The stripping rates were 58% (95% CI 54–61%) and 15% (95% CI 12–17%) respectively (p < 0.001). The confidences when using the normal and augmented screwdrivers respectively were 7.2 and 7.1 in unstripped insertions and 6.2 and 6.5 in stripped insertions. Performance improved with an augmented screwdriver, both in reduced stripping rates and greater accuracy in detecting stripping. Augmenting screwdrivers to indicate optimum tightness offer potentially enormous clinical benefits by improving screw fixation.Orthopaedic screws insertion can be trivialised as a simple procedure, however it is frequently performed poorly. Limited work exists defining how well surgeons insert screws or whether augmented screwdrivers can aid surgeons to reduce stripping rates and optimise tightness. We aimed to establish the performance of surgeons inserting screws and whether this be improved with screwdriver augmentation. 302 orthopaedic surgeons tightened 10 non-locking screws to what they determined to be optimum tightness into artificial bone sheets. The confidence in the screw purchase was given (1–10). A further 10 screws were tightened, using an augmented screwdriver that indicated when a predetermined optimum tightness was reached. The tightness for unstripped insertions under normal conditions and with the augmented screwdriver were 81% (95% CI 79–82%)(n = 1275) and 70% (95% CI 69–72%)(n = 2577) ( Screws are the most commonly inserted orthopaedic implant, with millions inserted each year just in the UK, in a global market expected to reach a value of $1.96 billion by 20282. Most screws are inserted manually, being tightened to a surgeon’s subjectively chosen amount. If screws are tightened too much, they strip the surrounding bone, reducing strength by > 90% and increasing fixation failure rates3. If failure occurs, treatment costs at least double alongside increased morbidity and mortality for patients3.The majority of people will sustain a fracture in their lifetime, often requiring screw fixation to restore function and mobility4, with most studies limited by having either one surgeon insert all screws, or many surgeons insert very few screws. However, these studies have shown that surgeons repeatedly perform poorly with more than one in every four screws inserted, stripping (irreparably damaging) the screw hole4. If representative of clinical practice, this would equate to millions of screws being poorly inserted each year. Currently there are no data to support how tight surgeons insert screws given the limitations and underpowering of previous studies, or data on how tight surgeons think they should be inserting screws.Data is limited on how well surgeons insert screws and is based on only 145 surgeons inserting a total of 1510 screws6, though no work has utilised augmented screwdrivers in an attempt to aid surgeons to reduce stripping rates and optimise tightness. What torque should be targeted for optimum fixation has until recently been unknown. Previously, we have shown optimum non-locking screw tightness in certain conditions to be between 70 and 80% of the maximum torque6. Knowing what tightness to target, and augmenting screwdrivers to indicate when the optimum tightness has been reached, offers the promise of greatly improving surgical performance when inserting screws, though their use has not been explored.Awareness of the torque applied during screw insertion improves surgical performanceThe aims of this study were to identify for a large sample of orthopaedic surgeons what tightness is achieved when inserting non-locking screws, how tight surgeons think screws should be, how confident surgeons are in their insertions, their accuracy in detecting screw hole stripping, how screw insertions change when using an augmented screwdriver that indicates when optimum tightness is reached and how training and experience impact on outcomes.The study protocol, procedures and questionnaires were developed and approved under local institutional ethical approval (AO Research Institute Davos), in agreement with the Declaration of Helsinki. All attending surgeons, both faculty and participants attending an international orthopaedic course were eligible for inclusion in the study. Surgeons were invited to participate and/or surgeons presented themselves for testing. All participants gave informed, written consent to participate and for their anonymised data to be analysed. Neither financial nor material incentives were offered for participation—surgeons were told they would receive individually their results if they participated. Having read the conditions related to the study, participants completed a questionnaire in English for demographic information, before being asked to read instructions for the screw insertions.3 were made into sheets of 4 mm thickness using a custom-made milling process  with pilot holes of 2.5 mm diameter; these sheets were designed to mimic the difficult situation of performing insertion into low density bone  of a density of 0.32 g/cm8. A torque measuring screwdriver ) was used for all screw tightening. Participants were asked to insert each screw to what they determined to be the optimum tightness for that screw. The screwdriver displayed the applied torque via a digital reading which was recorded by researchers; participants were blinded to these values. At a separate episode, a researcher calculated the surgeon’s achieved tightness by creating a ratio between the torque chosen by the surgeon (stopping torque) and the maximum torque the screw hole could receive (stripping torque). If the stopping torque was found to have been greater than the stripping torque, the insertion was defined as having been stripped by the surgeon. Following each screw insertion, participants rated the achieve purchase from 1 to 10 . They also reported whether they felt the screw hole had been stripped—yes or no.Surgeons were instructed to wear non-sterile gloves before sequentially tightening non-locking, 3.5 mm self-tapping, cortical screws into the artificial bone sheets in a vertical orientation in two testing phases Fig. . All scr6, was calculated to be 70% of the average stripping torque for 3.5 mm screws in 2.5 mm screw holes in the 4 mm thick artificial bone sheets, established as 0.15 Nm from pilot testing. The instructions for inserting screws in Phase 2 were to stop inserting when the optimum tightness was indicated by the screwdriver. Again, confidence (1–10) in the screw purchase and the surgeon’s assessment of whether the screw hole had been stripped were recorded.For Phase 2, 10 screws were tightened in exactly the same fashion except for the same screwdriver was set to beep and vibrate when a predetermined theoretical optimum torque value was achieved: 0.105 Nm. This value, defined as optimum tightness9. All data are available in an online repository10.Statistical analysis was performed using paired t-tests for comparisons between phases of screw tightness, confidence in unstripped insertions, confidence in stripped insertions, sensitivity, specificity and accuracy in predicting screw hole stripping and prediction for optimum tightness and actual tightness achieved. Chi squared tests were used for stripping rate comparisons between phases. Regression analysis was performed using backwards stepwise regression to select from the following variables: age , gender , job level , continent of work , number of years in speciality, value thought to generate optimum tightness and engineering qualification . Surgeons were categorised based on their techniques into either good performance (stripping rate ≤ 10% and an accuracy of ≥ 80%) or poor performance (stripping rate > 10% and/or accuracy < 80%) for both testing phases. Results were considered significant at a family wise error rate of 0.05 and confidence intervals were calculated at 95%. Statistical tests were performed with ‘R’, version 4.0.2Three hundred and two surgeons were recruited Table . They tip < 0.001), with a lower mean average screw tightness for unstripped insertions of 70% (95% CI 69–72%)(n = 2577)(p < 0.001). In Phase 1, 56 surgeons (19%) stripped all 10 screw holes. This reduced to just seven surgeons (2%) in Phase 2 (p < 0.001).In Phase 1, using a normal screwdriver, 58%  54–62%)n = 1745/3020) of screw holes were stripped, with the mean average screw tightness for unstripped insertions being 81% (95% CI 79–82%)(n = 1275) , which was different to the average tightness achieved (81%) with a normal screwdriver (p = 0.441) nor did confidence in stripped insertions: Phase 1—6.2 (95% CI 5.9–6.4), Phase 2—6.5 (95% CI 6.2–6.7) (p = 0.218) .Unstripped screw confidence did not change between phases: Phase 1—7.2 (95% CI 7.0–7.4), Phase 2—7.1 (95% CI 6.9–7.3) , predicted optimum  and years of experience (0.4% higher per year of experience). For Phase 2, variables associated with overall average tightness were: having an engineering qualification (3.8% less tight), years of experience (0.1% tighter per year of experience) and continent of work . Variables associated with a change in stripping rates were: years of experience (0.6% higher per year of experience) and continent of work . The three regression models with significant predictor variables were compared to an intercept only model which resulted in significant p-values of 0.0004, 0.0050 and 0.0001 respectively.Using the categories based on stripping rate and accuracy in detecting stripping, using an augmented screwdriver led to good performance being seen in 69% of surgeons compared to just 9% under normal conditions Fig. .11, fixation strength12 and contribute to fixations failing3, stripping rates should ideally be zero. An ability to immediately critique a screw insertion should enable the detection of stripping by the surgeon, should it occur, so that remedies can be enacted, such as changing the position of the fixation or increasing the screw size12. However, we found, as did Stoesz et al. previously, that detection of stripping when inserting screws with unaugmented devices is rare, and only when the bone is greatly damaged13. The high stripping rate seen with a normal screwdriver combined with the low accuracy in detecting stripping by some surgeons may indicate that sub-optimal fixations are being routinely performed. Equally, given the poor techniques seen with a normal screwdriver, it may be that current fixation strategies use more screws than are needed to compensate for some screws being inserted poorly. Using augmentation to indicate optimum tightness, the significant improvements in techniques seen could mean that fewer, better-inserted screws would provide the same fixation if they were inserted correctly. This could reduce surgical exposures, surgical time and implant costs.Surgical performance varied greatly amongst surgeons, with a considerable proportion of screws being inserted poorly. Use of an augmented screwdriver indicating when optimum tightness had been reached dramatically improved surgeons’ techniques. As stripped screw holes impact on bone healingOnly a couple of the recorded demographic factors were associated with changes in technique, particularly number of years of experience and continent of work, implying that a spectrum of techniques is seen in all countries, ages and surgical experiences. Surgeons with higher values for the suspected optimum tightness were associated with higher stripping rates, perhaps due to a desire to achieve more tightness.Whilst confidence did reduce significantly when screw holes were stripped, it still remained high in both normal and augmented insertions. Indeed, many surgeons reported mid-range and even high confidence in a screw that had clearly stripped the screw hole. This may reflect a general inability to critique insertions, or a lack of understanding by some surgeons of what proprioceptive feedback they should be feeling for. Perhaps even not knowing how a screw works. Thankfully, this improved with augmentation, demonstrating the safety benefits quantitative feedback can offer; in this study, techniques could be described as ‘good’ in 69% of surgeons when using augmentation, compared to just 9% under normal conditions. Unfortunately, it is not known, and we did not investigate, what a surgeon would do with a low confidence in a screw, i.e. at what confidence score would a surgeon change the screw or alter the fixation.14, but the tightness to target, and then how to target this have been unknown. Recently however, whilst only in-vitro biomechanical studies, optimum tightness for non-locking screws has been found between 70 and 80% of the stripping torque. Additionally, using bone characteristics16, the stripping torque can be calculated or at least estimated prior to insertion. This study demonstrates that assuming appropriate estimations of the optimum tightness can be made—which are straightforward in the controlled, artificial bone testing environment used—augmenting screwdrivers to indicate the optimum torque has great fixation benefits. With advancements in drill design, bone density estimates can be made based on the energy required to create the pilot hole17. This characteristic can be incorporated into the calculations for the maximum torque, and thus what 70 to 80% would be.Previous work on screwdriver augmentation has shown its benefits4. It is the first study to look at different surgeon characteristics and whether these are associated with changes in performance and the first to look at augmented screwdriver insertion on a large sample. Furthermore, a diverse spectrum of surgeons was tested, increasing the generalisability of the findings.This is the largest study to date into surgeon performance when inserting screws. Most previous studies were underpowered due to the small number of screw insertions for each tested variable and/or a small number of surgeons7. The optimum tightness for this model was not investigated in this study and used previous investigations in bovine and human bone as a reference point for was tightness to target. The optimum tightness for artificial bone may be different, meaning that augmentation should have been set to a different level, however, the primary objective of screw insertion is to not stripping the surrounding bone, which augmentation was highly successful in achieving, whereas optimum tightness (which for this model is not known) is a secondary objective. Neither assessments of fixation strength nor of the impact on bone healing were performed, though stripped screws are known to worsen bone healing11. Finally, the benefits seen with the augmented screwdriver may have been enhanced due to greater familiarity with the material and stripping torque for Phase 2 compared to Phase 1. However, no difference has been seen in previous biomechanical studies between the first 10 insertions of a screw and more insertions8.Limitations with this study include that whilst having an international group for testing makes the findings more generalisable, there may have been language issues that made instructions harder to understand by some participants—though the courses they were attending were also being delivered in English. The homogeneity of the bone model used removed confounders from the testing material, though as it is artificial bone, its properties may differ from in vivo fixation techniques and outcomes. However, a previous study has shown that techniques in artificial bone mimic those in human boneUsing augmentation reduces stripping rates, improves accuracy whilst optimising screw tightness. Developing methods for real-time evaluation of the stripping torque and use of torque-controlled screwdrivers will improve surgical performance through less failed insertions, saving time, money and likely improving patient outcomes.


