Visual functions are reviewed that coincide with conscious as opposed to unconscious vision. Four stages of vision are identified, going from the fully invisible, to subjectively invisible, unattended, and clearly visible. It is proposed that feature extraction, categorization, and some aspects of visual inference occur during full and subjective invisibility. Functions related to perceptual organization, such as grouping and figure-ground segregation, occur during inattention as well as full visibility. It is argued that perceptual organization is the function that is central to understanding the transition from unconscious to conscious seeing. It is discussed what this implies for theories of consciousness such as Recurrent Processing Theory, Higher Order Thought Theory, Integrated Information Theory, and Global Neuronal Workspace Theory. This is generally considered “objective invisibility,” the level at which conscious sensations are trusted to be absent.What does it mean to consciously see or not see? The matter is simple at the extreme ends. Suppose someone is shown a face, which is then recognized by the subject, who insists that he was seeing that face and can describe all sorts of features. Then it will be safe to assume there was a conscious sensation of that stimulus. At the other end, suppose someone is shown visual stimuli (or blanks) that are strongly masked, while all attention is focused on the screen, and the only task is to press one button when seen, and another when not seen. If the subject then insists not having seen anything at any trial, which is confirmed by d-prime above 0 may get rated “unaware” (level 1 on the PAS) in such subjective scaling experiments , where subjects report the amount of subjective awareness on a scale, say from 1 to 4 . The scaeriments , implyinOther variations on this theme exist. Some have combined objective measurements with confidence ratings or post-decision wagering, asking subjects how much they trust their yes-no responses. When confidence is equally high (or low) for correct and incorrect trials , stimuli are considered metacognitively unconscious. A big advantage of this approach is that objective and subjective (or rather metacognitive) visibility can be directly compared in the framework of signal detection theory, using a d-prime value , and henFrom this, we arrive at three stages of visual processing :Fully unconscious, i.e., stimuli that are below the threshold for objective visibility.Subjectively unconscious, i.e., stimuli that are reported as unseen in experiments using perceptual awareness scales, meta d-prime, confidence ratings etc., yet are above the threshold for objective invisibility.Subjectively conscious, i.e., stimuli that get a PAS score above 1, where meta-d prime is above 0, etc., and hence are reported as seen.On another dimension are phenomena of “unconscious” vision that relate to attention. Well-known are the change blindness (CB) or innatentional blindness (IB) demonstrations where huge changes between scenes are not seen, or gorillas pass by unnoticed. Often used is the attentional blink (AB) paradigm, where the reporting of a first target renders a shortly following second target “invisible.”A separate distinction thus is between :4. Unattended, i.e., stimuli that are reported as not seen while attention is diverted away from them . These are potentially visible, yet momentarily escape cognitive access.5. Attended, accessed, reported, memorized, etc.How to compare these stages? Stages 3 and 5 are in fact similar, since in both cases we have subjects reporting (and most likely having attention for and access to) subjectively visible stimuli. It is less clear to what extent “unattended” overlaps with subjective invisibility. Stimuli that are subjectively easily visible become subjectively invisible when attention is diverted away (which is in fact the striking point of the change blindness phenomenon). When attention happens to be directed at these stimuli, the subjective invisibility is resolved. This is clearly different from invisibility from masking, flash suppression, etc. In those cases, there is nothing the subject himself can do to see the stimulus, it seems irrevocably “buried” in the unconscious. It thus seems that inattentional invisibility is a more “cognitive” or higher-level form of invisibility than invisibility from masking, flash suppression, etc. As will be shown below, indeed attention as manipulated in CB, IB or AB paradigms does not interfere with visual functions per se. It therefore seems warranted to categorize “unattended” as “potentially visible,” yet not reportable or accessed right now, as others have done before .A potential “hierarchy” of visual phenomenology thus may be (above levels in parentheses)Stage 1: Fully invisible (1)Stage 2: Subjectively invisible (2)Stage 3: Unattended (but otherwise subjectively visible) (4)Stage 4: Subjectively visible and attended (3/5)An alternative is to view the two types of manipulations of conscious vision – those manipulating visibility and those manipulating attention- as orthogonal [as I and others have done before ]. In facof that conscious percept). That has been discussed in previous work extensively, resulting in an apparent stalemate of opinions about the essence of conscious vision  is truly unconscious, for example because subjective awareness may be influenced by all sorts of non-visual conditions, like the type of PAS scale used, whether it is followed or preceded by other questions , decisios vision  , otherwise stage 2.Manipulations like backward or dichoptic masking, CFS, and rivalry, or conditions like blindsight render stimuli stage 1 when Attentional manipulations like AB, IB, and CB, or attentional deficits like neglect speak only to the transition between stages 3 and 4 (see above for the underlying reasoning).vially – relate to several cognitive functions. All measurements of consciousness in one way or another rely on subjects reporting, mostly via button presses. The required motor machinery consists of a whole set of cognitive functions that are not relevant to the discussion here. Metacognition and attention are trivially linked to conscious vision, simply because they are part of the way the different stages are distinguished. Metacognition is intertwined with various aspects of decision making. Closely associated with attention is a function called “access,” or the making available of sensory information to other cognitive processes  . A potenvia the LGN seems unaffected by it  is generally considered to be independent of consciousness. Similarly, the transmittance of retinal signals to the cortex ed by it . The extoupings”  such as oupings” ] regardlAt some point in vision, all the features and categories that have been extracted start to interact. Some examples of these are phenomena of perceptual interference, where the luminance or color of one part of the image is influenced by that of another, potentially resulting in striking visual illusions of brightness or color differences between physically identical patches of gray or color. The neural correlates of such interactions are found in many visual areas, and to some extent also in anesthetized animals . MoreoveA related visual function is that of inference. These are most poignantly illustrated by many visual illusions. Take the well-known Kanizsa triangle, where the physical stimulus consists of three “Pac-men” shapes. From that configuration we infer a triangle overlying three disks, with clearly visible yet illusory contours, and even an increased brightness for the triangle region. Neural correlates of such illusory contours have been found in early visual cortex, in awake animals. Some have shown these illusions to persist during CFS , others In sum, it seems that the visual processes of interference and inference, where local features start to interact and put into global perceptual context  find their basis in stage 2, and may even operate to completion during this stage. Some have called this stage the formation of the 2.5D sketch , i.e., gThere are clear limits, however, to what is achieved in terms of moving toward a more global interpretation of the scene during stage 2. An important aspect of scene interpretation is perceptual organization, where distant features are combined into coherent objects and their relative layout. An example is figure ground organization as depicted in Effects of attention on visual processing are well-documented. In general, unattended stimuli generate weaker responses than attended ones. But the quality of visual representation seems to suffer much less, if at all. For example, the width of orientation tuning curves in V1 does not change during inattention, only their amplitude does . Higher Processes of interference and inference proceed as well, as Kanizsa illusion representations are identical whether attended and reported or inattended . FahrenfIn sum, although attention has strong effects on the strength of visual representations in visual cortex [and may also lower thresholds for otherwise invisible stimuli ], the esWith everything visual completed already during stages 1, 2, and 3, one may wonder what exactly is added in stage 4. The short answer is: nothing in terms of visual operations. What is added are cognitive functions that may operate on the visual representation that is now complete. These may include attentional selection , working memory (storing some parts of the scene), access , decision processes (deciding on which object to act), metacognition [re-representing the visual representation somewhere else ], and reA summary of the results presented above is given in What does this say about the NCC? As noted, finding the NCC is ultimately aimed at unraveling the set of neural operations (hence functions) that “generate” the conscious  percept. Often, this is framed as operations being both necessary and sufficient for consciousness. In practice, however, this is hard to establish. What mostly occurs is that some correlation is found between conditions or manipulations that affect conscious experience on the one hand, and neural signals and their associated processes on the other hand. On the basis of what is summarized here, some visual functions can be excluded from the NCC in this correlational sense: feature extraction, categorization, some