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In 2000, Pincott and Branthwaite published 'Nothing New Under the Sun?' which discussed the role of the internet in research both in terms of the hazards and opportunities this confers. Specifically, Pincott and Branthwaite argued that whilst the advent of the internet offers up new approaches and techniques, this must not be at the detriment of methodological rigour and standards we have grown accustomed to in the industry. In this paper, it is argued that the advent of big data, big qual and the emerging potential of the metaverse present new opportunities in research but pose the same questions originally conceived by Pincott & Branthwaite, 2000. Moreover, methodological rigour and standards are framed by three interconnected themes, namely, the role of data science and big data, the importance of softer skills of interpretation and narration and finally, the role of theory in elevating and grounding research. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this article, we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-naïve evaluation algorithm on any datalog program. [ABSTRACT FROM AUTHOR]

Supply chain decarbonisation has become a strategic requirement in the era of a net-zero economy. Despite the significant role of Big Data Analytics (BDA) in decarbonising the supply chain (SC), no prior study has evaluated it systematically. The present study aims to provide a systematic literature review on the applications and outcomes of big data analytics in SC decarbonisation. A total of 69 papers on applying BDA technology for supply chain decarbonisation published between 2016 and 2021 have been selected following the PRISMA protocol. The findings show that the topic is evolving. Studies employed methods such as surveys (30), case studies (11), and conceptual research designs (8). Thematic analysis reveals that 65% of the studies are grounded in resource-advantage theories, organisational theories, and system theories. Studies from India and China (35%) dominate the topic, while most studies have been conducted on the food and manufacturing industries. Further, this study applied the Antecedent-Decision-Outcomes (ADO) framework in BDA-based SC decarbonisation. Antecedents include BDA resources and capabilities, workforce skills, and supplier capabilities. Decisions refer to improving decision-making across the supply chain. Outcomes refer to improving decarbonisation, sustainable growth, and sustainable innovativeness. Future research directions and questions are provided using the Theory-Context-Methodology (TCM) framework. [ABSTRACT FROM AUTHOR]

Polygenic scores (PGSs) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PGSs in breast cancer to date is PGS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PGS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular electronic health record (EHR) system, genotyping platform, and quality control process. We found that the PGS313 achieved overlapping areas under the receiver operator characteristic (ROC) curve (AUCs) in females of HL (AUC = 0.68, 95% confidence interval [CI] = 0.65-0.71) and EA ancestry (AUC = 0.70, 95% CI = 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC = 0.61, 95% CI = 0.56-0.65; EAA: AUC = 0.64, 95% CI = 0.60-0.680). While PGS313 is associated with hormone-receptor-positive (HR+) disease in EA Americans (odds ratio [OR] = 1.42, 95% CI = 1.16-1.64), this association is lost in African, Latinx, and Asian Americans. In summary, we found that PGS313 was significantly associated with breast cancer but with attenuated accuracy in women of AFR and EAA descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to the clinical implementation of PGSs.

PurposeUse of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR.DesignLiterature review and quantitative analysis.SubjectsPublished manuscripts.MethodsFour graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions.Main outcome measuresNumber of studies included and numeric counts of billing codes used to define codified cohorts.ResultsIn total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts.ConclusionsSubstantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Big data technologies change the way in which data and labor combine to create knowledge. Is this a modest innovation or a data revolution? Using hiring and wage data, we estimate firms' data stocks and their knowledge production functions. Quantifying changes in production functions informs us about the likely long-run changes in output, in factor shares, and in the distribution of income, due to big data technologies. For the investment management industry, our structural estimates predict a 5% decline in the labor share of income; that change is comparable to similar estimates for the industrial revolution. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

Although research in the social media and sport domain continues to uncover key insights related to content, there has been a push toward identifying the social media metrics that serve as the antecedents to relationship marketing engagement. Along that vein, the purpose of this study was to analyze social media activity (i.e., impressions and engagements) from all teams in a given professional sport league over a 3-year period. Contextually set with Major League Soccer teams for the 2017, 2018, and 2019 calendar years, 66,745 Instagram posts were retrieved using MVPindex and parsed for focal social media metrics (i.e., impressions and engagements) for each team using a temporal lens (i.e., by month and by day). Findings of this study align with past work indicating the need for sport properties to focus on posting outside of game-day windows, harnessing the ongoing, instantaneous nature of social media. [ABSTRACT FROM AUTHOR]