Two prominent features of obsessive-compulsive disorder (OCD) are the inability to inhibit intrusive thoughts and behaviors and pathological doubt or intolerance of uncertainty. Previous study showed that uncertain context modeled by equiprobable presentation of excitatory (Go) and inhibitory (NoGo) stimuli requires non-selective response inhibition in healthy subjects. In other words, it requires transient global inhibition triggered not only by excitatory stimuli but also by inhibitory stimuli. Meanwhile, it is unknown whether OCD patients show abnormal brain activity of the non-selective response inhibition system. In order to test this assumption, we performed an fMRI study with an equiprobable Go/NoGo task involving fourteen patients with OCD and compared them with 34 healthy controls. Patients with OCD showed pathological slowness in the Go/NoGo task. The non-selective response inhibition system in OCD included all brain areas seen in healthy controls and, in addition, involved the right anterior cingulate cortex (ACC) and the anterior insula/frontal operculum (AIFO). Moreover, a between-group comparison revealed hypoactivation of brain regions within cingulo-opercular and cortico-striato-thalamo-cortical (CSTC) circuits in OCD. Among hypoactivated areas, the right ACC and the right dorsolateral prefrontal cortex (DLPFC) were associated with non-selective inhibition. Furthermore, regression analysis showed that OCD slowness was associated with decreased activation in cingulate regions and two brain areas related to non-selective inhibition: the right DLPFC and the right inferior parietal lobule (IPL). These results suggest that non-selective response inhibition is impaired in OCD, which could be a potential explanation for a relationship between inhibitory deficits and the other remarkable characteristic of OCD known as intolerance of uncertainty. Obsessive-compulsive disorder (OCD) is a widespread debilitating illness characterized by the occurrence of intrusive thoughts (obsessions) and repetitive behaviors or mental acts (compulsions). The lifetime prevalence of OCD is about 2.5%, which makes it the fourth most common mental illness ,2,3. TypIt can be assumed that the detector of errors acquires a sort of independent, self-sufficient significance in other, more “natural” conditions of pathology and transforms from a detector—an optimizer of activity—into the determinator of its impairments. Such a process can also underlie psychopathological syndromes, particularly those that are characterized by an obsessive repetition of actions, inappropriate behavior etc. A constant, undetermined by error, primary in relation to some action activity of the structure which usually plays the role of the detector of errors, is always signaling the inconsistency of the executed action  with the plan, regardless of whether the action is correct”“ (p. 101)To date, two neurobiological hypotheses of OCD development are most common: (1) pathological hyperactivity of the error detection or action monitoring mechanism, and (2) an impairment of inhibitory control caused by imbalance in excitatory-inhibitory cortico-striato-thalamo-cortical (CSTC) pathways. According to the first hypothesis, first suggested by N.P. Bechtereva in 1971 ,9, OCD mLater, a similar idea was proposed in the article “A cybernetic model of obsessive-compulsive psychopathology” by Pitman 1987), who considered the septo-hippocampal system as an “error detector” or a “comparator” . In more87, who cThe second hypothesis highlights the fact that patients with OCD are unable to suppress certain repetitive thoughts and actions, while they also generally demonstrate inhibition deficits ,32,33,34A recent meta-analysis of functional magnetic resonance imaging (fMRI) studies by Norman et al., 2019  confirmeAnother prominent feature of OCD is pathological doubt, or intolerance of uncertainty ,44,45,46Typically, researchers look for a selective increase in neuronal activity triggered by rare inhibitory stimuli compared to frequent Go stimuli (“NoGo > Go” effect) ,55. InfrMeanwhile, the issue of the relationship between impaired inhibitory control and intolerance of uncertainty in OCD still requires in-depth investigation . Particut = 1.8, p = 0.0776). The proportion of females to males was higher in the HC group . All participants were right-handed , with a probability of thrP = 0.95, then the hypothesis of the null “NoGo = Go” effect will be accepted. The hypothesis on the presence of the “Go > NoGo” effect will be accepted if the posterior probability of finding the effect that does not exceed the negative effect size threshold, θ < −γ, is greater than thrP = 95%. If none of the above criteria are satisfied, the data in particular voxel are insufficient to distinguish the null hypothesis from the alternative hypothesis (“low-confidence” voxels). The group-level effect size threshold γ was set at one standard deviation of the prior variance of the contrast, which is the default in SPM12 [It is important to note that the “50/50% Go/NoGo blocks > 100% Go-control blocks” contrast reveals areas not only related to response inhibition, but also non-inhibitory cognitive functions, such as preparatory attentional processes and uncertainty perception. We used the cue-target Go/NoGo paradigm, which was designed to separate preparatory attentional processes from the execution and suppression of prepared motor programs . The firry 2023) . For thery 2023) . It reprin SPM12 . A simulin SPM12 . The onein SPM12 . For vishttp://www.alivelearn.net/xjview, accessed on 7 January 2023).To evaluate non-selective response inhibition impairments in OCD group compared to HC group, we used “Go + NoGo” contrasts, as non-selective inhibition should appear in both Go and NoGo trials. The between-group comparison was performed using classical frequentist NHST with a primary significance threshold of 0.005 and cluster-level FWE-corrected threshold of 0.05. We used the more common frequentist inference for the between-group comparison because we did not aim to look for practically equivalent neuronal activity in both groups. Finally, to look for dependencies between response times (RTs) and brain activity in NoGo and Go trials (“Go + NoGo” contrasts), we used a linear regression model with one regressor corresponding to a linear contrast of interest (“Go + NoGo”) and another regressor corresponding to individual mean RTs centered to the overall mean. For this analysis, we also used a primary significance threshold of 0.005 and cluster-level FWE-corrected threshold of 0.05. Anatomical localization of clusters was identified using the xjView toolbox .The mean response omission in the Go trials was 2.97 ± 3.92% for the HC group and 3.21 ± 3.45% for the OCD group. The mean of false alarms in NoGo trials was 0.35 ± 0.64% for the HC group and 0.43 ± 0.94% for the OCD group. The between-group differences in response omission and false alarms were not statistically significant. The mean response time (RT) was 384 ± 60 ms for the HC group and 491.1 ± 117.5 ms for the OCD group. The between-group difference in RT was statistically significant  and patients’ mean RT in several brain regions: bilateral ACC, middle cingulate cortex, posterior cingulate cortex, precuneus, right IPL, and right DLPFC see . The oveThe results obtained in the present study speak in favor of the hypothesis about the impairment of non-selective response inhibition in conditions of uncertain context. It was shown that the mean RT was increased in the OCD patients compared to the HC subjects while performing a response inhibition task in the condition of equiprobable presentation of Go and NoGo stimuli. The response slowness in OCD patients may be referred to as the “pathological inertness” described by I.P. Pavlov in the “experimental neurosis” model caused by the “collision” of excitatory and inhibitory stimuli ,47. The The current research is the first work that discovers the brain areas related to non-selective response inhibition in OCD patients. This set of brain areas was almost identical to the previously revealed one for the HC control group and included: the right DLPFC, IPL, TPJ, PMC, FEF, and the left AIFO . HoweverAn important addition to the previous studies is that we found an overlap between hypoactivated structures and brain areas related to non-selective response inhibition in patients with OCD. These were the right ACC, which was not observed in the HC group , and theFinally, we revealed a significant dependency between OCD slowness and hypoactivation during inhibitory control. The higher mean RT was associated with lower BOLD signal in equiprobable Go and NoGo trials within bilateral ACC, middle cingulate cortex, posterior cingulate cortex, precuneus, right IPL, and right DLPFC. Moreover, an overlap was found between these areas and non-selective response inhibition areas in the right DLPFC and right IPL. Since the subjects were instructed to respond to Go stimuli as quickly as possible, the RT was taken as a measure of the inhibitory task performance effectiveness ,75. WhenCombined with previous neuroimaging studies, the results of our research allow us to link the impairment of inhibitory control with another prominent characteristic of OCD, intolerance of uncertainty, within the framework of the proposed “two-component” model of OCD development. It can be assumed that in OCD, hyperactivity of the error detection system, including ACC, may be referred to as undetermined or misspecified error signals. These constant error signals induce anxiety and a subjective sensation that “something is wrong” or NJREs ,11,76. NIn the current study, we have for the first time demonstrated a dysfunction of the non-selective response inhibition system in OCD patients that operates under conditions of context uncertainty in the cued Go/NoGo task. The uncertain context was created by the “collision” of two equiprobable excitatory (Go) and inhibitory (NoGo) stimuli. Under such conditions, the inhibitory process is non-selectively triggered by both types of stimuli. The altered non-selective inhibitory control in patients with OCD was associated with the extension of the non-selective response inhibition network. Compared to healthy controls, the non-selective response inhibition in OCD involved two additional regions in the right ACC and the right AIFO. At the same time, a direct comparison between the OCD and the HC groups revealed hypoactivity in OCD within two regions related to non-selective response inhibition: the right ACC and the right DLPFC. The decrease in the efficiency of the inhibitory task performance manifested as a reaction speed slowdown in the OCD patients compared to healthy controls. Regression analysis allowed us to connect pathological slowness to decreased brain activity in the cingulate regions, as well as the right DLPFC and the right IPL, related to non-selective response inhibition. The current findings extend previously observed hypoactivity of the cingulo-operular and CSTC regions in OCD during selective response inhibition and cognitive inhibition. Impairment of non-selective response inhibition sheds light on the link between inhibitory deficits and intolerance of uncertainty seen in OCD.The first limitation of the current study is a relatively small sample size for the OCD group. However, the obtained sample size was enough to reveal statistically significant differences between OCD and HC groups after the correction for multiple comparisons. The larger samples would potentially allow to reveal OCD subgroups, and their relevance to non-selective response inhibition impairment could be explored.The second limitation is the question of how precisely observed impairments are related to inhibitory processes per se. In the current study, we used cue-target design to separate preparatory attentional processes from the executive motor and inhibitory processes. However, the processes of non-selective inhibition, uncertainty perception and attentional orienting may be near impossible to disentangle, because orienting to salient task-related signals is inevitably accompanied by broad motor suppression . Both prFinally, future research should consider how the identified hypoactivation, which has been related to impaired non-selective response inhibition and uncertainty perception, is associated with aberrant functional and effective connectivity between cortical and subcortical structures that form inhibitory and excitatory CSTC circuits.