interference and inference occur regardless of whether one is conscious of the visual stimulus or not. Other functions are likely candidates: some inference and integration (like in the Kanizsa illusion), processes of grouping, perceptual organization and figure-ground segregation depend strongly on the stimulus that is evoking these operations being consciously perceived.Others have argued for the NCC also needing to have some explanatory power toward consciousness. The properties of conscious experience most in need for such explanation according to Some argue that a mere visual representation, however integrated and organized, is not sufficient for having a conscious percept of it. Either some re-representation of it is deemed necessary, as in Higher Order Thought Theory (HOTT) , or funcPerhaps the most difficult aspect of conscious experience is that of it having this ego-perspective, or “I”-point of view. This is primarily caused by the “I” being hard to define, let alone pin down to some specific functions or neural processes. In some versions of HOTT, the re-representation of visual information is thought to be a pointer indicating whether the visual representation is coming from within (as in imagination) or from the outside (as in perception) . It is aIn sum, therefore, the transition from unconscious to conscious vision, key to understanding the NCC of seeing, is probably best explained by visual processing going from (low and high level) feature extraction toward integrating those features into a coherent and integrated whole, thereby constituting the organized and unitary percept that sits so prominently in the mind of the visual animal we are.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Our work is focused on the future clinical use of oncolytic viruses (OVs) for the treatment of aggressive thyroid carcinomas. Therefore, we provide a brief description of the overall use of OVs in the clinic. Rigvir is among the few OVs that have already been used for the treatment of patients, and studies describing its effects have been briefly commented and cited in our text .The comment of Dr. Alberts aims to clarify the actual state of the oncolytic ECHO-7 virus Rigvir and its approval as a therapeutic opportunity. The comment properly declares that Rigvir has been approved in former Soviet Union countries such as Latvia in 2004, Georgia in 2015, Armenia in 2016, and Uzbekistan in 2018 [® story” by Dr. Alberts, where “the results will be confirmed and updated by modern clinical studies”. In our work, it is not stated that the use of Rigvir was rejected by European or U.S. agencies, but likely that more detailed studies are required to propose Rigvir for their approval. Overall, if the readers require more specific information regarding Rigvir, proper references have been included by us and we are sure that more focused reviews such as those of Dr. Alberts, might clarify any “unclear” sentences.In the sentence stated in our publication: “This virus has not met the required standard regulation for European or U.S. approval, further studies are in progress to better evaluate its therapeutic potential”, we did not doubt the efficacy of Rigvir. Indeed, many pre-clinical and clinical studies have been performed. However, it is clear that Rigvir use needs to be supported by further and updated preclinical and clinical studies as also argued in the paper “The advent of oncolytic virotherapy in oncology: The Rigvir


This study aimed to determine the associations between parental monitoring and affection and three adolescent lifestyle aspects: constructive leisure, non-constructive leisure and substance use. A cross-sectional study was conducted in four countries . Adolescents aged 12–15 self-completed a multi-purpose questionnaire. Multiple logistic regressions were performed to analyse the association between the parental monitoring and affection variables and the outcomes in terms of the children’s lifestyles. The results indicate that parental monitoring is conducive to more constructive leisure and less non-constructive leisure and seems to be conducive to the prevention of substance use. Furthermore, parental affection is conducive to constructive leisure and the prevention of substance use. The discussion focuses on the fact that the family can be a protective resource associated with positive adolescent development. Historically, the adolescent stage has been considered a turbulent and conflictive growth period ,2,3 charDeficit models focus on pathology and identify the problems and imbalances of this growth stage . HoweverThe “positive youth development” (PYD) model considers the adolescent as an individual in a period of psychological, emotional, social and intellectual growth ,4. This The PYD model is based around the concept of resources or assets for development . SpecifiIn this sense, the literature indicates that the functional family is a protective factor. It can directly affect adolescents and increase the probability they will obtain positive health results ,9,17,18.Family assets include important factors connected to family, such as family support, positive communication, clear rules and consequences and supervision . These vThe family and family functioning play an important role in the types of leisure activities performed by adolescents . LeisurePrevious studies have analysed the associations between these adolescent behaviours and different variables, including parental monitoring and affection ,27. Howe▪To examine how monitoring and parental affection differ according to sex, age and other sociodemographic variables of adolescents.▪To confirm the association of monitoring and parental affection with constructive and non-constructive leisure activities using the same sampling and survey tools in an international cohort of four Spanish-speaking countries.▪To confirm the association of monitoring and parental affection with the consumption of substances such as tobacco, alcohol and marijuana in the mentioned international cohort.Therefore, this study had the following objectives:To achieve the proposed objectives, the data from an international research project called YOURLIFE were analysed ,30,31,32The article presented here is framed under a cross-sectional design covering four countries .A convenience sampling was carried out in public and private schools in Chile, Mexico, Spain and Peru. Invitations were sent out, and 52 schools voluntarily decided to participate. An online questionnaire was completed by 3443 secondary school students of approximately 13 years of age. After eliminating those who did not indicate their age or sex and those who were outside the age range of 12–15 years, a sample of 3300 students remained.https://proyectoyourlife.com/index.html). The questionnaire is anonymous, self-completed and answered online. It is a multi-purpose questionnaire with questions of various types. The items used in the present study are described below.For data collection, one of the three questionnaires of the YOURLIFE project was used: The c13 questionnaire aimed at students between 12 and 15 years  . This inThe questions regarding parental affection were formulated as follows: Regarding your parents: “Do they know you well and understand you?”; “Do they set an example?”; “Do they listen to you?”; “Do they take your opinions into account when doing something?”; “Do they speak kindly to you?”; “Do they help you when you feel insecure?”; “Do you feel that they love you and that they accept you as you are?”; “Do you feel comforted and supported by them?”; “Do you feel that your things interest them?”; “Do they take time to talk to you?”; and “Do they try to be with you and help you?” Each question had five possible answers . The mean of the scores of all the items was calculated, and we eliminated the participants who had responded to fewer than half of the items. This variable  was dichotomised around the median into “high affection” and “low affection”. Internal consistency was high .The questions regarding parental monitoring were formulated as follows: Regarding your parents: “Do they require you to follow a schedule?”; “Do they decide with you what you must do?”; “Do they limit what you spend?”; “Do they limit the time you can watch television?”; “Do they control your use of cell phones or the Internet?”; and “Do they control your books and magazines?” Each question had five possible answers . Again, the mean of all the scores was calculated, and the participants who responded to fewer than half of the items were eliminated. This variable was dichotomised around the median into “high monitoring” and “low monitoring”. Internal consistency was acceptable .The questionnaire included questions to determine the frequency with which students participate in different leisure activities. For each activity, a response scale from 0 to 4 was established . The leisure activities were divided into two groups: “constructive leisure” and “non-constructive leisure”. The election of activities and the division into constructive or non-constructive leisure was based on previous literature . We haveConstructive leisure included the following questions: “In the last 12 months, how often have you done the following activities?”: “Play some sport, go to the mountains, etc.”; “Volunteer ”; “Attend artistic and educational activities ”; and “Activities with your parents ”.Non constructive leisure included the following questions: “In the last 12 months, how often did you perform the following activities?”: “Hang out in the street, in a park, on the beach, or in other public places”; “Go to shopping centres, game rooms, billiards, or football stadiums”; and “Gather in a place with friends, without adults present”.For each group of activities, the mean of all the corresponding item scores was calculated and the participants who responded to fewer than half of the items were eliminated. Again, each variable was dichotomised into: “high and low frequency of constructive leisure activities” and “high and low frequency of non-constructive leisure activities”.Participants were asked how often they consumed various substances, such as alcohol, tobacco, marijuana and other drugs . We decided to create a new variable separating those who answered “never” for all of these substances from those who had ever consumed any of them. This decision was based