Farmers’ entrepreneurship is an important engine to comprehensively help promote rural revitalization. Based on data from the China Household Finance Survey (CHFS2019), this paper empirically examines the effect of digital literacy on farmers’ entrepreneurial behavior using the Probit model and instrumental variable method; and examines the mediating role of health status in this effect using the mediation effect model, combined with the Sobel and Bootstrap tests. The results of the study showed that (1) digital literacy positively influenced farmers’ entrepreneurial behavior at the 1% significant level, and this positive influence showed some differences across gender and age; (2) health status played a mediating effect in the positive influence of digital literacy on farmers’ entrepreneurial behavior. Accordingly, policy recommendations are made to foster farmers’ digital literacy, encourage farmers’ entrepreneurship, and ensure farmers’ "digital health" services. Rural revitalization strategy, as the overall grasp of the "San Nong" work in the new era, is a complex and huge systemic project. Fully mobilizing the enthusiasm and creativity of hundreds of millions of farmers and actively encouraging and supporting farmers’ entrepreneurship are essential measures to promote the rural revitalization strategy comprehensively , as wellTotally can digital literacy promote farmers’ entrepreneurial behavior in real time? If the effect is confirmed, what is the mechanism of action behind it? How does the impact of digital literacy on farmers’ entrepreneurial behavior differ across individual characteristics? Existing studies still need to answer the answers to these questions clearly. Most of the existing studies on farmers’ entrepreneurial behavior have been analyzed in terms of essential individual traits , entreprThe remainder of the paper is structured as follows: the second part is a literature review. The third part shows a mechanical analysis and research hypothesis. The fourth part presents the study design. The fifth part manifests an analysis of the empirical results. The sixth part discusses the mechanism of action, and the seventh part is a conclusion and recommendations.With the improvement of people’s cognitive abilities and the development of digital technology, the connotation of digital literacy has been deepened and expanded. The concept "digital literacy" was first defined by P. Gilster  in his bFarmers’ entrepreneurial activities have become a meaningful way to promote rural economic growth and maintain rural economic vitality and are directly related to the process of realizing rural revitalization in China. Regarding the research on the factors influencing farmers’ entrepreneurial behavior, the existing studies can be broadly divided into the following aspects: the first one is individual traits. Farmers will engage in entrepreneurial activities on family farms to survive and succeed, and their entrepreneurial skills significantly impact entrepreneurial activities. One of its main challenges is to enable farmers to develop their entrepreneurial skills, which requires economic support and greater emphasis on education and training .KomppulaA synthesis of the existing literature manifests that there are many research results on farmers’ entrepreneurial behavior, while there needs to be more in-depth research on farmers’ entrepreneurial behavior from the perspective of digital literacy. Based on this, this manuscript may have the following contributions. First, it measures rural people’s digital literacy levels in rural areas of China, deeply analyzes the influence of digital literacy on farmers’ entrepreneurial behavior, extends the study of digital literacy to rural economic activities, and enriches and supplements the research perspective on rural people’s entrepreneurial intentions. Second, it deepens the study of the path mechanism of digital literacy affecting farmers’ entrepreneurial behavior,. As a human capital, health plays an important role in rural economic activities. Analyzing the relationship between digital literacy, health, and farmers’ entrepreneurial behavior and identifying the possible channels through which digital literacy affects farmers’ entrepreneurial behavior, helps to clarify the mechanism path and further exploit the depth of the study. In summary, this paper intends to use the factor analysis method to measure the level of digital literacy based on China Household Finance Survey (CHFS2019) survey data, and investigate the effect of digital literacy on farmers’ entrepreneurial behavior through the Probit model regression and instrumental variables method. Moreover taking health as the entry point, the study of the influence mechanism between digital literacy and farmers’ entrepreneurial behavior was attempted through the mediating effect test. As a result, suggestions are made to improve farmers’ entrepreneurial behavior from the perspective of digital literacy to help rural revitalization, to effectively complement existing studies to a certain extent.Information economics theory suggests that prices are obtained at a cost through search and, when received, require further investigation and correction. The relatively high and uneven cost of access leads to an asymmetry in information and competition. Although the information is eternally asymmetric, digital literacy, as "skills and assets in the digital economy," has broken the original temporal and spatial differences and is balancing the information asymmetry to a greater extent, making the information gap between subjects gradually narrowing. Digital literacy is a comprehensive ability that enables farmers to use digital tools, digital technology, digital resources, and digital communication and collaboration with others in a digital context in a proficient, correct, and reasonable manner and to integrate well into digital life and production. It can not only help farmers solve the problems they face in production life by understanding and using digital information and its technology tools, but also help them better integrate into the wave of informatization to obtain resources and advantages in various aspects. Besides, it breaks the information barriers brought by the original geography, distance, and economic development level in rural areas, improves the problem of information asymmetry to a certain extent, and substantially improves their management level. Based on the theory of information economics, this paper argues that the impact of digital literacy on farmers’ entrepreneurial behavior is manifested in the following aspects:Firstly, digital literacy meets the information needs of farmers’ entrepreneurship. Information is considered a "core resource for entrepreneurial success." Adequate information resources are able to help farmers grasp market dynamics and policy regulations, which in turn help them understand the market and capture business opportunities . In the Secondly, digital literacy improves farmers’ entrepreneurial capabilities. Digital literacy is essentially a manifestation of competence  and can Lastly, digital literacy increases the social capital of farmers’ entrepreneurship. Social capital is an explicit and potential resource possessed by individuals, and the more abundant the social capital, the broader the social interaction and the more it can promote individual entrepreneurship . On the H1: Digital literacy has a positive effect on farmers’ entrepreneurial behavior.As a human capital, digital literacy has a significant impact on the health status of rural people. Access to health information can help people actively learn about various types of health knowledge, which can better help them identify health problems and adopt appropriate coping strategies . With thHealth is an all-around good state of physical, mental, and social relationships , which iH2: Health has a mediation effect on digital literacy affecting farmers’ entrepreneurial behaviorBased on the above analysis, this paper constructs a mechanism diagram on the effect of digital literacy on farmers’ entrepreneurial behavior see .Considering the data accessibility and the research topic, this paper adopts the 2019 China Household Finance Survey (CHFS) data published by the China Household Finance Survey and Research Center of Southwest University of Finance and Economics (SWUFE). The CHFS questionnaire covers detailed information on various aspects such as household demographic characteristics, farmers’ entrepreneurship, subjective attitudes, and information about digital literacy, which in turn provides a good data environment for this study. During the data processing, it firstly screened the samples of rural household registration aged 18–80. Then, the main variables, including the relevant variables indicating digital literacy and farmers’ entrepreneurial behavior and the control variables, were selected according to the study objectives and the CHFS questionnaire item settings. Finally, it removed the missing values and outliers of the relevant variables. After the above processing, 25,173 valid samples were obtained in this research.(1) Explained variable: Farmers’ entrepreneurial behavior. Farmer entrepreneurial behavior is a market economy activity of farmers with the household as the primary decision-making unit, which mainly refers to the process of value creation by farmers through identifying opportunities, allocating and utilizing resources, and innovating business forms or opening up new business fields , includi(2) Core explanatory variables: digital literacy. In this research, five measurement topics were finally selected to measure farmers’ digital literacy at the level of computer use, payment methods, online communication spending, online shopping spending, and online borrowing, as shown in Using principal component analysis, two common factors were extracted using the principle that the characteristic root is greater than 1. The cumulative variance contribution of the two factors was 61.45%; the specific results are shown in (3) Control variables. Li et al.  showed t(4) Instrumental variables. This paper uses "frequency of online shopping" and "mode of receiving delivery" as instrumental variables for digital literacy. The specific validation and interpretation of the instrumental variables are explained in detail below.