Nature Human Behaviour最新文献

Loneliness—the subjective experience of social disconnection—is now widely regarded as a health risk factor. However, whether the associations between loneliness and multiple diseases are consistent with causal effects remains largely unexplored. Here we combined behavioural, genetic and hospitalization data from the UK Biobank to examine the associations of loneliness with a wide range of non-overlapping diseases. During a median 12.2-year follow-up, loneliness was associated with greater risks in 13 of 14 disease categories and 30 of 56 individual diseases considered. Of the 30 diseases significantly associated with loneliness, 26 had genetic data available for Mendelian randomization (MR) analyses. After Benjamini‒Hochberg correction and multiple sensitivity analyses within the MR framework, non-causal associations were identified between genetic liability to loneliness and 20 out of the 26 specific diseases, including cardiovascular diseases, type 2 diabetes mellitus, obesity, chronic liver diseases, chronic kidney disease, most neurological diseases and the other common diseases. Genetic liability to loneliness was only potentially causally associated with the remaining six diseases. Socioeconomic factors, health behaviours, baseline depressive symptoms and comorbidities largely explained the associations between loneliness and diseases. Overall, our study revealed a dissociation between observational and genetic evidence regarding the associations of loneliness with multiple diseases. These findings suggest that loneliness may serve as a potential surrogate marker rather than a causal risk factor for most diseases tested here.

Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioural goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgements directly onto the brain. Our results reveal broadly distributed representations of behaviourally relevant information, demonstrating selectivity to a wide variety of novel dimensions while capturing known selectivities for visual features and categories. Behaviour-derived dimensions were superior to categories at predicting brain responses, yielding mixed selectivity in much of visual cortex and sparse selectivity in category-selective clusters. This framework reconciles seemingly disparate findings regarding regional specialization, explaining category selectivity as a special case of sparse response profiles among representational dimensions, suggesting a more expansive view on visual processing in the human brain.

Previous work has identified characteristic neural signatures of value-based decision-making, including neural dynamics that closely resemble the ramping evidence accumulation process believed to underpin choice. Here we test whether these signatures of the choice process can be temporally dissociated from additional, choice-‘independent’ value signals. Indeed, EEG activity during value-based choice revealed distinct spatiotemporal clusters, with a stimulus-locked cluster reflecting affective reactions to choice sets and a response-locked cluster reflecting choice difficulty. Surprisingly, ‘neither’ of these clusters met the criteria for an evidence accumulation signal. Instead, we found that stimulus-locked activity can ‘mimic’ an evidence accumulation process when aligned to the response. Re-analysing four previous studies, including three perceptual decision-making studies, we show that response-locked signatures of evidence accumulation disappear when stimulus-locked and response-locked activity are modelled jointly. Collectively, our findings show that neural signatures of value can reflect choice-independent processes and look deceptively like evidence accumulation.

Warning labels from professional fact-checkers are one of the most widely used interventions against online misinformation. But are fact-checker warning labels effective for those who distrust fact-checkers? Here, in a first correlational study (N = 1,000), we validate a measure of trust in fact-checkers. Next, we conduct meta-analyses across 21 experiments (total N = 14,133) in which participants evaluated true and false news posts and were randomized to either see no warning labels or to see warning labels on a high proportion of the false posts. Warning labels were on average effective at reducing belief in (27.6% reduction), and sharing of (24.7% reduction), false headlines. While warning effects were smaller for participants with less trust in fact-checkers, warning labels nonetheless significantly reduced belief in (12.9% reduction), and sharing of (16.7% reduction), false news even for those most distrusting of fact-checkers. These results suggest that fact-checker warning labels are a broadly effective tool for combatting misinformation.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein’s heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.

Emotions have been theorized to be important drivers of economic choices, such as intertemporal or risky decisions. Our systematic review and meta-analysis of the previous literature (378 results and 50,972 participants) indicates that the empirical basis for these claims is mixed and the cross-cultural generalizability of these claims has yet to be systematically tested. We analysed a dataset with representative samples from 74 countries (n = 77,242), providing a multinational test of theoretical claims that individuals’ ongoing emotional states predict their economic preferences regarding time or risk. Overall, more positive self-reported emotions generally predicted a willingness to wait for delayed rewards or to take favourable risks, in line with some existing theories. Contrary to the assumption of a universal relationship between emotions and decision-making, we show that these relationships vary substantially and systematically across countries. Emotions were stronger predictors of economic decisions in more economically developed and individualistic countries.