Predicting implicit attitudes with natural language data.

Large-scale language datasets and advances in natural language processing offer opportunities for studying people's cognitions and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach achieves substantially higher correlations than existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures variance in implicit attitudes that is largely unexplained by explicit attitudes. Overall, our results shed light on how implicit attitudes can be measured by combining standard psychological data with large-scale language data. In doing so, we pave the way for highly accurate computational modeling of what people think and feel about the world around them.

Workplace inequality is associated with status-signaling expenditure.

Regional inequality is known to magnify sensitivity to social rank. This, in turn, is shown to increase people's propensity to acquire luxury goods as a means to elevate their perceived social status. Yet existing research has focused on broad, aggregated datasets, and little is known about how individual-level measures of income interact with inequality within peer groups to affect status signaling. Using detailed financial transaction data, we construct 32,008 workplace peer groups and explore the longitudinal spending and salary data associated with 683,677 individuals. These data reveal links between people's status spending, their absolute salary, salary rank within their workplace peer group, and the inequality of their workplace salary distribution. Status-signaling luxury spending is found to be greatest among those who have higher salaries, whose workplaces exhibit higher inequality, and who occupy a lower rank position within the workplace. We propose that low-rank individuals in unequal workplaces suffer status anxiety and, if they can afford it, spend to signal higher status.

Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.

BACKGROUND: Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used in combination with Electronic Health Records for prediction of depression. METHODS: Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022). RESULTS: Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques. LIMITATIONS: The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography. CONCLUSION: This review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns with generalizability and interpretability.

Computational Methods for Predicting and Understanding Food Judgment.

People make subjective judgments about the healthiness of different foods every day, and these judgments in turn influence their food choices and health outcomes. Despite the importance of such judgments, there are few quantitative theories about their psychological underpinnings. This article introduces a novel computational approach that can approximate people's knowledge representations for thousands of common foods. We used these representations to predict how both lay decision-makers (the general population) and experts judge the healthiness of individual foods. We also applied our method to predict the impact of behavioral interventions, such as the provision of front-of-pack nutrient and calorie information. Across multiple studies with data from 846 adults, our models achieved very high accuracy rates (r2 = .65-.77) and significantly outperformed competing models based on factual nutritional content. These results illustrate how new computational methods applied to established psychological theory can be used to better predict, understand, and influence health behavior.

A feasibility study of the delivery of online brief cognitive-behavioral therapy (CBT-T) for eating disorder pathology in the workplace.

OBJECTIVE: CBT-T is a brief (10 sessions) version of cognitive behavioral therapy for non-underweight eating disorders. This report describes the protocol for a single center, single group, feasibility trial of online CBT-T in the workplace as an alternative to the health-service setting. By offering mental health services for eating disorders in the workplace, greater accessibility and increased help-seeking behaviors could be achieved. METHOD: Treatment will be delivered online over 10 weeks and offered to employees based on self-referral rather than meeting diagnostic criteria, making treatment available to employees with sub-threshold eating disorder symptoms. RESULTS: Assessments will be conducted at baseline, mid-treatment (week 4), posttreatment (week 10) and at follow-up (1 month and 3 months posttreatment). For the primary outcome, measures will include recruitment, attrition and attendance data using pre-set benchmarks to determine high, medium or low feasibility and acceptability. Qualitative participant experiences data will be analyzed using thematic analysis. Impact on work engagement and effect sizes will be determined from secondary outcome measures; the latter enabling sample size calculations for future study. DISCUSSION: These pilot data will provide insights to recruitment, acceptability, effectiveness and viability of a future fully powered clinical trial of online CBT-T in the workplace. PUBLIC SIGNIFICANCE STATEMENT: This study will present feasibility data from an eating disorders intervention (online CBT-T) using the workplace as an alternative to the healthcare setting to recruit and treat workers. Recruitment will be based on self-reported eating and weight concerns rather than diagnosis potentially enabling treatment to employees who have not previously sought help. The data will also provide insights to recruitment, acceptability, effectiveness, and future viability of CBT-T in the workplace.

Can a 'rewards-for-exercise app' increase physical activity, subjective well-being and sleep quality? An open-label single-arm trial among university staff with low to moderate physical activity levels.

