Optimizing the human learnability of abstract network representations.

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core-periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

Why religion and spirituality are important in human biological research.

The study of human biology includes exploration of all the genetic and environmental influences on human variation and life history, including impacts of sociocultural and physical environments. Religious practice and spirituality may be one of these influences. There are more than 5.8 billion religiously affiliated adults and children, accounting for 84% of the world's 6.9 billion people. Furthermore, 70% of Americans consider themselves spiritual in some way, including 22% who do not consider themselves religious, and the numbers for Europe are lower but proportionally similar. Such a high rate of religious affiliation and spiritual belief suggests that religion and spirituality could be sociocultural influences on human variation, but human biologists have scarcely attended to their impacts, as indicated by the limited numbers of relevant articles in the two flagship human biology journals. In this article, we discuss why human biologists may have overlooked this important force for human variability and highlight foundational work from human biology and other disciplines that can give our colleagues directions forward. We review the impacts of religion and spirituality at population and individual levels and call for human biologists to attend to the many aspects of religion and spirituality that can impact human biology and are much more than simply influences of denominational affiliation.

The value of publicly engaged podcasting in human biology.

Podcasting about science may be popular, but does it motivate engagement? The Human Biology Association and American Journal of Human Biology (AJHB) cosponsor a podcast that highlights recent articles. We hypothesized that AJHB articles discussed on the podcast (n = 42) receive more online views than other articles from the same issues. We used Student's t-tests to compare mean article views and found podcasted articles received more views, but the difference was not significant. When extreme outliers were removed, podcasted articles had over twice the views of control articles (p = .04), indicating that journal-affiliated podcasting can increase science engagement.

Quantifying the compressibility of complex networks.

Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding-or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here, we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization-which is characterized by modular structure and heterogeneous degrees-facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network's structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks.

Broken detailed balance and entropy production in the human brain.

Living systems break detailed balance at small scales, consuming energy and producing entropy in the environment to perform molecular and cellular functions. However, it remains unclear how broken detailed balance manifests at macroscopic scales and how such dynamics support higher-order biological functions. Here we present a framework to quantify broken detailed balance by measuring entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain nearly obeys detailed balance when at rest, but strongly breaks detailed balance when performing physically and cognitively demanding tasks. Using a dynamic Ising model, we show that these large-scale violations of detailed balance can emerge from fine-scale asymmetries in the interactions between elements, a known feature of neural systems. Together, these results suggest that violations of detailed balance are vital for cognition and provide a general tool for quantifying entropy production in macroscopic systems.

Decomposing the Local Arrow of Time in Interacting Systems.

We show that the evidence for a local arrow of time, which is equivalent to the entropy production in thermodynamic systems, can be decomposed. In a system with many degrees of freedom, there is a term that arises from the irreversible dynamics of the individual variables, and then a series of non-negative terms contributed by correlations among pairs, triplets, and higher-order combinations of variables. We illustrate this decomposition on simple models of noisy logical computations, and then apply it to the analysis of patterns of neural activity in the retina as it responds to complex dynamic visual scenes. We find that neural activity breaks detailed balance even when the visual inputs do not, and that this irreversibility arises primarily from interactions between pairs of neurons.

Impacts of family and fieldwork on career/family balance among tenure-track and tenured anthropologists.

OBJECTIVES: A common dilemma rarely mentioned during doctoral training is career/family balance-especially the timing of children. This dilemma extends to professional anthropologists and includes questions surrounding the benefits and challenges of taking children to the field. We analyzed survey data from tenure-track and tenured anthropologists to determine if fieldwork in general and fieldwork with or without their children on site impacts career/family balance. We hypothesized that having children and the burden of fieldwork puts more strain on women than on men in anthropology. METHODS: We compared 297 tenure-track and tenured anthropologists by gender, tenure status, and mean annual time spent conducting fieldwork using survey data collected for the Family and the Field study. Analysis was conducted using a combination of hierarchical multivariate analysis of variance and regression to analyze the differing factors affecting various groups. RESULTS: The primary differences in career/family balance occurred between tenure-track and tenured anthropologists, not women and men, and that fieldwork requirements do not necessarily make achieving personal balance harder for anthropologists than other academics. CONCLUSIONS: The need to conduct fieldwork may dissuade some people with or planning to have children from becoming anthropologists. However, upon obtaining tenure-track positions, the burdens impacting "balance" taper off and become less stressful. We offer policy suggestions for buffering stress related to career/family balance that may negatively impact decisions to pursue or continue in anthropology as a career.

