Although previous research showed that telehealth services can reduce the misuse of resources and urban–rural disparities, most healthcare insurers do not include telehealth services in their health insurance schemes. Therefore, no target variable exists for the classification approaches to learn from or train with. The problem of identifying the potential recipients of telehealth services when introducing telehealth services into health welfare or health insurance schemes becomes an unsupervised classification problem without a target variable.We propose a HDTTCA approach, which is a systematic approach (the main process of HDTTCA involves (1) data set preprocessing, (2) decision tree model building, and (3) predicting and explaining of the most important attributes in the data set for patients who qualify for telehealth service) to identify those who are eligible for telehealth services.This work uses data from the NHIRD provided by the NHIA in Taiwan in 2012 as our research scope, which consist of 55,389 distinct hospitals and 653,209 distinct patients with 15,882,153 outpatient and 135,775 inpatient records. After HDTTCA produces the final version of the decision tree, the rules can be used to assign the values of the target variables in the entire NHIRD. Our data indicate that 3.56%  of the patients are eligible for telehealth services in 2012. This study verifies the efficiency and validity of HDTTCA by using a large data set from the NHI of Taiwan.This study conducts a series of experiments 30 times to compare the HDTTCA results with the logistic regression findings by measuring their average performance and determining which model addresses the telehealth patient classification problem better. Four important metrics are used to compare the results. In terms of sensitivity, the decision trees generated by HDTTCA and the logistic regression model are on equal grounds. In terms of accuracy, specificity, and precision, the decision tree generated by HDTTCA provides a better performance than that of the logistic regression model. When HDTTCA is applied, the decision tree model generates a competitive performance and provides clear, easily understandable rules. Therefore, HDTTCA is a suitable choice in solving telehealth service classification problems. The concept of telehealth, which first appeared in the 1900s when physicians began discussing diseases by telephone and has evolved to such a sophisticated level of performing robotic surgery, regardless of the geographical restrictions, is possible at present , 19. TheThe emergence of wireless technologies and the advancements in on-body sensor design can facilitate changes in the conventional healthcare system by replacing it with wearable healthcare systems centered on individuals . For exaA new concept called elderly welfare, which incorporates health welfare and the development of a telehealth system for the aging population, also has emerged. The telecare industry has expanded worldwide. Many countries, such as Japan, the United Kingdom, the United States, and Canada, have developed long-term care assistance policies to utilize telehealth systems , 6, 18. If patients must pay their own expenses for telehealth services without insurance reimbursement, then extremely few patients will have motivation to use these services , 15. HowWhen Taiwan introduced its National Health Insurance (NHI) plan in 1995, the Department of Health also introduced a pilot project in providing telehealth services . NeverthGiven that health insurance policy has not officially recognized telehealth services as an efficient treatment, we have no information to compute the cost and benefit of using them if they are reimbursed by health insurances. All patients must be classified into two groups, namely, “need telehealth service” and “do not need telehealth service” which will be a time-consuming task without computer aid. Thus, a proper classification algorithm developed with telehealth experts’ assistance is necessary. Among the many classification algorithms, decision trees are the most suitable one because they are simpler to understand and interpret than association rules or logistic regression. Decision trees also require a simple data preparation stage and can handle categorical data.This study aims to address the problem of identifying the patients who are the best candidates in receiving telehealth services subsidized by health insurance reimbursements. Specifically, patients with certain chronic diseases can benefit from noninvasive monitoring devices such as those evaluating blood pressure, blood glucose levels, and cardiac activity . HoweverAlthough previous research showed that telehealth services can reduce the misuse of resources and urban–rural disparities , most heThe first challenge of this study is to generate the target variable for the unsupervised telehealth classification problem. The type of target variable  determines which data-mining techniques can be used. In classifying patients into recipients and nonrecipients, the target variable is generally the patient’s status . The target variable can also be defined according to different classes in matching their various meanings. For example, we can classify all patients into several classes, such as “necessary”, “maybe necessary in the long term”, and “unnecessary”. However, having many classes, leads to frequent misclassification, because the individuality of each class becomes diluted, which results in misclassification for similar classes. This condition also can lead to an overfitting problem caused by the excess number of predictor variables for a multiclass target variable but insufficient data points. Consequently, this study limits the number of target variable’s classes into two as binary. Thus, the target variable can be transformed into a 0/1 code and the telehealth service classification problem can be applied easily in many data-mining techniques, including decision trees and logistic regression.The second challenge of this study involves generating the required information from the existing attributes for the insurance providers to determine whether the applicant is a suitable recipient of telehealth services. In a telehealth classification problem, the attributes are the patients’ personal and outpatient information they provided when they submit telehealth service applications. However, applying these attributes directly from the data set to a data classification technique may be inappropriate. For example, the decision tree–building algorithm does not handle numeric attributes uniformly. When applying the numeric attributes to generate the decision tree, numeric attributes may be used more than once with different thresholds. Some important attributes are excluded in the data set; thus, this attributed need to be derived from other attributes. For example, the patient’s traveling distance or transportation time to the hospital is generally not included in the healthcare data set. Thus, these data need to be generated. Solving this challenge can ensure that insurance providers receive the patients’ detailed medical-related data that can be used to generate the telehealth service classifier and applied directly in the classifier in determining the status of an applicant.Third challenge is building a classifier to solve the problem of identifying candidates in receiving health insurance reimbursement for telehealth services. In this study, we choose decision trees in generating the classifier because the rules they generate are simple to interpret, such that the results can be easily understood for both medical professionals and patients. Constructing a decision tree–based classifier involves three main steps, namely, variable selection, node splitting, and tree pruning . Generalk-fold validation technique divides a data set into k subsets and takes k – 1 subsets as the training data, with the remainder as the validation data set. Then, the model is trained for k times, and each iteration uses the subset i one at a time. However, the problem considered herein has a relatively small training data set for the experts to classify the patients as candidates in receiving telehealth services. Given that the training data set is extremely small, we will not split  or cross-validate the training set in the validation step. We need to develop a new validation method suitable for an unsupervised classification problem with an extremely small training set and an exceedingly large test data set.Finally, a fourth challenge is selecting a validation method. Validation is the process of assessing how well the classification models perform against the validation data  by verifying whether the models’ misclassification rates meet the established requirements. The validation techniques consider the probability of the worst-case scenario, wherein a model’s complexity is high. For example, the widely used In summary, this study aims to solve the unsupervised classification problem of identifying the patients who are the best candidates in receiving telehealth services. Four challenges, such as (1) generating the target variable, (2) generating the needed information from the existing attributes, (3) building a classifier, and (4), selecting a validation method, are addressed.To classify candidates to receive telehealth services through health insurance reimbursements, we propose a new decision tree approach, that is, heuristic decision tree telehealth classification approach (HDTTCA), which consists of three major steps, namely, 1) data analysis and preprocessing, (2) decision tree model building, and (3) prediction and explanation, as shown in Fig.  data anaAs mentioned before, four challenges are addressed in HDTTCA: step 1 tackles challenges 1, 2 and 4, while step 2 tackles challenges 3 and 4. Finally, in step 3, HDTTCA predicts and explains incoming data by using the decision tree classification model that was chosen previously in step 2. In other words, after building the decision tree model, we use this model to predict the applicability of telehealth services. In the following subsection, we explain the details of steps 1 and 2 and then clarify the time complexity of HDTTCA.As discussed previously, the target variable and some important attributes are excluded in the original data set. Therefore, HDTTCA first needs to derive some attributes from the current data set and then used them in determining the value of target variable. To validate the performance of the decision tree classification model, HDTTCA divides the data set into several subsets.Given that the raw data containing the critical attributes are often collected from different sources, these data should be integrated into a single data set first. We focus on the two primary actors involved in healthcare activity, namely, patients and hospitals. The patient-related data sets describe the information about those who have seen physicians and contain two types of information, namely, basic information  and clinical information . We retain only the important attributes of patient-related data sets for telehealth services, such as the hospital where a patient seeks treatment, the code for the international classification of diseases, and the number of days of prescription. The hospital-related data sets describe the information about hospitals that patients visit to see their physicians. For our purposes, we only need the hospital’s location and size.FAge), to represent the age of ≤30 years as young, 30 years ≤ age ≤ 70 years as middle-aged and age of ≥70 years as elderly. Similarly, we transcode the monthly insurance amount into an attribute, that is, insurance level (FIL), which is categorized as low, middle, and high with the suggested percentages of 20, 60 and 20%, respectively. We also mark the situations when patients are not required to pay the copayments with an attribute, that is, copayment exemptions (FCEM) mark as Y and N.To reduce the numbers of age categories and balance the percentages among them, we use an attribute, that