Empirical Data Research Articles

Representational geometry explains puzzling error distributions in behavioral tasks

Measuring and interpreting errors in behavioral tasks is critical for understanding cognition. Conventional wisdom assumes that encoding/decoding errors for continuous variables in behavioral tasks should naturally have Gaussian distributions, so that deviations from normality in the empirical data indicate the presence of more complex sources of noise. This line of reasoning has been central for prior research on working memory. Here, we reassess this assumption and find that even in ideal observer models with Gaussian encoding noise, the error distribution is generally non-Gaussian, contrary to the commonly held belief. Critically, we find that the shape of the error distribution is determined by the geometrical structure of the encoding manifold via a simple rule. In the case of a high-dimensional geometry, the error distributions naturally exhibit flat tails. Using this insight, we apply our theory to visual short-term memory tasks, and find that it can account for a large array of experimental data with only two free parameters. Our results challenge the dominant view in the mechanisms and capacity constraints of working memory systems. They instead suggest that the Bayesian framework, which explains various aspects of perceptual behavior, also provides an excellent account of working memory. Overall, our results establish a direct connection between neural manifold geometry and behavior, and call attention to the geometry of the representation as a critically important, yet underappreciated factor in determining the character of errors in human behavior.

Stress responsiveness in a wild primate predicts survival across an extreme El Niño drought.

We know more about the costs of chronic stress than the benefits of the acute stress response-an adaptive response that buffers organisms from life-threatening challenges. As yet, no primate study has empirically identified how the stress response adaptively affects evolutionary fitness. Here, we take advantage of a natural experiment-an El Niño drought-that produced unprecedented mortality for wild white-faced capuchins. Using a reaction norm approach, we provide evidence from primates that a more robust stress response to a challenge, measured using fecal glucocorticoids, predicts a greater likelihood of survival. We show that individuals with greater stress responsiveness to previous droughts later had higher survival across a severe El Niño drought. Evolutionary models need empirical data on how stress responsivity varies in adaptive ways. While we cannot buffer subjects from catastrophic events, we can use them to understand which aspects of the stress response help animals to "weather the storm."

Global initiatives for industry 4.0 implementation and progress within the textile and apparel manufacturing sector: a comprehensive review

ABSTRACT A comprehensive picture on global initiatives to Industry 4.0 (I4.0), technological evolution within fibre-to-apparel manufacturing and its progression so far are reported here. Germany is the global progression leader while other developed countries; France, Italy, Spain, Portugal, and even the UK and USA have generic initiatives for upgraded manufacturing. Their initiatives are I4.0 strategies, funding, platforms, and model projects. Developing countries like Indonesia, Malaysia, Thailand, Brazil, Turkey, Vietnam, India, etc. have also taken I4.0 national initiatives. However, the apparel and textile manufacturing industry lags far behind other sectors, although applying I4.0 technologies in this sector has started. CPS, IOT, Cloud computing, Automatic bobbin changing, RFID, Blockchain, augmented reality, and digital twins, are found in the textile production and fashion retailing. Moreover, advanced machineries offer incorporated automation, remote maintenance and operability, cloud systems, self-optimization, and predictive maintenance facilities. The review identifies that technological gaps between concept and reality hinder commercial application. The readiness evaluation is predominantly grounded in academic literature, which requires field investigation from the start to the end of textile processing. Hence, case studies for empirical data, sustainability mapping for the upgraded technologies and integrated policy development are the future direction in this domain.

Are Bayesian regularization methods a must for multilevel dynamic latent variables models?

Due to the increased availability of intensive longitudinal data, researchers have been able to specify increasingly complex dynamic latent variable models. However, these models present challenges related to overfitting, hierarchical features, non-linearity, and sample size requirements. There are further limitations to be addressed regarding the finite sample performance of priors, including bias, accuracy, and type I error inflation. Bayesian estimation provides the flexibility to treat these issues simultaneously through the use of regularizing priors. In this paper, we aim to compare several Bayesian regularizing priors (ridge, Bayesian Lasso, adaptive spike-and-slab Lasso, and regularized horseshoe). To achieve this, we introduce a multilevel dynamic latent variable model. We then conduct two simulation studies and a prior sensitivity analysis using empirical data. The results show that the ridge prior is able to provide sparse estimation while avoiding overshrinkage of relevant signals, in comparison to other Bayesian regularization priors. In addition, we find that the Lasso and heavy-tailed regularizing priors do not perform well compared to light-tailed priors for the logistic model. In the context of multilevel dynamic latent variable modeling, it is often attractive to diversify the choice of priors. However, we instead suggest prioritizing the choice of ridge priors without extreme shrinkage, which we show can handle the trade-off between informativeness and generality, compared to other priors with high concentration around zero and/or heavy tails.