Multiple hypotheses have been proposed for possible functions of zebra stripes. The most thoroughly experimentally supported advantage of zebra stripes is their visual unattractiveness to horseflies (tabanids) and tsetse flies. We propose here a plausible hypothesis why biting horseflies avoid host animals with striped pelages: in sunshine the temperature gradients of the skin above the slightly warmer blood vessels are difficult to distinguish from the temperature gradients induced by the hairs at the borderlines of warmer black and cooler white stripes. To test this hypothesis, we performed a field experiment with tabanids walking on a host-imitating grey test target with vessel-mimicking thin black stripes which were slightly warmer than their grey surroundings in sunshine, while under shady conditions both areas had practically the same temperature as demonstrated by thermography. We found that horseflies spend more time walking on thin black stripes than surrounding grey areas as expected by chance, but only when the substrate is sunlit. This is because the black stripes are warmer than the surrounding grey areas in the sun, but not in the shade. This is consistent with the flies’ well-documented attraction to warmer temperatures and provides indirect support for the proposed hypothesis. The frequent false vessel locations at the numerous black–white borderlines, the subsequent painful bitings with unsuccessful blood-sucking attempts and the host’s fly-repellent reactions enhance considerably the chance that horseflies cannot evade host responses and are swatted by them. To eliminate this risk, a good evolutionary strategy was the avoidance of striped (and spotted) host animals. The main hypotheses are the following6: (1) Apparent size increase9: The stripes could induce a visual illusion increasing the apparent size of zebras and could ensure an advantage over their predators. (2) Decreased visibility in twilight12: At dusk and dawn or in moonshine, stripes might be difficult to discern. (3) Dazzling the eyes of predators by moving stripes16: The moving stripes of escaping zebras may dazzle and confuse their predators, which could difficultly select an individual zebra from the herd. (4) Optical camouflage from predators21: Stripes may dissolve the animal contour and thus could blend in zebras with their background. (5) Intraspecific communication by individual stripe patterns24: Zebras could recognize each other by means of their individual stripe patterns, which can be visual markers for group bonding and/or particular body parts for grooming. (6) Warning colouration, or aposematism17: The conspicuous black-white striped pattern of zebras may offer protection by advertising antipredator defenses, such as dangerous kicks and bites, and thereby warn predators to avoid them. (7) Visual defence against blood-sucking tsetse flies and tabanids carrying pathogens of serious diseases36: Since tsetse flies and tabanids avoid striped targets and prefer homogeneous objects, they do not bite zebras as frequently as solidly coloured host animals. (8) Visual signalling of fitness37: Irregularities in the stripe pattern because of injuries or other dysfunctions could visually signal the poor physical condition of a zebra for mate-seeking conspecifics. (9) Cooling by convective air eddies above sunlit stripes39: Sunlit black and white stripes may induce rotary convections, which air eddies could cool the striped animal.Since the debate among Galton40, Egri et al.32 and Horváth et al.41 have shown the following: (i) Female horseflies rarely attack host animals with a coat of inhomogeneous (striped or spotted) intensity and/or reflection-polarization patterns. The attractiveness to tabanids quickly decreases with increasing inhomogeneity (increasing number and decreasing width of stripes/spots) of the coat, and below a critical stripe/spot width (= threshold) the target becomes unattractive to horseflies relative to a homogeneous coat. The stripe widths of the three living zebra species  fall below this threshold. The exact reason for the tabanid-repellency of inhomogeneous coat patterns is unknown34. In this work, we propose a new explanation, which does not exclude the validity of the earlier ones. (ii) Zebra-striped patterns do not lose their tabanid-unattractiveness even if tabanid-attractive gases  are emitted to an extent that is characteristic to the host animals. Thus, the visual tabanid-repellency of zebra stripes overwhelms the attractiveness of zebra-specific odours. (iii) In the case of sunlit targets with inhomogeneous patterns, the few attracted female horseflies land mainly on dark stripes/spots instead of bright ones. The main reason for this behaviour is that in sunshine dark surfaces are warmer than bright ones, and tabanids prefer warmer host animals42, because they can escape from warmer targets more easily to avoid the parasite-repellent reactions of the attacked host43.In field experiments Blahó et al.44 found no evidence for cooling by stripes, and using schlieren imaging  in laboratory experiments, Pereszlényi et al.46 demonstrated that cooling air eddies do not form above sunlit zebra stripes. Both findings contradict theory 9 mentioned above. According to Caro et al.47, Caro and Stankowich48 and Caro6, the most important selection factor in the evolution of zebra stripes is the defence against tabanids, which results support theory 7. Kojima et al.49 showed that black cows painted with white stripes imitating zebra stripes minimally attract blood-sucking stable flies , horn flies (Haematobia irritans) and tabanids (Tabanus sapporoensis). This observation also supports theory 7. Currently, the most thoroughly proven evolutionary advantage of zebra stripes and other striped/spotted coat patterns is the visual unattractiveness to biting flies, especially horseflies and tsetse flies49. However, it is still unclear why striped/spotted host animals are unattractive to female blood-seeking tabanids.In a thermodynamic field experiment, Horváth et al.21 suggested that compared to the homogeneous pelage of other sympatric herbivores, it seems highly unlikely that zebra stripes serve as antipredator camouflage. Caro et al.34 showed that up close, striped surfaces prevent horseflies from making a controlled landing. As a consequence, very few tabanids land on zebras, stay a long time, or probe for blood. How et al.36 found that horseflies avoid landing on, fly faster near, and do not approach as close to striped and checked targets compared to homogeneous grey ones, as has already demonstrated Waage27 experimentally.Melin et al.50 on the legs51 or antennae52 or mouthpart53 which sense the slightly higher skin temperature above a vessel relative to its surroundings. Thus, vessel location could happen with thermoreception by means of vessel-induced temperature gradients. Tabanids evidently prefer sunlit, warm host animals and targets56. Horváth et al.43 showed that biting horseflies prefer a warmer host, because they can escape easier from a warmer target compared to a colder one. If the sunlit coat of a host is striped, on this inhomogeneous pattern all borderlines of adjacent dark and bright stripes have temperature gradients (differences), because in sunshine the dark stripes are warmer than the bright ones59. The temperature gradients at the borderlines deceptively may imitate vessel-induced temperature differences for a blood-seeking female horsefly, if she looks for vessels by thermoreception. Thus, the borderlines of sunlit black and white stripes may hamper successful thermal vessel detection. According to our hypothesis, tabanids do not prefer to land on striped (or spotted) hosts, because on such targets it is difficult to locate vessels by thermoreception due to the disruptive temperature gradients of the numerous borderlines of black and white stripes in sunshine. Therefore, female horseflies wanting to suck blood attack less frequently striped host animals, because on homogeneous body surfaces, the temperature gradients above blood vessels can be detected more easily than on sunlit striped patterns.In this work, we propose the following new explanation for this phenomenon: A blood-seeking female horsefly landed on a host animal should find a blood vessel directly below the host's body surface. This may be performed by her thermoreceptorsTo test this hypothesis, we performed a field experiment with tabanids: We attracted female horseflies by a medium grey barrel with thin black stripes mimicking a visually attractive host. The thin horizontal and vertical black stripes on the barrel’s cylinder imitated thermally the slightly warmer subsurface blood vessels when the studied half of the cylinder was sunlit. When the striped cylinder’s half was shady, the temperature of the thin black stripes was practically the same as that of their grey surroundings. We measured the total time periods of alighted and walking horseflies spent on the grey and the black areas of the sunlit or shady cylinder. Our aim was to elicit whether tabanids prefer the black stripes only if they are sunlit and thus warmer than their grey surroundings. If the stripes were preferred to the grey areas only under sunlit conditions, this preference could be explained exclusively with their higher temperature, rather than with their darkness and/or higher polarization. This would corroborate the supposed thermal vessel recognition in horseflies, and could support that sunlit stripes hamper the thermodetection of host’s vessels by tabanids.Our field experiment was performed with horseflies (Tabanidae), which