University residential college students (college students) are at particularly high risk for experiencing alcohol-related harms compared to non-college students. Interventions to reduce alcohol consumption in colleges may thus constitute a means to reduce the likelihood of potential harm. However, college students have been reluctant to accept top-down interventions, as they may be perceived as excessively paternalistic. In young adults, a co-design approach has successfully produced acceptable and effective interventions. However, research exploring college students’ suggestions for co-designed interventions is lacking.

This study sought to explore college students’ suggestions for co-designed interventions to address previous findings that harm-minimisation approaches have been largely ineffective in reducing high-risk drinking and that college students have been reluctant to accept top-down interventions. Accordingly, the present study aimed to elucidate college students’ views about alcohol consumption in college and explore desired interventions. Specifically, this study sought to identify: 1. The form and content of interventions college students believe would be most effective in reducing high-risk drinking in residential colleges 2. College students’ perceptions as to what facilitates high-risk drinking in residential colleges. 3. Barriers college students perceive there would be to reducing alcohol consumption in residential colleges.

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Individual semi-structured interviews were conducted via Zoom and were between 12-27 minutes (M = 18.60, SD = 4.90) in duration. Interview questions investigated students' perceptions of the facilitators of high-risk drinking, barriers to its reduction, and students' suggestions for interventions. Transcripts were imported into NVivo for qualitative coding and semantic thematic analysis

Eight primary themes were identified. Drinking to feel included and drinking to increase social confidence were primary facilitators and students’ desire for autonomy and the minimization and misperception of harms were primary barriers. The most frequently cited desired interventions included initial and ongoing education sessions, peer-led interventions, and hosting enjoyable, alcohol-free events. The most frequently cited undesired intervention was any that could be perceived as paternalistic.

Findings suggest that co-designed interventions that are peer-led, encourage autonomous decision making, and promote alcohol-free activities, would likely be well accepted by college students, while previously suggested paternalistic interventions would likely elicit resistance. These findings will be transposed from thesis format to a journal article format, with the aim of publication in an academic journal.

Drug misuse is a major health issue with wide-ranging economic and social implications. In Australia, tobacco use is responsible for 9% of the nation’s disease burden, while alcohol and illicit drug use account for 5.1% and 1.8% respectively (AIHW, 2016). The total economic and social cost of these drugs is estimated at more than $55 billion per annum (AIHW, 2011). Nonetheless, high proportions of Australians 14 years or older report recent (preceding 12 months) drug use: 14.9% are current smokers, 77.5% indicate consuming alcohol, and 15.6% have used illicit drugs (AIHW, 2017). While relatively few of these individuals will transition to dependent use, the factors influencing this transition, as well as those that may have a mitigating impact, remain unclear. Numerous studies have consistently reported the association between various cognitive factors and dependent drug use. For instance, compared to healthy controls, individuals dependent on various substances exhibit difficulties on tasks that assess executive function, particularly those related to decision-making and inhibitory control (Bickel et al., 2012; MacKillop et al., 2011; Smith et al., 2014; Verdejo-García et al., 2008). Alcohol and substance dependent individuals additionally show significant impairments across tasks that assess various types of learning and memory (Lundqvist, 2005; Potvin et al., 2014; Stavro et al., 2013). At the neural level, attention is focused on the interplay between bottom-up dopamine-mediated reward circuitry, and top-down prefrontal regions involved in self-control (Goldstein & Volkow, 2011; Verdejo-García et al., 2006). Substance dependent individuals show dysfunction across both systems (Goldstein & Volkow, 2011; Verdejo-García et al., 2006; Verdejo-García & Bechara, 2009). The cognitive deficits evident among substance dependent individuals are considered both a cause and consequence of drug use (De Wit, 2009). Longitudinal studies involving both children and adolescents have shown how executive function performance in early life predicts illicit drug use and the onset of problematic alcohol consumption, independent of other risk factors (Audrain-McGovern et al., 2009; Fernie et al., 2013; Kollins, 2003; Nigg et al., 2006; Verdejo-García et al., 2008). At the same time, drug taking appears to exacerbate cognitive issues related to memory, learning and executive function (Colzato et al., 2007; De Wit, 2009; Fillmore & Rush, 2002; Mendez et al., 2010; Verdejo-García et al., 2008). Given these findings, studies concerned with identifying non-dependent drug users at risk of transitioning to dependency often focus on intake patterns and their relationship to (usually single) assessments of executive function, memory and learning; such studies tend, however, to yield inconsistent results (Fernie et al., 2010; Field et al., 2007; Henges & Marczinski, 2012; MacKillop et al., 2011; Moreno et al., 2012; Murphy & Garavan, 2011; Poulton et al., 2016; Rossiter et al., 2012; Smith & Mattick, 2013). This may be due to a number of factors. For instance, intake is frequently recorded using retrospective summary surveys, which can underestimate consumption and are limited in their ability to capture within- person variability and change over time (Piasecki et al., 2007; Poulton et al., 2018; Runyan et al., 2013). At the sub-clinical level, it is possible variability of intake is linked to performance – or, indeed, variability of performance – on cognitive tasks. To investigate this proposition, we are utilising mobile-based ecological momentary assessment (EMA) – which involves using a smartphone app to collect data repeatedly, in close proximity to the event, and in the natural environment (Shiffman, 2009; Trull & Ebner-Priemer, 2013) – to assess both intake and cognition. We hope this enables a more detailed exploration of the in-the-moment cognitive antecedents and consequences of intake behaviour. EMA techniques additionally provide a unique opportunity to assess how individualized feedback about intake and cognition delivered in real-time might alter the trajectory of licit and illicit drug use over time. Early brief interventions have already been identified as a potentially effective means of curbing intake among non-dependent drug users, particularly when they are individualized, though a number of difficulties have been noted (Bernstein et al., 2009; Das et al., 2016; Riper et al., 2009; Tait & Hulse, 2003). Namely, there are limited professionals to deliver these interventions; it can be difficult contacting users; and, implementation and delivery costs can be high (Riper et al., 2009). Interventions delivered via smartphone apps potentially address these limitations.

