With each of the five scenarios, 24 participants had to spell six letter words (at least 18 selections with this three-steps speller). The fastest feedback modalities were different for the users, there was no visual feedback which was generally better than the others. With the used interface, six users achieved significantly better Information Transfer Rates (ITRs) compared to the “no feedback” condition. Their average improvement by using the individually fastest feedback method was 46.52%. This finding is very important for BCI experiments, as by determining the optimal feedback for the user, the speed of the BCI can be improved without impairing the accuracy.In this paper we compared different visual feedback methods, informing users about classification progress in a steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) speller application. According to results from our previous studies, changes in stimulus size and contrast as online feedback of classification progress have great impact on BCI performance in SSVEP-based spellers. In this experiment we further investigated these effects, and tested a 4-target SSVEP speller interface with a much higher number of subjects. Five different scenarios were used with variations in stimulus size and contrast, “ Brain–computer interfaces (BCIs) use brain signals to control computer applications or external devices. By measuring the normal electrical brain response, i.e., to specific stimuli, an application can be controlled without the need for hand-movements . ElectriOne of the commonly used methods in BCIs are steady-state visual evoked potentials (SSVEPs) which can be detected from the occipital area of the brain when the users are looking at a light source flickering with a constant frequency between 4 and 90 Hz . FlickerWhile most other BCI modalities require training before the first use of the system, SSVEP-based BCIs can be operated with little to no training , althougCommonly, BCIs are developed for people with motor disabilities, who have restricted means for communication. While BCIs are a viable solution for communication without muscle use, their speed is still lower as compared to other methods of communication. Therefore, BCIs can benefit greatly from improvements in performance. Several solutions exist targeting BCI performance, such as changes in classification algorithms, or personalising the parameters of the system. In some cases, these personalised parameters result in a faster or an overall more accurate system, for example, bigger stimuli can result in stronger brain responses .Several other BCI parameters can also be adjusted, for example, the flicker frequencies of the stimuli, which manifest in faster or slower flickering. On the one hand, faster flickering can be more user friendly, on the other hand, the brain response is weaker, which can result in inferior performance ,12. AnotSome stimulus parameters that can influence the amplitude of the evoked neural response are the size , contrasno feedback” condition using the personalised feedback. The number of participants in these studies was low, however, already with this lower number of subjects we noted only minor effects due to duty-cycle changes. For this reason, and to limit the overall duration of the experiment, in the current study we focused on size and contrast changes.As it can be seen from this multitude of parameters, BCIs require, or at least function better with, personalisation. Apart from classification parameters of the BCI, the users themselves influence the performance greatly. This can be the consequence of fatigue, lack of attention, or even the attitude of the user, not to mention muscular activity which can cause noise in the recorded EEG data. Therefore, it seems reasonable to involve the users more in the use of the BCI. In our previous experiments ,17, we aAlthough using auditory feedback and not visual,  found thThe user comfort was also taken into account during the design phase of the experiment. For some of these stimulus parameters, a better brain response is associated with a lower user comfort ,15. To aIn this experiment, we further investigated the effects encountered in our previous studies, by testing an interface with less stimuli . 24 subjects , with a mean (SD) age of 25.2 (4.1) years participated in this study. All participants were healthy adult volunteers. They all gave written informed consent prior to the study in accordance with the Declaration of Helsinki. Information needed for the analysis was stored anonymously; results cannot be traced back to the participants. This study was approved by the Ethical Committee of the Medical Faculty of the University Duisburg-Essen, the ethical approval code is 17-7693-BO. Participants had the opportunity to opt-out of the study at any time. Subjects received a financial reward for their participation in this study.Participants were seated in front of a LCD screen  at a distance of about 60 cm, the setup is shown in The Graphical User Interface (GUI) of the 4-target interface is shown in The speller logic was implemented as follows: when a box was firstly selected, its content was spread over three boxes and resulted in three characters per box. After the next selection phase, the three boxes contained only one letter each. With the third selection the selected letter or special character was selected. In the second and the third phases, the fourth, “Delete” target changed to “Back” to give the option to go back to a previous layer in case of an error. For more details of this 4-target speller logic please refer to .The list of frequencies displayed was (in Hz): 6.10, 7.15, 8.39, 9.84. The flickering on the monitor screen was generated using the method proposed in . In shorFor each feedback parameter, minimum and maximum values were determined. The size limits for the stimuli were between For the contrast, only the stimulus RGB values were modified, the black background remained constant. The stimulus colour values ranged from RGB , which corresponds to dark grey, to RGB , which is white, and the R, G, and B values always remained equal to each other, i.e., these three RGB values were always changing simultaneously. This resulted in colour changes from dark grey through light grey to completely white according to the probability for that stimulus, as calculated by the classifier.For the contrast changing, the sizes of the stimuli were kept constant . The exact values for the changing parameters were calculated as in our previous study, with the formula:Minimum energy combination method (MEC) ,24 was uThe classifier did not require training; however, a classification threshold (ranging from 0 to 1.0) was determined individually for each subject before the experiment. For the thresholds (separate threshold for each stimulus), users were instructed to select each target twice, and the thresholds were adjusted until this was achieved without misclassifications and with a reasonable speed. An automated method could be utilised to set up thresholds, such as described in . Since tThe EEG data were transferred to the PC in blocks of 2500 samples (0.5 s recordings with the sampling rate of 5000 Hz). Down-sampling resulted in blocks of 125 samples (same 0.5 s with 250 Hz sampling rate). The classifications were performed by four separate classifiers. These were of fixed length . When enough data were available (more than 5.0 s) all of them were running concurrently classifying after each incoming block of data (every 0.5 s). As an example, after 4.5 s recording the first three classifiers were active, analysing the last 2.0, 3.0 and 4.0 s of data, respectively. The probabilities calculated by the classifiers were combined by weighted averaging, with weights assigned to the classifiers based on the amount of data processed, as more data usually yield more accurate results .The probabilities used for overall classification were calculated from the individual classifier results, by calculating the weighted average. If none of the probabilities exceeded the threshold, the output was rejected. If one of the probabilities exceeded the threshold, the following classifier outputs were rejected for a short duration, in order to negate the effect of the previous stimuli on the EEG data, and to allow users to find the next target. The duration of this gaze shifting period was set to 1.5 s. During this period the targets did not flicker, allowing the users to find the desired target and change their focus. As movements cause artefacts in the recorded EEG data, the gaze shifting time also lowers the chance of false classifications. False classifications caused by movements could only occur if the movements lasted long enough, exceeding the allocated gaze shifting time. The length of the gaze shifting time was set based on our previous experience. For more details please see .Moreover, an audio file of the spelled character was played after the third selection step to inform the user what character was selected. The audio file “Select” was following the previous selections or alternatively “Back” and “Delete” when those commands were selected. Compared to visual feedbacks, which were used continuously, audio feedback occurred only after selecting one of the targets (when the classifier output was higher than the threshold). Moreover, the audio feedback was executed during the gaze shifting period; therefore, it only helped users decide on the next target (whether the next letter or “Delete” had to be selected). The users had to correct any mistakes in the written text using the last box, labelled “Delete” or “Back”, which removed the last character of the user-written string or returned to the previous step, respectively.no feedback”, “size increasing”, “size decreasing”, “contrast increasing”, and “contrast decreasing”. The order of the spelling tasks was randomised to avoid bias.In this experiment users had to spell altogether five words, each with five different feedback methods: “no feedback” condition no information was given to the users regarding classification progress. In the “size increasing” and the “size decreasing” conditions, the stimuli were getting bigger or smaller, respectively, as the stimulus was assigned a higher probability by the classifier. “Contrast increasing” and “contrast decreasing” were realised through changing the intensity/shade  of the stimulus, which in the off phase was always black, and in the on phase a shade of grey. For a more detailed description please refer to the contrast increasing” scenario.As indicated by the name, in the “After signing the consent forms, users were prepared for the EEG recording. For each user the individual thresholds for classification were set. The values set this way were not altered throughout the rest of the experiment. If no selection was made with a scenario for several minutes, caused by the probabilities calculated by the classifiers not reaching the corresponding threshold, or the classification accuracy was insufficient to continue, the scenario in question was marked as not finished.The spelling tasks all required six letters, which could be