is, age group (Fop) to categorized op_time into none when its value is 0, low for 1 ≤ op_time ≤ 12, middle for 13 ≤ op_time ≤ 36, and high when its value ≥37. We also denote an attribute, that is, inpatient frequency (Fip), as 0 for ip_time = 0, 1 for ip_time = 1, 2 for ip_time = 2, and 3+ for ip_time ≥ 3.We summarize the number of times in a year that each patient visits a hospital (outpatient) or is hospitalized (inpatient) into op_time and ip_time, respectively. We use an attribute, that is, outpatient frequency (FCBR), which is the number of CB days divided by EB day + CB day, to distinguish those whose symptoms cannot be helped by telehealth services and eliminate the patients with FCBR = 0. We also summarize the number of drug prescription days as drug day and the total amount of the medical fees as total amount.Some diseases are inapplicable for telehealth services . Therefore, we differentiate the total number of days that a patient uses an emergency bed (EB day) and a chronic bed (CB day). We use an attribute, that is, chronic bed rate (FDis), by combining the zip codes of the hospital location and the patient’s residence location and the assistance of Google Maps. We use the great-circle distance to estimate the shortest distance between two points on the surface of a sphere, which is calculated as follows:1, λ1) and  denote the latitudes and longitudes of points 1 and 2 (in radians), respectively; and r is the mean earth radius (approximately 6371 km). For example, the longitude and latitude of the zip codes 100 and 700 are  and , respectively. Therefore, the distance between the two points is calculated as follows:In previous studies, the critical influence factors for adopting telehealth services include the patient’s traveling distance or transportation time to the hospital, health status, and financial status , 6, 20. FDD) to record whether a drug is administered for an extended period of time. We use the attribute economic priority (FEco), to distinguish patients with special conditions, such as low income or disability. Telehealth services are mostly needed by patients living in rural areas, even if their traveling distances to the hospitals are shorter than those of the other patients. Given that remote area is undefined, we use an attribute, that is, remoteness (FR), to distinguish patients residing in rural areas by changing their addresses.Telehealth services are beneficial for the patients with chronic diseases because the administration period is > 7 days. We create a special attribute, that is, drug duration (FTD), with Y indicating suitability and N indicating unsuitability for telehealth services, respectively. Another way to mark the potential telehealth users is differentiating the treatment that a patient receives. We create an attribute, that is, target treatment (FTT), to record the specific treatments for diabetes, hypertension, and hyperlipidemia symptoms, with telehealth-applicable as A, other chronic diseases as B, and nonchronic treatment as N. For special cases that do not fit in the preceding categories, we create an attribute, that is, Reim_Spe (FRS), to record these special telehealth applicable cases, with applicable denoted as Y and nontelehealth-applicable as N. Table As mentioned previously, telehealth equipment can monitor only some physiological values, such as blood pressure, blood glucose level, and cardiac activity, at present . Thus, tFDis_C by using the formula in Table The decision tree–building algorithm does not handle numeric attributes uniformly. When applying these attributes to generate the final decision tree, numeric attributes may be used more than once with different thresholds, and these numerical attributes should be transferred to categorical ones. First, for the numeric attributes that are already converted , we remove the numeric attributes. Second, we can use mean and standard deviation to compute the thresholds and categorize the attributes . Table This study aims in classifying potential chronic patients who are suitable to receive telehealth services subsidized by health insurance reimbursements. This target variable does not exist in most healthcare data set. Thus, HDTTCA generates the target variable, that is, adoptability first. This approach involves asking experts to assign a value for the variable. However, because the data set is extremely large, HDTTCA samples a comparatively small data set as the training data set for experts’ opinions. To solve the problem of this study, we interview three experts in the telehealth-related fields  in identifying the target variable, that is, adoptability (labeling the adoptability Y or N for each record) in the sampled data. Then, the experts reveal the criteria and rules they used in their decisions during the interview by showing all the attributes for all records in the sampled data in Table The experts label only a comparatively small data set. Therefore, HDTTCA generates a decision tree for each expert independently after collecting their opinions. The possible values of the target variable are “yes”, “no”, or “in consideration”. Given the importance of telehealth services in monitoring the physiological values and eliminating the urban–rural gap and socioeconomic gaps, we combine “no” and “in consideration” into one group. HDTTCA uses the attributes in Table Although patients suffering from chronic diabetes, hypertension, and heart diseases increase, our potential target population is still comparatively rare, accounting for 10% or less of the total population. These imbalance characteristic may reduce the predictability of a decision tree model; over- and undersampling are helpful techniques to overcome this problem. Oversampling resamples the existing minority data with slight modifications to be close to the proportion of majority, whereas undersampling abandons some existing majority data and keep all minority data. These techniques balance the data set distribution. We also adopt stratified sampling to increase the proportion of our target.After sampling, we divided the data sets into training and validating subsets. We use the former to build the classification model and the latter in validating the overfitting problem and compare the prediction rate of different models. The overfitting problem occurs when the model used extremely many attributes to generate a decision tree and fit the data with extremely few objects. We may find overfitting clues in the model prediction step. If the decision tree’s accuracy is high in the training data set  but comparatively low in the validation data set, then an overfitting problem occurs. Therefore, we need to decide the proportion of training and validation data sets carefully. Given that HDTTCA samples a comparatively small data set similar to the training data set for experts’ opinions, we use all the experts’ opinions as the training data set and randomly took additional samples from the remaining data set as the validation data sets.In this step, we build a decision tree classifier, on the basis of the training data set and validate the classifier by using the validation data set. However, the telehealth service classification problem we consider the involved > 20 attributes, which are not all essential to identify the characteristics of the target variable. Correlations among the attributes can also cause multicollinearity and inaccuracy in some data-mining models. Thus, HDTTCA uses an attribute selection mechanism to choose the most discriminative attributes when building a decision tree classifier.T as the data set, which was constructed by m attributes and had n records, x1, x2, …, and xn. The target variable, which was denoted as yi = {0, 1} where 1 ≤ i ≤ n, was “adoptability for telehealth service”. Therefore, a record can be expressed as xi =  and T = {xi | 1 ≤ i ≤ n}. Decision tree algorithms classify records by conjunctive rules (e.g. age group = elder and distance ≥60). Several decision tree algorithms, such as ID3 and C4.5, apply information theory to separate data by iteratively calculating the entropy, which was denoted as H(T) and the information gain, which was denoted as IG, from splitting data on the basis of the attribute a. Entropy, which was denoted as H(T), is the expected value of the information contained and can be defined as H(T) = −Σp(b)log(b), where T is the training data set, Y is the target variable in T, b is a classified value in Y, and p(b) is the probability that an object in T is classified as b.Before detailing the construction of a classifier, we define IG = H(T) – Σp(a)H(a), where A is an attribute for which a split has occurred, p(a) is the probability that an object in T exhibits attribute A = a, and H(a) is the entropy of the subset of T, where attribute A = a. The decision tree selects the locally best attribute  as a splitting criterion. After calculating the information gain of each attribute, the decision tree algorithm selects the attribute with the maximum information gain to be a node, which splits the data set into two or more subsets. The process iteratively proceeds until a full decision tree is built.Information gain is the amount of uncertainty reduced due to the split, which can be defined as A well-fitted decision tree model can predict the training data set with the least misclassification cost or the highest accuracy. The advantages of decision trees over other classification algorithms are their coherent and consistent rules from the tree root to the leaves and the ease of interpretation. We use J48 in Weka [When using J48 in Weka [In Weka , J48 offAn appropriate model should be able to predict future data sets consistently and effectively. Different perspectives and criteria are available to identify the performance across different settings of the confidence factor and the minimum number of objects per leaf. The validation techniques in J48 in Weka can assess the performances of various settings. Considering that the training data set is small, HDTTCA will not split or cross-validate the training set. Instead, HDTTCA asks J48 in Weka to randomly produce 30 data sets from the test data set and apply validation and evaluation on these data sets. If the error between the training and validation data is high, then overfitting or underfitting needs to be considered.rs) among these attributes and eliminate the coefficient with rs > .7 to avoid the multicollinearity problem. Then, we perform stepwise logistic regression to select the significant attributes from the remaining attributes. We remove unrelated attributes according to coefficient tests or p-value greater than 5%, until all remaining attributes are significant.Then, we compared the final decision tree results against the results obtained from a logistic regression model that was constructed by Weka . We firsŷ, with the actual result of the target variable, y to test for predictive capability. Table Err(T), and the probability of being correct, which is denoted as Accuracy(T). Here, Err(T) is computed as (FP + FN) / (FP + TN + TP + FN), and Accuracy(T) is computed as 1 – Err(T) or (TP + TN) / (FP + TN + TP + FN), where FP is the false positive count, FN indicates the false negative count, TP is the true positive count, and TN reflects the true negative count , that can be computed as TP/(TP + FN), such that the denominator represents actual positive cases. Sensitivity can identify the positive case of a model correctly. Hence, high sensitivity implies that few Type-II errors have occurred when applying the model. Low sensitivity suggests a poor performance in identifying the wrong patients for telehealth services. Specificity, which is computed as Specificity(T) = TN/(FP + TN) = 1 – FP/(FP + TN), is the true negative rate and indicated how accurately our model will identify true negatives. Precision, which is computed as Precision(T) = TP/(TP + FP) = 1 – FP/(TP + FP), is the exactness or percentage of tuples that the classifier labeled as positive that are actually positive. Precision denoted the accuracy