This is an ongoing study that aims to investigate whether individual differences in cognitive function are related to licit and illicit drug use in the general population. Specifically, we seek to assess if cognitive factors – related to memory, learning, and executive function – are associated with particular patterns of drug use, including occasional, social and binge intake behaviours. We additionally hope to determine if these cognitive factors can predict changes in drug use over time and if feedback about cognition and/or intake precipitates reduced drug-taking behaviour. The current study aimed to assess whether personalised impulsivity feedback would augment the benefit of alcohol-related feedback. It also aimed to validate whether differences in impulsivity can be observed between groups of binge and non-binge drinkers.

Participants included 41 first-year university students who were randomly allocated to one of three conditions: no feedback, alcohol use feedback only, and impulsivity combined with alcohol use feedback. Thirty participants completed follow-up measures at 6 weeks post-intervention.

Data collection was cross-sectional, intensive (14 days), and longitudinal. Participants were requested to take part in two intensive assessment periods. They were recruited remotely. At the commencement of each assessment period, participants were required to complete online baseline (Time 1) or subsequent (Time 2) surveys. They were then required to complete a cognitive stop-signal task online. Finally, they were asked to download an app and record alcohol intake for a period of 14 days. Feedback condition participants were provided with feedback about their alcohol intake and/or cognition at the conclusion of each assessment period. Control condition participants received no feedback about their intake or cognition.

This is an ongoing study. Key variables of interest are binge drinking, total alcohol consumption, choice impulsivity, and response inhibition. Choice impulsivity is indexed by the Monetary Choice Questionnaire (MCQ) overall k-score; higher scores are indicative of greater impulsive decision making. Response inhibition is indexed by stop-signal reaction time, which is derived from Stop-Signal Task (SST) data. Longer stop signal reaction times are indicative of reduced response inhibition. Prior to primary hypothesis testing, the three groups will be compared on key demographic, alcohol use, and impulsivity variables measured at Time 1. A one-way analysis of variance (ANOVA) will be conducted to test the groups are matched on continuous variables, with a chi-square test of independence used for gender. The primary hypotheses will be tested using a mixed between- within subjects ANOVA for each alcohol use variable, with three feedback conditions (No Feedback, Alcohol Feedback, and Alcohol + Impulsivity Feedback) and two time periods (Time 1 and Time 2). Effect sizes will be computed using partial eta squared and interpreted according to Cohen's guidelines: .01 = small, .06 = moderate, and .14 = large effect (Cohen, 1988).

We expect binge drinking and total alcohol consumption to be reduced at Time 2 across all groups as recording alcohol consumption has been found to reduce alcohol intake over time. We expect participants in the Alcohol Feedback group to have reduced drinking to a greater extent than those in the No Feedback group, and those in the Alcohol + Impulsivity Feedback group to have reduced drinking to a greater extent than those in any other group. Data from S1 & S2 will be analysed and form part of 4th year theses in 2021. Data will continue to be collected in 2022 with a view to publishing results later that year.