on the low prevalence of consumption at the ages covered by our analysis and on scientific evidence that indicates that any consumption of these substances during adolescence is considered a risk behaviour .The multivariate analyses adjusted for age, sex, educational level of the parents, family structure and religiosity.Religiosity was assessed with three different variables. Participants were asked what religion they belonged to. Then, if they belonged to any religion, they were asked how often they went to the church/temple of their religion and how often they prayed (from 0 = Never to 5 = More than once a week). They were finally asked how much they agreed with this statement: “My faith is an important influence in my life, and I am willing to take it into account in my decisions” (from 0 = Strongly disagree to 4 = Strongly agree). For each of these issues, a dichotomous variable was generated into “high” and “null/low” attendance/prayer/salience.Complete details of project YOURLIFE and its procedures are available elsewhere . BrieflyThe characteristics of the participants are provided as absolute frequencies and percentages by country. To determine which variables were associated with affection and parental monitoring, we first analysed the bivariate associations between each of these two variables and various sociodemographic variables . Second, two logistic regressions were performed. The dependent variable was parental monitoring in one case and parental affection in the other. In both regressions, the independent variables were the aforementioned sociodemographic variables.Then, the associations between the family variables  and the three outcomes in terms of the children’s lifestyles  were analysed. The two family variables were divided into quintiles. For each quintile, the percentage of participants performing each of the three behaviour outcomes was calculated to perform a preliminary description of these associations.Three logistic regressions were then performed, each with one of the three lifestyle outcomes as a dependent variable. The independent variables were affection and monitoring, in addition to various sociodemographic variables to adjust for possible confounding.Finally, we performed a structural equation model (SEM) to test all associations in a single model.The data were analysed in the statistical programme Stata 12.1. The significance level was set at 0.05.In total, 3443 students from Spain, Mexico, Chile and Peru were recruited. In total, 89 participants were excluded because they presented missing data in the age variable or because they reported being younger than 12 years or older than 15 years. In total, 54 participants who did not indicate their sex were also eliminated. Finally, a sample of 3300 students was analysed .The main characteristics of the sample are described in To achieve the first objective, the bivariate and multivariable associations of monitoring or parental affection with various sociodemographic variables were explored . After aTo work towards the two other objectives, a bivariate analysis was first carried out to determine the association between each parental variable (monitoring and affection) and each outcome  . ParticiAfter these first analyses, multiple logistic regressions were carried out to estimate the associations between the monitoring and parental affection variables and the different outcomes . First, Second, the association between monitoring and parental affection and non-constructive leisure was explored. Parental monitoring  was significantly associated with a lower frequency of non-constructive leisure activities. In contrast, no significant association was found between affection  and the frequency of non-constructive leisure. Other variables associated with a lower frequency of non-constructive leisure activities were being younger, being Peruvian (compared to being Spanish), having parents without a university education and having a high frequency of church attendance.p < 0.001)  , it was The structural equation model obtained excellent fit indices . The results are shown in The data obtained suggest that both monitoring and parental affection are associated with a greater frequency of constructive leisure activities. Constructive leisure activities are associated with more positive adolescent development and promote better health outcomes ,20,38,39The results of this article are consistent with the theory of parental socialisation. This theory indicates that, when parents invest more hours in structured activities with their children, they dedicate more time to this type of activity . Doing fThe present study, in agreement with the literature, indicates that a higher educational level in parents ,42 and hThe results show an association between parental monitoring and less non-constructive leisure activities, but no clear association was found between parental affection and this type of leisure. The engagement in non-constructive leisure entails a low development of personal skills and lower levels of motivation. This promotes a sense of worse well-being in adolescents . This feGreater parental monitoring might decrease the engagement in non-constructive leisure activities . There iOur results also confirm that non–constructive leisure activities were lower among those with a high frequency of church attendance, but not among those with high frequency of praying or with high religious salience. A previous study had found that both attendance and salience predicted lower frequencies of unstructured leisure, although this association varied according to the country studied .Our results are not clear with regard to the association between parental monitoring and substance use. Both in the regression and in the SEM, monitoring seems to be protective, but the effect is significant only in the SEM. These results would stand in the same protective direction observed in previous studies ,50,51,52The results indicate that greater parental affection is significantly associated with a lower prevalence of substance use. Previous research has found similar associations between parental warmth and substance use ,54,55,56Our results also confirm, in line with previous studies, that substance use is lower among women ,59 and iWith regard to the role of religiosity, church attendance predicted lower odds of substance use in the regression analyses, but prayer and salience did not. Previous studies have found results with similarities and differences with ours. Most of them found that church attendance is negatively associated with substance use ,63,64,65Regarding our main variables, the results presented in this study, where we used the same questionnaire in four different countries, seem to confirm that the family can be a protective resource associated with positive youth development. Specifically, the results confirm the associations between parental monitoring and affection, and health assets such as the reduction and prevention of substance use and with greater time spent in constructive leisure activities. These findings are very much in line with previous results about the fundamental role that parents have in the decision-making of adolescents with respect to the establishment of certain lifestyles ,49.This study also found that three dimensions of the religiosity are associated with parental monitoring and affection. This would imply that religiosity would have a double effect on adolescent behaviour: a direct effect (as discussed above) and an indirect effect through the role of parenting. These associations should be further explored because recent studies found that only spirituality was associated with these parenting variables .Few studies have included models that examined the associations between monitoring and parental affection ,27,57 anMost studies have focused on European or North American populations. Some studies which include Hispanic preadolescents focus on risk behaviours such as drug abuse , delinquDifferent limitations should be taken into account when interpreting the results of this study. First, the results come from a cross-sectional study that does not necessarily allow for causal inferences to be made about the associations found. In cross-sectional studies inverse causality can explain some associations. For example, a low religiosity can determine riskier behaviours, but risky behaviours could also determine an adolescent to decrease their religiosity. However, inverse causality is not a likely explanation for the association between low monitoring and higher substance use. It is not likely that parents would lower their monitoring upon knowing that their adolescent is consuming drugs. The associations found could also be due to the role of confounding variables; however, we tried to adjust for confounders and had a good sample size to do so. In any case, longitudinal studies should confirm these results.In addition, we used a convenience sample, and the response rate is unknown. However, this issue may have been partially compensated by our large sample size that enabled adjusted good adjustment for several potential confounders.It should also be noted that, by filling in a self-completed questionnaire in which information was requested about their own perceptions, the students could have given answers that were socially well accepted and not so much their true perceptions. To avoid this possible bias, we insisted on anonymity, and we ensured that the responses were confidential, which reduces this kind of bias .This study also has several strengths. First, the large sample size allowed the results to be generalised and allowed the analysis to be adjusted for various potentially confounding variables. In addition, the sample came from four underrepresented countries in the literature, which share some cultural traits but have significant differences between them. In addition, the fact that the main associations remained stable among the four countries gives strength to the generalisation of the results.This study adds to the evidence that parental monitoring and affection may be one health asset leading to positive and healthy lifestyles. The results could help to take into account the family as a resource when designing health interventions in order to lead adolescents to participate in constructive leisure activities and avoid risky behaviours.