#Hackademics: Hacks toward success in academia.

OBJECTIVE: The Sausage of Science is a podcast for the Human Biology Association and the American Journal of Human Biology through which we introduced a special series called #Hackademics. The podcast was initially used to highlight the scholarship and humanity of fellow human biologists through interviews about "how the sausage is made" in research-that is, the nuts and bolts that go into our publications. However, we soon realized there is much more a weekly podcast can do to help colleagues in our interrelated fields. METHODS: Here we introduce the different contributions to this #Hackdemics special issue. Through #Hackademics, we address under-discussed topics in academia such as work-life integration, hostile academic environments, decolonization of research and teaching, and science communication, among others. RESULTS: The feedback received from listeners for the #Hackademics series reinforced a need for more tools for navigating the academic side of our disciplines. Furthermore, there was a need to decentralize what is often termed "institutional knowledge" by sharing it in a more authoritative medium-a special issue of peer-reviewed articles in our Association's esteemed journal. This special issue's contributions also elevate diverse voices with multiple authors at different career stages. CONCLUSIONS: Using the institutional credibility of peer-review to legitimize these topics, this issue's articles can be a resource for those looking to initiate conversations or change within their departments or institutions with the hope of creating a kinder, more respectful, collaborative, equitable, and accessible academic environment.

Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state.

At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity-in concert with network structure-affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions' baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations.

The evolutionary adaptation of body art: Tattooing as costly honest signaling of enhanced immune response in American Samoa.

BACKGROUND: Tattooing has been practiced globally for thousands of years. From an evolutionary perspective, this tradition seems counterintuitive because it is a dermal injury that risks infection. Previous research indicates tattooing may habituate the immune system for subsequent stress, as with exercise or vaccination, an important benefit in high-risk areas. Visible injuries through tattooing may be a form of costly honest signaling-consciously or unconsciously drawing attention to immunological quality. OBJECTIVES: We tested this habituation effect of tattooing in American Samoa, where its practice is common and extensive and infectious disease rates high. We hypothesized that people with more tattoo experience would have enhanced immune response related to the stress of being tattooed. We compared total and rate of tattoo experience to determine if tattooing is more analogous to exercise or vaccination. METHODS: We measured secretory immunoglobulin A (SIgA), cortisol, C-reactive protein (CRP), and tattoo experience in 25 adults receiving tattoos. We compared post-tattoo SIgA to total and rate of tattoo experience using analyses of covariance, controlling for pre-tattoo SIgA, tattoo duration, age, marital status, and stress and baseline health (cortisol, CRP, body mass index, and cigarette use). RESULTS: Post-tattoo SIgA positively correlated with total tattoo experience (P < .05). Furthermore, when dichotomized by experience, participants with low tattoo experience showed little to no stress-related immune change, whereas high-experience participants exhibited elevated SIgA, suggesting habituation to repeated tattooing. CONCLUSIONS: The historical and cultural popularity of tattooing may be partly due to honest information tattoos convey about adaptive biology, similar to physical benefits of exercise.

Zika virus in American Samoa: challenges to prevention in the context of health disparities and non-communicable disease.