Previous studies have found that regardless of the condition, the more confident an individual is, and the faster their responses are, and the more accurate their identification is. Previous research has also found that increasing the length of the confidence rating scale led to poorer recognition performance. This research aims to find out if the same effects of recognition can be found when participants have enough practice on using the response keys while being motivated to use all keys strategically in order to maximise their gains. Although there exists many models that aim to explain the relationship between confidence, response time and accuracy in decision making (e.g., Ratcliff & Starns, 2009; Voskuilen & Ratcliff, 2015, Wixted & Mickes, 2014), those models were inadequate to provide a unified account that relates confidence and RT to the accuracy of choices in recognition. Therefore we proposed a new model, the Multiple Threshold Race model (MTR) that has the potential to provide such unified account.

The aim of the present research is to conduct a visual recognition experiment that aim to test and validate the use of MTR in predicting relationship between confidence, RT and accuracy. The peripheral aim of the present research is to test what effect of varying confidence scale length would have on recognition performance. It is hypothesised that we will observe longer RT for larger confidence scale length.

A total of 35 REP participants completed the study.

Participants first went through a response key practice block where they were presented with a series of confidence options, one at a time. Their task was to press the corresponding key as quickly and accurately as possible. Then they completed 3 practice and 15 experimental cycles of computer-based visual recognition tasks. During each trial, they were presented with a large square which consists of 400 small grids of two different colours (orange and blue) that flashed on the screen. The task was to decide whether the colour orange or blue appeared more frequently. They were also asked to report how confident they were in that decision on either a 2-point, or a 4-point, or a 6-point confidence scale.

When the length of the confidence scale is increased, there is a slowdown in RT, which is consistent with the findings of Benjamin et al. (2013). We plan to fit the MTR model with the data using hierarchical bayesian techniques, and see what parameter variations are needed to improve model fit. By fitting MTR with different combination of parameter variations, the validity of MTR could possibly be established.

The findings of the experiment suggest that longer confidence rating scales could induce decision noise that contributes to recognition decisions. The MTR model has not been fitted to this dataset. Yet based on our modelling results from previous recognition memory dataset, it is possible for us to conclude that the validity of MTR could be established. This gives support to the single-staged confidence theory using Balance of Evidence hypothesis, as it could simultaneously account for the relationships between confidence, accuracy and RT. The results from this study will be written up as a journal article for publication, and will be presented at various conferences on psychological research.

A majority of people care about climate change, however, very few act upon those concerns. This gap between concern and behaviour, often referred to as the value-action gap, is at the crux of many problems that are faced by society in the 21st century. This phenomenon cuts across a variety of modern problems: people care about the way their society is governed yet they fail to vote and Individuals have empathy and concern for animals yet they continue to eat from factory farms. These diverse scenarios all share the same foundation – they are collective action problems. A collective action problem refers to situations where a number of people are required to work together to achieve some common goal, but, because of conflicting interests between individuals (e.g., short term benefits from not cooperating), joint action is discouraged (Roser-Renouf et al., 2016). Why do individuals choose the uncooperative option? A myriad of studies have targeted this particular question, with a majority putting it down to free-riding behaviour. Free-riding is placing an individual’s self-interest above that of the group’s interest, and is commonly referred to as the tragedy of the commons. While a majority of research assumes defection (i.e., the noncooperative option) occurs because of free-riding, work is beginning to emerge demonstrating other motives underscoring non-cooperative behaviour. For one, individuals may choose not to cooperate as they do not trust others to cooperate (Van Lange et al., 2013). Because acting is costly, an individual may not want to cooperate if they believe others are also not doing their part. This explains why people cooperate more when individual action is transparent and when there is a possibility of punishment for non-cooperators (Wu et al., 2020). A third and under-explored motivation for defection is the feeling that one’s behaviour is ‘a drop in the bucket’. The drop in the bucket bias may be observed across a variety of circumstances, and several commonalities underpin these situations. For one, the drop in the bucket bias will only occur in collective action problems, where many individual actions are required to achieve a goal. As it suggests, one act cannot resolve the problem on their own. Second, the action must be costly for an individual, otherwise, in the absence of cost, the incentive to defect is absent. Finally, an individual must acknowledge the issue, and wish to resolve the issue. It’s this third criterion that separates the drop in the bucket bias from the tragedy of the commons – an individual is not choosing self-interest over the collective interest. Rather, they want to help achieve the solution for the problem, but they feel there is little their individual action can do. As noted, the drop in the bucket bias can only occur in collective action situations, where many individuals are required to act to achieve a solution. Using this logic, the bias should be less likely to occur in circumstances where only a few individuals are needed to achieve an outcome. As it suggests, smaller numbers of actions required to solve a problem would make an individual’s action more effective at alleviating said problem. Thus, when fewer actions (or people) are needed to solve a problem, cooperation should increase. Research demonstrates that increased group size does indeed lower cooperative behaviour in public goods games (Shank et al., 2015; Wu et al., 2020). However, whether this relationship can be explained, in part, by the drop in the bucket bias is unexplored.