(5) Mediating variable: health status. Firstly, from the subjective perspective, it chooses rural residents’ evaluation of their physical condition to measure health status. Self-rated health satisfaction is adopted to indicate health . Among tThe definition, assignment and descriptive statistics of each variable are shown in This empirical model is divided into two parts. First, adopt the binary Probit model to analyze the effect of digital literacy on farmers’ entrepreneurship behavior. Second, adopt the mediating effect model to analyze the logical relationship between digital literacy, health status, and farmers’ entrepreneurial behavior.EWi, while EWi = α1DLi + ∑Xi + εi, and since EWi cannot be observed, all that can be observed is a binary variable. When EWi > 0, EWi = 1; conversely, when EWi > 0, EWi = 0. Finally, the specific form of the model to analyze the effect of digital literacy on farmers’ entrepreneurial behavior is as follows.Probit model. Farmers’ entrepreneurial behavior is a typical binary categorical variable with values of 1 and 0 for the "yes" and "no" options, which is estimated by a binary Probit model . SupposeEW denotes farmers’ entrepreneurial behavior, EW = 1 shows farmers with entrepreneurial behavior, and EB = 0 presents farmers without entrepreneurial behavior. φ manifests the cumulative distribution function of normal distribution, DLi indicates numerical literacy, and γi are coefficients to be estimated for each variable. Besides, Xi denotes control variables affecting farmers’ entrepreneurial behavior at the individual level and household level, and εi are random disturbance terms.In the model, the explanatory variable Step 1, testing the impact of digital literacy on farmers’ entrepreneurial behavior.Step 2, testing the impact of digital literacy on health status.Step 3, integrating digital literacy and health status into the model at the same time.Mediating effect model. Since the mediating effect model can analyze the influence process and mechanism of the independent variable on the dependent variable and is extensively used in economics research, this paper applies the model to empirically test the mediating role of health status between digital literacy and farmers’ entrepreneurial behavior. In this paper, the stepwise regression method is used to test the mediating effect by referring to Wen et al. , which iEBi denotes farmers’ entrepreneurial behavior, Healthi presents health status, and DLi indicates digital literacy. Moreover, ∂1, ∂2, ∂3 are constant terms, βi is the coefficient to be estimated for each variable, Xi represents the control variables affecting farmers’ entrepreneurial behavior at individual and household levels; and εi is a random interference term.In the above equation, The Sobel test and Bootstrap (repeated sampling 5000 times) mediating effect test are further applied respectively. The Sobel method is one of the coefficient product tests, which is different from the stepwise regression method which directly tests the significance of coefficients a and b, without considering the significance of coefficient c. According to the assumption of the Bootstrap method, "confidence interval does not contain zero", that is, when the upper and lower limits of the confidence interval are both positive or negative, it indicates that there is a significant positive or negative effect of mediation.This paper conducts a multicollinearity test, and the variance inflation factors are all less than 2, indicating that there is no multicollinearity among the respective variables. To make the results more reliable, this paper adopts a stepwise regression method see , and modThis research considers the possible endogeneity of the baseline regression results. On the one hand, digital literacy and farmers’ entrepreneurial behavior may be causally related to each other. Digital literacy affects farmers’ entrepreneurial behavior, and on the contrary, farmers’ entrepreneurial process may be exposed to digital technology and services, and their digital literacy may be higher. On the other hand, there may be omitted variables. Some unobservable factors, such as rural clan power factors, may also affect farmers’ entrepreneurial behavior. As a result, in order to alleviate the endogeneity problem, this manuscript selects "frequency of online shopping" and "whether to choose Cainiao post to receive express delivery" as two instrumental variables of digital literacy in the questionnaire. In addition, it adopts the instrumental variable method to solve the endogeneity problem.On the one hand, the higher frequency of online shopping indicates that farmers are more familiar with all online shopping processes and skilled in using a cell phone or computer shopping APP, all of which will promote the formation of digital literacy. On the other hand, when picking up the express delivery at the Cainiao station, farmers will enter the pickup code or APP to complete the pickup, and all these processes reflect the digital literacy of farmers. Moreover, "frequency of online shopping" and "whether to choose Cainiao post to pick up express delivery" do not directly affect farmers’ entrepreneurial behavior. Therefore, theoretically instrumental variables may be influential instrumental variables of digital literacy.In terms of empirical tests, firstly, the DWH test of whether there is an endogeneity problem was conducted, and the results predicted that the p-value of the DWH test was less than 0.01 and digital literacy was considered as an endogenous variable. Secondly, the minimum eigenvalue statistic of the test is 111.254, which is greater than the critical value of the 10% level test (19.93). Meanwhile, The two instrumental variables are significant at the 1% and 5% levels, respectively, and there is no weak instrumental variable problem. Finally, the p-value in the over-identification test is more significant than 0.1, so the selected instrumental variables can be considered exogenous. In conclusion, it can be assumed that the selected instrumental variables are appropriately and error-free selectedrefer to .Based on the above analysis, this paper constructs two-stage least squares (2SLS) to deal with endogeneity using farmer entrepreneurial behavior as the explanatory variable, digital literacy as the core independent variable, and control variables in line with the baseline model. The regression results of the model in Replacing the explanatory variables: To verify the reliability and robustness of the estimated results of the baseline regression model, the quantitative indicators of the core explanatory variables were replaced by directly selecting the questionnaire "Do you intend to start a business? Yes = 1, No = 0" to redefine farmers’ entrepreneurial behavior. The intention is an essential antecedent variable of behavioral response, directly determining whether and how strongly individual behavior occurs. The stronger an individual’s choice to start a business means that they are more willing to invest more effort in entrepreneurship, thus contributing more to various entrepreneurial activities and exhibiting more entrepreneurial behaviors . In thisReplacing the regression model: The previous section of the benchmark regression uses the probit model, and the logit model as a reference to ensure the reliability of the results. The logit model is applicable when the dependent variable is a binary categorical variable or the incidence of an event is a numeric variable. From To examine the differences of the positive effect of digital literacy on farmers’ entrepreneurial behavior across genders and ages, this paper examines the heterogeneity of digital literacy affecting farmers’ entrepreneurial behavior using respondents’ gender and age as grouping variables, respectivelyrefer to . FirstlyThe above results confirm that digital literacy can promote farmers’ entrepreneurial behavior. Regarding the impact mechanism of digital literacy on farmers’ entrepreneurial behavior, this paper takes health status as the entry point and uses the mediating effect model to verify. This research further tested the significance of the mediation effect using the Sobel and Bootstrap (5000 replicate sampling) methodsrefer to . First, In the context of implementing the rural revitalization strategy, farmer entrepreneurship has greatly contributed to farmers’ income and the sustainable development of rural areas. This paper constructs a model of the relationship between digital literacy and farmers’ entrepreneurial behavior. Based on CHFS2019 microdata, it adopts the Probit model and further uses the Ivprobit model with two-stage least squares (2SLS) to deal with endogeneity, systematically examines the influence of digital literacy on farmers’ entrepreneurial behavior, and explores the mediating role of health in the effects of digital literacy on farmers’ entrepreneurial behavior. The study finds that: first, digital literacy is conducive to promoting farmers’ entrepreneurial behavior; second, the positive effect of digital literacy on farmers’ entrepreneurial behavior is more pronounced in the sample of females and the sample of 18 to 40 years old in terms of different genders and ages; third, there is a mediating effect of health in digital literacy in promoting farmers’ entrepreneurial behavior. Previous studies have primarily focused on the relationship between social capital, infrastructure, policies and institutions, and cultural dimensions and the occurrence of entrepreneurship at the national or regional level. Still, few have linked digital literacy to entrepreneurial activities at the micro-individual level. Compared with previous studies, this paper interprets the underlying mechanism of farmers’ entrepreneurship from the digital literacy perspective and considers health status as a mediating variable embedded in the path of "digital literacy influences farmers’ entrepreneurial behavior." Besides, it empirically analyzes the causal relationship between digital literacy first influencing health status and then influencing farmers’ entrepreneurial behavior, which shows the variability of different individual characteristics in digital literacy influencing farmers’ entrepreneurial behavior to cultivate a realistic path for other farmers’ personal digital literacy to achieve optimal entrepreneurship.According to the above findings, this paper puts forward the following policy recommendations:First, the level of digital literacy of farmers can be improved. The government and relevant departments can offer training courses to train the skills in using digital tools, digital platforms, etc. Manuals on the use of digital communication tools, digital trading platforms, etc., can be produced, and experts can also visit villages at appropriate times to hold lectures on digital knowledge topics and digital knowledge competition activities. We can arrange for farmers of different genders and ages to be trained in groups and arrange for exceptional people to provide them with targeted training and guidance on using smartphones in an easy-to-understand manner to bring them closer to advanced technologies such as digital technologies. At the same time, according to the personality characteristics and cultural customs of different groups of farmers, some technology-based enterprises should also develop software suitable for different farmers to use so that those groups with limited knowledge can quickly get started with the application. At last, we can provide targeted training for different groups to meet their different entrepreneurial needs through the above methods.Second, farmers with high digital literacy are encouraged to prioritize entrepreneurial activities. The higher digitally literate farmers are able to start their own businesses through the netroots economy, live-streaming with goods, e-commerce, etc. The government is supposed to encourage these farmers to give priority to entrepreneurial activities so that they can share their new ideas and thinking with neighboring farmers, play a demonstration and leading role, and encourage and guide farmers to establish mutual help entrepreneurial groups. Moreover, it can profoundly broaden the social network system of farmers, promote the smooth flow of digital resources, entrepreneurial information, and capital among farmers, and then give play to the spillover effect of digital literacy so that the driving impact of digital literacy on farmers’ entrepreneurship can be better played.Third, the government should protect farmers’ "digital health" services. As an essential human capital, health plays a vital role in rural economic activities. In the era of "digital health," on the one hand, farmers should be taught the most practical mobile Internet applications in their daily lives, such as travel and medical treatment, so that they can "check" their health online and "self-diagnose" online through digital platforms, select and use online medical treatment, and conduct online medical treatment and purchase medicines. On the other hand, relevant departments can also provide more convenient and accessible health-intelligent services and promote the construction of health-intelligent service platforms, such as cell phone "intelligent health butler," "Internet + health" fine service platforms, etc., which can effectively meet and protect the health service needs of farmers.The research in this manuscript has certain limitations that need to be explored in depth. On the one hand, due to the restriction of data, this paper has examined digital literacy in general, and the measurement of digital literacy needs to be further deepened. Further research could be done on digital social literacy, digital security literacy, digital creative literacy, digital business literacy, and so on. On the other hand, this paper only studies farmers’ entrepreneurial behavior, and further analysis of entrepreneurial mode, entrepreneurial types, and entrepreneurial performance can be attempted.