Organisations that develop analytical capabilities can leverage advanced data platforms and cloud-based solutions; they may also experiment with sophisticated machine learning algorithms. But when business analysts or data scientists fail to bridge the gaps among data, analytics, and decision-making, it might imply a premature implementation of complex data analytics. This article aims to derive clear guidelines from management literature to formulate a stepwise approach for deploying marketing analytics with increasing levels of complexity. Furthermore, we demystify the relevant jargon. [ABSTRACT FROM AUTHOR]

Despite the growing diversity of customer needs, little empirical evidence would guide managers to leverage customer need diversity (CND) for radical innovation. Building on the knowledge-based view and dynamic capabilities, the authors explore relationships among CND, strategic flexibility, big data analytics capabilities, and radical innovation performance. With 308 survey responses from innovation managers, the authors find that CND impacts radical innovation performance via (1) mediation through strategic flexibility and (2) moderation by volume, variety, and velocity capabilities associated with big data analytics. The moderation effects of volume and velocity capabilities are positive and negative simultaneously, depending on the purpose for which CND is utilised. Therefore, to leverage customer need diversity, firms need to build strategic flexibility and exercise caution when utilising big data analytics capabilities. [ABSTRACT FROM AUTHOR]

Global change is currently impacting ecosystems and their contributions to people (i.e. ecosystem services). These impacts have consequences for societies and human well-being, especially in Africa. Historically, efforts have focused on assessing global change from a social or biophysical perspective, treating them as separate entities. Yet, our understanding of impacts to social-ecological systems remains limited, particularly in the Global South, due to a lack of data, tools, and approaches accounting for social and ecological aspects of ecosystem services. This is especially relevant for cultural ecosystem services as they are less tangible. We use a simple indicator and important provider of a multitude of cultural ecosystem services, birding, to understand how climate, biodiversity, and land use change will impact cultural ecosystem services across Africa. We explore how emerging tools and data can overcome limitations in mapping and modeling cultural ecosystem services, particularly in analyzing human preferences and behavior at large spatiotemporal scales and in data-poor regions. Leveraging crowdsourced data from eBird and using machine learning techniques we map and model recreational birding to assess the underlying social-ecological relationships and the impact of future climate and environmental change. We show that bird species richness, protected areas, accessibility, and max temperature contribute most to birding suitability across the continent. Further, we show spatial shifts in the suitability of birding under three future climate scenarios (SSP126, 370, and 585). Models suggest climate and biodiversity change will increasingly constrain the flow of birding related cultural ecosystem services across Africa. This has implications for human-nature interactions, development of countries, management of protected areas, and overall human well-being in the future. More generally, we highlight opportunities for crowdsourced datasets and machine learning to integrate non-material ecosystem services in models and thus, enhance the understanding of future impacts to ecosystem services and human well-being.

Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focussing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies. [ABSTRACT FROM AUTHOR]

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Big data and innovative research methods are two rapidly evolving trends that are transforming how we conduct research in sport management. Considering the natural relationship between social media, which is widely recognized as a major big-data source, and sport, this commentary centers on contemporary research method applied to social media data. In doing so, it discusses contemporary innovative techniques for social media data, focusing on exploring ways to access social media data, the natural language-processing techniques used, the challenges they address, the strengths and limitations of different techniques, and the ethical and privacy considerations associated with their use. Furthermore, the commentary demonstrates that using sentiment-analysis tools (e.g., Syuzhet, Bing, and AFFIN) is appropriate and efficient in analyzing sport's social media data. Thus, a rigorous application of contemporary innovative techniques can significantly shape the future of sport management research. However, researchers must exercise caution when considering the source and preprocessing of the data prior to applying advanced analytical techniques. [ABSTRACT FROM AUTHOR]

We have a simple thesis: the relationship between academic and industry-based cognitive science is broken, but can be fixed. Over the last few decades, there has been a huge increase in the representation of cognitive science in industry. Beyond just machine learning, businesses are increasingly interested in human behavior and cognitive processes. Large proportions of our Ph.D. students, post-docs, and even faculty choose to go through a largely one-way door to corporate jobs in data science, behavioral experimentation, machine learning, user experience, and elsewhere. Currently, people who choose industry careers often lose their social and intellectual networks and their ability to return to tenure-track positions. Valuable insights from industry about memory, decision-making, learning, emotion, distributed cognition, and much more never return to the academic community. We believe that deep, theory driven, theory building work is being done in industry settings–and that the rift between communities makes all our work less effective