Experiment 1 This study will explore whether drop in the bucket thinking occurs in collective action problems, and thus, in problems where many actions are required to achieve an outcome. Additionally, we aim to explore whether drop in the bucket thinking relates to reduced cooperation in these circumstances. We hypothesise that more actions needed to solve a problem will result in greater drop in the bucket thinking, which will, in turn, be associated with reduced cooperation. Pilot 1 & 2 A series of surveys will be included as filler surveys so participants are not aware of the main aim of the study. However, we have included several surveys that will represent exploratory pilot studies for other research questions. First, we will assess the relationship between qualitative descriptions of the stereotypical middle class and quantitative measures of the wealth distribution. Second, we will assess the link between the likelihood of moralising different behaviours (i.e., the extent to which one thinks many issues are moral issues) and rational/dialectical thinking.

In total, 200 REP participants completed the study. Participants were excluded for either not passing the attention check (i.e., a question asking participants to select option '3') or for completing the study in less than 1/3 of the median completion time. The final sample included 182 participants.

Study 1 Participants were asked to pledge some of their time to edit letters from school children directed towards politicians urging them to do something about an important cause (e.g., climate change). Participants were either be told there were a) only a small amount of letters left to edit before they can be sent out to politicians or b) there were a large amount of letters left to edit before they can be sent out to politicians. Critically, participants were then asked how much of their time they would like to pledge to help edit the letters as well as reasons for why they chose that particular time (e.g., whether or not they felt their part wouldn't make a difference for climate change). Pilot 1 & 2 Participants filled out several surveys measuring their perceptions of the wealth distribution, as well as surveys measuring their engagement in rational/dialectical thinking and how wrong they thought particular scenarios were.

Study 1 Using independent samples T-tests and correlation analyses, we will look at the relationship between condition and cooperation, as well as condition and how much one thinks their behaviour is 'just a drop in the bucket'. Depending on our findings, we may also see if the feeling that one's behaviour is a 'drop in the bucket' mediates or moderates the relationship between condition and cooperation. Pilot 1 & 2 In a series of correlation analyses, we will examine the relationship between quantitative measures of the wealth distribution and qualitative descriptions of the size of the middle class (e.g., that most people struggle to afford a house or make ends meet). We will also run a series of correlation analyses to assess how moralising relates to various measures of rational and dialectical thinking.

This study will hopefully shed light on why people do not cooperate in situations when their behaviour affects others. We believe the feeling that your contribution is small and won’t affect the outcome may be a reason why this occurs, and this feeling will be enhanced when you make decisions in the face of larger problems. Depending on our findings, we may run several follow up studies and hope to publish the results in a journal article and present the work at conferences.

The ability to monitor ongoing performance is a function critical to adapting behaviour. Detecting an error may signal that the task has increased in difficulty and adapting our approach to the task is necessary to improve performance, or avoid making the same mistake again. There is an ongoing need to examine how we recognise and adapt our behaviour following an error in performance. Further, the question of whether post-error slowing is non-functional requires greater exploration.

RQ: Does post-error slowing differ as a function of the uncertainty of errors? Hypotheses: We expect greater post-error slowing following errors in a condition where lure trials are presented randomly, as opposed to a condition where lures are presented in a predictable manner. We expect greater post-error slowing following aware errors than unaware errors.

80 participants completed the study. No exclusions have been applied.

A serial choice reaction time task was developed with modifications based on the error awareness task (EAT; Hester et al., 2005). The task was designed and presented using jsPsych and responses were recorded via the computer keyboard. Colour words were presented in a serial stream in either a congruent or incongruent colour. Participants were required to respond to the presentation of each stimulus by pressing the letter ‘A’, unless the stimulus was a lure. Lure trials occurred when the word and colour were congruent (Colour lure) or when the same word was presented on two consecutive trials (Repeat lure). Adopting competing inhibition rules exploits the different strengths of the stimulus-response relationships such that the overlearned behaviour of reading the word makes the Repeat rule more salient than the Colour rule. Previous research has suggested that this may cause competitive suppression of the Colour rule, producing more colour errors and affecting participants’ awareness of those errors (Hester et al., 2005). On lure trials, participants were required to respond by pressing a ‘hotkey’ (1, 2, 3, or 4). The appropriate lure response was determined by the participant’s response on the preceding lure trial. At the start of each block participants were instructed to use the hotkey ‘1’ unless they committed an error. To indicate an error, participants were required to change their lure response on the subsequent lure trial to ‘2’, and to continue using ‘2’ on lure trials until they committed another error. Error awareness was thus inferred when the participant’s lure response changed to the next key (2, 3, 4, 1). This modification to the original EAT design dissociates the error awareness response from any post-error adjustments in reaction time and was introduced to allow comparisons between post-lure behaviour following aware and unaware errors. If a participant were to make multiple consecutive errors before progressing their lure response to the next number key, however, it would not be possible to determine which of those errors was aware and which were unaware. To avoid this ambiguity, when participants made two consecutive errors they were then presented with a feedback screen that informed them they had just committed an error and queried what response they should have made. This feedback screen replaced the Go trial following the second consecutive error. If the participant’s response differed from what they should have used on previous lure, it was inferred that they were aware of the error. If they did not progress to the next hotkey in the series, however, the error was classified as unaware. Participants were administered two versions of the task over two separate conditions. The order of administration was randomised. Both tasks presented 5 blocks comprising 265 trials. Lures were presented on 25% of trials. In one task, lures were presented on average every 6 trials (SD = 2; range = 3-12). In the second task, lures were presented on average every 4 trials (SD = 2; range = 2-6).