Network Analyses to Explore Comorbidities Among Older Adults Living With Dementia.

Older persons living with dementia (PLWD) often have multiple other chronic health conditions (i.e., comorbidities). Network analyses can describe complex profiles of chronic health conditions through graphical displays grounded in empirical data. Our study compared patterns of chronic health conditions among PLWD residing in and outside of long-term care (LTC) settings. Population-based administrative data, including outpatient physician claims, inpatient records, pharmaceutical records, and LTC records, for the study were from the Canadian province of Manitoba. We included PLWD, ages ≥ 67 years, with two or more other chronic health conditions, who resided in Manitoba from 2017 to 2020. A total of 138 chronic health conditions were ascertained using a modification of the open-source Clinical Classification Software. Networks defined by nodes (health conditions) and edges (associations between nodes) were stratified by residence location (in versus outside LTC). Network properties were described, including: density (ratio of number of edges to number of potential edges), and modularity (associations between and within clusters of health conditions), and the median and interquartile range (IQR) for node degree (number of associations per node). The population comprised 19,672 PLWD, of which 17,534 (89.1%) had two or more chronic health conditions. The median number of co-occurring conditions was similar among PLWD in LTC (median: 6, IQR: 3-10) versus outside LTC (median: 7, IQR: 4-10). Network properties were similar for PLWD and multiple comorbidities residing in versus outside LTC, including node degree (median 11 vs. 12), density (0.15 vs. 0.14), and modularity (0.18 vs. 0.26). Multiple chronic diseases characterize PLWD residing in and outside of LTC. Using network analyses, chronic diseases among PLWD do not form easily distinguishable groups or patterns. This suggests the need for comprehensive clinical assessments, individualized approaches for disease management, and highlights the importance of person-specific care.

Study of the impact of innovative polycentricity on carbon productivity: empirical data from China

Study of the impact of innovative polycentricity on carbon productivity: empirical data from China

An integrated multiphase dynamic genome-scale model explains batch fermentations led by species of the Saccharomyces genus.

During batch fermentation, a variety of compounds are synthesized, as microorganisms undergo distinct growth phases: lag, exponential, growth-no-growth transition, stationary, and decay. A detailed understanding of the metabolic pathways involved in these phases is crucial for optimizing the production of target compounds. Dynamic flux balance analysis (dFBA) offers insight into the dynamics of metabolic pathways. However, explaining secondary metabolism remains a challenge. A multiphase and multi-objective dFBA scheme (MPMO model) has been proposed for this purpose. However, its formulation is discontinuous, changing from phase to phase; its accuracy in predicting intracellular fluxes is hampered by the lack of a mechanistic link between phases; and its simulation requires considerable computational effort. To address these limitations, we combine a novel model with a genome-scale model to predict the distribution of intracellular fluxes throughout batch fermentation. This integrated multiphase continuous model (IMC) has a unique formulation over time, and it incorporates empirical regulatory descriptions to automatically identify phase transitions and incorporates the hypotheses that yeasts might vary their cellular objective over time to adapt to the changing environment. We validated the predictive capacity of the IMC model by comparing its predictions with intracellular metabolomics data for Saccharomyces uvarum during batch fermentation. The model aligns well with the data, confirming its predictive capabilities. Notably, the IMC model accurately predicts trehalose accumulation, which was enforced in the MPMO model. We further demonstrate the generalizability of the IMC model, explaining the dynamics of primary and secondary metabolism of three Saccharomyces species. The model provides biological insights consistent with the literature and metabolomics data, establishing it as a valuable tool for exploring the dynamics of novel fermentation processes.IMPORTANCEThis work presents an integrated multiphase continuous dynamic genome-scale model (IMC model) for batch fermentation, a crucial process widely used in industry to produce biofuels, enzymes, pharmaceuticals, and food products or ingredients. The IMC model integrates a continuous kinetic model with a genome-scale model to address the critical limitations of existing dynamic flux balance analysis schemes, such as the difficulty of explaining secondary metabolism, the lack of mechanistic links between growth phases, or the high computational demands. The model also introduces the hypothesis that cells adapt the FBA objective over time. The IMC improves the accuracy of intracellular flux predictions and simplifies the implementation process with a unique dFBA formulation over time. Its ability to predict both primary and secondary metabolism dynamics in different Saccharomyces species underscores its versatility and robustness. Furthermore, its alignment with empirical metabolomics data validates its predictive power, offering valuable insights into metabolic processes during batch fermentation. These advances pave the way for optimizing fermentation processes, potentially leading to more efficient production of target compounds and novel biotechnological applications.