written, in case of no errors, using 3 × 6 = 18 selections. Mistakes had to be corrected. After each spelling task users were asked to rate the used scenario on a Likert scale from 1–10, where 1 noted a very negative impression, and 10 corresponded to a very positive impression. The experiment lasted approximately one hour. This included informing the users about the experiment as well as electrode preparation. Usually these steps took about 20–25 min. The time for the spelling tasks varied greatly, as some scenarios were more difficult to complete (shown by the lower ITRs). The fastest users took a few minutes only to complete all spelling tasks, while in the slowest took 17.15 min. Additionally, users had to rate each scenario and they were allowed to have short breaks between the spelling tasks. As in most cases no breaks were requested, the time between spelling tasks was only 1–3 min long.The complete word list, from which the five spelling tasks were randomly selected: “COVERS”, “HORSES”, “STRIKE”, “STORES”, “MISTER”, “RUBBER”, “SOLELY”, “LITTLE”, “BUFFER”, “TIMING”, “REFUSE”, “FORCES”, “LIKELY”, “MODIFY”, “FLAVOR”, “LIKING”, “MOSTLY”, “POLICE”, “LOSING”, “CLOSER”, “MERITS”, “OFFICE”, “WIDELY”, “COUPLE”. These were selected according to the criteria described in https://bci-lab.hochschule-rhein-waal.de/en/itr.html.The achieved accuracies and ITRs are presented in The mean (SD) ITR was 23.9 (7.1) bit/min, while the mean accuracy was 95.2 (6.3)%. These values show that the BCI was working with high accuracy and reasonably fast most of the time.t-tests and Wilcoxon signed ranks test, respectively, when necessary. Sphericity for ITR results was also considered; however, the sphericity assumption was not violated . Bonferroni correction was used for multiple comparisons.Statistical analysis was conducted to investigate performance differences between the different feedback scenarios. By using d’Agostino–Pearson tests for checking the normality of the results we found that ITR results can be used for parametric tests; however, accuracy results require non-parametric equivalents. Therefore, for ITR comparisons repeated measure one-factor ANOVA was used, while for accuracy comparisons Friedman’s test was used. These were followed by pairwise comparisons with paired contrast increasing” scenario proved to be slower than all other scenarios, the “no feedback” (p = 0.002), the “size increasing” (p = 0.041), the “size decreasing” (p = 0.021), and the “contrast decreasing” (p = 0.002) scenarios. All other pairwise comparisons showed no significant differences. Accuracy, on the other hand showed no significant differences using the Friedman test (p = 0.142).ITR showed significant difference To further investigate our hypothesis, we organized for each subject the ITR from fastest to slowest, and compared the fastest methods to the no feedback condition for that subject. To be able to compare the results statistically, we calculated the ITR value for each step of writing the target word. This means that 18 values were calculated, three for each letter. ITR was used for comparisons as it incorporates accuracy as well as selection times, therefore, it is a practical measure of performance.To calculate ITR values for each stage, the following methods were used. Firstly, the gaze shifting time was included in all selection times, in order not to generate outliers in the statistical analysis. Regarding accuracy, the same logic was utilised as for the whole words, error corrections were considered correct. ITR values were calculated for each stage. E.g., with the word “COVERS”, an ITR value was calculated upon selecting the target with “ABCDEFGHI” letters, also when selecting “ABC” and when selecting “C” with the 4-target interface. If a letter was accidentally deleted afterward, it had to be rewritten and the next stage reached to calculate the next ITR value.no feedback” spelling tasks. It is noteworthy, that for some cases the amount of samples is lower than optimal. Therefore, these statistical tests are weaker. This means that there is an increased chance of accepting the null hypothesis, no difference between the feedback methods in our case, while in truth there is a difference. In turn, this means that any differences found significant here would remain significant with more samples. Furthermore, as the highest ITR values are expected to be the same or higher than the “no feedback” ITR values and never lower, the one-tail tp-values were used. p-values are shown in Paired t-tests were used to compare the ITR values between the fastest and the “contrast increasing” scenario is the least often marked as best.The subjective user opinions are shown in no feedback” condition. Our results confirm our hypothesis and the findings of our previous studies, that the effect of user feedback can hugely impact BCI performance. With the used scenarios, the fastest feedback method was found to be significantly faster for 6 users, for them 46.52% faster on average than the “no feedback” scenario. Additionally, since differences between ITR values are low, the length of the spelling tasks is not always sufficient, and the statistical analysis became weak, resulting in a high chance of a Type II error. This means possibly more significant differences would have been found with a larger sample size.We investigated the effects of different feedback methods on online BCI performance. This experiment was conducted to examine the effects of different visual feedback with a different number of stimuli. Therefore, we used a 4-target spelling interface with four different feedback scenarios and the “contrast increasing” scenario as significantly slower than most others. That said, there were a few users, for whom this feedback resulted in the fastest selections. This means that even though it is generally slower than other methods, this mechanism should also be considered for further implementations.We did not find any effect of the visual feedback on classification accuracy, the users were able to complete all tasks with approximately the same amount of mistakes, which confirms the robustness of 4-target spellers. Contrary to our previous studies, with more users even the generalised results showed significant differences, marking the “As mentioned, the experiments utilising real-time feedback methods in a similar manner are very rare in the literature. There are many occasions when feedback methods are used in another sense, such as in , where aFeedback methods similar to the ones employed in this paper were used by . They us/min. In , the aut/min. In  were gotcontrast increasing” scenario, it is possible that the low contrast at the start of the flickering had a negative effect on the performance for some users. Investigating these effects requires an experiment with multiple different constant contrasts and also with changing contrasts (as feedback); however, due to the amount of tests necessary for both size and contrast changes, this would have likely doubled the duration of our experiment, and would have likely introduced bias from fatigue or lack of attention from BCI users. Nevertheless, these effects require further investigation. Furthermore, we suggest that the stimulus parameters such as size and contrast should be also investigated individually for users, as it could also prove to be user dependent whether they improve or degrade BCI performance. If this is true and the optimal feedback methods/stimulus parameters are determined, they could be adapted for each user for reaching an optimal speed with the BCI.Stimulus parameters can influence the amplitude of the evoked neural responses ,8,14,15 no feedback” condition proved to be the fastest, was six. This value is higher than in our previous study, and this could be the consequence of excluding duty-cycle changes , which could have been faster for these users. Moreover, additional mechanisms of feedback could be used, such as changing not the contrast but the colour of the stimuli , or changing the shape of them. Another possibility is that for the 4-target speller the effects of the feedback methods are diminished.The number of users for whom the “The feedback modalities used in this experiment were chosen according to their simplicity in implementation, as size and colour changes are easily realisable on a computer screen. This makes the ideal for implementation in various SSVEP BCI systems. However, the possible feedback modalities are not limited to these. Changes in shape, colour , opacity and any other method could be tested as a feedback modality in the future, to hopefully find effective and user-friendly solutions.contrast increasing” scenario was found to be performing significantly worse than the other methods, the user’s subjective scores do not reflect this. This could be a consequence of better user-friendliness or mean that slower performance is not perceptible by the participants. The topic could be further investigated with elaborate questionnaires for more detailed user opinions.Interestingly, although the “We utilised the same classification method as previously; however, the effect of these feedback modalities with different classification methods is still not investigated, and requires further experiments.We utilised a 28-target interface with the same feedback methods; however, as explained above, the amount of samples was too short, resulting in a high chance of a Type II error . This means we missed significant differences because of the low sample count, in the case of this interface only 6 samples. In these results we found for two users significant differences, where the improvement in ITR was 25% and 53.7%, respectively. By employing longer spelling tasks in the future, more robust statistics could be expected, which will likely result in more significant differences discovered. As this would have quite substantially expanded this experiment and would have introduced bias based on user fatigue/user motivation, we refrained from using longer spelling tasks.In this experiment we aimed to investigate the effects of feedback methods with 4-target BCI spelling interface, as well as to confirm our previous results, that visual feedback about the classification progress improves online BCI performance of SSVEP-based BCI spellers . We usedWhen the effects of different feedback modalities are averaged for users, the beneficial effects are not noticeable. However, by checking the results individually, the optimal feedback can have a huge impact on ITR. By determining the optimal feedback method for the user, the speed of the BCI can be improved without affecting the accuracy negatively.This topic needs to be investigated further, most importantly to check whether the optimal method is consistent over time (if the best method is the same after a couple of days or weeks). Methods that are different from those presented here, such as colour or shape changes, could also be investigated, as well as interfaces with more targets.Our results suggest that by determining the optimal feedback method and using it for the BCI system, ITR can be greatly improved for many users.


