Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: However, whereas Watson considers this skill from the perspective of agents who wish to gather information and use it for themselves (an important topic), in a Socratic dialogue the epistemic authority uses it to foster understanding in others. And an important condition for this application of the skill of good questioning, it seems, is the ability to exercise epistemic empathy. Only if you can project yourself into another agent’s thinking in the ways we have outlined—only if you can construct a sufficiently detailed picture of the interlocutor’s noetic network, with both its good parts and its problematic areas—will you be able to ask questions that prompt her to improve that system and bring it into equilibrium. In Sect. 4 we distinguished experts from authorities, but conceded that the two classes overlap. Footnote 33 We can add now that an (expert) authority who is able to guide her interlocutors in their attempts to achieve or improve their understanding, will be epistemically empathetic. Note that we are not claiming that epistemic empathy is necessary for producing understanding in one way or another. We maintain instead that epistemic empathy is necessary for having the ability (deliberately and reliably) to produce understanding in others. Footnote 34 Surely an authority could produce understanding by happy accident and without epistemic empathy. Yet in such cases, this result would not be attributable to the authority’s ability to guide or teach her interlocutor. (Abilities are standardly construed as dispositions or potentialities which, in the right circumstances, reliably produce certain results. )The above examples illustrate this point. William of Baskerville may be an expert in philosophy, theology, church dogma, and so on. But he would not be a systematic, reliable source of understanding for Adso if he could not epistemically empathize with him. Similar observations apply to our Holmes and Socrates examples. Arguably, these agents too have special expertise. Yet without epistemic empathy, they could not teach and guide their interlocutors in improving their understanding. Consider the following analogy. Imagine a vast network of water reservoirs, akin, e. g. , to a system of Nepalese rice terraces, most of which are connected through a complex channel network. [SEP] Justify:

Output: The text corroborates the claim by asserting that epistemic empathy is necessary for individuals to reliably produce understanding in others. It emphasizes that to guide interlocutors effectively, one must be able to project oneself into another's thinking and construct a detailed picture of their noetic network. By stating that epistemic empathy allows authorities to help improve understanding in others, the text supports the notion that empathy is a crucial factor in enhancing social connections and mutual comprehension.


Input: Task: contrast the claim using the given text. Claim: AI will replace all healthcare workers in the next decade. [SEP] Text: The next phase involves identifying the most appropriate places within the workflow to collect specific data elements and to run a trial of collecting those data – using pre-coded forms or checklists on paper – and produce initial copies of the reports. At that stage a final selection of the most valuable reports is made and only the data required to support them are "hard wired" into their electronic medical record and other electronic data collection tools. With regular feedback of the reports to clinicians and scheduled annual minor and triennial major reviews of the shared baseline protocols, the standards are continuously updated to reflect the latest evidence – both from the published scientific literature and from the accumulated observations and interpretation of Intermountain Healthcare's own protocol variations data. At present no Canadian health organizations or systems have created a system for the design of care and active learning from variation as well-developed as that of Intermountain Healthcare. Group of the Initiative (2012) have followed Intermountain Healthcare's lead in design of a shared baseline protocol in vascular surgery – and developed the measurement system following the method used by Intermountain Healthcare, but the province still lacks the information systems infrastructure to build measurement seamlessly into workflow, and has not addressed the issues of clinical management structure or financial incentives. Alberta's Networks seem to have important elements of clinical management structure in place but have yet to reliably deliver the hoped-for improvements in care. The entire package of changes to care design, measurement, management structure and incentives that will work in a Canadian context has yet to be realized. From a measurement perspective, It is important to note that the successful approach used by required an investment in improvement and measurement expertise that could be embedded with clinical standards development teams for an extended period – initially to help develop, and then to help maintain the shared baselines approach. Decade in Quality and As a country and as provincial/territorial healthcare systems, we will continue to need standardized, comparable metrics that can be used to identify areas where improvement is needed or to document trends in improvement (or not) over time among jurisdictions and organizations. There will continue to be areas of care where the existing evidence base relating specific processes to desired outcomes is quite solid, where there will need to be mechanisms such as large-scale campaigns or collaboratives to facilitate the spread of implementation of these better practices. Each of these approaches has an important role to play and needs further investment to improve their effectiveness. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of integrating data systems and improvements in healthcare delivery, indicating that the transition requires substantial expertise and cannot simply lead to the replacement of healthcare workers. It points out that a complete system like that of Intermountain Healthcare is not yet developed in Canada, and the healthcare system still relies on human management and decision-making, suggesting that AI will not fully replace healthcare workers in the foreseeable future.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: A further limitation of this study is the relatively small sample size, since small differences in the magnitude or the variability of phase shifts between subjects with different iris colors might be observed with the greater statistical power that a larger sample size provides. A final note about this study is that we did not measure pupil size, and it is possible that subjects with blue irises had more pupil constriction than subjects with brown irises, diminishing the difference of retinal irradiance between the groups. However, because the light levels used in our study were above those that elicit a maximal pupil constriction in humans [48], and we think it is unlikely that pupil diameter contributed substantially to our results. a single day of a 2-hour bright light pulse at night and a 4-hour delay of the sleep episode, the human circadian clock can be delayed an average of ~1. 5 hours. This is a larger delay than studies that have administered a single phase delaying bright light pulse combined while maintaining habitual wake time. There were no differences in the phase delay between subjects with blue versus brown irises, and no association between objective measures of iris color or lightness/darkness and the magnitude of the circadian phase delay. Therefore, there was no evidence that iris color influenced the circadian phase delays produced by nighttime bright light exposure and a moderate delay of the sleep episode. Future studies could confirm that iris color does not, and racial differences do, influence the magnitude of light-induced circadian phase shifts. Competing interests The authors declare that they have no competing interests. Authors' contributions JLC helped design the study, supervised staff and subjects, screened and ran participants, performed data analyses, and prepared figures. MRS conceived the study, helped design the study, wrote the subject informed consent document, and commented on data analyses. H-SC performed iris photography. CIE helped design the study, was principal investigator on the grant supporting this research, and commented on data analyses. Each author contributed to manuscript composition and approved the final manuscript. 10. 1126/science. 2734611 Honma, K and Honma, S (1988). A human phase response curve for bright light pulses Journal of Psychiatry and Neurology 42: 167–168. and Wirz-Justice, A (1991). A human phase-response curve to light 133: 36–40, [PubMed] DOI: Revell, VL and Eastman, CI (2005). [SEP] Justify:

Output: The text does not directly address the claim regarding circadian rhythms being regulated by light-sensitive proteins. Instead, it discusses various factors influencing circadian phase delays, such as pupil size and iris color, and mentions a study design without any clear connections to the regulatory role of light-sensitive proteins in circadian rhythms.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: Some of the reservoirs are filled, others contain some water but are not full, and other areas are entirely dry—either the higher-level reservoirs and the channels and ditches that would fill them are dry or closed off. Suppose that the system is so large and complex that the farmer who is in charge, though he tries hard to fill as many lakes and ponds as possible so as to keep as many areas as possible fertile, can only progress slowly and laboriously on his own. Many fields remain dry, many remain with little if any water, various channels are blocked, and watering other fields would require building new channels from scratch. One day, an experienced fellow-farmer visits the site and offers help. She brings maps of the whole area and realizes where things go wrong. Importantly, she has the competence to identify areas of minor or major significance and to judge which operations would be most effective. Some actions will have an “explosive” effect: when certain channels are opened, large and important areas of the whole territory become flooded or imbued simultaneously. We may add that, since the area is vast but time is short, during each visit the visitor can only inspect and improve limited parts of the territory. This picture illustrates the holistic way in which epistemic authorities operate when exercising epistemic empathy. The lakes, we may suppose, correspond to significant beliefs, important commitments, central acceptances, or pivotal epistemic goals of their interlocutors; ponds correspond to less significant subsystems, and the channels and ditches illustrate support relations. An authority may initially gain insight only into limited parts of an interlocutor’s system, but is in a position to extrapolate where the most effective work can be done in order to pursue the cognitive goal of filling and connecting cognitively “dry” areas. To summarize what we have argued so far: epistemic empathy is a social-epistemic virtue, because it helps improve people’s epistemic situation (as opposed e. g. to their emotional situation). Moreover, it is a social epistemic virtue, because its primary or immediate target is other peoples’ epistemic states and episodes. Footnote 35Epistemic empathy is a social epistemic virtue because it constitutes an epistemic excellence. Corresponding to different traditions in virtue epistemology, this may be spelled out in different ways. [SEP] Justify:

Output: The text supports the claim by discussing 'epistemic empathy' as a social-epistemic virtue that aids in improving people's epistemic situations. It indicates that through empathy, one can better understand the beliefs and commitments of others and identify areas where they can be supported. This aligns with the idea that empathy enhances social connections and understanding.