The development of professional identity is a fundamental element of medical education. There is evidence that in Germany, students’ perceptions of the ideal and real doctor differ, and that of themselves as physicians falls between these constructs. We sought to compare students’ perceptions of themselves, the ideal doctor, and the ‘real’ doctor and investigate differences from first to final year in the relationships between these constructs, as well as differences between Australian and German cohorts.Students in the first and final years of their medical program at one Australian and one German university were invited to complete the Osgood and Hofstatter polarity profile, involving the description of their mental image of the ideal and real doctor, and the doctor they hope to become, with adjectives provided.One hundred sixty-seven students completed the survey in Australia  and 188 in Germany . The perception of the ideal doctor was consistent across all respondents, but that of the real doctor and self-image differed between country and year. Differences existed between country cohorts in perceptions of ‘confidence’, ‘strength’, ‘capability’ and ‘security’.The pattern previously reported among German students was maintained, but a different pattern emerged among Australian students. Differences between countries could reflect cultural differences or variations in the overt and hidden curricula of medical schools. Some of the constructs within the profiles are amenable to educational interventions to improve students’ confidence and sense of capability. For medical schools in many parts of the world, admissions processes aim to select students who are not only academically capable, but who possess important skills and values such as altruism, teamwork, and communication , 2. WhilMedical students often enter the medical profession with aspirations about providing care and ‘helping people’. Influences upon students throughout medical school include their own ideals and stereotypes, their academic teachers and clinical supervisors, and encounters with other physicians and patients. These influences can vary greatly, as evidenced by the vast literature relating to the hidden curriculum; the difference between students’ own values and what they are taught in their ‘on-campus’ learning, and the explicit or implicit messages they are exposed to in clinical environments .In recent years, medical program accrediting bodies internationally have required medical undergraduate and postgraduate curricula to include teaching and assessment of professionalism. Cruess et al. describe the objective of teaching medical professionalism as being to assist learners to develop professional identities . Two imp‘cultural mores that are transmitted, but not openly acknowledged, through formal and informal educational endeavours’.  and 46 in year 5 [23.4%]) and 188 in Germany (164 in year 1 [94.8%] and 24 in year 6 [57.1%]). The average age of first year students was 21.3 (Standard Deviation [SD] 4.92) in Australia and 21.5 (SD 5.01) in Germany, and of final year students was 24.8 (SD 3.88) and 25.9 (SD 4.17) respectively. No significant age difference was found between the cohorts. In Australia, females made up just under half of the sample, while in Germany approximately 65% of the sample was female. This difference was significant for the first year cohort only (χ2 (1) = 7.020, In general, patterns were similar in the German and Australian cohorts, but the levels of ‘positivity’ or ‘negativity’ varied between the cohorts, as did the relationships between the constructs  and ‘realistic’ versus ‘idealistic’ in which first year German students favoured ‘realistic’ more than final year students (p < 0.05).Overall, the ideal physician is perceived as ‘understanding’, ‘thorough’, ‘reliable’, ‘trustworthy’, ‘likeable’, and ‘capable’. Scores are lower for some items, favouring ‘realistic’, ‘rich’, and ‘authoritarian’. In general, perceptions were similar between cohorts with the exception of some small but significant differences (please see Table Perceptions of the real doctor were markedly different between Australian and German, with significant differences on the majority of items. Among Australian students, a significant shift occurred from first to final year on several items (please see Table Differences between Australian and German students existed for all but one item in their self- reflection. The only item not statistically significantly different was ‘realism’ versus ‘idealism’. While the questions were subtly different between cohorts, the patterns were generally in the same direction as observed for the real doctor, so they may reflect actual differences between cohorts. Some key differences also existed between first and final year for Australian students (please see Table In particular, for German students, greater distance was observed between the perceptions of ideal, real, and self, with self-perception laying between the others see Fig. . In the In general, the data shows that all four cohorts regarded the ideal doctor very highly, with high scores on core characteristics such as ‘understanding’, ‘thorough’, ‘likeable’, ‘reliable’, ‘trustworthy’, ‘capable’ and ‘friendly’. Scores for the real doctor were lower, with these characteristics perceived to a lesser extent. In the German cohort, self-perception lay between these two, perhaps indicating that personal ideals and values have students striving to be better than that which they perceive as the current reality. In the Australian cohort, students generally perceived themselves as more similar to the ideal doctor rather than the real, which is not surprising given the slight differences in the translated version of the survey. It seems though, that in both cohorts, ideals exist which they do not see emulated in the clinicians to whom they are exposed during their training, and in the case of the German students, which they believe that they are not able to embody.In general, all cohorts had a similar perception of the ideal doctor, reinforcing the notion that this ideal is a widely held truth about the medical profession. Unlike those relating to the real and self, perceptions of the ideal doctor remained stable from first to final year, and were consistent with values previously identified as desirable by students at the same Australian university . DespiteIn contrast, some differences were observed between Australian and German students, and to a lesser extent between first and final year students in Australia in relation to perceptions of the real doctor. This is likely to reflect cultural differences in the modelling of medical practice in both society as a whole, and in medical education approaches between the two countries. Increasing exposure to hidden curricula factors such as the professional behaviour observed in clinical role-models, organisational pressure such as work hours and pressure to meet key performance indicators may influence students’ perceptions of the real doctor .Differences exist in the medical school cultures between the two countries, including a predominant problem-based and self-directed learning approach in Australia, compared to a more traditional, didactic approach in Germany. In clinical rotations, Australian students are supervised and supported to a variable degree depending on their site and team allocation. While some variation also exists in the German medical school, the majority of faculty associated with the medical school undertake mandatory training in education as part of their induction, offering a higher degree of standardization. These fundamental differences are likely to impact on students’ experiences, and on their perceptions of doctors based on the teachers to whom they are exposed, and the degree of involvement of those teachers.The most pronounced cross-cultural differences were seen for ‘indifferent’ versus ‘understanding’, ‘conservative’ versus ‘progressive’, ‘superficial’ versus ‘thorough’, ‘authoritarian’ versus ‘democratic’, ‘untrustworthy’ versus ‘trustworthy’ and ‘incapable’ versus ‘capable’. These differences could reflect cultural differences such as those described Hofstede et al. in their 6-dimension model, which enables direct comparisons between countries . ParalleSeveral studies of the hidden curriculum describe students’ experiences of becoming more confident and mature in their clinical work and being able to learn from positive role models who emulate knowledge, respect, and patient-centred care –26. ThesThe process of medical identity formation is one of socialisation into the profession , which lThe most pronounced differences in perceptions between cohorts were observed in relation to the image of ‘self’ – but the difference between countries must be interpreted with caution due to the nuances of the question asked. The absence of difference from first to final year somewhat contradicts previous literature suggesting a decline in empathy and altruism , 8, but While many of the