This paper outlines the historical development of the concept of the visual aura of migraine, from the first comprehensive published description by the physician Hubert Airy, in 1870. Airy’s description of the phenomenon he called “transient hemiopsia” became widely copied and highly influential as a consequence of the language and images that he used in his presentation. This paper outlines the subsequent development of theories of aura from the time of Airy’s publication to the first demonstration of spreading oligaemia by Lautitzen and Olesen in the 1980s. Hubert Airy, to the assembled members of the Royal Society of London. It was, at that time, customary for such papers to be communicated and read by Fellows of Society. Hubert Airy’s paper, “On a distinct form of Transient Hemiopsia”, was subsequently published in the  Society . ThroughPhilosophical Magazine noted that even in the short period between its composition and publication, he had uncovered two more cases. George Airy had compared his own experiences with those of his friends, pointing out similarities in the phenomena, but also differences, both in the triggering circumstances , and in related symptoms . Hubert Airy mentions at least three other people’s experiences. It is not clear whether these are patients or acquaintances, although he does present an entry from his diary in which he recorded his first ever attack, commenting that a female acquaintance was subject to these attacks of “half-blindness”. The common thread that runs through all these accounts is a degree of slight bafflement as to why such a common human experience should have essentially escaped the notice of the medical profession [In his 1870 paper, Airy presented a precis of previous accounts of hemiopsia, including several published in the 19th century by eminent men of science: William Hyde Wollaston, Sir David Brewster, and Airy’s father, George. Wollaston, Brewster, and the elder Airy all seem to have been predominantly interested in their visual experiences as potentially instructive in the broader context of the increasing interest in the physical basis of light, the laws of optics, and the psychology of visual perception, all of which were current and predominant interests in 19th century British science. Hubert Airy approached the phenomena from a more purely medical point of view, commencing his account by asking, perhaps only semi-rhetorically, why members of his profession had not taken more interest in such a common and fascinating phenomenon. Wollaston believed the condition “to be far more common than is generally supposed; and I might with as much reason have expressed surprise at its having so far escaped notice, were I not aware how many facts commonly remain disregarded, merely for want of explanation”. An addendum to Wollaston’s article in the ofession .Rereading Airy’s account after 150 years have passed, one finds much that remains contemporary and relevant. That it does so is testament to the influence this paper has had in the subsequent understanding of migraine. It is important to remember, however, that it was by no means self-evident at the time that this would be the case. Airy was not a well-known doctor. The subject matter was not something of particular interest to the medical profession or indeed to men of science in general. It was true that eminent scientists had written about migraine aura, but they had had their own reasons for doing so, and Airy’s reasons for including those accounts have more to do with establishing the validity of the phenomenon, as with the interpretation that previous men of science had put upon it. There are perhaps three main reasons why Airy’s paper has come to be regarded as a classic: first (and perhaps foremost) the drawings of the phenomena that Airy included; second, the language that he used to describe the phenomena, and in particular his use of metaphors which subsequently became commonplace; and thirdly, the central role that subsequent neurologists, in particular the renowned William Gowers, gave his account when they came to discuss the subjective visual sensations that could be associated with migrainous headaches.Airy did not use the term “aura” for the phenomenon that he described. As the historian and biographer of migraine Esther Lardreau has demonstrated, “aura” was a term that had predominantly been applied to sensory symptoms that preceded an epileptic seizure; this usage continues to the present day . Airy inAiry’s second neologism for the phenomenon—“teichopsia”—proved more pervasive. This term became the most successful manifestation of the multiplicity and outflowing of metaphor in Airy’s paper. The term, Greek for “town-wall vision”, rested on a series of descriptions of the positive phenomena of aura being angular, like those of fortifications, dating back to a 1778 paper by the physician John Fothergill. By “fortification”, Fothergill meant the triangular pattern of the outer walls of a mediaeval town or castle seen from above, or on a plan, rather than the crenelations atop a castle wall, as some subsequently interpreted it. The eminent 19th century mathematician and astronomer Sir John Herschel was precise in comparing the phenomena to “salient and re-entering angles, bastions, and ravelins”. The 20th century neurologist Macdonald Critchley recounts how Charcot compared his own aura to “one of Marshal Vauban’s fortifications with its salients and recesses” (Vaubon was responsible for the defence of Paris in the 1870 Franco-Prussian war). The same thought appears to have suggested itself spontaneously to Hubert Airy, who in his diary in 1854 recorded that his first experience of aura was “like a fortified town with bastions all round it, these bastions being coloured most gorgeously”. Airy also compared the phenomena to a “cloud”, or a “thick liquid all alive”: “boiling”, “seething”, full of “turbulence and trembling”.At one point, Airy calls the visual phenomenon a “strange intruder”. The “otherness” of aura—the sense that aura is something at once familiar, yet alien—is reinforced by Airy’s recurrent use of military metaphors, an increasingly common trope in medicine from the 1860s onwards, and which still pervades migraine literature  to the present day. Throughout he refers to the episodes as “attacks”. He talks of “the disease” extending and spreading outwards to “invade the more distant parts of the field of vision”. Occasionally, he observes “the rudiments of a fresh attack, beginning nearly where the first began, and sometimes advancing so far as to exhibit its bastioned margin”. With this sense of being under attack comes an oppression, a horror of the experience: “I have seen a person”, writes Airy, “terribly subject to these attacks, shudder at the very name, and turn away from a drawing of the ugly sight, quite content to bear serious illness “if only the ‘half-blindness’ would keep away””. The visceral otherness of the experience proved a fertile ground for contemporary writers, who found the descriptions of fragmented vision, bodies, and consciousness a relevant symbol for the prevalent anxieties of the 19th century .Brain in 2007, pointed out the essential role of illustration in demonstrating uniformity, and providing a heuristic for the interpretation and understanding of the underlying physiological processes [That Airy should show a drawing of the phenomenon to discover whether his friends and acquaintances were familiar with it is not surprising. Airy’s father had included a representation of his experiences in his short paper on the