Neuroimaging, especially through Functional Magnetic Resonance Imaging (fMRI), plays a pivotal role in understanding brain activity by leveraging blood-oxygen level dependent (BOLD) signals to estimate neural activities across the brain. The interpretation of these signals through functional connectivity (FC) matrices facilitates the application of Graph Neural Networks (GNN) for analyzing brain network structures, offering insights into both normal and abnormal brain functions. Despite the potential of centralized learning methods in this domain, challenges related to data privacy and the feasibility of sharing sensitive medical datasets across institutions limit their application. This study introduces the Federated Graph Learning Framework for Brain Connectome (FGLBC), addressing these concerns. This novel approach enables the collaborative training of GNN models across multiple entities, such as hospitals, without compromising data privacy. The FGLBC framework implements a privacy-preserving local GNN training (PPGT) algorithm that incorporates Differential Privacy (DP) to safeguard sensitive information during model training. Furthermore, we introduce a unique similarity-weighted aggregation (SWA) algorithm that enhances the aggregation process, thereby boosting the global model's utility and performance. Our comprehensive evaluation across benchmark datasets demonstrates that the FGLBC not only preserves user privacy but also achieves or surpasses the performance of existing methods.

Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.

What factors contribute to the growth of children's early vocabulary? One method for exploring this question is investigating predictors (e.g., frequency) that differentiate words learnt earlier from those learnt later. A more comprehensive account requires the examination of multiple language families and multiple levels of linguistic representation (e.g., phonological, morphosyntactic, semantic). Here, we studied 10 predictors of word ages of acquisition across 27 languages and dialects. We found that words that were more frequent, concrete, and associated with babies were learnt earlier, whereas words that had greater length in phonemes and mean length of utterance were learnt later. There was no reliable effect of other morphosyntactic predictors, or of phonological neighbourhood. We found evidence of a tradeoff between a language's morphological complexity and the effect of syntactic complexity for predicates, supporting the competition model. Predictor coefficients revealed broad consistency across all languages, along with variability that reflected language family classifications.

Dependency length minimization is a universally observed quantitative property of natural languages. However, the extent of dependency length minimization, and the cognitive mechanisms through which the language processor achieves this minimization remain unclear. This research offers mechanistic insights by postulating that moving a short preverbal constituent next to the main verb explains preverbal constituent ordering decisions better than global minimization of dependency length in SOV languages. This approach constitutes a least-effort strategy because it's just one operation but simultaneously reduces the length of all preverbal dependencies linked to the main verb. We corroborate this strategy using large-scale corpus evidence across all seven SOV languages that are prominently represented in the Universal Dependency Treebank. These findings align with the concept of bounded rationality, where decision-making is influenced by `quick-yet-economical' heuristics rather than exhaustive searches for optimal solutions. Overall, this work sheds light on the role of bounded rationality in linguistic decision-making and language evolution.

Both prediction and working memory constraints have been established as key factors in characterizing incremental sentence processing difficulty. Here we investigate the less explored question: Whether and how predictive expectation and working memory interact with each other using data from naturalistic reading time corpora. We provide broad-coverage evaluations of two hypotheses that make divergent predictions regarding the interaction of expectation and memory constraints: the Information Locality and Prediction Maintenance hypotheses. We first confirmed the predictions of both expectation- and working memory-based theories. Regarding their interactions, we find support the Information Locality hypothesis: Strong mutual predictability can enhance locality effects. We argue that future theory building in sentence processing should therefore take into consideration both prediction and memory constraints, as well as their potential interaction.

Predicting Click-Through Rate (CTR) is crucial in product and content recommendation, as it involves estimating the likelihood of a user engaging with a specific advertisement or content link. This task encompasses understanding the complex cognitive processes behind human interactions with recommended content. Learning varied feature embeddings that reflect different cognitive responses in various circumstances is significantly important. However, traditional methods typically learn fixed feature representations, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components:(I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context;(II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-the-art performance among current CTR methods.

Language is fundamental in human cognition and communication, helping us to encode the world around us. Adjectives represent a linguistic form used extensively, particularly in the social domain. Adjectives vary in both their valence and their breadth (e.g., “punctual” is narrower than “dependable”). Variations in adjectival breadth have not been studied extensively but may have significant consequences across various domains. The present study explores how subtle distinctions in adjective use may relate to descriptions of experiences that people share. To assess linguistic breadth in online communication, we examine whether online reviews with different star ratings are associated with differences in adjectival breadth. Through an analysis of over 200,000 reviews from Amazon digital music (Study 1) and Yelp restaurants (Study 2), we find evidence that linguistic desirability and breadth of adjectives in reviews positively correlate with their ratings. Specifically, higher-rated reviews tend to use broader and more desirable adjectives. However, this relationship varies between product categories, with high-rated music reviews showing increased linguistic breadth and desirability, while top-rated restaurant reviews demonstrate a decrease in breadth. This paper contributes to understanding linguistic breadth in social media contexts, highlighting how subtle language variations in evaluations can reflect different cognitive and communicative processes.