Input: Task: contrast the claim using the given text. Claim: Patients universally prefer AI interactions over human doctors. [SEP] Text: Patients prefer artificial intelligence to a human provider, provided the AI is better than the human: A commentary on Longoni, Bonezzi and Morewedge (2019) Published online by Press: 01 January 2023 Mark V. Pezzo andJason W. author details Mark V. Pezzo* Affiliation: Department of Psychology, University of St. W. Beckstead Affiliation: College of Public Health, University of Tampa * *Email: pezzo@usf. edu. 0. 08 mb) View PDF [Opens in a new window] Save to to Save to & Permissions [Opens in a new window] call attention to an important, but overlooked finding in research reported by Longoni, Bonezzi and Morewedge (2019). Longoni et al. claim that people always prefer a human to an artificially intelligent (AI) medical provider. We show that this was only the case when the historical performance of the human and AI providers was equal. When the AI is known to outperform the human, their data showed a clear preference for the automated provider. We provide additional statistical analyses of their data to support this claim. Keywords algorithm aversionautomationartificial intelligencehealthcareuniquenessmedical decision makingtrust Type 10. 1017/S1930297500007221 [Opens in a new window] The authors license this 3. 0 License. © [2020] This is an 3. 0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. 1 Longoni, Bonezzi and MorewedgeLongoni, Bonezzi and Morewedge (2019) recently reported a series of ten clever experiments demonstrating that people prefer to receive medical care (i. e. , diagnosis, screening, and treatment) from a human provider rather than from an equally competent artificially intelligent computer (AI provider). Particularly interesting is their finding that perceived “uniqueness neglect” on the part of an AI provider can explain this preference. There is an important message, however, that we believe became lost among the other interesting findings: People actually did prefer the AI provider so long as it outperformed the human provider. [SEP] Justify:

Output: The text contrasts the claim by discussing research from Longoni, Bonezzi, and Morewedge (2019), which found that patients did not prefer AI to human providers when both were equally competent. Specifically, it states, 'People actually did prefer the AI provider so long as it outperformed the human provider,' indicating that preference for AI is conditional and not universal as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: Journal of Circadian RhythmsStart SubmissionBecome a ReviewerReading: Maximum entropy spectral analysis for circadian rhythms: theory, history and practiceDownloadXML (US English)PDF (US English)Alt. DisplayShareShareReviewMaximum entropy spectral analysis for circadian rhythms: theory, history and practiceis an array of numerical techniques available to estimate the period of circadian and other biological rhythms. Criteria for choosing a method include accuracy of period measurement, resolution of signal embedded in noise or of multiple periodicities, and sensitivity to the presence of weak rhythms and robustness in the presence of stochastic noise. (MESA) has proven itself excellent in all regards. The MESA algorithm fits an autoregressive model to the data and extracts the spectrum from its coefficients. Entropy in this context refers to "ignorance" of the data and since this is formally maximized, no unwarranted assumptions are made. Computationally, the coefficients are calculated efficiently by solution of the Yule-Walker equations in an iterative algorithm. MESA is compared here to other common techniques. It is normal to remove high frequency noise from time series using digital filters before analysis. filter is demonstrated here and a danger inherent in multiple filtering passes is discussed. Year: 2013Page/10. 1186/1740-3391-11-6Submitted on Apr 16, 2013Accepted on Jun 25, 2013Published on Jul 11, 20134. 0 processes in almost all plants and animals have adapted to the cycles in the environment, be they daily (circadian), tidal, lunar, synodic lunar monthly or annual [1]. Oscillatory behavior with periods of less than 24-h, termed ultradian, are also commonly found, occasionally embedded in circadian or other rhythms [2]. This adaptation to cycles in the environment has occurred through the evolution of a biological timekeeper, a true temperature-compensated oscillator providing temporal information at all levels of physiology and behavior [1]. Thorough investigation of these oscillators requires that the periodic evolution of the processes in time be characterized precisely as to the length of the periods seen, as these are the manifestation of the clock process [3]. In addition, the relative robustness and regularity of the rhythms is of considerable interest. Numerical samplings of any process that evolves in time, taken at appropriate intervals, form time series, the stuff and substance of biological rhythm research. Analysis of time series may be simply done. [SEP] Justify:

Output: The text does not address the claim directly, as it discusses various techniques for analyzing circadian rhythms without mentioning the role of light-sensitive proteins in their regulation. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: According to virtue reliabilists such as or John Greco, epistemic virtues are cognitive faculties or abilities the exercise of which reliably promotes epistemic ends. Traditional examples are vision, memory, introspection, rational intuition, etc. Whereas such faculties have individualistic epistemic targets, however, epistemic empathy is essentially a social virtue, since it is concerned with other people’s intellectual condition. As for responsibilist traditions in virtue epistemology, as influentially developed, e. g. , by Linda Zagzebski, conceive of epistemic virtues not only as reliable faculties, but also as intellectual character traits that agents can deliberately influence. Whereas reliabilist virtues can operate sub-personally, character traits are so-called personal excellences. However, as (VE) has argued, both kinds of virtues contribute to our overall intellectual wellbeing. If this is so, the contrast between the two traditions is less dramatic than meets the eye. What matters for present purposes is that epistemic empathy can also combine both sorts of virtue. It is a competence which, if operating under the right conditions, reliably furthers the corresponding epistemic goals (in some standard reliabilist sense); but it is also a personal excellence that agents can deliberately train and cultivate in their attempts to form their intellectual character. [SEP] Justify:

Output: The text corroborates the claim by discussing 'epistemic empathy' as a social virtue concerned with understanding others' intellectual conditions. It posits that epistemic empathy reliably promotes epistemic ends and can be cultivated as a personal excellence, which supports the idea that empathy can enhance social connections and understanding among individuals.


Input: Task: contrast the claim using the given text. Claim: Patients universally prefer AI interactions over human doctors. [SEP] Text: The effects of accuracy could not be tested in seven of their ten studies, either because participants received incomplete accuracy information that did not allow for a direct comparison between human and computer (Studies 1 and 4) or were explicitly told that the human and computer had equal accuracy rates (Studies 2, 5, 6, 7 and 8). We believe, however, that the findings of Studies 3A to 3C provide strong support that people do sometimes embrace AI. Studies 3A to 3C were identical in design, differing only in the type of service provided (screening for skin cancer, triaging a potential emergency, surgical implant of pacemaker). Participants rated provider X (human) and Y (human or comupter) on a 7-point scale such that scores above the midpoint of the scale indicated a preference for Y, below the midpoint indicated a preference for X, and scores exactly at 4 indicated indifference. One variable that they manipulated was the relative accuracyFootnote 1 rate of the X and Y providers. Accuracy rates were either equal (X = Y), slightly favored provider Y (X < Y), or strongly favored provider Y (X << Y). They also manipulated choice set: was always human, but provider Y was either another human or a computer (AI provider). Longoni et al. report significant main effects of both relative accuracy and choice set, but no interaction (see Figure 1). Before discussing the results, here is their stated goal for these three studies: Figure 1: Preference for human vs. AI providers as a function of relative accuracy (adapted from Reference Longoni, Bonezzi and MorewedgeLongoni et al. 2019, Figure 2). A response of 4 indicates indifference. Our main hypothesis was that participants would be more reluctant to choose an automated provider if a human provider was available, even when the automated provider performed better than the human provider (p. 7). First, their reported main effect shows that, overall, increases in accuracy for provider Y did lead to a preference for this provider. On the far left of each graph in Figure 1, we see that when accuracy rates are identical (X = Y), ratings are at or below the midpoint, indicating either indifference between humans (black bars) or a preference for the human over AI (white bars). This replicates the findings of studies 2, and 5–8. [SEP] Justify:

Output: The text contrasts the claim by indicating that when participants rated humans vs. AI providers in studies, a preference for human providers was observed when accuracy rates were identical. Specifically, it mentions that 'ratings are at or below the midpoint, indicating either indifference between humans or a preference for the human over AI'. This suggests that patients do not universally prefer AI interactions, as the findings show a preference for human doctors when given the choice.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: In early work by Bünning on bean plant leaf movements, periodicity was estimated by measuring peak-to-peak intervals on chymographs that registered leaf position [4]. Analysis technique has progressed considerably since that time and now offers an array of possibilities for estimates of period length [5]. This paper deals with a very useful method called Spectral Analysis, or MESA, developed by Burg in the 1960s in answer to shortcomings of the principal analysis technique up to that time, Fourier analysis [6 - 8]. We will first discuss Fourier analysis, noting the problems that MESA was developed to fix and how they can be circumvented with MESA. We will pay attention to the theoretical underpinnings so that this popular method will not be a “black box” and will show the basics of how the spectrum is computed. Given that the biologist necessarily works with time series that are either inherently irregular or contain major trends, tools that can ameliorate these problems when used in conjunction with MESA will be introduced and examples of their benefits discussed. Biological rhythm data Circadian rhythms are studied in systems ranging from intracellular fluorescence to complex behaviors such as running wheel activity; data acquisition and format vary accordingly. For example, when studying the activity of an enzyme, the variable may be continuous and an appropriate sampling interval must be chosen. This must be rapid enough to avoid “aliasing” in the periodicity region of interest. This occurs when the sampling interval is longer than the period being recorded and is famously seen in old western movies when the spokes of wagon wheels seem to be going backwards; sampling frequency must be no less than twice the frequency of the cyclic process of interest. This constraint is the Nyquist or foldover frequency [9]. Faster sampling is normal, however, to ensure no detail is lost and that accurate period estimates result. There are two important things to consider. The first, is resolution, which is the ability to separate two frequencies as being distinct, for example a 24-h circadian peak with a 24. 8-h lunar daily peak. This is equivalent to optical resolution, in which two objects in an image can be separable [10]. Resolution is theoretically limited by the number of cycles in the data set, or in optics, by the diameter of the lens. A completely separate problem is the ability to discern a periodic signal in noise. This is sensitivity, and both are important. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses techniques for analyzing biological rhythms, including circadian rhythms, but does not mention light-sensitive proteins or their role in regulating these rhythms. The focus is on various analytical methods and challenges in collecting biological rhythm data without providing any evidence or details related to light-sensitive proteins.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: find that individual differences in Cold-ToM as measured by the Index do weakly correlate with the DG offers. This finding is in line with previous studies on non-strategic social tasks that have found a positive relationship between pro-social behavior and ToM [37], [38]. A potential explanation suggested by the previous studies is that the origins of human social intelligence lie in the need for cooperation. In this case, people who aim to be fair also need to have a high functioning ToM. At the same time, the lack of monetary incentives in both studies might have not provided an adequate test-bed for assessing the role of ToM among selfish individuals. Notably, we find that risk aversion as measured by the Holt-Laury lottery task significantly and positively influences UG offers, suggesting that strategic considerations in the UG are moderated by individual differences in risk p2. The relationship between ToM and accuracy of beliefs in games. possible explanation for the lack of a relationship between the accuracy of beliefs and psychometric ToM measures is that a neutrally framed laboratory experiment is a context in which many of the cues that psychometric tests rely on are stripped away. Even the IRI measure of ToM which is based on abstract description of social interaction likely does not relate well to the simple set-up of games. Yet, the present laboratory setting provides a very specific measure of how good a participant is in judging the action of others which closely relates to ToM: participants stated what they belief others do in the games and were rewarded for their accuracy. The resulting score of accuracy of beliefs then is a laboratory specific measure in how far ToM might inform action. To test our third hypothesis that fair participants have more accurate beliefs than selfish participants, we use a mean split of the DG offers to distinguish between fair and selfish participants. Alternative definitions of selfishness (e. g. , median split or offering nothing) yield qualitatively very similar results to those obtained with the mean split applied here. Note that we do not find evidence to support the conjecture that participants' risk aversion is associated with the dispersion of reported beliefs. This result is robust to different measures of statistical dispersion (variance and kurtosis). Similarly, behavior in the three belief elicitation tasks is not affected by risk aversion. [SEP] Justify:

Output: The text discusses the relationship between Theory of Mind (ToM) and pro-social behavior, indicating that individuals with a high functioning ToM tend to exhibit more fair and cooperative behaviors. This supports the claim that empathy, which is closely connected to ToM, plays a critical role in improving social connections and aiding in the understanding of others. The finding that pro-social behavior is correlated with ToM suggests that empathy contributes positively to social interactions.


Input: Task: contrast the claim using the given text. Claim: Patients universally prefer AI interactions over human doctors. [SEP] Text: However, when accuracy rates are better for provider Y (X < Y or X << Y), mean responses increased above the midpoint of the scale indicating a preference for provider Y. The lack of an interaction showed this to be the case regardless of whether provider Y was another human (black bars) or a computer AI (white bars). Clearly, participants did not always resist AI technology. In fact, they arguably embraced AI when its historical performance was described as being better than that of the human (Reference Carlson, Desai, Drury, Kwak and YancoCarlson et al. , 2011). Note that the effect size for accuracy is quite large, with partial eta squared values of η2p =. 445 to. 479. These effects are 2 to 3 times larger than effects for choice set (η2p =. 131 to. 222). Using the means and standard deviations provided in their Table 2, and assuming that the sample size was equal across conditions, we calculated the one-sided lower 95% confidence limit on each reported mean. In all six conditions involving a superior automated provider, the lower bound for the mean response was above the null value of 4. 0 (i. e. , the value indicating no preference) suggesting that participants preferred the more accurate automated provider to the less accurate human one. Study 3A: X < Y: M = 4. 64, lower limit = 4. 07; X << Y: M = 5. 97, lower limit = 5. 60. Study 3B: X < Y: M = 4. 75, lower limit = 4. 21; X << Y: M = 5. 54, lower limit = 5. 07. Study 3C: X < Y: M = 5. 25, lower limit = 4. 81; X << Y: M = 5. 48, lower limit = 5. 11. Given these lower bounds, we were surprised that Longoni et al. made the following claim: Together, studies 3A-3C provided evidence that consumer resistance to medical AI emerges across a variety of medical domains. Resistance to medical AI was robust across … providers’ performance rates (i. e. , accuracy/success vs. complications/failure)…. Participants were resistant to medical AI even when the performance of AI providers was explicitly specified to be superior to that of human providers. (p. 8) We respectfully disagree with this conclusion. [SEP] Justify:

Output: The text contrasts with the claim by indicating that participants did not always prefer AI over human providers. While the findings suggest that participants showed a preference for AI when it was more accurate than a human provider, this does not equate to a universal preference for AI interactions. The text states that 'resistance to medical AI was robust across providers’ performance rates,' suggesting that despite some preferences for AI in specific scenarios, overall resistance and complexities around preference exist.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: Biological rhythm data are commonly not continuous and consist of unary events unlike the record left on a kymograph by a bean plant. Here, other constraints begin to play a role. Running wheel activity in mammals and the breaking of an infrared light beam by Drosophila are useful examples. Here, individual events occur, and are summed across arbitrary intervals or “bins”. Bin size affects the output of time series analysis and this effect can be profound when bin size is too small (Review: [11]). Bin sizes of 10 min up to an hour are common in rhythms work. For the purposes of illustration of the techniques being discussed, we will analyze a simulated data set. This is useful in this sort of discussion, as one may know precisely what the parameters of the signal are. The series considered here is a 23-h square wave with 20% white noise added consisting of 336 values produced at half hour intervals for 7 days using our own software. I chose a square wave as it is important to show that any analysis be effective for waveforms deviating markedly from the badly overused sinusoid. A simple time plot of the data is shown in Figure 1. Figure 1 An artificially produced time series with an arbitrary maximum amplitude of one. It is a square wave with 20% white noise added. The power in the series is: 0. 62. The autocovariance and autocorrelation functions Given a particular signal, even if it appears clearly rhythmic in a simple time plot, it is important that an objective statistical test be employed to determine if significant periodicity is present. Such a robust test is autocorrelation analysis [5]. In this analysis, the time series is initially lined up with itself in register and correlation analysis is applied yielding the coefficient, r. In this case, no matter what the signal looks like, correspondence is one to one and r = 1. The two series are then set out of register or “lagged” by one interval. The result is a decrement in r. The drop depends on the series; if it is a noiseless sinusoid, the change will initially be small, but if it is white noise, the drop will be very large, since the value of any given point has no relation whatsoever to any other point either near or far in time. Lagging proceeds one interval at a time up to about N/3. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not mention circadian rhythms, light-sensitive proteins, or their regulatory mechanisms. Instead, it discusses data analysis techniques for biological rhythms without addressing the specific claim regarding the role of light-sensitive proteins.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: How does empathy help us to understand others? What does it mean to understand someone else? Some would say it requires knowledge of what that person is going through. Others would say shared personal experience is needed. One requirement for understanding is empathy, the skill of sharing another person’s perspective. At the University of Liverpool in the UK, Schramme is investigating this connection between empathy and interpersonal understanding. With a team of researchers, he is developing a philosophical theory to explain the concepts behind interpersonal understanding, an ability that is increasingly important in today’s politically and culturally divided world – the ability to share the perspectives and feelings of another being – to gain appreciative knowledge of thoughts and feelings of others – the feeling of pity or sorrow for someone else – the study of foundational questions regarding all aspects of human and natural being, including knowledge, the mind, ethics and existence – the scientific study of how human thought and behaviour change over a lifetime – the ability to manage and respond to our emotions Many of us find it hard to understand the decisions that other people make. We may be concerned that our actions are not understood by others, we get frustrated when our friends and family cannot see something from our point-of-view, and we struggle to comprehend how someone could commit an atrocious act like murder. But why is it so hard to understand each other, and what prevents us from putting ourselves in someone else’s shoes? Empathy is a skill that enables us to take the perspective of other beings and feel what they feel. Schramme, a philosopher at the University of Liverpool, is using philosophical discussion and psychological findings to develop a theory relating empathy to interpersonal understanding. In a world that is becoming increasingly politically and culturally divided, this theory will aid future discussions by increasing our knowledge of how we understand each other. “Empathy is not itself a moral skill,” says Thomas, “but a mode of gaining understanding of another being. ” For example, some con artists are very good at ‘getting into the heads of others’, and so are good at empathy, but they are not caring or moral, rather they use their skill in empathy to cheat others. Empathy is also often confused with sympathy, which is the feeling of sadness or pity you may feel if someone else is sad, whereas empathy involves a broader understanding of others. [SEP] Justify:

Output: The text supports the claim by detailing how empathy is essential for understanding another person's perspective and feelings. It describes empathy as a skill that aids in sharing the experiences of others and gaining appreciative knowledge of their thoughts and emotions. Furthermore, the research conducted by Schramme at the University of Liverpool focuses on developing a philosophical theory to explain the connection between empathy and interpersonal understanding, reinforcing the idea that empathy is important for improving social connections.