topic. Scientists were interested in constant, reproducible phenomena that could be measured, quantified, and understood. Visual aura was transient, disconcerting; it undermined the very foundations of objective record by distorting the senses. Illustrations were necessary to fix the clinical phenomena, to prevent them from decaying in the same way that formalin prevented the otherwise inevitable putrefaction of flesh. The neurologist Geoffrey Schott, writing in the journal rocesses . Schott On Megrim, the picture he chose was Hubert Airy’s [When the physician Edward Liveing needed a diagram to illustrate aura in his monograph t Airy’s . In the t Airy’s . Both LiManual of Nervous Diseases. In the Manual, Gowers includes a long discussion of the visual accompaniments of migraine, largely drawn from Airy’s paper, supplemented with additional cases, including that of his patient, Mr. Beck. He noted that the sensory symptoms of migraine sometimes occurred without headache [British Medical Journal), Gowers continued to use Airy’s diagrams as the archetype of visual aura [The central figure in the wider dissemination of Airy’s images was the neurologist William Gowers. Gowers recognised the association of migraine with a number of typical subjective sensory symptoms . Gowers cited Airy’s work in his chapters on migraine for his headache : “These headache , he askeual aura .Leçons sur les maladies des système nerveux in the 1880s [Across the Channel, the great French neurologist Jean-Martin Charcot, describing “migraine ophthalmique” in his he 1880s , used laWörterbuch der Augenheilkunde of 1887 [Text-book of Ophthalmology, references Airy’s nomenclature, and closely follows Airy’s description of the phenomenon [The German ophthalmologist Julius Hirschberg cited Airy’s paper in his  of 1887 . In the enomenon .Principles and Practice of Medicine in 1886, reprinted portions of Airy’s paper verbatim, even whilst calling into question the usefulness of the term “teichopsia” [A Manual of the Practice of Medicine is clearly based closely on Airy’s text and diagrams [Principles and Practice of Medicine, from its first edition in 1892, stressed how, in deference to Airy’s illustrations, “the so-called fortification spectra (teichopsia)… may be illuminated with gorgeous colours” [Manual [By the 1880s and 1890s, the term “teichopsia” started to appear in accounts of migraine in standard medical textbooks. Charles Hilton Fagge, in the first edition of his chopsia” . The desdiagrams . The moscolours” . The neuSystem of Medicine, for example, the general practitioner James Mackenzie (subsequently to become renowned as a pioneer of the use of ECG technology in assessing diseases of the heart) presented an account, complete with coloured plates , struggled to explain the phenomena, only devoting eight out of the 600 pages that made up the first edition of his monograph  in 1948 . Wolff hIn 1940, the possibility that aura was a primarily neural process, consisting of a wave of excitation and then inhibition, was raised again by the visual physiologist Karl Lashley, who published meticulous descriptions of his own visual aura . In 1958caused reduced blood flow to the affected areas; that is, spreading oligaemia, and by extension aura, was mediated by a primary disturbance of nerve cell function, rather than being primarily vascular [It was not until the early 1980s, however, that a physiological process was firmly linked to the experience of migraine aura in humans. It was known that ~50% of patients who were prone to visual aura might have an attack after carotid angiography. The Danish neurologists Martin Lauritzen and Jes Olesen used this as an opportunity to study cerebral circulation in the initial phases of a migraine attack by injecting radioactive xenon-133 into the carotid arteries. They found that, in their migraine patients, this caused a decrease in regional cerebral flow that began in the posterior part of the brain and progressed anteriorly in a slow wave, independent of the territories that were supplied by the large cerebral arteries. They theorised, therefore, that aura was the experience of CSD: a wave of neural excitation initiated in the posterior part of the brain, progressing anteriorly with a constant speed of approximately 2–3 mm/min, and that it was the transient absence of nervous activity that vascular .Subsequent research studies confirmed Lauritzen and Olesen’s original findings, and replicated them by other means, such as functional MRI scanning . In addiHallucinations, the neurologist Oliver Sacks recalled the intellectual excitement and emotional engagement of seeing migraine patients in a migraine clinic in New York in the mid-1960s [Writing in his 2012 book id-1960s .“I was fascinated by the range of symptoms and phenomena that could occur in migraine attacks. These attacks often included an aura, a prodrome in which aberrations of perception and even hallucinations occurred. They were entirely benign and would only last a few minutes, but those few minutes provided a window onto the functioning of the brain and how it could break down and then reintegrate. In this way, I felt, every attack of migraine opened out into an encyclopedia of neurology”.Sacks, himself a lifelong migraine sufferer, went on to write a book on migraine which, whilst it has its detractors, remains the richest modern monograph on the subject. Sacks places aura front and centre in his book, for the reasons, both intellectual and emotional, outlined in the quote above. Sacks also praises the Victorian pioneers of the English language migraine literature, especially Liveing, who he sees as his direct intellectual ancestors.As I hope I have shown in this paper, those of us who see patients with migraine remain indebted to the doctors who carefully and meticulously detailed the clinical phenomena of the condition. Even now, an internet search for pictures of migraine aura will bring up, nestled amongst photoshopped representations of scintillating scotomata, Airy’s original plates from his 1870 paper, testament to the enduring power of his images. “Teichopsia” nestles securely in the medical dictionaries and textbooks, demonstrating the ability of metaphor to pervade and persist in the medical literature. Airy’s aura continues to be, in many ways, the archetype of migraine aura.


Visual snow syndrome (VSS) is a relatively rare, unusual and enigmatic medical condition . It freqBased on the patient’s case history and the aforementioned possible symptomatology, we have developed a VSS symptom questionnaire to assist in diagnosis, as well as to assess the effect of a therapeutic intervention . This quper se was reported as early as 1944 in association with the use of digitalis for heart problems [et al. [The presence of VS problems . Howeverproblems , with emHence we have taken a different, neuro-optometrically based, approach in our evolving clinical studies  – especiOur primary focus has involved the use of specialized spectacle tints, typically of a chromatic nature . DiffereA second therapeutic approach has been directed to reduce the perceived intensity of the palinopsia, especially with trailing , in addiIn conclusion, we have presented some evolving, and positive, neuro-optometric clinical approaches and considerations in patients with VSS, especially regarding the symptoms of VS, photosensitivity and palinopsia. The patient with C/mTBI and VSS now has a range of simple and effective therapeutic options, with more likely to be on the horizon as our understanding of VSS and related technology advances.