BACKGROUND: Zika virus (ZIKV) is linked to deleterious foetal and neonate outcomes. Maternal exposure to ZIKV through mosquitoes and sexual fluids creates a public health challenge for communities and policymakers, which is exacerbated by high levels of chronic non-communicable diseases in American Samoa. AIM: This study aimed to identify structural barriers to ZIKV prevention in American Samoa and situate them within locally relevant cultural and epidemiological contexts. SUBJECTS AND METHODS: This study assessed knowledge, attitudes and access to ZIKV prevention among 180 adults in American Samoan public health clinics. It queried knowledge about pre-natal care, protection against mosquitoes and condom use. RESULTS: Women were most likely to identify pre-natal care as important. The majority of participants were able to identify how to prevent mosquito bites, but may have been unable to follow through due to socioeconomic and infrastructure limitations. Few participants identified condom use as a preventative measure against ZIKV. Prevention misconceptions were most pronounced in women of low socioeconomic status. CONCLUSIONS: These findings reinforce the need for a multi-pronged approach to ZIKV. This study highlights the need for information on culturally specific barriers and recognition of additional challenges associated with dual burden in marginal populations where social inequalities exacerbate health issues.

Tattooing to "Toughen up": Tattoo experience and secretory immunoglobulin A.

OBJECTIVES: A costly signaling model suggests tattooing inoculates the immune system to heightened vigilance against stressors associated with soft tissue damage. We sought to investigate this "inoculation hypothesis" of tattooing as a costly honest signal of fitness. We hypothesized that the immune system habituates to the tattooing stressor in repeatedly tattooed individuals and that immune response to the stress of the tattooing process would correlate with lifetime tattoo experience. METHODS: Participants were 24 women and 5 men (aged 18-47). We measured immune function using secretory immunoglobulin A (SIgA) and cortisol (sCORT) in saliva collected before and after tattoo sessions. We measured tattoo experience as a sum of number of tattoos, lifetime hours tattooed, years since first tattoo, percent of body covered, and number of tattoo sessions. We predicted an inverse relationship between SIgA and sCORT and less SIgA immunosuppression among those with more tattoo experience. We used hierarchical multiple regression to test for a main effect of tattoo experience on post-tattoo SIgA, controlling for pretest SIgA, tattoo session duration, body mass, and the interaction between tattoo experience and test session duration. RESULTS: The regression model was significant (P = 0.006) with a large effect size (r(2) = 0.711) and significant and positive main (P = 0.03) and interaction effects (P = 0.014). CONCLUSIONS: Our data suggest that the body habituates over time to the tattooing stressor. It is possible that individuals with healthy immune systems heal faster, making them more likely to get multiple tattoos. Am. J. Hum. Biol. 28:603-609, 2016. © 2016 Wiley Periodicals, Inc.

Emergent scale-free networks.

Many complex systems-from the Internet to social, biological, and communication networks-are thought to exhibit scale-free structure. However, prevailing explanations require that networks grow over time, an assumption that fails in some real-world settings. Here, we explain how scale-free structure can emerge without growth through network self-organization. Beginning with an arbitrary network, we allow connections to detach from random nodes and then reconnect under a mixture of preferential and random attachment. While the numbers of nodes and edges remain fixed, the degree distribution evolves toward a power-law with an exponent γ = 1 + 1 p that depends only on the proportion p of preferential (rather than random) attachment. Applying our model to several real networks, we infer p directly from data and predict the relationship between network size and degree heterogeneity. Together, these results establish how scale-free structure can arise in networks of constant size and density, with broad implications for the structure and function of complex systems.

Human learning of hierarchical graphs.

Humans are exposed to sequences of events in the environment, and the interevent transition probabilities in these sequences can be modeled as a graph or network. Many real-world networks are organized hierarchically and while much is known about how humans learn basic transition graph topology, whether and to what degree humans can learn hierarchical structures in such graphs remains unknown. We probe the mental estimates of transition probabilities via the surprisal effect phenomenon: humans react more slowly to less expected transitions. Using mean-field predictions and numerical simulations, we show that surprisal effects are stronger for finer-level than coarser-level hierarchical transitions, and that surprisal effects at coarser levels are difficult to detect for limited learning times or in small samples. Using a serial response experiment with human participants (n=100), we replicate our predictions by detecting a surprisal effect at the finer level of the hierarchy but not at the coarser level of the hierarchy. We then evaluate the presence of a trade-off in learning, whereby humans who learned the finer level of the hierarchy better also tended to learn the coarser level worse, and vice versa. This study elucidates the processes by which humans learn sequential events in hierarchical contexts. More broadly, our work charts a road map for future investigation of the neural underpinnings and behavioral manifestations of graph learning.