of our model in identifying true positives.Another important criterion is sensitivity or true positive rate, which is denoted as Accuracy(T) evaluates classification models. However, Accuracy(T) cannot distinguish type-I/type-II error. For the telehealth service classification problem in this study, type-II error is crucial. Patients can suffer extensively if the model misidentified an eligible patient as ineligible. Therefore, sensitivity is the first criterion that we will apply in the evaluation process, followed by accuracy, specificity and precision.In general, the misclassification rate or We acquired a data set from the NHIRD provided by the NHIA, Ministry of Health and Welfare in Taiwan, which contains data from 1996, when Taiwan first introduced NHI, to 2012 in demonstrating the applicability of HDTTCA. For calculative convenience, we use the 2012 data as our research scope, which consists of 55,389 distinct hospitals and 653,209 distinct patients with 15,882,153 outpatient and 135,775 inpatient records. After HDTTCA’s generating derivative attribute step, the size of the data set decreases to approximately 100 MB compared with the 4.45 GB size of the original data set. This study (REC no: 20141HS007) has been approved by the Research Ethics Committee of National Taiwan University and classified as exempt on November 14, 2014 in accordance with the Social and Behavioral Research Ethical Principles and Regulation of National Taiwan University and governmental laws and regulation of Taiwan.After Step 1.1, HDTTCA has derived 22 attributes  for experts’ opinions, HDTTCA used all experts’ opinions as the training set and took 30 random samples of 20,000 patients each from the remaining data set as the validation data sets when training the decision tree. When using J48 in Weka [t-test to compare the top two trees, that is,  and , and the t statistics and p-value for the sensitivity metric are 19.0614 and 3.01E-18, respectively, which rejects the null hypothesis. Thus, we can select the best trees by using the sensitivity criterion, which is tree .In total, J48 in Weka [Table In conclusion, three main dimensions influenced the decision about using telehealth services. First, patients’ clinical status is important because of the limitation of telehealth equipment. Patients with diseases that do not need to be monitored by physiological values are not recommended for telehealth services. Second, telehealth services play important roles for patients who live in inconvenient areas or who have long travel times to healthcare facilities. Third, telehealth service benefits elderly and low socioeconomic level patients. With telehealth service subsidized by insurance, these patients can have a healthy quality of life.After HDTTCA produces the final version of decision tree, the rules can be used to assign the values of the target variables in the entire NHIRD. Our data indicate that 3.56%  of the patients are eligible for telehealth services in 2012. The following step is setting up different cases of experiments to compare the results of HDTTCA with those of a logistic regression model.rs among these attributes and eliminate the coefficient with rs > .7 to avoid the multicollinearity problem. We perform stepwise logistic regression to select significant attributes from the remaining 13 attributes. The final logistic regression model consists of only 5 significant attributes , as shown in Table Then, we then construct the logistic regression model by Weka  as the bThis study proposes HDTTCA to determine the eligibility for the insurance reimbursements of telehealth services. After finding the feasible combinations of factors, models, and corresponding parameters in the Method Section, we conduct a series of experiments 30 times to compare the HDTTCA results with the results of the logistic regression by measuring their average performances and determining which model addresses the telehealth patient classification problem better. As mentioned in the Method section, four important metrics including sensitivity, accuracy, specificity, and precision, are used to compare the results. These metrics reflect the usability and accuracy of a model. We also discuss the interpretability of the result as a crucial criterion when applying different classification methods in practice.t-tests between the results classified by the decision tree of HDTTCA , of the pairwise t-tests between the decision tree generated by HDTTCA and the logistic regression model. In terms of sensitivity, the decision tree generated by HDTTCA and the logistic regression model are on the equal ground. In terms of accuracy, specificity, and precision, the decision tree generated by HDTTCA provides a better performance than that of the logistic regression model. The decision tree model generates a competitive performance and provides clear, easily understandable rules by applying HDTTCA. Hence, HDTTCA is a suitable choice in solving telehealth service classification problems.Given that this data set is unbalanced , HDTTCA result shows extremely high sensitivity, accuracy, and specificity  but low precision. The results in Table In conclusion, this study has three contributions. The first contribution is confirming that the use of decision trees is a good approach in identifying the potential receivers of telehealth services. A decision tree telehealth service classifier can produce clear and understandable rules within extremely fast training time by applying HDTTCA. The second contribution indicates that HDTTCA determines the three most important dimensions of reimbursing patient for telehealth services, namely, clinical records, convenience, and social-economic status. The third contribution is proving that HDTTCA is essentially applicable on a real data set from NHIRD in Taiwan.Two matters need to be illustrated in this study. First, the ethical question of denying some people access to a service because it is not covered by insurance or they live extremely near the hospital or service provider has not been addressed. That limitation indicates that the universal healthcare coverage is not universal if some cannot access it. Second, HDTTCA involves human judgment to determine the target variable. In the future, the actual value of the target variable may be acquired from the NHIRD if the policy of reimbursing patients for telehealth services is implemented. We can compare the HDTTCA results and the attributes of actual applicants and modify the classifier rules. Future work can also focus on building the utility model of users and in designing an appropriate billing mechanism of telehealth services.


In this article we explore the relationship between learning and the conjunction fallacy. The interpretation of the conjunction effect as a fallacy assumes that all observers share the same knowledge, and that nobody has access to privileged information. Such situations are actually quite rare in everyday life. Building on an existing model of surprise, we prove formally that in the more typical scenarios, where observers are alert to the possibility of learning from event outcomes, the conjunction rule does not apply. Scenarios which have been engineered to produce the so-called conjunction “fallacy” (e.g., Tverksy and Kahneman,  Every day, people deploy their knowledge to carry out a bewildering array of cognitive tasks, ranging from motor tasks to perception, to reasoning and decision making. This knowledge is tenuous. Not only are the facts themselves uncertain, but so too are the assumed causal models that allow facts to be inferred in the first place. At any moment we may be faced by a surprising revelation which requires us to fundamentally re-evaluate our representation of what we thought was the case. As such, our knowledge is not set in stone, but continually modified, resulting in a dynamic and ever changing mental representation of the world.In this article we highlight the fact that there are two very different types of uncertainty, one we refer to as “objective uncertainty,” which is shared by all observers and does not result in any representational updating, and the other we refer to as “subjective uncertainty,” which is asymmetric, and causes different people to learn in different ways. These two types of uncertainty invoke different sets of probabilistic rules, a divergence which, we will argue, lies behind the conjunction effect. Specifically, most scenarios in everyday life involve subjective uncertainty, for which the conjunction rule does not hold.Objectively uncertain outcomes are those that involve pure randomness, from which no learning takes place. Each event is independent, and completely unconnected to the next.When we roll a dice, for example, and observe the result, it does not lead us to update our expectations for future rolls. We already know everything about the dice. Such gambling tools are designed to guarantee that everybody is equally uninformed. Nobody can have privileged knowledge of a fair dice.Over the last century, probability theory has emerged as the ubiquitous approach for modeling objective uncertainty. At its heart lies the assumption that all observers share the same information: probabilities are expressed relative to a fixed stochastic model, which is available to all observers. The theory was formalized by Kolmogorov in the 1930s through the notion of probability space, whereby a set of possible outcomes is mapped to a number that represents its likelihood by a probability measure function.objective in the sense that there is a fixed stochastic model which processes randomness, and this model can be sampled repeatedly without changing one's beliefs about the model. If we agree with all of these assumptions, then a person who judges the sequence of coin tosses HTHHTH as more likely than HHHHHH is reasoning fallaciously, since both are equiprobable  uncertainty are extremely rare. Instead, people typically expect to When we henceforth use the terms “subjective” and “objective” uncertainty in this paper, we are referring, not to the holder of the uncertainty, but to the issue of whether outcomes are viewed as dependent or independent. Ramsey  for instFor example, what if a tossed coin repeatedly shows heads 10, 100, or even 1,000 times in a row? Classical probability theory says that 1,000 heads in a row is just as likely as any other particular sequence of 1,000 coin tosses, so no need to do anything. But in the real world, would we really accept this? Would we be so confident as to maintain the assumption of independence between coin tosses? The greater the deviation from a typically random sequence, the more our faith in the neutrality of the coin is tested. We know that somebody might be tricking us. A repeated pattern of heads appears to leak subjective information about some bias in the coin. This lack of absolute faith in outcome independence reflects a source of “subjective uncertainty” that is incompatible with classical probability theory.Even generative mechanisms which are strongly believed to be random can produce sequences that expose latent subjective uncertainty, and lead people to question the assumption of independent outcomes. Such an event occurred in 2009 in the Bulgarian national lottery, when the same set of six numbers was drawn in two consecutive draws. An unprecedented record number of 18 people shared the winnings from the second draw, having correctly anticipated a repeat of the previous week's numbers. The officials of the lottery insisted that manipulation was impossible and that it must have been a freak co-incidence. Nevertheless, the Bulgarian minister of Physical Education and Sport established a commission to investigate the incident, indicating a lack of complete confidence in the randomness of the draw. Maybe the balls were not equally weighted, maybe the drum mechanism was defective, or maybe the lottery officials were corrupt. In sum, the occurrence of the same set of numbers on two consecutive occasions was so surprising that it served to