Systematic Online Living Evidence Summaries (SOLES) to accelerate evidence synthesis. In this approach, we integrate automated processes to continuously gather, synthesise and summarise all existing evidence from a research domain, and report the resulting current curated content as interrogatable databases via interactive web applications. SOLES can benefit various stakeholders by (i) providing a systematic overview of current evidence to identify knowledge gaps, (ii) providing an accelerated starting point for a more detailed systematic review, and (iii) facilitating collaboration and coordination in evidence synthesis.Systematic reviews and meta-analysis are the cornerstones of evidence-based decision making and priority setting. However, traditional systematic reviews are time and labour intensive, limiting their feasibility to comprehensively evaluate the latest evidence in research-intensive areas. Recent developments in automation, machine learning and systematic review technologies have enabled efficiency gains. Building upon these advances, we developed  Systemaresearch . Many chresearch . For exaresearch ,6. SRs oresearch .There are proven benefits of conducting SRs for evidence-based research and decision making. Here, we focus on the application of SRs to preclinical and clinical evidence. However, traditional SR methodology is no longer optimal for the current research landscape where rates of publication are increasing tremendously . Here, wOne of the key strengths of SRs over more traditional narrative literature review is that they aim to capture all the available evidence. However, even SRs are not universally successful in this regard . MethodoBy the time of their publication, SRs are frequently many months or years out of date ,17. ManuWithout co-ordinated efforts to prioritise and conduct which SRs should be done, the degree of overlap can be significant , with liSetting aside the usefulness of SRs and meta-analyses, alternative approaches such as rapid reviews, or scoping reviews  may be sRecently, several groups have sought to facilitate faster evidence synthesis through the application of automation technologies . By incoSystematic Online Living Evidence Summaries (SOLES). SOLES are systematically obtained, continuously updated, curated, and interrogatable datasets of existing evidence, enabled by automation. They are likely of greatest benefit to research-intensive areas with high rates of publication, as seen in much of biomedicine. Here, we describe where SOLES fit into the evidence synthesis ecosystem  without capturing too many irrelevant publications to screen . Using automation approaches for record screening allows for broader systematic search strategies with higher sensitivity, to prioritise sensitivity over specificity. At the most extreme, one might imagine record screening being applied to an entire bibliographic database rather than to the results of a systematic search, the specificity being derived from downstream, automated tasks applied to full text articles. However, current computational limitations and tool performance mean that this is not possible at present.Further, the sensitivity of the SOLES approach to literature searching does not rely on the presence of specific PICO elements in the title or abstract of publications. The pool of potentially relevant publications is much larger but, as we have shown in exemplar SOLES projects, the evidence can then be further subdivided into PICO categories by applying natural language processing techniques  on the full-text of the publication.Automated Systematic Search Deduplication tool [A highly sensitive search to retrieve as many relevant citations as possible comes with the caveat that many individual citations will have been identified from several databases. With extremely large searches, this issue is amplified and difficult to manage using conventional deduplication tools. We have recently developed ASySD – the ion tool . ASySD iUsing SOLES as a starting point for obtaining the latest and most relevant research enables researchers and funding agencies to quickly gain a credible and systematic overview of current evidence. This can facilitate decision making, ensuring that future research is focused on the areas of greatest need and minimising research waste.exist for several platforms including PubMed (NBCI), Scopus, Dimensions and Web of Science collections – and schedule these search queries to run at scheduled time periods. Attempts to automate some searches have been hampered by issues in retrieving citation data of the same quality (with abstracts and other meta-data present) and cost for access. Depending on institutional access and specific database needs, this step may require some human input. The retrieval of new citations on a regular basis also facilitates attempts to update SRs with minimal additional effort.A major goal of SOLES projects is to synthesise new research evidence as soon as it becomes available, thus preventing any delay in making use of that evidence to inform future research and decision making. SOLES developers can implement fully automated searches for publications using accessible Application Programming Interfaces (APIs) – which Systematic review questions generally focus on specific PICO . To enable SOLES users to identify the latest relevant research, categorisations can be added to publications using regular expressions (regex) search dictionaries for animal models, interventions and outcome measures. A regex is a sequence of characters relating to a pattern of text, and are commonly used in library and information science and more widely. Overall, these approaches are time-saving and can be highly sensitive, especially if sufficient effort is placed into designing regex probes to account for common synonyms and deviations in punctuation and spelling . In this way, using a curated citation data set derived from a SOLES platform can be an accelerated starting point for a more detailed SR.To ascertain study quality, and to gain insights over whether primary study quality may be improving over time, similar text-mining tools can be applied to research articles to measure reporting quality or measures to reduce the risk of bias . More reThe automated PICO annotation and quality assessment tools allow for flexibility of the approach to analysis, depending on the needs of the research user. For example, the quality of a whole domain  of evidence can be quickly determined for funders making decisions in the design of new funding calls. Alternatively, current data on highly specific research questions can be interrogated by primary researchers designing a new experiment. Together, these approaches bring us a few steps closer to making use of new evidence at a much quicker pace. Further, measuring research quality indicators in an automated way may allow for the benchmarking of research improvement activities over time ,53.SOLES approaches require substantial human effort to set-up and ensure that the automated processes used to screen and annotate information function optimally. Many of the automation tools used to power SOLES require initial human input. For example, record screening uses supervised machine learning algorithms, requiring in turn large, curated training datasets. A random subset of search results should be screened manually to train an algorithm on the inclusion and exclusion criteria of a particular SOLES project, even if such an algorithm has already been validated in a different context. This calls for efficient collaboration and co-ordination to ensure that training data can be collected quickly, and that data are reliable enough to train the algorithms.Large projects and collaboration provides an opportunity to use a crowdsourcing approach, which, if well-co-ordinated, allows for quicker data collection. Crowdsourced approaches work best if participants are given ‘micro-tasks’ to perform, such as screening or annotation of only a few pieces of information at a time . ParticiFurther collaboration can be used to establish the creation of new SOLES projects, ensuring that domains of most interest to researchers and funders are prioritised, and maximising crowd engagement to generate ideas for model, intervention and outcome assessment ontologies.Furthermore, the ability of SOLES to provide a continually updated, comprehensive, annotated summary of current evidence, uncertainties and gaps of an entire research domain may prove instrumental in evidence-based policy making and research priority setting where broad-scope evidence synthesis produced within the timeframe of policy cycles are preferrable over traditional SRs which typically emphasises accuracy and depth of a narrower field .SOLES projects are an emerging methodology, and we have thus far focussed our efforts on synthesising evidence from preclinical laboratory experiments within research-intensive areas where there is an urgent need to develop new therapeutics.The evolution of the SOLES approaches began with a SR of animal models of depression which used machine learning for title and abstract eligibility screening and text-mining techniques to automatically extract key methodological details from publications including the type of animal model used and the pharmacological interventions tested . The finLater, we expanded the SOLES approach to