Gendered Patterns in Manifest and Latent Mental Health Indicators Among Suicide Decedents: 2003-2020 National Violent Death Reporting System (NVDRS).

Objectives. To investigate differences in the documentation of mental health symptomology between male and female suicide decedents in the 2003-2020 US National Violent Death Reporting System (NVDRS). Methods. Using information on 271 998 suicides in the 2003-2020 NVDRS, we evaluated precoded mental health-related variables and topic model-derived latent mental health themes in the law enforcement and coroner or medical examiner death narratives compiled by trained public health workers. Results. Public health records of male compared with female suicides were less likely to include notations of mental health conditions or treatment interventions. However, topic modeling of death summaries revealed that male suicide decedents were more likely to evidence several subclinical cognitive and emotional indicators of distress. Conclusions. Suicide death records vary by gender, both in recorded evidence for mental health conditions at time of death and in accompanying narratives describing proximal circumstances surrounding these deaths. Our findings hint that patterns of subclinical mental health changes among men might be less well captured in commonly used mental health indicators, suggesting that prevention efforts may benefit from measures that also target assessment of subclinical distress. (Am J Public Health. 2024;114(S3):S268-S277. https://doi.org/10.2105/AJPH.2023.307427).

A Simple, interpretable method to identify surprising topic shifts in scientific fields.

This paper proposes a text-mining framework to systematically identify vanishing or newly formed topics in highly interdisciplinary and diverse fields like cognitive science. We apply topic modeling via non-negative matrix factorization to cognitive science publications before and after 2012; this allows us to study how the field has changed since the revival of neural networks in the neighboring field of AI/ML. Our proposed method represents the two distinct sets of topics in an interpretable, common vector space, and uses an entropy-based measure to quantify topical shifts. Case studies on vanishing (e.g., connectionist/symbolic AI debate) and newly emerged (e.g., art and technology) topics are presented. Our framework can be applied to any field or any historical event considered to mark a major shift in thought. Such findings can help lead to more efficient and impactful scientific discoveries.

Integrating topic modeling and word embedding to characterize violent deaths.

SignificanceWe introduce an approach to identify latent topics in large-scale text data. Our approach integrates two prominent methods of computational text analysis: topic modeling and word embedding. We apply our approach to written narratives of violent death (e.g., suicides and homicides) in the National Violent Death Reporting System (NVDRS). Many of our topics reveal aspects of violent death not captured in existing classification schemes. We also extract gender bias in the topics themselves (e.g., a topic about long guns is particularly masculine). Our findings suggest new lines of research that could contribute to reducing suicides or homicides. Our methods are broadly applicable to text data and can unlock similar information in other administrative databases.

Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat.

Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight "fill in the blanks" about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.

Not by transmission alone: the role of invention in cultural evolution.

Innovation-the combination of invention and social learning-can empower species to invade new niches via cultural adaptation. Social learning has typically been regarded as the fundamental driver for the emergence of traditions and thus culture. Consequently, invention has been relatively understudied outside the human lineage-despite being the source of new traditions. This neglect leaves basic questions unanswered: what factors promote the creation of new ideas and practices? What affects their spread or loss? We critically review the existing literature, focusing on four levels of investigation: traits (what sorts of behaviours are easiest to invent?), individuals (what factors make some individuals more likely to be inventors?), ecological contexts (what aspects of the environment make invention or transmission more likely?), and populations (what features of relationships and societies promote the rise and spread of new inventions?). We aim to inspire new research by highlighting theoretical and empirical gaps in the study of innovation, focusing primarily on inventions in non-humans. Understanding the role of invention and innovation in the history of life requires a well-developed theoretical framework (which embraces cognitive processes) and a taxonomically broad, cross-species dataset that explicitly investigates inventions and their transmission. We outline such an agenda here. This article is part of the theme issue 'Foundations of cultural evolution'.

A quantitative workflow for modeling diversification in material culture.

Questions about the evolution of material culture are widespread in the humanities and social sciences. Statistical modeling of long-term changes in material culture is less common due to a lack of appropriate frameworks. Our goal is to close this gap and provide robust statistical methods for examining changes in the diversity of material culture. We provide an open-source and quantitative workflow for estimating rates of origination, extinction, and preservation, as well as identifying key shift points in the diversification histories of material culture. We demonstrate our approach using two distinct kinds of data: age ranges for the production of American car models, and radiocarbon dates associated with archaeological cultures of the European Neolithic. Our approach improves on existing frameworks by disentangling the relative contributions of origination and extinction to diversification. Our method also permits rigorous statistical testing of competing hypotheses to explain changes in diversity. Finally, we stress the value of a flexible approach that can be applied to data in various forms; this flexibility allows scholars to explore commonalities between forms of material culture and ask questions about the general properties of cultural change.

Choosing experiments to accelerate collective discovery.

A scientist's choice of research problem affects his or her personal career trajectory. Scientists' combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity's importance corresponds to its degree centrality, and a problem's difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies.

Dynamic landscapes: a model of context and contingency in evolution.

