Nature Human Behaviour最新文献

The formation of predictions is essential to our ability to build models of the world and use them for intelligent decision-making. Here we challenge the dominant assumption that humans form only forward predictions, which specify what future events are likely to follow a given present event. We demonstrate that in some environments, it is more efficient to use backward prediction, which specifies what present events are likely to precede a given future event. This is particularly the case in diverging environments, where possible future events outnumber possible present events. Correspondingly, in six preregistered experiments (n = 1,299) involving both simple decision-making and more challenging planning tasks, we find that humans engage in backward prediction in divergent environments and use forward prediction in convergent environments. We thus establish that humans adaptively deploy forward and backward prediction in the service of efficient decision-making.

Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT and confidence and does so better than all current alternatives. To test RTNet’s ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants performing a digit discrimination task. We found that the accuracy, RT and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet’s predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.

US consumers may turn to the private market for credit when income and government benefits fall short. The most vulnerable consumers have access only to the highest-cost loans. Prior research on trade-offs of credit with government welfare support cannot distinguish between distinct forms of unsecured credit due to data limitations. Here we provide insight on credit–welfare state trade-offs vis-à-vis unemployment insurance generosity by leveraging a large sample of credit data that allow us to separate credit cards, personal loans and alternative financial services loans and to analyse heterogeneity in credit use by household income. We find that more generous state unemployment insurance benefits were associated with a lower probability of high-cost credit use during the first seven quarters of the coronavirus disease 2019 (COVID-19) pandemic. This inverse association was concentrated among consumers living in low-income households. Our results support theories that public benefits are inversely associated with the use of costly credit.

Understanding the interactions between genetic risk and lifestyles on different types and age onsets of cardiovascular disease (CVD) risk can help identify individuals for whom lifestyle changes would be beneficial. Here we developed three polygenic risk scores, called MetaPRSs, for coronary artery disease, ischaemic stroke and intracerebral haemorrhage by combining PRSs for CVD and CVD-related risk factors in 96,400 participants from the prospective China Kadoorie Biobank. Genetic and lifestyle risks were categorized by the disease-specific MetaPRSs and the number of unfavourable lifestyles. High genetic risk and unfavourable lifestyles were found to be more strongly associated with early than late onset of CVD outcomes in men and women. Change from unfavourable to favourable lifestyles resulted in 14.7-, 2.5- and 2.6-fold greater reductions in incidence rates of early-onset coronary artery disease and ischaemic stroke and late-onset coronary artery disease in high than low genetic risk group. Young adults at high genetic risk may have larger benefits in preventing CVD from lifestyle improvements.

It is unclear whether poverty and mental illness are causally related. Using UK Biobank and Psychiatric Genomic Consortium data, we examined evidence of causal links between poverty and nine mental illnesses (attention deficit and hyperactivity disorder (ADHD), anorexia nervosa, anxiety disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, post-traumatic stress disorder and schizophrenia). We applied genomic structural equation modelling to derive a poverty common factor from household income, occupational income and social deprivation. Then, using Mendelian randomization, we found evidence that schizophrenia and ADHD causally contribute to poverty, while poverty contributes to major depressive disorder and schizophrenia but decreases the risk of anorexia nervosa. Poverty may also contribute to ADHD, albeit with uncertainty due to unbalanced pleiotropy. The effects of poverty were reduced by approximately 30% when we adjusted for cognitive ability. Further investigations of the bidirectional relationships between poverty and mental illness are warranted, as they may inform efforts to improve mental health for all.