Headache is the clinical syndrome most commonly observed by neurologists in daily practice. Pharmacological and non-pharmacological treatments are commonly used for the management of headaches; however, the clinical reasoning behind these interventions is not properly applied. We conducted a narrative literature review using as data sources for academic PubMed, MEDLINE, EMBASE, AMED, CINAHL, EBSCO, PEDro, Cochrane Database of Systematic Reviews, Cochrane Collaboration Trials Register, and SCOPUS. This narrative literature review mainly considered systematic reviews, meta-analyses, randomised clinical trials, and expert opinions published after the year 2000 discussing clinical reasoning for application of non-pharmacological interventions in individuals with tension-type, migraine, and cervicogenic headaches. After the data extraction, we organized the literature thematically as follows: (1) mapping of theoretical aspects of non-pharmacological interventions; (2) summarizing most updated literature about effectiveness of non-pharmacological interventions grouped by targeted tissue and headache; (3) identifying research gaps in the existing literature and proposing hypotheses for better understanding of current clinical reasoning. We found that there are many non-pharmacological treatment strategies used for headaches, including beyond the tissue-based impairment treatments (bottom-up) and strategies targeting the central nervous system (top down). Bottom-up strategies include joint-biased, soft-tissue biased, or needling interventions, whereas top-down strategies include exercise and cognitive interventions. Evidence shows that the effectiveness of these interventions depends on the application of proper clinical reasoning, since not all strategies are effective for all headaches. For instance, evidence of non-pharmacological interventions is more controversial for migraines than for tension-type or cervicogenic headaches, since migraine pathogenesis involves activation of sub-cortical structures and the trigemino- vascular system, whereas pathogenesis of tension-type or cervicogenic headaches is most associated to musculoskeletal impairments of the cervical spine. We conclude that current literature suggests that not all non-pharmacological interventions are effective for all headaches, and that multimodal, not isolated, approaches seem to be more effective for patients with headaches. Most published studies have reported small clinical effects in the short term. This narrative literature review provides some hypotheses for discrepancies in the available literature and future research. Clinical reasoning should be applied to better understand the effects of non-pharmacological interventions. In the 21st century, headache is probably the most common clinical syndrome most attended by neurologists. Headache causes substantial pain and related-disability to the individuals and it is associated with considerable costs to health care systems ,4. The GThe International Classification for Headache Disorders (ICDH-III) presents headache diagnosis in a hierarchical fashion with migraine as the first consideration, TTH as the second, and CeH and other headaches next . NeverthScientific evidence of non-pharmacological interventions is highly heterogenous and several systematic reviews have been conducted with contradictory results. For instance, systematic reviews conducted by Chaibi et al. concluded that: (1) the efficacy of physical therapy seems to be similar than the efficacy of tricyclic antidepressants at mid-term in chronic headaches ; (2) mulFurther, chronic headaches and non-pharmacological interventions are broad terms including different headaches and different therapeutic approaches. In fact, published systematic reviews and meta-analyses only assessed isolated interventions  on a specific headache types , and results are somewhat conflicting. Since current evidence suggests that non-pharmacological interventions may play an important role in headache treatment, it is important to identify gaps in existing knowledge in relation to the clinical reasoning to be applied for identification of the proper therapeutic strategies to specific headaches. Therefore, we conducted a narrative literature review aimed to: (1) discuss relevant pain mechanisms underlying non-pharmacological interventions; (2) identify and map the most updated scientific evidence for non-pharmacological therapies in TTH, migraine, and CeH; and (3) discuss the clinical reasoning explaining discrepancies on effects of non-pharmacological interventions based on their underlying nociceptive mechanisms.Proper management of chronic pain syndromes, including headaches, have begun to target various pain mechanisms as one strategy for combatting chronic pain . In factAdditionally, interventions for people with headache should be based on current knowledge of nociceptive pain mechanisms. There is evidence supporting the presence of an altered nociceptive pain processing both TTH  and migrCentral sensitization is initiated by long-lasting/prolonged peripheral noxious inputs ; however, once central sensitization has been established, only minimal nociception is required to maintain the sensitization state . Hence, Current therapeutic models should integrate the use of bottom-up (Hands-ON) and top-down (Hands-OFF) interventions where the peripheral stimuli induced by any bottom-up (Hands-ON) strategy is able to precipitate and modulate different responses at spinal cord, sympathetic nervous system and brainstem . In suchThis narrative literature review was based on a methodological framework for scoping reviews described in Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) , but sliA search strategy based on potential themes and topics considered relevant to the headache field according to the current literature and hot topics was conducted. Electronic literature searches were conducted on the following database from January 2000 to January 2020: MEDLINE, EMBASE, AMED, CINAHL, EBSCO, PEDro, Cochrane Database of Systematic Reviews, PubMed, Cochrane Collaboration Trials Register, and SCOPUS. We also screened the reference lists of the papers that were identified in the database. Database search strategies were conducted with the assistance of an experienced health science librarian.To avoid missing any relevant study in the search, the following broadly heading terms were combined by using Boolean operators in the search strategy: “tension type headache”, “migraine”, “cervical headache”, “cervicogenic headache”, “manual therapy”, “physical therapy”, “exercise”, “muscle”, “joint”, “spinal manipulation”, “spinal mobilization”, “dry needling”, “soft tissue”, “trigger point”, “neck”, “cervical spine”, “cognitive behavior”, “clinical reasoning”, “psychological treatment”, “pain mechanisms”.We mainly considered systematic reviews, meta-analyses, randomised controlled trials, and expert opinions published in the last 20 years discussing either neurophysiological mechanisms, clinical reasoning or effectiveness of non-pharmacological interventions in TTH, migraine, and CeH. Two authors reviewed the identified publications on relevance by checking and by judging the title and abstract. Authors included the relevant papers on each of the topic based at personal discretion. Discrepancies in reviewers’ responses at any stage of the screening were resolved by asking a third author.After the data extraction, we organized the literature thematically, according to the following topics: (1) mapping of theoretical aspects of non-pharmacological interventions; (2) summarizing the most updated scientific literature about effectiveness of non-pharmacological interventions grouped by targeted tissue and headache; or, (3) identify research gaps in the existing literature and propose hypotheses for better understanding of the current literature.n = 4 justifying rational of joint-biased interventions; n = 9 systematic reviews/meta-analyses discussing most updated evidence, and n = 3 for discussing potential adverse events).The electronic searches identified 265 potential papers related to joint-biased interventions for headaches. After removing duplicates, 100 papers remained. Eighty-four were excluded based on examination of the abstracts, leaving 16 papers for discussion in the narrative review based on their relevance and upload date  delivered at the end of the available restricted joint motion .Scientific evidence of joint-biased interventions for the management of headache is highly conflicting. Previous systematic reviews, including the early Cochrane Review, concluded that there was insufficient evidence to support or refute the efficacy of spinal manipulation in TTH ,32, or mAdditionally, the use of cervical manipulations, mostly related to those targeting the upper cervical spine, remains controversial because of subsequent concerns about their safety. Adverse reactions to manipulative therapy range from minor events, e.g., pain or limitation in range of motion, to more serious events, e.g., vertebral or carotid arteries dissection . Puentedn = 80) papers were excluded based on examination of the abstracts, leaving 10 papers for discussion in the narrative review based on their relevance and upload date .The electronic searches identified 145 potential papers related to soft tissue-biased interventions for headaches. After removing duplicates, 90 papers remained. Eighty  and migraine , although the quality of evidence was still low . Again, n = 33) papers were excluded based on examination of the abstracts, leaving 7 papers for discussion