Input: Task: contrast the claim using the given text. Claim: Patients universally prefer AI interactions over human doctors. [SEP] Text: The data reported in studies 3A-3C show that accuracy rates did matter, and that when they favor the AI provider, consumers did not resist medical AI. We do not dispute the findings of the pairwise comparisons, but argue that these comparisons don’t ask the right question. Remember, the black bars represent a choice between two human providers; only the white bars represent a choice between a human and AI provider. Thus, the pairwise comparisons merely show that participants didn’t prefer superior AI over an inferior human as much as they preferred a superior human over an inferior human. The fact remains, however, that, when given the choice, participants did prefer the more accurate AI provider to the less accurate human. We should be clear that we are not arguing that algorithm aversion (Reference Dietvorst, Simmons and MasseyDietvorst, Simmons & Massey, 2015) doesn’t exist. Rather, we are merely noting that its effects did not overwhelm the effects of accuracy. This is important, because a long history of research comparing clinical and actuarial judgment has shown that in most cases algorithms are more accurate than humans (Reference Dawes, Faust and MeehlDawes, Faust & Meehl, 1989; Reference KleinmuntzKleinmuntz, 1990; Reference Grove, Zald, Lebow, Snitz and NelsonGrove, Zald, Lebow, Snitz & Nelson, 2000). Longoni et al. acknowledge this fact, and further suggest that “given the superior accuracy of statistical models over human intuition, people should prefer to follow the advice of statistical models…. ” (p. 2). We take an optimistic view of their data and suggest that this is exactly what they found. Footnotes 1 Study 3C provided complication rates instead of accuracy rates such that a lower value suggested better performance. For simplicity, we refer only to accuracy throughout this paper. 27. [SEP] Justify:

Output: The text contrasts the claim by explaining that while participants did prefer more accurate AI providers over less accurate human providers, it does not support the notion of universal preference for AI interactions. It emphasizes that participants preferred superior human providers over inferior human ones and questions the appropriateness of pairwise comparisons used to assess preferences. This indicates that the overall preference does not necessarily favor AI over human doctors in a universal manner.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: The process is usually limited to this point since the power of the test is reduced with the decrement of each pair of lags off the ends of the series. r values are plotted as a function of the lag yielding the autocorrelogram function. In a rhythmic series, r will continue to decline, becoming negative and reaching a minimum when the peaks and valleys in the two series are out of phase by one half cycle. A second positive peak will occur when the peaks and valleys are back in phase, but one cycle out of register. The envelope of decay of the autocorrelation peaks is a function of the regularity in the series and this can provide a useful way of characterizing the regularity in the signal, as will be discussed below (Reviews: [5,12]). When computing the correlation coefficient, the output is normalized by dividing by the variance of the complete data set, but this need not be so and the output is then “covariance”, or the autocovariance function [5]. Autocorrelation is commonly employed, as it allows comparisons among wide-ranging experiments. The autocorrelation function also yields a valuable way to quantify the regularity of the signal both in terms of variation in period and the presence of noise. The height of the third peak in this function, counting the peak at lag zero as one, is taken as the Rhythmicity Index, or RI. This value relies on the decay of the envelope of the function and is normally distributed so it may be used in statistical analyses (review: [12]). The RI of the test signal is 0. 697. It is useful to have a formal criterion for the significance of rhythmicity in data. The 95% confidence interval for testing the significance of a given peak in the autocorrelogram is 2/√N [5]. Plus and minus confidence intervals are commonly plotted as flat lines, the decrement in N as values are lost being ignored (see above discussion). Repeated peaks equaling or exceeding the confidence interval are usually taken to imply significant rhythmicity, but there is room for subjective interpretation [5,12]. Figure 2 shows the autocorrelation function for the test data set depicted in Figure 1. Since the lagging can be done in either direction, Lag 0, where r = 1 is at the center and the function is mirrored to the left as well. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses autocorrelation and statistical methods for characterizing rhythmicity in data without addressing the role of light-sensitive proteins in regulating circadian rhythms. There is no mention or connection made regarding circadian rhythms or light sensitivity within the provided text.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: Like any other skill, to be used successfully, empathy needs to be practised and developed over time. When used correctly, empathy provides a way to access the minds of others, helping us to understand their thoughts and actions, and enabling us to interact with them. Although empathy plays an important role in connecting with others, more seems to be needed for full understanding. To some people, ‘understanding’ means knowing why someone else is feeling a certain way, while for others, it requires a shared personal experience. “For example, we struggle to empathise with people who commit hideous crimes,” explains Thomas. “In consequence, we can fail to understand them, even though we might know their motives. ” Empathy and understanding, therefore, appear to be heavily linked, but full understanding likely requires more than just empathy. ? Thomas is a member of a research group investigating this connection between empathy and interpersonal understanding. Philosophers research a topic by checking reality (known as ‘phenomena’) against a specific concept. “We are concerned with conceptual problems and try to develop terminology related to these topics that is clear and unambiguous,” says Thomas. For example, Thomas may ask whether ‘emotional contagion’, such as our ability to pick up the mood of a room full of people, is really a form of empathy. “Does this phenomenon fall under the concept of empathy? ” Thomas is not only concerned with theoretical concepts. “We want to use findings from developmental psychology to reveal the reality of empathy,” he says. Hopefully, this will lead him to an empirically informed theory of empathy in relation to interpersonal understanding. “We want to explain how empathy works. ” today’s world, it often seems like we are getting worse at understanding each other. Thomas believes that psychological mechanisms such as biases and poor emotion regulation are hindering our performance of empathy. And our increasing interaction through technology is not helping, as we lose so many social cues such as tone of voice and body language when we do not communicate face-to-face. When no personal connection exists, it is hard to ‘read’ someone, and so therefore harder to empathise with them. We also struggle to grasp what understanding actually means, and what a successful version of empathy looks like. [SEP] Justify:

Output: The text corroborates the claim by explaining that empathy, when practiced correctly, enables individuals to 'access the minds of others' and 'understand their thoughts and actions,' which facilitates social connections. Furthermore, it describes the importance of empathy for human interaction, implying that it is a key factor in enhancing our understanding of others. While the text also acknowledges that full understanding may require more than empathy, it firmly supports the idea that empathy plays a significant role in improving social connections and understanding.


Input: Task: contrast the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: This edited volume arose from the first on Translation and Accessibility in held at the Universitat Autònoma de Barcelona (UAB) on 2 -3 December 2010, organised by the Transof UAB, which is part of the Centre for and Accessibility in Catalunya (CAIAC). The focus of the conference was unique in its comprehensive approach combining game accessibility, game translation and localization and also related issues in broader environments of virtual worlds. Such combination was due to a broad view of accessibility, which not only encompasses users with functional diversity, but also those who due to age or skill are not able to play a game successfully, those who do not speak the original language of the game, and those who due to socioeconomic conditions cannot access games or virtual platforms. In addition, both game localization and accessibility are issues that ideally need to be taken into account early in the development process of a game, in order prevent costly modifications at a later stage. If accessibility and localization are considered since the conception of the game, it should not be too costly or cumbersome to translate the game into other languages or include accessibility ← 9 | 10 → features, such as the possibility to remap the controls or have intralingual subtitles. Gathering together researchers in academia as well as practitioners from the industry, the conference provided an ideal forum to exchange different perspectives to add to the emerging areas of research in translation and media accessibility of digital content. The conference demonstrated how research on game localization and research on game accessibility are gathering momentum, albeit they are still relatively unexplored academic fields. To date, most of the research conducted on game accessibility has been carried out by academics from engineering and computer studies backgrounds, such as the Games (UA-Games) at the Human-Laboratory of ICS-FORTH in Greece, who have developed a number of universally accessible games, and a handful of industry professionals interested in the topic, such as the Group (SIG) at the (IGDA). Most information and guidelines on game accessibility can be found on specialised blogs and websites, and are addressed to developers, to help them make their games accessible to the widest range of population. However, there seems to be a lack of awareness by mainstream developers and publishers about accessibility issues and due to the pressures and time limits associated to the release of a game, accessibility services usually are not a priority. [SEP] Justify:

Output: The text contrasts the claim by discussing accessibility in the context of game design rather than healthcare. It highlights that 'there seems to be a lack of awareness by mainstream developers and publishers about accessibility issues' which implies that there are challenges in making technology universally accessible, thus opposing the notion of universal accessibility and affordability of AI technology in healthcare.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: This view can make visual interpretation easier. Figure 2 This is the autocorrelation of the data depicted in Figure1. Note the height of the third peak, which is the RI and equals 0. 697. Fourier analysis Beginning in the late 19th century, the process of producing a spectrum from digital time series was largely accomplished by Fourier analysis. Fourier showed that any function showing certain minimal properties called the “Dirichlet Conditions” can be approximated by a harmonic series of orthogonal sine and cosine terms [6,7]. The series must have a finite number of maxima and minima, be defined at all points and not have an infinite number of discontinuities, conditions met by most data encountered in biology [12]. Here we take a function f(t) and approximate it with a Fourier series: (1) ft≅a0/2+a1sint+a2sin2t+…b1cost+b2cos2t+… If our function consists of an ordered set of values x(t), then the “power” in the series is the ensemble average of the squared values. If the mean is zero, this is variance. transform is an extension of a fit of the Fourier series and has the property that the coefficients approximate the spectrum of the power, meaning the power at each frequency for which a computation can be made (review: [5]). For a continuous series, we have: (2) Fω=∫−∞∞fte−iωtdt The exponential function consolidates the sine and cosine terms. F(ω) is the spectrum of the function, with ω being the angular velocity, or 2πf, where f is frequency. This process was carefully described by Schuster [13] and he termed it the “periodogram” of the function. This procedure should not be confused with the Whittaker-Robinson algorithm [14], improperly given the same name, which was largely discredited by Kendall on formal mathematical grounds [15]. (See [12] for further discussions and examples). Figure 3 depicts the Whitaker-Robinson “periodogram” for the data set. Figure 3 This is the so-called Whitaker-Robinson “periodogram”, which is not the same as the true periodogramss of Schuster. [SEP] Justify:

Output: The text does not address the claim regarding circadian rhythms and light-sensitive proteins. Instead, it discusses Fourier analysis, autocorrelation of data, and mathematical functions without providing any relevant information pertaining to circadian rhythms or their regulation by light-sensitive proteins.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: Empathy is also often confused with sympathy, which is the feeling of sadness or pity you may feel if someone else is sad, whereas empathy involves a broader understanding of others. Like any other skill, to be used successfully, empathy needs to be practised and developed over time. When used correctly, empathy provides a way to access the minds of others, helping us to understand their thoughts and actions, and enabling us to interact with them. Although empathy plays an important role in connecting with others, more seems to be needed for full understanding. To some people, ‘understanding’ means knowing why someone else is feeling a certain way, while for others, it requires a shared personal experience. “For example, we struggle to empathise with people who commit hideous crimes,” explains Thomas. “In consequence, we can fail to understand them, even though we might know their motives. ” Empathy and understanding, therefore, appear to be heavily linked, but full understanding likely requires more than just empathy. ? Thomas is a member of a research group investigating this connection between empathy and interpersonal understanding. Philosophers research a topic by checking reality (known as ‘phenomena’) against a specific concept. “We are concerned with conceptual problems and try to develop terminology related to these topics that is clear and unambiguous,” says Thomas. For example, Thomas may ask whether ‘emotional contagion’, such as our ability to pick up the mood of a room full of people, is really a form of empathy. “Does this phenomenon fall under the concept of empathy? ” Thomas is not only concerned with theoretical concepts. “We want to use findings from developmental psychology to reveal the reality of empathy,” he says. Hopefully, this will lead him to an empirically informed theory of empathy in relation to interpersonal understanding. “We want to explain how empathy works. ” today’s world, it often seems like we are getting worse at understanding each other. Thomas believes that psychological mechanisms such as biases and poor emotion regulation are hindering our performance of empathy. And our increasing interaction through technology is not helping, as we lose so many social cues such as tone of voice and body language when we do not communicate face-to-face. When no personal connection exists, it is hard to ‘read’ someone, and so therefore harder to empathise with them. [SEP] Justify:

Output: The text corroborates the claim by discussing how empathy facilitates understanding others' thoughts and actions, thereby enhancing social connections. It states, 'When used correctly, empathy provides a way to access the minds of others, helping us to understand their thoughts and actions, and enabling us to interact with them.' This implies that empathy is a critical factor in fostering social relationships and understanding, consistent with the claim that empathy improves social connections.


Input: Task: contrast the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: It could be argued that by not taking into account accessibility, developers are leaving behind an important segment of the population: people with disabilities, who according to the UN account for 10–12% of the word population (Bank, 2009). People with sensorial (visual, auditory), cognitive, and physical disabilities also can enjoy playing video games, as they are fun and provide hours of entertainment. As regards the topic of game translation, we wish to share a few observations on the development of the domain that is beginning to emerge as a legitimate research field in Translation Studies. Following the official declaration of year one of game studies in 2001 (Aarseth, 2001), academic research on video games has multiplied, helped by its increasingly ubiquitous presence in modern life and, most of all, by its inherent richness, inspiring many a research avenues. When the Handbook (Chandler, 2005) was first published, the domain was a somewhat esoteric practice, little known beyond the immediate circle of practitioners and gamers involved in fan translation of games. Despite its close link to ← 10 | 11 → software localization, which has been integrated into translator training at many universities for some time now, localization practices applied in video games have remained under reported until recently in academia. Similarly, the localization industry has focussed mainly on business-oriented productivity software. For example, in the early days games were not even included in the official statistics of the software industry as they were categorised as consumer goods and part of the entertainment sector (Berry, 2008:66). Localization found early difficulties gaining recognition both in industry and academia, is now an essential part of the game industry which relies on global sales while rapidly establishing itself as an interdisciplinary area of research in academia. Contemporary games are sophisticated technological as well as cultural artefacts with multiple perspectives that lend themselves to study and analysie from many and distant fields: from Psychology to Neuroscience or Sociology. Studies game localization is often introduced as a hybrid mode of (AVT) and (Munday, 2008:190) in an attempt to locate this newly emerged practice within an established category. The research trajectory so far in game localization illustrates how the field tagged itself to these pre-existing domains, motivated by the fact that video games are indeed audiovisual media and also that they are pieces of software. In the sense that both are dynamic fields sensitive to advances on technology, the boundaries between the two currently separate divisions are increasingly becoming blurred. [SEP] Justify:

Output: The text contrasts the claim by highlighting that developers may be ignoring significant accessibility issues for a segment of the population, specifically people with disabilities. It suggests that not considering accessibility in AI technology development could leave out those who are already marginalized, thereby implying that AI technology in healthcare is not universally accessible. This supports the view that there are barriers to access related to disabilities that are not being addressed.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: Fourier analysis has undergone considerable development and sees a great deal of use in many fields, with chronobiology prominent among them; it has done yeoman service. If the spectrum is calculated directly from data sampled at intervals, it is termed the Transform or DFT. Fourier spectra are seldom computed directly from the raw data however, rather they are produced from either the autocovariance or autocorrelation functions. One argument for using the autocovariance function is that the output is equivalent to partitioning the variance in the signal by frequency and the area under the curve is the power (review: [5]). Figure 4 depicts the DFT of the test data set which is the most basic way to visualize the process. The period is reported as 22. 4 h which contrasts with the known value of 23. The reason for this discrepancy is discussed below. Figure 4 of the test time series. The period is calculated to be 22. 4. Note in particular the paucity of spectral estimates in the crucial range between 20 and 30 hours. This would normally be corrected in more advanced algorithms, but at a cost (see text). Compromises inherent in Fourier analysis Since the autcovariance and autocorrelation functions lose power with each pair of points lost, usually no more than one third of the data are used to compute correlation coefficients, adversely affecting the potential resolution in the spectrum. To alleviate this, the rest of the function is padded out with zeroes. This is an outright falsification of data points not in evidence, since there is no reason to suspect these data points would all be zeroes. An added problem occurs at the point where the zeroes abruptly start, since this abrupt discontinuity will cause artifactual peaks in the spectrum called “side lobes” owing to the Gibbs phenomenon [16]. To correct for this, the real data are blended into the zeros to soften the transition. Here, yet more actual data must be altered to allow for side lobe suppression. One further development that exacerbates these compromises is the Transform, or FFT. In this algorithm, computational efficiency, and concomitantly speed of calculation, are increased by constraining the input series to consist of 2N data points. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses Fourier analysis and its application in chronobiology without addressing how circadian rhythms are regulated by light-sensitive proteins. It does not provide any information that directly supports or contradicts the claim.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: We also struggle to grasp what understanding actually means, and what a successful version of empathy looks like. We often fail to understand what is good for others, meaning empathic decisions might not lead us to morally sound conclusions. And barriers to fully understanding someone are always going to exist. “It is a common feature of empathy that we cannot ever fully succeed in empathising, because we cannot ever be the other person,” says Thomas. A common topic in philosophical discussions of empathy is whether we can empathise with people who have completely different backgrounds to us. Some theorists argue that to understand someone requires shared knowledge or experience with them, however Thomas sees limitations in this argument. “We can at least partly overcome different backgrounds,” he says. “If we can accept there is always going to be a boundary to other minds, then cultural differences do not pose an insurmountable hurdle to successful understanding. ” With this viewpoint, he highlights the importance of successful understanding, rather than full understanding, when interacting with others. empathy is a skill, how we can improve our success at using it? suggests, our capacity to empathise is not fixed or limited by personal or social identities. “Empathic skill can be improved by self-knowledge and imagination,” he says. “If you know your weaknesses and biases, and can distance yourself from them, you have a good chance of improving your empathic abilities. ” Thomas believes an important requirement for empathy is to focus on the other person and not on yourself. “When we empathise, another person is the target of our efforts,” he explains. “So we mustn’t ask ‘How would I feel in such a situation? ’ but ‘How does this person feel? ’” Successful empathy is the ability to, as much as possible, understand the other person. And while it can be difficult to understand those with completely different life experiences, this is not a barrier to showing empathy. If we all developed our skills of empathy and used them to increase our interpersonal understanding, the world would likely be a much kinder and more compassionate place. of : : Investigating the relationship between empathy and interpersonal understanding : Arts and of : : Investigating the relationship between empathy and interpersonal understanding : Arts and is the study of knowledge, the mind, ethics and our very existence. [SEP] Justify:

Output: The text corroborates the claim by emphasizing the importance of empathy in improving interpersonal understanding. It states that although complete understanding may be challenging, successful empathy can lead to greater understanding, as noted by Thomas when he mentions that empathy is the ability to understand another person as much as possible. The text suggests that enhancing our empathic skills can foster improved social connections, making the world kinder and more compassionate.