Results have not yet been conducted. In line with Schroder et al. (2019), response speed adjustments following lure trials will be assessed by calculating the difference in reaction time for each of the post-lure trials and the target trial immediately preceding the lure (a subtraction of post-lure reaction time from the pre-lure reaction time). Pre-lure and lure trials in which the participant made no response will be excluded from analysis. Mixed effects models will be estimated using the mixed function from the afex package (Singmann et al., 2020). Consistent with recommendations by Singmann and Kellen (2019), Type III tests will be used as they are more reasonable under conditions of unbalanced data. Satterthwaite approximation for degrees of freedom will be used to obtain p-values as it provides the best control for Type I errors under the restricted maximum likelihood estimation and is less computationally intensive than the Kenward-Roger approximation (Singmann & Kellen, 2019). Post-hoc tests will be undertaken using the emmeans package (Russell et al., 2020). Marginal means and standard errors will be computed using the emmeans function and pairwise comparisons will be conducted using the pairs function. The emmip function will be used to create interaction style plots for estimated marginal means.

If our findings adhere to our hypotheses, our study will demonstrate that post-error slowing is not necessarily adaptive. Rather, it is influenced by the uncertainty of errors. These findings contest dominant functional accounts of post-error slowing whereby post-error slowing is suggested to reflect the adoption of a conservative response strategy that aims to improve performance subsequent to error detection. Our results lend support to non-functional accounts such as the orienting account of post-error slowing which consider post-error slowing to be a time-consuming orientation to an infrequent or salient event and a reorientation to the task at hand. We aim to submit our research for publication in a journal article.

This experiment delves into research on empathy, pain and observational learning. Current research has established that pain-related beliefs can be formed through personal experience and observation (Goubert et al., 2011). We expect that through observing facial and other cues provided, participants are able to learn novel words by pairing them to the model’s reactions. The experiment aims to understand if learning pain-related words is different to learning non-pain-related words and extend upon this by observing participant’s helping behaviours when seeing others in pain. Research suggests that closeness to those in pain and similar past experiences influences our desire to help others with no benefit to ourselves (Grynberg & Konrath, 2020; Nordgren et al., 2011). The study aims to intersect this understanding with empathy research. While it is generally understood that dispositional traits play a role in helping behaviours, literature into which facets of dispositional empathy influences behaviours and how these influences present themselves is divided (Daly, 2018; Davis, 1983). We aim to observe how dispositional empathy traits influence learning (of new pain-related words) and helping behaviours towards those with past experiences of physical or social distress vs. those with no distress. References Daly, A. (2018). The Empathy Mitigation: Empathy and its Impact on Pain Perception and Altruistic Motivation (Master’s Thesis, Abilene Christian University). https://orcid.org/0000-0003-3620-2264 Davis, M. H. (1983). The effects of dispositional empathy on emotional reactions and helping: A multidimensional approach. Journal of personality, 51(2), 167-184. https://doi.org/10.1111/j.1467-6494.1983.tb00860.x Goubert, L., Vlaeyen, J. W., Crombez, G., & Craig, K. D. (2011). Learning about pain from others: an observational learning account. The Journal of Pain, 12(2), 167-174. https://doi.org/10.1016/j.jpain.2010.10.001 Grynberg, D., & Konrath, S. (2020). The closer you feel, the more you care: Positive associations between closeness, pain intensity rating, empathic concern and personal distress to someone in pain. Acta Psychologica, 210, 103175. https://doi.org/10.1016/j.actpsy.2020.103175 Nordgren, L. F., Banas, K., & MacDonald, G. (2011). Empathy gaps for social pain: Why people underestimate the pain of social suffering. Journal of Personality and Social Psychology, 100(1), 120–128. https://doi.org/10.1037/a0020938

The purpose of this project is to observe people’s word learning capabilities when faced with others in pain through a word learning paradigm. We investigate the role of dispositional empathy - which is how well we can relate to, and understand another person’s emotional experiences - in influencing how an individual might help others. Our main research question is: Do participants with higher levels of dispositional empathy learn better (in this case, words) from the experience of someone else in pain? Our main hypotheses are: a) We hypothesise that there will be a correlation between pain word learning and empathy levels, where participants with higher empathy levels will show better performance on word learning through observation (higher accuracy and lower reaction times). b) It is hypothesised that participants will show better performance (faster response times and accuracy) to identifying and prescribing treatments when they have additional information about the model in the videos (e.g., that the model has had a history of physical or social distress, vs. no history of distress).