Previous work has shown that isolated non-canonical sentences with Object-before-Subject (OSV) order are initially harder to process than their canonical counterparts with Subject-before-Object (SOV) order. Although this difficulty diminishes with appropriate discourse context, the underlying cognitive factors responsible for alleviating processing challenges in OSV sentences remain a question. In this work, we test the hypothesis that dependency length minimization is a significant predictor of non-canonical (OSV) syntactic choices, especially when controlling for information status such as givenness and surprisal measures. We extract sentences from the Hindi-Urdu Treebank corpus (HUTB) that contain clearly-defined subjects and objects, systematically permute the preverbal constituents of those sentences, and deploy a classifier to distinguish between original corpus sentences and artificially generated alternatives. The classifier leverages various discourse-based and cognitive features, including dependency length, surprisal, and information status, to inform its predictions. Our results suggest that, although there exists a preference for minimizing dependency length in non-canonical corpus sentences amidst the generated variants, this factor does not significantly contribute in identifying corpus sentences above and beyond surprisal and givenness measures. Notably, discourse predictability emerges as the primary determinant of constituent-order preferences. These findings are further supported by human evaluations involving 44 native Hindi speakers. Overall, this work sheds light on the role of expectation adaptation in word-ordering decisions. We conclude by situating our results within the theories of discourse production and information locality.

Extensive instruction tuning of large language models(LLMs) has proven to be a powerful technique, extending the outstanding performance of general LLMs to new tasks. Consequently, the integration of state-of-the-art(SOTA) general open-source models with specific domains leverages instruction tuning to unlock the emerging capabilities of large language models(LLMs) in those domains. Current practices in instruction tuning often rely on expanding the size of the dataset without a clear strategy to ensure data quality. This can inadvertently introduce noise and degrade model performance. Furthermore, there is currently no unified approach for the quantity, quality, and diversity of instruction tuning data, as well as the methods for instruction tuning. As a result, this severely hampers the practicality and universality of instruction tuning.\\ Addressing these issues, we propose a \textbf{UNI}fied \textbf{F}ramework for \textbf{I}nstruction \textbf{T}uning(\textbf{UNIFIT}), namely Concept Tree generation for instruction tuning data, instruction following difficulty selecting data for high-quality instruction tuning, and the incorporation of random noise embeddings(NE) to enhance model performance during tuning. Through experiments involving multiple models, domains, and orders of magnitude, our proposed instruction tuning framework not only enhances the diversity of instruction tuning data but also achieves a remarkable 60\% reduction in training time consumption, with a mere 6\% of all instruction tuning data, surpassing the performance of using all instruction tuning data by 11\%. This universally applicable instruction tuning framework signifies a substantial advancement in the generality of large language model instruction tuning, marking a revolutionary leap forward and promising efficiency gains while being resource-conscious.

In recent years, with the widespread popularity of location-based social network platforms, the data generated by users on social networks has grown exponentially. There has been a growing focus on the problem of POI (Point-of-Interest) recommendations. Unlike traditional sequence recommendation that primarily considers the temporal dimension, POI recommendation needs to account for the influence of geographical information to a large extent. However, previous works in the graph construction process often only consider the places users have visited, neglecting those they haven't been to. To address this, we propose a Dual Weighted Graph Convolutional Network for POI recommendation called DualPOI. Specifically, we first leverage graph neural networks and attention mechanisms to capture users' local trajectory preferences for visited POIs. A delicately designed spatiotemporal encoder is conducted to model users' local spatiotemporal preferences. Subsequently, using a dual graph convolutional approach, we transfer the user's local preference information to a global scope, thereby modeling novel preferences for unvisited locations. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method in enhancing the accuracy of POI recommendations. Comprehensive ablation studies and parameter analysis further confirm the efficacy of the proposed modules.