With the approval of three treatments for spinal muscular atrophy (SMA) and several promising therapies on the horizon, the SMA adolescent and young adult populations are expected to evolve in the coming years. It is imperative to understand this cohort as it exists today to provide optimal care and resources, as well as to assess possible treatment effects over time. In 2018, Cure SMA launched two initiatives geared towards understanding adolescents and young adults with SMA, ages 12–25. First, Cure SMA launched a Quality of Life (QoL) survey to capture quantitative and qualitative information on this specific age demographic. Concurrently, Cure SMA invited SMA-affected individuals, ages 12–25, to create a three-minute video on their everyday experiences living with SMA. An inductive thematic analysis of the free-text survey questions along with the video contest findings are reported here.Eighty-five individuals—6 type Is, 58 type IIs, and 21 type IIIs—completed the Quality of Life free-response, while six individuals participated in the SMA awareness video contest. In both settings, individuals detailed a variety of challenges, including but not limited to forming or maintaining close relationships, experiencing feelings of isolation, challenges with accessibility, independence, and dealing with the stigma of being perceived as mentally disabled. Individuals also discussed their successes, including but not limited to higher education enrollment and attendance, development of quality friendships, and perseverance through obstacles. Additionally, notably in the survey, 39% of respondents requested the creation of an SMA peer support group in efforts to connect with each other as well as collectively navigate the aforementioned challenges they face.Together, these findings provide a rare glimpse into the unique mindsets, challenges and motivations of SMA adolescents and young adults, via patient-reported measures instead of caregiver proxy. The adolescent and young adult age demographics assessed represent a critical transition period in life and in SMA care. No one understands the needs of an adolescent or young adult with SMA better than the individuals themselves, and it is critical to encapsulate their insights to affect change. Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disease characterized by progressive muscle weakness often resulting in loss of movement, significant disability, and premature death –3. SMA iWith the approval of three treatments as of August 2020, the phenotypic presentation and prevalence of SMA in adolescent and young adult populations is expected to change. Due to this shift, disease classification is moving away from subtype and towards demographics based on motor function. All data presented below will be displayed in accordance with motor function while information related to SMA type is used to validate.In previous years, a variety of initiatives have qualitatively captured clinical meaningfulness and the experiences of the adult SMA population , 18–20. The Quality of Life Survey consists of four open-ended questions. The objective of this survey was to provide an opportunity for adolescents and young adults affected by SMA to share, in their own words, individual experiences about living with SMA as well as highlight topics important and specific to their stage of life. The four free text questions inquired on biggest obstacles, the impact of SMA on schooling and socialization, and potential resources to improve quality-of-life. No guidelines were given towards these responses in order to help identify a variety of themes. The Quality of Life Survey was distributed as part of a larger survey which was comprised of three sections: (1) demographics information, (2) SMA Health Index instrument  . Written informed consent was obtained online from all adult respondents prior to the conduct of any research related activities. For minor adolescent respondents (ages 12–18), written consent was obtained online from a parent/guardian, and assent was obtained from the survey respondent thereafter.The survey was advertised through targeted email blasts sent to over 400 families using Cure SMA’s membership database as well as Cure SMA’s social media platforms. Cure SMA is a non-profit patient advocacy organization for SMA, which currently maintains a robust database that features data from over 8000 individuals affected with SMA . As of JAn inductive thematic approach was adopted for qualitative analysis of the free text collected in the survey. All questions were evaluated for themes independently of one another. For each question, all responses were manually sorted by the primary author into themes through the identification of key words and sentiments. Key words were identified retroactively of survey completion, for purposes of data analyses. Although the prompts were open-ended, there was a high concordance in responses across participants, particularly regarding the final question. Once all responses were categorized, they were sorted by age, SMA type, and respondent’s maximum motor function at time of diagnosis and time of survey completion, with all demographic information being self-identified. Maximum motor function status was defined as; non-independent sitter , sitter/non-independent walker , and independent walker . Statistical significance based on response age and maximum motor function at time of survey completion was assessed using Fischer’s exact test. For detailed information regarding key words and phrases included in each theme as well as statistical significance results, please see Additional file The objective of the SMA awareness video contest was to raise awareness about the impact/burden of SMA on adolescents and young adults, and learn about the strategies they use/have developed to help navigate their day to day life and the challenges that come with managing this multifaceted and complex disease. Contest guidelines included general topic suggestions that participants could discuss within a 3-min time limit. Topics suggestions included but were not limited to schooling, everyday routine, relationships with family and friends, and activities/interests. Post-completion, this project was submitted for and received IRB exemption for the purposes of retrospective analysis . For more information on contest guidelines, please review Appendix 1.The contest was announced on Cure SMA’s website and social media platforms in October 2018 and ran through January 2019. Targeted e-mail blasts featuring contest guidelines were disseminated to over 400 families of SMA on a biweekly basis, using the Cure SMA membership database; recruitment reminders were also posted on Cure SMA’s web and social media pages. The contest submission period was 6 weeks in duration, after which point all videos were reviewed and approved by Cure SMA staff for video length and use of appropriate language, and subsequently uploaded to Cure SMA’s YouTube channel. All submitters received an Amazon gift card, as a thank-you for their participation. The three videos with the most "likes" received an additional incentive. Video submissions are still accessible; a full link along with contest results can be found in Appendix 2. Video submissions were analyzed for key content themes as well as percentage of time spent on each theme.In the results detailed below, individuals involved with the survey will be referred to as ‘respondents’, while individuals involved with the video contest will be referred to as ‘participants’. All individuals quoted will be identified by their age, gender, and motor function level at the time of completion, and all quotes cited are directly their words..The QoL survey elicited eighty-five (85) responses, representing a 21.3% response rate, with a 95% confidence level and a 9.4% margin of error. Of the respondents, forty-four (44) were adolescent minors , while forty-one (41) were young adults . Fifty-three (53) individuals identified as female, thirty-one (31) as male, and one as non-binary. The most prevalent maximum motor function level at both time of diagnosis and at time of survey completion was sitter/non-independent walker. As respondents were recruited from the survey through the Cure SMA database, demographic information could be verified. Sample demographics and clinical characteristics can be found on Table 1The first free text question of the QoL survey addressed the most difficult aspect of balancing SMA symptoms with the everyday lives of adolescents and young adults . There was a statistically significant difference in discussing accessibility by motor function (p = 0.005), as the majority of individuals reporting accessibility had a maximum motor function of independent walker. Fatigue was most frequently mentioned by individuals with a maximum motor function status of sitter/non-independent walker than an independent walker. One respondent remarked, “Everything is difficult because depending on the day, my muscles get tired which makes me tired” . Regarding socialization, one respondent wrote, “It’s hard to always keep up with my friends” .Other factors mentioned with less frequency included the burden of pain and physical symptoms, finding proper care and finding a healthy life balance. Multiple non-independent sitting individuals cited pain as their most difficult aspect of balancing life, one writing, “Just dealing with the pain.” . There was a statistically significant difference between adolescents and young adults citing finding one-on-one care to be the most difficult aspect of balancing life, with only young adults citing this factor (p < 0.05). Responses are categorized by age and motor function at the time of survey completion in Table ” . This response was primarily indicated by individuals with a maximum motor function of non-independent sitter or sitter/non-independent walker.The second question of the QoL free text inquired about the impact of SMA and SMA symptoms on schooling . Aid and assistance also received 9 responses. One respondent wrote, “It forces me to require a 1 on 1 aide” . 8 individuals discussed homeschooling, for reasons including germ exposure and placement struggles. This response was primarily indicated by adolescents and is statistically significant by age groups (p = 0.006). A respondent detailed, “I stopped going to public school, now I do it at home. They put me with all of the other special needs kids because I was in a wheelchair” . Fatigue was also discussed by 8 individuals, this time primarily indicated by young adults. This finding is also statistically significant (p = 0.026) by age groups. One respondent stated, “Because of my lack of energy, I could not handle being a full-time student” . Although these individuals have faced academic challenges, many of them report being highly successful. Of the young adult survey respondents, 16 are currently enrolled in college, with an additional 12 individuals having graduated college and 3 individuals holding a graduate degree. Responses are categorized by age and motor function at the time of survey completion in Table The third free-text question asked how SMA affects socialization . Conversely, some survey respondents felt the opposite, with little to no socialization. “What social life?” one respondent remarked . Another respondent wrote, “SMA affects my social life very much. I never want to go out because everyone stares at me everywhere I go” . This answer was discussed by 6 respondents of all motor function levels.Other factors impacting socialization include activity limitations, communication difficulties, and feeling judged or misunderstood. While describing activity limitations, one respondent wrote, “I am not able to do many activities. It affects my social life and my love life (by that I mean I don’t have one)” . Additionally, one respondent noted communication difficulties, stating, “People have a hard time understanding me” . Regarding feelings of judgement, one participant wrote, “Many adults and teens speak to me as though I’m much younger or will talk to the person I’m with acting as though I’m not really there” . Dependence as a response in this section once again obtained statistical significance by motor function categories, with all respondents being sitter/non-independent walker (p = 0.04) rather than independent walker. Responses are categorized by age and motor function at the time of survey completion in Table Another respondent remarked, “Support groups to help cope with the emotional and mental toll of SMA” . Separate, but closely related, was the idea of a big/little buddy support program. One respondent wrote, “It would be useful to pair young adults with older individuals with SMA, like a buddy to learn from” .The final question of the QoL free text asked respondents what resources they would like to see created for this specific SMA population . Other ideas included improved access to equipment and technology, accessible transportation maps and activities, and accessible video games. Regarding improved access to equipment and technology, one respondent wrote, “Help with better access. Help to get a van for using power chair away from home and school. Grants for families” . Regarding accessible activities, respondents suggested the creation