in the narrative review based on their relevance and update .The electronic searches identified 95 potential papers about needling therapies for headaches. After removing duplicates, 40 papers remained. Thirty-three  and dry  interventions, are commonly used for the management of people with headaches . Wet neeThe Cochrane review about the use of acupuncture for TTH concluded that acupuncture is effective for the management of this primary headache, but clinical trials comparing acupuncture with other therapeutic strategies are clearly needed . LikewisIt is important to note that acupuncture is different than dry needling. The main difference between both approaches is that this dry needling inserts the needle on muscle TrPs, and not in standardized acupuncture points. Although the literature suggests that dry needling may be a useful addition to physical therapy, the only systematic review on this topic remarked that further research is required . After tn = 180) were excluded based on examination of the abstracts, leaving 20 papers for discussion in the narrative review based on their relevance and upload date .The electronic searches identified 452 potential papers related to exercise and headaches. After removing duplicates, 200 papers remained. One hundred and eighty , aerobic exercises; and (2), local exercises targeting specific musculoskeletal disorders of the cervical spine. This differentiation is clinically relevant since aerobic exercises could be the best option in migraine prophylaxis, whereas specific neck/shoulder strengthening exercises may be a potential better choice for TTH . This asThere is evidence supporting the effectiveness of aerobic exercise for the management of TTH and migraine . A recenn = 7 justifying rational of cognitive techniques; n = 7 systematic reviews or meta- analysis, and n = 3 clinical practice guidelines).The electronic searches identified 341 potential papers of cognitive/psychological interventions for headaches. After removing duplicates, 185 papers remained. One hundred and sixty-eight were excluded based on examination of the abstracts, leaving 17 papers for discussion in the narrative review based on their relevance and upload date  is also needed . A recenDifferent meta-analyses have also concluded that biofeedback is effective for reducing the intensity and the frequency of headache attacks in TTH  and migrOf particular interest could be the use of therapeutic pain neuroscience education. Therapeutic pain neuroscience education can be defined as educational sessions, usually directed by health care professionals, providing patients’ knowledge about underlying mechanisms of their condition and proper skills to manage their lives. A meta-analysis reported strong-moderate evidence for intermediate effects of therapeutic neuroscience education on headache frequency  and related-disability  in people with migraine . No effeFinally, the problem with cognitive/psychological treatments in clinical setting is that they are more time consuming than, e.g., pharmacological treatment, and require the active participation and motivation of the individual. Further, treatment costs and their coverture by health care systems largely differs in some countries.This literature review has summarized the most updated current evidence and hypotheses about non-pharmacological interventions for the treatment of TTH, migraine or CeH. It seems that most non-pharmacological interventions have a small clinical effect on these headaches, and most importantly is that not all treatment strategies are indicated for all headache syndromes .It is important to consider that not all headache patients will benefit from non-pharmacological interventions. It is probably that patients with more severe headaches attending to specialized clinics of chronic pain will seek more frequently for this kind of treatment. Nevertheless, current literature also supports that non-pharmacological interventions are also effective for patients with the episodic forms of TTH or migraine, patients which are more frequently attended at primary care.There is preliminary evidence trying to identify subgroups of patients who could be most likely to benefit from some specific non-pharmacological interventions, particularly soft tissue-biased strategies. In fact, it has been suggested that not all patients with headache will benefit from manual therapy . In lineThe existence of a wide range of management interventions commonly applied for headaches without clear clinical effectiveness of their efficacy can be the main consequence of an inconclusive understanding of the underlying pathophysiology or an inappropriate clinical reasoning considering these headaches  equally . The chaIn fact, effectiveness of physical therapy is different on each headache disorder; for instance, evidence in migraine is more controversial than in TTH. One potential explanation could be the fact that migraine pathogenesis involves the activation of sub-cortical structures as well as the trigemino-vascular system, whereas the pathogenesis of TTH is essentially considered a generation of muscle/myofascial pain. Similarly, it is expected that spinal manipulation/mobilization would be effective for CeH because this secondary headache is considered a referred pain elicited by nociceptive stimulation of upper cervical joints (C1-C3). Accordingly, individuals with painful disorders of upper cervical zygapophyseal joints may exhibit significant benefits with treatments (joint-biased) directed at cervical pain generators . On the Another clinical reasoning would be that headache patients presenting with musculoskeletal impairments of the neck may particularly benefit of non-pharmacologic bottom-up approaches . A recenOn the contrary, in those patients with co-morbid anxiety and mood disorders, problems for managing stress, significant headache-related disability and/or medication overuse, cognitive strategies and exercise programs should be better applied. Therefore, it is clinically important to identify, if possible, the main sensitizer driver on a particular patient. Nevertheless, clinical and scientific evidence supports the conclusion that proper management of patients with headaches should be multimodal, including appropriate use of pharmacological  and non-pharmacological  interventions.The current literature review has identified several gaps that need to be addressed in future studies. First, most evidence has mainly investigated short-term effects, so we do not currently know the duration of pain relief. This is a relevant topic since headaches are chronic pain syndromes of long duration. Our literature review proposes some hypotheses behind a potential clinical reasoning explaining the conflicting results observed in the current literature in relation to the effectiveness of non-pharmacological interventions. Future studies should introduce the propose therapeutic model based on a clinical reasoning considering nociceptive pain mechanisms for improving the clinical outcomes in specific subgroups of patients with TTH, migraine or CeH.Another important topic for investigating in future studies would be cost-effectiveness of these non-pharmacological interventions. This is a relevant topic to investigate since non-pharmacological therapies are usually time and personnel consuming and also relatively expensive which could limit their availability to specialty centers and public health care system in many cases. In fact, the is a lack of studies in this area. Only two studies have investigated the costs associated to the use of acupuncture for the management of headache and both suggest a potential beneficial cost for this intervention, although future studies are needed ,103.This literature review summarizes most updated data on non-pharmacological treatments for patients with TTH, migraine, or CeH and the underlying clinical reasoning explaining discrepancies their therapeutic effects. Several non-pharmacological interventions have shown positive effects for the management of these headaches, but their clinical relevance was small and the duration of the effect was mainly in the short-term. It is important to identify, if possible, the main sensitizer driver for proper clinical reasoning about the selection of the appropriate non-pharmacological therapeutic strategy. In addition, not all patients will benefit from the same interventions and identification of these subgroups of patients should give future research. Appropriate therapeutic management of patients with headache should be multimodal, including beyond tissue-based impairment therapies (bottom-up interventions) and central nervous system interventions (top down interventions).Treatment strategies used for the management of headache should include beyond tissue- based impairment therapies (bottom-up interventions) and central nervous system interventions (top down interventions).Bottom-up (hand-on) strategies can include joint-biased, soft-tissue biased, and needling therapies whereas top-down (hand-off) strategies include exercise and cognitive interventions.Evidence shows that the effectiveness of non-pharmacological interventions for headaches depends on a clinical reasoning since not all strategies are equally effective for all headaches.Evidence of non-pharmacological interventions is more controversial for migraine than for TTH or CeH, since migraine pathogenesis mainly involves activation of sub-cortical structures and the trigemino-vascular system whereas pathogenesis of TTH or CeH is associated to musculoskeletal impairments of the cervical spine.