Every day from the moment we wake up until the moment we sleep, we have the option to look outward or look inward. This is more than an outlook; it is also a metaphor. As we – citizens, ethicists, clinicians, health professionals, lawyers, theologians, academicians, researchers, and educators – prepare for our day we choose our disposition to our world, in fact, our worldview.Those who look outward are mindful of their surroundings, their responsibilities, their obligations, and indeed their place in the world, from the social microcosm of their city to their contribution to their nation, to their impact on the ecosystem. Concretely, this affects their engagement in fundamental aspects of social life – commerce and recreation; privacy and technology; transportation and energy; participation in just institutions, and active reform of racist, sexist, and heterosexist structures.Those who look inward are also in a microcosm, often facilitated by the immediate and persistent attachment to handheld digital devices. The din of self-imposed boundaries of exclusivity between the agent and other dignified beings restricts intellectual movement, creating a recursive echo chamber that is difficult to escape, as it is self-contained and self-created.The metaphor of outward and inward attitudes extends to collective bodies, like committees, task forces, groups of affinity, and executive boards. It extends to concepts like “bioethics” and “globalization” as well.égoïsme à deux between patient and the medical industrial complex instead of maintaining an expansive, even politically aware stance as an ethical system in service to the common good. Particularly as even those who are outside the traditional medical setting are scanned as potential patients, subjected to the medical gaze as businesses decide who is a health risk based on their ability to procure a pass sanitaire , the integrity of bioethics not only as liberation from medical imposition (hence the principle of respect for autonomy) but also an ethical philosophy dedicated to solidarity and justice is further compromised.Conceptually, bioethics – constructed by Fritz Jahr and Sass  and lateThus, we seek here a return to the concept of bioethics, which requires reaffirmation of its global nature. Bioethics was always “global,” holistic, multifaceted. Global does not alter – but rather affirms – Jahr and Potter’s vision. In this way, it ideologically conserves, but not preserves, as bioethics is not a petrified forest or museum of dead thoughts. Hence, our vision for global bioethics is outward, forward, upward. It is nascently contained here in the selection of essays for this, our inaugural issue, under the new editorial leadership of Cheryl Cox Macpherson and Cristina Richie.Ruth Macklin’s lead article “A New Definition for Global Bioethics: COVID-19, a Case Study” describes the “relations between and among countries … (as) a clue to what is needed for understanding what global bioethics is and does.” In particular, her case study on Covid-19 and the insulated response of many countries to the Covid pandemic in the form of “vaccine nationalism” highlights domestic inwardness. Macklin writes that vaccine equity agreements, which “began as a good-faith effort to allocate vaccines in an equitable manner quickly gave way to self-interested behavior on the part of rich and powerful nations.”Self-interested behavior, of course, is not limited to pandemic responses but is symptomatic of inward-looking countries. Yet, as Gustavo Ortiz Millán’s commentary on Macklin’s article suggests, “a more globalized world presents bioethics with new challenges; cases that call for a global response and also a global bioethics.” These challenges include wildlife poaching and trade, zoonotic outbreaks, biodiversity loss, and climate change. His call in “Bioethics, Globalization and Pandemics” that “global bioethics should take the concept of One Health more seriously” extends to poverty-based global health disparities as well as integrative approaches to health for people, animals, and the environment.feminist global bioethics” . She, too, observes the splits in feminist thought between values like autonomy and community, which tend to fall along Western/non-Western lines, respectively. The often inward-looking agenda of Western feminism is at odds with the outward-facing decolonization of both feminism and ethics, and thus both humanism and bioethics. Therefore, instead of balking at concepts, such as care, gender essentialism, and interdependence, a global bioethics is truly inclusive since it values these ideas in addition to justice, gender constructivism, and independence. To be sure, the dichotomy of outward/ inward does not fall predictably on other dyads. Eva Feder Kittay’s characterization of “rights/justice-based ethics/bioethics on the one hand and relationships/care-based ethics/bioethics on the other hand,” noted in Tong’s essay, could be both inward or outward in application.Rosemary Tong’s “Towards a Feminist Global Ethics” outlines the impacts of globalization on both feminism and women by carving out “room in global bioethics for a certain type of non-imperialistic, non-colonial universalism that is present in some formulations of intersectional approach to the values of care and justice” . In doing so, ethical dilemmas may be solved and resolved, thus widening the aperture of bioethical impact.Indeed it is just this pluralistic, non-binary approach that Christine Overall presents as she interrogates “The Role of Care” in her commentary on Tong’s article. Since “looking at the lengthy list provided by Dr. Tong of issues that global bioethics deals with it is hard to see how care can resolve them,” Overall suggests that “in order to develop a feminist global bioethics, what is needed is an In taking perhaps the broadest (re)interpretation of bioethics, Henk ten Have observes, “the phenomenon of a pandemic clearly reactivates the notion of global bioethics” by facilitating the opportunity to address issues typically overlooked by  bioethics, like “community, the common good, solidarity and fairness.” His article “The Challenges of Global Bioethics” distills both Macklin and Tong’s articles, thus poising global bioethics for significant methodological work in both scope – that is, problems of a “global nature … intimately connected to processes and practices of globalization which have characterized international relations” – and the moral orientation of “unity of humanity, solidarity, equality, openness to differences, and focus on what human beings have in common.”unwelt. We welcome dialogue and contributions in this endeavor.Taken in aggregate, the articles which were solicited for our thematic issue, which attempts to re-define global bioethics and “deepen understandings of what ‘global bioethics’ is and does” simply does just that. Global bioethics connects rather than separates; builds paths rather than blocks them. Global bioethics replicates the intricate matrixes found in nature as both a holistic ethical system and as a spatial location. As such, our vision for the future of the journal henceforth explores the many facets of global bioethics – with an eye towards effecting significant change in the most pressing and perennial ethical issues of the


In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies.Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term  interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are  What are these restricted task scenarios? “short, well-defined lessons delivered with limited adaptation to individual learners or flexibility in curriculum.” Results from studies of single-session tutoring interactions with limited personalization paint an overall picture of benefits that are stable, positive, and modest. In order to improve the impact of social robot tutoring technology, researchers are looking toward educational interactions where personalization plays a larger role, and to long-term interactions to develop deeply personalized models.A 2018 review  on the uDespite general recognition that long-term interactions enable a more impactful approach for the field, developing agents capable of sustaining long-term interactions is no simple feat. Some of the challenges researchers face in sustaining long-term interactions include lower student engagement (due to repetitive interactions and declining novelty), personalized models that represent only limited aspects of student mastery (narrowly focused models are more straightforward to implement and require less data to train), and early stopping .transferrable across tasks throughout the long-term interaction, i.e., data collected from earlier interactions with a student on a prior task can be used to improve personalized model learning in a new task.To address some of these challenges, we advocate an approach to long-term interaction design called “multi-task personalization” in which students interact with a social robot across different task contexts throughout a long-term interaction. Within each task, the robotic interaction partner learns a task-specific personalized model of the student that is A multitask personalization approach has potential to address many of the practical challenges associated with sustaining long-term interactions. Student engagement is likely to remain higher over time when engaged in different, varied tasks with a learning partner, compared to repeating the same task multiple times. Personalized student models can also draw on data from a wider variety of task contexts