of a wheelchair accessible waterpark, video game console and sport specifically modified to fit the needs of all SMA severities. Lastly, a few respondents wrote that a cure would be an excellent resource. Responses are categorized by age and motor function at the time of survey completion in Table The video contest received six submissions: four from adolescent minors and two from young adults. Five of the six individuals identified as type II; the remaining participant was a young adult with type III. Ambulatory status was assessed from the video submissions. Please see Table Although the video contents had a much smaller sample size, the findings align with those of the QoL survey. Video content was divided into 6 subthemes discussed by all participants: social engagement, everyday life, physical health, emotional health, other obstacles and overcoming obstacles . Additionally, participants spoke to both ends of the spectrum while describing the impact of SMA on schooling. One participant noted, “I needed an aid as well as many other accommodations, which resulted in a fair amount of arguments with people who just don’t understand”  while another detailed, “I don’t want to be known as the girl in the wheelchair, I want to be known as this cool teenage girl. I’m really into student government, yearbook, French club, Humane society…” . While the video submissions did not discuss specific resource recommendations, an inspiration for quite a few participants to create videos was to be a role model for struggling members of the Cure SMA and disabled adolescent communities. One participant said, “I hope to be an inspiration to all the kids out there that grew up like me, who are in wheelchairs and have self-doubt.” ..One element of the video contest that was not covered in the QoL survey is overcoming adversity. Each video participant highlighted challenges they experienced while also discussing their motivations and strategies of resilience. One participant said, “I keep a blog and make YouTube videos to show you can have a disability and still have a life that’s full and fulfilling. I hope to be an inspiration to all the kids out there that grew up like me, who are in wheelchairs and have self-doubt.” . Another participant detailed, “My family and I, we love to get out and travel. We don’t let inaccessible routes or people tell us ‘You can’t go that way.’ Usually, we figure out a way to do it. There are challenges, but there are ways to help, that’s what SMA is all about” . For more information on the topics discussed in the video contest, please refer to Table 7Across the platforms, mental health was discussed by twelve different survey respondents. When detailing difficult obstacles, one survey respondent elaborated, “I was just diagnosed with depression and anxiety mostly from the constant worrying about my life and having SMA, I think that in some cases having SMA makes me want to work harder and prove others wrong, but at the same time there are a lot of days where I just want to give up and say what's the point” . Other respondents were more succinct but equally impactful. One respondent wrote in the difficult obstacles question, “Depression. It makes everything worse and less bearable” . Survey respondents also discussed the emotional impact of being considered disabled simply by having a wheelchair. In the socialization question, one respondent explained, “I feel like people judge me just by seeing the wheelchair. People treat me like I’m a baby or mentally disabled or too innocent and I’m none of those things!” . Another respondent wrote, “It makes me sad when people stare at me. I know they’re probably just ‘curious’, but still, it makes me upset” .In the awareness video contest, mental health issues were discussed in-depth by two participants. One participant elaborated, “After losing my aide of three years and realizing how I was being treated by my peers, depression overcame me. I could no longer stay well” . Another said, “As soon as middle school started, I was thrown into a deep depression, and I still have not fully recovered…It is the physical demands of being disabled that would eventually shape my personality to being an introvert.” .Although not directly addressed in either incitive, it is evident that many adolescents and young adults affected by SMA are leading productive lives despite challenges. In the schooling section of the QoL survey, a few individuals highlighted their academic accomplishments. One survey respondent wrote, “Graduated from the Wharton School at the University of Pennsylvania”  while another wrote, “I get As and Bs” . Many survey respondents also emphasized their interests outside of their disease, such as video games, swimming and wheelchair sports. In response to the question regarding socialization, one respondent wrote, “I still go dancing!!!!!” .Similar successes were also emphasized in the awareness video contest. One video contest participant highlighted their future plans. “After I finish my degree, I plan on working with children who are similar to me, hopefully inspiring them to do what they want to do with their lives. Just because you can’t physically do something doesn’t mean that you can’t follow your dreams” . All contest participants detailed their interests, ranging from clubs to YouTube, attending concerts, and traveling. Additionally, many participants highlighted unique experiences. One participant reflected, “If you want to live twenty hours away from your parents, go for it. If you want to go hunting for [sic] your dad, do it. If you want to go skydiving, why not? My only advice is, don’t be afraid of a challenge. Be excited for it” .In comparing our population to the SMA adult population captured in previous publications, our top responses coincide with their key findings. Both accessibility and fatigue were reported as significant in multiple questions. Accessibility was a high response in our study in relation to schooling and socialization. This correlates with the identification of accessibility and limited mobility as symptoms with the greatest impact on SMA adults in the SMA Patient-Reported Impact of Symptoms (PRISM-SMA) study (n = 359) . FatigueWhen breaking our population into cohorts of adolescents (ages 12–18) and young adults (ages 19–25), it is evident that certain themes are most relevant to certain age groups versus others. Only young adults requested the creation of college and independent transition materials and young adults also more frequently reported fatigue issues in school and issues finding care. Conversely, adolescents were more likely to report being homeschooled. With these differences noted between age groups, majority of the key factors impacting the day to day lives of adolescents and young adults were largely shared by both groups.There was a statistically significant difference between maximum motor function and identifying accessibility and dependence as the most difficult aspect of balancing SMA; accessibility concerns were mainly voiced by participants who previously walked independently or were able to walk independently at the time of survey completion. While accessibility was a main issue for these individuals, the sentiment was not shared by those with a maximum motor function of non-independent sitters, who more frequently detailed pain or communication difficulties. Non-walkers were the most likely to voice challenges regarding dependence or reliance on others, and these results were significant with respect to socialization and considered a difficult aspect of disease management. While motor function status was correlated with some responses, many responses had no correlation. At least one individual of every motor function group discussed difficulties regarding mental health, feeling left out, or needing more time and effort in school. Additionally, the no effect responses detailed in schooling and socialization all incurred varied responses from all levels of motor function.In both the schooling and socialization questions of the free text, a parabola effect was observed in high frequency answers. A similar number of respondents felt that either, SMA greatly affected, or had no effect on their schooling or socialization, two significant aspects of life in this age period. The majority of those who felt that SMA did not impact schooling did not overlap with individuals who felt that SMA did not impact their socialization. It is important to note that neither of these questions received a large group consensus unlike the categories of everyday life or resources, with similar frequencies reported amongst many responses.Although mental health was not the most frequently cited factor in any category, the impact of SMA on mental health can be severe , 19, 20.The specific psychosocial and developmental challenges observed in our research are similar to those found in the larger adolescent and young adult populations. These population archetypes struggle with feeling left out at school, navigating adulthood, and finding independence . Our resAs discussed in the corresponding results section, most of the SMA adolescent and young population surveyed are leading successful and productive lives. The resilient spirit exhibited in our findings complements existing publications in which adversity, resilience and grit were found to be key components of adolescence and young adulthood with SMA affected individuals. Study participants described strategies including focusing on abilities rather than disabilities, and prioritizing self-worth and value .A peer support group was the most requested and most desired resource to support this population. Such a resource is not uncommon for disease populations. In a July 2013 publication from Patient Education and Counseling, researchers found that while there were benefits to both mentor/mentee programs and age-matched support groups, the latter had the greatest potential for symmetrical and mutually beneficial relationships leading to greater emotional support . This isFurthermore, peer support groups are common resources for this age demographic within and outside of the rare disease community. In a 2015 study regarding cystinosis support groups for emerging adults (n = 49), participants reported valuing the connection and support they received from those who understand their lived experience . AdditioThere are several important limitations to note with our research. Although the size of our population is a strength, there is a low participation of type I individuals. Traditionally, most of those affected with SMA type I do not survive past the age of two and those who do survive present with a very severe phenotype (requiring respiratory and feeding support and little to no movement) that would make direct participation in these studies very challenging. Furthermore, there is a greater number of females than males in our study. This limitation makes it difficult to summarize our findings to the greater SMA population, as it does not represent the entire population. Additionally, because this was a qualitative research study using open ended responses, rather than a validated patient reported outcome measures with prior usage in the SMA population, predications about possible differences in responses between sub-populations were not feasible, making a priori power calculations impossible. Instead, confidence intervals were calculated when comparing sub-populations to provide indication of statistical precision. Furthermore, as the survey is directly patient-reported, there may have been mis-reported data on SMA type and other demographic information. Some information that could impact results, such as mental health status, economic status, and motor function gain/loss was not collected. Lastly, the awareness video contest response rate is hypothesized to be much lower than the survey response rate due to the high demands of the contest design, including creating and editing a video submission, publicly posting to YouTube, and the subsequent contest campaigning. Although the small cohort is not intended to represent the entire SMA population, their anecdotes and insights are intended to supplement a fuller picture of this age cohort and largely align with the findings of the larger QoL free response findings.The findings of this research corroborate previous findings on disease burden and quality of life in adults and caregivers with SMA –16, 19. As SMA treatments and care expand, more and more SMA-affected children will live to become teenagers and young adults with bright futures ahead. It is imperative to understand the current life experience in order to improve the quality-of-life for this and future generations. It is evident that this population has a big voice with valuable insights, and no one understands the needs of the SMA community better than those directly affected. Despite all of the challenges and complexities that come with living with SMA as adolescents and young adults, these teens are living full and successful lives. Indeed, via this work, we have learned that these individuals have SMA but SMA does not have them.Additional file 1. contains Supplementary Tables I-IV, denoting the quality of life survey questions' key words and response breakdown by SMA type.