undermine confidence in the assumption of objective uncertainty.In classical probability theory, the probability distributions themselves remain invariant. However, in the case of subjective uncertainty, observers learn from outcomes and update their expectations accordingly. The invariant in this case is a system for extracting and processing the information derived from observations. Adherence to a fixed stochastic model  precludes any possibility of learning, a disposition which Baldi and Itti  argue woBoth Baldi and Itti  and MaguWhereas classical probability is founded on certainties and fixed causal models, Bayesian probability instead focuses on subjective uncertainties and inductive inference. It extends the classical approach by viewing probability not as an objective phenomenon, but as the state of knowledge of an uncertain observer, a state which is therefore subject to refinement  = δ(x|p)/log1/p(x). It is a normalized value between 0, 1 up to a logarithmic factor. This measure gives us bits of surprise per bit of observation, in other words, the proportion of the probability-based encoding that is superfluous according to the computational point of view. The more an observation can be compressed , the greater the associated level of surprise.The surprise of a string Maguire et al.  show thaThese theories of surprise (Bayesian and AIT-based) allow us to quantify the subjective informativeness of observations, and thus model how people reason about the likelihood of outcomes that could potentially leak subjective information .In the case of a surprising lottery sequence such as the Bulgarian example, the two possible explanations, “biased draw” and “coincidence”, are incommensurable. In order to apply probability theory, a single invariant model of reality must be identified, which supports the notion of a repeated experiment  = P(A)+P(B)−P(A∩B), no longer holds. For example, the coin toss pattern HHHHTT features less randomness deficiency than HHHH, even though the latter is more probable if we assume informational symmetry. The fact that subjective likelihood considers the informational content of an outcome relative to an observer's existing representation  adds an additional dimension which undermines its monotonicity.Because additional conjunctive events can serve to reduce randomness deficiency, thus increasing subjective likelihood, the addition law of probability, In the following section we formalize this result by demonstrating that for every situation involving subjective uncertainty there is a conjunction of events which is less surprising  than either of its constituents in isolation.p, there are always two strings of events x, y, such that x is a substring of y but y has higher subjective likelihood. The idea of the proof is that any long enough typical string of events can always be decomposed into a substring of events that carries greater subjective information.We prove that given any hypothetical model Theorem 1. Let E1, E2, …Em be m independent events, and let p be the associated computable probability measure function. Let α>0 be a surprise threshold. There exists a conjunction of events A = A1∧A2∧…∧An with a constituent B  < p(B)) such that B is -surprising  and A is -typical .Proof.E1, E2, …Em, p and α>0 be as above. Without loss of generality m = 2k and p can be seen as a probability on strings of length k (each coding one event Ei) extended multiplicatively i.e., p:2k →  is extended multiplicatively by p(xy): = p(x)p(y).Let n be a large integer. Let y∈2kn be a -typical string. y can be viewed as the concatenation of n strings of length k . By the pigeon hole principle, there must be such a string that occurs at least n/2k times. Denote this string by s, and let l be the number of occurrences of s in y, i.e., l≥n/2k. Because y is -typical, we have p(s)>0. Thus p(s) = 2c− for some c>0. Let x be l concatenations of s. Because p is extended multiplicatively, we have p(x)>p(y).Let x is -surprising. To describe x it suffices to describe l plus a few extra bits that say “print s l times.” Since l can be described in less than 2logl bits (by a prefix free program) we have K(x) < 3logl for n large enough. We haveLet us show that n large enough. Thus x is -surprising, but y is not, which ends the proof. The conjunction rule does not hold for subjective likelihood.for In the remainder of the article we investigate whether evidence in support of the so-called conjunction “fallacy” might instead reflect the interpretation by participants of subjective uncertainty in the experimental scenarios. Specifically, we investigate whether rephrasing scenarios to be more objectively uncertain mitigates the conjunction effect, and whether giving participants the time necessary to “learn away” their subjective uncertainty mitigates the conjunction effect.Since the 1970s, the dominant view in psychology and behavioral economics has been that humans are prone to making sub-optimal decisions .All of these explanations assume that probability theory provides the normative rules for quantifying likelihood. For instance, even Juslin et al. , who conAn alternative approach is to question the appropriateness of probability theory itself in this context  Linda is a bank tellerb) Linda is a bank teller and is active in the feminist movement.Tverksy and Kahneman  reportedIn the Linda example, some information about Linda is provided, but there is much about her that remains unknown. What kind of person is Linda? Has she settled down since her student days? Participants do not know the answers to these questions, yet the experimenters might, since they are the ones referring to “Linda.” This scenario seems to imply the presence of subjective uncertainty.If we assume that the description of the Linda scenario is in any way incomplete , then the outcomes have the potential to be subjectively informative, meaning that classical probability theory cannot be applied. For example, if we find out that Linda is a bank teller, we might infer that she has become more conservative and has less time for activism, completely altering our understanding of who she is. In contrast, hearing that Linda is still active in the feminist movement suggests that she holds the same beliefs as before. Because these two models of Linda are incommensurable, there is no single objective model relative to which classical probability  can be expressed. The association with representativeness noted by Tverksy and Kahneman  may simpabout Linda (subjective uncertainty), the uncertainty in the scenario must be processed by the Linda (objective uncertainty). This involves interpreting the details provided about “Linda” as being a complete description of a stochastic mechanism that processes randomness.In order for the Linda scenario to be compatible with classical probability theory, the outcomes must be interpreted as being uninformative, or random, relative to the model of Linda. Instead of being For example, in a study investigating the 2-4-6 task, Van der Henst et al.  hypothesIn order to achieve a similar shift in perspective, we qualified the original Linda scenario so that the outcomes could be interpreted as independent, random events produced by an unchanging completely specified generative model, like the roll of a dice, or the output of a jackpot machine.One hundred and ninety-one undergraduate students from Maynooth University participated voluntarily in this study. These were all second year computer science students who had previously taken introductory mathematics modules in logic, calculus and algebra.qualified condition was presented as follows:The scenario in the Some programmers run a filter on a large social media database. They input the following randomly selected parameters.university_degree = “philosophy”1) marital_status = “single”2) IQ > 1303) name = “Linda”4) age = 315) By chance, a single database record is returned by the filter. Which is more probable?a) The record states ‘occupation = “bank-teller” and political_outlook = “feminist”’b) The record states ‘occupation = “bank-teller”’This qualification of the original scenario is compatible with classical probability theory, because it clearly identifies the description as a completely defined process for selecting a Linda who is a random product of that stochastic model. The outcomes “bank-teller” and “feminist” do not provide any information about the Linda selection filter. All observers share the same information about “Linda,” hence the conjunction rule holds.original condition was presented as follows, so as to be compatible with the information presented in the qualified condition:The scenario in the Linda is 31 years old, single, and has an IQ greater than 130. Her university degree was in philosophy.Which is more probable?a) Linda is a bank teller and a feministb) Linda is a bank tellerOur hypothesis is that the above description invites the interpretation of subjective uncertainty: Linda is a person, she has more features than those stated in the description, and the outcome provides subjective information about these additional features. Accordingly, neither classical probability nor the conjunction rule hold in this case. Probability is judged based on how subjectively informative the outcomes are. Option a) is less subjectively informative, hence more probable relative to the observer. We therefore hypothesized that the conjunction effect would be observed more frequently in the original condition than in the qualified condition.n = 85) or qualified condition (n = 106) and selected one of the two options, which were randomly ordered for each participant. The task was carried out individually on a desktop PC and the results were saved in a database.Participants were randomly assigned either to the original . Had they interpreted the description as informationally complete, then there would have been no question of some outcomes providing “better” matches than others: every outcome would have been equally uninformative. Instead, they viewed the outcomes as These results illustrate just how counter-intuitive it is to interpret a brief description of a person called Linda as informationally complete. Participants taking part in a psychological experiment expect that the experiment is designed to test some aspect of how they naturally think and behave. Accordingly, it makes sense for them to interpret the description of Linda as if it was a natural everyday situation, presented in the form of text for the sake of convenience. The description of a person called Linda invites participants to think of a particular person who has a personality and other idiosyncratic features, which are known to others, yet subjectively uncertain.In everyday life it is Linda the person that is relevant, and never Linda the selection mechanism. The form of probability that Tverksy and Kahneman  advocateThe idea that a conjunction can be more likely than either of its constituents seems plain wrong. However, Fuchs et al.  point ouClearly, betting on a conjunction will never pay out as often as betting on its constituents in isolation. Subjective likelihood thus appears to violate Dutch-book coherency.The key to resolving this apparent paradox is that betting only takes place when bettors share the same information. In situations involving asymmetric information, some competitors will hold additional information that gives them an edge, meaning that nobody is going to place a bet. Individuals who are judging probability based on subjective uncertainty do not bet, and do not expose themselves to a monetary loss. They only use the judgment to make decisions for themselves. Indeed, in an empirical study, Maguire et al.  found thWhen betting is taking place on the toss of a coin there is complete transparency on the nature of the coin, how the coin is going to be tossed and how the call of “heads” or “tails” will be determined. In a fair bet, everyone shares the same objective model of the mechanism that produces the random output, and no outcome is subjectively