in order to learn a more multifaceted picture of a student’s mastery. And transferring data from interactions on prior tasks can help speed up model learning on a new task, reducing the risk of early stopping from cold-start learning.RhymeRacer and WordBuilder, were developed in partnership with experts in children’s media and early literacy learning, and were designed to help young students practice different literacy skills, namely rhyming and spelling.In prior work  we laid We developed a flexible Gaussian Process-based approach to modeling student knowledge in each game task, with an instance-weighting protocol based on task similarity that allowed for data transfer across tasks. We showed that multi-task personalization improved the sample-efficiency of model training, and was particularly useful for avoiding the problem of “cold-start” modeling. This research was conducted with the assumption that student knowledge was static i.e., that students’ level of knowledge was fixed throughout the interaction sequence. In order to further validate the potential of multitask personalization for real-world scenarios—and recognizing that in real human-robot educational interactions, a student is not a fixed target but a dynamic one—we look to augment our original approach to multitask personalization with methods that better support personalized modeling of dynamic/non-stationary targets.shift over time, recognizing that, in the real world, temporal data are not independent and identically distributed (“i.i.d”), but rather drawn from a distribution that may change over time, but without a clear signal of such a shift  – a “learning paradigm where the data distribution and learning objective change through time, or where all the data … are never available at once”  is preci a shift . Continui.i.d, and, frequently, the training data arrive in a batch, rather than over time. These broad distinctions can largely be characterized by a focus on task “shift” vs. task “switch” However, this boundary is not always strict, and researchers often work to address both issues simultaneously  generaldirect applicability to the empirical content of the rest of the paper, we wish to briefly highlight some particularly helpful articles that inspired our thinking in the area of transfer learning and nonstationary modeling, as applied to Gaussian Processes.In general, rather than compiling lists of relevant citations, we prefer to introduce and cite prior work at relevant sections throughout this paper. However, owing to the more abstract nature of the following articles and less StudentModels  to each word in the Curriculum, based on observations of their prior gameplay. Each game has undergone playtesting validation and the Curriculum was curated by experts in early childhood learning to ensure a representative set of 74 words that are generally phonetically, orthographically, and semantically  age-appropriate, and form distinct rhyme groups.As the child and robot play each game together, the robot tutoring agent learns a Gaussian Process model, which we refer to as the RhymeRacer centers around the family of literacy skills known as “phonological awareness and phonemic articulation.” It is a two-player game that proceeds in a series of rounds, each of which offers a chance for either the robot tutor or the student to select a word that rhymes with a central “prompt word”. In this paper, the gameplay of RhymeRacer is unchanged from its presentation in The gameplay and design of RhymeRacer is … a fast-paced, competitive, 2-player game that proceeds through a series of discrete game rounds. At the start of each round, the tablet shows a picture of the “Target” word in the center of the screen ?” as the images are displayed. The first player to correctly tap on the rhyming Prompt word graphic is awarded points, after which the graphics clear and the next round begins.“reen see , surrounThe robot player is presented to the human player as a co-playing peer, and its outward behavior affirms this framing: the robot player selects Prompt word graphics just as the human player does, gives a mixture of correct and incorrect responses, and responds with appropriate socio-emotional behaviors to in-game events .”WordBuilder is the second game we developed to study multitask personalization in long-term interactions. It was specifically designed to complement RhymeRacer and went through a similar design process, including playtesting, consultation with educational experts, and content and asset revision by experienced children’s media designers. Most of the visual assets are shared across both games, including the graphics of the Curriculum words, both to help reinforce students’ understanding, and also, practically, to help ensure that the correlation between student performance in the two games is based on students’ mastery of the underlying skills, not on factors related to the game interface design. The gameplay of WordBuilder is also unchanged from its presentation in WordBuilder is a brand-new game developed to complement RhymeRacer. The two games use a similar design process and share some game assets to maintain a consistent visual style, most notably the Target word graphics that depict the words from the Curriculum … WordBuilder serves as a counterpart to RhymeRacer in two main ways: First, WordBuilder is designed to help students practice spelling , rather than rhyming (a phonetic skill), to broaden the curricular coverage of the unified system. Second, WordBuilder features collaborative, rather than competitive, gameplay; the robot and child work together to solve a spelling puzzle posed by the tablet, as opposed to the “first-to-answer-wins” style of RhymeRacer.“RhymeRacer, gameplay proceeds through a discrete series of rounds, each associated with a round “Target” word whose graphic is displayed at the top of the screen. The letters which make up the Target word are randomly placed into letter blocks surrounding a set of  letter slots in the center. For example, if the round Target word is SNAKE, the tablet shows an image of a snake and the letters S-N-A-K-E in letter blocks in a random order and location, surrounding five empty letter slots  or whether the robot responds . This paradigm of interwoven “samples” and “demonstrations” that mix assessment and learning is an example of the “stealth assessment” design pattern, commonly used to achieve educational goals in interactive games without breaking immersion and experience flow  in In order to effectively teach a student, the agent must know what words the student has already mastered and which it has not. Therefore the agent faces the twin challenge of simultaneously estimating a student’s individual knowledge state while using its latest estimate to teach new content, though the act of teaching itself may change the student’s underlying knowledge. The The fundamental modeling approach we use to represent student’s cognitive task mastery is Gaussian Process regression. A Gaussian Process (GP) is a distribution over functions, defined over some input domain, where the joint distribution of the functions at any finite set of domain points is jointly Gaussian, i.e., at any particular domain point  to incorporate personalized data obtained from an interaction focusing on a different literacy skill . We also extend this approach to nonstationary environments, by augmenting the conventional Gaussian Process with a “continuous active training data management” protocol, that acts as a mirror to the active learning protocol pursued by the ContentModel–instead of selecting domain points to add to the training set based on the GP posterior, the CATDaM protocol selects points already in the GP training set to remove (see Gaussian Process can flexibly represent a wide variety of domains and can be tailored by model designers to incorporate domain knowledge move see RhymeRacer and WordBuilder), which we refer to as the StudentModel for that game. Each StudentModel is an estimate of a student’s personal mastery of that game’s intended literacy skill (rhyming or spelling). In our implementation, the StudentModels of both games take the form of a Gaussian Process, defined over the same domain space: a discrete set of words , a set of 74 words selected in partnership with experts in childhood literacy, supplemented with age-appropriate words to complete the rhyme groups.Over the course of a long-term study , the covariance functions functionally distinguish, and therefore define the game task (with respect to each other). In other words, because the two StudentModels share an input space, mean function, and noise hyperparameters, the difference in their posterior estimates, if provided with the same training data, is solely driven by the differences in their covariance functions.Under the framework of supervised learning, the GP mean function is conventionally set to 0 everywhere, leaving the covariance as the primary way for researchers to encode domain knowledge in the model “design”. In fact, because the two task RhymeRacer and WordBuilder game inputs give only a binary “correct” or “incorrect” answer signals. To map from the binary signal of response correctness, we blend that information with continuous contextual features like timing. The final score , where −1 represents complete lack of mastery, 1 represents full mastery, and 0 represents neutral mastery, providing an intuitive scale for interpreting training data and, hence, the GP posterior. Gaussian Processes can handle a continuous range of inputs, but