BACKGROUND: This study examined the impact of a 'rewards-for-exercise' mobile application on physical activity, subjective well-being and sleep quality among 148 employees in a UK university with low to moderate physical activity levels. METHODS: A three-month open-label single-arm trial with a one-year follow-up after the end of the trial. Participants used the Sweatcoin application which converted their outdoor steps into a virtual currency used for the purchase of products available at the university campus' outlets, using an in-app marketplace. The primary outcome measure was self-reported physical activity. Secondary measures included device-measured physical activity, subjective well-being (i.e., life satisfaction, positive affect, negative affect), and self-reported sleep quality. RESULTS: The findings show an increase in self-reported physical activity (d = 0.34), life satisfaction (d = 0.31), positive affect (d = 0.29), and sleep quality (d = 0.22) during the three-month trial period. CONCLUSION: The study suggests that mobile incentives-for-exercise applications might increase physical activity levels, positive affect, and sleep quality, at least in the short term. The observed changes were not sustained 12 months after the end of the trial.

The More Who Die, the Less We Care: Evidence from Natural Language Analysis of Online News Articles and Social Media Posts.

Considerable amount of laboratory and survey-based research finds that people show disproportional compassionate and affective response to the scope of human mortality risk. According to research on "psychic numbing," it is often the case that the more who die, the less we care. In the present article, we examine the extent of this phenomenon in verbal behavior, using large corpora of natural language to quantify the affective reactions to loss of life. We analyze valence, arousal, and specific emotional content of over 100,000 mentions of death in news articles and social media posts, and find that language shows an increase in valence (i.e., decreased negative affect) and a decrease in arousal when describing mortality of larger numbers of people. These patterns are most clearly reflected in specific emotions of joy and (in a reverse fashion) of fear and anger. Our results showcase a novel methodology for studying affective decision making, and highlight the robustness and real-world relevance of psychic numbing. They also offer new insights regarding the psychological underpinnings of psychic numbing, as well as possible interventions for reducing psychic numbing and overcoming social and psychological barriers to action in the face of the world's most serious threats.

Psychological mechanisms of loss aversion: A drift-diffusion decomposition.

Decision makers often reject mixed gambles offering equal probabilities of a larger gain and a smaller loss. This important phenomenon, referred to as loss aversion, is typically explained by prospect theory, which proposes that decision makers give losses higher utility weights than gains. In this paper we consider alternative psychological mechanisms capable of explaining loss aversion, such as a fixed utility bias favoring rejection, as well as a bias favoring rejection prior to gamble valuation. We use a drift diffusion model of decision making to conceptually distinguish, formally define, and empirically measure these mechanisms. In two preregistered experiments, we show that the pre-valuation bias provides a very large contribution to model fits, predicts key response time patterns, reflects prior expectations regarding gamble desirability, and can be manipulated independently of the valuation process. Our results indicate that loss aversion is the result of multiple different psychological mechanisms, and that the pre-valuation bias is a fundamental determinant of this well-known behavioral tendency. These results have important implications for how we model behavior in risky choice tasks, and how we interpret its relationship with various psychological, clinical, and neurobiological variables.

Engagement with MyFitnessPal in eating disorders: Qualitative insights from online forums.

OBJECTIVE: Using calorie-counting and fitness-tracking technologies is concerning in relation to eating disorders. While studies in this area typically assess one aspect of use (e.g., frequency), engagement with a device or application is more complex. Consequently, important relationships between the use of these technologies and the eating disorder symptomatology might remain undetected. The current study therefore used comments from online eating disorder-related forums to generate comprehensive qualitative insights into engagement with a popular calorie-counting and fitness-tracking application, MyFitnessPal. METHOD: First, we extracted every comment mentioning MyFitnessPal made on three eating disorder-related forums between May 2015 and January 2018 (1,695 comments from 920 commenters). Then, we conducted an inductive thematic analysis using these comments to identify important aspects of engagement with MyFitnessPal. RESULTS: The analyses resulted in three themes: Preventing misuse, describing ways in which MyFitnessPal attempts to prevent pathological use and actions taken by users to circumvent its interventions; Accuracy, outlining distrust of MyFitnessPal's accuracy and ways in which perceived inaccuracy is reduced or compensated for; and Psychosocial factors, comprising cognitive, behavioral, and social factors that influence, or are influenced by, engagement with MyFitnessPal. DISCUSSION: The qualitative insights provide a detailed overview of how people with high levels of eating disorder symptomatology likely engage with MyFitnessPal. The insights can be used as a basis to develop valid, quantitative assessment of pathological patterns of engagement with calorie-counting and fitness-tracking technologies. The findings can also provide clinicians with insight into how their patients likely engage with, and are affected by, these devices and applications.

Association and response accuracy in the wild.