The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes.

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models - a middle, third way for using theory to inform design.

The Structure and Dynamics of Creative Tourism: An ePortfolio Approach

Contributing to limited knowledge of creative tourism product development and operationalization processes in small cities and rural areas, ePortfolios were used as an online fieldnote depository for photographs and texts, which were entered by geographically dispersed researchers within the CREATOUR research-and-application project. The ePortfolios, as a type of research diary, record and examine the ways in which 48 different creative tourism activities were structured, organized, and implemented. The thematic analysis of this empirical data identified five temporally distinct organizational moments that comprise the creative tourism activities and reveal the nuances of active participation, learning, and community engagement as key elements characterizing creative tourism activities. Findings indicate that creative tourism activities are fertile ground for the contemporary tourist trends of wanting to go beyond “gazing” and to experience active involvement, which in these activities involved four different types of active participation. The ePortfolios also highlight the significance of the learning component within creative tourism, illustrating some ways in which learning is integrated into tourism activities. Links to the community are revealed with, for example, elderly women acting as artisan teachers, through which local participation is enabled and communication between visitors and residents is fostered. The use of ePortfolios as an “exploratory” research platform and tool in tourism research is also discussed, for example, in terms of stimulating creative expression and offering researchers freedom to present discovered aspects that may not be captured in “preplanned” data forms and processes.

Upstreamness and downstreamness in input–output analysis from local and aggregate information

Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

Examining the influence of streamer source characteristics on viewers’ continuous viewing intentions in tourism live-streaming: A SEM and fsQCA approach

ABSTRACT In tourism live-streaming, streamers play a key role in sharping viewers’ psychology and behavior. This study explores the influence of streamer characteristics on viewers’ continuous viewing intention. Using a mixed-methods approach, empirical data were collected from 410 respondents in China. Structural equation modeling (SEM) revealed that streamer characteristics (attractiveness, trustworthiness, expertise, and interactivity), along with viewers’ emotional states (pleasure, arousal, and dominance), significantly impact continuous viewing intention. Furthermore, a fuzzy-set qualitative comparative analysis (fsQCA) identified five configurations that lead to continuous viewing, including two key conditional configurations. Interactivity emerged as a supplementary, rather than core, condition for continuous viewing, complementing SEM results. The findings highlight the complexity of continuous view intention and offer practical insights for tourism practitioners to better engage viewers by addressing their preferences.

Jens Kraft’s account of Indigenous peoples’ principal institutions: an eighteenth-century perspective on political anthropology

ABSTRACT This article examines the political anthropology developed by the Danish philosopher Jens Kraft in his study of Indigenous peoples and their institutions, customs, and opinions. Kraft aims to reconstruct human history by amalgamating insights on man, nature, and society from scholars like Wolff and Rousseau with empirical knowledge on Indigenous cultures worldwide. Through comparative analysis of diverse Indigenous groups, Kraft seeks to identify institutions, customs, and opinions that are identical and thus universal to human nature and history. This examination not only allows Kraft to formulate general laws governing humanity’s progress but also to deduce fundamental natural principles – specifically, natural liberty and charity – against which ‘civilised’ society should be measured and improved. The article argues that, for Kraft, human history is the history of intellectual advancement, driven by humanity’s pursuit for rational solutions to challenges arising from external circumstances such as population growth and the emergence of property. In his work, Kraft, much like Rousseau, criticises contemporary ‘enlightened’ European society as morally debased, but maintains that humans are capable of rectifying their deficiencies and improving their imperfect civil societies through the use of progressively developed right reason.