SAHLPA-23 was more highly correlated with all study variables than SAHLPA-18. Although both instruments displayed acceptable discriminative power, SAHLPA-23 had better accuracy than SAHLPA-18 . The SAHLPA-23 is an independent, feasible and innovative tool for estimation of health literacy in the Portuguese adult population.The goal of this study was to adapt, improve and validate a short, self-administered health literacy assessment tool for European Portuguese-speaking adults. Health literacy tools are of great importance to health authorities and professionals, as low or inadequate health literacy, that is, a limited capacity to handle health-related information, is associated with higher morbidity and mortality. The 18-item Short Assessment of Health Literacy for Brazilian Portuguese-speaking adults (SAHLPA-18) was adapted into European Portuguese. The European Portuguese tool (SAHLPA-23) includes five additional items. The SAHLPA-23 was tested in a convenience sample of 503 participants from two Portuguese regions. Socio-demographic data, literacy and cognitive indicators were collected. Participants also completed a questionnaire on comprehension of written health materials. Construct validity was assessed through correlations between SAHLPA-23 scores and education, literacy, and cognitive variables and score on the comprehension questionnaire. The psychometric properties of the new tool were compared with those of the SAHLPA-18. The mean SAHLPA-18 and SAHLPA-23 scores were 13.9  and 18.3 , respectively. Both tools showed adequate reliability (Cronbach’s  In the late 20th century literacy was mainly understood in terms of reading and writing skills, but the concept has been extended and now includes the ability to interpret texts, perform calculations and participate actively in society : participants are required to read and recognise health words .The Test of Functional Health Literacy in Adults : participants are required to perform a modified cloze procedure  .The Newest Vital Sign : participants are required to answer comprehension questions about the nutrition label for an ice cream .The Short Assessment of Health Literacy - Spanish and English versions : participants are required to read and recognise health words .Various tools and methodological approaches have been used to evaluate health literacy  ; this demonstrates that the decision-makers have recognised the population’s low level of health literacy and the need to improve it. In addition, Portuguese, taking the European, Brazilian, African and Asian varieties together, is the world’ seventh most commonly spoken language (with around 200 million of speakers) , ‘adverse reactions’ (efeitos secundários), ‘excipient’ (excipiente), ‘fertility’ (fertilidade) and ‘precautions’ (precauções). Two of the five new items were deliberately designed with two-word targets in European Portuguese (‘active ingredient’ and ‘adverse reactions’) as it was thought that longer targets would provide better discrimination between levels of health literacy. The positioning of the five additional items in the tool is random  and assess comprehension of health-related documents. Comprehension was evaluated with two questionnaires on the comprehension of PLs of medicinal products. These last two questionnaires were developed in accordance with the European guidelines on medicine PLs clarity and comprised eight questions  Europea. They wecarro (car), vaso (vase), bola , lápis (pencil) and relógio (watch) (maximum score: 5 points).The spelling of the words et al., The mental calculus task: 100−7(maximum score=1 point), plus two additional calculation tasks (maximum score=2 points). The two additional calculation tasks were as follows: a sum followed by a subtraction to calculate how much money respondents will get back in a purchase and a division to calculate the price of a certain amount of a product. These last two calculation tasks were adapted from the last national Portuguese literacy study  books ; and (2) magazines or journals . Participants’ writing habits were also assessed, using questions about how often they performed simple writing tasks . The response options were as follows: daily, at least once a week, at least once a month, rarely and never (maximum score: 4 points). These questions were also adapted from the last national Portuguese literacy study 1995, reported by Benavente et al. . The maxet al., Before administering the SAHLPA-23 and the study questionnaires a brief screening test was administered to detect any cognitive problems  that might affect participants’ judgement and reasoning capacity. The test consisted of the following questions and simple tasks: (1) ‘What month is it?’ (2) ‘Where are we ?’ (3) ‘The researcher is about to say three words in loud voice: please write them down ,’ (4) ‘How much is 100 minus 7?’ (5) The researcher shows the participant a pencil and asks him or her to write down the name of the object, (6) The researcher shows the participant a watch and asks him or her to write the name of the object and (7) ‘Write down the three words that the researcher said in loud voice a few minutes ago, again.’ These questions and tasks were based on the full version of the Mini-Mental State Examination  The discriminatory ability of both tests was examined using receiver operating characteristic (ROC) curves and area under the curve (AUC). The optimal cut-off point was defined as the maximum value of the Youden index, calculated as: J=max [sensitivity c+specificity c–1], where c represents all possible criterion values  exploratory factor analysis (EFA) was carried out [Kaiser–Meyer–Olkin Measure (KMO) of sampling and adequacy and the Bartlett’s Test of Sphericity]. Principal component factor analysis with oblique rotation  was conducted based on eigenvalues values  .A cut-off score for adequate health literacy was identified using ROCs for the established literacy tasks. This procedure resulted in exclusion of some items and the definition of a cut-off score for distinguishing between adequate and inadequate health literacy. (4) Spearman’s rank test was used to assess correlations between score on the new short health literacy tool (SAHLPA-23) and the construct variables. Construct validity was evaluated by calculating correlations between SAHLPA-23 scores and the following variables: years of schooling, cognitive index, literacy tasks, reading and writing habits, score on the comprehension questionnaire, and the socio-demographic variables. The Statistical Package for Social Sciences (SPSS) version 22  was used for all analyses except for ROC analyses, which were conducted using MedCalc version 14.8 . All statistical tests were two-tailed  were male, with a mean age of 38.7 year  and an average of 10.3 years (SD=4.8) of schooling , the military (21.3%) and university undergraduates  (19.9%). The remaining participants were evenly distributed across the other institutions. Overall, 49.3% of the participants were from Lisbon and Tagus Valley, 53.1% (chooling . Table 3χ2(253)=2764.4, P<0.001], indicating that the correlation matrix was suitable for factor analysis. According to the ratio-of-first-to-second-eigenvalues-greater-than-three rule  the SAHLPA-23 displayed a one-dimensional structure (5.97/1.78=3.36).The SAHLPA-23 data yielded a good KMO score (0.89) and Bartlett’s sphericity test was significant , indicating that the correlation matrix was suitable for factor analysis. As with the SAHLPA-23, the ratio-of-first-to-second-eigenvalues-greater-than-three rule also indicated that a one-dimensional structure (4.97/1.52=3.32). Visual inspection of the scree plots for both instruments also revealed a multidimensional factor structure  and Bartlett’s sphericity test was significant , the cognitive index [rs(482)=0.374], the comprehension questionnaire [rs(482)=0.561], direct measures of literacy [rs(482)=0.308] and indirect measures of literacy [rs(482)=0.234], suggesting that the SAHLPA-23 had good construct validity. The SAHLPA-23 was more highly correlated with all relevant study variables, except the direct and indirect measures of literacy, than the SAHLPA-18 . Similarly, the association with the direct literacy index was larger in the case of the SAHLPA-23 than the SAHLPA-18 . The rest of the variables were similarly associated with both instruments (P>0.05).Fisher In accordance with classical data reduction procedures , which suggests the tool is a moderately accurate method of detecting inadequate health literacy. The recommended minimum AUC for diagnostic tests is 0.75  (maximum score=24) on our structured protocol for comprehension of PLs were defined has having inadequate health literacy. This cut-off score was based on the recommendations of the European Medicines Agency as expressed in the  Larner, .Figure 1J=0.42; sensitivity=68.2%; specificity=73.5%)  . This cu et al., .Z=3.36, P<0.001. The SAHLPA-23 also had a larger AUC than the direct measure of literacy, ΔAUC=0.10, Z=3.40, P<0.001 and the indirect measure of literacy, ΔAUC=0.15, Z=4.70, P<0.001 . Comparisons of the ROC curves for SAHLPA-23 and other instruments are shown in Years of formal schooling  had the highest AUC, which was higher than the AUC for the SAHLPA-23  were classed as having inadequate health literacy with the SAHLPA-23. This figure is slightly higher than that obtained in the last Portuguese health literacy study, in which only 38.5% of the 1001 participants were classified has having adequate or excellent health literacy  was higher than the mean score on the direct literacy index (numeracy tasks), it is likely some participants overestimated their reading and writing habits . Additionally, writing habits were evaluated based on self-reported frequency of performance of simple tasks , which may reduced the specificity of this variable as an index of health literacy.et al., This variable contributed to support construct validity. The mean score for this prose task was low, with only around half of the participants scoring ⩾75%  to nine years (in 1986) and eventually 12 years (in 2009).SAHLPA-23 may be particularly useful to Portuguese health professionals and researchers as a quick method of obtaining an indication of health literacy in clinical and research settings, in order to anticipate potential problems with health literacy and facilitate more effective communication. Where possible socio-demographic data, such as years of education, should be collect and analysed together with this literacy measure.The SAHLPA-23 is an independent, effective and innovative self-administered tool for discriminating between adequate and inadequate health literacy. The SAHLPA-23 is a self-administered tool, unlike previous versions of the tool.


