Nature Human Behaviour最新文献

Whether high-frequency phase-locked oscillations facilitate integration (‘binding’) of information across widespread cortical areas is controversial. Here we show with intracranial electroencephalography that cortico-cortical co-ripples (~100-ms-long ~90 Hz oscillations) increase during reading and semantic decisions, at the times and co-locations when and where binding should occur. Fusiform wordform areas co-ripple with virtually all language areas, maximally from 200 to 400 ms post-word-onset. Semantically specified target words evoke strong co-rippling between wordform, semantic, executive and response areas from 400 to 800 ms, with increased co-rippling between semantic, executive and response areas prior to correct responses. Co-ripples were phase-locked at zero lag over long distances (>12 cm), especially when many areas were co-rippling. General co-activation, indexed by non-oscillatory high gamma, was mainly confined to early latencies in fusiform and earlier visual areas, preceding co-ripples. These findings suggest that widespread synchronous co-ripples may assist the integration of multiple cortical areas for sustained periods during cognition.

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.

Although immunization can dramatically curb the mortality and morbidity associated with vaccine-preventable diseases, vaccination uptake remains suboptimal in many areas of the world. Here, in this meta-analysis, we analysed the results from 88 eligible randomized controlled trials testing interventions to increase vaccination uptake with 1,628,768 participants from 17 countries with variable development levels (for example, Human Development Index ranging from 0.485 to 0.955). We estimated the efficacy of seven intervention strategies including increasing access to vaccination, sending vaccination reminders, providing incentives, supplying information, correcting misinformation, promoting both active and passive motivation and teaching behavioural skills. We showed that the odds of vaccination were 1.5 (95% confidence interval, 1.27 to 1.77) times higher for intervention than control conditions. Among the intervention strategies, using incentives and increasing access were most promising in improving vaccination uptake, with the access strategy being particularly effective in countries with lower incomes and less access to healthcare.

Natural language contains and communicates social biases, often reflecting attitudes, prejudices and stereotypes. Here we provide evidence for a novel psychological pathway for the expression of such biases, in which they arise as a consequence of the automatized mechanisms by which humans retrieve words to produce sentences. Four experiments show that, when describing events, speakers tend to mention people who are more like them first and, thus, tend to highlight the perspectives of their own social groups. This ‘like me’ effect was seen in speakers from multiple demographic groups, in both English and Chinese speakers and in both first- and second-language English speakers. Psycholinguistic manipulations pinpoint that the bias is caused by greater accessibility in memory of words that refer to in-group than out-group members. These data provide a new cognitive explanation for why people produce biased language and highlight how detailed cognitive theories can have social implications.

Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson’s r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.

Have humans always sold and purchased things? This seemingly trivial question exposes one of the most conspicuous blind spots in our understanding of cultural evolution: the emergence of what we perceive today as 'modern' economic behaviour. Here we test the hypothesis that consumption patterns in prehistoric Europe (around 2300-800 BCE) can be explained by standard economic theory, predicting that everyday expenses are log-normally distributed and correlated to supply, demand and income. On the basis of a large database of metal objects spanning northern and southern Europe (n = 23,711), we identify metal fragments as money, address them as proxies of consumption and observe that, starting around 1500 BCE, their mass values become log-normally distributed. We simulate two alternative scenarios and show that: (1) random behaviour cannot produce the distributions observed in the archaeological data and (2) modern economic behaviour provides the best-fitting model for prehistoric consumption.