informative. The outcome of the bet never leads a bettor to update their understanding of how the outcome was produced (or else they would not agree to pay up). Given that there is independence between the model and the outcomes it produces, classical probability theory can be applied.However, in most everyday situations where likelihood judgements must be made, knowledge of the generative mechanism is not uniform among observers. Typically, people learn from their observations, adjusting their models in light of what they experience. Because some people have access to privileged information, betting is impossible in such situations. Gambling only takes place when participants are confident that everyone in the game experiences the same objective uncertainty.Tverksy and Kahneman's  Linda scBy contrast, the qualified Linda scenario in our experiment supports betting. Here, the Linda scenario is no longer about a particular individual called Linda, but rather constitutes a definitive set of instructions for how a Linda will be selected. Specifically, a Linda will be randomly selected from a social media database to match the description of being 31 years old, single etc. Betting makes sense in this case, as everybody agrees on the model, the outcome is random relative to the model , and hence no learning takes place. No matter what emerges as the Linda's current occupation, it will not affect the bettor's beliefs about the mechanism by which the Linda was selected.In sum, probability is the inverse of informativeness. When a situation involves objective uncertainty, then everybody identifies the same probabilities, the conjunction rule holds and betting can take place. However, when information is asymmetric, and subjective uncertainty applies, then different people see different probabilities: outcomes can leak new information, meaning that the conjunction rule no longer holds, and betting does not take place.If it was feasible, we would replicate every experiment in Tverksy and Kahneman's  study anFor example, Sides et al. , investiTake as another example Tversky and Kahneman's Wimbledon scenario. Participants were asked to predict what would happen in the 1981 final. “Which is more likely,” they were asked “- that Borg will lose the first set, or that he will lose the first set but win the match?” People who bet on sports assume that they know as much about the event as anybody could possibly know, that is, they assume that objective uncertainty applies. This expert knowledge allows professional gamblers to identify value bets. However, sports bettors only reach this stage of objective uncertainty after years of studying a particular game and its athletes. How many of Tverksy and Kahneman's 93 subjects would have felt they knew enough about Bjorn Borg to place a bet on one of his matches? Most likely zero. Hence, those subjects, on being required to make a prediction about the Wimbledon final, undoubtedly viewed themselves as being in a position of informational disadvantage, undermining the applicability of the conjunction rule.subjectively more likely; objective probability does not apply. Specifically, if Bjorn Borg lost the match, this would be surprising because Bjorn Borg is supposed to be a great player. If this were to happen, naive observers would have learned something about Bjorn Borg's weaknesses. Given Shannon's observation that information and likelihood are inversely associated concepts, Bjorn Borg losing is viewed as more informative and less likely, as supported by the 72% of participants in Tversky and Kahneman's study who rated it as more probable. In order for the scenario to be interpreted as one involving objective uncertainty, Tverksy and Kahneman's experiment would have to be carried out with seasoned tennis sports bettors. For these professional gamblers, neither outcome is surprising: they've seen it all before and, no matter what happens, they learn nothing new.For naive participants, the outcome of Borg losing the first set but winning the match is more likely, because it conveys less subjective information. This is not a fallacy, it is an acknowledgement of not being an expert in sports betting. Because such participants would never dream of placing a bet on a Bjorn Borg match, there is no violation of Dutch-book coherence: the conjunction is The only other experiment in Tverksy and Kahneman's  study th1) RGRRR2) GRGRRRor3) GRRRRmust be more likely. This, of course, assumes that the scenario is interpreted as one involving objective uncertainty. In this regard, there are several mitigating factors.When awarded real payoffs, 65% of participants selected Sequence 2 as most likely, while 62% selected it when the payoffs were merely hypothetical. Sequence 1 can be obtained simply by deleting the first G in sequence 2, therefore, according to the rules of classical probability, the first sequence Firstly, the inclusion of the third sequence, which is highly unrepresentative, suggests that the question is targeting the objectivity of the dice. Its inclusion hints that the sequences of rolls should be evaluated for how much subjective information they convey about the surprising possibility that the dice is biased. Second, it's not obvious that the second sequence is contained in the first. Unless one goes counting and matching letters, it's not obvious that there is a conjunction here. Tverksy and Kahneman  themselvIn the following experiment we replicated Tverksy and Kahneman's  originalHogarth and Soyer  investigOne hundred and fifty-seven undergraduate students from Maynooth University participated voluntarily in this study. These were all second year computer science students who had previously taken introductory mathematics modules in logic, calculus and algebra. None of these students had participated in Experiment 1.Before the process began, participants were introduced to a computer simulated dice with four green faces and two red faces, which they could roll by pressing a button. The experimenter explained that, after making a prediction, participants would roll the dice twice to see if the prediction was correct or not. To incentivise focused decision-making, students were told that the top 20 performers would earn bonus continuous assessment marks. The question was posed as follows:Which outcome do you wish to gamble on?a) RED will be rolled secondb) GREEN will be rolled first, and RED will be rolled secondParticipants selected an option by clicking a box on the screen. They were also asked to select on a Likert scale “how confident are you that you are playing the game well?.” The dice was then rolled twice and the results displayed on the screen. Participants were invited to repeat this process a total of 30 times, with the cumulative number of correct guesses being tracked.Participants carried out the task individually on a desktop PC and the results were saved in a database. In the end, all participants with 11 or more correct guesses were awarded the bonus marks, with the top performer achieving 15 correct guesses out of 30.In line with Tverksy and Kahneman's  results,In total, the number of participants displaying the conjunction effect dropped from 61% to 29% after 30 trials, with a continuing downward trend. Over the same period, the percentage of participants reporting the highest level of confidence increased from 17% to 30%.F = 3.666, p < 0.001, MSe = 0.172. As expected this data displayed a linear trend, F = 29.701, p < 0.001, MSe = 0.414, indicating that participants were more likely to exhibit the conjunction fallacy in the initial stages of the experiment than in the latter stages of the experiment.In order to determine whether a trend existed across participants' responses over time, a repeated measures ANOVA was conducted. This revealed a significant difference in responses for the 30 trials, F = 3.686, p < 0.001, MSe = 0.069. A linear trend was also apparent here, F = 13.180, p < 0.001, MSe = 0.414, indicating that participants became more confident in their responses over time.A similar trend emerged in participants' confidence ratings over time. These differed significantly across the 30 trials Figure These results show that people quickly adjust to the mechanics of objective uncertainty following a period of learning. Although 61% of participants failed to appreciate the applicability of the conjunction rule based on the initial verbal description at the start of the experiment, they successfully updated their stance once the objectivity of the process was communicated through repeated interaction with the system. In other words, learning about the process by actually carrying it out in practice served to extinguish the conjunction effect.These results strengthen our claim that Tverksy and Kahneman's  conjunctThe best way to eliminate subjective uncertainty is to allow people to engage in a judgment task as many times as they want, until they are utterly assured that there is nothing left to be learned. Unfortunately, a hallmark of conjunction fallacy inducing tasks is that they are not repeated: participants are asked a given question a single time, raising the spectre of subjective uncertainty. After 30 trials, nearly a third of our participants were still demonstrating a conjunction effect. Additional research is required to identify whether a true fallacy can ever be observed, or whether the effect would drop to zero with sufficient trials.Given that it quickly drops off as participants learn about the task, the conjunction fallacy is unlikely to be observed in practice. For example, it is unlikely that professional gamblers could increase their winnings by exploiting the effect. The results of Experiment 2 show that a few seconds of gambling is sufficient for participants to adopt the correct model. Thus, the failure here lies not in people's ability to think rationally. Instead, it lies in the inadequacy of verbal descriptions for communicating objective uncertainty. When people read or hear words, they instead think in terms of subjective uncertainty. Why?We posit that, rather than exposing a blind spot in human rationality, the conjunction effect simply reflects the unnaturalness of objective uncertainty, especially when communicated via words. Nearly all the decisions that people make in everyday life are based on personal uncertainty, not objective uncertainty. The idea that a diverse set of people might share the same uncertainty is extremely unusual, and only arises when contemplating mechanisms that are capable of manufacturing randomness . In everyday life, judgments of likelihood, like judgments of informativeness, are nearly always a personal construct .Admittedly, the experiments we have presented in this paper are mere sketches whose results might well be compatible with other competing explanations for the conjunction effect. Further research is required to confirm our theory, specifically focusing on the use of likelihood judgements in ecologically valid settings, where tasks are repeated many times in succession, as they are in real life. The main advantage of our theory is that it has no psychological component: it does not make any assumptions about the idiosyncracies of human behavior. Our theory simply constitutes a mathematical model of how information should be processed in order to optimize decision making in the context of subjective uncertainty; the results of our experiments suggest that people make decisions which reflect the optimal model. Given that our theory is more parsimonious, we suggest that the burden of proof should be placed on those theories that presume that a psychological component is needed to explain the conjunction effect.Uncertainty is an intrinsic and omnipresent feature of the real world environment. However, the type of uncertainty we deal with in everyday life is unlike that involved in rolling a dice. We do not assume that our observations are independent of each other. Instead, we know that other people know things that we don't know. We accept the idea that different observers hold different uncertainties, and we watch out for patterns that may be subjectively informative. Accordingly, likelihood judgements in the real world rarely, if ever, obey the conjunction rule.The notion that the conjunction effect represents a fallacy is firmly entrenched. However, our results suggest that the error is not on the part of the participants, who are sensitive to the subjective informativeness of the outcomes, but on the part of the experimenters, who naively expect classical probability, which assumes objective uncertainty, to apply in real world situations. In everyday life, where subjective uncertainty dominates, it is subjective informativeness rather than classical probability which provides the correct approach for decision-making.Tverksy and Kahneman  posed thThese studies, which involved the application of logic and probability, were presented as a component of the module being taught. All subjects participated voluntarily as a learning experience.PMa: lead author and experimenter; RM: secondary author and experimenter; PMo: formal theory; MK: general adviser.