constructs in the polarity scale reflect the complex processes of socialisation explored above, others are more amenable to ‘intervention’ through educational approaches. Differences between the cohorts were observed in the perception of ‘insecurity’ versus ‘security’, likely closely linked with ‘weakness’ versus ‘confidence’, and ‘incapability’ versus ‘capability’. While these are just a few of the constructs for which the cohorts differed, these reflect important markers of confidence and self-assurance, with the German students consistently rating themselves lower than the Australian students in these measures. These constructs may be a reflection of the difference between self-directed and traditional learning models, and likely indicate students’ sense of unpreparedness for clinical practice . These fThis study is limited by small cohorts of students from just two universities in two different countries. The culture of professional identity and the hidden curriculum could vary greatly across universities and of course across other countries. We feel however, that these findings offer a useful starting place for further exploration of identity-formation among medical students in a range of schools and cultures. It is possible that some of the differences between Australian and German students are associated with differences in demographic characteristics such as previous study  as well Further, the study is limited by low response rates among final year students in both Australia and Germany, and a smaller pool of invited final year respondents in Germany. This is a product of the different recruitment approaches used, with Australian final year students invited largely by email, and only those completing Psychiatry as their final rotation being invited in person. Similarly, in Germany, the final year students invited were those placed at the University Hospital for at least one of their three final year elective placements. While this did limit the participant pool, the fact that the majority of the cohort spend at least one placement at the hospital means that the participants are likely to be a representative sample of their respective cohorts.This preliminary study has highlighted some important patterns in medical students’ perceptions which could influence the development of their professionalism and medical identity. Importantly, students’ confidence and sense of security and capability were identified as areas of difference between the cohorts and represent potential targets for educational interventions to improve students’ overall experiences and attitudes. The erosion of self-perception over time is a concern in the context of graduate’s preparedness for practice and the risk of burnout. Further work is needed to better understand the trajectory of change over time in perceptions, and how these constructs relate to students’ and graduates’ performance in assessment and as clinicians. It may be possible to target modifiable constructs among these polarities, in order to improve educational efforts to shape medical identity. Critically, faculty development should be considered as an important target for enhancing the modelling of positive medical identities among those teaching in medical programs, and preventing the erosion in perceptions of the self and the real doctor over time. Medical schools could consider their own perceptions to characterise their desired graduate attributes on a scale such as this, encouraging transparency and efforts towards alignment of perceptions regarding ideal, real and self.


In this editorial, we focus on recent problematic developments in sport science, and more specifically, problems related to periodization research. Primary areas discussed are (1) appreciation of history, (2) considerations for training studies, (3) the development of concepts, and (4) programming-driven training models. Over the last several years, disturbing trends related to sport science research and education related to the periodization of training have been noted by the authors of this editorial. Specifically, it appears that there is an increased push by researchers to promote “outside the box thinking”. Certainly, questioning poorly supported dogma and popular belief(s) is at the root of the scientific process. However, for areas of research that have a solid underpinning supported by good scientific data and considerable previous work, appropriate appreciation and understanding should take place before a dismissal of these works occurs. This does not mean that previous findings cannot be challenged or cannot be further developed, as this is a key aspect of the scientific process. However, we believe that most accepted conceptual paradigms of basic and applied science, including sport science, are firmly rooted in logical reasoning and supporting evidence. We also believe that most good conceptual paradigms develop and evolve over time as a result of critical thinking, sound research (often painstaking), and a diligent search for clarity. As such, a central goal of this editorial is to inspire future research. A troubling current trend in academics appears to be an “everything we have been taught is wrong” attitude, carried out with an intent to create controversy, and this trend has spilled over into sport science research, particularly as it pertains to periodization. Perhaps some (much?) of these issues are related to/influenced by social media (of which we readily admit that we are not experts). Researchers are now more connected virtually, with others across the globe leading to constant commentary, for better and for worse. Several of the authors have been scientists for a long time >30 years), and we unanimously feel that today’s students and young scientists are developing in a climate that is too focused on self-promotion and too often misses the big picture. Over the last several years, we have read many review and opinion papers ,2,3,4 on0 years, Recently, it seems that a more commonly held belief has arisen that “older” research is out of date and less helpful than recently published research. This can result in authors selectively choosing references or statements; potentially missing important work; or worse, specifically choosing studies that fit one’s personal narrative. It is particularly surprising how research in the areas of resistance exercise, strength-power development, etc., performed in 1970s, 1980s, and 1990s was perhaps the biggest “lift” in knowledge the field has experienced , and yetA lack of appreciation of older literature may lead to improper citing of that older literature (when it is cited) or at the very least lead to misinterpretation of this research. Perhaps most importantly, there does not seem to be an appreciation for the development and evolution of ideas and concepts. We have been particularly dismayed at several recent papers that have used details from older references to dismiss entire concepts that evolved from these initial studies ,2. AdditOne example of a common oversimplification is the reference of Lenoid P. Matveyev (USSR) being recognized as the “Father” of periodization. While Matveyev did formalize the periodization conceptual paradigm, he clearly built on previous work and contributed to the evolution of the paradigm. The historical development of periodization has a long and rich history dating back several centuries to the ancient writings of Philostratus; Galen; Avorroes; and, more recently, L. Pihkala , L. Nadori , and N.G. Ozolin . A few of the earliest texts, written in English, that describe breaking up the training plan into “periods” include books by G. Dyson , W. Bresnahan and G. Tuttle , and K. Doherty  [Indeed, Matveyev’s contribution to periodization, particularly his observational work on periodization monitoring of the Soviet track and field athletes preparing for the 1952 and 1956 Olympic Games, and later, his text describing an annual training plan (1965), cannot be underappreciated . It is wDiscussions on the importance of the applied nature of sport and thus aspects related to that understanding are well documented ,11. Too When discussing short-term training studies (the duration of most training interventions) , it is i“Virtually any method of strength training will enhance the strength of a novice during the first few months, provided the intensity, in particular, is kept at a safe level. This is a major reason why it is misleading and counterproductive to apply the results obtained from scientific studies of less than six months’ duration. It is also a major reason why relatively inexperienced coaches manage to have initial success with athletes…” .We certainly appreciate how difficult long-term periodization studies are, such as ,14,15, aAnother important consideration is the amount of detail related to the training that is performed. Mujika  has prevLastly, most training studies referred to as periodization studies are really programming studies (see discussion below), as the manipulation and