In total, 50 first-year undergraduate students at the University of Melbourne completed our experiment; no exclusions were applied.

The study consisted of a questionnaire and experimental conditions. Participants completed the Interpersonal Reactivity Index (IRI) and completed three stages of tasks where they associated pseudo-words (called ‘creams’ in the experiment) with specific reactions to hot/cold sensations. Participants would watch videos of four models reacting to the application of said creams, and determine whether they could recognise the cream applied, and prescribe the cream most relevant to alleviating their pain. During the trials, participants would view the pseudo-word, a thermometer indicating what type of sensation the models are experiencing (hot/cold), and a reaction from the model. Participants were also given histories on three out of the four models, providing a glimpse into some of their background and experiences. These histories were adapted from DeWall & Baumeister’s (2006) study, ‘Alone but Feeling No Pain: Effects of Social Exclusion on Physical Pain Tolerance and Pain Threshold, Affective Forecasting, and Interpersonal Empathy’ and were intended to evoke empathy in the participants. This was a minor concealment as participants were not told the histories were fictional but were later disclosed in the debriefing statement.

As the experiment only competed this week, data analysis is still in progress. Overall, we will use reaction times for recognition of form and meaning of painful and non-painful words tested with each of the phases of the experiment, and compare them directly, taking into account participants sex and level of empathy (a regression model will be applied). We expect the main effect of pain (faster reaction times to painful vs. non-painful words) and an interaction between the effect of pain and empathy (faster reaction times in more empathic participants). We further expect these to be affected by the knowledge of models' histories (which are physical, social, no pain or no background information).

We anticipate that this research will provide some understanding on how knowledge of patients can influence care in clinical settings. We also expect it to broaden our understanding of how dispositional empathy traits, particularly one’s ability to regulate personal distress and empathic concern, is an important skill to have when faced with patients experiencing physical or mental pain. We will plan to make the findings available through academic talks and research publications.

Research has found that insomnia symptoms and severity are associated with higher levels of perfectionism and specific aspects of perfectionism. However, it remains unknown whether this relationship exists only with the perception of poor sleep or whether the same relationship can be found with objective sleep measures.

1. Explore the relationship between perfectionism and insomnia using objective sleep parameters obtained from actigraphy. 2. Investigate the association between facets of multidimensional perfectionism and sleep disturbances. 3. Explore pathways whereby perfectionism may influence sleep through anxiety, stress, depression and sleep-related cognitions.

150 participants signed up for this pre-screening survey. Exclusion criteria: 1. Current diagnosis of psychosis, bipolar affective disorder, substance use disorder (including alcohol use disorder), central nervous system disorder, prior head injury and shift work. 2. Medication use (including sedatives, hypnotics and antidepressants) that may affect sleep may be permitted to participate if usage pattern is unstable.

All questionnaires were completed online via Qualtrics.

The REP survey is used as a pre-screening survey to find potential participants to participate in the two-week actigraphy study. Some eligible participants will be invited to proceed with the next phase of the study via email.

This project aims to contribute to the understanding of the relationship between perfectionism and sleep. Perfectionism may be a factor in determining the success of cognitive-behavioural therapy for insomnia (CBT-I) and incorporating perfectionism in CBT-I may benefit insomniacs who are highly perfectionistic. Results are planned for journal publication.

We are interested in how people’s personality, emotions and behaviour may change across the days of their menstrual cycle. Previous research has not yet provided a clear answer to this question, so our study seeks to gain a clearer and more detailed understanding of these changes. We intend this research to form the foundations of a larger scientific and social movement that supports women’s understanding of their own menstrual cycle, and by extension improves their health and wellbeing.

In this phase of data collection, we were interested in understanding the effect of fertility on competitiveness and self-objectification. We hypothesised that fertility would be associated with an increase in competitiveness and self-objectification in naturally cycling women but not hormonal contraceptive users.

100 REP Participants in semester 2, 2021. Exclusion criteria included people without a regular menstrual cycle, anyone with a fertility abnormality, POCS or endometriosis. People who are trying for pregnancy, are breastfeeding or who are currently experiencing menopause were also excluded.