StudentModels is their covariance kernels, which compute a distance metric between words in the Curriculum, bringing pairs of words “closer” together when their task outputs  are more highly correlated. In RhymeRacer, the covariance function is based on the cosine distance between the GloVe semantic word vectors ? Because the two tasks are broadly related , we could consider simply adding all observed source task data to the target task training set. However, this approach ignores that some source task data points are more informative to the target task than others. In other words, the correlation of source task output with target task output varies over the word space domain. Moreover, we can use the definitions of the covariance kernels to compute a metric of task similarity at each domain input point, which gives us a score of how similar the local geometries are for each task. We can interpret this instance-specific task similarity metric as a transfer coefficient. “Instance-weighting” refers to a family of transfer learning methods that training a target task model on source task data, where the source-task data is re-weighted (a very simple form of task-transformation) before incorporation into the target-task training set  . When computing the instance weight of ‘’, if knowing DOG impacts the inference of other words in the source task in a way similar to how knowing DOG impacts inference in the target task, then DOG should be weighted roughly equally (i.e. close to 1) in the target task. More concisely, the greater the source-target similarity in word-space geometry around a domain point, the higher the transfer weighting of any source task data at that domain point.“The covariance function of or FROG) . Likewisw in the Curriculum, W”  difference between source and target task covariances of the instance word and each other word, giving a measure of how similarly instance word data impacts inference overall in the source and target tasks. Transfer weight, ulum, W” A transfer coefficient of 1 indicates “perfect” transfer i.e., that instance word conveys the same information in both source and target tasks, whereas a transfer coefficient of 0 indicates that the source and target task are uninformative to each other, with respect to that instance word. To avoid undue complications in evaluating this method, we reweight data instances only once in our evaluations, from the originating source task to the target task. If the model switches tasks multiple times, previously transferred data is not re-weighted and re-transferred back to the original source-task model.λ can never change the sign of a training instance i.e., a negative demonstration in RhymeRacer remains a negative demonstration in WordBuilder.By design, the range of possible training data scores lies within , which, in addition to providing a natural interpretation of scores as “mastery”, also simplifies the instance-weighting transfer procedure. Because positive values are interpreted as positive mastery and negative values as lack of mastery, multiplying by the (positive) transfer coefficient nonstationary domains. We accomplish this primarily via a novel extension to the Gaussian Process modeling framework described thus far. In prior work (high variance Gaussian (indicating a newly uncertain prediction), but rather a low-variance Gaussian . Unlike other uncertainty-based estimation methods  in which uncertainty is updated over time, Gaussian Processes lack a mechanism for increasing uncertainty around previously observed training data. Our solution, aimed at adapting GPs to lifelong learning scenarios, is an active “learning” protocol we call “continual active training data management”, or CATDaM.In this paper, we propose to move from multitask personalization to “lifelong personalization” by extending transferrable personalized models to ior work  we notedCATDaM is comprised of a data structure that organizes the observed training data temporally, and an active learning algorithm that marks “stale” data points and removes them from the active training set. Much as the active learning method used by the ContentModel  and explaining out loud its reasoning to the student. As described in Curriculum word demonstrated are, in fact, coordinated by the StrategyModel, the ContentModel, and the StudentModel. The decision to take the DEMONSTRATE strategy action comes first, and then the ContentModel selects the word which the StudentModel is most confident the student has not mastered .Demonstrations by the tutoring agent represent the most direct opportunity for the agent to influence CATDaM! It signals that a student’s mastery with respect to that domain point  may have shifted, and that prior observations of student performance may no longer reflect their current mastery. In order to address this potential distribution shift in student mastery, CATDaM marks prior observations of student response to that target word in the memory data structure and removes them from the active training set. Not only does the CATDaM protocol remove training data that may no longer reflect the current “distribution”, but it also has the additional advantage of directly increasing model uncertainty at the demonstrated domain point, signaling to the ContentModel that it is a good candidate for observing student performance at a future opportunity.A demonstration represents important contextual information for CATDaM to a StudentModel through a series of simulation experiments.In the following sections, we evaluate the effect of adding simulations of human behavior. “Oz of Wizard” experiments involving “simulated” students are rarely publicized, despite the widespread use of simulators in other areas of robotics . In part this is because real student behavior is not easy to simulate. Real students act unpredictably, capriciously, and in ways that even the students themselves struggle to articulate.Although uncommon, it is by no means a new idea within HRI to simulate human data to evaluate robot behavior, models or algorithms under gentler (and more repeatable) conditions. The benefits of this practice are most clearly articulated in a paper that describes the “Oz of Wizard” paradigm, inverting the better-known “Wizard of Oz” paradigm in which real humans interact with a robot whose behavior is actually produced by a human . Under tde rigeur. Despite a simulation fidelity gap larger than many physical environment simulations, we believe simulated student evaluations can advance research in long-term human-robot interactions by providing a more principled starting point for systems prior to conducting long-term in-person studies. For instance, studies on simulated student data can confirm that modeling algorithms perform as expected on simplified data distributions, simulated student data can also help algorithm designers tune hyperparameters to useful values or establish reasonable performance baselines without having to conduct pilot tests on live students. They can also allow for many different comparisons to be made in parallel, whereas human-subjects studies are more typically tightly controlled owing to the generally small number of participants, which has the unfortunate side effect of limiting the number of hypotheses that can be evaluated. We believe that the use of simulated student tests should not be considered a substitute for an in-person evaluation, but rather an important and insightful part of the system implementation and preparation before a study of in-person long-term interaction is launched.In many fields of engineering where the “actual” live test of a system is expensive, overly time-consuming, or carries substantial risk, simulation studies are considered In a 2021 review published in the Proceedings of the National Academy of Sciences, roboticists highlighted HRI as an area where simulation has great potential, but also faces many challenges.Development of simulation tools that better represent the psycho-social nature of HRI and enable a common operating “picture” of possible solution sets for decision making may … establish a baseline for more effective collaboration … Creating (simulated human) avatars is as difficult as humans are diverse, each person a unique and complex web of intertwined physical, social, emotional, cognitive, and psychological threads … Numerous questions remain unanswered in relation to abstracting in mathematical models the psychological underpinnings that trigger in humans states of anxiety, fear, comfort, stress, etc. In this context, the ability to control and display emotions in (simulated human) avatars represents a prerequisite for endowing smart robots with a sense of empathy in their interaction with humans viaCATDaM. We outline our implementations of two classes of simulated “students” (referred to as “simple” and “dynamic” SimStudents), describe the theoretical assumptions on which these simulations are based, and discuss the implications of subsequent simulation experiments. SimStudent is unchanged from that prior work in the new experiments and results reported here.In this section we describe how we derive simulated student performance data, which we use to analyze the effects of multitask personalization through cross-task model transfer and a “lifelong personalization” extensions SimStudent has an internal “true mastery” . The process for generating true mastery values varies by game, and is used to simulate a student’s gameplay actions during the game via a noisy sampling process.Each SimStudent’s “performance data” for a