comparisons of different strategies are usually dictated by differences in set-rep schemes, intensities, etc., and not by over-arching timelines or adaptation-based fitness phases and goals. Training concepts  can be astonishingly multifaceted and complex. Sometimes in attempting to summarize basic, translational and applied science, the large amount of physiology that goes into training concepts  is lost. Related to bullet point #1 (history), to truly appreciate an established training concept one must really know the historical literature. Additionally, for sport research it is also likely helpful to understand aspects well beyond the literature . For example, using a very specific issue, that is a relative component within a larger concept, and then dismissing the entire concept as a mistake: disregarding a mechanistic concept such as Selye’s work on stress response because his original studies were not based on exercise as a paradigm and therefore suggesting that the General Adaptation Syndrome (G.A.S.) cannot be used conceptually in explaining the response to exercise and training . TraininLastly, we have read several articles and frequently observe discussions on social media incorrectly portraying the idea that block periodization  is stagnate and that the programming within the periodized plan cannot be modified if needed during its implementation. Recently, there has been an increased push to use a training strategy that is more in-the-moment-focused. Many terms have been used to describe these programming centered training plans that involve a day to day organization template, for example, agile periodization, flexible periodization, and fluid periodization ,27,28. IWithin U.S. Collegiate sport and Australian professional sports, we continue to be surprised by how often we hear “periodization doesn’t work for team sports” or the more general statement “periodization doesn’t work for my situation.” We have speculated that perhaps avoiding this long-term planning approach is due to how strength coaches are often placed in a servant type role to the head sport coach , and perRecently, we  authored a letter to the editor (LTE)  addressi


The literature on neuroscience has grown rapidly in recent years with the emergence of new domains of research. In the context of this progress, creating a knowledge organization system (KOS) that can quickly incorporate terms of a given domain is an important aim in the area. In this article, we develop a systematic method based on word representation and the agglomerative clustering algorithm to semi-automatically build a hierarchical KOS. We collected 35,832 research keywords and 11,497 research methods from PubMed Central database, and organized them in a hierarchical structure according to semantic distance. We show that the proposed KOS can help find terms related to the given topics, analyze articles related to specific domains of research, and characterize the features of article clusters. The proposed method can significantly reduce the manual work required by experts to organize the KOS. As the literature on neuroscience continues to grow, new subjects of research continue to emerge. Researchers need to identify these new topics and adapt to the changing landscape of the discipline. Knowledge organization system (KOS) “are used to organize materials for the purpose of retrieval and to manage a collection” . KOS wit1, one can find terms representing all subdomains of research in the area. Other, similar, KOS include Disease Ontology (DO)  is among the most prominent KOS in biomedical science. It is a massive collection of terms organized in a hierarchical manner that are used as recommended keywords for articles in the life sciences and medical research. It consists of a “new and thoroughly revised version of lists of subject headings compiled by NLM for its bibliographies and cataloging” , p. 265.ogy (DO)  and Geneogy (DO) . All the2 contains only 11 terms, far fewer than the actual number of keywords related to cognition.It is important for researchers that the KOS be updated extensively and quickly. However, manual organization requires large amounts of time and labor, which indicates that a manually organized KOS cannot both update quickly and contain a comprehensive list of the relevant terms. According to Automatic and semi-automatic methods have been developed to organized terms based on the given corpus. Previous work has combined word representation with clustering algorithms. Word2vec  is amongCreating KOS automatically using word representation and clustering algorithm has another problem to be overcomed: the clustering result is not necessarily accordant with semantic groups. On one hand, the criteria of semantic groups are vague, which makes it challenging to develop objective criteria. Some words are core members of a given semantic group while others are marginal members. On the other hand, A qualified clustering algorithm should have two features: First, it should be able to create a hierarchical structure according to the similarity of words. Second, it should allow us to manually adjust the similarity threshold for word representation while minimizing the manual work required. We think that the agglomerative clustering algorithm  can fulfWe devise a method to significantly reduce the manual work required to pick nodes with explainable meanings and, in this way, adjust the similarity threshold for word representation. For example, as shown in Based on the idea describe above, we created a KOS for neuroscience containing 11,497 research methods and 35,832 keywords. We trained their vector representations based on PubMed Central , containWe then assessed the proposed KOS on two tasks related to the analysis of scientific literature. First, to show that the KOS could retrieve certain categories of articles, we investigated the relationship between research methods and keywords in neuroscience. The strategic space for researchers can be described simply as one where appropriate methods are applied to the corresponding research domains. Applying a new approach to a known subject of research, and applying an available method to a new research topic are two of the most convenient ways to design novel research. The adaptability between the clustered research methods and keywords is apparent at a glance.Second, to show that the KOS can help describe the features of article clusters, we divided articles related to learning and memory into seven clusters and analyzed features of each using the KOS. Many methods are available to cluster documents, but it is challenging to depict features of specific clusters. In summary, we first develop a systematic procedure to semi-automatically organize a KOS with a hierarchical categorization of the neuroscience literature. The KOS can be used to retrieve articles related to certain research domains and characterize their features. 3, and all fields of all available articles, published from January 1, 1000, to October 8, 2018 were scanned for the keywords. On the search results’ page, we used the “Send to” function to send the results to file. The file format was set to XML and the sort order to “default.” A total of 613,184 articles were hence acquired.All articles were downloaded from PubMed Central (PMC) using the search keywords “brain,” “neural,” and “neuron” on October 8, 2018. The data were downloaded using the site’s advanced search functionThe keywords were extracted from the keyword section of the XML files extracted from PMC. The term list of research methods was extracted from the “Materials and Methods” sections of the scanned articles. The subtitles in this section usually featured names of experimental methods, and thus they were extracted as the names of research methods. The “Materials and Methods” section was identified by finding the word “method” or “procedure” in the title. Section titles like “Methods,” “Experimental procedures,” and “Methodology” were all thus identified as identical to the “Materials and Methods” section. Some words or phrases appear both in the keyword list and in the methods’ list. We would like to analyze the correlation of keywords and methods in articles, so we need to avoid duplicated terms. We deleted these terms from the keyword list.All words and phrases were converted to lowercase. Each word or phrase in the list was assigned a number according to its ranking in terms of frequency. The most frequently occurring keyword was “hippocampus,” and was assigned the ID kwd1. The most frequently occurring method name was “statistical analysis,” and was assigned the ID mtd1.All the articles collected were transferred to a corpus for word embedding training. Words and phrases are recognized from natural language in a “longer phrase-first” manner. For example, from the sentence “visual short-term memory for high-resolution associations is impaired in patients with medial temporal lobe damage.” we recognized the embedded keyword phrases “visual short-term memory” (kwd5577) and “short-term memory” (kwd1284). However, the keyword “short-term memory” was not counted because the longer phrase “visual short-term memory” had a higher priority. This setting was chosen because embedded phrases would have otherwise interfered with word embedding training. If the embedded phrases were all listed out in the training corpus, “image analyses” and “gene analyses” would have similar word embeddings, for instance, because they are both related to “analyses.” The training algorithm would capture shallow semantic relations rather than deeper ones. All other words not recognized as keywords or methods were discarded. Not many of these remained at the end of the training, except for stop words.We used the Doc2Vec  functionThe keywords and methods were clustered separately using the agglomerative clustering algorithm .When calculating the distances between clusters, the agglomerative clustering algorithm has three kinds of linkage calculating algorithms: single linkage, complete linkage, and average linkage. The word vectors were normalized so that the norm was one. Keywords appearing more than five times in the keyword section of the articles, and more than 10 times in the corpus were used for further analysis. Research methods that had appeared more than two times in the “Methods” sections of articles, and more than 10 times in the corpus were also used. If a term appeared rarely in the corpus, it meant that the word representation might not have been trained well and introduced noise to the subsequent steps of clustering. The keyword tree and the method clustering tree were fully printed in Each branching node in the tree was labeled by a number based on the node-labeling function in the sklearn package. If there were n terms in the tree, n-1 branching nodes were needed to merge them with the root node. Node numbers 1 to n were used to label the leaf nodes representing the terms. When the first two nodes were merged together to form a new branching node, this node was labeled as n+1. When two more nodes merged to create yet another branching node, it was labeled as n+2. There were a total of 38,036 terms in the keyword clustering tree and 12,488 terms in the method clustering tree. The root node in the keyword clustering tree was labeled as 76,072, 2 × 38,036.The clustering tree was manually organized into 378 keyword clusters  and 277 Sometimes, when the paraphyletic branch of a clustering tree was more likely to belong to one cluster with a consistent explainable meaning, we gave the same cluster name to both the small monophyletic branches in the paraphyletic branch.The results of the clustering of methods and keywords were reorganized into a more general, level-3, cluster result , S6.For each cluster of keywords and research methods, the average normalized word vector was calculated as its representation. The results of visualization were calculated by the TSNE  algorith1, K2, K3, …, Km terms. Suppose also that there are n method clusters in the article, and each has M1, M2, M3, …, Mn methods. Then, for keyword cluster i and method cluster j, the correlation index on the given article is:We calculated the relation between the clusters of research keywords and methods by calculating their co-occurrence in the articles. If keyword “kwd1” in keyword cluster A for a times and method “mtd1” in method cluster B appeared b times in one article, the relation between clusters A and B for this article was a × b. We also considered the weight of an article (W) as its number of citations in PubMed. Suppose there are m keyword clusters in an article, and each has KBy adding the correlation indices of all articles together, we acquired the correlation index between all keyword clusters and method clusters. The log value of the matrix of correlation indices was visualized using a heat map . The colAll 12,391 articles containing at least one keyword from the cluster “learning and memory” were selected. Their representation as calculated by Doc2Vec was obtained from the genism word embedding model. The articles were clustered using the affinity propagation algorithm  in the sTo create a comprehensive KOS with hierarchical categorization for neuroscience, we extracted 35,832 keywords, and 11,497 research methods, calculated word vector representations for each, and generated a binary clustering tree. Each node in given a digit as its ID, like 71,260. The meaning of the ID is explained in section “Word Clustering.” The hierarchical binary tree can organize terms according to distance, but cannot directly serve as a KOS because not all nodes in it have explainable meanings. For example, if a tree node contains 20 chemical receptors with similar functions, there would be 19 intermediate tree nodes in the binary tree, but 19 explainable meanings to categorize these receptors are unlikely to be available. Some terms were simply parallel to one another. To better reveal the structure of the clustering tree, we chose 378 nodes of the keyword tree and 277 of the method tree. Each cluster contained a group of keywords or methods with specific, explainable meanings, and most of them represented a subdomain of research. Each cluster was a monophyletic tree in the clustering, and was given a name to describe its contents. Some clusters, like the method cluster 24,328, “methods,” in line 166 of To measure the accuracy and completeness of our categories of terms, we compared our clustering tree with the MeSH ontology tree. In most domains related to neuroscience, our collection of terms was more detailed than that of MeSH. For example, when searching “glutamate” in MeSH, only one result, “Glutamic Acid” [D12.125.427.300], representing the chemical itself, is obtained. But in our collection of keywords, 60 terms were found referring to glutamate, which belonged to several clusters, including cluster 75,631 , cluster 75,552 (“synapse”), and cluster 74,742 (“glutamate”). The MeSH category “Learning” [F02.463.425] contained 115 terms , whereas our keyword branch 75,751 (“learning and memory”) of the clustering tree contained 565 terms. Neuroscience research is highly dependent on lists of certain kinds of proteins, genes, and brain structures. Thus, a detailed and well-structured list can be valuable for researchers.We collected all terms related to regions of the brain in MeSH 2019 to determine whether their presence and location in the ontology tree. Terms associated with regions of the brain were collected under the category of “Brain” [A08.186.211]. Of the 77 terms collected from MeSH, 16 (21%) were not found in our term list, 37 (48%) belonged to the category “brain regions, circuits, and neurons,” and the other terms were scattered in other categories.The key difference between our KOS and MeSH is that nodes of the latter can have multiple categories. For example, the MeSH node “Glymphatic System” [A08.186.211.150] is both a part of the brain and the lymphatic system. In our KOS, it was classified into the category “interstitial fluid and lymphatic system,” whereas it was under the nodes of the Brain [A08.186.211], Lymphatic System [A15.382.520], and Cardiovascular System [A07] in MeSH. Similar examples include “Olivary Nucleus,” which was classified into “hearing related brain structures” in our system but under the node Medulla Oblongata [A08.186.211.132.810.591.500] in MeSH. This brain structure is closely related to the hearing function, and is not categorized into “brain regions, circuits, and neurons” in our system. The structure of the binary tree in our KOS thus could not incorporate all relations between tree nodes.We conclude that our method can incorporate more terms and is more convenient to update than MeSH. However, although the binary tree can help organize terms according to similarity, the hierarchical structure cannot incorporate all relations in the semantic space. Manual work or an advanced algorithm is required to excavate all relations between nodes of the binary tree. The method used here alone cannot generate a KOS to replace MeSH, but can provide a surplus of new and well-organized terms for people building a KOS like MeSH. It can also help build a preliminary KOS quickly without requiring large amounts of manual work.Scientists, especially bibliometric scientists, need to locate articles in certain research areas. The term clusters in the KOS can be used as search keywords to find articles. In this and the following section, we perform scientific literature tasks using the KOS to demonstrate that although the KOS is semi-automatically created with much less efforts, it can perform function just like other KOS. Here we demonstrate that the KOS can retrieve articles related to certain research domains.The strategic space for researchers is centered around the research methods they can use and the topics in which they are interested. A comprehensive analysis of the adaptability between research methods and keywords can help researchers expand their strategic space for research. To analyze the