Compositionality is considered a key hallmark of humanlanguage. However, most research focuses on item-level compositionality, e.g.,to what extent the meanings of phrases are composed of the meanings of theirsub-parts, rather than on language-level compositionality, which is the degreeto which possible combinations are utilized in practice during language use.Here, we propose a novel way to quantify the degree of language-levelcompositionality and apply it in the case of English adjective-nouncombinations. Using corpus analyses, large language models, and humanacceptability ratings, we find that (1) English only sparsely utilizes thecompositional potential of adjective–noun combinations; and (2) LLMs struggle topredict human acceptability judgments of rare combinations. Taken together, ourfindings shed new light on the role of compositionality in language andhighlight a challenging area for further improving LLMs.

People sometimes explicitly announce that they are being sarcastic. The announcement appears to be particularly common in text-based conversations where prosodic cues are more difficult to identify. In certain cases, the tone of a comment is sufficient to determine non-literal meaning. However, what happens in the absence of these features, or when context forces us to explicitly caveat our sarcasm? In this study, we examined Reddit comments from r/AskReddit for the features that are present in comments tagged with "/s", a convention on the platform for users to denote sarcasm. We found that a host of cues which mimic prosody, and other aspects of figures of speech, were inconsistent predictors of announced sarcasm. In contrast, when talking about sociomoral topics such as politics or race, users were more likely to tag their comments with "/s". This suggests that users are more likely to announce sarcasm in text-based conversations where misinterpretation would be socially detrimental.

The scientific potential of digital game studies in psychology is limited by the observational nature of the data that they investigate. However, digital environments present us with a perfect opportunity to incorporate experimental paradigms in complex interactive and multivariate worlds where each decision made by participants can be tracked and recorded. In this study, we demonstrate an industry-academic research collaboration that offers a proof-of-the-concept on how minor modifications of the game settings could be used to test psychological research questions. We modify the settings of the Zool platform game, where players allocated to the experimental group are provided with more information when in danger of dying in the game. Results of the study show that manipulation does not influence behaviour in the game, such as achieved score or number of deaths, but it changes the overall player's response of whether they will continue playing the game after the disappointing event of losing all their lives, game over event. In line with previous studies, the additional information provided through the experimental manipulation made death in the game more informative to the players.

Various methods of dimensionality reduction have been used to apply a quantitative approach to the study of complex skill acquisition. This work builds upon past approaches, offering a comparative analysis of principal component analysis, logistic regression, and linear discriminant analysis to quantify expertise in the domain of competitive video gaming, or “eSports.” We present a novel, robust dataset of expert and non-expert gameplay data from professional and amateur players of the Super Smash Bros. Melee competitive fighting game. We assess each quantitative model via the metrics of providing accurate expertise classification, predictive utility, and a pragmatic window into the features of complex skill performance that hold the most weight in overall performance outcomes, thereby also providing insights for direction of future training. We posit that linear discriminant analysis provides the best performance for all relevant metrics. The nuances are discussed here, and suggestions for the field are offered for future study of other complex skill domains.

Cognitive science has historically been introduced as a multidisciplinary and, sometimes, an interdisciplinary study of the mind. Recent critical views of the field have questioned the foundational core and its multidisciplinary nature by suggesting that psychology has come to dominate cognitive science. As these are actively debated issues, we need further investigations. This study examines the degree of overlap between cognitive science and psychological science by comparing article keywords and departmental affiliations of authors extracted from flagship journals over the past decade (2012-2022). The results reveal that over 50% of published authors stem from psychology departments. The topics of study between the two remain quite similar as well. However, network analyses found fragmentation in terms of the methodological approaches and a considerable focus by the community of cognitive scientists on formal modeling. Based on the topics and socio-institutional analysis, we suggest that cognitive science is largely (cognitive) psychology. Implications for the field of cognitive science and its claims of multidisciplinarity are discussed.

The idea of curriculum learning, whereby a model is first exposed to simpler examples before an increase in complexity, has long fascinated the AI community. Unfortunately, the experimental successes of curriculum learning have been mixed, particularly applied to natural language, where a vast body of literature appears to evidence its failures. However, recent work has shown that language models trained on transcribed-child-directed-speech (CDS) learn more grammar compared to those trained on Wikipedia. To a lesser extent, the same trend has been observed through training on transcribed speech and simple text data. Motivated by these findings, we revisit the idea of curriculum learning starting from CDS, before moving to simple data, and finally finishing with complex long form text. Unfortunately, through experimentation with an array of models and training step sizes, only in the smallest models trained for the least steps does curriculum learning show any advantage over random sampling.