word consists of a binary “correctness” variable corresponding to whether they successfully applied the primary literacy skill of the game to the word , plus a scalar “timing” variable corresponding to the amount of (simulated) time taken to answer. Each word-performance pair  are averaged, and Gaussian noise , both skills are indicators of a meta-linguistic skillset known as phonemes , uniformphonemes . After rcorrectly answering a question without true mastery or incorrectly answering a question despite true mastery. For RhymeRacer, we set guess and slip rates at 0.25 and 0.1, based on the multiple-choice nature of the round gameplay. For WordBuilder, due to a game design less conducive to successful guessing, the guess and slip rates are set at 0.1 and 0.1.The “correctness” component of student performance is determined by whether the student’s true mastery of that word is greater or less than 0 (corresponding to correct/incorrect). However, the value of this component is randomly flipped at a rate equal to “guess” and “slip” binomial variables. “Guess” and “slip” parameters are common formulations in educational student modeling research , which wSimStudent’s true mastery, mixed with Gaussian noise. For these experiments, we capped the maximum timing at 10 s. The student’s true mastery score is binned into deciles, and the final score is calculated by sampling from a Gaussian centered on The “timing” component of student performance is determined by the numerical value of the SimStudent largely keeps the same implementation as the simple SimStudent, and extends it by adding a learning rate and a learning gain parameter. Whenever the tutoring agent gives a demonstration, the learning rate parameter determines the probability that the student’s mastery increases, simulating student learning. The magnitude of the score rise in the student’s underlying word mastery is set by the learning gain parameter . In the experiments reported here, the learning rate was set to 0.66 and the learning gain was set to 0.50 . Other than the probabilistic shift in word mastery in response to tutor demonstrations, the dynamic SimStudent’s word mastery and performance data are simulated identically to the simple SimStudent.The dynamic SimStudent), then explore how introducing robot demonstrations, student learning updates, and the addition of continuous active training data management (CATDaM) affects our evaluation of multi-task personalization.Our primary research goal with this paper is to extend prior results on multi-task personalization to a more realistic simulation scenario that emphasizes the non-stationary nature of a student learner during a tutoring interaction. Therefore, we will quickly recap prior results on multitask personalization in the stationary case  Viability: does incorporating source task data improve target task performance at all, or do we find that source task data is worse than no data i.e., negative transfer? 2) Proficiency: Does a target task model trained on source and target task data perform better than a target task model trained on the same amount of total data, exclusively from the target task? 3) Efficiency: Does a target task model trained on source and target task data perform better than a target task model trained on the same amount of target task data only?The primary questions we were interested in answering with this work were fundamental measures of transfer learning systems: sign  of the SimStudent’s true word mastery. While this may seem a coarse metric for simulated study—we could, for instance, look at L1 or L2 regression loss—the sign of the word mastery is the primary determinant of the correctness of the student’s response (guesses and slips notwithstanding). In a study with real students, we do not have access to a numerical form of a student’s “true” mastery; student models are evaluated based on their ability to predict student’s actual response behaviors. Therefore, in the spirit of keeping our simulation as close as possible to human subject study, we focus our evaluation on the same metric: binary classification of student mastery with respect to individual curricular components.Our primary metric for evaluating models is the F-1 classification score, which combines precision and recall. The classification task is whether the model correctly predicts the RhymeRacer single task, WordBuilder single task, and a transfer model (color shading indicates standard error of the mean). At the start of each rollout, a new SimStudent (with newly randomized word mastery) is created to represent a unique student. Each rollout consisted of 60 samples, intended to mirror the structure of many common studies of long-term interactions—four interaction “sessions” each of which provided 15 useful samples [roughly in line with the actual number of samples collected in live human-robot experiments reported in via game performance.Each figure below shows the results of the average of 20 “rollouts” of 60 “samples” for each of three classes of model: StrategyModel determines whether the tutoring agent DEMONSTRATEs or OBSERVEs. For our simulation study, we adopt a simple rule-based StrategyModel: the robot chooses to DEMONSTRATE after a fixed number of samples and OBSERVE otherwise. In the case of a typical 60 sample rollout, the tutoring agent DEMONSTRATES twice after every three samples it OBSERVEs, starting after the first nine samples. So in a 60 sample rollout, the student receives 34 “demonstrations” from the robot , 2 each after 9, 12, 15 … samples.In live gameplay, the StrategyModel determines whether the tutoring agent DEMONSTRATEs or OBSERVEs. For our simulation study, we adopt a simple rule-based StrategyModel: the robot chooses to DEMONSTRATE after a fixed number of samples and OBSERVE otherwise. In the case of a typical 60 sample rollout, the tutoring agent DEMONSTRATES twice after every three samples it OBSERVEs, starting after the first nine samples. So in a 60 sample rollout, the student receives 34 “demonstrations” from the robot , two each after 9, 12, 15 … samples.In live gameplay, the RhymeRacer is the starting task, and WordBuilder is the starting task.In prior work, we showed that multitask personalization can improve the efficiency of target task model learning, and that this effect is most pronounced within the first few samples collected during an interaction. This is a critical step towards reducing the problem of cold-start learning in interactive machine learning. The results in this section were previously reported in First, we discuss the single-task models. Both single-task models learn good representations of their respective game tasks over 60 samples, consistent with prior experimental results , suggestx-axis determined by the amount of target task data. This representation is best suited for exploring questions of model efficiency—how well do transfer models trained on a mix of source and target task data compare to single-task models trained on the same amount of data exclusively from the target task?The transfer model data is depicted in two separate representations, each of which is better suited to answering different questions. The “continuous” representation  shows the transfer model data as a single rollout of 60 samples, with each session segment colored to show transfer. This representation is best suited for exploring questions of final proficiency—how well do transfer models trained on a mix of source and target task data compare to single-task models trained on the same amount of data exclusively from the target task? The “discontinuous” representation  shows the transfer model data split into discrete session segments, with their position on the RhymeRacer to WordBuilder is substantial and positive, and that a WordBuilder model trained on prior data from RhymeRacer outperforms a single-task WordBuilder model, particularly during crucial early interaction rounds. WordBuilder). In this case, we can see that transfer from WordBuilder to RhymeRacer boosts initial performance, but that subsequent transfer effects are less impactful as more target task data is gathered, suggesting that the benefits of task transfer may not be perfectly reflexive .Overall, these results from the simplified simulation environment paint a compelling enough picture to merit further investigation of multitask personalization in the nonstationary setting. Positive transfer is evident in both directions, and there is strong evidence that multi-task personalization is most impactful in crucial early phases of an interaction, before a model has an opportunity to acquire significant target-task training data.CATDaM) to the GP StudentModel for “lifelong” learning, and present evidence that including CATDaM can improve both model performance and student learning in nonstationary scenarios.Now we turn our attention to evaluating qualities of multitask personalization in a more complex, nonstationary simulation scenario that incorporates the effects of a tutoring agent’s actions on dynamic  students. In these evaluations, results for all models were derived over the same simulation timeline of 60 samples, even though in studies with real students, there is often a trade-off between opportunities for the tutoring agent to respond (“demonstrations”) and the student to respond (“samples”). Because we are primarily interested in understanding data and performance trade-offs between different kinds of computational models, we chose to evaluate them over consistent data sample timelines. Even though the models