integrate more automation technologies, such as study quality and risk of bias annotation using a regular expressions approach, in an early version of our Alzheimer’s disease dashboard. Further, by implementing a crowd-sourced approach to build up sets of annotated data at a quicker pace, we could train tailored automation approaches. Using this approach, we created a pilot COVID-SOLES, a resource which was updated on a weekly basis with new evidence in the emerging pandemic . More rein vivo studies and MND in vitro studies.In parallel, we have created MND-SOLES-CT ,60,61 toBringing together the advances and learning points from each of these projects, we are now in the position to develop a functional automated workflow that can be replicated to form the skeleton of all new and ongoing SOLES projects. Our early SOLES approaches made great use of the R programming language and R Shiny Apps (RRID:SCR_001626). As the technological functionality has evolved, subsequent SOLES approaches have drawn in more programming tools such as database APIs, SQL databases and tools built in Python (RRID:SCR_008394), capitalising on interoperability.There are several limitations within the automated SOLES approaches we have outlined. Publications with no indexed abstract text may be incorrectly handled by machine learning-assisted screening approaches, while publications with no accessible full-text documents cannot be further categorised programmatically. The evolution of scientific publishing towards open-access publishing and improvements to the way citations are indexed in biomedical databases  will support evidence synthesis approaches such as SOLES moving forward. The limitations of supervised machine learning algorithms for record screening of large corpuses have been discussed elsewhere .A further barrier to a fully automated approach, is that the way preclinical research publications are currently structured does not allow for accurate detection of PICO elements for every experiment. A publication may describe several experiments in different animal models, testing different treatments and measuring those animals on different outcomes. Further, it is difficult to determine how many regex matches indicate that a PICO element is part of an experiment described within a publication, rather than a match to a reference to other research.These caveats suggest that while automated approaches are useful, they cannot completely replace human reviewers; a SOLES project will still require some human input to ensure quality of the data that is output.Our key short-term goal for SOLES is to continually optimise the automated approaches we use, improving the performance of existing tools and optimising our approaches to take advantage of new tools which may become available. In particular, we recognise the growing need for high-level database management functionality for running automated scripts and updating records, and the implications this has in maintaining the quality of synthesised data.In the long-term, we hope to focus on the interface between SOLES and SRs derived from SOLES projects. Interoperability between SOLES data visualisation apps and SR platforms such as the Systematic Review Facility  strengtin vitro experiments. Furthermore, the same interventions, drug targets and underlying biological pathways may be studied within different contexts. Combining evidence from across SOLES projects could enable us to uncover hidden links across typically distinct research silos. While aspects of SOLES overlap with evidence gap maps, we aim to leverage novel automation tools to support and expand our approach going forward to provide more advanced insights and drive hypothesis generation.Having developed SOLES across several research domains, we hope to expand our approach and triangulate diverse evidence streams including a greater focus on clinical evidence and results from Finally, we seek greater collaboration with key research stakeholders who may benefit from the data that a SOLES can provide, such as laboratory researchers, clinicians and other healthcare decision makers. Only with effective collaboration can we ensure that SOLES are fit for purpose and beneficial to the wider scientific community.Systematic Online Living Evidence Summaries are an emerging approach to benefit multiple key stakeholders in the evidence synthesis process, including primary researchers, evidence synthesists, information specialists and statisticians, funders, regulatory agencies and policymakers.We believe this approach can bring these stakeholders together to work collaboratively to answer pressing research questions in biomedicine, forming communities to bridge the translational gap and make the most effective use of research.The approach is flexible, data processing pipelines can be expanded to add new validated tools as they become available, to meet the diverse and growing needs of evidence stakeholders in biomedicine.


BackgroundWhen treating periprosthetic femoral fractures (PPF) around polished taper slip stems (PTS), determining which patients can be successfully treated with internal fixation can be challenging. We have described the subsidence-in-centraliser (SINC) sign as a radiographic feature of PPF around PTS stems. We hypothesise that a positive SINC sign can help predict a poorer outcome for the fixation of these fractures.Patients and methodsRetrospective identification of PPFs around cemented PTS with an appreciable centraliser on radiographs was conducted at a single centre. A positive SINC sign was defined as a post-injury radiograph demonstrating >50% reduction in the radiographic lucency representing the stem centraliser when compared to pre-injury films or complete obliteration of distal lucency when no pre-injury film was available. The primary outcome was the rate of subsequent stem subsidence on follow-up radiographs comparing SINC-positive and SINC-negative fractures, which were managed with open reduction and internal fixation (ORIF).ResultsFifty-four patients were included in the analysis. The mean age was 76.8 years, and the mean follow-up for all patients was 12.7 months. Thirty-five fractures were deemed SINC-positive, and 19 were SINC-negative. 17/17 (100%) SINC-positive fractures managed with fixation underwent further subsidence . A positive SINC sign demonstrated a sensitivity of 90.5% and specificity of 100% for subsequent stem subsidence in fractures treated without revision. SINC positive fractures underwent significantly more subsidence compared with SINC negative fractures when fixed  at a mean follow-up of 12.7 months. The SINC sign demonstrated strong inter- (k=0.96) and intra-rater (k=0.86) reliability.ConclusionThe SINC sign can serve as a useful adjunct in the decision to fix or revise PPF around PTS. A positive SINC sign may represent a cement mantle that cannot be reconstituted anatomically, leading to subsidence after treatment with ORIF. Periprosthetic hip fractures (PHF) are common post-operative complications following total hip arthroplasty (THA) ,2. PHFs The Vancouver classification  and subsHigh failure rates amongst B1 fractures treated with ORIF have been attributed to the underdiagnosis of loose implants -17. LindCritically, the Vancouver classification does not distinguish between types of stem fixation. Fractures around cemented stems account for a large volume of PHFs globally . PolisheModern PTS has a distal hollow centraliser allowing for a central position of the stem  and consWe have previously described a radiographic sign in fractures around cemented polished tapered stems and termed this the ‘SINC’ sign, referring to ‘subsidence-in-centraliser’, whereby a fracture can cause the stem to acutely subside into the centraliser .We hypothesise that a positive SINC sign is indicative of a cement mantle that cannot be adequately reconstituted and can therefore help to predict those fractures that will perform worse with internal fixation. Therefore, the central question of our study is whether the SINC sign is an indicator of poor performance with osteosynthesis in periprosthetic fractures around the THA.Study designA positive SINC sign was defined as a >50% reduction in the distal lucency representing the stem centraliser when compared to pre-fracture radiographs Figures -3 or comRetrospective identification of all cases of periprosthetic hip fractures presenting to a single hospital system in the United Kingdom between January 2011 and December 2020 was performed. Available imaging and notes were reviewed using the electronic medical record (EMR) and picture archiving and communication system (PACS).Patient selectionThe inclusion criteria were patients with periprosthetic femoral fracture (PPF) around cemented stems whose radiographs demonstrated an appreciable centraliser on initial fracture and post-injury radiographs. Cases were excluded if the femoral stems had no appreciable centraliser on radiographs , if the stems displayed radiographic evidence of pre-injury loosening or osteolysis, if the stem was uncemented, or if a minimum follow-up radiograph of eight weeks was not available. Treatment was determined as either conservative (non-operative), ORIF, or revision arthroplasty and was decided upon following discussion amongst surgeons at a trauma meeting. ORIF was conducted by general orthopaedic trauma surgeons and