We studied contestant accuracy and error in a popular television quiz show, "Jeopardy!" Using vector-based knowledge representations obtained from distributional models of semantic memory, we computed the strength of association between clues and responses in over 5,000 televised games. Such representations have been shown to play a key role in memory and judgment, and consistent with this work, we find that contestants are more likely to provide correct responses when these responses are strongly associated with their clues, and more likely to provide incorrect responses when correct responses are weakly or negatively associated with their clues. This effect is stronger for easier questions with low monetary values, and for questions in which contestants compete to respond quickly. Our results show how distributional models of semantic memory can be used to predict human behavior in naturalistic high-level judgment tasks with skilled participants and significant monetary and social incentives.

Text-mining as a methodology to assess eating disorder-relevant factors: Comparing mentions of fitness tracking technology across online communities.

OBJECTIVE: Text-mining offers a technique to identify and extract information from a large corpus of textual data. As an example, this study presents the application of text-mining to assess and compare interest in fitness tracking technology across eating disorder and health-related online communities. METHOD: A list of fitness tracking technology terms was developed, and communities (i.e., 'subreddits') on a large online discussion platform (Reddit) were compared regarding the frequency with which these terms occurred. The corpus used in this study comprised all comments posted between May 2015 and January 2018 (inclusive) on six subreddits-three eating disorder-related, and three relating to either fitness, weight-management, or nutrition. All comments relating to the same 'thread' (i.e., conversation) were concatenated, and formed the cases used in this study (N = 377,276). RESULTS: Within the eating disorder-related subreddits, the findings indicated that a 'pro-eating disorder' subreddit, which is less recovery focused than the other eating disorder subreddits, had the highest frequency of fitness tracker terms. Across all subreddits, the weight-management subreddit had the highest frequency of the fitness tracker terms' occurrence, and MyFitnessPal was the most frequently mentioned fitness tracker. DISCUSSION: The technique exemplified here can potentially be used to assess group differences to identify at-risk populations, generate and explore clinically relevant research questions in populations who are difficult to recruit, and scope an area for which there is little extant literature. The technique also facilitates methodological triangulation of research findings obtained through more 'traditional' techniques, such as surveys or interviews.

Income inequality and status seeking: searching for positional goods in unequal U.S. States.

It is well established that income inequality is associated with lower societal well-being, but the psychosocial causes of this relationship are poorly understood. A social-rank hypothesis predicts that members of unequal societies are likely to devote more of their resources to status-seeking behaviors such as acquiring positional goods. We used Google Correlate to find search terms that correlated with our measure of income inequality, and we controlled for income and other socioeconomic factors. We found that of the 40 search terms used more frequently in states with greater income inequality, more than 70% were classified as referring to status goods (e.g., designer brands, expensive jewelry, and luxury clothing). In contrast, 0% of the 40 search terms used more frequently in states with less income inequality were classified as referring to status goods. Finally, we showed how residual-based analysis offers a new methodology for using Google Correlate to provide insights into societal attitudes and motivations while avoiding confounds and high risks of spurious correlations.

Incomparability and Incommensurability in Choice: No Common Currency of Value?

Models of decision-making typically assume the existence of some common currency of value, such as utility, happiness, or inclusive fitness. This common currency is taken to allow comparison of options and to underpin everyday choice. Here we suggest instead that there is no universal value scale, that incommensurable values pervade everyday choice, and hence that most existing models of decision-making in both economics and psychology are fundamentally limited. We propose that choice objects can be compared only with reference to specific but nonuniversal "covering values." These covering values may reflect decision-makers' goals, motivations, or current states. A complete model of choice must accommodate the range of possible covering values. We show that abandoning the common-currency assumption in models of judgment and decision-making necessitates rank-based and "simple heuristics" models that contrast radically with conventional utility-based approaches. We note that if there is no universal value scale, then Arrow's impossibility theorem places severe bounds on the rationality of individual decision-making and hence that there is a deep link between the incommensurability of value, inconsistencies in human decision-making, and rank-based coding of value. More generally, incommensurability raises the question of whether it will ever be possible to develop single-quantity-maximizing models of decision-making.

Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records.