By the 1960s two crises emerged: the first was an inability to evaluate, compare, and judge different pattern recognition systems; the second was an inability to articulate what made pattern recognition constitute a distinct discipline. The resolution of both crises through the problem-framing strategies of supervised and unsupervised learning and the incorporation of statistical decision theory changed what it meant to provide an adequate description of the world even as it caused researchers to reimagine their own scientific self-identities.The slow and uneven forging of a novel constellation of practices, concerns, and values that became machine learning occurred in 1950s and 1960s pattern recognition research through attempts to mechanize  Wherever and whenever groups of people come to agree about what knowledge is, they have practically and provisionally solved the problem of how to array and order themselves. To have knowledge is to belong to some sort of ordered life; to have some sort of ordered life is to have shared knowledge.Leviathan and the Air-Pump— Steven Shapin and Simon Shaffer, Machine learning is often seen as either an early-twenty-first or late-twentieth-century subfield of artificial intelligence drawing on techniques in the fields of operations research, cybernetics, cognitive science, and statistics.contextual significance that could be performed by humans or machines, and that emphasized performance reliability in parity with human judgment and robust performance in the face of never-seen-before new data. This notion of contextual significance, as discussed in section “OCR), and encouraged a form of problem solving that sought to produce useful decisions purely through learning from examples. The multiple meanings of learning in pattern recognition, identified in section “could and should be taught to do. This polysemous learning, instantiated mathematically in late 1950s pattern recognition as a loss function borrowed from statistical decision theory, fused day-to-day technical decisions for building pattern recognition systems with attitudes that linked creativity to mechanical schemes for generating contextual significance. The theoretical conception of “learning” from examples offered as a solution to the problem of contextual significance was analogically seen as a solution for deciding social questions. This occurred, as sketched out in section  “supervised and unsupervised learning.Early mid-1950s pattern recognition researchers articulated a conception of significance in pattern recognition was tied to a way of problem-framing for which learning was seen as the answer, mere narratives of the development of supervised and unsupervised learning miss the point. Textbook definitions of supervised and unsupervised learning as self-evident synchronic ordering principles of machine learning miss the point: for the historian it is precisely why these forms of learning became stable, durable ordering principles that need to be explained.learning in the larger matrix of tacit problem-framing strategies shared by pattern recognition researchers, we need to look at “substitut[ing actions] for concepts and practices for meanings.”Researchers seeking to make pattern recognition into a reputable field of inquiry saw their object of study as the mechanical identification of significance and the reproduction of human judgment. They emphasized building “learning machines” that reliably performed a specific task within a narrow context despite wide variations in input, and were tied to a way of formulating problems that encompassed disparate activities: spotting missiles in the sky or spotting card catalog subjects in thousands of papers; disambiguating polysemy in machine translation or disambiguating particle tracks in bubble chambers; identifying unstable governments in the Cold War world or identifying individual alphanumeric characters on paper. The contextual flexibility and robustness of pattern recognition was tied, in part, to letting the phenomenon of interest be defined by the statistical properties of the data. “Solutions to the problem of knowledge,” Shapin and Shaffer have compellingly argued, “are solutions to the problem of social order.”learning in the then-nascent field of pattern recognition in the 1950s and 1960s, including how technical and professional contingency informed practices of communal- and self-valuing, by a careful examination of the central research problem of pattern recognition at that time: optical character recognition. OCR research in the 1950s and 1960s did not center on one algorithm or one broad collection of techniques, but articulated a set of specific concerns built into the pattern recognition problem-framing for which messy data was no more or less preferable to high-quality, expensively produced, well-curated data sets. While OCR work did not provide prestige and visibility to the field of pattern recognition, the development and circulation of pattern recognition work in the 1960s across a variety of domains, and its attendant forms of learning, provided a blueprint for how to solve a problem and, more importantly, what counted as a good problem.Examples from the history of science are instructive. To historicize scientific objectivity, Daston and Galison  adopt a Perspective API uses a machine learning classifier to estimate the likelihood of a comment to be “perceived as ‘toxic,’” and was intended to “help increase participation, quality, and empathy in online conversation at scale” by curtailing “abuse and harassment online.”63 million Wikipedia talk page comments spanning 2004–2015 as “toxic” or not.ignorance-as-generality may rightly give us pause when applied to high-stakes decisions, the advantages of this strategy in low-stakes decisions become more apparent when applied to the problem of OCR discussed in the next section.Today machine learning reformulates the original sin of incomplete knowledge into an epistemic virtue of greater contextual sensitivity. For example, Google’s New York Times, the Economist, Wikipedia, and the Guardian emblazoned on Perspective’s website as “partner experiments,” Perspective’s public release in 2017 by Google and Jigsaw soon exposed that Perspective’s classifier rated comments with words denoting historically marginalized gender, sexual, and racial identities  as more probable to be perceived as toxic.did achieve the desired technical result, and, for our purposes, illustrated two key epistemic values. First, for Perspective’s “toxicity” classifier to work, it needs to be able to identity contextual significance: namely, that the same data  may produce different effects  in different contexts . Second, in order to be useful even when encountering new forms of toxic speech, the classifier needs contextual robustness: namely, that decisions or categories  developed from past data  will continue to be correct for future as-yet-unseen data. As we shall see in the following section, contextual significance, contextual robustness, and ignorance-as-generality were carefully constructed epistemic hedges tied together through a conception of mechanized significance in mid-century pattern recognition problems.We can see other problem-framing concerns endemic to machine learning in the case of Perspective, especially in how machine learning systems are fixed when discovered to make improper decisions or classifications. With the to design a machine which will recognize patterns. We shall call any possible visual image, no matter how cluttered or indistinct, a configuration. A pattern is an equivalence class consisting of all those configurations which cause the same output in the machine.“ Pattern recognition,” Oliver Selfridge wrote for a 1955 Western Joint Computer Conference session on “Learning Machines,” is “the extraction of the significant features … from a background of irrelevant detail” performed by a machine that “learns” by “build[ing] up sequences [of mathematical operations] ‘like’” but not identical to those previously efficacious at a specific task.A and O.,“Good” features were obtained by mathematical “sequences of operations” performed on the digitized images whose values empirically distinguished between equivalence classes for the letters As and Os the machine had never encountered and even beyond what the Lincoln Lab researchers could anticipate.not the same as AI’s mid-1950s obsessions with heuristic problem solving of mathematical questions, automatic translations facilitated by programming compilers, or logistical operations of bureaucracies. Pattern recognition for Selfridge and Dinneen was about mechanizing a particular notion of contextual significance that valorized the reliability of machine performance  for a task given novel data with deformations that are not known or anticipated at the outset.Rejecting straight lines as “too simple” a task and faces as “too complicated,” Selfridge and Dinneen settled on identifying printed black “A”s and “O”s on paper, which were chosen for their respective asymmetry and symmetry.A might differ for two different sets of “A”-labeled images in potentially contradictory ways. This might appear to be a wildly idiosyncratic way of identifying anything—whether letters, EKGs, faces, fingerprints, or cats—hopelessly sensitive to the peculiarities of the features selected and the training data used. However, Selfridge reframed this sensitivity to individual examples as a virtue. By identifying a problem in which such a strategy was preferable—namely, that of optical character recognition—and noting analogically how this sensitivity to training data was what facilitated humans’ comparably superior capacity for finding significance, Selfridge made experience the sole source of contextual significance:experience alone affects the kind of thing we regard as significant.Now significance is a function of, first, context, and second, experience…. [O]f course, context is a function of experience. But more than that, Such contextual significance meant that the letter If humans could learn significance  solely through experiencing the world, Selfridge contended, why couldn’t machines learn an image recognition task solely from labeled images? The “recognition of simple shapes in a visual field, such as block capital letters,” was a useful test case.A and O each constituted an equivalence class, which was defined as whatever feature values consistently picked out “A”-labeled images as As and “O”-labeled images as Os.A or O around which each letter image was an imperfect copy. Rather, an equivalence class defined the values of a feature (or features) that categorized images as the same letter: say, many different “A”s as the letter A, even if all “A” images have “no one thing in common that makes us use the same [letter name] for all.”But how did identifying two letters generalize in any way to a method for identifying contextual significance? As seen above, for Selfridge and Dinneen the letters features , the data configurations , and equivalence classes . Building, evaluating, and improving a learning machine could only be achieved by understanding the relations between individual features, configurations, and equivalence classes themselves and the machine’s performance as a coherent whole in relation to these individual parts. More or less useful features and