Symbolic incentives can affect the frequency of blood and plasma donations: Evidence from 12 years of donors’ behavior and donation awards

We use panel data (from 2008 to 2019) covering the entire population of bloodand plasma donors affiliated to an association of donors (AVIS) in an Italian region(Tuscany) to examine how donors respond to a nonlinear scheme of symbolic incentives (medals) related to donation quotas and length of membership. The structureof the award scheme allows us to study the impact of two distinct characteristicsof the symbolic awards: the private vs. public assignment, and the donation-onlyvs. donation-and-membership criterion (although this latter characteristic can onlybe distinguished for private assignments). When we control for unobservable individual characteristics potentially affecting the evolution of performance along adonor’s career we find that: (i) public assignment of medals has an impact which isnot appreciably different from the one with private assignment, (ii) the donation-and-membership criterion induces an ex-post increase in the frequency of donations,while the donation-only criterion does not. This evidence suggests that symbolicawards are useful if given to less performing donors, that they affect behavior aftertheir obtainment rather than before, and that they operate when their obtainmentdoes not exclusively depend on performance, but also on membership.

Population coding in the cerebellum and its implications for learning from error

The cerebellum resembles a feedforward, three-layer network of neurons in which the “hidden layer” consists of Purkinje cells (P-cells), and the output layer consists of deep cerebellar nucleus (DCN) neurons. However, unlike an artificial network, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? To consider these questions, in this review I apply elementary mathematics from machine learning and assume that the output of each DCN neuron is a prediction that is compared to the actual observation, resulting in an error signal that originates in the inferior olive. This signal is sent to P-cells via climbing fibers that produce complex spikes. The same error signal from the olive must also guide learning in the DCN neurons, yet the olivary projections to the DCN are weak, particularly in adulthood. However, P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppresses activity of the DCN neuron that produced the erroneous output. Viewed in the framework of machine learning, it appears that the olive organizes the P-cells into populations so that through complex spike synchrony each population can act as a surrogate teacher for the DCN neuron it projects to. This error-dependent grouping of P-cells into populations gives rise to a number of remarkable features of behavior, including multiple timescales of learning, protection from erasure, and spontaneous recovery of memory.

Semantically Explainable Fuzzy Classifier

In machine learning, there are many high-performance classifiers. However, because of lack of transparency, they are not able to explain the data in a human-friendly form. In this paper, Cumulative Fuzzy Class Membership Criterion (CFCMC), a recently proposed fuzzy modeling classifier, is modified and utilized for a novel approach of information extraction from the labeled data. This approach is able to explain the classifiability of the data in the form of semantics. Extracted semantics give information about the structure of the data and the similarities between classes. To get a relevant image of its classification performance, it is compared to three well-known and frequently used classifiers, which are considered as black boxes, namely, SVM, MLP, and kNN, and to a similar transparent approach, MF ARTMAP. To validate extracted semantics, they are compared to visualization of classified data and to confusion matrices generated during the evaluation of the created CFCMC models. The experimental result shows that CFCMC is not necessarily the best classifier, although, in most cases, it is not too far from the best performing methods. However, the semantical explanation potentially allows the classifier to be applied as a support for human decision processes in real-world problems.

Resisting a Genetic Identity: The Black Seminoles and Genetic Tests of Ancestry

In July 2000, the Seminole Nation of Oklahoma passed a resolution that would effectively expel a significant portion of its tribal members. The resolution amended the Nation's constitution by changing its membership criteria. Previously, potential members needed to show descent from an enrollee of the 1906 Dawes Rolls, the official American Indian tribal rolls established by the Dawes Commission to facilitate the allotment of reservation land. The amended constitution requires possession of one-eighth Seminole Indian blood, a requirement that a significant portion of the tribe's membership cannot fulfill. The members of the Nation who fail to meet this new membership criterion all have one thing in common: they are black.Descendents of former slaves who came to live among the Seminole Indians of Florida in the seventeenth and eighteenth centuries, the black Seminoles have been officially recognized by the U.S. government as members of the Seminole Nation of Oklahoma since 1866.

Flare Activity and Cluster Membership Probability of Flare Stars

Mirzoyan (1976) showed that the concentration of flare stars around the center of the Pleiades cluster (Alcyone) was the same, irrespective of their proper motions. At that time, however, proper motions of only a few flare stars were known. The result was confirmed on the basis of more extensive observational material (Chavushian 1979, Mirzoyan 1983). Photographic observations of stellar flares in the general galactic field during 181 hours yielded the detection of only a single flare, i.e. the percentage of flare stars in the general galactic field is about 10% of the total number of flare stars detected in the regions of stellar clusters and associations (Chavushian 1979, Mirzoyan et al. 1988).This result shows that flare activity can be considered as a definitive cluster membership criterion, which appears to be a better one than the cluster membership probability, which is based on proper motions. To prove this, cluster membership probabilities (Stauffer et al. 1991) for 408 Pleiades cluster flare stars from the catalogue by Haro et al. (1982), are used (Table 1).