Mindshaping, conditional games, and the Harsanyi Doctrine

ABSTRACT Much work in game theory concerns mechanisms by which players can infer information about the utilities and beliefs of other players based on actions within games and pre-play signals. When game theory is applied to interactions among people, such analysis interprets them as ‘mindreading’. Recent work in cognitive science, however, suggests that human coordination rests more centrally on ‘mindshaping’, where interactants determine preferences jointly. As mindshaping is strategic, there is motivation to extend game theory to accommodate it. Conditional Game Theory (CGT) is a strategic theory of mindshaping. We show how it can be used to help players of standard games identify correlated equilibrium, and thus solve games. We then extend CGT to address a challenge to the relevance of correlated equilibrium to empirical choice data, by showing how pre-play analysis using CGT can reconcile the Harsanyi Doctrine – assumed common priors among Bayesian players – with rank-dependent choice as observed in economic experiments.

The evolution of signaling and monitoring in plant–fungal networks

Experiments have shown that when one plant is attacked by a pathogen or herbivore, this can lead to other plants connected to the same mycorrhizal network up-regulating their defense mechanisms. It has been hypothesized that this represents signaling, with attacked plants producing a signal to warn other plants of impending harm. We examined the evolutionary plausibility of this and other hypotheses theoretically. We found that the evolution of plant signaling about an attack requires restrictive conditions, and so will rarely be evolutionarily stable. The problem is that signaling about an attack provides a benefit to competing neighbors, even if they are kin, and so reduces the relative fitness of signaling plants. Indeed, selection is often more likely to push plant behavior in the opposite direction-with plants signaling dishonestly about an attack that has not occurred, or suppressing a cue that they have been attacked. Instead, we show that there are two viable alternatives that could explain the empirical data: 1) the process of being attacked leads to a cue (information about the attack) which is too costly for the attacked plant to fully suppress; 2) mycorrhizal fungi monitor their host plants, detect when they are attacked, and then the fungi signal this information to warn other plants in their network. Our results suggest the empirical work that would be required to distinguish between these possibilities.

HEMA-free versus HEMA-containing adhesive systems: a systematic review

BackgroundHydrophilic monomer 2-hydroxyethyl methacrylate (HEMA)-free adhesive systems are gaining increasing popularity nowadays. Although the addition of HEMA to dental adhesives improves dentin wettability and resin diffusion into demineralized collagen fibrils, HEMA’s high hydrophilicity can lead to hydrolytic degradation of the adhesive interface. Thus, HEMA-free adhesive systems have been developed. Unfortunately, the lack of HEMA in the adhesive composition may lead to a separation phase between hydrophobic and hydrophilic components. The aim of this systematic review was to evaluate the clinical performance of HEMA-free adhesive systems and compare them with HEMA-containing ones.MethodsAn electronic search of The National Library of Medicine (MEDLINE/PubMed) was conducted. Eligibility criteria were reporting empirical data from clinical studies published between 2013 and 2023 about the clinical performance of HEMA-free adhesive systems for direct resin composite restorations. Studies with at least 2-year clinical follow-up done in permanent dentition in any form of cavities were selected. The included studies were assessed for risk of bias using the modified Cochrane Collaboration tool criteria.ResultsThe database search returned 147 studies; a total of 7 studies were included in this review; the majority of studies reported no significant difference between the two types of adhesives for the parameter of retention.ConclusionsHEMA-free adhesive systems exhibited good clinical performance with regard to retention. There was some concern about their influence on marginal adaptation and marginal discoloration due to the conflicted results reported by the included trials. Thus, the results need to be confirmed with long-term evaluations.Systematic review registrationPROSPERO CRD42023448952.

Cross-border higher education and quality assurance: the role of international organisations

Purpose Over the years, International Organisations have started to look at Cross Border Higher Education (CBHE) with more attention, namely, at the impact and challenges entailed by this type of educational provision due to the emergence of low-quality offers and degree mills. This paper aims to discuss the role of these organisations in the development and promotion of CBHE and its quality assurance in Europe. Design/methodology/approach This paper conducts interviews using a previously drafted guideline with top-level managers of seven relevant international organisations involved in quality assurance at European and international level, namely, INQAAHE, EUA, ENQA, ESU, EQAR, ECA and EURASHE. It analysed the data collected using a grid created through the articulation of the guidelines’ questions and the empirical data. Findings The interviews’ content analysis indicated that these international organisations are important stakeholders in the development of CBHE quality assurance in Europe, particularly through the promotion of cooperation and trust between the different actors involved in it. These organisations can assume two different roles: a Technical-Professional Role and a Community Building Role. Originality/value This work’s originality stems from the fact that at least to our knowledge there is no other empirical work so far on the role and the views of different international organisations on the challenges CBHE entails for quality at the European level.