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The BHT procedure involves attaching the donor body (D) to the head of the recipient (R), and discarding the body of R and head of D. Canavero’s proposed procedure will be incredibly difficult from a medical standpoint. Aside from medical doubt, the BHT has been met with great resistance from many, if not most bio- and neuroethicists.Neurosurgeon Sergio Canavero proposed the HEAVEN procedure – i.e. Given both the known challenges and unknown outcomes of HEAVEN, several important neuroethical and legal questions have emerged should Canavero be successful, including: (1) What are the implications for transplantology in the U.S., inclusive of issues of expense, distributive justice, organizational procedures, and the cost(s) of novel insight(s)? (2) How do bioethical and neuroethical principles, and legal regulations of human subject research apply? (3) What are the legal consequences for Canavero (or any other surgeon) performing a BHT? (4) What are the tentative implications for the metaphysical and legal identity of R should they survive post-BHT? These questions are analyzed, issues are identified, and several solutions are proposed in an attempt to re-configure HEAVEN into a safe, clinically effective, and thus (more) realistically viable procedure.avant garde techniques are successful.Notably, the permissibility of conducting the BHT in China fosters additional, important questions, focal to (1) whether Western ethics and professional norms be used to guide the BHT – or any neuroscientific research and its use - in non-Western countries, such as China; (2) if the models of responsible conduct of research are identical, similar, or applicable to the intent and conduct of research in China; and (3) what economic and political implications (for China and other countries) are fostered if/when such These questions are discussed as a further impetus to develop a globally applicable neuroethical framework that would enable both local articulation and cosmopolitan inquiry and oversight of those methods and approaches deemed problematic, if and when rendered in more international settings. Wherefore not? Thus far I have gone, tracing a secure way over the pathless seas... Why not ... proceed over the untamed yet obedient element? What can stop the determined heart and resolved will of man?”Mary Shelley [-  Shelley head anastomosis venture; and the operation is being viewed as exciting and inspiring, as well as with doubt, scorn and resistance.When Mary Wollencroft Shelly wrote these words in 1818, she had no idea that exactly 200 years in the future, her fictional Dr. Frankenstein’s endeavors would be so akin to what neurosurgeon Sergio Canavero views as the the crowning culmination of his life’s work. Canavero has recently made headlines by planning to perform the first human body-to-head transplant (BHT) in China during the coming year. By definition, the BHT procedure involves attaching the donor body (D) to the head of the recipient (R), and discarding the body of R and head of D. Canavero, who has now performed the procedure on two cadavers , i.e. thAmerican Journal of Bioethics - Neuroscience [Bioethicist Arthur Caplan, has called HEAVEN “rotten scientifically” and “fake news” that merits “contempt and condemnation.”  A recentoscience  was devooscience . SimilarMars Attacks! [Get Out [The idea of a “head transplant” has been popularized as the stuff of fiction. In Greek mythology, the Minotaur  was composed of the body of a man and the head of a bull. In the film Attacks! , head tr[Get Out  centers [Get Out .Canavero’s proposed procedure will be incredibly difficult: demanding that the recipient’s cerebral bloodflow be maintained in order to avoid imminent brain damage from hypoxia; requiring meticulous re-attachment of the spinal cord to preserve neurologic function important for both keeping autonomic functions of the body intact and for providing input to the brain, which many have argued is vital for what is referred to as “embodied consciousness;” and necessitating extensive and life-long immunosuppression to prevent transplant rejection. While doubt and considerable caution may be warranted, why the resistance? Why is the term ‘monster’, typically applied to Frankenstein’s creation, now being associated with the creator? The abundant ethical and legal questions are likely to provide an answer.In the United States, transplant practices are governed by the United Network for Organ Sharing (UNOS) , an orgaThe costs of transplants can be exorbitant. For example, the average cost of a kidney transplant (i.e. the most common organ transplant) is $400,000 , whereasBelmont Report [If Canavero is to “pioneer a new way, explore unknown powers, and unfold to the world the deepest mysteries of creation,”  as he hat Report  regardint Report . The basThis is why Canavero has relied heavily on the precepts of informed consent . Similarwere to perform such a procedure in the U.S.? Would he face criminal charges? The HEAVEN protocol necessitates that R be “killed” , as the Uniform Declaration of Death Act [Clearly, the BHT will not be permitted to be undertaken in the United States. But what if Canavero eath Act  defines eath Act . HoweverContrarily, some may attempt to defend and justify Canavero’s actions via the ‘Principle of Double Effect’ – a moral doctrine permitting an otherwise untenable action (and outcome) when achieved via a legitimate act. This doctrine has several key criteria: the action itself must be morally good or neutral; the bad effect must not be the means by which the good effect is achieved; the actor cannot intend the bad effect; and the bad effect must be proportionate to the good effect. This principle is typically instantiated in debates regarding the permissibility of abortion via hysterectomy, or in cases of terminal palliative sedation. However, it seems clear that the BHT would not satisfy all of these criteria: with the “bad effect” (i.e. temporary cessation of bodily function) being the intended means by which the “good effect” (i.e. completed transplant and restoration of bodily function) is achieved.In any case, it is unclear what repercussions Canavero would face. Far more interesting are the consequences for the patient:“Who was I? What was I? Whence did I come? What was my destination?”  One can In order to query what a BHT “feels like,” the patient would need to not only live, but also retain consciousness, communicative ability, and memory of their prior embodied experience. While Canavero may not be worried about people remembering him, this is untrodden territory, and if the patient can’t remember who he or she is, we may never know what the pre- versus post-surgical experience is like in subjective terms. And R’s significant memory loss must be carefully considered given the primary procedural risk of HEAVEN is brain hypoxia, with the hippocampus – the part of the brain responsible in large part for memory functions –most prone to anoxic injury. In which case, how do we identify our amnestic anybody?identical twins share 99.99% similarity; this has already been problematic when identifying the culprit in the cases of a jewelry theft [While the law does not establish a concrete definition of identity, two methods are currently used – one physical and one functional. Physically, DNA is commonly used in a host of identification practices, including in criminal evidence and in paternity testing. But DNA is non-definitive, as ry theft  and rapery theft . Moreove(viz. “caputs”) sharing the same body (viz. corpus) – to show that in such a case, the government still recognizes the existence of two people with distinct identities and rights to self-determination. Furthermore, commonly held views of personhood that rely on the ability to feel pain, including those used in debates about abortion, are also neuro-centric. And if neither of these views seem sufficient, perhaps R should simply be treated  as the same person as before, consistent with other conceptualizations of identity in persons with memory disorders. The law already treats amnestic patients – those who do not retain past memories, cannot form new ones, and/or behave entirely differently following, for example, traumatic brain injury or contraction of a memory disorder – as the same person prior to amnesia .Alix Rogers elegantly argues that the law has typically taken a functionally “neurocentric” view of identity . Rogers Legal identification is vital because the implications, including citizenship, inheritances, and assets, extend to others – e.g. marriage, parenthood, debts, and wills. Therefore, two things need to occur. First, the legal system must establish a clear definition of identity. Second, until this is done, identity must be established prior to the BHT. At first glance, this would require: (1) that R must agree to preserve their prior legal identity (to account for old responsibilities and adopt new DNA); (2) that R cannot be held in any way accountable for civil, criminal, and contractual responsibilities of D (e.g. paternity); and (3) that D’s healthcare proxy and family must forgo all claims to D’s body.Even if legal identity can be established, how will R incorporate a new body into the ‘old self?’ The patient may struggle, going through life “just not feeling like their self.” Similar concerns originally plagued surgeons performing face and hand transplants. But evidence has shown that these transplant recipients actually feel more like themselves (i.e. renewing their pre-illness identity) and/or obtain a more complete sense of agency (i.e. regaining lost capacity) after the operation because they can engage public life without the stigma of their prior appearance (e.g. in the case of face transplantees), as well perform previously lost physical functions (e.g. in the case of limb transplantees) . But recHowever, it must be noted that these assertions are rendered in the context of the U.S. legal system and reflect a Western perspective. Canavero intends – and has been authorized – to undertake the BHT in China – where culture, ethics, and law(s) differ not only from the U.S., but from neighboring Asian countries as well. The expanding neuroscientific enterprise in China – perhaps the fastest growing share of the neuroscience market, predicted to reach $34.8 billion by 2024  – as welAmerican Journal of Bioethics-Neuroscience peer commentaries, and numerous BHT-‘themed’ articles and op-eds in the popular media have focused on the history of human rights’ violations in China and the lack of sufficient research delineating patient risks;. But in the main, such writings have generally failed to ask whether the models of responsible conduct of research, informed consent, and the neuroethical principles underlying their analyses are identical, similar, or applicable to the intent and conduct of research in China.But simple moral  relativism may also be untenable. The Consequences of performing the BHT in China extend beyond those of individual patient harm , and incavant garde use of methods and tools). Previously, we have proposed a risk assessment and mitigation approach, and set of principles that could be utilized to leverage neuroethical analyses and guidance, which could be applicable to both local and global contexts [Perhaps, more of a ‘middle-ground’ or, rather a more globally applicable stance must be found for the professional ethics informing international laws relevant to Canavero’s procedure  be attempted. Such an agenda should insure that further animal studies are conducted and subject to peer review. And observation and scrutiny of any such work, and regard for its realistic translation to a human trial, should be encouraged, supported, and welcomed.The capability and potential of current and emerging neuroscientific tools and techniques may well harken Shelley’s query, “With how many things are we on the brink of becoming acquainted, if cowardice or carelessness did not restrain our inquiries?” . CanaverCanavero has been called a “cowboy”; and if that is a fitting title, it should demand a proverbial “white hat” atop a white lab coat. If the intent is to benefit patients, the process of informed consent must be undertaken with greater humility and utmost stringency. UNOS – which has claimed to recognize at least the meritable intent of a BHT – should be engaged to establish guidelines that direct and govern the type and extent of preliminary research necessary to provide sufficient (or at least satisficing) “medically-based evidence” to translate the procedure to human application. All in all, numerous medical, ethical, and legal steps must be taken globally before HEAVEN can be realized on earth. If and when such steps are taken, success will not just crown Canavero’s endeavors, but will propel – and sustain – the right and good use of neuroscience in what may be an inevitable, and we hope inspiring, march forward.


Clinical anxiety is common during the perinatal period, and anxiety symptoms often persist after childbirth. Ten to 30 % of perinatal women are diagnosed with panic disorder (PD)—far more than the 1.5–3% rate among the general population. Although cognitive behavioral therapy (CBT) has been determined to be an effective treatment for PD, few studies have been conducted on CBT effectiveness in treating postpartum PD and, to the best of the knowledge of the present authors, no research has been conducted on postpartum PD among Japanese women. In this manuscript, we report on our administration of CBT to three postpartum patients with PD, detailing the improvement in their symptoms.All patients in this study were married, in their thirties, and diagnosed using the Mini-International Neuropsychiatric Interview as having PD with agoraphobia. The Panic Disorder Severity Scale (PDSS) was used to evaluate patients’ panic symptoms and their severity. All patients received a total of 16 weekly 50-min sessions of CBT, and all completed the treatment. All patients were exceedingly preoccupied with the perception that a “mother must protect her child,” which reinforced the fear that “the continuation of their perinatal symptoms would prevent them from rearing their children”. After treatment, all participants’ panic symptoms were found to have decreased according to the PDSS, and two no longer met clinical criteria: Chihiro’s score changed from 13 to 3, Beth’s PDSS score at baseline from 22 to 6, and Tammy’s score changed from 7 to 1.CBT provides a therapeutic effect and is a feasible method for treating postpartum PD. It is important that therapists prescribe tasks that patients can perform collaboratively with their children.The online version of this article (10.1186/s40359-019-0330-z) contains supplementary material, which is available to authorized users. In the field of women’s health, anxiety and depression symptoms are common in the postpartum period . A recenThe CBT model for treating PD indicates that patients with PD can misinterpret normal physical sensations such as increased breathing, palpitations, and dizziness, and this can lead to panic attacks . The CBTThe use of CBT to address PD has been consistently found to be effective by meta-analysis including randomized controlled trials (RSTs) . HoweverA previous case series reported a mother who had become concerned that her child was isolated from the community and felt disappointed in herself . The womn = 8541, 48 effect sizes overall) suggested that “inflated responsibility beliefs may be associated also with symptoms of different forms of psychopathology other than OCD, specifically anxiety disorders. A possible explanation could be that responsibility beliefs play as a transdiagnostic cognitive factor for both OCD and anxiety disorders [A sense of responsibility for child care can promote excessive control of perinatal physiological and healthy responses. Obsessive compulsive disorder is a disease that strives excessively to fulfill one’s own sense of responsibility for things that can’t be originally controlled. The leading cognitive models of OCD posited that “inflated responsibility” beliefs play as a vulnerability and maintenance cognitive factor for obsessional thinking , 19. Preisorders ”. Hence,The objective of this study was to investigate the adaptability of CBT for postpartum PD of Japanese patients. Here, we present the results from clinical practice about postpartum PD of three patients. All patients had inflated responsibility for anxiety symptoms and physical sensation. In the current study, we focus on the CBT model of postpartum PD. We performed a retrospective study by three case series to assess the efficacy and feasibility of our CBT model for adult PD .Participants were three women aged 36 to 38 years who met DSM-IV-TR criteria for PD . Two patIn the assessment session before CBT, first author conducted interviews using the Mini-International Neuropsychiatric Interview (M. I. N. I.) –24, all To provide data on the effectiveness of the therapy, participants completed assessment surveys reporting panic, general anxiety, and mood during daily life in the first, middle, and final CBT sessions. The primary outcome was measured using the PDSS , 26. TheAs the aim of this case series was to learn how to adapt CBT for postpartum PD, it was essential that the therapy and supervision be conducted by individuals fully trained in adult PD. All sessions were delivered by Matsumoto K., an experienced clinical psychologist who had been trained in CBT for anxiety disorder during a clinical placement at the Center for Cognitive Behavioral Therapy, Chiba University Graduate School of Medicine, Japan. Matsumoto K had been provided weekly individual face-to-face supervision by Shimizu E who developed the CBT model for PD.Therapy was delivered in accordance with the standard adult protocol . The CBT model is administered once per session, with the exception of the “behavior experiments,” which were repeated five times in a row. After each session, Matsumoto K carefully reviewed the session and discussed with Shimizu E the effect panic seemed to have on the patient’s cognition and behaviors, along with plans for the next session. All patients completed 16 sessions of the CBT model .Assessment and goal setting: The therapist performed hearing of mental health history, evaluated severity of panic symptoms by PDSS, and provided feedback to patients. Through this, the patients could gain an understanding of themselves (including their symptomology), establish therapeutic goals, and increase their focus on addressing PD by CBT. In Session 1, the patients were asked, as homework, to think of the thoughts and behaviors related to a feeling of panic that they experience or perform in their daily lives.Psycho-education regarding the CBT model: The application of Seki and Shimizu’s CBT model was determined collaboratively with the postpartum women, based on their own thoughts, images, and safety-behaviors . The modCase formulation: Fig. Safety behaviors: Therapist helped the patients identified safety behaviors. To examine the functions of the patients’ safety behaviors, in Session 4, two ways of role-playing (both with and without safety behaviors) were demonstrated. In the first trial, the patients were asked to focus their attention on themselves and think of a panic attack, while also performing their habitual safety behaviors . In the second trial, they were encouraged to focus externally, not to perform safety behaviors, and instead to involve themselves in their situation . Typically, patients with PD discover that the habitual safety behaviors by which they perceive internal physical sensations  makes them feel more anxious. It is important that patients empirically recognize their unconscious safety behaviors and maintain an awareness of their internal and external attention conditions. As homework, patients were recommended to repeat the two approaches on a daily basis.Re-constructing the catastrophic self-image associated with internal physical sensations: During panic-related episodes, visual images that caused pain may also occur, along with symptoms of post-traumatic stress disorder , and theAttention-shift training: Patients with PD tend to excessively focus their attention on internal physical sensations  , and becBehavioral experiments regarding catastrophic beliefs: It was necessary to conduct behavioral experiments across multiple sessions, including interceptive exposure and in vivo exposure. Hence, we collaboratively devised experiments to examine the patients’ catastrophic beliefs regarding their physical sensations. During the experiments, in order to collect new information about their panic, patients were encouraged to stop performing their safety behaviors and to focus their attention externally. This was designed to help patients realize that the feared catastrophic outcome is less likely to occur than they originally believed  occurs, patients need to be aware that others will not evaluate them as negatively as they believe. To assess the criteria and viewpoints of others, public opinion surveys were conducted.Schema work: Negative nonfunctional beliefs/assumptions (schema) were identified in this session. For example, an extreme cautionary warning is: “I must always be careful about chest palpitations.” For this, the conditional belief is: “I could die unless I carefully monitor myself for chest palpitations.” Meanwhile, the unconditional belief is: “No matter what I do, I will suddenly die.” To address this, patients were asked to create positive functional beliefs/assumptions instead of relying on schema, and to write them on cards so that they could be referred to at any time. For example, “If I feel heart palpitations, it is not an actual heart attack; for example, I can still walk.” As homework, patients were asked to recite the contents of the cards they created every day and record evidence of positive emotions supporting the new belief.Preventing relapse: The therapist listened to the skills and knowledge the patients had acquired through their treatment and gave them feedback regarding their demonstrated level of awareness. In addition, to generalize what the patients had learned from previous PD-related episodes, the therapist held collaborative discussions with the patients.The following treatment components were conducted with the aid of worksheets delivered during the session and also as homework:Chihiro was 36 years old at the first session. She graduated from college and worked full-time as a retailer for a decade. She then married in her early thirties and retired from full-time work, taking a part-time position as a clerk. After 2 years, Chihiro became pregnant; she consequently retired from her career and began living a happy life at home. Chihiro’s decision to retire was an easy one; caring for her children was her priority. Four years later, she had a second child, for whom she had hoped and planned. For this second