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The basic unit of decision making in an inclusive democracy is the demotic assembly, i.e. the assembly of demos, the citizen body in a given geographical area which may encompass a town and the surrounding villages, or even neighbourhoods of large cities. An inclusive democracy today can only take the form of a confederal democracy that is based on a network of administrative councils whose members or delegates are elected from popular face-to-face democratic assemblies in the various demoi. Thus, their role is purely administrative and practical, not one of policy-making like that of representatives in representative democracy.

The citizen body is advised by experts but it is the citizen body which functions as the ultimate decision-taker . Authority can be delegated to a segment of the citizen body to carry out specific duties, for example to serve as members of popular courts, or of regional and confederal councils. Such delegation is made, in principle, by lot, on a rotation basis, and is always recallable by the citizen body. Delegates to regional and confederal bodies should have specific mandates.

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structure. The guiding philosophy is that people should have decision making power in proportion to how much they are affected by the decision. Local councils of 25–50 people are completely autonomous on issues that affect only them, and these councils send delegates to higher level councils who are again autonomous regarding issues that affect only the population affected by that council.

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Cosmopolitan democracy, also known as Global democracy or World Federalism , is a political system in which democracy is implemented on a global scale, either directly or through representatives. An important justification for this kind of system is that the decisions made in national or regional democracies often affect people outside the constituency who, by definition, cannot vote. By contrast, in a cosmopolitan democracy, the people who are affected by decisions also have a say in them. Clearance Shop Buy Cheap Clearance AOYINI Plue Size Height Increasing Wedges Lace Up Flats Discount Authentic Online Discount From China 1PbsEH4

Based on previous literature, somatic pain was administered on the left limbs, and the visual stimuli used to evoke vicarious pain showed injuries to the right limbs. This mirrors the side of painful stimulation from the observer’s point of view in an allocentric reference frame, but not an egocentric one; thus, lateralization of stimulus presentation could potentially determine some of the functional properties of the VPS. To test this, we repeated the signature response analysis with a left-right flipped version of the VPS pattern (excluding the occipital cortex), which preserves the pattern but with opposite laterality. The results remained the same: The flipped VPS pattern did not track somatic pain intensity ( t UL (27) = 1.47, n.s., t LL (27) = 0.04, n.s. , accuracy Hsom-Lsom = 50%, n.s. ), but did track vicarious pain intensity ( t UL (27) = 2.68, p<0.05, t LL (27) = 2.53, p<0.05, accuracy Hvic-Lvic = 79%, p<0.005). These results indicate that the laterality of the VPS, and by extension the laterality of the stimuli it was trained on, are not an important determinant of its functional properties.

Another issue that may make the NPS and VPS responses appear more dissimilar is differential habituation or sensitization across trials. To test this, we estimated NPS and VPS amplitudes for each trial using a ‘beta series’ approach ( APRUUK Genuine cow leather SZ053 2018 square block heel Get The Latest Fashion QEzhvSYQG
) and examined their stability across trials. For this analysis, we used single-trial beta estimates for the stimulation period (one per trial), and applied the NPS and VPS patterns. For each participant, we then regressed the NPS responses against the reported somatic pain rating, and obtained the residual activity for somatic pain. We followed the same procedure for each participant for vicarious pain, and regressed the VPS responses against the reported vicarious pain rating to obtain the corresponding residual activity for vicarious pain. We then averaged the responses trial-by-trial across participants, and plotted the dynamic changes for the behavior, predicted signature response, and residual activity for somatic and vicarious pain. We did not see any evidence for systematic variation [e.g., sensitization or habituation; ( Jepma et al., 2013 )] across trials ( Appendix 1—figure 4 ).

Appendix 1—figure 4
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The first row depicts subjective ratings for somatic pain (top panel) and vicarious pain (bottom panel) respectively, the second row depicts the ratings predicted by NPS (top panel) and VPS (bottom …

Patterns tailored to predict somatic and vicarious pain for each individual might be more accurate, and thus provide better markers for representation that could yield different results. Therefore, we performed within-participant classification of vicarious pain patterns across the whole brain and also within the bilateral aINS and dACC. We used unsmoothed (but corrected for slice-acquisition-time and motion, and warped to SPM’s normative atlas) images for these analyses. Single-trial level analyses were conducted where first-level betas for each trial were estimated by modeling the 2s cue presentation, the 5, 7, or 11s variable pre-stimulus fixation period, the 11s stimulation (9 levels) and 4s rating periods, and the fixation cross epoch was used as an implicit baseline. Each of the 9 levels of the 11s stimulation period were modeled trial-by-trial with a total of 81 trial-level betas for each participant per experimental session. Trials with high variance inflation factors (VIFs > 2.5), and unreliable ratings were excluded from further analysis. The estimated betas were used for training classifiers (i.e., LASSO-PCR and SVM) for each participant. For each of the cross-validated iterations, one run with upper limb stimulation and one run with lower limb stimulation were excluded from the analysis. The classifier was trained on the trials from the remaining runs and the resultant pattern was tested both on the left out runs from the training modality (i.e., within modality cross-validation) and all trials from the other modality (i.e., between modality cross-prediction).

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