blood-sucking parasites are non-protected insects in Hungary. Furthermore, as a demonstration, we recorded the thermograms of a horse with the permission of the horse owner (Mr. István Simon). For our purpose (i.e. to show the fine temperature gradients of the hairy skin above blood vessels), one such demonstration on a single horse was enough. This thermography was passive, that is the infrared radiation originating from the hairy horse skin was recorded by a thermal camera from a distance without any contact of the horse. From the viewpoint of the horse, this undangerous thermal recording was equivalent with a common photography, for which no approval from an ethical committee is necessary. We confirm the followings: (i) For both studies no institutional permission, licence or approval were necessary. (ii) No animals  were killed specifically for the purpose of these studies.62. On the experimental days (Supplementary Table g = 50%) cylindrical plastic  barrel , and the vessels were imitated with 10 vertical and 5 horizontal black, thin (width w = 0.5 cm) stripes 13 cm apart  with an abundant horsefly population, where several other experiments with tabanids have been conducted in the last decade44). Such a comparison was unnecessary, because in our present field experiment, the most important aspect was to imitate the fine temperature gradients of the host’s skin above blood vessels and at the borderlines of sunlit adjacent black and white zebra stripes, by adding sunlit thin black stripes to the grey barrel. According to the thermograms presented in this work  is very artificial relative to the real host animals of horseflies, we did not measure the spectral reflectance of the black stripes of our grey barrel, and did not compare it with that of black stripes of zebras measured earlier in another field experiment /Am = 5w(h + rπ)/(rπh) ≈ 0.07, where w = 0.5 cm, h = 80 cm and r = 22 cm. The portion of the total area of the grey rectangles between the black stripes relative to the half cylinder is Qgr = (Am − Av − Ah)/Am = (rπh − 5hw − 5rπw)/(rπh) ≈ 0.93. Hence, 93% of the half cylinder was grey, and only 7% was covered with the vessel-imitating thin black stripes.The surface area of the half barrel’s cylinder  is pes Fig.  each witpes Fig.  each witThe black-striped grey barrel was placed on a white tetrapodal plastic stool (height = 46 cm), and the tabanids landing and walking on the barrel were observed by one  person from a distance of 2 m Fig. . A landeThe observer was sitting on a chair and wore white clothes and a bright hat against sunshine and to reduce his visual attractiveness to tabanids. He observed either the sunlit (solar: facing toward the sun) or the shady (antisolar: facing toward the antisolar point) half of the barrel’s cylinder. His direction of observation followed (changing in every 30 min) the solar-antisolar meridian. He was interested only in the walking tabanids which obviously monitored the substrate and looked for something. Using two stop-watches, the observer measured the time periods spent by the walking tabanids on the grey rectangles (with a stop-watch held in the right hand) and the black stripes (with a stop-watch held in the left hand) of the barrel’s surface. When a female tabanid landed on the grey/black area and started to walk, the right/left stop-watch was started and was stopped when she flew away, or walked onto a black/grey area. This procedure was repeated throughout the whole session of observation on a given day from morning to afternoon. If after alighting a tabanid stayed in one place before flying away, her staying period was not registered with the stop-watch, because this reaction was irrelevant with respect to vessel seeking.Since we aimed to measure the time periods spent by tabanids walking on the grey and black regions of the barrel’s surface, a non-sticky (dry) barrel was used. Thus, the same tabanid individual might have landed more than once on the barrel. Such pseudoreplication could have been avoided only with a sticky barrel, on which an alighted tabanid could not have walked and flew away, consequently the time period spent by the fly on the barrel could not have been measured.64 and female tabanids are polarotactic insects (i.e. they are attracted to linearly polarized light during host seeking65) we measured the reflection-polarization characteristics of the test black-striped grey barrel used in our field experiment with imaging polarimetry in the red (650 ± 40 nm), green (550 ± 40 nm) and blue (450 ± 40 nm) parts of the spectrum67. These polarization patterns helped to interpret the tabanid attractiveness of this target.Since horseflies are polarization sensitive42).The temperature distributions (thermograms) of the half cylinder of the black-striped grey barrel used in the experiment were measured with a thermal camera  having a nominal precision of ± 1.5 °C, which was validated by calibration with a contact thermometer  possessing a nominal precision of ± 1 °C  or shady (antisolar) half of the barrel’s cylinder were registered under a clear sky in sunshine. These thermograms served as a demonstration of the typical temperature patterns of our test targets under sunny conditions when host-seeking female horseflies are very active. There was no need to capture the thermograms continuously throughout each experimental day, because this experiment was performed always under a cloudless sky in full sunshine, when the thermograms were qualitatively very similar: on the sunlit half of the barrel’s cylinder the black stripes were always slightly warmer than the surrounding grey areas, while on the shady half the black stripes had practically the same temperature as their grey surroundings (see later).44 and zebra hides in a laboratory46. Furthermore, for the thermographic demonstration of the temperature gradients of the shady horse skin above the slightly warmer blood vessels, a horse of any colour is appropriate.We also recorded thermograms of the hairy body surface of a brown horse in a horse farm  with the permission of the farm owner (Mr. István Simon). Since this thermographic recording was quite undangerous for the horse, we did not need permission or approval from an institutional or licensing committee. We chose a brown horse, rather than a white or a black one, because among the natural hosts (mainly ungulates) of horseflies in Africa, where zebras live, there are neither white, nor black animals. White  animals are quickly selected out due to their striking bright appearance under any illumination conditions, while black animals could not tolerate the heat in sunshine. Natural hosts typically have medium bright/dark grey or brown fur. Earlier, we have measured the thermograms of sunlit and shady living zebras in a zooT along straight lines on the target surfaces was determined with our custom-developed software. On the thermograms horizontal, or vertical, or tilted lines were selected, along which the average and standard deviation of T were calculated.The surface temperature I of sunlight on the experimental days, because this was unnecessary. For our experiment the following criteria were the only important: (i) Flying blood-seeking female tabanids were abundant in the field. (ii) The test surface  was either sunlit or shady. Thus, it offered similar gradients of the surface temperature T for landing female tabanids as T-gradients forming on the coat of sunlit or shady zebras. This was demonstrated by comparison of the thermograms of our test surface and those measured earlier on living zebras and zebra hides . (iii) These T-gradients were measured and visualized by our thermocamera. All these made superfluous the measurement of I of sun- and skylight illuminating our test target. The result, that is the T-gradient induced by the ambient radiation (sun- and skylight) was the relevant point, rather than the I of illumination causing it.Our experiment was conducted in Hungary (at a latitude of 47°52'N), while zebras live in Africa, around the Equator. The intensity of celestial radiation (sun- and skylight) is different between these two regions. In spite of this fact, we did not measure the intensity In the experiment, we compared the ratios of total time tabanids spent on the black stripes to the surface ratio of black stripes on the barrel. We calculated the mean and standard deviation of the measured time ratios. Then, we divided these time ratios with the corresponding (grey or black) area of the barrel to get relative time values, independent of the grey or black parts of the barrel. Using Student’s t-test for the sunlit and shady half of the barrel, we compared the relative times for the black and grey areas. We chose a significance level of p = 0.01. For statistical computations, we used the statistical function package of Microsoft Excel 2021.black = 6.4% and τgrey = 93.6% of their total time on the black stripes and the grey areas, respectively. The average time ratio that female tabanids spent on the black stripes of the shady half of the barrel’s surface was 6.4 ± 0.7% (mean ± standard deviation). This ratio is approximately the same as the surface ratio Ablack = 7% of the black stripes, since 7% is within the scattering range of the average time ratio. Since the surface percentage of the black and grey areas is Ablack = 7% and Agrey = 93%, respectively, we conclude that the walking tabanids preferred neither the shady black stripes, nor the shady grey rectangles.Female horseflies have an ommatidia-free thin dorsal zone between their two compound eyes, while in males both eyes connect dorsally. These characteristics of the tabanid eyes could have been observed in our experiment, in which only female tabanids alighted on the black-striped grey barrel. According to Fig. black = 19.6% ± 3.9%  than that reflected from the grey rectangles (d ≤ 24%) between the stripes under both sunlit and shady conditions. The reason for this is the law of Umow68: the darker a surface in a given spectral range, the higher is the degree of polarization of reflected light. Apart from two highly polarized patches (43% ≤ d ≤ 51%) at the uppermost and lowermost regions of the barrel’s cylinder , the sunlit grey rectangles reflected light with lower d (d ≤ 15%) than the shady ones (d ≤ 24%). The angle (direction) of polarization α of light reflected from a surface is always perpendicular to the plane of reflection determined by the dominating incident ray of sun- and/or skylight, the point observed and the observer’s viewing direction . Therefore, both the left and the right vertical sides of the barrel reflected nearly vertically polarized light, because the plane of reflection was nearly horizontal there. On the other hand, the central vertical zone of the barrel’s cylinder reflected mainly horizontally polarized light, since the plane of reflection was approximately vertical in that case. These reflection-polarization patterns of the black-striped grey barrel imitated well those of sunlit and shady zebra hides and zebra-striped test surfaces used