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Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. While the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function which – in combination with Bayes’ rule – determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, i.e., by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to this day. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.
We introduce the Berlin Numeracy Test, a new psychometrically sound instrument that quickly assesses statistical numeracy and risk literacy. We present 21 studies (n=5336) showing robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk web panels). Analyses demonstrate desirable patterns of convergent validity (e.g., numeracy, general cognitive abilities), discriminant validity (e.g., personality, motivation), and criterion validity (e.g., numerical and non-numerical questions about risk). The Berlin Numeracy Test was found to be the strongest predictor of comprehension of everyday risks (e.g., evaluating claims about products and treatments; interpreting forecasts), doubling the predictive power of other numeracy instruments and accounting for unique variance beyond other cognitive tests (e.g., cognitive reflection, working memory, intelligence). The Berlin Numeracy Test typically takes about three minutes to complete and is available in multiple languages and formats, including a computer adaptive test that automatically scores and reports data to researchers (http://www.riskliteracy.org). The online forum also provides interactive content for public outreach and education, and offers a recommendation system for test format selection. Discussion centers on construct validity of numeracy for risk literacy, underlying cognitive mechanisms, and applications in adaptive decision support.
We summarize what we assess as the past year's most important findings within climate change research: limits to adaptation, vulnerability hotspots, new threats coming from the climate–health nexus, climate (im)mobility and security, sustainable practices for land use and finance, losses and damages, inclusive societal climate decisions and ways to overcome structural barriers to accelerate mitigation and limit global warming to below 2°C.
We synthesize 10 topics within climate research where there have been significant advances or emerging scientific consensus since January 2021. The selection of these insights was based on input from an international open call with broad disciplinary scope. Findings concern: (1) new aspects of soft and hard limits to adaptation; (2) the emergence of regional vulnerability hotspots from climate impacts and human vulnerability; (3) new threats on the climate–health horizon – some involving plants and animals; (4) climate (im)mobility and the need for anticipatory action; (5) security and climate; (6) sustainable land management as a prerequisite to land-based solutions; (7) sustainable finance practices in the private sector and the need for political guidance; (8) the urgent planetary imperative for addressing losses and damages; (9) inclusive societal choices for climate-resilient development and (10) how to overcome barriers to accelerate mitigation and limit global warming to below 2°C.
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Science has evidence on barriers to mitigation and how to overcome them to avoid limits to adaptation across multiple fields.
The ability to seek out new information is crucial in many situations of our everyday lives. In general, people can display quite elaborated exploration behavior. However, exploration has mainly been studied in multiarmed bandit tasks and theories have predominantly focused on simple directed and random exploration strategies. In this chapter, we review the results of prior studies and argue that the repertoire of human exploration strategies is much more diverse than how it is portrayed in the literature. However, to find evidence for more sophisticated strategies, more complex paradigms than multiarmed bandits are required. In particular, we argue that Markov Decision Processes offer an interesting new setting that allows us to capture strategies beyond random and directed exploration, such as empowerment-based strategies or strategies that explore using explicit goals. We conclude this chapter by discussing several new experimental paradigms that could advance our understanding of human exploration to the next level.
This book has covered a wide range of new and exciting research in the science of information-seeking. Yet many open questions still remain. For example, how is information-seeking related to reward-seeking? What are the principles that enable us to acquire useful information with computational efficiency, despite possessing limited cognitive capacities and knowledge? Which aspects of our neural machinery are unique to information-seeking, and what is shared across other cognitive systems? How does the science of information-seeking inform important societal issues, such as fake news, conspiracy theories, and education?
Humans constantly search for and use information to solve a wide range of problems related to survival, social interactions, and learning. While it is clear that curiosity and the drive for knowledge occupies a central role in defining what being human means to ourselves, where does this desire to know the unknown come from? What is its purpose? And how does it operate? These are some of the core questions this book seeks to answer by showcasing new and exciting research on human information-seeking. The volume brings together perspectives from leading researchers at the cutting edge of the cognitive sciences, working on human brains and behavior within psychology, computer science, and neuroscience. These vital connections between disciplines will continue to lead to further breakthroughs in our understanding of human cognition.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus.
We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection.
Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data.
We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies.
We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.
Different treatment options for pulmonary hypertension have emerged in recent years, and evidence-based management strategies have improved quality of life and survival in adults. In children with pulmonary vascular disease, therapeutic algorithms are not so clearly defined; this study determined current treatment initiation in children with pulmonary hypertension in participating centres of a registry.
Through the multinational Tracking Outcomes and Practice in Pediatric Pulmonary Hypertension registry, patient demographics, diagnosis, and treatment as judged and executed by the local physician were collected. Inclusion criteria were >3 months and <18 years of age and diagnostic cardiac catheterisation consistent with pulmonary hypertension (mean pulmonary arterial pressure ⩾25 mmHg, pulmonary vascular resistance index ⩾3 Wood units×m2, and mean pulmonary capillary wedge pressure ⩽12 mmHg).
At diagnostic catheterisation, 217/244 patients (88.9%) were treatment naïve for pulmonary hypertension-targeted therapy. Targeted therapy was initiated after catheterisation in 170 (78.3%) treatment-naïve patients. A total of 19 patients received supportive therapy, 28 patients were not started on therapy, and 26 patients (10.7%) were on targeted treatment before catheterisation. Among treatment-naïve subjects, treatment was initiated with one targeted drug (n=112, 51.6%), dual therapy (n=39, 18%) or triple-therapy (n=5, 2.3%), and calcium channel blockers with one targeted medication in one patient (0.5%). Phosphodiesterase inhibitors type 5 were used frequently; some patients with pulmonary hypertension related to lung disease received targeted therapy.
There is a diverse therapeutic approach for children with pulmonary hypertension with a need of better-defined treatment algorithms based on paediatric consensus for different aetiologies including the best possible diagnostic workup.
Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA. (JINS, 2016, 22, 240–249)