Perceptual decisions are accompanied by feelings of confidence that reflect the likelihood that the decision was correct. Here we aim to clarify the relationship between perception and confidence by studying the same perceptual task across three different confidence contexts. Human observers were asked to categorize the source of sequentially presented visual stimuli. Each additional stimulus provided evidence for making more accurate perceptual decisions, and better confidence judgements. We show that observers’ ability to set appropriate evidence accumulation bounds for perceptual decisions is strongly predictive of their ability to make accurate confidence judgements. When observers were not permitted to control their exposure to evidence, they imposed covert bounds on their perceptual decisions but not on their confidence decisions. This partial dissociation between decision processes is reflected in behaviour and pupil dilation. Together, these findings suggest a confidence-regulated accumulation-to-bound process that controls perceptual decision-making even in the absence of explicit speed-accuracy trade-offs. Feelings of confidence reflect the likelihood that decisions are correct. Here the authors show that confidence taps partially dissociable evidence from that used for perceptual decisions, and that, rather than passively monitoring, confidence controls the depth of sensory information processing. The perceived objects are under-constrained by sensory evidence, so these inferences are fundamentally probabilistic. In recent years, there has been a growing interest in the ability of human observers to estimate the validity of their perceptual decisions. This form of metacognitive judgement can be obtained by asking an observer to rate how confident she is that one of her perceptual decisions is correct. While the perceptual decision is used to quantify the percept itself, each confidence rating quantifies the certainty the observer has about her own percept. These two types of judgements are known as Type-I and Type-II decisions, respectively2.Sensory perception results from inferring the causes of uncertain sensory evidence3. The evidence for the Type-I decision can be computationally described by sequential sampling processes, or diffusion models7 (for a recent review8), wherein, samples of noisy evidence are accumulated over time and a decision is made once the evidence reaches a bound. The Type-II decision estimates the likelihood that the Type-I decision is correct, given the accumulated evidence. The likelihood of the Type-I decision is moderated by both the quantity of evidence (determined by the relative placement of the decision bound) and by the quality of evidence . The ideal Type-II decision therefore requires the estimation of the quantity and quality of Type-I evidence.By definition, an ideal Type-II decision-maker uses the exact same evidence as for the Type-I decision11), and they may ignore evidence in favour of other decision alternatives14 . Furthermore, observers may integrate additional evidence into their Type-II decision that was not used for making their Type-I decision16, allowing them to report errors in the absence of feedback19, and to change their mind after the initiation of a response20. These departures from ideal are yet to be fully characterised by a unifying framework of Type-II decision-making.However, a large body of evidence demonstrates that human observers do not make their Type-II decisions in accordance with this ideal Type-II decision maker. For example, observers poorly incorporate estimates of sensory noise into their Type-II decisions . Based on the work of Drugowitsch et al.22 each orientation offered a specific amount of evidence in favour of each category, such that the quality and quantity of Type-I evidence can be carefully monitored over the course of each trial. This allowed for the disambiguation of different suboptimalities affecting observers’ Type-I decision-making using computational modelling. Given the suggestion that Type-II evidence accumulation may continue after the Type-I decision bound has been reached, we were especially motivated to understand the relationship between Type-I and Type-II decision-making relative to the point at which the Type-I evidence crosses the decision bound. In the first Type-II context we therefore measured observers’ ability to set and maintain appropriate bounds on their Type-I evidence accumulation by asking them to make their Type-I judgement when they thought they had reached an instructed target performance level. We used the second Type-II context to measure observers’ default bound—i.e., how much evidence each observer feels they need to accumulate to commit to a perceptual decision. Last, in the third Type-II context, we tested whether observers implement a covert bound when they are presented with more evidence than needed to reach their default bound, as measured in the second context. In this third Type-II context, observers also rated their confidence that their Type-I decision was correct on each trial—i.e., an explicit Type-II decision. In addition to behavioural responses, pupil dilation was monitored throughout the experiment, as the literature has suggested strong links between pupil dilation and Type-I23, and Type-II25 decision-making via pupil-linked dynamics of the noradrenergic system27. Both behaviour and pupillometry reflect a partial dissociation between Type-I and Type-II evidence, which could allow for a confidence-regulated accumulation-to-bound process that controls perceptual decision-making even in the absence of an explicit speed-accuracy trade-off.To clarify this relationship, we asked observers to make the same Type-I judgement but in three distinct Type-II contexts. In all three contexts, the Type-I judgement required observers to make a two-alternative categorisation decision after viewing a series of visual stimuli (a variation of the weather prediction taskN = 20) made the same Type-I decision: whether the orientations of the sequence of Gabor patches presented on each trial were drawn from the orange or the blue category. These categories were defined by circular Gaussian probability distributions over the orientations of the Gabor patches, as shown in Fig. Across all three Type-II contexts, observers (d’) of 1.2 [±0.17], 1.7 [±0.14], and 1.9 [±0.19] in each target performance condition, which was found to significantly increase across target performance conditions using a Wilcoxon sign rank test; Z (70% vs. 85%) = 3.78, pbonf*2 < 0.001; Z (85% vs. 90%) = 2.35, pbonf*2 = 0.037, with these p-values Bonferroni corrected for two comparisons). Observers also chose to enter their response later in the higher target performance conditions  = 3.71, pbonf*2 < 0.001; Z (85% vs. 90%) = 2.80, pbonf*2 = 0.010; additional analyses are provided in Supplementary Note In the first session, observers were placed in a Stopping task context in which they were continually shown samples until they entered their Type-I response. Importantly, they were asked to enter their response when they felt they had accumulated enough evidence to reach a certain probability of being correct (target performance). There were three target performance conditions  in which observers scored an average proportion correct [95% between-subjects CI] of 0.72 ±0.018]; 0.80 [±0.021]; and 0.82 [±0.023]. This corresponded to a Type-I sensitivity  = 0.82, p = 0.41, uncorrected). In the Less condition, two fewer samples corresponded to a substantial decrease in performance within-subjects  but in the More condition, four additional samples did not significantly improve performance within-subjects .In a second session, completed on a separate day, observers were placed in a Free task context, followed by a Replay task context. In the Free task context, observers were also continually presented with samples until they entered their response. Unlike in the Stopping task, observers were not given specific performance targets, but instead asked to enter their response as soon as they ‘felt ready’. Observers scored an average proportion correct [95% between-subjects CI] of 0.80 [±0.025], with observers choosing to respond after 10.5 [±1.51] samples on average. The same trials were then repeated to observers in the Replay task, completed immediately after the Free task, but in the Replay task observers were presented with a fixed number of samples and could only respond after the response cue. The number of samples presented on each trial was determined relative to how many samples the observer chose to respond to for the three repetitions of each pre-defined trial in the Free task. There were three intermixed conditions: Less (−2 samples from the minimum), Same (same number of samples as the median), and More (+4 samples from the maximum). This resulted in a median number of samples of 5.4, 10.4, and 18.5 in the Less, Same, and More conditions, respectively. We compared performance in the 100 trials of the Less, Same, and More conditions to the corresponding sets of 100 trials in the Free task exhibiting the minimum, median, and maximum number of observed samples across the three pre-defined trial repetitions. Performance in the Same condition was on par with performance on those same trials  in the Free task . These ratings were used to compute observers’ Type-II sensitivity using meta-22. The model describes the observer’s choice on each trial as an accumulation of evidence with each additional sample, as shown in Fig. σ and α, respectively (see Methods for details). These parameters were adjusted for each observer to best describe their behavioural choices. In order to achieve the performance targets, the observer imposes a bound on the accumulated evidence. To maintain a constant probability of a correct response, the ideal bound decreases with the number of samples—i.e., a collapsing bound (see Methods for more details). That is, as additional samples are accumulated, the observer requires less evidence per sample on average for the same probability of a correct response. We found that observers adjusted the rate of decline of the bound function over the number of samples (parameterised by λ) to accommodate for the different target performance conditions. On average, λ increased from 2.17 [±0.38] to 3.55 [±0.53] to 4.38 [±0.69] and this alone was sufficient to explain the differences between conditions in Fig. Observers’ behavioural responses were determined by the orientations they were presented with on each trial, and by their internal processing of these samples, which is affected by several sources of suboptimalities. These suboptimalities were quantified using computational modelling, based on the work of Drugowitsch et al.This computational model was then used to examine the process of accumulating evidence for Type-I and Type-II decisions in the context of our three main questions: the relationship between efficient bound-setting for Type-I decision-making and Type-II sensitivity, the implementation of covert bounds on Type-I and Type-II evidence accumulation, and the relationship between suboptimalities in evidence accumulation for Type-I and Type-II decisions.Observers’ ability to set and maintain appropriate bounds on Type-I evidence accumulation was measured in the Stopping task. As shown in Fig. y = 1.41× + 0.25; p = 0.004, using non-parametric bootstrapping; y = 1.5× + 0.2, p = 0.026 with two outliers removed). Importantly, the bound efficiency was not found to relate to observers’ Type-I sensitivity in the Replay task (p = 0.64), nor was Type-II efficiency related to the parameters contributing to Type-I sensitivity in the Stopping task (inference noise (σ); p = 0.51; and temporal biases (α) p = 0.17). This indicates that the relationship between the bound efficiency and Type-II efficiency did not arise from other, potentially confounding computational parameters underlying Type-I and Type-II sensitivity.There was a strong, significant relationship between bound