in our earlier field experiments41.According to Fig. d-values, independently of α60, one could have expected that in our experiment the landing and walking tabanids always preferred visually the black stripes of the grey barrel. In the subsection “Since host-seeking female tabanids are strongly attracted by high Under sunlit conditions, the black stripes of the grey barrel used in the field experiment were about 1.5–2 °C warmer than the grey areas because of the lower albedo of black areas relative to the higher albedo of the grey ones Fig. . Under sFigures T  imitating thermally either blood vessels or borderlines of sunlit neighbouring dark and bright stripes. This finding proves that in sunshine the T-gradients induced by the hairs at the borderlines of warmer black and cooler white zebra stripes can be confused with the T-gradients of the skin above the slightly warmer blood vessels. All these experimentally corroborate our new hypothesis that biting horseflies avoid host animals with striped pelages (e.g. zebras) because of the risk of such thermal confusions, which reduce the chance of a successful vessel finding.With our experimental setup, we showed that blood-seeking female horseflies landing on an artificial test surface mimicking a visually attracting host animal prefer fine gradients of surface temperature 76. After landing on a host, their behaviour is also influenced by the temperature distribution of the host’s body surface78. In this work, we proposed a new explanation of the unattractiveness of zebra stripes to blood-seeking horseflies and presented the results of a field experiment which support our proposal.From a remote distance, horseflies are attracted to their host animals by visual and olfactorial cues79. Both internal and external factors have a significant influence on the body surface temperature T of horses. The T-pattern depends on the movement of the animal, as the skin overlying muscle has an increased T due to muscular activity. Because of evaporative cooling, sweating also affects T. At low ambient temperatures the subcutaneous vasculature constricts resulting in the cooling of the skin surface, while at high ambient temperatures it dilates resulting in the warming of the skin surface79.Currently, thermography is routinely used in equine sport and veterinary medicine as an efficient diagnostic tool for detection and monitoring of injuries, diseases, inflammations and other pathological areas of the musculoskeletal system and the hairy skin of horses57. In direct sunlight, black-hair-covered skin areas are much warmer than white(unpigmented)-hair-covered ones, while in shade there is no significant T-difference between black and white hairy areas46. The T of the hairy body surface exposed to sunlight depends on the shape, posture, curvature of the surface as well as on the variations in hair thickness and colour (pigmentation)57.The hair layer is a good thermal insulator blocking heat emission from the underlying skin. The equilibrium temperature of the hairy skin surface is determined by the balance between the heat loss by radiation and convection to the air, and the heat conduction through hairs80: The thermograms of horses are related to the vasculature and tissue metabolism and is influenced by the ambient air temperature. The skin overlying major blood vessels is always warmer than skin areas farther from these vessels. Normally, the skin surface above veins is warmer than above arteries, because the former are in metabolically active areas and closer to the skin surface. Venous drainage from tissue with a high metabolic rate is warmer than venous drainage from normal tissue. Areas of increased skin temperature follow the vascular pattern, while areas distant from vessels are cooler.For the interpretation of the results presented in this work, the most important thermal characteristics of the hairy skin surface of horses are the following43 recorded several thermograms of black, brown, beige and white horses with a thermal camera under sunny and shady conditions. These thermograms and Fig. In earlier field studies Horváth et al.The temperature distribution of the skin surface covered by hairs can be sensed by vessel-seeking female horseflies landed and walking on horses only if their thermoreceptors contact directly the skin. Therefore, the flies have to penetrate between the hairs to reach the skin surface.T of black hairs is always larger than T of white hairs. Due to the heat conduction of the hair layer, the hair-specific T-characteristics are transferred down to the skin surface, which is heated better by the warmer black hairs than by the cooler white hairs. On the other hand, independently of hairs, the skin surface above a blood vessel is slightly warmer than the surrounding areas, because the blood is slightly warmer than the surrounding tissues. The interaction of heat conduction by hairs and tissues results in the following four typical T-levels of the skin surface  of the sunlit body surface changes spatially suddenly perpendicular to these borderlines. Due to such a T-gradient at a sunlit borderline, a female horsefly searching for a blood vessel by means of thermal gradients can erroneously assume a vessel, because the dark area is warmer than the bright one, but there is actually no vessel there. On the skin surface above a vessel positioned next to the surface, a positive/negative T-gradient (i.e. increasing T) points toward/away from the vessel being slightly warmer than the surrounding tissues. Therefore, if a sunlit coat has a more inhomogeneous pattern, it is more difficult to find vessels with thermoreception, because the numerous T-gradients at the borderlines of stripes/spots have nothing to do with vessel-induced ones. Especially on the head and legs of zebras, the stripes are thin (< 1 cm), and these body regions should be defended the most against biting flies.The attractiveness of host animals to tabanids decreases with increasing inhomogeneity of the host’s coat pattern59, and therefore there are no temperature gradients at their borderlines. In this case, tabanids could find blood vessels by thermoreception on inhomogeneously coloured host animals as easily as on homogeneously coloured ones. However, blood-seeking female tabanids attack their hosts mainly in sunshine, because they prefer warmer hosts42, since they can escape easier from warmer targets43.Under shady conditions, there are practically no temperature differences between shady dark and shady bright stripes/spots54. Tabanids avoid striped/spotted hosts, and usually do not land to seek for blood vessels on dark stripes/spots despite of their higher temperature in sunshine. This avoidance is an enigmatic behaviour, since on thicker dark/bright stripes or larger dark/bright spots—where only vessel-induced T-gradients occur on the skin surface below the hair layer—horseflies could find vessels by thermoreception as easily as on the skin of homogeneous dark/bright hosts. It is also an observational fact that the very few horseflies that land on sunlit striped/spotted targets alight mainly on the thick/large dark stripes/spots and avoid thin/small ones40. The reason for this may be twofold: they prefer thick/large dark sunlit stripes/spots because (i) due to the local higher temperature they can easier evade dangerous host responses, and (ii) they can easier find vessels by thermoreception due to the lack of T-gradients induced by the borderlines of stripes/spots. Thus, our results are consistent with the results of the mentioned previous investigations.How do our results relate to previous studies showing that horseflies prefer warmer temperatures, because it helps them evade host responses? It is an observational and experimental fact that horseflies prefer sunlit homogeneous dark hosts against sunlit hosts with bright or heterogeneous (striped/spotted) coat patternsI of reflected light is proportional to the intensity I0 of incident light, and the proportionality factor is the reflectivity R  of the surface at a given angle of incidence and wavelength λ: I = R·I0. Therefore, the intensity contrast Cint,grey = (Igrey – Iblack)/Igrey = (Rgrey – Rblack)/Rgrey or Cint,black = (Igrey – Iblack)/Iblack = (Rgrey – Rblack)/Rblack between the black stripes and their grey surroundings is independent of the incident intensity I0 and depends only on the reflectivities/albedos Rgrey and Rblack of reflecting surfaces. Thus, if the incident intensity changes by a factor, because the sunlit black-striped grey barrel becomes shady or vice versa, then both intensities of light reflected from the black and grey surfaces also change by the same factor, and therefore the intensity contrast between the black stripes and the grey surroundings does not change. Consequently, the intensity contrast between the black stripes and their grey surroundings of our test barrel was practically the same for both the sunlit and the shady situations.The intensity Ipol of reflected light (i.e. intensity of the linearly polarized component of light) is proportional to the incident intensity I0. If I0 changes by a factor, because the shady black-striped grey barrel surface becomes sunlit, or vice versa, then the polarized intensities of light reflected from the black and grey surfaces also change by the same factor. Therefore, the degree of polarization d = Ipol/I0 is independent of the incident intensity I0 and depends only on the polarizing capability of the reflecting surface at a given angle of incidence and wavelength λ. Since the degrees of polarization dblack and dgrey practically do not change when the sunny/shady barrel becomes shady/sunny, the polarization contrast Cpol,grey = (dblack – dgrey)/dgrey or Cpol,black = (dblack – dgrey)/dblack between the black stripes and their grey surroundings does not change. This is seen in the polarization patterns of our sunlit and shady barrel . At both β = 0° and 90°, d = 0. If β increases from 0° to 90°, then d first increases, at the Brewster’s angle βBrewster = arc tan (n) reaches its maximum dmax, then decreases to 0, where n is the refractive index of the reflecting surface. Thus, for both the shady and the sunlit barrel, the maximum of the polarization constrast Cpol,grey = (dblack – dgrey)/dgrey or Cpol,black = (dblack – dgrey)/dblack between the black stripes and their grey surroundings can occur at βBrewster, while if β nears 0° or 90°, both dblack and dgrey nears zero, and therefore both polarization contrasts Cpol,grey and Cpol,black approximate zero, too. These polarization characteristics are also visible in the polarization patterns of our shady and sunlit barrels  surface parts—the reflection spectra of which are approximately constant as the wavelength λ of light changes—, its reflection-polarization characteristics are practically independent of λ , rather than also in the intensity and polarization. Consequently, our test target demonstrated only thermal effects, and the results can be explained unambiguously by this thermal difference.Supplementary Information.