equivalence classes could be determined via iterative computational guessing and checking different combinations of features, configurations, and equivalence classes. Any “sequence of operations” that produced features with values that distinguished As and Os could work and could be checked via  trial and error—whether digital computers or humans were doing the trial and error, for Selfridge, mattered not.This method illustrated what researchers would later call the “two subsidiary problems” of pattern recognition: “what to measure and how to process the measurements.”learning that reflected these researchers’ ambitions for a machine able to distinguish contextual significance while maintaining contextually robust performance when encountering new data.Learning as feature selection improvement in pattern recognition did not foreground expertise, in part, because, as Ware noted, Selfridge’s and Dinneen’s learning machine papers dealt with “the learning of pattern recognition by a maturing individual”  while Newell’s problem of chess-playing expertise examined “the action of an entire man as he copes with complicated problems of his life.”Cybernetics.A is never known or pregiven, but an effective equivalence class can always be learned. For all their similarities, however, pattern recognition distinguished itself from cybernetics in valorizing knowledge as a kind of classification. Representing phenomena as aggregates-of-features was a strategy for reproducing (human) judgment (insofar as these “judgments” could be translated into classification decisions) that also held the possibility of being robust to unexpected new data.What could people do using pattern recognition that they couldn’t do before? What made pattern recognition’s problem-framing not merely rhetorically compelling but intellectually preferable for some communities? Which communities celebrated and were empowered by these capacities? Pattern-learning machines offered a way of imperfectly knowing the world via its provisional and piecemeal traces.Researchers working on pattern recognition problems were engaged in an intellectually stimulating but professionally fraught endeavor in the 1950s and 1960s since pattern recognition was seen by many as applied engineering and not an intellectual arena able to facilitate professional advancement. Work in pattern recognition was accordingly often conducted as a tertiary research pursuit. Selfridge, Dinneen, and others did pattern recognition research informally, during lunch hours.Those working on pattern recognition and seeking professional advancement through the pursuit of a disciplinary identity were confronted by pattern recognition’s methodological disunity and a vast sea of clever, ad hoc mechanical jury-rigged prototypes. The strategies that developed in response to the problems of recognizing alphanumeric letters in particular, both typed and handwritten, on paper and elsewhere, was a key site in which virtues were formed and justified. Pattern recognition work, especially OCR work in laboratories, in panels at international conferences, government funding agencies, and in individual lives was a critical trading zone that shaped these concerns about how to know the world. What was shared by early pattern recognition research was a set of intellectual ambitions, problem-framing strategies, institutional priorities and norms, and computing practices endemic to OCR work exemplified by Selfridge and Dinneen’s 1955 efforts. OCR facilitated the circulation of pattern recognition’s methods and values because of its practical application in a variety of disparate domains as well as for the ease with which its methods were generalized and repurposed for non-OCR problems.learning. In practice, these different meanings of learning were expressed mathematically by the use of the loss function that represented the numerically-defined “costs” of a particular decision or action. While pattern recognition learning had not yet become synonymous with minimizing the loss function by the early 1960s, this form of learning was seen as a viable strategy to realizing three ambitions. First, this conception of learning could serve as a decision procedure for mimicking (human) judgment for narrow, well-defined cases. Second, this learning generated trust through its repeated use when, as researchers described it, “we do not understand our problem well enough to pre-program reasonable sets of characterizers.”A key facet of the “black box ontology” in pattern recognition’s framing of good problems was the plurality of meanings of any classification humans could make for a finite collection of images  by definition. Insofar as classification was a synecdoche for significance, identifying importance became a problem of engineering. Finding the image features correlated to whatever humans employed in the same classification was a problem that could itself be partially automated. Picking out As and Os became possible not only for labeled images , but also for As and Os in unexpected images not anticipated by programmers themselves. This strategy could be applied to a universe of decision problems, as was articulated by pattern recognition researchers by the late 1960s:[W]e often speak of the character-recognition problem, the problem of analyzing electrocardiograms, the photointerpretation problem, and so on. Yet each of these is in reality not a single problem but a problem area, an entire ensemble of problems. The problems within an area are generally governed by a number of important parameters. Each proposed application, each choice of parameters, specifies a new problem from the ensemble—there are literally an infinity of problems.Pattern recognition provided a strategy for reproducing This ambition to produce a methodology able to handle any and all decision problems led to a disciplinary identity crisis for pattern recognition similar to that experienced by statistics in the twentieth century and data science today, and which manifests in the confusion and conflation of the terms artificial intelligence and machine learning.reliability for machines directly interfacing with a messy, inconsistent, and ever-changing real world. What reliability was, or, how it could be measured, often came down to local laboratory decisions about what constituted a “task” and how that task was to be integrated into the sensibilities, infrastructures, and worldview of those needing the task to be done.limited to a specific context of use for a particular agent was considered a virtue of the approach. Pattern recognition implicitly had “the notion of pattern-for-an-agent” built in such that “the problem of pattern-recognition is always ill-defined until the class of agents in question, and their repertoire, have been specified in relevant detail.” Pattern recognition was a hodgepodge of technical and social strategies for inventing and ensuring want to appeal. Nor, for the problems like OCR or image classification, was there a special set of data to appeal to because the techniques were a strategy for machine performance reliability.Today we are sensitive to how the repurposing of an existing pattern-for-an-agent can produce fictitious inductive claims, and that when such pattern learning machines are used to arbitrate or assist in the adjudication of social decisions involving historically marginalized groups, such reused “patterns” produce unjust, unfair, and inegalitarian decisions. Selfridge’s articulation of pattern recognition and its implementation by Lincoln Lab researchers may strike contemporary readers as woefully unconcerned about the idiosyncrasies of their labeled training data.with each allowable size and style variation of each character counting as a separate member.”For pattern recognition researchers, a particular learning machine gained its credibility not through mathematical proof but by its consistent uniform performance at a specific task that was responsive to and not incapacitated by unexpected, even contradictory, information. Researchers building learning machines funded by the military and employed in university labs and in commercial corporations didn’t need a machine to generate its own goals in a well-defined axiomatic system as was espoused by some prominent mid-century researchers in artificial intelligence.performed and judged. Which statistical techniques were brought to bear in a particular pattern recognition project varied, in part, on whether the “pattern” in question was for use in (1) textual work , (2) social and scientific judgment, data processing, and surveillance , or (3) the studies of “ perception,” including efforts to build and employ neural networks.Insofar as pattern recognition had a methodological theory, it was through an “effective transformation of a perception-recognition problem,” such as optical character recognition,“into a classification problem.”The overwhelming predominance of OCR systems as the de facto use case of pattern recognition ensured that the process of accounting for variance in training “features” was often decisive to a system’s effectiveness. In practice, as late as 1968, commercial OCR systems compared test characters against ideal character “masks” for typed text, which, while a technique of pattern recognition, was not an example in the machine learning tradition that this chapter has been tracking.Most of the reading machines built today are used in business where the OCR machine reads a prescribed font. In most other situations the number of fonts is quite limited and 5 to 10 specific fonts are usually more than enough. In the case of the Post Office, particularly in the case of zip codes, we can normalize the size and do some feature analysis and guess the numbers. But if one wanted to build a self-programming alphanumeric reader for the Post Office, one would be faced with the fact that there just isn’t enough information. This is true both because the number of characters to be read on any envelope is too small, and because one envelope has no relationship to the one before…. As far as I know, there is no self-programming machine, but we all think it would be fun to make one.The practice of learning typed letters and typed letter features empirically from training data as outlined by Selfridge in 1955 remained more of an aspiration in the early 1960s than a physically realized working system, but this may have reflected a belief about the demand for “self-programming reading machines.”Yet there were already considerable efforts underway to identify hand-printed alphanumeric characters in the same conference proceedings containing Rabinow’s statement. An ARPA funded project at RAND called GRAIL tracked a user’s light pen strokes to correctly recognize character inputs “with about” (Groner’s words) 90–95% accuracy largely for the purpose of making programming flowcharts. An OCR problem similar to Selfridge’s was pursued by John Munson at Stanford Research Institute (SRI) with funding from the US Army and Navy. Here the task was to take paper “Fortran coding sheets” at the Applied Physics Laboratory at SRI, filled out by hand, and accept them as Fortran programs to be run directly by the machine after being scanned. First, the SRI team used a TV camera to ingest the handwriting characters as 24 by 24 black-and-white grids for “3 alphabets of FORTRAN