efficiency and Type-II efficiency, shown in Fig. σ (inference noise) and α (inference leak). Second, and more interestingly, observers could be employing a covert bound on evidence accumulation, where observers do not accumulate evidence beyond this bound, even when additional evidence is available. To compare these hypotheses, we fit two models, one with and one without a covert bound, fixing the suboptimalities to those fit to the Same and Less condition. There was a significant improvement in model fit with a covert bound relative to the model without a covert bound, assessed using a five-fold cross validation . Further details on reflexive vs. absorbing29 covert bound comparisons can be found in the In the Replay task, the additional samples in the More condition should have driven increased performance compared to the Same condition, but from the statistical analysis reported in preliminary analysis, this was not the case Fig. . This la30 of Wilcoxon sign rank tests against baseline, this increase became significantly greater than baseline from 0.28 s prior to the response (pbonf*3 < 0.002 in each condition). In contrast, in the Replay task there were substantial between-condition differences. In the Same and the Less conditions the increase relative to baseline did not survive cluster-level comparisons , but in the More condition pupil size was significantly greater than baseline from −3.92 to −1.86 s relative to the response (pbonf*3 = 0.018). Pupil size was also found to peak earlier in the More condition (M = −2.46 s) relative to the Less condition (M = −2.8 s) (using jackknife resampling t(17) = −13.06, pbonf*3 < 0.001) though neither peak was significantly different from the Same condition (p > 0.1). The difference in the median number of samples shown in the More and the Same condition was about 8. If the difference in the peaks were due to the different number of samples, there would also be a difference between conditions in the Stopping task, where there was also a difference of about 8 samples between the 70% and 90% target performance conditions , and significant decrease in temporal bias . If the Type-I and Type-II evidence accumulation processes were entirely independent, then there should be no significant correlation between the suboptimalities affecting each separate accumulation process. On the contrary, we found both σ and α to correlate when allowing model parameters to vary independently across the Type-I and Type-II evidence accumulation processes  while the Type-II parameters remained significantly increased relative to the Type-I parameters . The results are therefore consistent with a partially dissociable model, where the Type-I and Type-II accumulators receive the same noisy decision evidence, but thereafter the Type-II accumulator incurs additional noise. This additional noise could occur either at the accumulation stage or the decision output stage, however these partially correlated models could not be distinguished (the difference in the log-likelihoods of the models is 0.67). Figure p = 0.007), furthermore, estimating the placement of Type-I bounds based on the confidence evidence provided a good estimate of bound efficiency (see Confidence ratings were modelled by placing criteria on the accumulated evidence such that the confidence rating was given based on the position of the evidence relative to these criteria (further details in the Methods). The ideal Type-II observer is defined as the one that is using the exact same evidence as for the Type-I decision. If this were the case, there would be no systematic difference in the parameters fit to describe both Type-I and Type-II responses, compared to when only Type-I responses were fit. On the contrary, we found a significant increase in inference noise (p = 0.004). Comparing trials predicted (based on fitted model parameters) to have crossed observers’ covert bounds to those that did not cross the covert bound also revealed a significant difference in pupil size following the response . There was also a significant difference prior to the response, reflecting the earlier peak in pupil size when the bound is crossed . It should be noted that these trial divisions were not completely independent: 61% of the trials in which the bound was crossed were high-confidence trials, and 67% of the low-confidence trials were trials where the bound had not been crossed. Fig. A partial dissociation between Type-I and Type-II decision processes was also evident from observers’ pupil dilation. There was a significant difference in pupil size between trials rated with high confidence (ratings of 3 and 4) and trials rated with low confidence (ratings of 1 and 2) from 1.2 s following the response , which was temporally aligned with the response in the Stopping task, but occurred much earlier in the More condition of the Replay task, supporting the behavioural and modelling results that suggest that observers were covertly committing to their Type-I decisions early. This implementation of covert bounds could help to optimise perception for efficient Type-I decision-making. However, in the Replay situation, the bounds did not improve efficiency in terms of overall time to complete the task , but rather in terms of cognitive resources (by terminating the effortful evidence accumulation process early). The Type-I evidence accumulation system is thus not only hasty, but also indolent.31, and may continue to accumulate Type-II evidence even after their Type-I response with additional Type-II noise16, and explains how some observers are able to show superior performance in their Type-II decisions compared to what would be expected from their Type-I decisions32. The additional accumulation of Type-II evidence, in combination with the additional inference noise affecting Type-II evidence accumulation, can accommodate for all levels of metacognitive performance described in the literature thus far. Moreover, the particular relationship between the suboptimalities in Type-I and Type-II evidence accumulation, with more noise but less temporal bias in Type-II accumulation supports the characterisation of these processes as a hasty but efficient (Type-I) process moderated by a cautious but inefficient Type-II process3. Here, these effects were found without any explicit experimental manipulation: there was no speed-accuracy trade-off to induce the early boundary on Type-I accumulation in the Replay task, and there was no manipulation of the timings of Type-I and Type-II responses to induce additional Type-II accumulation. In other words, the observed relationship between Type-I and Type-II evidence accumulation arose from observers’ intrinsic predispositions.In comparison, there was no evidence that the Type-II evidence accumulation system was subject to the same premature termination: the model with covert bounds did not improve the description of observers’ confidence judgements, and the confidence of the observer was not discernible from their pupil dilation until after the Type-I response. This is consistent with other findings in the literature, suggesting that observersʼ Type-II responses inherit noise from the Type-I process33. This pupil response to covert decisions has been demonstrated in previous experiments34, as has the distinct profile we saw related to confidence36. While phasic pupil dilation was associated with crossing the decision bound , a distinct effect was seen related to confidence: faster pupil constriction following the response (possibly due to an indirect effect of confidence on task disengagement). This means that these two distinct responses visible in the pupil dilation likely correspond to distinct processes that can be measured simultaneously, perhaps corresponding to the functional differences related to tonic and phasic activity of noradrenergic neurons26.This partial dissociation between Type-I and Type-II evidence accumulation processes was also evident from observers’ pupil dilation responses, suggesting differential activations of the noradrenergic system. The pupil response to reaching the decision bound may be associated with the phasic activity of Locus Coeruleus (LC) neurons to task relevant stimuli, the timing of which is more tightly related to the response time (though occurring prior to the response) than to the timing of the stimulus39. Our interpretation goes further by postulating that the Type-II process is acting on the Type-I evidence accumulation online, as accumulation occurs. Indeed, we were able to reproduce observers’ bound efficiency by simulating bounds based on the Type-II evidence estimate of the probability of correct responses  in the Replay task and bound efficiency in the Stopping task. Likewise, there was no relationship between Type-II efficiency in the Replay task and the magnitude of the suboptimalities in evidence accumulation in the Stopping task. The strongest interpretation of the correlation is that there is a causal relationship between Type-II efficiency and bound efficiency: Type-II evidence is being used to set and maintain boundaries on Type-I evidence accumulation . This ‘metacognitive control’ has been previously suggested to operate in a post-decisional manner: the current confidence will influence future Type-I evidence accumulationIn summary, perceptual and confidence decisions were best explained by partially dissociable, yet causally related evidence accumulation processes. This intricate relationship allows human observers to simultaneously represent sensory evidence as a categorical variable while maintaining a graded representation of the uncertainty in that variable, which can then signal the commitment to a perceptual decision. This allows for fast and efficient perceptual processing under the control of more cautious confidence evaluation. Rather than adding complexity, this characterisation of perceptual processing as concomitant with the processing of uncertainty provides a general computational framework for describing several features of human decision-making. That confidence controls perceptual decision-making explains how observers can adjust decision-making across speed-accuracy trade-offs, learn to make decisions in volatile environments, incorporate priors and loss functions into perceptual decisions, and optimise perceptual processing flexibly across instances requiring detailed scrutiny and instances requiring the integration of global cues.https://osf.io/gy2t3/), in adherence with the declaration of Helsinki, and participants gave written informed consent prior to completing the experiment. Two participants were removed from the analysis based on pre-registered criteria (performance not significantly above chance), leaving 20 participants in the full analysis as planned. One further participant was removed from the pupillometry analyses due to a technical problem with the recording.A total of 22 participants with normal or corrected-to-normal vision were recruited via the French Relais d’Information en Sciences de la Cognition (RISC) mailing list. The experimental protocol was approved by the Conseil en éthique pour les recherches en santé and pre-registered on the Open Science Framework platform (2. Stimulus generation and presentation was controlled by MATLAB (Mathworks) and the Psychophysics toolbox49, run on a Mini Mac (Apple Inc). An EyeLink 1000 infrared monocular eye-tracker system , running at 500 Hz on a dedicated PC, was used to monitor blinks and pupil dilation in the observer’s dominant eye. Observers viewed the monitor from a distance of 60 cm, with their head supported by a chin rest. Stimuli were oriented Gabor patches subtending 4° of visual angle with a Michelson contrast of 0.2 and spatial frequency of 2 cycles/degree. Gabors were embedded in spatially filtered noise with an amplitude spectrum of 1/f1.25 and contrast of 0.15. The orientation of each Gabor was chosen from one of two Von-Mises distributions with concentration parameter κ = 0.5, and means of –45° and + 45° from vertical (0°). An