While both icefields have been losing mass rapidly, their responsiveness to various climate drivers, such as the El Niño-Southern Oscillation, is not well understood. Using the elastic response of the earth to loading changes and continuous GPS data we separated and estimated ice mass changes observed during the strong El Niño that started in 2015 from the complex hydrological interactions occurring around the PIF. During this single event, our mass balance estimates show that the northern icefield lost ~ 28 Gt of mass while the southern icefield lost ~ 12 Gt. This is the largest ice loss event in the PIF observed to date using geodetic data.The Patagonia Icefields (PIF) are the largest non-polar ice mass in the southern hemisphere. The icefields cover an area of approximately 16,500 km The ENSO quasi-cycle has positive, neutral, and negative phases, with one whole ‘cycle’ typically lasting about 4 years. The positive and negative phases correlate with rainfall and temperature anomalies, and changes in windfields, cloud cover and solar insolation that generate large deviations from average seasonal weather patterns. The negative phase of ENSO, EN, for instance, correlates with negative precipitation and positive temperature anomalies in Southern Patagonia5. Inter-annual changes in weather patterns can have a significant impact on seasonally-adjusted ice loss, driving both positive and negative accelerations in the rate of change of ice mass6. Long-term climate change has slowly but relentlessly increased global oceanic and atmospheric temperatures, making it easier for atmospheric and oceanic quasi-cycles, such as the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), or the ENSO to push local conditions beyond thresholds for major increases in ice loss. Approaching or crossing such thresholds was not possible even as little as 3 decades ago7.The climate in South America is strongly affected by the El Niño-Southern Oscillation (ENSO), with El Niño (EN) being ENSO’s negative phase. ENSO events are produced by an unstable interaction between the Pacific Ocean and the atmosphere in the equatorial zone8. ENSO events are tending to strengthen with each new cycle, as manifested by the Oceanic Niño Index  as well as ice loss for specific outlet glaciers that may be influenced by the intensification of EN events. To date, no studies have reported the PIF ice loss during a specific EN event, mostly due to a lack of observations and/or the low temporal resolution of the available datasets. Due to the paucity of on-site observations, most previous studies are based on remote sensing data from satellite images12, and time-variable gravity data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO).There is strong evidence that the ENSO has responded to human activity, specifically greenhouse gas emissions, since the early 1990’s13). Both SAR and visible images, however, need to be paired with other observation techniques, such as GRACE and GPS, to correctly constrain mass changes. For time-variable gravity data from GRACE and GRACE-FO, the inherent spatial resolution of ~ 330 km is too coarse to independently estimate the ice loss at the scale of the internal drainage basins in the PIF. Also, due to the discontinuity between GRACE and GRACE-FO missions, there is a data gap of ~ 12 months, during part of the ENSO cycle between 2017 and 2018. Although this gap has been successfully filled with other models14, it should be noted that the later GRACE time series are noisier due to strategies to extend the mission.Satellite images and GRACE present some challenges when attempting to develop models with dense temporal and spatial resolution. For the case of visible satellite images, cloud coverage in Patagonia is frequent, limiting the number of usable images available for analysis. Other observational methods, such as synthetic aperture radar (SAR), penetrate cloud cover  data are therefore required to fully capture the crustal displacements due to short-term oscillations and trends in ice mass.Other studies analyzed long-term trends using campaign GPS data, measured every 1–2 years, to understand glacial isostatic adjustment (GIA) and short-term elastic response signals17, stage data from all major lakes in southern Patagonia, SRTM data, ice velocity from satellite images18, glacier surface height change grids19, and the Global Land Data Assimilation System20 (GLDAS) terrestrial water storage (TWS) model, we produced a least-squares ice loss estimate for the North and South PIF. Our results show a significant acceleration in ice loss during the first months of 2016 in agreement with previous studies2. Our study, however, provides better spatial resolution that allows separating the northern and southern PIF ice loss signals. Thus, our results show an increased ice loss in the northern PIF compared to previous estimates of ice loss rates. Our analysis supports the hypothesis that this increased ice loss rate may have been driven by the 2015–2016 EN event, and the mass loss is likely permanent as the ice mass had not recovered by late 2018, the end of our study period.CGPS data provide higher spatiotemporal resolution (relative to campaign data) to constrain the PIF ice loss during the 2015–2016 EN event. Although the density of the CGPS data in the region is low, the addition of the CGPS ground-based data with satellite data facilitates estimating the episodic or transient ice loss during the 2015–2016 EN. Using an 8-year time series of daily positions from seven CGPS stations, an elastic response model21). A major driver of crustal loading are changes in TWS. Galván et al.9 has shown the effects of the 2015–2016 EN in the Amazon basin and Li et al.2 used GRACE to constrain the fluctuations in PIF inter-annual ice loss during the 2015–2016 EN event. GRACE studies of the PIF2, however, reported accelerating ice loss at the beginning of 2016 without isolating the ice loss during the 2015–2016 EN. Additionally, the coarse resolution of GRACE does not permit direct observation of the separate Northern (NPIF) and Southern PIF (SPIF) ice mass losses.The elastic response of earth’s crust due to loading by large hydrological cycles, which are rarely steady or strictly periodic, is well established , a quadratic termFigure 31, and the elastic response to a disk load (using 5 km diameter disks), we computed the vertical displacement of the crust produced by the regional changes in TWS at the seven CGPS stations between 2010 and 2019. Because GLDAS does not correctly account for ice fields and water bodies, we removed the model cells within these areas. Using the same elastic disk load and elastic models, we computed the water storage change signal for the lakes in Patagonia using lake stage observations . Thus, the ice load signal can be found from Eq.  at each CGPS site asThe entclass1pt{minimaentclass1pt{minimaentclass1pt{minima19. Although the spatial distribution of dh/dt rates in the PIF during the 2015–2016 EN are likely to show differences relative to the average dh/dt rates reported by Braun et al., we will use this pattern as a more reasonable estimate of the spatial variation for ice loss during the 2015–2016 El Niño than a uniform one, and scale it to match the CGPS data. In either case, the total ice mass loss is controlled by the CGPS data, not the ice mass loss distribution. The elastic response of each disk-station pair can be converted from rate, dh/dt, to a height change by multiplying the rate by a time interval, Δt. It should be noted that if one knows the ice height change, the disk loading least-squares problem can be interpreted as a solution for the density of the disks, rather than the equivalent water height, that generates the observed CGPS displacements. The dh/dt grids, however, represent the average ice height change in the data used by Braun et al., and these values are not representative of the ice loss during the 2015–2016 EN. We can assume, however, that scaling the dh/dt rates by a factor (one for NPIF and another for SPIF) will bring them into agreement with the ice loss during this EN event. In other words, we wish to transform the average dh/dt values into an ‘instantaneous’ ice loss that fits the CGPS data during the 2015–2016 EN. Therefore, the loading problem presented here consists of estimating the appropriate Δt scaling factor for each field such that the densities estimated from the elastic response (at the CGPS stations) are within the known range of ice density. This Δt scaling factor gives the ratio of ice loss rate during 2015–2016 EN with respect to that obtained by Braun et al.As previously discussed, the GLDAS + L time series do not include any loading effects due to changes in ice mass. Thus, the ice loss can be estimated from Eq. , hereaft3 at the lowest and highest elevations of the PIF, respectively. The second and third approach, models B and C, categorized the disks into two and three groups, respectively, based on elevation, ice velocity, and dh/dt values. The elevation of each disk was obtained using 30 m SRTM data, and the ice velocities were obtained from the NASA Regional Glacier and Ice Sheet Surface Velocities retrieved from the ITS_LIVE project18.The mass balance problem was solved using three distinct approaches to characterizing ice density variation in the PIF. This diversity of approach helps us to assess the robustness of our conclusions. In the first approach, hereafter model A, we assume that density is a linear function of height, assuming an ice density of 918 and 500 kg/m32 and Schaefer et al.33, a velocity threshold , and the dh/dt value for each disk (negative/positive for height loss/gain). Disks were classified as group 1  when dh/dt > 0 , as group 2  when dh/dt < 0, and velocity < VT or height > HT, and group 3  when height < HT and dh/dt < 0. We combined G1 and G2 to simplify the problem (model B) and we used the three groups for model C. For all three models, we solved for disk densities using the average CGPS-GLDAS-L difference after 2016.5. Observations were weighed in the least-squares adjustment using the squared inverse of each CGPS-GLDAS-L standard deviation calculated using data before 2016.The classification into groups was determined using a height threshold  following Bravo et al.To obtain the three pairs of Δt for the NPIF and SPIF dh/dt grids (one for each model), we performed a grid search over Δt and selected those that minimized the root mean square (rms) misfit between estimated and a priori density parameters  and is likely the result of specific climate dynamics related to the strong EN event. Other sources that can explain part of the deviation from the mean dh/dt also include contributions below lake or sea level which are not captured by the dh/dt models19. Figure The range of Δt for the three models does not show significant changes, and it is readily visible that the NPIF Δt is between 7 and 8 times the SPIF Δt value. This shows that the NPIF and SPIF dh/dt relationship for the 2015–2016 EN event is different from that obtained by Braun et al., which used SAR interferometry data over the years 2000 to 2011/2015. This deviation from the mean dh/dt behavior has been identified in previous studies  and we found that the overall effect is negligible. We also perturbed the a priori ice densities by 7% to test the uncertainty in the mass balance solution and found that the changes in ice loss were always below 1% (0.1–0.2 Gt). This is in part because any misfit in the a priori ice densities will be corrected by the scale factor applied to the dh/dt grids. Thus, the most significant effect in the mass balance estimates is from the relative uncertainty between the GLDAS + L model and the CGPS data. We therefore perturbed the CGPS-GLDAS-L values using their standard deviations and found that for model C, the NPIF ice loss uncertainty is ± 8 Gt while the SPIF uncertainty is ± 2 Gt.4 showed that strong modes of climate variability produce large temperature, wind, and precipitation anomalies in Southern Patagonia. It should also be noted that the 2015–2016 EN correlated with the largest temperature and precipitation anomaly in the region since 20032, which may have pushed local conditions beyond thresholds for major increases in ice loss.A strong link between ENSO and surface mass balance has not been demonstrated in Patagonia, although Weidemann et al.2 during the same period (after correcting for the linear trend between 2013 and 2016 reported by the authors), which used GRACE data and the GLDAS model. We speculate that GRACE data noise may have introduced a bias in the results from Li et al. Results from Ciraci et al.14  also show a clear offset in the PIF mass loss trends, although the authors did not discuss it.After removing modeled reference frame, GIA, and periodic effects from the time series, we found the total mass loss for the PIF to be ~ 40 Gt ± 8.2 Gt. Our results are in agreement with previous studies, showing a dramatic ice loss in the first few months of 2016. Yet, our total ice loss estimate is approximately half of the mass loss reported by Li et al.19), our mass balance results attribute 75% of the ice loss to NPIF, which lost an equivalent of ~ 7 years of ice, while SPIF only lost ~ 1 year, at the average rate in the previous decade reported by Braun et al. We hypothesize that the location of NPIF relative to SPIF (further north) may have favored the NPIF ice loss compared to SPIF, which is supported by previous studies showing that accumulation in NPIF has decreased more significantly than at SPIF7.While previous studies attribute the largest ice loss signal to SPIF (e.g.34, RACMO235, and MERRA-236 could provide means to extrapolate the ice loss time series to before the deployment of the CGPS network to investigate the sensitivity of the PIF to prior EN events.This work shows, for the first time, the PIF ice loss during the 2015–2016 El Niño event using ground-based CGPS data. Our method combined direct observations of crustal deformation and remote sensing datasets to produce a robust mass balance estimate that has low sensitivity to variations in the a priori ice densities. Although the continuous geodetic data in Patagonia is sparse, the available dataset of seven stations allowed us to obtain ice loss estimates for both ice fields for this specific EN event. Our results suggest that the 2015–2016 EN had a large impact on the PIF, and highlights the necessity to further study the effects from strong modes of climate variability using ground-based GNSS/GPS data. Additionally, surface mass balance estimates from models such as MARSupplementary Information.