Each day participants were asked to complete a 5 minute survey. Each survey contained a select number of questions, drawn from a larger pool of psychological measures. We also asked questions about the menstrual cycle and birth control.

The results of this study will be analysed in a number of ways. For example, we are interested in identifying differences between naturally cycling women and women that use contraceptives. However, we are also interested in whether or not there are clear differences in behaviour/psychology at different times in the cycle (e.g., during the menstrual phase compared to when ovulating).

The results of this research are planned for a number of journal articles. Each article is designed to educate the scientific community about how the menstrual cycle influences individual psychology.

Anxiety disorders are a kind of mental disorder defined by problematic anxiety, fear and avoidance (Craske & Stein, 2016). They are the most common type of mental disorder worldwide, develop early in life, and tend to be chronic. Those with anxiety disorders have a heightened sense of threat and danger that is excessive and not in proportion to the situation of concern. Exposure therapy is an effective psychological treatment for anxiety disorders. Exposure means approaching a feared (but actually safe) situation or thing (as in “exposing” yourself to what you are scared of). Exposure therapy involves approaching feared (but actually safe) situations with the support of a therapist until it is learned that these situations are safe. Decades or research and clinical experience demonstrates that exposure therapy improves all types of anxiety disorder. Despite this, researchers are not sure how and why exposure works. The leading view currently is that exposure works by providing experiences where clients can learn that their fears don't come true. The technical term for when a fear doesn't come true is "threat prediction-error". Discrepancy between anxious predictions and actual outcome vary from being a lot worse than expected, as-expected, to a lot better than expected. Current theory posits that larger threat prediction error during an exposure will lead to more safety learning. Exposure therapists should thus try to create conditions where people are surprised by the outcome and see that their fears don’t come true to the greatest extent possible. Evidence supporting this theory of exposure therapy comes from basic fear learning research with animals and humans and using controlled experimental procedures in laboratory settings. This fear learning research often involves participants viewing trials of abstract geometric shapes on a computer screen. Some shapes pair with an electric shock or loud noise and some don't. Researchers measure fear to see how it changes through different stimuli combinations. This research shows that fear in this kind of procedure reduces after threat prediction errors. Some researchers assume that the findings from this kind of research will translate to the "realworld" context of therapy in the clinic. However, findings may not transfer to the more complex and "messy" settings that characterise everyday life. We need more research to determine whether the threat prediction error model of exposure translates to real-world contexts outside of the laboratory. It is important to know the therapeutic mechanisms of our treatments. If we don't know the true treatment mechanisms, we may be subject clients to unnecessary procedures. Instead, we could drop unneeded procedures to focus on the real mechanisms of change. This study aims to address these gaps by exploring whether threat prediction error is associated with anxiety reduction through a procedure that resembles exposure therapy in the clinic. This procedure involves give six short speeches to a researcher over two days and rating anxiety, threat perception, and perception of threat outcome for each speech.

Questions: 1. How often do speeches turn out better, worse, and as-expected? 2. How does threat expectancy and perceived outcome relate to speech anxiety change over time? 3. How does social anxiety symptom level relate to speech anxiety, threat prediction, and perceived outcome? Hypotheses: 1. Anxiety, threat expectancy, and perceived outcome scores will vary significantly between participants, and also with within participants over time. 2. Discrepancy between speech threat expectancy and perceived outcome will relate to speech anxiety change over time. i. Speech anxiety will be lower after speeches that went better than expected. The better the outcome was compared to what was expected, the lower anxiety will be at the next speech. ii. Speech anxiety will be higher after speeches that went worse than expected. The worse the outcome was compared to what was expected, the higher anxiety will be at the next speech. iii. Speech anxiety will remain similar after speeches that went as-expected. 3. Higher social anxiety symptoms will be associated with higher overall levels of speech anxiety, threat expectancy, and perceived outcome.

69 people have participated in the study so far. 57 completed both part one and two. We need 100 participants in total and will keep recruiting next semester.

It is a longitudinal and observational study conducted remotely via Zoom. Participants give six 3 minute speeches to a researcher with one minute to prepare, while rating their anxiety, sense of threat, and threat outcome. These six speeches are done across two sessions separated by a week (3x speeches per session). Social anxiety and mood questionnaires are completed before each session.

Multi level modelling will be used where threat expectancy, threat outcome, and anxiety at time X will be entered as predictors of anxiety at time X + 1. A discrepancy effect will be supported if threat expectancy and outcome predictors are both significant and opposite in sign (Humberg et al, 2018), and this is what we are expecting. We will also test whether the joint relationship between threat expectancy / outcome and anxiety is more complex than a simple discrepancy effect by adding a set of higher order terms to the model (e.g see Nestler et al, 2019) and evaluating whether this improves fit with the data (Edwards, 2002). If adding higher order terms do not improve fit, then it allows more confidence in establishing that the relationship between the two predictors and anxiety represent a simple discrepancy effect.