Although the basic mechanics of evolution have been understood since Darwin, debate continues over whether macroevolutionary phenomena are driven by the fitness structure of genotype space or by ecological interaction. In this paper we propose a simple model capturing key features of fitness-landscape and ecological models of evolution. Our model describes evolutionary dynamics in a high-dimensional, structured genotype space with interspecies interaction. We find promising qualitative similarity with the empirical facts about macroevolution, including broadly distributed extinction sizes and realistic exploration of the genotype space. The abstraction of our model permits numerous applications beyond macroevolution, including protein and RNA evolution.

Metaknowledge.

The growth of electronic publication and informatics archives makes it possible to harvest vast quantities of knowledge about knowledge, or "metaknowledge." We review the expanding scope of metaknowledge research, which uncovers regularities in scientific claims and infers the beliefs, preferences, research tools, and strategies behind those regularities. Metaknowledge research also investigates the effect of knowledge context on content. Teams and collaboration networks, institutional prestige, and new technologies all shape the substance and direction of research. We argue that as metaknowledge grows in breadth and quality, it will enable researchers to reshape science-to identify areas in need of reexamination, reweight former certainties, and point out new paths that cut across revealed assumptions, heuristics, and disciplinary boundaries.

Clustering drives assortativity and community structure in ensembles of networks.

Clustering, assortativity, and communities are key features of complex networks. We probe dependencies between these features and find that ensembles of networks with high clustering display both high assortativity by degree and prominent community structure, while ensembles with high assortativity show much less enhancement of the clustering or community structure. Further, clustering can amplify a small homophilic bias for trait assortativity in network ensembles. This marked asymmetry suggests that transitivity could play a larger role than homophily in determining the structure of many complex networks.

Edge direction and the structure of networks.

Directed networks are ubiquitous and are necessary to represent complex systems with asymmetric interactions--from food webs to the World Wide Web. Despite the importance of edge direction for detecting local and community structure, it has been disregarded in studying a basic type of global diversity in networks: the tendency of nodes with similar numbers of edges to connect. This tendency, called assortativity, affects crucial structural and dynamic properties of real-world networks, such as error tolerance or epidemic spreading. Here we demonstrate that edge direction has profound effects on assortativity. We define a set of four directed assortativity measures and assign statistical significance by comparison to randomized networks. We apply these measures to three network classes--online/social networks, food webs, and word-adjacency networks. Our measures (i) reveal patterns common to each class, (ii) separate networks that have been previously classified together, and (iii) expose limitations of several existing theoretical models. We reject the standard classification of directed networks as purely assortative or disassortative. Many display a class-specific mixture, likely reflecting functional or historical constraints, contingencies, and forces guiding the system's evolution.

A model of sequential branching in hierarchical cell fate determination.

Multipotent stem or progenitor cells undergo a sequential series of binary fate decisions, which ultimately generate the diversity of differentiated cells. Efforts to understand cell fate control have focused on simple gene regulatory circuits that predict the presence of multiple stable states, bifurcations and switch-like transitions. However, existing gene network models do not explain more complex properties of cell fate dynamics such as the hierarchical branching of developmental paths. Here, we construct a generic minimal model of the genetic regulatory network controlling cell fate determination, which exhibits five elementary characteristics of cell differentiation: stability, directionality, branching, exclusivity, and promiscuous expression. We argue that a modular architecture comprising repeated network elements reproduces these features of differentiation by sequentially repressing selected modules and hence restricting the dynamics to lower dimensional subspaces of the high-dimensional state space. We implement our model both with ordinary differential equations (ODEs), to explore the role of bifurcations in producing the one-way character of differentiation, and with stochastic differential equations (SDEs), to demonstrate the effect of noise on the system. We further argue that binary cell fate decisions are prevalent in cell differentiation due to general features of the underlying dynamical system. This minimal model makes testable predictions about the structural basis for directional, discrete and diversifying cell phenotype development and thus can guide the evaluation of real gene regulatory networks that govern differentiation.

Link and subgraph likelihoods in random undirected networks with fixed and partially fixed degree sequences.

The simplest null models for networks, used to distinguish significant features of a particular network from a priori expected features, are random ensembles with the degree sequence fixed by the specific network of interest. These "fixed degree sequence" (FDS) ensembles are, however, famously resistant to analytic attack. In this paper we introduce ensembles with partially-fixed degree sequences (PFDS) and compare analytic results obtained for them with Monte Carlo results for the FDS ensemble. These results include link likelihoods, subgraph likelihoods, and degree correlations. We find that local structural features in the FDS ensemble can be reasonably well estimated by simultaneously fixing only the degrees of a few nodes, in addition to the total number of nodes and links. As test cases we use two protein interaction networks (Escherichia coli, Saccharomyces cerevisiae), the internet on the autonomous system (AS) level, and the World Wide Web. Fixing just the degrees of two nodes gives the mean neighbor degree as a function of node degree, k;{'}_{k} , in agreement with results explicitly obtained from rewiring. For power law degree distributions, we derive the disassortativity analytically. In the PFDS ensemble the partition function can be expanded diagrammatically. We obtain an explicit expression for the link likelihood to lowest order, which reduces in the limit of large, sparse undirected networks with L links and with k_{max}L to the simple formula P(k,k;{'})=kk;{'}(2L+kk;{'}) . In a similar limit, the probability for three nodes to be linked into a triangle reduces to the factorized expression P_{Delta}(k_{1},k_{2},k_{3})=P(k_{1},k_{2})P(k_{1},k_{3})P(k_{2},k_{3}) .