Information content of note transitions in the music of J. S. Bach.

Music has a complex structure that expresses emotion and conveys information. Humans process that information through imperfect cognitive instruments that produce a gestalt, smeared version of reality. How can we quantify the information contained in a piece of music? Further, what is the information inferred by a human, and how does that relate to (and differ from) the true structure of a piece? To tackle these questions quantitatively, we present a framework to study the information conveyed in a musical piece by constructing and analyzing networks formed by notes (nodes) and their transitions (edges). Using this framework, we analyze music composed by J. S. Bach through the lens of network science and information theory. Regarded as one of the greatest composers in the Western music tradition, Bach's work is highly mathematically structured and spans a wide range of compositional forms, such as fugues and choral pieces. Conceptualizing each composition as a network of note transitions, we quantify the information contained in each piece and find that different kinds of compositions can be grouped together according to their information content and network structure. Moreover, we find that the music networks communicate large amounts of information while maintaining small deviations of the inferred network from the true network, suggesting that they are structured for efficient communication of information. We probe the network structures that enable this rapid and efficient communication of information--namely, high heterogeneity and strong clustering. Taken together, our findings shed new light on the information and network properties of Bach's compositions. More generally, our framework serves as a stepping stone for exploring musical complexities, creativity and the structure of information in a range of complex systems.

Exactly solvable statistical physics models for large neuronal populations.

Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of N~100 neurons. As N increases in new experiments, we enter an undersampled regime where we have to choose which observables should be constrained in the maximum entropy construction. The best choice is the one that provides the greatest reduction in entropy, defining a "minimax entropy" principle. This principle becomes tractable if we restrict attention to correlations among pairs of neurons that link together into a tree; we can find the best tree efficiently, and the underlying statistical physics models are exactly solved. We use this approach to analyze experiments on N~1500 neurons in the mouse hippocampus, and show that the resulting model captures the distribution of synchronous activity in the network.

Violations of the fluctuation-dissipation theorem reveal distinct nonequilibrium dynamics of brain states.

The brain is a nonequilibrium system whose dynamics change in different brain states, such as wakefulness and deep sleep. Thermodynamics provides the tools for revealing these nonequilibrium dynamics. We used violations of the fluctuation-dissipation theorem to describe the hierarchy of nonequilibrium dynamics associated with different brain states. Together with a whole-brain model fitted to empirical human neuroimaging data, and deriving the appropriate analytical expressions, we were able to capture the deviation from equilibrium in different brain states that arises from asymmetric interactions and hierarchical organization.

Tattooing as a phenotypic gambit.

OBJECTIVES: Tattooing is not an evolved behavior, but it may be a phenotypic gambit to highlight immunological health. Phenotypic gambits are traits or behaviors that appear costly but occur at high rates as a honing process of natural selection not constrained by genetics. Tattooing is an ancient practice that is increasing in popularity worldwide, but it involves wounding the body, which seems counterintuitive because it challenges the immune system and makes one more susceptible to infection. But tattooing may represent a costly honest signal of fitness by "upping the ante" in an era of hygiene or a means to stimulate the immune system in a way that improves and highlights underlying fitness. MATERIALS AND METHODS: We investigated this hypothesis by assessing bacteria killing activity (BKA) in saliva samples collected during two studies of tattooing (N = 40). We compared previous tattoo experience (extent of body tattooed and hours spent being tattooed) to BKA before and after getting a new tattoo. RESULTS: Tattoo experience positively predicts post-tattoo BKA (ß = 0.48, p = 0.01), suggesting that people with more tattoo experience have a relatively more immediate and active immune response than those with less tattoo experience. DISCUSSION: Tattoo experience may elevate innate immunological vigilance, which could aid in protecting against future dermal insults.