annular colour guide was drawn around each Gabor to aid participants in the visualisation of these distributions, where the red and blue RGB channels reflected the probability density of each angle in the two distributions, respectively22. This colour guide was present throughout the trial, in addition to a black, circular fixation point subtending 0.3° at the centre of the screen. These distributions and example stimuli are shown in Fig. Stimuli were presented on a 24-inch LCD monitor (BenQ) running at 60 Hz with a resolution of 1920 × 1080 pixels and mean luminance 45 cd/m22. On each trial the observer was presented with a sequence of stimuli and was asked to guess which of the two categories (defined by the distributions of the orientations) the stimuli were drawn from. Observers were instructed to press the left arrow key for the category with mean of –45° and the right arrow key for the category with mean of +45°, which was described to the participants using the colour guide. The stimuli were presented at a rate of 4 Hz, with Gabors presented at maximum contrast for 150 ms, temporally bordered by a 25 ms cosine ramp, with a 50 ms inter-stimulus interval. This same basic procedure was used across three variants of the task , but 0 points otherwise.In the first session, observers completed the Stopping task. In this task, samples were presented to the observer until they entered their response . There were three response conditions, where observers were instructed to enter their response as soon as they thought they had reached a certain performance target . These conditions were completed in a random order over six blocks of 100 trials (two blocks of each condition). Before starting this task, observers completed a practice block, where they were first shown 20 trials of 4, 8, 12, or 16 samples with immediate feedback as to which distribution the orientations were actually drawn from. They were then asked to practise responding at each of the performance targets over 10 trials for each target. During this part of the practice, observers were given feedback as to their average percent correct performance over the 10 trials. In the Stopping task, participants were informed of their average performance over mini-blocks of 20 trials, and were symbolically awarded 10 points for achieving the performance target over the 20 trials, or 5 points for coming within 5% of the target , and the median sequence length (number of samples observers saw) for each participant, for each experimental condition, and across experimental blocks. We present the average proportion correct across conditions in the Results. Non-parametric within-subject statistics were applied to sensitivity (d’) to examine differences in performance across conditions, and to the median sequence length. We also present parametric confidence intervals on the proportion correct data. Throughout the analysis, Bonferroni corrections were applied to p-values less than 0.05 when more than one statistical test was carried out per hypothesis (with this indicated by bonf*[number of tests corrected for] in subscript), while non-significant p-values were reported uncorrected.Raw behavioural data were used to calculate proportion correct, sensitivity  and relative to the response (−6 to 2 s). Epochs were baselined by subtracting the group mean for each condition at the start of the epoch from each participant. Pupil change was calculated based on the derivative of the pupil size, which was conducted with some smoothing, by taking the differences between the sum of five time-points before and after each time-point and dividing by five times the sample rate (50 Hz). Statistical inferences were performed using a cluster-based procedure30. Significant clusters were found using Wilcoxon sign rank tests at each time-point  and comparing the sum of the z-scores in these clusters to those obtained over 3000 permutations. For tests against baseline in the response-aligned epochs, data were permuted by shuffling the response times within each condition. For tests between conditions, data were permuted by shuffling the condition labels of the trials. Cluster-based corrections corrected for tests over multiple time-points, additional Bonferroni corrections were applied to significant p-values when more than one set of tests was performed . As a secondary check that the results were not influenced by the pupil response to stimulus onset, the analyses were also performed on data where the pupil impulse response function to stimulus onset was removed using an autoregressive model with exogenous inputs50, these results can be found in the Supplementary Note Blinks were identified using the EyeLink automatic blink detection algorithm and pupil dilation was linearly interpolated using a 100 ms window before and after each blink. Additional outlier pupil dilation data points (greater than 8 SD from the mean) were also interpolated. Data were downsampled to 50 Hz by averaging over consecutive windows of 20 ms. Time-points where the observer was not fixating within 200 pix from fixation were tagged for exclusion and trials were excluded from the analysis if there was more than 100 ms of exclusory data, or if any data were more than 3 SD from the mean . Each participant’s pupil size data were 22. The model takes the Bayesian optimal accumulation of sensory evidence in this task, and disrupts this process with several sources parametrically defined suboptimalities. The Bayesian optimal observer is assumed to know the category means, κ = 0.5, and takes the evidence in favour of category ψ (ψ = 1 or ψ = 2) based on the orientation presented on a specific trial, for a specific sample n, as the probability of the orientation θn given each category:I0(·) is the modified Bessel function of order 0. The optimal observer then chooses the category ψ with the greatest posterior probability over all samples for that trial, T , given a uniform category prior, A computational model was defined based onThis is achieved by accumulating the log probabilities of each category, given each orientation presented in the sequence:T):Given the evidence for each category is perfectly anti-correlated over the stimulus orientations, the evidence from each sample can be summarised as:z > 0 and 2 if z ≤ 0.Such that the Bayesian optimal decision is 1 if εn, is added to each evidence sample. The noise is Gaussian distributed with zero mean, and the degree of variability parameterised by σ, the standard deviation:This optimal decision-making was disrupted by several sources of suboptimality in order to account for each observer’s behaviour. First, variability is added to the evidence accumulation process, such that independent and identically distributed (i.i.d) noise, 22 suggests that the contribution of sensory noise to this task is minimal , thus no sensory noise parameter was explicitly fitted in reported analyses.This noise represents inference noise, as it is added to the decision update as opposed to the representation of stimulus orientation. It is noted that there could be some contribution of sensory noise, where the representation of the stimulus orientation does not veridically match the sensory input. However, previous evidenceα, before accumulating the next sample, so that when α > 1 this creates a primacy effect and later evidence affects the decision less than the initial evidence. In contrast when α < 1 this creates a recency effect, and the observer’s decision places greater weight on the more recent evidence than the initial samples. Thus, by the end of the sequence, the weight on each sample n is equal to:T is the total samples in that trial and n ∈ . Altogether, the suboptimal accumulation of decision evidence takes the following form:The suboptimal observer does not accumulate evidence perfectly. Functionally, during accumulation, the current accumulated evidence is weighted by n), has crossed a decision boundary, Λ. In order to maintain a constant likelihood of a correct response (as required by the task) this bound was found to decrease as sequence length increases . The likelihood f(n) of responding at sample n was estimated by computing the frequencies, over 1000 samples from εn (Monte Carlo simulation), of first times where the following inequality is verified:For the positive decision bound  is the likelihood of responding at sample n as above in Eq. f′(n) is the modified likelihood that takes into account a smoothing of the choices in time, and μU and variance n.As we do not have access to when the decision is made, only when the response is entered, two additional parameters are used to describe the mean, r at sample n on each trial, using a Bayesian Adaptive Direct Search51. As there is no known analytic solution to the likelihood function of the model, the probability of the observer making each response at each sample, given the parameters, was numerically estimated using Monte Carlo simulation. The sensitivity of this approach was tested using parameter recovery. Simulating 300 trials we found a significant correlation between the simulated and recovered parameters , for each condition of each task. The full model contained response bias and lapse rate parameters (not described above) that could be removed as they did not significantly improve the fit . There was also little evidence for response bias from the behavioural data (mean criterion = 0.03 [±0.05]). Thus, the final Type-I model contained seven parameters for fitting both what and when observers responded, and just two parameters for fitting only the categorisation response. Next, we examined whether any of the parameters systematically varied across conditions within tasks. For the Replay task, this is described in the Results. For the Stopping task, this was used to assess which parameters observers were adjusting to control their bounds. The only parameter to significantly vary across conditions in the Stopping task was λ  with all other p > 0.1 . We then fit all conditions of the Stopping task together, with three λ, one for each condition.The full model was then simplified using a knock-out procedure, by comparing the Bayesian Information Criterion (BIC) of the full model with the BIC of models with each parameter fixed to a neutral value in turn using Bayesian Model Selection  and producing responses to the orientations shown to observers over 1000 samples of noise from each observers’ εn. Bound efficiency was then calculated as the actual difference in observers’ λ in the 70% and 85% target performance condition, divided by the simulated difference in the λ that would have achieved 70% and 85% correct, leaving all other parameters the same. This meant that the bound efficiency really corresponded to the ability of the observer to appropriately adjust their bound, irrespective of any absolute bias to set bounds too high or too low. The data from the 90% correct condition was not used in this calculation as the model predicted that 13 observers would have never reached 90% correct, and indeed, no observer actually reached 90% correct overall in any task of the experiment.In order to calculate bound efficiency, observers’ actual bound separation was divided by the optimal bound separation. The optimal bound separation was estimated by simulating observers’ performance across all bounds, and taking the bounds that produced the target performance. Performance was simulated by fixing all parameters except 1,Λ2,Λ3) as the upper limit on the evidence for each confidence rating (with the highest confidence rating having no upper limit). The three bounds were modelled with the same a and b, but different λ. Model fitting was performed using the same method as for the Type-I behaviour, except both the Type-I and the Type-II response were fit, such that the model would respond :To fit Type-II confidence ratings, additional criteria were required to partition the evidence for each confidence rating. We examined the absolute evidence, z for Type-I and Type-II responses. Entirely separate