revision by fellowship-trained hip arthroplasty surgeons.Additionally, 100 patients with 107 consecutive cases of primary THA using PTS were identified from the trust coding database between January 2018 and April 2018, with no history of trauma and with an appreciable centraliser on radiographs. The presence or absence of the SINC sign was documented. The purpose of this was to determine whether a positive SINC sign is a normal finding in follow-up radiographs of PTS in the absence of trauma.Data collection and analysisInter-observer reliability of the SINC sign was assessed by an independent review of all pre-operative radiographs by an arthroplasty and trauma consultant (PS) and a joint reconstruction fellow (FI). Cohen’s Kappa was used to determine the level of agreement between the two assessors’ ratings.Intra-observer reliability was assessed by the review of radiographs by a senior resident (SM) on two separate occasions, separated by three weeks. Cohen’s Kappa was used to determine the level of agreement between ratings on each occasion . For botSubsequent stem subsidence on follow-up radiographs was measured using digital templating software  by two authors (PS and FI). The femoral head diameter was used for calibration of the magnification of the radiographs in the template. The vertical distance from the shoulder of the stem to the tip of the greater trochanter along the longitudinal femoral shaft axis was used to calculate the subsidence. Subsequent stem subsidence was measured at the final available follow-up radiograph in comparison to immediate post-injury radiographs. The degree of subsequent stem subsidence was compared between the SINC-positive and SINC-negative groups using the Mann-Whitney U test. The inter-observer reliability of the digital templating software method of measuring subsequent stem subsidence was calculated using the intraclass correlation coefficient (ICC).To assess the accuracy of the SINC sign in predicting subsequent subsidence, sensitivity, and specificity were calculated using 2 × 2 contingency tables, with exact Clopper-Pearson confidence intervals calculated for 95%. A SINC-positive radiograph was defined as a positive test, and subsequent stem subsidence as a dichotomous outcome was defined as a true positive finding. This analysis was only applied to PHFs that were managed conservatively or with fixation as opposed to revision surgery. In addition, the Phi coefficient was calculated to assess the association between a SINC-positive radiograph and the rate of subsequent stem subsidence. Statistical analyses were conducted using IBM SPSS Statistics software, version 25 . A p-value of <0.05 was considered statistically significant.Of the 100 consecutive patients with no history of trauma who underwent 107 THA, two post-operative radiographs demonstrated a positive SINC sign with complete obliteration. This was evident at the initial post-operative radiographs and the latest follow-up. The remaining 105 THA had negative SINC signs at the initial post-operative and latest follow-up. The mean follow-up in this group was 19.6 months (range: 12-68 months).Out of 268 PHFs originally identified, following exclusions, a total of 54 patients with 54 PHFs were included in the analysis, all of which occurred around a cemented, polished, tapered stem with an appreciable centraliser on radiographs . Thirty-two patients (67%) were female, and 17 (33%) were male. Forty-seven femoral stems were CPT , three were C-Stem  and four were Exeter .The mean follow-up of cases managed non-operatively or with fixation was 12.8 months . The minimum follow-up was two months. The median time to surgery was three days for ORIF and six days for revision arthroplasty. Pre-injury radiographs were available for 25/37 patients managed conservatively or with fixation (67.6%).SINC positiveThirty-five PHFs were deemed to be ‘SINC’ positive Table . Of thesNineteen of nineteen (100%) SINC-positive PHFs that were managed conservatively or with ORIF, with appropriate follow-up radiographs available, demonstrated subsequent stem subsidence  at a mean of 11.3 months of follow-up (SD 10.8). Seventeen of seventeen (100%) SINC-positive fractures were managed with fixation subsided , with 3/17 resulting in further fixation or arthroplasty. Three of three (100%) of SINC-positive patients managed conservatively subsided, including one patient who underwent further revision surgery due to loosening and pain.SINC negativeNineteen fractures were deemed to be ‘SINC’ negative Table . Five ofThirteen of nineteen (68.4%) SINC-negative patients were treated with ORIF. Twelve patients underwent fixation with a single lateral locking plate, including two with supplementary strut grafts, and one patient was treated with cerclage cables alone. Two of thirteen (15%) of these fractures demonstrated subsequent stem subsidence, with a mean subsidence of 0.28 mm (SD 1.1) and a mean follow-up of 15.1 months (SD 9.8).Sensitivity and specificity of SINC signThe SINC sign demonstrated a sensitivity of 90.5% in the prediction of subsequent stem subsidence (95% CI 69.6-98.8) and a positive predictive value (PPV) of 100% Table . The SINInter- and intra-rater reliabilityThe SINC sign demonstrated strong inter-rater reliability when measured with two independent assessors, with Kappa 0.96 (95% CI 0.89-1.03). Intra-rater reliability was also found to be strong, with Kappa = 0.86 (95% CI 0.73-0.99). Measurement of subsequent stem subsidence demonstrated strong inter-rater reliability (ICC 0.9).This study has demonstrated that the SINC sign is associated with a high level of sensitivity (90.5%) and specificity (100%) for the detection of subsequent subsidence in periprosthetic fractures around PTS that are treated without revision surgery. When comparing the degree of subsidence, SINC positive fractures underwent significantly more subsidence compared with SINC negative fractures when fixed  at a mean follow-up of 12.7 months.As such, this tool may serve as a useful adjunct to the existing diagnostic armamentarium for identifying PTS stems that will fare worse with fixation. We propose that SINC-negative PHFs are equivalent to Vancouver B1 fractures and can be safely treated with fixation. By contrast, SINC-positive fractures could be considered for revision arthroplasty either to a distal-fit uncemented stem ,26 or a We have previously described the SINC sign, noting that this phenomenon has been alluded to in the literature before . GrammatAll cemented PTS are associated with some degree of subsidence within the cement mantle . The majIt is well documented that the underlying principle of PTS fixation involves controlled subsidence within the cement mantle leading to hoop stress generation at the cement-bone interface and that PTS stems do not achieve true fixation at the cement-stem interface . Some auThe strong inter- and intra-rater reliability demonstrated in this study gives us confidence that the SINC sign is a simple and reproducible radiographic sign that could be utilised by the majority of orthopaedic surgeons with minimal training. Further data on a larger scale using surgeons from multiple centres and of differing experience levels would be useful in further assessing the reproducibility of the sign.A negative SINC sign demonstrated an 89% negative predictive value for subsequent stem subsidence. It is notable that SINC-positive fractures were more likely to undergo revision surgery compared with SINC-negative (41% vs. 10%), suggesting that the SINC sign correlates with other clinical and radiographic features that led surgeons to opt for revision arthroplasty rather than fixation.The reoperation rate for PHFs treated with fixation was 23.5% in the SINC-positive group versus 15.4% in the SINC-negative group, and whilst statistical analysis was not performed for this comparison due to a lack of power, this may represent a higher likelihood of failure as a result of symptomatic stem subsidence.LimitationsOur study is subject to several limitations. A sizeable proportion of patients 6) were lost to follow-up following the management of their PHF. Regular clinics and radiographs are not always possible for this group of frail, elderly patients ,6. The S were losThe sample size used to calculate sensitivity and specificity is not large enough to make conclusive statements. Larger, multi-centre studies with longer clinical, functional, and radiological follow-up would better establish the validity of our findings and the consequent effect on outcomes. This study includes different stem designs, different indications for index arthroplasty, and different fixation methods for those treated with ORIF. Different rates of PHF have been documented for different stem types ,37, indiThe SINC sign can serve as a useful adjunct in the decision to fix or revise PHF around PTS. A positive SINC sign may represent a cement mantle that cannot be reconstituted anatomically, leading to subsidence after treatment with ORIF. This sign demonstrates strong inter-rater reliability and is a simple, reproducible tool. The SINC sign may indicate poorer outcomes with osteosynthesis, but further, larger-scale studies with longer-term follow-up, patient-reported outcome data, and additional markers of failure are required to confirm these findings and allow incorporation into treatment algorithms for PHFs.