evaluated with a dynamic student incorporate demonstrations and student learning and models evaluated on static students do not, we evaluate them both with respect to the same 60 sample timeline. We also provide new results from adding “continuous active training data management” (CATDaM) directly to a nonstationary simulation with agent demonstrations and dynamic students.First, we show what happens when we apply the original, static Gaussian Process model , compared to (0.84–0.87) when modeling static students, a drop of 10 percentage points. The final proficiency of the multitask model also declines across both tasks, though the performance loss is less than in the single-task case. Despite this hit to overall proficiency, the most notable trends of the multitask transfer model, positive transfer and early-sample efficiency gains, remain.CATDaM) into Gaussian Process student models.CATDaM) single-task and transfer models on the right. For both classes of model, underlying student performance is derived from a dynamic SimStudent that receives demonstrations.Next, we examine the benefits of incorporating continual active training data management . Augmenting the GP model with CATDaM helps the Gaussian Process better model the non-stationary effects of tutor demonstrations, and performance performs as well in a more complex, nonstationary environment as a stationary model does in a stationary environment.To summarize these results: when we increase the complex and realism of the simulation environment by adding in a non-stationary efficiency results from multitask transfer. Nor are efficiency results clearly impacted when GP model proficiency rises as a result of incorporating CATDaM. This result that the efficiency benefits of a multitask personalization approach are independent of the proficiency benefits of a continual learning approach.And, while non-stationarity lowers the final proficiency of static GP models, it does not appear to materially impact the CATDaM on student learning, the increase in mastery due to the tutoring agent demonstrations. We quantify these results by calculating the number of “newly mastered” words (mastery went from negative to positive) for each model type over rollouts guided by both static and dynamic GPs. SimStudents in the dynamic GP case learned five more words on average, compared to students in the static GP rollouts. We hypothesize this result is due to the dynamic GP picking “better” words to demonstrate, on account of a more up-to-date estimate of word uncertainty enabled by CATDaM.In addition to enabling more sophisticated evaluation of proficiency and efficiency of personalized model learning, by integrating tutoring agent actions and dynamic student learning into our simulation experiments, we can also study the effect of Throughout these experiments, we have strived to carefully contextualize the results as supporting evidence in support of future in-person studies with human students. There is truly no substitute for actual human experimental data. At the same time, we think that these results provide confidence to proceed with live student studies, and are demonstrative of the kinds of benefits for long-term HRI that can come from simulation analysis. When previously introducing this evaluation framework, we wrote:an evaluation of the proposed transfer method, it is not a definitive evaluation. Further research with human subjects will be necessary, not least, because one of the major hypothesized benefits of the multi-task personalization paradigm—increased student engagement—could not be realistically evaluated by simulation experiments.”“In advocating for researchers to evaluate their systems in the real world, Rodney Brooks famously quipped “simulations are doomed to succeed” . We findCATDaM) mechanism to each task model. This memory-based active data management allows each model to proactively prune away “stale” data from its training set based on interactive context features .In summary, the main contribution of this paper is extending our prior study of multitask personalization to encompass more realistic aspects of human-robot tutoring interactions, chiefly that student learning is a nonstationary target for cognitive modeling when the robot tutor is actively teaching. To investigate this more complex scenario, we introduced a method for our Gaussian Process transfer learning approach to better handle nonstationary targets, adding a continual active training data management  and stochastic student learning updates also allows us to analyze estimated student learning gains in simulation. We found that in simulated interaction with a tutoring agent using a complementary to a multitask personalization approach. Finally, we show that adopting a continual learning approach to dynamic student modeling also has benefits for student learning in addition to model learning.We also find evidence that the extension of our modeling approach to nonstationary domains does not alter the positive transfer benefits of data efficiency and cold-start avoidance previously observed in evaluating multitask personalization. In other words, adopting a continual learning approach appears to be Overall, these results paint a bright picture for future research in long-term HRI. Combining multitask personalization and continual learning into a “lifelong personalization” approach appears to benefit both the data efficiency of model learning, the final proficiency of learned student models, and the amount of student learning gain. The simulation experiments presented here provide useful insight as technical validation in advance of a long-term in-person study, and may also prove useful in persuading institutions to engage in long-term HRI research as a scientific partner. Naturally, more research will be needed to confirm these effects in studies with real students. But these results bring us one step closer, providing compelling evidence that combining continual learning and multitask personalization can be a successful path toward truly lifelong personalized companions.


Although individuals may self-identify with a given race and ethnic group, as multidimensional beings exposed to differential life influencing factors that contribute to disease risk, additional social determinants of health (SDOH) should be explored to understand the relationship of race or ethnicity to health. Potential health effects of structural racism, defined as “the structures, policies, practices, and norms resulting in differential access to goods, services, and opportunities of society by “race,” have been largely ignored in medical research. The Women’s Health Initiative (WHI) was expected to enroll a racially and ethnically diverse cohort of older women at 40 U.S. clinical centers between 1993 and 1998; yet, key information on the racial and ethnic make-up of the WHI cohort of 161,808 women was limited until a 2020–2021 Task Force was charged by the WHI Steering Committee to better characterize the WHI cohort and develop recommendations for WHI investigators who want to include “race” and/or “ethnicity” in papers and presentations. As the lessons learned are of relevance to most cohorts, the essence of the WHI Race and Ethnicity Language and Data Interpretation Guide is presented in this paper. Recommendations from the WHI Race and Ethnicity Language and Data Interpretation Guide include: Studies should be designed to include all populations and researchers should actively, purposefully and with cultural-relevance, commit to recruiting a diverse sample; Researchers should collect robust data on race, ethnicity and SDOH variables that may intersect with participant identities, such as immigration status, country of origin, acculturation, current residence and neighborhood, religion; Authors should use appropriate terminology, based on a participant’s self-identified “race” and “ethnicity”, and provide clear rationale, including a conceptual framework, for including race and ethnicity in the analytic plan; Researchers should employ appropriate analytical methods, including mixed-methods, to study the relationship of these sociocultural variables to health; Authors should address how representative study participants are of the population to which results might apply, such as by age, race and ethnicity.The online version contains supplementary material available at 10.1186/s40695-021-00071-6. In the summer of 2020, the Women’s Health Initiative (WHI) Steering Committee assembled a Race and Ethnicity Task Force to evaluate the strengths and limitations of the WHI race and ethnicity data and to provide guidance on language and data interpretation of WHI analyses and manuscripts. We present the WHI Race and Ethnicity Language and Data Interpretation Guide in this paper, as a means to support advancements in the study of race and ethnicity in public health research.It should be well recognized that “race” and “ethnicity” are each socially constructed terms that are not rooted in biology –3; in faUnfortunately, the effects of structural racism, defined as “the structures, policies, practices, and norms resulting in differential access to the goods, services, and opportunities of society by ‘race’” , have bewhich was dropped from the 2020 Census), “colored”, “oriental”, “Asiatic”, and “Caucasian”, among others, should be avoided. In fact, Flanagin et al state that the general term “minorities” should also be avoided when describing groups or populations, and although they recommend that one specify “racial or ethnic minority groups”, and