parameters (leading to independent z for Type-I and Type-II responses) were fit in the parallel model, where the Type-I parameters were fixed to those fit to only the Type-I responses. In partially correlated models some parameters for the Type-II z were fixed to be the same as those affecting the Type-I z. These models compared all combinations of fixed/varied noise and leak, and compared whether additional noise was added with each sample of evidence, or a single sample of noise irrespective of sequence length. Model comparison showed a partially correlated model, where Type-II z is affected by additional noise and a different leak, best accounted for Type-I and Type-II responses: the exceedance probability of this model over the model fit using the same z for Type-I and Type-II responses was xp > 0.99; the exceedance probability over the parallel model was xp > 0.999; the exceedance probability over the next best partial model (a model with leak fixed to the Type-I leak) was xp = 0.54. We then compared models in which the observer accumulates Type-II evidence over all samples and models implementing a bound on Type-II evidence accumulation . There was no evidence for a Type-II accumulation bound , and the model would sometimes become stuck in a local minimum. We therefore implemented plausible lower and upper bounds on the parameters, based on initial fits to participants where the model was successfully able to apply the bounds. These plausible bounds are used by the Bayesian Adaptive Direct Search to design the initial mesh of the parameter search, and by specifying increasing but overlapping plausible bounds on the λ’s, the model was able to successfully recover sensible parameters for all participants, while not limiting the model’s ability to describe the behaviour of some of the more extreme participants.If the Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


With the increasing number of Internet of Things (IoT) devices, a huge amount of latency-sensitive and computation-intensive IoT applications have been injected into the network. Deploying mobile edge computing (MEC) servers in cloud radio access network (C-RAN) is a promising candidate, which brings a number of critical IoT applications to the edge network, to reduce the heavy traffic load and the end-to-end latency. The MEC server’s deployment mechanism is highly related to the user allocation. Therefore, in this paper, we study hierarchical deployment of MEC servers and user allocation problem. We first formulate the problem as a mixed integer nonlinear programming (MINLP) model to minimize the deployment cost and average latency. In terms of the MINLP model, we then propose an enumeration algorithm and approximate algorithm based on the improved entropy weight and TOPSIS methods. Numerical results show that the proposed algorithms can reduce the total cost, and the approximate algorithm has lower total cost comparing the heaviest-location first and the latency-based algorithms. Internet of Things (IoT) enables billions of sensors, devices, and actuators, as well as the human to be interconnected via the Internet over a distributed environment to work together. The European Commission has predicted that there will be 50 to 100 billion devices connected to the Internet by 2020  and mobiIn spite of the promising benefits brought by deploying MEC servers in C-RAN, a large number of users with various requirements pose new challenges. First, due to the limited computing and storage capacity of a single MEC server, a large number of MEC servers need to be deployed in order to meet the quality of service (QoS) of the fast-growing IoT traffic. However, considering the deployment cost constraints, it is unrealistic to deploy the MEC server at each access point . Therefo(i)We present a hierarchical architecture for MEC servers’ deployment and requests allocation based on C-RAN, where MEC servers can be deployed at the distributed units or at the central units.(ii)We cast a mathematical framework to investigate the average latency-deployment cost tradeoff problem by associating with computation and communication resource allocation. The average latency of requests includes network latency and computing latency: network latency depends on the length of the optimal path selected between the location of request and MEC server by using the routing and wavelength assignment scheme and computing latency is modeled as an M/M/1 queuing model.(iii)We propose an enumeration algorithm for MEC servers deployment and request allocation as a benchmark, which enumerates all possible deployment sets. The requests allocation scheme considers the master-slave characteristic of candidate locations at central unit (CU) and distributed unit. In order to find the deployment set for MEC servers in a reasonable time scale, we propose an approximate algorithm for solving the MEC servers’ deployment problem effectively, which combines entropy weight method and TOPSIS method based on unassigned requests ratio.In this paper, we leverage the C-RAN architecture  and propThe rest of this paper is organized as follows. The MEC servers/cloudlets deployment and requests allocation are of great importance in networks, which have been attracting more attention in recent years . The latIn the respect to latency, Xu et al.  studied In the respect of deployment cost, Mondal et al.  proposedThere are some authors focusing on the tradeoff between latency and deployment cost. Fan et al.  proposedIn this paper, we propose a hierarchical deployment scheme based on C-RAN to exploit the tradeoff between deployment cost and average latency by jointly optimizing MEC servers’ deployment, requests allocation, and routing and wavelength assignment. Instead of regarding the latency as the length of the shortest path, we investigate the latency issue from a much more detailed perspective, i.e., the network latency takes into account available wavelength of links, and the computing latency is modeled as an M/M/1 queuing model.In this section, a three-tier C-RAN architecture supporting hierarchical deployment of MEC servers is given, and the network model of MEC servers deployment is elaborated.C-RAN: In C-RAN, the original BaseBand Unit (BBU) and Remote Radio Units (RRU) are reconstructed as three functional entities in 5G. A typical three-tier C-RAN, as shown in MEC: MEC as an alternative for resource-intensive and latency-intolerant applications, which is considered to be an extension of the cloud computing paradigm to edge networks . MEC serFor the MEC servers at DU layer, point-to-point fiber links between a MEC server and each RRU connected to it are installed. Because MEC servers of DU layer are in close proximity with the IoT devices, the network latency and bandwidth consumption are lower. However, the coverage of MEC servers at DU layer is limited, and the cost is high. The idea of installing MEC servers at central unit layer can serve a large number of users, and has a lower cost. However, the network latency and bandwidth consumption increases more in MEC servers at DU layer .Therefore, MEC servers at central unit layer are more economical option over MEC servers at DU layer, especially in dense deployment scenarios. Compared with the MEC server deployed at DU layer, MEC server deployed at the central unit layer has more computing and storage capacity. However, the network latency and bandwidth are the key drawbacks, due to the relatively long distance between MEC servers and users.I = { 1,2,…,i,…} as the set of requests, R = { 1,2,…,r,…} as the set of RRUs, N = { 1,2,…,n,…} as the set of DUs, M = { 1,2,…,m,…} as the set of central unit s, and J = N + M = { 1,2,…,j,…} as the set of candidate locations of MEC servers. W denotes the number of wavelengths of each fiber link in mid-haul and backhaul network. The network is shared by multiple requests. A request i is defined as , where id is the computing resource demand of request i, and iλ is the average generation rate of request i. A candidate location j of MEC server is defined as , where jf is the cost of rental site of location jj, SC is the number of physical machines of location j. The detailed notations and definitions used in this paper are summarized in We define In this section, for the MEC servers’ deployment problem, we defined a mathematical model. We assume that we have already known the locations of the RRU, DU, and CU, irrespective of the deployment scenario.i follows a Poisson distribution with the average generation rate of request equal to iλ  × 105 cycles. The average service of MEC server is proportional to the capacity of MEC server. The average service ju of each MEC server deployed at DU layer is chosen according to the Normal distribution with an average of 800 and a variance of 20, i.e., N. The average service ju of each MEC server deployed at CU layer is chosen according to the Normal distribution with an average of 2000 and a variance of 100, i.e., N. We set the length of link in the simulation network topology as in [In this section, we evaluate the performance of the proposed algorithms. We consider a simulation network topology as shown in gy as in : the lengy as in . ConsideWe analyzed the proposed algorithm from two perspectives, from the perspective of the total cost, we start the numerical analysis by contrasting enumeration algorithm and approximate algorithm against Heaviest-Location First Algorithm (HLFA)  and LateA good deployment scheme not only ensures low average latency of requests, but also needs to consider the deployment cost of service providers. η1. First, it is clearly observed from η1 is set to 0.6 as an example, as shown in 1η is 0.6, LBA always outperforms HLFA. This is because when η1 equals 0.6, the dominant cost is the average latency cost. The average latency of HLFA is greater than the average latency of LBA. Therefore, the total cost of HLFA is greater than the total cost of LBA. Note that, for 1400 requests, no feasible deployment scheme is found by using HLFA. This is because the MEC servers deployed by using HLFA are all located at CU layer, which is far from the requests. When assigning requests, a large number of wavelengths need to be occupied, resulting in insufficient wavelength resources in C-RAN.Take the tradeoff coefficient Secondly, we observe that the enumeration algorithm and approximate algorithm keep relatively stable performance with different numbers of requests in Thirdly, as further shown in The main goal of the simulation is to illustrate the impact of the deployment cost and latency on the total cost. We fixed the number of requests as 800. η1. When η1 increases, approximate algorithm starts to pay more attention to the average latency. As a result, it tends to deploy more MEC servers and select optimal location at DU layer to reduce the average latency while sacrificing deployment cost. Therefore, we can just adjust the relation between deployment cost and average latency by selecting a suitable η1 according to the actual requirement of the network.In this paper, we have introduced a novel hierarchical deployment of MEC servers’ optimization strategy in C-RAN over WDM ring networks to minimize deployment cost and average latency. We have proposed enumeration algorithm and approximate algorithm to solve MEC servers’ deployment and user allocation problem. Simulation results showed that the proposed approximate algorithm based on the improved entropy weight and TOPSIS method can obtain lower total cost comparing the HLFA and LBA. Additionally, the hierarchical deployment of MEC servers could tradeoff deployment cost and latency. The proposed algorithms will serve as a theoretical foundation for further MEC server network planning.As a future work, we are currently developing a joint MEC servers’ deployment and content caching  from the perspective of content providing. By analyzing the different video attributes , we determined the optimal locations of MEC servers in the network, and selected the MEC servers to cache videos.