Inhibition is considered a process essential to goal pursuit and as a result has become a central construct in many disciplines in psychology and adjacent fields. Despite a century’s worth of debate, however, there is little consensus about what inhibition actually is. We suggest that it is time to abandon the concept of inhibition as it currently stands, given that its definition has been problematic. Instead, we propose an alternative framework in which inhibition is the target outcome, rather than a process to obtain a goal. We leverage existing process models to elucidate how people can achieve an inhibition goal by actively regulating impulses and desires. Although the field has been led astray by classifying inhibition as a process, our framework is intended to provide greater practical utility to the study of goal pursuit moving forward. Inhibiting mental processes is considered essential for successful goal pursuit, whether the goals relate to basic motor movements, thoughts and emotions, or higher-order objectives . The concept of inhibition has deep roots, tracing back to ancient philosophers as well as to the father of modern psychology, William James, who stated that inhibition is “an essential and unremitting element of our cerebral life” , p. 583.In this article, we suggest that it is time to abandon the concept of inhibition as it currently stands. To advance research on inhibition, we first provide an overview of what is problematic about the concept of inhibition. To address these concerns, we then present an alternative framework that reclassifies inhibition as an outcome rather than a process, thereby providing greater conceptual and practical utility to inhibition as a construct. Finally, we close by outlining promising directions for future research.Over the years, scholars have raised numerous concerns about inhibition. Here, we consider three interrelated issues: (a) Different subfields disagree on how to define inhibition, which has led to a proliferation of different operationalizations; (b) many areas have seemingly lowered the threshold for what counts as inhibition, diluting its meaning as a construct; and (c) the term “inhibition” has been overextended to concepts that can be more parsimoniously explained by other constructs. It is important to note that we make few exceptions to these concerns, the most notable being in regard to neural inhibition: It is widely accepted that, from a biological standpoint, neurons can inhibit each other . Here, wOne pressing concern is that researchers rarely explicitly define inhibition , and wheAs is the case with many concepts in psychology, inhibition has undergone many semantic and conceptual shifts over time. As a result of this concept creep .We propose that conceptual clarity has been elusive because inhibition has been inaccurately operationalized as a process to obtain a goal , when in fact inhibition is the goal itself. People do not Consider an illustrative example. The stop-signal task is a cognitive reaction time task in which a person is asked to respond quickly to a target stimulus  but inhibit the response when presented with a designated stop signal  that appears after a short delay period. Thus, the goal is to stop one’s response on trials that randomly include the delayed stop signal. In this context, inhibition is not a process to be “used” to downregulate a motor response; rather, inhibition is the goal of stopping a motor response, and this goal can be achieved in a number of ways  is chosen, it is then implemented using specific tactics that are most effective in the moment, such as leaving the room when angry in order to be alone and cool down for a bit , thinking about the negative consequences of eating a tempting food , or intensely focusing attention on the computer screen while waiting for the auditory signal in the stop-signal task . If the tactic is successful , the inhibition goal is successfully attained. Finally, the progress at each stage is actively monitored so that one can maintain, stop, or switch from a particular process when necessary.Conceptualizing inhibition as an outcome rather than a process has many important implications for the study of goal pursuit. Rightfully operationalizing inhibition as a goal allows the field to better focus on the actual processes people use to pursue their goals. As a result, researchers not only are able to determine which strategies are actually most effective, but also can focus on important questions, including how personal factors can influence the processes people use (who uses what processes and when?), how contextual factors influence the effectiveness of different processes (when are particular processes most effective in the short run?), and whether certain processes are more adaptive than others . However, comparing processes with inhibition is methodologically problematic: Because inhibition is not a process, telling people to “use inhibition” actually assigns them the goal to inhibit the target response. They then rely on whatever processes they have at their disposal, which creates noisy variability , which refers to the ability to pursue goal-directed behavior, even in the face of more habitual or immediately compelling behaviors . AlthougWithin clinical psychology, various forms of psychopathology are characterized by the inability to inhibit responses appropriately. Indeed, not being able to inhibit responses is associated with a range of negative mental-health outcomes, including depression, attention-deficit/hyperactivity disorder, bipolar disorder, and substance abuse . One attFor centuries, the concept of inhibition has played a prevalent role in understanding human functioning. Given the many conceptual issues surrounding inhibition, we propose that it is time to abandon this concept as it currently stands. We suggest treating inhibition as an outcome and turning researchers’ attention to studying the actual processes through which people achieve their goals. Our hope is that reframing inhibition this way will push the field toward greater conceptual and empirical clarity. Finally, inhibition is merely one of many constructs in psychology (and adjacent fields) that have fallen victim to overexpansion and are now conceptually ambiguous. Although our aim here has been to help clarify the concept of inhibition specifically, we hope that this work inspires other researchers to tackle these issues within their respective disciplines.Gross, J. J. (2015). (See References). A detailed overview of the process model of emotion regulation , including how people identify which regulatory goals to pursue, select strategies for pursuing them, and subsequently implement those strategies.Annual Review of Psychology, 72, 319–345. https://doi.org/10.1146/annurev-psych-061020-105721. A comprehensive overview that integrates several models of self-regulation from different subfields  and highlights their points of convergence and divergence on important components of regulation, including conflict, emotion, and cognitive functioning.Inzlicht, M., Werner, K. M., Briskin, J. L., & Roberts, B. W. (2021). Integrating models of self-regulation. MacLeod, C. M., Dodd, M. D., Sheard, E. D., Wilson, D. E., & Bibi, U. (2003). (See References). A “reader’s digest” history of inhibition, including a variety of case studies highlighting how the concept of inhibition has been used in different areas in cognitive science, as well as some of the definitional issues surrounding inhibition.Werner, K. M., & Ford, B. Q. (2021). (See References). An application of the process model of emotion regulation to self-control, accompanied by discussion of the importance of having a well-equipped strategy toolbox and engaging in regulatory flexibility , as well as polyregulation .