The findings from this study will help determine whether threat prediction-errors are related to safety learning in settings that are similar to those in exposure therapy. Basic research in the laboratory suggests that they will be, but this proposition hasn't been tested enough in naturalistic settings. To our knowledge, no other studies to date have explored this explicitly in the area of social anxiety. Giving a speech is more complex than experimental procedures used in basic fear learning research, so it can't be assumed that safety learning works the same way in these two scenarios. Clinicians are conducting exposure based on the prediction error model, so it is important to know if this practice is correct or whether they should focus their practice on other mechanisms. This study will contribute to knowledge about how fear and safety learning works for humans in everyday settings. Achieving these aims will help refine understanding of exposure therapy mechanisms and may lead to better treatment outcomes for people living with anxiety. Anxiety disorders are the most common mental disorder worldwide, so research that may help improve outcomes for these issues is important.

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

Please visit the following link for full information about this study: https://knowlabunimelb.github.io/SCHEDULE_RT/index.html

There are over three decades of research suggesting that Self-Determination Theory is highly relevant for conceptualising wellbeing, particularly in university students (Ryan & Deci, 2017). Self-Determination Theory proposes that three basic psychological needs (i.e., needs for autonomy, competence and relatedness) must be satisfied for wellbeing and academic achievement to ensue. However, scales measuring need satisfaction and need frustration in a university setting are still being developed. This study aimed to investigate the psychometric properties of the Need Satisfaction and Need Frustration scale (Longo et al., 2016), and investigate the relationship between need satisfaction and frustration with other measures of wellbeing, illbeing and academic achievement.

Our research questions were: 1. Are the psychometric properties of basic need scales similar to those reported in studies with different contexts? 2. How well do scores on basic need scales moderate the relationship between student wellbeing and university academic achievement? 3. What is the relationship between need satisfaction at university and need satisfaction in general?

Participants were 341 first-year psychology students who were eighteen years old or older from the University of Melbourne. Most participants identified with one gender, which was female (77.7%) or male (21.7%), and two participants (0.6%) preferred not to say. No participants responded with alternative gender or genders. The mean age for participants was 19.90 years (SD = 4.46 years; range = 18 to 60 years). Most participants were studying full-time (94.4%; part-time = 5.6%) and in their first year of studies (83%). Forty two percent were international students and 58 percent were domestic students.

Participants completed an anonymous, 20 minute, online survey in their own time. Participants were asked to respond to demographic questions, to provide their weighted average mark (WAM) and to rate the effort they have expended on their studies so far. Participants then rated two measures of basic needs (one for a university setting and one for daily life), and four other measures of wellbeing, as well as measures on negative affect and psychological exhaustion. Measures used were: the Need Satisfaction and Frustration Scale (Longo et al., 2016) the Balanced Measure of Psychological Needs scales (Sheldon & Hilpert, 2012) the Warwick Edinburgh Mental Well- Being Scale (Clarke et al., 2011) the Scale of Positive and Negative Experience (Diener et al., 2010); the Vigour subscale from the Utrecht Work Engagement Scale for students (Schaufeli et al., 2002) the Exhaustion subscale from the Maslach Burnout Inventory- Student Survey (Schaufeli et al., 2002) the intrinsic motivation subscale of the Motivation at Work Scale (Gagne ́ et al. 2010) adapted for university students.

The results were consistent with previous research of student need satisfaction from other countries. The results showed that the Need Satisfaction and Need Frustration scale was a valid and reliable measure with this student cohort from the University of Melbourne, and need satisfaction positively correlated with wellbeing whereas need frustration positively correlated with illbeing. The results also showed need satisfaction/frustration at university was moderately correlated with need satisfaction/need frustration in daily life, which further supported the validity of the Need Satisfaction and Need Frustration scale. Lastly, the results showed that Relatedness Frustration and Competence Satisfaction positively predicted WAMs and effort expenditure, suggesting these needs were particularly important for this cohort in relation to academic achievement.

These results suggested that the Need Satisfaction and Need Frustration scale is a useful scale for measuring basic needs in university students, and by extension, for measuring university student wellbeing. Implications included using this scale to assess and monitor university student wellbeing and course engagement. While these results were limited to undergraduate psychology students, it extended on research conducted in other countries with undergraduates and high school students. Students from different faculties may demonstrate that other basic needs are important for academic success, but it appeared that competence and relatedness were import for this psychology student cohort. It is anticipated that these results will be published in an academic journal in 2022.