Although some national recommendations for the role of radiology in a polytrauma service exist, there are no European guidelines to date. Additionally, for many interdisciplinary guidelines, radiology tends to be under-represented. These factors motivated the European Society of Emergency Radiology (ESER) to develop radiologically-centred polytrauma guidelines.https://www.eser-society.org) and a short version also covering all recommendations (this article).Evidence-based decisions were made on 68 individual aspects of polytrauma imaging at two ESER consensus conferences. For severely injured patients, whole-body CT (WBCT) has been shown to significantly reduce mortality when compared to targeted, selective CT. However, this advantage must be balanced against the radiation risk of performing more WBCTs, especially in less severely injured patients. For this reason, we recommend a second lower dose WBCT protocol as an alternative in certain clinical scenarios. The ESER Guideline on Radiological Polytrauma Imaging and Service is published in two versions: a full version . The second protocol should be dose reduced and used for clinically stable and oriented patients who nonetheless require a CT because the history suggests possible serious injury (variant B). Reading, interpretation and communication of the report should be structured clinically following the ABCDE format, i.e. diagnose first what kills first. If indicated, whole-body-CT (WBCT) saves lives in severely injured patients.However, WBCT radiation dose risk versus benefit depends on severity of injury.Two WBCT protocols should be established .Protocol A should be used for clinically unstable patients/life-threatening conditions.For all other patients, protocol B should be selected.The European Society of Emergency Radiology (ESER) is an apolitical, non-profit organisation, exclusively and directly dedicated to promoting and coordinating the scientific, philanthropic, intellectual and professional activities of Emergency Radiology. The Society’s mission at all times is to serve the health care needs of the general public through the support of science, teaching, research and the quality of service in the field of Emergency Radiology . One parEmergency Radiology encompasses medical and surgical subspecialties including polytrauma services. Concerning the latter, past and present ESER board members had taken part in several interdisciplinary guideline processes. However, the ESER board has observed the lack of dedicated separate independent radiological recommendations for a radiological polytrauma service. The ESER has therefore created such recommendations with the hope that this will start to bring corresponding diverse national and international radiological societies together in order to refine the statements, gain visibility for national societies and in particular, strengthen the role of radiology in upcoming interdisciplinary polytrauma guideline development.As ESER also wants to be a promotor of future scientific work, we hope to give advice on specific questions as well as for a more general principal direction. To update this guideline, ESER will refine the statements at appropriate time intervals, (currently estimated to be two years).The Guideline on Radiological Polytrauma Imaging and Service is published simultaneously in a comprehensive short version (this article) and a full version (download of the full version from the ESER homepage ). This cThe ESER Board instructed the former ESER President (S.W.) to divide the entire field of radiological polytrauma care into individual sections. S.W. assigned parts of the project to J.H. as part of his doctoral thesis at the Ludwig-Maximilian-University, Munich, Germany. Each section was processed and prepared according to a fixed schedule: determination of key issue(s), literature research, selection of literature, classification of literature, rating of literature, determining a level of evidence, suggesting a grade of recommendation, suggesting a statement for each key issue as a basis for the consensus conferences.Each section was related to at least one key issue/question. The consensus conference members had to discuss and vote on one (or more) answer(s) to each key question, but were also allowed to delete or change answers.For each key issue, a literature search was conducted with subjectively fitting keywords from the MeSH terms  includiPublication period from January, 1 2010 to February, 15 2019Study population: n ≥ 50, adults Language of publication: English or German Full text accessible, free of charge via the university portal usedClinical relevance of the literature included with regard to the key issue It is published as a guideline, i.e. using the word ‘guideline’ in the titleThe guideline is described as being current or no updated version is availableAdditional criteria for guidelines:Allowed study types: meta-analyses, systematic reviews, randomised controlled trials, cohort studies, case–control studies, cross-sectional studies, before—after studiesp-value < 0.05 and/or confidence interval (CI) > 95%Outcome: Additional criteria for studies:The literature was selected on the basis of a catalogue of inclusion criteria. The search key words were determined together by S.W. and J.H. according to ‘sections’ and related ‘key issue(s)’. Data were excluded if at least one of the inclusion criteria listed below was not fulfilled.Classification In case of studies, the algorithm according to Hartling et al. [g et al.  was usedRating Systematic reviews and meta-analyses were rated using AMSTAR 2 [AMSTAR 2 , randomiAMSTAR 2 , and cohAMSTAR 2 . The CocAMSTAR 2  (e.g. weAMSTAR 2 ).Evidence Level For every study, the level of evidence of was assigned using the scheme of the Oxford Centre for Evidence—Based Medicine in the 2011 version [ version  (e.g. le version ).Using the evidence levels according to the AWMF Guidance Manual  one out If there was insufficient evidence in the literature included, the degree of recommendation—GPP  wFor each key issue, S.W. and J.H. proposed a statement with a corresponding level of recommendation (GoR or GPP) to the consensus conference. This served as a basis for the discussion during the consensus conference. If present in person, each member of the last and the current ESER Board (from 2017 until now) had exactly one equal vote. As the consensus conferences were during congress meetings, not all consensus members were able to present for the entire consensus conferences. According to our constitution, attendance by 2/3 of members was considered quorate for each vote on each statement. All members reviewed each statement during the publication process. The suggested statements and grades of recommendation as well as the corresponding literature were sent to the participants in advance by email.The procedure for each key question is as follows  asThe results of the consensus conferences are presented here and structured into ten sections. Each section may be subdivided into several key issues that were presented as tables in the following. Each table also holds a collection of ‘key literature’ that corresponds to the literature included. The tables also include a path through the literature classification as well as the evidence levels of the included literature Tables , 14, 15.For a detailed literature discussion of more than 50 print pages we have to refer to the guideline in full length (access via ESER homepage ).As a relatively young society, ESER overcame challenges during the guideline development, consensus and publication process. The members of the consensus group were distributed throughout diverse nations, making the necessary distribution of information and communication time consuming. Financial limitations restrict the whole group from coming together face to face to only once or twice a year, during the European congresses of radiology in Vienna and the ESER congress meetings. This huge project required two sittings of the consensus group to adequately provide time for discussion, this, in addition to restrictions of the SARS-Cov-2 situation and cancellation of the European Congress of Radiology (ECR) 2020 caused unexpected delay in manuscript production Tables , 18, 19.Only one person was involved in suggesting key issues (S.W.)Literature search was limited to two persons Literature preparation  was also limited to S.W., J.H.The preparation of the consensus conference(s) including suggested statements and respective grading of them was limited to S.W., J.H.Literature inclusion was limited to free full access via the Ludwig-Maximilians University of Munich, Germany. However, this quote was about 95 percent in mean and always above 90 percent.German was the only non-English language that could be included in the literature search (because S.W. and J.H. were able to understand and translate it for the consensus conference members).The guideline does not cover special topics like paediatric patients or interventional radiology; these are an aspiration for future editionsBy developing this guideline, the ESER aimed to redress the lack of dedicated separate independent radiological recommendations for radiological polytrauma service. ESER recommends that a patient should first be assessed as ‘polytrauma’, who will therefore receive whole-body CT (WBCT) or ‘non-polytrauma’ . For a polytrauma service, the CT distance to the Emergency Trauma Room should not exceed 50 m—the closer, the better , 19, 20.ESER endorses abandoning a ‘one-size-fits-all-concept’ (-p.1142).In contrast, the ‘Time/Precision protocol’ is optimised for very fast, very high diagnostic accuracy and will more or less correspond to the institutional protocol used so far. The key advantage is the more sensitive detection of active bleeding , 108. ThThe ESER hopes that this guideline motivates diverse national and international radiological societies to come together in order to refine the statements over time. The ESER acknowledges that these guidelines do not focus on the radiological polytrauma service for children and Interventional Radiology. Rather the ESER invites the corresponding national and international radiological (sub)societies to contribute in the future. Where the guidelines do overlap with other radiological communities on topics such as Musculoskeletal, Abdominal & Urogenital imaging, the ESER anticipates arriving at a consensus in the future.ESER sees this as way to gain visibility for national societies in the field and in particular to strengthen the role of Radiology in upcoming interdisciplinary polytrauma guideline processes. As ESER is active in the whole field of emergency radiology, we also aim to expand the guideline to non-traumatic Emergency Imaging in upcoming versions.