characters from each of 12 writers” from which character features were calculated with “three 85-weight dot product units per category[.]”We view the problem [of handprinted text recognition] in the five stages of data preparation, data input, preprocessing, classification, and context analysis…. We believe that these stages must all be kept in view, however, because a change in performance in one greatly affects the demands put on another. We have tried to establish a framework in which, if the outcome of an experiment is x% correct classification of characters or lines of text, the meaning and value of the result will be clear.Despite such optimism, Munson was painfully aware that such error rate measures were themselves highly idiosyncratic, depending on the way the data set was created, digitized, and stored, the various learning procedures employed, the kind of classification process attempted, and the criteria for success. This made it dubious to attempt to compare reported error rates between different systems or OCR projects. Munson concluded:The prevalence of a prototype-and-try attitude toward pattern recognition systems by engineers further exacerbated the difficulty of comparing the efficacy of different OCR programs across different data sets. An OCR program that worked well for one data set would do poorly for a different data set. The issue was not simply what contemporaries might call “overfitting,” but rather that the data sets might have antithetical feature values for the same equivalence class.Statistical Decision Functions published the year of his death.Theoretical studies of pattern recognition were grounded in statistical decision theory as well as signal-to-noise problems in radar, telecommunications, and other applications of information-theoretic measurements. Building on both Fisher’s and Pearson and Neyman’s hypothesis-testing frameworks, statistical decision theory involved calculating a problem-specific “decision function” to decide how many “stages” of experimental observations were necessary before making a “terminal decision” about which hypothesis to accept given the opportunity cost of incorrect decisions and the possibility of additional experimentation.game between an “experimenter” and “Nature.”game is the possible choices available to both experimenter and Nature. For the experimenter: a choice of a “decision function” from all possible decisions and the subsequent decision results . For Nature: a “choice of [the] true distribution.”infinite possible choices for both experimenter and Nature, incorporating the use of sequential testing so that after each observation a new decision  is made in response to the history of previous actions taken by the experimenter.Wald’s productive insight was to conflate statistical decisions— what Wald, following Neyman, labels “the problem of inductive behavior”—as a generalized zero-sum two-person [The optical character] recognition problem is considered to be that of testing multiple hypotheses in the statistical inference. Consequently, the design and evaluation of a recognition system is comparable to a statistical test. Results of decision theory can be applied.Here then we can begin to see the analogical and mathematical similarities between Wald’s decision theory and pattern recognition’s OCR problem. In 1957 C. K. Chow, working “in the Electromechanisms Department of the Burroughs Corporation Research Center at Paoli, PA,” suggested decision theory should be used in pattern recognitionThe criterion of minimum error rate is then no longer appropriate. Instead, the criterion of minimum risk is employed. Proper weights indicate the loss incurred by the system for every possible decision. Proper weights are assigned to measure the consequences of errors, rejections, and correct recognitions. These weights indicate the loss incurred by the system for every possible decision. The loss, which should be regarded as negative utility, may actually represent loss in dollars or unit of utility in measuring the consequence of the system decision.Mindful of the experimental difficulties of comparing error rates of different OCR systems, Chow noted, “Cases may arise where different misrecognitions have different consequences; e.g., the registering of a four as an asterisk may not be as serious an error as registering it as a nine.” Chow employed nomenclature already matter-of-factly used in Wald’s 1939 and 1945 papers, including the use of vectors as features typically found in pattern recognition in the 1960s, and was a very early example of Bayesian decision theory employed to a pattern recognition problem. Given “noise statistics, the signal structure, and preassigned weights,” all of which could be determined empirically for a particular OCR system, Chow could not only describe the optimal possible performance for any system according to a particular decision criteria (like the minimization of probability of misclassification), but, more importantly, Chow could describe the ways these different systems would degrade “from characters being corrupted by printing deterioration and/or inherent noise of the [OCR] devices.”Obtaining empirical probability distributions of actual machines was a formidable challenge to making Chow’s result usable. However, what did facilitate the circulation of Chow’s union of OCR to decision theory was the approach’s agnosticism as to (1) how to choose the features to be examined for a particular pattern recognition problem and (2) how patterns of features might be learned by a particular pattern recognition system. Though beyond the temporal scope of this chapter, agnosticism to these two questions in the 1960s structured the possibility for pattern recognition as a field, and set the stage for pattern recognition’s conflation with machine learning in the 1970s and then subsequent appropriation by artificial intelligence in the 1980s. Today’s machine learning ontology of supervised and unsupervised learning—“learning with a teacher” (using labeled data) and “learning without a teacher” (using unlabeled data), respectively—developed largely in 1960s pattern recognition communities concerned with language and judgement . The second register, in its extreme form, was the very pinnacle of hubris that sought to reproduce the contextual significance of human perception, and even what humans did or could not know, and that made even hyperbolic coeval claims about producing “ intelligence” in artificial intelligence seem levelheaded by comparison.Early pattern recognition in the 1950s and 1960s, as articulated by Selfridge and many others, inhabited two registers of discourse. The first register was pedestrian and born of a perceived need for reliability in the face of variegated social data that The search for generalized rules took many forms, including ways of thematically combining pattern recognition work as a discipline and making this knowledge available to researchers in different disciplines. In the second IEEE conference, one researcher observed the tendency in pattern recognition “towards [...] solutions of very limited problems” and “the scarcity of and means for carrying out comparative evaluations of different approaches,” hindered the dissemination of pattern recognition research, and the “unif[ication] of the field.”Even given the mathematical tools of decision theory numerous researchers noted it was not “adequate for the solution of pattern recognition problems.”supervised and unsupervisedlearning were established as durable categories that continue to dictate possibility in machine learning to this day, but, as we have seen, were originally developed in the early 1960s. This ontology of learning forged in pattern recognition, and later incorporated into machine learning (the disciplinary formation) as machine learning (the set of techniques) did not dispel the practitioners’ sense of the diverse activities that were to constitute the discipline. This was demonstrated in part by a 1973 textbook entitled “Pattern Classification and Scene Analysis,” with chapters on “Bayes Decision Theory,” “ Supervised Learning,” “ Unsupervised Learning,” and “Linear Discriminant Functions,” that began by noting that “this diversity [of the contributions from many different disciplines] also presents serious problems to anyone writing a book on [ pattern recognition].”[Classification theory] provides formal mathematical procedures for classifying patterns once they have been represented abstractly as vectors. Attempts to find domain-independent procedures for constructing these vector representations have not yielded generally useful results. Instead, every problem area has acquired a collection of procedures suited to its special characteristics.These efforts ultimately highlighted two existential threats to pattern recognition: (1) a confusion among individual researchers about what constituted a good pattern recognition question, and (2) an ambiguity about what distinguished pattern recognition as a distinct field of inquiry . Both concerns were manifested through the singular inability of pattern recognition researchers to usefully compare different pattern recognition systems and approaches. By the mid-1970s, The inability to theorize how to vectorize the world and incorporate these non-necessary “extra-evidential factors,” in hindsight, became precisely one of the key concerns that differentiated pattern recognition as a discipline and shaped the manner in which supervised and unsupervised learning was subsequently valorized and circulated in the late 1970s and early 1980s. Pattern recognition did not attain the kind of disciplinary prominence or prestige implied by Watanabe’s claim of “the most ambitious scientific ventures of [the twentieth] century.”The Art of Computer Programming and ACM Turing Award winner Donald Knuth, who has argued that more recent histories of computer science are “dumbed down” in an ill-advised attempt to make it accessible to a reading public beyond computer scientists.only a particular set of results. Perhaps most readers who have made it this far into the paper will disagree with such a constrained understanding of what history does or should do.Histories of machine learning, insofar as they are offered as standalone pieces or as addendums to the history of artificial intelligence, tend to narrate the period from the late mid-1950s to the late 1970s as one in which little progress in machine learning occurred, often using neural network “connectionist” touchstones as a proxy for machine learning progress and foregrounding “AI winters” as a primary driver of machine learning success or failure in both the twentieth and twenty-first centuries. This reflects an instrumental view of the history of science in line with Robert Merton’s sociology of science in which the objective is to help scientists do better science.The Theory of Everything, The Imitation Game, or Hidden Figures as a playbook for a career in cosmology, computing, or aerospace do so. However, historians are painfully aware of how stories we tell about ourselves produce the possibilities for our own action in both subtle and not subtle ways. Our understandings of race, gender, sexuality, and class are profoundly inflected by how we narrate our histories. The U.S. Civil War was about slavery. We shape our world and selves by