pregnancy, Chihiro needed to be hospitalized and underwent a cesarean section. Immediately after hospitalization, Chihiro began to experience symptoms of anxiety, such as feelings of compression, stuffiness, cold sweats, and a strong fear that she was “about to scream and lose control.” However, as the birth of her child was imminent, the nurse asked Chihiro to “please stay in your room,” so she remained in her hospital room, experiencing repeated panic attacks. Chihiro gave birth without complications, and she felt relief upon meeting the new member of her family. Unfortunately, Chihiro’s panic attacks did not cease after discharge, so she visited a psychiatrist for help. Chihiro thought, “if I keep having panic-attack symptoms, I will not be able to be a good mother because I won’t be able to do things such as take the children to the park.” The second author, who became Chihiro’s attending psychiatrist, diagnosed her with PD and agoraphobia using DSM-IV-TR criteria . At the KM: If your speculation is correct (if you don't cope with it right away you will asphyxiate), you must always get a panic attack when you feel of dyspnea, right?Chihiro: I agree. Even with this care, I always feel like my throat is full.KM: Another way of thinking is explained in the CBT model of PD. It is a hypothesis that you are catastrophically interpreting the physical sensations and focusing attention on internal information has made it easier to notice the unpleasant physical sensations.Chihiro: That might be true. It is too difficult for me to protect my children if panic attach occurs to me, so that I always be concerning about the condition of my throat after a panic attack has occurred.KM: It is a natural thing to happen to a healthy person to feel tightness when exercising or doing activities. Chihiro's breathlessness may also be healthy and harmless. Dare, Could you do activities that feel of dyspnea and reassess the danger? First task is climbing the stairs of this hospital with me, from the first floor to the ninth floor.Chihiro: Well…. OK, I will try.KM: Brave decision. Let's challenge now!To expose Chihiro to compression and breathlessness from physical internal sensations, the following homework tasks based on behavioral experiments were established: to expose internal sensations such as breathless or compression, she would exercise by climbing up and down a low platform for 10 min, wrap threads for cooking around her wrists, and use a hug string when hugging her baby. Through the series of behavioral experiments, Chihiro learned that her fear of physical sensations was unfounded. After performing the experiments, Chihiro found it easier to go out with her children. Finally, she was able to bring her children to a show for children, which required her to get on a limited express train whose doors remain sealed for over 10 min after the train has departed. She was glad that her children, especially the eldest son, seemed very happy to be able to go out with their mother. Chihiro’s PDSS scores for panic symptoms fell from 13 pre-treatment to 3 post-treatment: GAD-7 scores from 10 to 5; PHQ-9 scores from 7 to 7 , without performing safety behaviors; in one task, in order to simulate a breathless experience Chihiro was asked to climb stairs quickly, which she performed with the therapist in the hospital, as follow:In Chihiro’s case, the change in the her living environment seemed to cause the absence of a change in the PHQ-9 value from before and after treatment, despite marked improvement in panic symptoms. In Japan, there is customary for a postpartum daughter live for a while at her mother’s house. After a daughter has recovered from the fatigue of childbirth, she will return to her husband’s home. At the end of the treatment program, Chihiro was returning home with her children, and so she no longer had the support of her parents’ constant presence. Therefore, the relative increase in Chihiro’s nurturing role may explain the post-treatment PHQ-9 score .Beth was 38 years old at the first session. After graduating from university, Beth worked full-time as a general clerk for 15 years. Marrying in her mid-thirties, she soon gave birth to her first child at the age of thirty-six. On an otherwise normal day when Beth had been driving, she suffered a panic attack. She felt a sharp panic manifest in the form of a rapid heartbeat, breathlessness, and serious fear. Since then, Beth often experienced palpitations, difficulty breath, trembling, and high levels of fear, both in her car and at home. In the past, Beth had been treated with psychosomatic medicine, and further anti-anxiety medicine was prescribed  to her to take regularly for 1 year, but it was ineffective. Further, due to circumstances regarding her husband, she relocated to a European country, and her treatment was temporarily suspended. Although Beth wanted to take walks with her child in the beautiful European townscape, she never did because she feared “if I feel dizzy and my breathing gets difficult, I may faint; and this will reflect badly on my husband and could have a negative effect on his career.”Beth’s strongest supporter was her husband, who listened to her problems and anxieties every day. Beth was able to go out with her husband and baby every weekend, and this made her happy. Then, Beth was pleased to find that not long after moving to Europe, she had become pregnant with her second child. However, her anxieties about her panic attacks also increased. Approximately 10 months after moving abroad, Beth returned to Japan to give birth. After giving birth she felt unable to fulfill her responsibilities as a mother with respect to her baby’s care and education, saying “I can’t bring my child anywhere as it is, and I feel depressed all day if I do not go out.” As a result, she visited a psychiatric department for the purpose of curing her panic and anxiety. Using DSM-IV-TR criteria, Beth was diagnosed with PD with agoraphobia . As BethAs part of the CBT, Beth performed role play that involved boarding a train. She was asked to notice her strong anxiety when she became worried and to pay attention to her respiration. Although she appropriately engaged in external attention during the session, she later reported that she was unable to do so during homework because her child’s “playing was very noisy, and I could not concentrate.” Consequently, we adjusted the task to flexibly accommodate and bring attention to the sounds of children playing as well as her physical sensations. Beth answered, “I will do it while breastfeeding,” and later reported that she had succeeded, stating that: “I became confident in my ability to flexibly manage my attention, and I was able to go to a hairdresser for the first time in a few months.” In a behavioral experiment, Beth sat on a rotating chair, and was spun around five times, making her dizzy. She then rushed up some stairs (with the therapist behind her as a safety precaution). Through this activity, Beth experienced heavy breathing and dizziness. Additionally, focusing on her internal physical sensation allowed her to develop the reasonable interpretation that “if I feel difficulty breathing, I will not succumb to a little dizziness.”KM: "If your friend raise panic attack in front of you, would you evaluate her or him as a bad mother/wife?"Beth: "No at all."KM: "Why do you evaluate yourself negatively?" “Is that reasonable?”Beth: "Well, now that you say that, I may be too harsh for myself."Beth also believed that “when a person feels dizziness or shortness of breath, because my husband’s boss or colleague and their wife will not respect a person whose family member has a PD, it reflects badly on my husband.” To help her understand that others would not make this evaluation, the therapist first confirmed that Beth herself did not negatively evaluate others based on their spouse’s physical symptoms as follows:KM: "According to the questionnaire, no one answered that they could not trust anyone who had panic attacks. How would you interpret this result?"Beth: "Unsurprisingly everyone was kind, they don't evaluate negatively. I found that I was the most critical of my own symptoms."Next, we performed an opinion survey with 10 people, affirming that, if a person collapses from a panic attack, none believed that “that person’s family can’t be trusted,” or “that person’s family can’t function.” The people recruited for the survey were Beth’s three family members  and seven colleagues of the authors. These efforts allowed Beth to become aware that “people do not think critically about others in a negative way,” and somewhat relieved the anxiety that caused Beth’s panic attacks as follows:In the final session, Beth told the therapist that “I would like to take my children to a beautiful national park near my residence in Europe,” to which she later returned. Her panic symptoms pre- and post-treatment changed from 22 to 6 on the PDSS scale, respectively. Her GAD-7 scores changed from 10 to 4, and PHQ-9 scores changed from 6 to 4 . In recognition of this behavior, tasks in behavioral experiments involving exposure to internal senses were established, including breathing through a straw, excessive breathing, putting warm water on her face, and submerging her face in hot water while in the bath. Every task served to reinforce a new belief that breathlessness is not related to panic attacks. Tammy later reported that: “I can start house keep, but I cannot carry on doing them.” Therapist found that she had traumatic memory about panic attack. She changed the meaning of trauma memory to a safer thing: from “I suffer from panic symptoms that never recover to helpless” to “I know how to cope with panic and the panic attack will vanish over time.” In intervention for her traumatic memory, She advised past-herself that “I know you were afraid. Don’t worry, paying attention to bodily sensations and uneasy thoughts makes you uncomfortable, so you should read some magazines or engage in some other behaviors that distract you.”Tammy used empathic words to give advice to her past self. Additionally, Tammy was fearful of getting on a train, but, by regularly performing an activity involving boarding a bus or train, she was finally able to board a bullet train and remain on board for over an hour. The severity levels of her pre- and post-treatment panic symptoms decreased from 7 to 1, respectively, as measured by the PDSS. GAD-7 scores changed from 3 to 2 and PHQ-9 scores changed from 2 to 2  were significantly effective in reducing anxiety from which a patient with anxiety disorder suffered , 49. TheThis study is limited in several ways. First, this study is observed retrospectively, it cannot be explained with a causal relationship. To prove the causation of postpartum PD in pregnant women by their strength of their sense of responsibility, future research has to investigate the risk of perinatal PD in a cohort study. The second limitation relates to cultural differences. This study is the first in the world to report the efficacy of the application of the CBT model to Japanese women suffering from postpartum PD, and, as such, should not be extrapolated to the other populations without further research on those specific populations. Since similar studies have not yet been conducted in other countries, this paper cannot predict the efficacy of the CBT model to non-Japanese patients. The final limitation is the failure to ascertain conclusively the effectiveness of the CBT model for perinatal PD. This study cannot claim certain effectiveness, because the series included only three case studies. In order to examine the efficacy of the CBT model for postpartum PD in the future, it is necessary to conduct clinical trials designed to include random controls.In the present case studies, the patients showed improvements regarding their panic symptoms pre- and post-intervention and did not report any adverse events. Our results show the viability of CBT for patients with PD after childbirth.Additional file 1:Behavioral-experiments. (DOCX 16 kb)