BACKGROUND: Recent advances in machine learning combined with the growing availability of digitized health records offer new opportunities for improving early diagnosis of depression. An emerging body of research shows that Electronic Health Records can be used to accurately predict cases of depression on the basis of individual's primary care records. The successes of these studies are undeniable, but there is a growing concern that their results may not be replicable, which could cast doubt on their clinical usefulness. METHODS: To address this issue in the present paper, we set out to reproduce and replicate the work by Nichols et al. (2018), who trained predictive models of depression among young adults using Electronic Healthcare Records. Our contribution consists of three parts. First, we attempt to replicate the methodology used by the original authors, acquiring a more up-to-date set of primary health care records to the same specification and reproducing their data processing and analysis. Second, we test models presented in the original paper on our own data, thus providing out-of-sample prediction of the predictive models. Third, we extend past work by considering several novel machine-learning approaches in an attempt to improve the predictive accuracy achieved in the original work. RESULTS: In summary, our results demonstrate that the work of Nichols et al. is largely reproducible and replicable. This was the case both for the replication of the original model and the out-of-sample replication applying NRCBM coefficients to our new EHRs data. Although alternative predictive models did not improve model performance over standard logistic regression, our results indicate that stepwise variable selection is not stable even in the case of large data sets. CONCLUSION: We discuss the challenges associated with the research on mental health and Electronic Health Records, including the need to produce interpretable and robust models. We demonstrated some potential issues associated with the reliance on EHRs, including changes in the regulations and guidelines (such as the QOF guidelines in the UK) and reliance on visits to GP as a predictor of specific disorders.

Fenneman et al.'s (2022) review of formal impulsivity models: Implications for theory and measures of impulsivity.

In Fenneman et al.'s (2022) review of theories and integrated impulsivity model, the authors distinguish between information impulsivity (i.e., acting without considering consequences) and temporal impulsivity (i.e., the tendency to pick sooner outcomes over later ones). The authors find that both types of impulsivity can be adaptive in different contexts. For example, when individuals experience scarcity of resources or when they are close to a minimum level of reserves (critical threshold). In this commentary, we extend their findings to a discussion about the measurement of impulsivity. We argue that a common method for measuring temporal impulsivity in which people make decisions between outcomes that are spaced out in time (intertemporal choice tasks), puts individuals in a specific context that is unlikely to generalize well to other situations. Furthermore, trait measures of impulsivity may only be modestly informative about future impulsive behavior because they largely abstract away from important context. To address these issues, we advocate for the development of dynamic measures of the two types of impulsivity. We argue that measuring temporal impulsivity in naturalistic contexts with varying environmental and state parameters could provide insights into whether individuals (i.e., humans and nonhuman animals) react to environmental changes adaptively, while trait measures of impulsivity more generally should collect and provide more contextual information. Dynamic measurement of different types of impulsivity will also allow for more discussion about adaptive impulsive responses in different contexts, which could help combat the stigmatization of various disorders associated with impulsivity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Political Attitudes and Disease Threat: Regional Pathogen Stress Is Associated With Conservative Ideology Only for Older Individuals.

What environmental factors are associated with individual differences in political ideology, and do such associations change over time? We examine whether reductions in pathogen prevalence in U.S. states over the past 60 years are associated with reduced associations between parasite stress and conservatism. We report a positive association between infection levels and conservative ideology in the United States during the 1960s and 1970s. However, this correlation reduces from the 1980s onwards. These results suggest that the ecological influence of infectious diseases may be larger for older people who grew up (or whose parents grew up) during earlier time periods. We test this hypothesis by analyzing the political affiliation of 45,000 Facebook users, and find a positive association between self-reported political affiliation and regional pathogen stress for older (>40 years) but not younger individuals. It is concluded that the influence of environmental pathogen stress on ideology may have reduced over time.

Content and Validity of Claims Made about Food Parenting Practices in United Kingdom Online News Articles.

The objective of this study was to qualitatively summarise the content of online news articles pertaining to food parenting practices and determine whether this content is substantiated by the scientific literature. News article data were identified and collected from United Kingdom online news published during 2010-2017 period using the News on the Web corpus. A coding framework was used to categorise the content of news articles to identify information related to food parenting practices. Then, claims made about food parenting practices were extracted from relevant news articles. Each claim was evaluated to determine the extent to which any claims were supported by the available scientific research evidence. The study identified ten claims across thirty-two relevant online news articles. Claims made across the news articles reported on the following food parenting practices: food restrictions, food-based threats and bribes, pressure to eat, use of food to control negative emotions, food availability, food preparation, and meal and snack routines. Eight out of the ten claims identified did not refer to scientific research evidence. News articles frequently lacked detail and information to explain to readers why and how the use of certain food parenting practices could have a lasting impact on children's health outcomes. Considering the influence that news media has on parents, the reporting of food parenting practices in news articles should aim to provide a balanced view of the published scientific evidence and recognise the difficulties and barriers that prevent the use of helpful and healthy food parenting practices. The study results in this paper could be used to aid and structure of the dissemination of food parenting practice research findings in the media, inform public health education to influence perceptions of unhelpful food parenting practices, and promote parental use of responsive food parenting practices.

Psychosocial factors influencing the eating behaviours of older adults: A systematic review.