Predictors of evacuation behavior: dataset on respondents’ route choice and web interaction

Empirical data on human evacuation behavior are invaluable for adjusting and training computational algorithms that simulate evacuation processes, including agent-based modeling. We provide a dataset on human decision-making during evacuations from virtual buildings, captured using experimental methods that controlled specific building layout parameters. An online experiment assigned participants a random subset of tasks featuring T-intersections. Data from 208 respondents, aged 17 to 71, were analyzed, considering education levels and excluding those with significant technical issues. Quantitative data on user interaction and evacuation route choices included decision time, mouse rotation, and the selected corridor, recorded through mouse clicks on invisible areas of interest. Respondents also self-reported their choice confidence on a Likert scale. Additionally, responses to final retrospective evaluation questionnaires were recorded. This dataset offers diverse research opportunities, particularly in emergency evacuation planning, where understanding evacuation choices in simulations can inform real-world strategies. It supports the development of models to predict human behavior in emergencies using machine learning and predictive modeling and is accessible for both academic and commercial use.

FASHION AS A FORM OF SELF-IDENTITY: A SEMIOTIC ANALYSIS OF MILLENNIALS' DRESS STYLE

Fashion plays a significant role in shaping self-identity, particularly among Indonesian millennials living in the digital and globalized era. This study aims to analyze the role of fashion as a visual communication medium in expressing millennials' self-identity through Roland Barthes' semiotic approach. Using a qualitative literature review method, data were collected from relevant secondary sources, such as journal articles, books, and dissertations. The analysis reveals that fashion functions as a system of signs containing denotative meanings, such as color or design, and connotative meanings reflecting personality, social status, or local culture. Social media platforms, such as Instagram and TikTok, serve as key arenas for millennials to showcase their fashion styles and construct their public identities. The findings affirm that fashion not only reflects but also shapes self-identity within the context of negotiating globalization and locality. This study contributes theoretically to semiotic studies and practically to the fashion and marketing industries. However, the absence of direct empirical data is a limitation, suggesting that future research should integrate field methods to deepen understanding of the interaction between fashion and millennial identity.

SHOPPE GAMES: HOW SHOPPE UTILIZES GAMIFICATION TO INCREASE APP UPTIME

This study examines how Shopee leverages gamification to increase user engagement, particularly by enhancing the time spent on its application. The research employs a qualitative approach with a literature review method, analyzing relevant studies on gamification in e-commerce. Findings reveal that gamification elements such as rewards, challenges, and social interaction significantly contribute to prolonged app usage. Rewards motivate users with tangible incentives, challenges create engaging experiences through progressively difficult tasks, and social interaction fosters connections through competition and collaboration. These results align with Self-Determination Theory and Flow Theory, which emphasize the role of fulfilling psychological needs and creating immersive experiences in driving engagement. The study provides practical insights for app developers, highlighting the importance of designing effective gamification strategies to enhance user retention and loyalty. However, limitations include the absence of primary empirical data, suggesting future research should explore the direct impact of gamification on diverse user demographics through quantitative or mixed-method approaches.

Do gritty salespersons mitigate relationship damage in unethical sales subcultures?

ABSTRACT Previous research has shown that salesperson grit is not a consistent predictor of ethical sales behaviors. Our study relies on the integrated theory of ethical decision-making to evaluate the influence of the sales ethics subculture and the interaction of salesperson grit across two distinct types of unethical behavior. Unethical sales behaviors are motivated by self-interest, but pro-social rule breaking is more selfless in nature. Empirical data from 197 salespeople were analyzed via structural equation modeling. We find that higher levels of salesperson grit result in lower levels of selfish, unethical sales behaviors, but grit does not change the amount of selfless pro-social rule breaking. Moreover, unethical sales behaviors damage the relationship between salespersons and their clients, but contrary to our beliefs, pro-social rule breaking neither damages nor improves these relationships. Our findings support the notion that salespersons operate according to dual processes when facing ethical dilemmas.