When making discrimination decisions between two stimulus categories, subjective confidence judgments are more positively affected by evidence in support of a decision than negatively affected by evidence against it. Recent theoretical proposals suggest that this “positive evidence bias” may be due to observers adopting a detection-like strategy when rating their confidence—one that has functional benefits for metacognition in real-world settings where detectability and discriminability often go hand in hand. However, it is unknown whether, or how, this evidence-weighting asymmetry affects detection decisions about the presence or absence of a stimulus. In four experiments, we first successfully replicate a positive evidence bias in discrimination confidence. We then show that detection decisions and confidence ratings paradoxically suffer from an opposite “negative evidence bias” to negatively weigh evidence even when it is optimal to assign it a positive weight. We show that the two effects are uncorrelated and discuss our findings in relation to models that account for a positive evidence bias as emerging from a confidence-specific heuristic, and alternative models where decision and confidence are generated by the same, Bayes-rational process. Specifically, a decision should be based on the difference in the number of ants between the kitchen and the living room, but not on the total number of ants found in both rooms together .When considering two alternative hypotheses, the probability of the chosen hypothesis being correct is a function of the availability of evidence supporting not only the chosen hypothesis but also the unchosen one. For example, when deciding that there are more ants in the kitchen than in the living room, confidence should not only positively weigh the number of ants found in the kitchen , it is highly informative with respect to detection decisions about the presence or absence of a signal. For example, when deciding that an ant colony is nesting in the house, we should also care about the total number of ants, irrespective whether they are found in the kitchen or living room see Fig. .Fig. 1DiA surprising finding is that, despite the irrelevance of sum evidence to the accuracy of discrimination decisions, people are systematically more confident in their perceptual discrimination decisions when sum evidence is high. For example, Zylberberg et al.  had subjDifferent models identify the origin of this evidence-weighting asymmetry at different levels of the cognitive hierarchy, ranging from positing a metacognitive bias that ignores conflicting information  in discrimination confidence. The same models also make predictions for evidence weighting in detection judgments and confidence ratings. In four experiments, reverse correlation analysis revealed evidence weighting patterns that only partly agree with the predictions of our models. Most notably, our four models fail to account for a negative evidence bias we observed in detection decisions and confidence: a tendency to irrationally place a negative weighting on evidence, such as being more confident in the presence of a bright stimulus when one of the presented stimuli was unusually dark. In what follows we first describe the four models and the predictions they make, before turning to empirical findings from our four experiments.E1 and E2. The agents’ task is to decide which of the two channels was the signal channel (discrimination), or whether any of the channels had signal in it at all (detection). When a signal is present in a channel, evidence E is sampled from a normal distribution E′, corrupted by additional internal sensory noise. After each time step, they update their belief about the relative likelihood of the observed samples under the two possible world states , and given full knowledge of the true sample-generating process, including the properties of sensory noise. Each trial comprises 12 time steps. At the end of a trial, agents report the world state that maximizes the likelihood of the observed evidence, and rate their confidence as the objective probability that their decision was correct given the accumulated likelihood estimates. The four models vary in the properties of sensory noise, and in the selection of some channels for inspection by selection mechanisms.We model a setting in which agents are presented with a sequence of samples from two noisy sensory channels: In the basic, vanilla model, sensory noise is sampled from a normal distribution mentclass2pt{minimThe firing rate model is similar to the vanilla model, with the exception that perceived values are sampled from a Poisson, rather than a normal distribution. An important property of the Poisson distribution family, commonly used to model firing rates in neuronal populations, is that their mean and variance are lawfully coupled: the stronger the activation, the more variable it is. When applied to sensory neurons, this results in strong stimuli being subjectively perceived as noisier, consistent with the Weber–Fechner law  where S is a sigmoid function with a steep slope of 5 and LLRr is based on all previous sensory samples in the trial. A conceptually similar drift diffusion model was previously shown to produce a positive evidence bias in confidence ratings . On each discrimination trial, the signal channel was designated as right or left with equal probability. On half of the detection trials both channels were noise channels. We then sampled, for each trial, 12 values from each channel. These 24 values were then passed on to the simulated agent, who returned a decision and a confidence rating. We then subjected the agents’ decisions and confidence ratings to a reverse correlation analysis. We now turn to describe this analysis, which will also be used to analyze the behaviour of human participants in Exps. 1–4.E) and not internal stimulus energy (E’) leaving internal noise hidden.Following Zylberberg et al. , we tookThe positive evidence bias in decision confidence is often seen as particularly striking, given that positive and negative evidence are equally weighted in forming a decision , both positive and negative evidence should still be predictive of decisions. The effect of positive evidence is then driven by those trials in which the agent selected the attended alternative, and the effect of negative evidence by those trials in which the agent selected the ignored alternative . Put differently, asymmetries of positive and negative evidence cannot affect the decision itself, because at the time of making the decision there is no positive and negative evidence to speak of—instead, there are two sources of evidence that may become positive or negative, depending on the decision that is selected. For this reason, in measuring evidence weighting in decision formation, we defined relative and sum evidence relative to the ground truth rather than the agents’ decision.tr) we extracted random fluctuations in perceptual evidence in the signal From each trial  was high  on decision confidence, such that agents were more confident when overall evidence was high  decision from evidence in trials that resulted in a ‘yes’ (target present) decision, separately for the signal and nonsignal channels:We similarly obtained detection kernels as a function of relative and sum evidence:In all four models, ‘yes’ responses were associated with stronger evidence in the signal channel  and confidence level (high or low). This was done separately for ‘yes’ and ‘no’ responses. Confidence kernels for the signal and nonsignal channels were then extracted by subtracting the mean low-confidence from the mean high-confidence evidence values for each channel and decision. For example, for ‘yes’ responses this meant computing:In all four models, agents were more confident in their decisions about signal presence when evidence in the signal channel was stronger . Ten participants were recruited via the UCL’s psychology subject pool, and gave their informed consent prior to their participation. Each participant performed four sessions of 600 trials each, in blocks of 100 trials. Sessions took place on different days and consisted of three discrimination blocks interleaved with three detection blocks.The experimental procedure for Exp. 1 largely followed the procedure described in Zylberberg et al. , Exp. 1.In both detection and discrimination blocks, participants indicated their confidence following each decision. Confidence was reported on a continuous scale ranging from chance to complete certainty. To avoid systematic response biases affecting confidence reports, the orientation  and polarity  of the scale was set to agree with the Type 1 response. For example, following an up-arrow press, a vertical confidence bar was presented where ‘guess’ is at the center of the screen and ‘certain’ appeared at the upper end of the scale see Fig. .Fig. 4TaTo control for response requirements, for five subjects, the dots moved to the right or to the left, and for the other five subjects, they moved upward or downward. The first group made discrimination judgments with the right and left keys and detection judgments with the up and down keys, and this mapping was reversed for the second group. The number of coherently moving dots (‘motion coherence’) was adjusted to maintain performance at around 70% accuracy for detection and discrimination tasks independently. This was achieved by measuring mean accuracy after every 20 trials, and adjusting coherence by a step of 3% if accuracy fell below 60% or went above 80%. We opted for a block-wise staircasing procedure in order to keep motion energy relatively stable across trials, allowing participants to optimally place their detection criterion. The staircasing procedure for both tasks started at a coherence value of 1.0.