relationship between the research methods and the keywords, we calculated the co-occurrence index of every research method and keyword. The resulting relation matrix is visualized in The three most commonly referenced method clusters were “behavioral and cognitive tasks,” “cell survival,” and “animal model.” The three most commonly referenced keyword clusters were “brain regions, circuits, and neurons,” “neurodegenerative disease,” and “biochemistry.” The 20 most commonly referenced combinations of the clusters of keywords and methods are listed in The research method “behavioral and cognitive tasks” occupied a significant position in this table. Of the 20 combinations, nine involved “behavioral and cognitive tasks.” It also ranked first in terms of the number of citations. It contained 200 terms, compared with an average number of 41 terms in each cluster, as well as experiment paradigms like “change detection task,” “line bisection task,” “Iowa gambling task,” and related terms like “sensitivity to reward,” “learning sessions,” and “experimental paradigm.” These methods examine the cognitive performance of organisms, mostly human beings. The term “experimental paradigm” seems not explicitly related to behavioral and cognitive tasks, but refers to experimental methods as “experimental paradigm” is indeed the preferred expression in this subdomain.To show that the KOS can be applied to bibliometric research, we present the following case: We plotted the correlation index of the 20 most commonly used combinations against their sequences and found that they followed a power law distribution . We thenCombinations with correlation indices lower than 10 were discarded because they were large in number. Combinations with correlation indices higher than 400 were also discarded because the long tail caused the number to become unstable.The research domain investigated above was defined by our categories of research methods and keywords. However, the scope of these categories was manually adjusted, because of which the size of the categories might have contained subjective factors. We thus collected terms related to regions of the brain and calculate whether all references containing these terms also followed the power law. The terms related to regions of the brain were extracted from the branches 73,705 and 75,966 of the clustering tree for keywords and branch 16,444 of the clustering tree for methods. A total of 780 terms were obtained. The articles containing these terms were retrieved and all total reference to these articles were obtained from PubMed. Using these data, we calculated the total number of references to each term related to regions of the brain and plotted the 30 most referenced regions of the brain . SynonymThe power law distribution of the correlation index suggests that the Matthew effect has been significant in the development of neuroscience . SuccessMost combinations between keywords and methods have received little attention, perhaps because popular research topics are more likely to attract new research. Using the KOS created here, researchers can find appropriate methods for subjects of their interest as well as promising issues to which their own research methods can be applied.A feature of an article, such as the method it uses, the topic it discusses, or the problem it intends to solve, can be depicted by its keywords and methods. Suppose we are interested in research in “learning and memory.” We want to know how many types of articles are there in the field and what their features are. We collected PMC articles containing terms in the keyword cluster “learning and memory,” acquired the document vector in the Doc2Vec model, and clustered them using the affinity propagation clustering algorithm . The firTo depict features of the article cluster, we calculate the coverage of article groups on the term clusters. If an article contained at least one term in a term cluster, it was said to cover the term cluster. For a group of articles, we defined coverage over a term cluster as the percentage of articles that covered it. For example, if 60% of articles in the group contained at least one term in term cluster A, the coverage of these articles over A was 60%. If the coverage of a group of articles over a certain term cluster was high, this meant that these articles frequently mentioned terms belonging to the term cluster. By calculating and ranking the coverage values, we found the term clusters with the highest coverage values, thus revealing the features of the articles.The sorted coverage vector of all articles related to learning and memory is listed in The coverage value is the percentage of learning- and memory-related articles containing terms related to the corresponding cluster. The keyword cluster “learning and memory” itself has been removed from the above list.This investigation procedure can be applied to all seven sub-clusters. To highlight features of the articles in each sub-cluster, we subtracted the coverage vectors of all articles from vectors of articles in a given sub-cluster to generate a “coverage difference vector.” A large value of the coverage difference vector suggested that the corresponding term cluster was closely related to the given sub-cluster, but not very closely related to all articles related to learning and memory. This difference reflects what is highlighted in the sub-cluster. To show how we analyzed features of each article cluster, we arbitrarily use cluster 1 as an example. The 10 clusters each of keywords and methods with the highest coverage difference are listed to depict their features .Drosophila, and used transgene methods and fluorescent tools. The article “Dietary choice behavior in Caenorhabditis elegans”  is difficult to build because it requires a large amount of manual work by domain experts. Our proposed method provides a fast and convenient way to build a KOS based on a corpus, which means that researchers can build their own KOS using their own versions of corpora. We showed that the KOS can perform many functions like other manually organized KOS: It can provide a full list of research domains with terms related to them, retrieve articles related to certain domains, and can be used to depict features of article clusters. A researcher need only follow the following steps to analyze a large corpus:1.Extract terms from the corpus.2.Train a word vector on the corpus.3.Cluster terms according to word vector distance.4.Investigate the clustering tree, and select branches containing terms related to a certain desired domain.5.Use the terms to retrieve articles related to a given domain.6.Cluster the articles.7.Use the term clusters to depict features of article clusters.This procedure does not require researchers to have extensive knowledge of algorithms, and does not demand model calibration. Our KOS is not as well organized as other KOS, like MeSH, but can reduce the amount of manual work needed, update more rapidly, and can handle more terms. It can also serve as a tool for KOS builders.Our research provided a useful strategic space for neuroscience researchers. The results of clustering provide systematic lists of cognitive functions, imaging protocols, descriptive terms, brain structures, behavioral paradigms, diseases, algorithms, cell types, and genes. The relation between methods and keywords can help scientists find new research projects. The popular combinations suggest successful application while obscure combinations may reflect subjects that have potential for fruitful research. Research in neuroscience has evolved to become so complex that it is challenging to obtain an overall perspective of the field without using natural language processing algorithms and other tools. Our research here provided a preliminary view of the field.The results of word representation training and clustering were not thoroughly examined. All parameters were used from past research. However, if we want to obtain a high-quality category of terms, we need to optimize the settings and use well-known objective methods to examine its quality. Furthermore, the binary tree structure cannot adequately depict the hierarchical relationship between terms. We need to either modify the algorithm or invest more manual work to add links to the category of terms. Moreover, this term system cannot help users without being built into a web application. Systematic user evaluation will be conducted in future studies.https://www.ncbi.nlm.nih.gov/pmc/; with accession numbers in Publicly available datasets were analyzed in this study. This data can be found here: YZ was involved in setting the framework of this research. YW provided opinions on algorithm and data visualization methods. CH designed and implemented the algorithm. All authors were involved in writing and refining this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer G-ZW declared a shared affiliation, though no other collaboration, with one of the authors, YZ, to the handling editor at time of review.