the stories we tell and the details we choose to make significant. The story of Geoff Hinton unable to find a job because he was working on neural networks isn’t just a story about the state of the field; it’s a subtle commentary by the story teller about the kinds of values or kind of life a researcher should have. Einstein invented relativity while working at a patent office. These stories do work for the people who repeat them even as they are also constrained by them. AI winters are the result of researchers overpromising and under delivering rather than a consequence of a Department of Defense shakeup for which artificial intelligence research was an afterthought—that story continues to constrain and suggest possibilities for many machine learning researchers today. The development of supervised and unsupervised learning was a particular answer to the problems of knowledge posed within a defuse, transnational network of individuals and institutions across dozens of countries that comprised the pattern recognition community from the 1950s to the 1970s. I sought to recover the epistemic assumptions of this community by tracing the various instantiations of OCR work in local laboratories and in the international technical literature to understand how machine learning came to be seen by some as a compelling method to make social decisions. This led me to a number of new actors who were largely elided in the histories of artificial intelligence, including women and non-white actors who I have cited in this paper that have made important contributions to machine learning and for which further study is required. The present prominence of machine learning in data science and computer science is often narrated in dramatic fashion as a sudden superiority of neural networks over other AI approaches at the end of the twentieth century and the start of the twenty-first. However, as has been shown here, much of the ontology of machine learning and its attendant epistemic justification was set out by the early 1960s. Contemporary debates about the generalizability of machine learning in social decisions rehearses many of the same debates pattern recognition researchers had with each other in the 1960s about how to compare different learning machines using different data sets.Different histories are written for different purposes, in response to different concerns, and by people with different lived experiences and worldviews. Let Knuth have his technical histories. Let everyone who wants to watch not because of any single technical advance but because the research problem of pattern recognition established virtues, practices, and organizing principles for how a useful question was to be identified. Such problem-framing strategies for technical problems circulated across geography and disciplines, in part, because it was also recognized as valuable knowledge by those not doing pattern recognition work. Rather than taking the machine learning terms of supervised and unsupervised learning as self-evident, I see them as research practices in need of explanation, in part, because they inform what constitutes evidence for questions of race, gender, and socioeconomic class now explicitly debated through the implementation of machine learning systems to make important social decisions. The history presented here reflects my questions on the interweaving of technical solutions as political solutions by many contemporary machine learning systems, including Perspective, that are offered as solutions to questions of social order. Narratives pertaining to machine learning are some of the props by which we envision possibility and feasibility. “Now anyone can deploy Google’s troll-fighting AI” said Wired when Perspective was publicly released, six months before the discovery of the model’s propensity to label identity words as “toxic” discourse.The early 1950s to mid-1970s was an intellectually capacious and generative period for machine learning in the twentieth century— Shapin and Schaffer , p. xlixMarcus and Davis , and JorPlasek  notes th Shapin and Schaffer , p. 332.Regarding computing infrastructures broadly interpreted and their impact on vulnerable populations, see the ACM Conference on Fairness, Accountability, and Transparency (FAccT*). Important references on the subject include Sweeney , O’Neil For the same reasons the usual list of mid-century neural network touchstones often provided as a stand-in for machine learning histories obscures more than it reveals. For common internalist touchstones of connectionist literature, see McCulloch and Pitts , Hebb 1, Selfrid Daston and Galison , p. 52.Daston and Galison , p. 52.New York Times classifier , McCorduck’s Machines Who Think (1979), and many others.This account is documented in Edwards’ For explication of trading zones in the history of science, see Galison , 2011; fUhr , p. 159.Fu , p. x. Munson , p. 417.existence of this confusion has historical origins that cannot be reduced to claims about discourse, rampant neoliberalism, or corporate takeovers. To do so erases precisely the history I seek to recover.This confusion is due to the appropriation of pattern recognition practices back into the field of artificial intelligence as machine learning in the late 1970s and 1980s. Katz  traces tprecision in nuclear intercontinental ballistic missiles (MacKenzie, The creation of  MacKay , p. 409. MacKay , p. 409.Dinneen writes in his acknowledgments that “It would have been impossible to obtain the experimental results without the excellent assistance of Miss B. Jensen and Mrs. M. Kannel in programming, coding, and operating the Memory Test Computer. I wish to acknowledge the excellent co-operation of those responsible for the operation of the Memory Test Computer” Dinneen, , p. 100.My thinking about the role of disciplinary repertoires in the construction of arguments has benefited and been informed by Slayton . MacKay , p. 410.See, for instance, Brick : “RedundBrick , p. 78.For machine-generated goals in artificial intelligence, see, for instance, Newell , p. 9.and may often be excused if it fails on some. We often find it worthwhile to introduce a heuristic method which happens to cause occasional failures, if there is an over-all improvement in performance. But imperfect methods are not necessarily heuristic, nor vice versa. Hence ‘ heuristic’ should not be regarded as opposite to ‘foolproof’; this has caused some confusion in the literature” ( Newell, Contrast this with Newell’s discussion of the meaning of “ heuristic”: “A ‘heuristic program,’ to be considered successful, must work well on a variety of problems, M. E. Stevens , p. 333.Rabinow , p. 14.Kanal , p. xi.The tripartite varieties of “pattern” activity I put forth is informed, in part, by the three-part organization of Kanal , p. 333. OCR was not a problem limited to pattern recognition or machine learning. Writing in 1961 for the National Bureau of Standards, Mary Elizabeth Stevens’ extraordinary 1958 survey observes, “‘That the blind may read’ is the earliest recorded objective for research attempts to develop devices capable of reproducing and transcribing printed, typed, or handwritten characters” (M. E. Stevens, Watanabe , p. vii.Rabinow , p. 20. Rabinow (Paradise Lost (see Selfridge, In 1958 Selfridge presented a pattern recognition strategy capable of learning letter features in a system he called “pandemonium,” in an explicit reference to Milton’s Rabinow (Groner , p. 103.Bernstein . Munson (Munson ( Munson , p. 136.Munson , p. 137.Munson , p. 139.Us and Cs example in note 30.See the Rabinow .For these last 4 examples, see Sheinberg (See, for instance, Chow , p. 252  Wald , p. 300.Weiss ( Wald’s 1939 paper was cited as his most important contribution by his friend and colleague Jacob Wolfowitz Wald, . In this Wald ; see alsWald (Wald , p. 27.Weiss  notes: “ Chow , p. 254. Chow ( Chow , p. 249.Chow , p. 249.Chow , p. 247.learning with/without a teacher have a long history. For early examples, see Turing (The phrases and metaphors of e Turing  and Boote Turing . In the e Turing , p. 99.Abramson and Braverman , p. S58.Abramson and Braverman , p. S58.For the historical source providing this explanation, see Cooper , p. 99.Cooper .Kanal , p. ix.Kanal, , p. x. SKanal , p. ix. Kanal , p. vii.Watanabe , p. vii.For an elegant articulation of both “registers,” see, for instance, Kanal and Chandrasekaran (Watanabe , p. vii.Watanabe , p. vii.Fein , p. 443.Fein , p. 445.Brick (Brick (Kanal and Chandrasekaran , p. 324.Kanal and Chandrasekaran , p. 324.Kanal and Chandrasekaran , p. 331.Watanabe (Lerner , p. 368.Lerner , p. 368.Lerner , p. 369.Lerner , p. 371.Duda and Hart , p. vii.Duda and Hart , p. vii.Watanbe , p. vii.See Zuckerman  for a diSee Haigh  for discFor an introduction to history of such internalist and externalist debates in the history of science and science and technology studies, see, for instance, Daston .Greenburg .


As I write, the United States has been held captive for the better part of a year. Across the nation, COVID-19 has spawned fear, anxiety, disruption, and tragedy. It also has forced professionals in every sector to confront new realities and reassess the ways in which they attend to “business as usual.”The traditional “art” of medicine relies heavily on face-to-face contact - close visual inspection, touch/palpation, picking up on a wide range of sensory and behavioral diagnostic clues. However, the critical need for social distancing to reduce the spread of COVID-19 has accelerated an inevitable shift from in-person to virtual communication. One positive consequence of this is that it forces us to take a hard look at the way we practice medicine.1 This supplement focuses on one such population.Already an accepted mode of health care delivery in the United States, there is convincing evidence that telehealth is efficient, effective, and beneficial for specific uses and patient populations.2 with associated costs approaching $1.3 billion.3 The current standard of care calls for a visual examination of external and internal structures as well as vaginal secretions, a “whiff test” to detect distinctive odors associated with specific infections, and on-site pH and wet mount diagnostic tests. A bimanual exam is also recommended.A substantial population of women with symptoms of vaginitis make up to 10 million outpatient visits per yearThere are several common causes of vaginitis, and differential diagnosis is challenging even under “normal” conditions. The literature suggests that a large percentage of patients do not receive an optimal diagnostic evaluation. The expected shift from in-person to virtual outpatient visits likely will further complicate the diagnostic dilemma.To me, the implications are clear: it is time to reconsider how best to approach care for this patient population under “the new normal” conditions. There is an obvious synergy between telehealth visits and state-of-the-art molecular technology that can be leveraged to improve the efficiency and accuracy of care we provide to women with vaginitis.I hope that, in addition to drawing needed attention to an important population health issue, this supplement leads clinicians, professional organizations, payers, and policy makers to revisit “business as usual” with an eye toward optimizing health care now and into the future.