∘), presented within a 6.5∘ circular aperture centered on the fixation point for 700 ms . Dots were grouped in two sets of 76 dots each. Every other frame, the dots of one set were replaced with a new set of randomly positioned dots. For each coherence value of c′, a proportion of c′ of the dots from the second set moved coherently in one direction by a fixed distance of 0.33∘, while the remaining dots in the set moved in random directions by a fixed distance of 0.33∘. On the next update, the sets were switched, to prevent participants from tracing the position of specific dots. Frame-specific coherence values were sampled for each screen update from a normal distribution centred around the coherence value c with a standard deviation of 0.07, with the constraint that c′ must be a number between 0 and 1.Stimuli for discrimination blocks were generated using the exact same procedure reported in Zylberberg et al. .1 TrialsStimuli for detection blocks were generated using a similar procedure, where on a random half of the trials coherence was set to 0%, without random sampling of coherence values for different frames.To probe global metacognitive estimates of task performance, at the end of each experimental block  participants estimated the number of correct responses they have made. Analysis of these global metacognitive estimates is provided in the Appendix.https://osf.io/z2s93/). Our full preregistered analysis is available in the Experiment x, y, and time. We then took the mean of this matrix across the x and y dimensions to obtain an estimate of the overall temporal fluctuations in motion energy in the selected direction. Using this filter, we obtained estimates of temporal fluctuations in the mean and variance of motion energy for upward, downward, leftward and rightward motion within each trial. We refer to these temporal estimates as motion energy vectors, where each such vector consists of 42 entries, one per time point. Additionally, for every time point we extracted the variance along the x and y dimensions, but given the high correlation between our estimates of mean and variance, we focused our analysis on the mean motion energy.For the reverse correlation analysis, we followed a procedure similar to the one described in Zylberberg et al. . For eacIn order to distill random fluctuations in motion energy from mean differences between stimulus categories, we subtracted the mean motion energy from trial-specific motion energy vectors. The mean motion energy vectors were extracted by averaging the motion energy vectors of all participants, separately for each motion coherence level and motion direction. We chose this approach instead of the linear regression approach used by Zylberberg et al.  in orderMD = 0.02, 95% CI , t(9) = 2.43, p = .038. This difference in task performance reflected a slower convergence of the staircasing procedure for the discrimination task during the first session. When discarding all data from the first session and analyzing only data from the last three sessions , task performance was equated between the two tasks at the group level MD = 0.00, 95% CI , t(9) =  − 0.05, p = .962; BF10 = 0.31. In order to avoid confounding differences between discrimination and detection decision and confidence profiles with more general task performance effects, the first session was excluded from all subsequent analyses.Overall proportion correct was 0.74 in the discrimination and 0.72 in the detection task. Performance in discrimination was significantly higher than in detection M = 0.59, 95% CI , t(9) = 3.45, p = .007). We did not observe a consistent response bias for the discrimination data  = 1.00, p = .344).In detection, participants were more likely to respond ‘yes’ than ‘no’  = 8.48, p < .001), whereas sum evidence  had no effect on discrimination accuracy, (t(9) =  − 0.70, p = .502 see Fig. Following Zylberberg et al. , we focuEconf − chosen, t(9) = 4.99, p = .001), but we found no significant decrease in confidence with stronger motion energy in the opposite direction,  =  − 0.25, p = .807; see Fig. Econf − relative, t(9) = 2.76, p = .022; Econf − sum: t(9) = 2.92, p = .017; see Fig. We next turned to the contribution of motion energy to subjective confidence ratings. The median confidence rating in each experimental session was used to split all motion energy vectors into four groups, according to decision (chosen or unchosen directions) and confidence level (high or low). Confidence kernels for the chosen and unchosen directions were then extracted by subtracting the mean low-confidence from the mean high-confidence vectors for both the chosen and unchosen directions. Motion energy in the chosen direction (positive evidence) significantly increased confidence  = 6.06, p < .001; see Fig. Edetection − n, t(9) =  − 2.89, p = .018; see Fig. Participants were significantly more likely to respond ‘yes’ when fluctuations in motion energy during the first 300 ms of the trial strengthened motion energy in the true direction of motion  = 4.59, p = .001; see Fig. Econf − yes − n; t(9) =  − 2.95, p = .016; see Fig. Confidence ratings were higher in detection ‘yes’ responses when random noise strengthened the motion energy in the true direction of motion  = 0.14, p = .892; see Fig. decisions were mostly sensitive to fluctuations in motion energy toward the true direction of motion, confidence judgments in detection ‘yes’ responses were equally sensitive (with opposite signs) to fluctuations in the true and opposite directions of motion. However, and to anticipate the results of Exp. 3, presented below, we note that this symmetric weighting of evidence in detection confidence was not replicated in a subsequent experiment designed to directly test this surprising result.Furthermore, unlike in the discrimination task, we found no effect of sum evidence on confidence ratings in ‘yes’ responses  were (1) to replicate a positive evidence bias in discrimination confidence, (2) to replicate the absence of a positive evidence bias in detection confidence, and (3) to replicate the absence of an effect of evidence on confidence in ‘no’ judgments.Importantly, our analysis treated energy in the leftward and rightward directions as two independently represented quantities. Although models of motion perception commonly include such direction-selective sensory channels . Our final data set includes observations from 102 included participants. The entire experiment took around 10 minutes to complete. Participants were paid £1.25 for their participation, equivalent to an hourly wage of £7.50.The research complied with all relevant ethical regulations, and was approved by the Research Ethics Committee of University College London (study ID number 1260/003). 147 participants were recruited via Prolific (prolific.co) and gave their informed consent prior to their participation. They were selected based on their acceptance rate (>95%) and for being native English speakers. Following our preregistration, we aimed to collect data until we had reached 100 included participants based on our prespecified inclusion criteria , 95% CI , t(101) = 18.01, p < .001. Unlike in Exp. 1, where we aimed to control for task difficulty, here we decided to match stimulus intensity between the two tasks, so a difference between detection and discrimination performance was expected . We did not observe a consistent response bias in discrimination (mean proportion of ‘right’ responses: 0.50). The two detection responses showed the typical asymmetries, with ‘yes’ responses being faster (median difference of 77 ms) and accompanied by higher levels of confidence . Unlike in Exp. 1, here we found no evidence for a difference in metacognitive sensitivity between ‘yes’ and ‘no’ responses (mean difference of 0.02 in AUC units). No asymmetries were observed between the two discrimination responses. For a detailed statistical analysis, see Stimuli in Exp. 2 consisted of two flickering patches, each comprising four gray bars presented for 12 frames. Together, this summed to 96 random luminance values per trial, which we subjected to reverse correlation analysis, following the analysis procedure of Exp. 2 in Zylberberg et al. .Erelative; t(100) = 14.29, p < .001; see Fig. Esum; t(100) =  − 2.29, p = .024; see Fig. First, we asked whether random fluctuations in luminance influenced discrimination responses. Similar to the results obtained by Zylberberg et. al., discrimination decisions were sensitive to fluctuations in relative evidence (the difference in mean luminance between the left and right stimulus) during the first 300 ms of the trial  = 6.76, p < .001), and negative  =  − 4.28, p < .001), evidence on decision confidence within the first 300 ms of the stimulus  = 2.56, p = .012; see Fig. Similar to Exp. 1, we observed a significant effect of positive  = 9.39, p < .001; see Fig. Edetection − n; t(101) =  − 4.64, p < .001; see Fig. Participants’ detection decisions were sensitive to fluctuations in the luminance of the target stimulus, such that ‘yes’ responses were associated with a brighter target stimulus  = 4.27, p < .001; see Fig. Econf − yes − n; t(99) =  − 4.98, p < .001; see Fig. Econf − yes − sum; t(99) =  − 0.28, p = .784; see Fig. Confidence in detection ‘yes’ responses was similarly sensitive to fluctuations in the luminance of the target stimulus (Econf − no − st(96) =  − 2.28, p = .025; see Fig. Econf − no − sum; t(96) =  − 2.04, p = .044; see Fig. Econf − no − n; t(96) =  − 0.71, p = .482), and the difference in luminance between the two stimuli  =  − 1.04, p = .301), had no significant effects on confidence in detection ‘no’ responses.Finally, unlike in Exp. 1, confidence in detection ‘no’ responses was sensitive to random fluctuations in the luminance of the target, such that participants were more confident in the absence of a signal when the target stimulus was darker (https://osf.io/hm3fn/) were (1) to replicate a positive evidence bias in discrimination confidence, (2) to replicate a positive evidence bias in detection decisions, and (3) to replicate the absence of a positive evidence bias in detection confidence.In Exp. 3, we aimed to replicate our findings using a direct experimental manipulation in addition to employing reverse-correlation analysis. Our preregistered objectives (see our preregistration document: https://osf.io/hm3fn/). Our final data set includes observations from 100 included participants. The entire experiment took around 20 minutes to complete. Participants were paid £2.50 for their participation, equivalent to an hourly wage of £7.50.The research complied with all relevant ethical regulations, and was approved by the Research Ethics Committee of University College London (study ID number 1260/003). A total of 173 participants were recruited via Prolific (prolific.co) and gave their informed consent prior to their participation. They were selected based on their acceptance rate (>95%) and for being native English speakers. Following our preregistration, we aimed to collect data until we had reached 100 included participants, based on our prespecified inclusion criteria (see on the project’s GitHub. Experiment A static demo of Exp. 3 is available MD = 0.21), 95% CI , t(99) = 30.35, p < .001, as expected.Overall proportion correct was 0.87 in the discrimination and 0.67 in the detection task. Performance in discrimination was significantly higher than in detection . We did not observe a consistent response bias in the discrimination task (mean proportion of ‘right’ responses: 0.50). The two detection responses showed the typical asymmetries, with ‘yes’ responses being faster (median difference of 69 ms) and accompanied by higher levels of confidence . As in Exp. 1, metacognitive sensitivity was higher for ‘yes’ than for ‘no’ responses (mean difference of 0.03 in AUC units). No asymmetries were observed between the two discrimination responses. For a detailed statistical analysis, see We first focused on reverse correlation analyses, pooling data from both high-luminance and standard trials (after mean-centering luminance in each), in order to replicate the findings of Exps. 1 and 2. We note the results are qualitatively similar when including standard trials only, with the exception of confidence in detection ‘no’ responses see .Erelative; t(99) = 19.17, p < .001; see Fig. Esum;t(99) = 0.23, p = .817).Discrimination decisions were sensitive to relative evidence during the first 300 ms of the trial  = 6.27, p < .001) and negative  =  − 5.29, p < .001) evidence within the first 300 ms of the stimulus  = 0.75, p = .455), but an effect of sum evidence was found when directly contrasting high- and low-luminance trials, as we show in the “Evidence-weighting” section below.Decision confidence was sensitive to positive  = 13.01, p < .001; see Fig. Edetection − n; t(99) =  − 8.91, p < .001; see Fig. Edetection − relative, or the difference in luminance between the target and nontarget stimuli; t(99) = 