’, ‘no’ and ‘don’t know’) were coded for to investigate whether prompted reading affected the use of simplistic answers, specifically whether prompted reading decreased affirmative answers. Questions asked by children, like ‘what?’ and ‘why?’ were also included in the affirmative category, as they reflect an aspect of speech where a child is requesting for more information to further engage with the parent. Child questioning was rare and therefore did not require a category of its own. ‘Fillers’, additional words that make up a sentence, were also totalled. This was to investigate whether prompted reading facilitated more structured sentences, and therefore increased the number of fillers children produced. This was of particular interest for the DS group, as children with DS present difficulties in sentence structure. The total amount of vocabulary produced (inc. affirmatives and fillers) would therefore be equal to the total number of words produced. Literal and Inferential Parental Questioning and Language Production Children’s ability to respond to literal and inferential questioning during shared reading sessions was coded for by adapting a four-level coding system previously used in studies investigating literal and inferential language in pre-school aged children (Van Kleek et al, 2003; Tompkins et al, 2013; Engevik et al, 2016). Previous coding schemes were designed to assess children’s literal and inferential speech across four linguistic domains, where the first two levels (Level 1 and Level 2) resemble children’s literal language, and the second two levels (Level 3 and Level 4) represent children’s inferential language (Blank, Rose & Berlin, 1978). For the present study, children’s linguistic responses to literal and inferential questioning was only assessed under a 2 level system, where Level 1 represented speech in response to literal questioning, and Level 2 represented speech in response to inferential questioning. This adaptation was done to take into account DS children’s linguistic abilities, as a four-level coding system would have been too advanced for the particular task. Since DS children’s understanding of cognitive concepts and inferential questioning is limited, their linguistic responses to such questions would also be limited, therefore a two-level coding system was more acceptable. For each set of 12 prompted questions used, 50% represented literal concepts (Level 1) and 50% represented inferential concepts (Level 2). Level 1 coded for children’s responses to labelling prompts (“What is that?”- pointing at Bear) and vocabulary prompts (“What does ‘hollow’ mean?”). Level 2 coded for children’s responses to inference prompts (“Why did Bear fall asleep?”) and general knowledge prompts (“What else could Bear have used to stick the spoon to the arrow?”). Parental prompts where also coded and separated between literal and inferential levels. The number of textual prompts and parental prompts where coded using a binary counting strategy, as well as the level of each question (literal or inferential) recorded. For each prompt, children’s responses where coded based their correct or incorrect response and vocabulary production (nouns, verbs, adjectives, adverbs and affirmatives) so each child had a score of response and vocabulary production for literal and inferential questioning, between prompted and unprompted reading conditions. (An example of the coding system can be seen in Appendix A).This particular coding method was designed to assess the extent to which textual and parental literal and inferential prompts enhanced children’s linguistic qualities, and pin point whether a specific type of questioning facilitated more correct responses and production of more vocabulary. Scaffolding Techniques and Language Production Parents ability to successfully utilise scaffolding techniques between reading conditions was assessed, through designing a coding system that recorded each time parents took a break from reading the text to direct questions, these were labelled as ‘turn-taking sections’. The total number of turn-taking sections was coded, as well as the total number of questions parents asked per section and whether each question was literal or inferential. This was done to assess whether prompted reading encouraged parents to take more breaks from reading the text to ask their child questions, whether each time parents took breaks they asked more literal or inferential questions to engage their child. In addition to this, whether parental scaffolding enhanced children’s linguistic abilities were also assessed. This was done by coding the total number of words children produced per section, which would show whether parental scaffolding techniques enhanced children linguistic contribution. (An example of the coding system can be seen in Appendix B). Accuracy The accuracy of children’s responses, in relation to literal and inferential questioning, was coded by using a three-level coding system, used by previous studies investigating accuracy of children language during shared reading (Engevik et al, 2016). Previously, children’s accuracy of response was coded for along a linguistic continuum, where ‘fully adequate’ represented accurate verbal responses, ‘partially adequate’ reflected verbal communication which is ‘on the right track’ but not necessarily accurate, and ‘inadequate’ which represented any response that was irrelevant (Sorsby & Martlew, 1991; Engevik et al, 2016). Previous studies investigating accuracy of speech in DS children have adapted the coding system to merge ‘fully’ and ‘partially’ accurate categories together, to take into account the linguistic and cognitive difficulties DS children face (based on Engevik et al, 2016). However, the present study uses a slightly adapted version of the original coding system, where children’s ‘fully’, ‘partially’ and ‘not’ accuracy of responses were coded, yet only children’s ‘fully’ accurate responses will be used in the final analysis. This was done so children’s fully accurate responses to literal and inferential parental questioning could be assessed. ‘Partially’ and ‘not’ accurate responses were not assessed in this particular study as the sole interest is children’s ‘fully’ accurate response. The reason as to why ‘fully’ and ‘partially’ categories weren’t merged for the present study was to gain a more realistic understanding of children’s fully accurate responses, and merging categories would not provide this. (An example of the coding system can be seen in Appendix C).