Our understanding of how eating behaviours change in later life have been dominated by the studies of physiological and biological influences on malnutrition. Insights from these studies were consequently used to develop interventions, which are predominantly aimed at rectifying nutritional deficiencies, as opposed to interventions that may enable older adults to eat well and enjoy their food-related life well into older age. The objective of the present review is to summarise the existing knowledge base on psychosocial influences on eating behaviours in later life. Following comprehensive searches, review, and appraisal, 53 articles were included (22 qualitative and 31 quantitative) to provide a greater understanding of the mechanisms underpinning the psychosocial factors influencing eating behaviours. Our analysis identified eight underpinning psychosocial factors that influences eating behaviours in later life; (1) health awareness & attitudes, (2) food decision making, (3) perceived dietary control, (4) mental health & mood, (5) food emotions & enjoyment, (6) eating arrangements, (7) social facilitation, and (8) social support. The importance and lasting influence of early food experiences were also identified as contributing to eating behaviours in later life. The review concludes with the call for further investigation into specific psychosocial factors that influence eating behaviour, calls for improvements in methodologies, and a summary of psychosocial barriers and enablers to eating well in later life.

Assessing physical access to healthy food across United Kingdom: A systematic review of measures and findings.

Background: Existing research suggests that physical access to food can affect diet quality and thus obesity rates. When defining retail food environment (RFE) quantitatively, there is a little agreement on how to measure "lack of healthy food" and what parameters to use, resulting in a heterogeneity of study designs and outcome measures. In turn, this leads to a conflicting evidence base being one of the many barriers to using evidence in policy-making. Aims: This systematic review aimed to identify and describe methods used to assess food accessibility in the United Kingdom (UK) to overcome heterogeneity by providing a classification of measures. Materials & Methods: The literature search included electronic and manual searches of peer-reviewed literature and was restricted to studies published in English between January 2010 and March 2021. A total of 9365 articles were assessed for eligibility, of which 44 articles were included in the review. All included studies were analysed with regards to their main characteristics (e.g., associations between variables of interest, setting, sample, design, etc.) and definition of RFE and its metrics. When defining these metrics, the present review distinguishes between a point of origin (centroid, address) from which distance was calculated, summary statistic of accessibility (proximity, buffer, Kernel), and definition of distance (Euclidean, network distance). Trends, gaps and limitations are identified and recommendations made for food accessibility research in UK. Results: Multiple theoretical and methodological constructs are currently used, mostly quantifying distance by means of Euclidean and ring-buffer distance, using both proximity- and density-based approaches, and ranging from absolute to relative measures. The association between RFE and diet and health in rural areas, as well as a spatiotemporal domain of food access, remains largely unaccounted. Discussion: Evidence suggests that the duration of exposure may bear a greater importance than the level of exposure and that density-based measures may better capture RFE when compared with proximity-based measures, however, using more complex measures not necessarily produce better results. To move the field forward, studies have called for a greater focus on causality, individual access and the use of various measures, neighbourhood definitions and potential confounders to capture different aspects and dimensions of the RFE, which requires using univariate measures of accessibility and considering the overall context in terms of varying types of neighbourhoods. Conclusion: In order to render ongoing heterogeneity in measuring RFE, researchers should prioritise measures that may provide a more accurate and realistic account of people's lives and follow an intuitive approach based on convergence of results until consensus could be reached on using some useful standards.

Rank-based alternatives to mean-based ensemble models of satisfaction with earnings: Comment on Putnam-Farr and Morewedge (2020).

How much satisfaction do we derive from a new salary or from receiving a bonus payment in an experiment? People do not judge monetary amounts in isolation but compare them to other amounts-judgments are context sensitive. A key question is, however, how context affects judgment. Across eight experiments, Putnam-Farr and Morewedge (2020) showed that people's self-reported satisfaction with a sum of money is predicted by the difference between that amount and the highest or lowest amount received by others. The authors found no evidence that people's judgments are sensitive to the ranked position of a monetary amount among other rewards. Putnam-Farr and Morewedge explained their results with reference to the ensemble representation literature, which shows that people can accurately estimate summary statistics, such as the maximum or mean, of stimulus distributions. In this commentary, we argue that their proposed interpretation is inconsistent with extensive theoretical and empirical research showing that judgments of stimuli reflect the relative ranked position of those stimuli within a comparison context. Building on this research, we show that the experimental results reported by Putnam-Farr and Morewedge can be explained on the assumption that people use contextual information to infer a distribution of monetary amounts and judge individual amounts by their relative ranked position within that inferred distribution. This inferred distribution theory accounts for empirical results reported in the original study while remaining consistent with the general and well-established principle of rank-based judgment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).