15.95, p < .001), and to sum evidence  = 4.29, p < .001; see Fig. Participants’ detection decisions were sensitive to fluctuations in the luminance of the target stimulus, such that ‘yes’ responses were associated with brighter target stimuli  = 8.37, p < .001), and negatively correlated with the luminance of the nontarget stimulus  =  − 3.58, p = .001; see Fig. Econf − yes − sum; t(99) = 2.83, p = .006; see Fig. Confidence in detection ‘yes’ responses was similarly positively correlated with the luminance of the target stimulus  =  − 2.72, p = .008; see Fig. Econf − no − relative; t(98) =  − 1.98, p = .050). The luminance of the nontarget stimulus and the overall luminance had no significant effects on confidence in detection ‘no’ responses (ps > 0.3).Finally, and in line with what we observed in Exp. 2, confidence in detection ‘no’ responses was sensitive to random fluctuations in the luminance of the target, such that participants were more confident in the absence of a signal when the target stimulus was darker , but this negative effect was weaker than the positive effect of the luminance of the target stimulus, resulting in an overall positive effect of sum evidence on detection confidence.In Experiments To shed further light on this issue, in Exp. 3, half of the trials were manipulated to include slightly brighter stimuli, thereby increasing statistical power for tests of the effects of sum luminance on discrimination and detection decisions and confidence.M = 0.09), 95% CI , t(99) = 8.73, p < .001, consistent with overall luminance providing a valid cue for signal presence.First, we established that participants were more likely to respond ‘yes’ on higher compared with lower luminance trials (M = 0.02), 95% CI , t(99) = 3.20, p = .002 , 95% CI , t(99) = 3.00, p = .003. Our preregistered Bayesian analysis provided strong evidence for the alternative hypothesis that detection confidence is affected by this manipulation (BF10 = 10.57). Furthermore, this increase in confidence in presence as a function of the brightness manipulation was not significantly different from that observed for discrimination confidence (M =  − 0.01), 95% CI , t(99) =  − 0.57, p = .573. Finally, and in line with Exp. 2, overall luminance had a significant negative effect on confidence in ‘no’ responses (M =  − 0.02), 95% CI , t(99) =  − 3.09, p = .003, indicating that participants were more confident in the absence of a target when overall luminance was lower.We next turned to the effects of our luminance manipulation on confidence. For discrimination judgments, participants were also more confident in higher compared with lower luminance trials . Our final data set includes observations from 100 included participants. The entire experiment took around 20 minutes to complete. Participants were paid £2.50 for their participation, equivalent to an hourly wage of £7.50.The research complied with all relevant ethical regulations, and was approved by the Research Ethics Committee of University College London (study ID number 1260/003). A total of 117 participants were recruited via Prolific (prolific.co), and gave their informed consent prior to their participation. They were selected based on their acceptance rate (>95%) and for being native English speakers. Following our preregistration, we aimed to collect data until we had reached 100 included participants, based on our prespecified inclusion criteria , 95% CI , t(199.32) = 16.52, p < .001, as expected.Overall proportion correct was 0.92 in the discrimination and 0.74 in the detection task. Performance in discrimination was significantly higher than in detection . A slight response bias in discrimination was not significant (mean proportion of ‘right’ responses: 0.51). The two detection responses showed the typical asymmetries, with ‘yes’ responses being faster (median difference of 48 ms) and accompanied by higher levels of confidence . A mean difference of 0.03 in metacognitive sensitivity (AUC units) was not significant. For a detailed statistical analysis, see Erelative; t(102) = 10.23, p < .001; see Fig. Esum; t(102) = 2.31, p = .023).Discrimination decisions were sensitive to relative evidence during the first 300 ms of the trial  = 6.00, p < .001) and negative  =  − 3.94, p < .001) evidence within the first 300 ms  = 1.92, p = .058). An effect of sum evidence was found when directly contrasting high- and low-evidence trials, as we show in the “Evidence-weighting” section below.Decision confidence was sensitive to positive  = 10.65, p < .001; see Fig. Edetection − n; t(101) =  − 4.22, p < .001; see Fig. Participants’ detection decisions were sensitive to fluctuations in the hue of the target stimulus  = 6.08, p < .001), and negatively sensitive to fluctuations in the hue of the nontarget stimulus  =  − 2.63, p = .010; see Fig. Econf − yes − sum; t(99) = 2.64, p = .010; see Fig. Confidence in detection ‘yes’ responses was positively sensitive to fluctuations in the hue of the target  =  − 2.23, p = .028; see Fig. t(97) =  − 1.95, p = .054). Relative evidence and negative evidence had no significant effects on confidence in detection ‘no’ responses (ps > 0.3).Finally, confidence in detection ‘no’ responses was sensitive to random fluctuations in the hue of the target, such that participants were more confident in the absence of a signal when the target stimulus was closer in hue to the background  = 17.39, p < .001), and became more confident in their discrimination judgments  = 3.75, p < .001), more confident in their detection ‘yes’ responses  = 4.76, p < .001), and less confident in detection ‘no’ responses  =  − 6.34, p < .001; see Fig. 10 = 2547.94). One difference in comparison to Exp. 3 was that in Exp. 4 the increase in ‘yes’ response confidence as a function of the hue manipulation was significantly stronger than that observed for discrimination confidence  =  − 2.61, p = .011).As in Exp. 3, on half of the trials , the hue of both patches was slightly shifted in the direction of the target stimulus . This allowed us to directly measure how sum evidence affects both detection decisions, and detection and discrimination confidence ratings. Overall, we obtained a similar pattern to Exp. 3: on high-evidence trials, participants were more likely to respond ‘yes’ in the detection task  or signal strength (Exps. 2–4). In order to measure the contribution of perceptual evidence to confidence in detection and discrimination judgments, we followed Zylberberg et al.  in applyless likely to say a target was present (in either channel) when the weaker channel had more evidence. In Exps. 1 and 2, this negative weighting of evidence in the nonsignal channel was strong enough to bring the effect of sum evidence on detection confidence down to zero . This negative weighting of evidence in the nonsignal channel remained significant in Experiments These same two models also made corresponding predictions for the detection task: When attempting to detect signal presence in either channel, decisions and confidence ratings should positively weigh evidence for both alternatives . Paradoxically, however, in the detection task subjects adopted a discrimination-like disposition, negatively weighing evidence in the nonsignal channel. In other words, subjects were What drives these discrimination-like evidence weighting profiles in detection? In Experiments Alternatively, changes to the global perception of overall stimulus intensity may have an internal source. For example, slow brain oscillations in the alpha band affect both detection criterion and discrimination confidence but have minimal effects on discrimination sensitivity: a nonselective effect on perception which has been attributed to a global change in the baseline firing rate of sensory neurons , may also be sensitive to sum evidence in their detection decisions and confidence. Surprisingly, however, we find no evidence for such an effect see . Across In four experiments, we replicated previous findings of a “positive evidence bias”: a detection-like evidence weighting in discrimination confidence. This pattern was accounted for by models that posit asymmetries either in the distributions of sensory noise or allocation of attention between target and nontarget channels. However, these same models could not account for a surprising finding of discrimination-like evidence weighting in detection decisions and confidence. We suggest that these seemingly irrational positive and negative evidence biases may reflect, at least in part, shared representational resources being harnessed for detection decisions and discrimination confidence.


Four categories of medication have been shown to reduce mortality following an acute coronary syndrome (ACS) event: (a) antiplatelets, (b) beta-blockers, (c) statins, and (d) angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs).To determine the association between use of 1 or more of 4 categories of evidence-based medications and patient-perceived health status.Data from the registry of a large university-based health system were used for an analysis of prescribing at discharge following an inpatient hospitalization for ACS. Use of evidence-based medications and patient perceived health status were measured in a telephone survey administered 6 to 12 months (mean [SD]=10 [3.5] months) after hospital discharge. Surveys were conducted from January 2002 through March 2005. Subjects were included in the survey if they were prescribed at least 1 of the 4 evidence-based drug categories at the time of discharge. Each patient was assigned to 1 of 5 groups (range: 0 to 4) based on the number of drug categories self-reported by the patient as current at the time of the survey. Patient-perceived health status was assessed using the question “How would you rate your health at the present time?” using a 5-point scale from excellent (1) to poor (5). Mean perceived health status scores for each of the 4 evidence-based medication categories were compared using Analysis of Variance (ANOVA). Multivariate logistic regression determined the association between patient-perceived health status—dichotomized to excellent/ very good/good versus fair/poor—and the evidence-based medication group, controlling for patient demographics and comorbidities. P values of  less than 0.05 were considered statistically significant.A total of 393 of 1,206 patients (32.6%) responded to the survey between 6 and 12 months after discharge for an ACS event. The mean (SD) patient-perceived health status ranged from 3.3 (1.1) for patients with no (0) self-reported evidence-based medications (n=14) to 2.5 (1.0) for patients with 4 evidence-based medications , indicating higher self-perceived health status for patients who were taking more of the evidence-based medications. Using patients with no (0) evidence based medications as the comparator, the odds of higher patient-perceived health status were multiplied by 8.2  for those with 4 medications, 9.3  for those with 3 medications, 4.9  for those with 2 medications, and not significantly different for those with 1 medication . Younger age, prior myocardial infarction, and recurrent ACS events occurring between discharge and the survey date were significantly associated with poorer perceived health status.Better patient-perceived health status was associated with use of a greater number of evidence-based medications for patients with ACS.