Visualization of inhibitory synapses requires protocol tailoring for different sample types and imaging techniques, and usually relies on genetic manipulation or the use of antibodies that underperform in tissue immunofluorescence. Starting from an endogenous ligand of gephyrin, a universal marker of the inhibitory synapse, we developed a short peptidic binder and dimerized it, significantly increasing affinity and selectivity. We further tailored fluorophores to the binder, yielding “Sylite”—a probe with outstanding signal‐to‐background ratio that outperforms antibodies in tissue staining with rapid and efficient penetration, mitigation of staining artifacts, and simplified handling. In super‐resolution microscopy Sylite precisely localizes the inhibitory synapse and enables nanoscale measurements. Sylite profiles inhibitory inputs and synapse sizes of excitatory and inhibitory neurons in the midbrain and combined with complimentary tracing techniques reveals the synaptic connectivity. A short dimerized peptidic probe for high contrast visualization of inhibitory synapses in neuron cultures and brain tissue is presented. The probe can be flexibly applied both for widefield and confocal 3D volumetric synapse visualization, for inhibitory circuits mapping in tissue and for super‐resolution imaging of synapses. A receptor (GABAAR) γ2 subunit[ARs at the postsynaptic density,[Reliable probes that label and visualize synapses are invaluable tools for clinical and fundamental neuroscience.2 subunit4, 5 fGephyrin is often visualized using genetic tagging. eGFP, mCherry, mEos2 and other fluorescent proteins can be fused with gephyrin and expressed in cells via transfection or infection.13, 14A powerful approach that bypasses some of the limitations of genetic tagging is immunostaining of endogenous proteins, a technique that is easily applicable to post vivo samples. Several antibodies have become the “gold standard” for gephyrin labeling and have been used for almost four decades.21, 22A promising alternative to genetically encoded probes and antibodies are synthetic affinity probes such as nucleic acid derived aptamers, peptides and small molecules.A succinct summary of the inhibitory synapse visualization methods is shown in Table Here we outline the development of a new, synthetic, gephyrin‐binding probe and demonstrate its application in cell culture and tissue, where it acts as a versatile tool to visualize neuronal structures on various scales, from macro‐ to nanoscopic studies of the brain.TMR2i is the only synthetic probe that has been successfully used to label gephyrin.Sylite, a dimeric probe with 9‐amino acid long binding sequences and a functional cysteine‐modified linker that enabled maleimide‐Cy5 conjugation showed the highest signal‐to‐background ratio (SBR) and colocalization with gephyrin , whereas SyliteM was the best monovalent counterpart Figure A. To appKD of 17.5 nM for Sylite, an 11‐fold affinity increase over the monomeric SyliteM (205 nM), indicating high probe affinity and confirming the expected 1 : 2 binding stoichiometry of the probe, in line with its dimeric design , confirming the selectivity of the probe for gephyrin. We next applied Sylites on primary neurons. Both Sylite and SyliteCy3 visualize the inhibitory synapses and the diffuse gephyrin in neuronal cell bodies, as confirmed by the co‐staining of primary hippocampal neurons with gephyrin 3B11 antibody Figure A, B. IntRecent work showed that the presynaptic active zone (AZ) protein RIM forms trans‐synaptic nanocolumns with gephyrin.stratum oriens of the CA3 region of the ventral hippocampus, an area densely packed with inhibitory interneuronsTissue staining of inhibitory synapses is an elaborate and time‐consuming procedure that is generally limited to thin brain sections (≤16 μm) to obtain reliable labeling.Sylite detected synaptic clusters throughout the entire section, demonstrating a complete penetration of the probe already after 1 hour of incubation . Strikingly, even at 72 hours of incubation with Sylite we did not observe any significant background fluorescence Movie S4,5). In contrast, after 24 hours, the antibody distribution appeared to have a “sandwich”‐like pattern, with the strongest labeling near the surfaces of the sections while the center remained largely unlabeled  is a midbrain region that plays an important role in orchestrating the defense reaction in response to a perceived threat. It has been postulated that intra‐PAG circuitry supports integration of multiple defense components, such as switching between active and passive behavioral coping patterns.Through systematic improvements of the binding sequence, fluorescent dye tailoring and dimerization to enable simultaneous attachment to two gephyrin molecules, we produced a highly selective and highly affine probe for the inhibitory synapse. Unlike antibodies that are raised against protein fragments that generally do not relate to a specific protein function, Sylite is derived from the GlyR, an endogenous ligand of neuronal gephyrin, binding gephyrin isoforms that actively cluster receptors at the synapse. Sylite has a linker with cysteine allowing flexibility for conjugation of maleimide containing dyes, enabling straightforward derivatization of a potent binder to a fluorescent probe with imaging capabilities only limited to the fluorophore of choice. However, since fluorophores can have a significant impact on the binding properties of a short peptidic probe , a pre‐application screening, like the one described for SyliteCy3 , is necessary.Sylite is easily implemented in standard immunocytochemical and immunohistochemical assays, where it can be used as a substitute to or alongside antibodies, visualizing inhibitory synapses in a one‐step application similar to direct immunolabeling. Sylite, conjugated to the Cy5 fluorophore, is particularly useful for super‐resolution microscopy, enabling nanoscale studies of the inhibitory synapse, such as the pre‐to‐post synapse distance measurements conducted here. In tissue, presumably because of its small size, Sylite attains previously unachievable staining efficiency, enabling greatly simplified and accelerated tissue processing without imaging artifacts often observed with antibodies. Sylite can thus be applied for advanced and multiplexed anatomical tracing techniques that address cell‐type‐ and projection‐specific connectivity as part of circuit‐centered approaches to brain function. By and large, techniques that rely on viral delivery systems do not provide specific information on the post‐synaptic site, such as the identity of the cellular structures, their size and organization. Due to its high labeling efficiency in tissue, Sylite can therefore greatly contribute to an easier and more reliable identification of inhibitory postsynaptic densities and their size. In addition to identifying target cells and synaptic sizes of specific projection pathways as highlighted here, Sylite has potential to advance studies of the functional significance of precise, i.e., subcellular, synaptic localization.Progressively complex neuroscientific research together with the technological advances in tissue imaging and super‐resolution microscopy create a strong demand for new probes for multiplex micro‐ and nanoscale studies of the brain.1, 43 H.M.M. and V.K. hold a utility model # 20 2021 002 515 on Sylites. Sylites are commercialized by NanoTag Biotechnologies GmbH, Cat.#P4001. Ca.S. is employed at Abbelight.1As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Studies of word production often make use of picture-naming tasks, including the picture-word-interference task. In this task, participants name pictures with superimposed distractor words. They typically need more time to name pictures when the distractor word is semantically related to the picture than when it is unrelated (the semantic interference effect). The present study examines the distributional properties of this effect in a series of Bayesian meta-analyses. Meta-analytic estimates of the semantic interference effect first show that the effect is present throughout the reaction time distribution and that it increases throughout the distribution. Second, we find a correlation between a participant’s mean semantic interference effect and the change in the effect in the tail of the reaction time distribution, which has been argued to reflect the involvement of selective inhibition in the naming task. Finally, we show with simulated data that this correlation emerges even when no inhibition is used to generate the data, which suggests that inhibition is not needed to explain this relationship. The cognitive processes underlying word production are often assessed using picture-naming tasks. In one such task, the picture-word interference task, participants are asked to name a picture in the presence of a superimposed distractor word see Fig. . ParticiDistributional analyses examine how an experimental effect evolves with the response time distribution . The fact that they saw a relationship with the slope of the slowest delta segment, but not with the fastest (with the exception of the one experiment), was taken to lend support to claims made by the activation suppression hypothesis by Ridderinkhof et al. . Reaction times were then sorted and divided into quantiles and the mean difference between conditions for each quantile was computed. We used five quintiles  as in Shao et al.  and ShaoFor each study, we fit a linear mixed-effects model using the lme4 package  and fastest delta segments (the slope between quintiles one and two) for each participant. We followed the procedure in, for example, De Jong et al. , Ridderislope = (delta(quintile 5) − delta(quintile 4))/(mean(quintile 5) − mean(quintile 4))slowest delta segment and the mean semantic interference effect and also for the slope of the fastest delta segment and the mean semantic interference effect. We then used the Fisher z-transformation to transform correlation coefficients  to z values , we assumed the following: Each study i has a true z-transformed correlation of ζi that is normally distributed with a mean of ζ and a variance of τ2 = 102. The observed z-transformed correlation zi in each study is assumed to stem from a normal distribution with mean ζi and variance zi represents the observed z-transformed correlation in each study i; ζ is the true z-transformed correlation estimated by the model; τ2 represents the between-study variance.For the meta-analyses of correlations .We additionally did sensitivity analyses for each meta-analysis. Effect sizes did not change with different priors for any of the meta-analyses, and details on sensitivity analyses can be found in Appendix 10) are reported.Results of the meta-analyses are summarized in Tables Our analyses suggest that the semantic interference effect increases with response times.Interestingly, the increase in the effect size increases with each quintile, with increases of 5 ms, 6 ms, 10 ms, and 21 ms, respectively. At first sight, this pattern does not seem to fit well with the hypothesis that at least a subset of participants applies more inhibition in the slower quintiles. However, we cannot rule out the possibility that without inhibition, the increase in the magnitude of the effect in the last two quintiles would have been even greater.only be due to an individual-specific ability to apply inhibition but suggests that the slope of the last delta segment also captures intra-individual variability in selective inhibition.The next meta-analysis showed that participants’ slopes of the slowest delta segment are positively correlated with the magnitude of their semantic interference effects. This is in line with the hypothesis that participants who apply less inhibition during the task as indexed by a steeper, more positive slope, show larger semantic interference effects. Our analyses confirm the same correlation when we calculated quintiles by item instead of by participant. In a within-item design, each participant names both related and unrelated trials for the same pictures. As a consequence, this correlation cannot fastest delta segment. According to the activation suppression hypothesis, inhibition takes time to build up, which is why the slope of the slowest delta segment is typically used to index inhibition .We additionally found a correlation between the semantic interference effect and the In the following paragraphs, we consider an alternative explanation to these correlations. Notably, the data in the last (or first) two quantiles are used in both the computation of the mean effect size for the participant (or item) and the computation of the slope of each delta segment. In other words, a correlation may be expected even in the absence of inhibition simply because some of the same data are used to compute the two measures that are then correlated with one another. Moreover, given the increase in effect size throughout the response time distribution, the correlation can be expected to be higher for delta segments where the effect is larger. As the semantic interference effect becomes greater between the last two quintiles, participants who show less of an effect overall could be expected to show less of an increase in the last two quintiles, irrespective of whether they deploy inhibition or not. The question therefore arises as to whether these correlations reflect something in addition to this, or whether they are only a by-product of circularity in the procedure.The mechanistic explanation assumes that the increase in effect size with response times combined with the fact that the data in the last two quantiles are used twice in the correlation suffices to generate the correlations we observe. The inhibition account assumes that there is an additional mechanism at play . In the first account, negative slopes are due to natural variation, whereas in the inhibition account, they are due to an additional mechanism of inhibition. The goal here is to simulate data under the assumption that no inhibition was applied to test whether the patterns we see in the meta-analysis are due to something more than what we would expect from distributional properties of the semantic interference effect alone.https://osf.io/v2fx5/).We chose to simulate data generated from an ex-Gaussian distribution. An ex-Gaussian distribution is a convolution of a normal and an exponential distribution and provides a good fit to most reaction time data , suggesting an inhibition account is not needed to explain this relationship. To be clear, we have not demonstrated that inhibition is not involved in the picture-word-interference task, nor that the slope of the last delta segment does not reflect inhibition. We have simply demonstrated that the correlation between the mean interference effect and the delta segments can also be produced from simulated ex-Gaussian distributions, in which the sigma and tau parameters are larger for the slower condition (see also Bürki & Madec, It is a reasonable assumption that inhibition is involved in a picture-word-interference task. To conclude that the slope of the last delta segment reflects inhibition in picture-naming tasks, we would need some evidence that is not taken from a correlation with some of the same response times. Shao et al.  provide