Input: Task: contrast the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: Hub and spoke model: making rural healthcare in India affordable, available and accessible 1 MBA (US) MBA (India - Gold Medalist), BS, Professional member * * 1 3 February 2016 : 30 January 2015 : 19 September 2015 : 2 December 2015. Hub and spoke model: making rural healthcare in India affordable, available and accessible. Rural and 2016; 16: 3476. © University 2016 Corrigendum to this 1. 1 million nurses, practitioner density is about one-fourth what it is in the US and less than half that of China. . . nominal per capita income is only $1,500 a year, patients typically have to pay 60% of health care expenses from their own pockets1. IssuesBackgroundThe aforementioned issues of availability and affordability in India led a few organizations to resort to the implementation of the hub and spoke model (HSM) in health care. At a high level, the fundamental tenets of HSM are to improve access to services and meet clients' needs. The model is derived from the visualization of a wheel and its central hub. The hub, or the center of the wheel, serves as the central operational headquarters of the overall mechanism. The activities vital to an enterprise's operation are located at the hub, which allows for capitalization of more specialized practices in the spokes. Airlines pioneered the use of the HSM in 19552, it was Bertrand Dawson, a physician of the Medical Corps, who suggested the use of an approach similar to HSM in health care in 1920. At that time, he referred to it as 'a hierarchical system which proceeded from the simple to the complex'3. 'Domiciliary services would revolve around doctor, pharmacist, and nurse' in 'Primary health centres staffed by general practitioners, laboratories and inpatient accommodation. ' These are followed by 'Secondary health centres which would provide a full range of general and specialized medical services. ' These secondary health centers would relate to university hospitals where the most difficult cases could be managed. Supplementary health centers would deal with 'patients suffering from other conditions such as mental and infectious diseases'4. 'The scheme would require a new pattern of medical administration, to ensure 'unity of purpose at all levels'. It was proposed that there should be a single health authority to unify and control all health services'3 (Fig1). Figure 1: Dawson's hierarchical system, London 19204. [SEP] Justify:

Output: The text contrasts the claim by discussing the specific situation of healthcare in India, where despite efforts, issues of availability and affordability persist. It highlights the problems faced, including low per capita income and the burden of healthcare costs on patients, which contradicts the idea that healthcare, particularly through AI technology, is universally accessible and affordable.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: Here again, the chances of a data set containing an integer power of 2 points are slim, and again, zeroes are added to pad the series out (Review [16]). Further data corruption can occur when tighter spacing of spectral estimates is required. If the series is long, consisting of multiple cycles, this is usually not a problem. However, when short data sets are at hand, as is commonly the case with circadian rhythm work, there will be few cycles available. Fourier analysis is based on harmonics and these are constrained. In practice, this means that the spacing between estimates can be very wide. For a one-week long experiment with data sampled at half hour intervals (as in the test data) and analyzed using a simple DFT, spectral estimates are produced only for 22. 4 and 24 h in the critical interval between 22 and 25 h. This leaves enormous gaps with little chance that the single estimate at 22. 4 is even remotely close to the true period which is 23. 0 h. Once again, it is straightforward to tighten up the interval between estimates, but once again, zeroes are added with further problems in using false data points for which there is no justification [16]. Maximum entropy spectral analysis In the late 1960’s, Burg developed a new method for producing a spectrum that tackles these problems [17,18]. It initially found acceptance in astrophysics and quickly spread to other fields. It began to be used for circadian rhythm work in the 1980s and is an excellent choice for a wide range of biological time series. This technique is called “Spectral Analysis” (MESA) [16 - 18]. MESA delivers the highest possible resolution, while eliminating side lobe problems. It is also extremely sensitive, as defined above. It is particularly useful in the short, noisy time series typical in biological systems [12,19,20]. The linchpin of this powerful technique is stochastic modeling. Time series evolve in time according to probabilistic laws and there are a number of models that can underlie such processes. One example is an autoregressive (AR) function (Review: [5]). The assumption is that the system moves forward in time as a function of previous values and a random noise component. [SEP] Justify:

Output: The text does not provide any information regarding the regulation of circadian rhythms by light-sensitive proteins. Instead, it discusses data analysis techniques related to circadian rhythm research, such as spectral analysis and maximum entropy spectral analysis, without mentioning the role of light-sensitive proteins in regulating these rhythms.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: My daughters are all like this to varying degrees. My son has strong feelings around social justice issues (he’s a libra) but he does not have quite the same sensitivity to others emotions as my daughters and I do. I think that emotional contagion does indeed lead to an increased potential for empathy. The reason is because even when someone is against me and a conflict is brewing; I can sense their emotions and put myself in their shoes. I believe that emotional contagion limits my ability to de-humanize people even if I wanted to. I know that de-humanizing someone is the first step towards the potential for violence, whether it be in thought, word or deed. So, in such ways, I think emotional contagion builds empathy. Please share your thoughts on this with me? I think that empathy is a fascinating topic for a philosophical discussion. Schramme on May 16, 2023 at 8:16 am Thanks Leslie, that’s a very interesting comment and an important question. Many empathy scholars try to make conceptual distinctions to better manage the often complex reality. In philosophical parlance, we accordingly speak of analytic distinctions. The distinction between emotional contagion and empathy is an example. It is usually said that in emotional contagion we don’t fully distinguish between ourselves and others. We “catch” an emotion but do not feel it in relation to someone else; as the other’s emotion. For empathy proper, some say, we need to be conscious of the other-oriented aspect of the feeling we have – that it originates from another person. Having said that, in reality it will often be very hard to actually diagnose such a difference between emotional contagion and empathy. Also, many scholars agree that they are closely related phenomena. So, in terms of your own and your children’s experience, you seem to have a high level of empathy, in the sense of a propensity or disposition. The performance of empathy, I’d say, still requires a lot of other elements; I don’t believe it is a fully automatic process. For instance, when you say that you sense angry emotions before a conflict breaks out, others might well “catch” that negative emotion and also get into an aggressive mood. You seem to be regulating this situation by adding imagination to the mix. [SEP] Justify:

Output: The text corroborates the claim by discussing how emotional contagion can enhance empathy, which is linked to improved social connections and understanding. The speaker describes their capacity to sense others' emotions and place themselves in their adversaries' shoes, which is a fundamental aspect of empathy. They mention that emotional contagion limits their ability to dehumanize individuals, emphasizing its role in fostering deeper human connections. Moreover, the discourse on empathy as an essential response to emotions highlights its importance in social interactions and relations.


Input: Task: contrast the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: Contemporary HSM experts also agree with that:On a larger level what this entails is the authorization for the larger, more established core units to essentially support the lesser ones while still allowing for the regulation and control of the overall 'wheel' movements5. HSM offers large market coverage with efficient use of resources and flexibility in workload distribution. However, capacity limitations and changes at the hub could have a domino effect on the spokes that could be disadvantageous to the network. Although the model can be applied to nearly any operational enterprise, its usefulness and overall efficiency is inherently driven by the core users' abilities to coordinate logistical operations within its entire practice5. Thereby, ICT-enabled HSM becomes necessary to help resolve potential logistical issues arising from the absence of such a concept. Role of ICTIn India, rural-urban connectivity is a major issue: 'The physical accessibility of public or private healthcare facilities is a challenge in rural areas'6. The innovativeness of ICT-enabled HSM lies in its emphasis on healthcare availability to populations spread over vast geographic areas without adding expensive infrastructure. It eliminates the need for travel unless necessary by ensuring that rural clinics (RCs) (spokes) take care of basic treatment and that patients would only need to travel to the urban hospital (UH) (hub) for specialized care. ICT system set-upThe ICT-enabled HSM incorporates the use of cloud computing over a secured network. The healthcare workers at the RCs are provided with handheld devices or laptops connected to a secure network. These devices can be equipped with:wireless network cards designed to work in low connectivity areas and without the need for any phone or ethernet wiressoftware with defined procedures and process flowsvarious security features (antivirus, password protection, data encryption)communication capabilities (email and text) (Fig2). Figure 2: Example of system set-up depicting the urban-rural connection. Data integrityPatient information is stored on a community cloud7. The community, in this case, is the organization, the RC and the UH. Only those users with approved credentials can share and access the information securely within the network using a laptop or a mobile device. These devices are equipped with antivirus software to protect the information from any type of data breach (hacking, phishing etc). This way, patient information is kept confidential, but details can be made available in case of referrals or escalations. [SEP] Justify:

Output: The text contrasts the claim by highlighting the challenges related to accessibility in healthcare for rural areas in India. It mentions that 'the physical accessibility of public or private healthcare facilities is a challenge in rural areas,' indicating that there are barriers to accessing healthcare technology, thus challenging the notion that AI technology in healthcare is universally accessible and affordable.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: The simplest example is a Markov process: (3) Xt=aXt−1+Zt. Where t is time, a is a coefficient derived from the data and Zt is white noise [5]. This simplest process may be extended by going backwards in time to earlier and earlier values, with each weighted by a coefficient derived from the known observed values [5]: (4) Xt=aXt−1+bXt−2+…+cXt−p+Zt. and, again, a, b, c,… are the model’s coefficients and p is the order of the filter. These coefficients form the prediction error filter (PEF) [21]. Crucially, it is possible to use the model to predict future values based on what is known of all the past values. In the case at hand, the analysis is functionally extending the autocorrelation function out to the needed number of values by prediction from those that can be reliably estimated [16]. Entropy, in information theory, is equivalent to ignorance. If one can formally calculate estimates that maximize ignorance, this means these values are the most honest based on what is known from data in hand and this is demonstrated through the calculus of variations [16 - 18]. A pile of zeros certainly does not fit this criterion. The spectrum is constructed from the coefficients as follows [17,18]: (5) Sω=P/1−∑k=1pake−iωk2 Where: S(ω) is spectral power as a function of angular velocity (see above), P is the power passed by the PEF, p is the order of the PEF and ak is the set of PEF coefficients. The algorithm commonly used by us and others calculates the filter in an iterative fashion and is based on the work of Anderson [22]. Each iteration extends the AR model by one. The number of coefficients in the prediction error filter employed to construct the spectrum is hence not fixed and requires some care in its choice. If a number that is too low is chosen, resolution and important detail can be lost. On the other side of the coin, if the number of coefficients is run up too high, there may be spurious peaks [21]. An objective method has been developed using the methods of Akaike [21], based on information theory. [SEP] Justify:

Output: The text is largely irrelevant to the claim regarding circadian rhythms and light-sensitive proteins. It primarily discusses a Markov process and prediction error filters in a mathematical context, without addressing the biological aspects of circadian rhythms or their regulation by light-sensitive proteins.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: - Previous document - Next document 18 | 2020Psychopathology and Phenomenology-2. Science in Progress. Frames for and for understanding others. The RdoC approach to Theory of mind and empathy deficits in Schizophrenia, Disorder and and p. 162-181 Abstract | Index | Outline | Editor’s notes | Text | 1. Introduction 2. The RdoC framework 3. Theory of Mind and Empathy 4. Schizophrenia 5. Borderline personality disorder 6. Mood disorders 7. Finding brain correlates or reducing? 8. Concluding remarksTop of page Editor’s notesDOI: Full text PDF 287k Share by e-mail 1. social life, the possibility of having successful and fulfilling relations and exchanges with other people, depends on our capacity to understand their actions and emotions, and to adjust our behaviour accordingly. Alterations of such capacity are associated with a wide range of disabling and distressing mental conditions, including autism but also schizophrenia, mood disorders and substance abuse disorders. > (Cotter et al. , 2018) 1Psychology has coined two concepts for the capacity of Understanding Others, namely Theory of mind and Empathy, both grounded in philosophical tradition, and now employed in neuropsychology. In this 1. In particular, it has been described as having a reductionist approach to mental disorders, as it identifies them with brain dysfunctions - a psychiatry without psyche (see eg. Parnas, 2014). The second goal of this 2. The RdoC framework 5RdoC provides a matrix for organizing research, publicly accessible online (NIMH 2019). At the rows of the matrix are 6 domains of functioning: for Social Processes, Arousal/Modulatory Systems, and Sensorimotor Systems. Each domain is specified by subordinate ones, called “constructs”. At the column of the RdoC matrix are the “units of analysis”, the six different classes of variables that can be used to measure constructs and subconstructs. They are and Self-Report (including patient verbal report). Circuits can refer to measurements of particular circuits as studied by neuroimaging techniques, and/or other measures validated by animal models or functional neuroimaging (e. g. , emotion-modulated startle, event-related potentials with established source localization). [SEP] Justify:

Output: The text supports the claim by stating that successful social interactions depend on our ability to understand others' actions and emotions and adjust our behavior accordingly. It discusses the importance of empathy and theory of mind in social functioning, linking deficits in these skills to mental health conditions, which implies that having these capacities can improve social connections.


Input: Task: contrast the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: Patient medical records are encrypted and only shared over a secured network via directed exchange8. Within the software, the security credentials are multi-layered (requiring the user to enter their password to perform important steps), and the access privileges are role based (a doctor could have access to features such as sharing information with the UH that a nurse might not have). Some companies train their health workers to respect information confidentiality and penalize workers who do not comply9. Patient advantages The companies implementing the HSM have strong connections to a network of doctors and facilities that provide subsidized prices and reduced treatment fees to patients, eliminating the need to shop for facilities in the first place. further explains this aspect of the HSM's connectivity:Some cancer care in India is delivered in a hub and spoke model. At the hub, they use very advanced surgical techniques for treating cancer. The spokes are the cancer centers where they do chemotherapy, radiation and rehabilitation. Anything you can do locally should be done locally10. Most rural Indians are daily wageworkers who make about 174 rupees a day (approximately $3)11. Considering this, it is important to acknowledge that the HSM approach saves the patient a lot of travel time, waiting time, money and an otherwise unnecessary trip to the UH. Since the rural-urban hospitals are within the HSM network, the fees charged by the UH for secondary and tertiary diagnoses to the patients will also be discounted. The spokes are constantly in touch with the hub hospital, and doctors can update the spokes about a patient's case. The ICT-enabled HSM network also connects patients to pharmacies for discounted medicines. DiscussionUsing the HSM approach:Hub hospitals in urban centers assemble the most advanced technologies and highly specialized surgical and diagnostic skills. Smaller hospitals and clinics in outlying towns are equipped with lower-level technologies and operated by non-specialist physicians, assisted by nurses and support staff. Providers at the spokes treat patients who have straightforward needs. They refer those with more complex issues back to the hub or use telemedicine technologies to consult with hub specialists on treatments12. This tightly coordinated web cuts equipment and expertise costs. It also creates specialists at the hubs who, while performing high volumes of focused procedures, develop the skills that will improve quality13. [SEP] Justify:

Output: The text contrasts the claim by highlighting the disparities in accessibility and affordability of healthcare technology, particularly in rural India. It discusses how most rural patients earn a low daily wage (approximately $3), which suggests that AI technology, while connected through networks to improve efficiency and cost, is not universally accessible or affordable to all segments of the population. Furthermore, it emphasizes that smaller hospitals have lower-level technologies and are operated by non-specialist physicians, pointing out that not everyone has equal access to advanced healthcare technologies.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: The filter length chosen is consistent with the most amount useful information that is being extracted as each iteration extends the length of the filter. This is used in the MESA software employed in our work, but we also commonly set a minimum filter length of about N/4 for biological rhythm analyses to ensure adequate representation of any long period cycles in the presence of noise, which can be considerable. N/3 is a good safe maximum [5,21]. MESA has proven itself superior to ordinary Fourier analysis as it does not produce artifacts from the various manipulations needed absent a model for the function and both resolution and sidelobe suppression are superior to standard Fourier analysis [16,23]. To show the difference between Fourier analysis and MESA in one critical area, it was noted above that the possible estimates that can be computed for period in Fourier analysis is constrained by the fact that these estimates can only be calculated for fixed values that are harmonics based on the length of the time series at hand. Longer series mean more tightly spaced estimates and this can be “faked” by adding zeros. MESA does not need to do this. Since the spectrum is extracted from an AR model, the spacing can be narrowed to any needed level. As an example, for the time series we have been working with, we have one week’s worth of data, sampled at half hour intervals. Note that shortening the bin size will have no effect on the spacing of the samples. Figure 5 shows the MESA for the test data set with the number of estimates increased by a factor of 32. Unlike the DFT, which had only 2 estimates in the interval between 22 and 25 h, MESA produced 60. Increasing MESA coefficients could go as high as needed, with the downside being a growing number of values that need plotting. Here 32X is more than sufficient to tease out a good estimate. Figure 5 This is the MESA spectrum with the coefficients upped to 32X. The period reported is 22. 88, compared to the known input of 23. 0. The tiny discrepancy is likely a result of the 20% added noise in the signal. Data conditioning Biological signals can be notoriously non-stationary and noisy. This variation can take the form of linear or nonlinear trends in amplitude, variations in period and the waveform. As with any signal analysis system, MESA output can be improved by conditioning the signal. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim as it discusses the MESA software and its applications in analyzing biological signals and rhythms, but does not provide any information regarding light-sensitive proteins or their role in regulating circadian rhythms.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: However, the matrix is meant to be a work in progress, and constructs and even domains may change as new evidence is being considered. For example, the Domain was added in 2018 (Harrison et al. , 2019). It is important that RdoC research unities do not suffer the same fate as DSM and ICD ones, in becoming reified and resistant to change (Charney, et al. ,2013). 9Let us focus now on the Domain more closely, in order to introduce the case studies of this 4. 3. Theory of Mind and Empathy 10Human social life critically depends on understanding the internal causes of behavior. ToM ability is considered to be crucial for successful social interactions (Samson et al. , 2007) and it is involved in affect regulation, impulse control and self-monitoring (Fonagy and Target, 1996). Two different subcomponent of ToM have been identified: the cognitive ToM (cToM) primarily concerns the representations of others’ knowledge, beliefs, intentions and other neutral states, while the affective ToM (aToM) is involved in others’ affective states (i. e. , emotions, feelings and desires) and is supposed to require the appreciation of the emotional states of others. (Brothers and Ring, 1992; Shamay-Tsoory, 2011). 11ToM deficits are key predictor of social function outcomes, mental health and quality of life (Milders et al. , 2006) and have been suggested to exert a crucial role in the developing and maintenance of different psychopathologies such as autism, anorexia, schizophrenia (SZ), borderline personality disorder (BPD) and bipolar disorder (BD) (Bora, 2009, Bora et al. , 2016, Lazarus et al. , 2014, Leppanen et al. , 2018). 12A large body of research indicates that ToM starts in the first few years, in typically developing children, but continues to evolve across childhood and adolescence: environmental experiences and biological maturation of involved brain regions co-occur and interact in determining ToM development (Wolf et al. , 2010). In addition, children’s developing language abilities (Milligan et al. , 2007) and executive functions (Moses et al. , 2010) play an important role in the acquisition of socio-cognitive skills. [SEP] Justify:

Output: The text corroborates the claim by discussing the importance of Theory of Mind (ToM) and empathy in understanding others, which is critical for successful social interactions. It states that affective ToM involves recognizing others' emotions and feelings, which is directly related to empathy. Furthermore, it emphasizes that ToM is key to social function outcomes and mental health, supporting the idea that empathy enhances social connections.


Input: Task: contrast the claim using the given text. Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: The advantage of having expensive and specialized equipment and practitioners at fixed centralized locations is that they alleviate the pressure for smaller healthcare facilities to spend their limited resources on equipment that is not frequently used but still critical for health care. By contrast, hospitals in the US are spread out and uncoordinated, duplicating care in many places without high enough volume in any of them to provide the critical mass to make the procedures affordable. Similarly, an MRI machine might be used four to five times a day in the US but 15 to 20 times a day in the Indian hospitals. We have to make the equipment sweat! 13In the cases of the more successful privatized hospitals, India, as well as the rest of the world, is paying careful attention to this 'configured networks of care and facilities in a hub-and-spoke design'12. One of the primary benefits of HSM that is being revealed in India is that 'This allows for a more-orderly progression of care and rational allocation of often-scarce resources'12. Thus, success is being found in India when practising medical facilities centralize their assets and diversify their more benign practices in the lower cost ambulatory locales. The end result is a lower centralized cost, as providers only procure the equipment and resources they need. Moreover, the Indian population, in general, is able to access greater coverage for fulfilling their basic healthcare needs. Reasons for successAttracting and retaining doctors: No matter how efficient a hospital may be, it cannot be deemed successful if its doctors and nurses are incompetent or unable to perform their primary tasks. As such, Indian hospitals operating under the HSM are actually retaining and attracting capable doctors who are keenly interested in 'high volume and sheer variety of cases that attract talent to these hospitals'. Also, 'doctors can build their capabilities faster in [HSM hospitals]. . . in turn, [this] contributes to the high quality of outcomes at these hospitals'14. Nurturing and updating treatment protocols: Perhaps most unique to India's HSM success is its incorporation of developed protocols for both mundane tasks and highly complex procedures. Knee and hip replacements, as well as cardiac and cancer surgeries, all have itemized procedures that are adhered to despite being executed in facilities outside of the central hub. [SEP] Justify:

Output: The text contrasts the claim by describing the disparities in healthcare access and affordability between different regions, particularly highlighting how expensive and specialized equipment is centralized and may not be accessible to smaller healthcare facilities. It mentions that 'hospitals in the US are spread out and uncoordinated, duplicating care... to provide the critical mass to make the procedures affordable,' which illustrates that there is a lack of universal accessibility and affordability in AI technology in healthcare.


Input: Task: pass the claim using the given text. Claim: Circadian rhythms are regulated by light-sensitive proteins. [SEP] Text: It should be noted, however, that MESA is robust in the face of such problems from the start. Incorporated into our MESA program is a detrending step which fits a line by regression and subtracts it. This eliminates linear trend and removes the mean. Mean removal is highly recommended, as this DC component can obscure the rhythmic one if it is excessive [12]. Removal of nonlinear trend can be accomplished by high-pass filtering by numerous methods and will not be discussed here as it is beyond the scope of this work. Low pass filtering to remove high frequency noise, however, is of considerable interest and is commonly done in preparation for spectral analysis (Review: [12,24]). We have had excellent success with Butterworth recursive filters [9,12,24]. They are considered recursive because in addition to incorporating the original time series data into the moving filtering process, previously filtered values are used as well. Butterworth filters are highly accurate and reliable, and the cutoff frequency is sharp [9]. 6, the artificial test signal depicted in Figure 1 is shown after filtering with a two-pole low-pass Butterworth filter with a ~3 dB amplitude rolloff at the specified period of 4 h. The number of poles reflects the depth of the recursion [9]. The filter equation showing the recursion is: (6) Yt=Xt+2Xt−1+Xt−2+AYt−1+BYt−2/C Figure 6 This is the original data set after being filtered twice with a Butterworth recursive digital filter. The second pass reverses the filter’s introduction of a four-hour phase delay owing to its recursive nature. is the original data series and Yt is the output series. A and B are the filter coefficients: A = 9. 656 and B = −3. 4142. C is the “gain” or amplitude change of the filter and equals 10. 2426. See [9,12,24] for a more detailed description of this filter. Owing to the recursion there is a 4-h phase delay in this example and this needs either to be made clear when the data are plotted or actually reversed. Reversal can easily be accomplished by running the filter in reverse. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the use of Butterworth filters in data processing and does not mention circadian rhythms or light-sensitive proteins at all. There is no connection to the regulation of biological rhythms in response to light.


Input: Task: corroborate the claim using the given text. Claim: Empathy improves social connections and helps in understanding others. [SEP] Text: )| falseSearch 174. SingerT. HeinG. (2012). Human empathy through the lens of psychology and social neuroscience. — In: The primate mind ( de WaalF. B. M. FerrariP. F. , eds). Press, Cambridge, p. 158-174. )| falseSearch 1162. SingerT. SeymourB. O’DohertyJ. KaubeH. DolanR. J. FrithC. D. (2004). Empathy for pain involves the affective but not sensory components of pain. — Science 303: 1157-1162. )| falseSearch 469. SingerT. SeymourB. O’(2006). Empathic neural responses are modulated by the perceived fairness of others. — Nature. 439: 466-469. )| falseSearch 48. TrevarthenC. AitkenK. J. (2001). Infant intersubjectivity: research, theory, and clinical applications. — J. Child Psychol. Psychiatry 42: 3-48. )| falseSearch 222. van der GaagC. MinderaaR. B. KeysersC. (2007). Facial expressions: what the mirror neuron system can and cannot tell us. — Soc. Neurosci. 2: 179-222. )| falseSearch 664. WickerB. KeysersC. PlaillyJ. RoyetJ. P. GalleseV. RizzolattiG. (2003). Both of us disgusted in My insula: the common neural basis of seeing and feeling disgust. — Neuron 40: 655-664. )| falseSearch 621. . . (2006). Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spectrum disorder. — Neuropsychologia 44: 610-621. )| falseSearch 126. ZallaT. BarlassinaL. BuonM. LeboyerM. (2011). Moral judgment in adults with autism spectrum disorders. — Cognition 121: 115-126. )| falseSearch 40. 00 Add to access options Rent on of the key questions in understanding human morality is how central are emotions in influencing our decisions and in our moral judgments. Theoretical work has proposed that empathy could play an important role in guiding our tendencies to behave altruistically or selfishly. [SEP] Justify:

Output: The text corroborates the claim by suggesting that empathy plays an important role in influencing decisions and moral judgments, which implies that it contributes to social connections and understanding others. It specifically mentions that theoretical work has proposed that empathy could guide our tendencies to behave altruistically or selfishly, supporting the notion that empathy affects social behavior and relationships.