state that other terms such as “underserved groups or underrepresented populations” may be used, provided the categories of individuals included are defined, and that “marginalized groups” can be suitable in certain contexts if rationale is provided The 10 “minority recruitment sites” sites averaged 43% enrollment of women in the targeted ethnic and racial groups, with only one site  achieving the ≥60% enrollment goal ; howeverEmbajadoras-led program Then, The WHI Race and Ethnicity Task Force (WHI R&E TF) recommended that WHI apply the 2003 (self-identified) categories to the baseline 1993–1998) categories, using a mapping algorithm which is presented as a diagram in Fig. 993–1998 One of the driving forces for this effort was the desire to determine how representative of the U.S. population of women, aged 50–79 the WHI was, with respect to race and ethnicity, at baseline. The R & E TF laid out Ethnicity and Race by 5 year age groups for these new categories, in juxtaposition with the 1995 US Census, the latter of which combined Asian and Pacific Islander women Table . When coRetention of WHI participants has differed by race and ethnicity, such that as of September 2019, with re-consenting required at two time points (2005 and 2010), 89.1% of participants in the current WHI Extension Study cohort, now aged 70 years and older, identified as White at baseline, while the percent of Black/African American had dropped (from 8.9%) to 6.3%, despite their younger baseline age, and the proportion of Hispanic/Latina participants dropped from 4.5%) to 3.4%  created the The WHI R&E TF recognizes that the concepts, terms and ideas in the WHI Race and Ethnicity Language and Data Interpretation Guide will continue to evolve and have recommended that it be reviewed and updated periodically, to reflect contemporary thinking.The WHI Race and Ethnicity Language and Data Interpretation Guide (WHI website link) includes key points that would apply to most study cohorts:Develop Questions and Methodological Strategies Informed by Conceptual Frameworks. In the study design and data interpretation stages of race- and ethnicity-focused research, identify conceptual models to target interpretation of the structural factors and racism underlying race and ethnic disparities. For example:Public Health Critical Race Methodology (PHCR) , 8offers“Scientists can consider using frameworks such as the National Institute of Minority Health and Health Disparities Research Framework  to develThose with limited expertise or experience with diverse populations should consider seeking this expertise and experience in the form of co-authors actively engaged in health disparities/ health equity research.ameworks. In the Data Collection.or ethnicity” at baseline as described above and self-identified ethnicity and race, per Census 2000 categories , in 2003; however, as many participants were no longer active, WHI mapped baseline categories onto the 2003 categories  have changed over time and will continue to change. For example, WHI participants self-identified “race Reporting of demographic data on race and ethnicityManuscripts should include an explanation of who identified participant race and ethnicity and the source of the classifications used .Rationale for use of race as a key variable: For papers and ancillary studies where race is the primary exposure of interest or where analyses are stratified by race and/or ethnicity, authors should provide a clear, written definition and rationale for why race is being used .Data Analyses, Interpretation & Reportingnot be required. However, when highlighting the heterogeneity and resilience available within racial and ethnic groups, within heritage group analyses are recommended.The term “other” has often been used as a “convenience” grouping or label for comparisons in data analysis when sample sizes for a given group are small compared to a dominant group, such as non-Hispanic Whites in most U.S. cohorts; however, combining “all other race and ethnic groups” has no valid scientific rationale and is clearly not informative across individual races or ethnicities and should, therefore, not be done. While there is considerable value in examining associations within select historically marginalized race and ethnic groups, the decision to make comparisons between race or ethnic groups should be informed by the research questions. Comparisons of race and ethnic groups to Non-Hispanic Whites by investigators should , i.e. based on the proportion of older women within each race and ethnic group. Discussion sections should address implications for analyses examining racial/ethnic inequities, which may be underestimated compared to those observed in the general U.S. older female population.Authors are encouraged to address how representative of the reference population a given cohort is, in the context of interpreting the generalizability of the analytical results. For example, when evaluating the context of results from WHI that includes a range of race and ethnic, socioeconomic, and/or educational subgroups, it is important to consider generalizability along with relevant confounders and mediators for women aged 50–79 at baseline (1993–1998) and ages 70 and over now (2021) by race and ethnicityStatistical power for race and ethnicity subgroup analyses:We have an ethical responsibility to present data on all race and ethnic sub-groups, but appropriate interpretation is important. As is the case for all subgroup analyses, race and ethnicity subgroup analyses should be sufficiently powered to detect differences by that group. Results from analyses with insufficient power, based on smaller sample size, should be reported with caution. When describing results across race and ethnicity groups, it is essential that authors provide a clear context for interpretation and for applicability to any subgroup. The discussion should clearly acknowledge that sample selection limits interpretation of findings to the overall U.S. population or country of origin or heritage group identified in the manuscript.Over time, sample composition of any given cohort will be influenced by selective drop-out that can be investigated through the use of inverse probability weighting and other methods. As noted above, WHI is currently analyzing known differences in retention by race and ethnic groups, recognizing inequality across all variables but also similarities compared to other women in their age range.Retention by Race and Ethnicity:Race and ethnicity are clearly important variables that should be collected to describe the population, but as both serve as a proxy for both historical and ongoing disadvantage in social, economic, environmental and structural factors arising from racism, considerable caution should be applied when discussing their relationships to disease risk or to support recommendations regarding medical treatment . ScientiStudies be designed with inclusion of all populations in mind; assured access to research centers or removal of barriers to participation, as well as promotion of analytical methods, including mixed-methods, to better understand these factors as interventions are designed.Researchers actively, purposefully and with cultural-relevance, commit to recruiting a diverse sample for all research seeking to improve health.The scientific community should meaningfully commit to training the next generation of diverse scientists and research staff.Authors should develop clear direction and rationale for manuscripts that include scientific hypotheses with regards to race and ethnicity as proxies for social determinants of health and racism.Principal investigators should collect robust data on race and ethnicity, as well as intersections of religion, immigration status, country of origin, acculturation and the social determinants of health to inform research.Authors should clearly define the concepts and context of race and ethnicity as proxies for social determinants of health and racism in describing the purpose of the research and related manuscripts.Journal editors should require appropriate language and descriptors be included in manuscripts to robustly describe the population of interest.Based on lessons learned and drawing on responsible research practices, current WHI Race and Ethnicity Language and Data Interpretation Guide recommend that:Finally, the scientific and medical communities should define race within a robust historical, political, and contemporary cultural framework. This will advance scientific understanding of racism as it impacts health and wellness, and how it can be effectively dismantled. Race, when considered as a biological construct, perpetuates White supremacy in medicine and shifts focus from the fundamental causes of such differences, thereby impeding ability to effect meaningful change in understanding how systems and structures affect health , 44. GreThere is a strong rationale for including race and ethnicity in health research such as longitudinal studies, like the Women’s Health Initiative. Race and ethnicity need to be clearly defined in testing health-related hypotheses as a social, not biological construct. Furthermore, National Institutes of Health (NIH)-funded cohorts such as the WHI should likewise extend to all participants across the nation the assurance of their commitment to report unbiased and rigorously quantified results intended to improve the health of all people groups.Additional file 1:.Additional file 2:.Additional file 3:.Additional file 4:.Additional file 5:.Additional file 6:.