Egocentric framing - one way people may fail in aswitch dilemma: evidence from excessive lane switching

types: ArticlePre-print version published in Munich Personal RePEc Archive. NOTICE: this is the author’s version of a work that was accepted for publication in Acta Psychologica. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Acta Psychologica, November 2013, 144, 604–616To study switching behavior, an experiment mimicking the state of a driver on the road was conducted. In each trial participants were given a chance to switch lanes. Despite the fact that lane switching had no sound rational basis, participants often switched lanes when the speed of driving in their lane on the previous trial was relatively slow. That tendency was discerned even when switching behavior had been sparsely reinforced, and was especially marked in almost a third of the participants, who manifested it consistently. The findings illustrate a type of behavior occuring in various contexts (e.g., stocks held in a portfolio, conduct pertinent for residual life expectancy, supermarket queues). We argue that this behavior may be due to a fallacy reminiscent of that arising in the well-known “envelopes problem”, in which each of two players holds a sum of money of which she knows nothing about except that it is either half or twice the amount held by the other player. Players may be paradoxically tempted to exchange assets, since an exchange fallaciously appears to always yield an expected value greater than whatever is regarded as the player’s present assets. We argue that the fallacy is due to egocentrically framing the problem as if the “amount I have” is definite, albeit unspecified, and show that framing the paradox acentrically instead eliminates the incentive to exchange assets. A possible psychological source for the human disposition to frame problems in a way that inflates expected gain is discussed. Finally, a heuristic meant to avert the source of the fallacy is proposed

The Distribution of Visual Information in the Vertical Dimension of Roman and Hebrew Letters

English and Hebrew native speakers read texts mutilated by removing a narrow or a wide strip at the top or at the bottom of the lines. Whereas reading the English texts was impaired more by mutilating the top, the reverse was found for the Hebrew texts. This result is ascribed to the different ways in which information is distributed along the vertical axis of Roman and Hebrew letters. Interactions between region and width of mutilation are argued to indicate that the effect is not due just to features at the very top and very bottom

Rational ligand design for metal ion recognition. Synthesis of a N-benzylated N2S3-donor macrocycle for enhanced silver(I) discrimination

Four previously documented ligand design strategies for achieving Ag(I) discrimination have been applied to the design of a new N-benzylated N2S3-donor macrocycle; the latter shows high selectivity for Ag(I) over Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Pb(II) in log K and bulk membrane transport studies

Does high income inequality signify inequality of opportunities?

It is often presumed that Gini coefficient values taken to reflect high income inequality are largely due to some combination of socioeconomic factors that gives rise to inequality of opportunities. We demonstrate, using computer simulations, that practically every Gini value within the entire range observed in state economies can be approximated by at least one of a set of possible models of an economy in which earning is totally due to random factors. Although that clearly does not prove that opportunities are in reality fairly equal, it does suggest that inequality of opportunities is not necessary for high income inequality. At the least, it relegates the burden of proof to whoever ascribes the latter largely to the former

Improved Generalization of Weight Space Networks via Augmentations

Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural fields (INRs, NeRFs), as well as making inferences about other types of neural networks. Unfortunately, weight space models tend to suffer from substantial overfitting. We empirically analyze the reasons for this overfitting and find that a key reason is the lack of diversity in DWS datasets. While a given object can be represented by many different weight configurations, typical INR training sets fail to capture variability across INRs that represent the same object. To address this, we explore strategies for data augmentation in weight spaces and propose a MixUp method adapted for weight spaces. We demonstrate the effectiveness of these methods in two setups. In classification, they improve performance similarly to having up to 10 times more data. In self-supervised contrastive learning, they yield substantial 5-10% gains in downstream classification.Comment: Under Revie

Data Augmentations in Deep Weight Spaces

Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various fields, from analyzing and editing neural fields and implicit neural representations, to network pruning and quantization. Recent works designed architectures for effective learning in that space, which takes into account its unique, permutation-equivariant, structure. Unfortunately, so far these architectures suffer from severe overfitting and were shown to benefit from large datasets. This poses a significant challenge because generating data for this learning setup is laborious and time-consuming since each data sample is a full set of network weights that has to be trained. In this paper, we address this difficulty by investigating data augmentations for weight spaces, a set of techniques that enable generating new data examples on the fly without having to train additional input weight space elements. We first review several recently proposed data augmentation schemes %that were proposed recently and divide them into categories. We then introduce a novel augmentation scheme based on the Mixup method. We evaluate the performance of these techniques on existing benchmarks as well as new benchmarks we generate, which can be valuable for future studies.Comment: Accepted to NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representation

Multi-clonal SARS-CoV-2 neutralization by antibodies isolated from severe COVID-19 convalescent donors.

The interactions between antibodies, SARS-CoV-2 and immune cells contribute to the pathogenesis of COVID-19 and protective immunity. To understand the differences between antibody responses in mild versus severe cases of COVID-19, we analyzed the B cell responses in patients 1.5 months post SARS-CoV-2 infection. Severe, and not mild, infection correlated with high titers of IgG against Spike receptor binding domain (RBD) that were capable of ACE2:RBD inhibition. B cell receptor (BCR) sequencing revealed that VH3-53 was enriched during severe infection. Of the 22 antibodies cloned from two severe donors, six exhibited potent neutralization against authentic SARS-CoV-2, and inhibited syncytia formation. Using peptide libraries, competition ELISA and mutagenesis of RBD, we mapped the epitopes of the neutralizing antibodies (nAbs) to three different sites on the Spike. Finally, we used combinations of nAbs targeting different immune-sites to efficiently block SARS-CoV-2 infection. Analysis of 49 healthy BCR repertoires revealed that the nAbs germline VHJH precursors comprise up to 2.7% of all VHJHs. We demonstrate that severe COVID-19 is associated with unique BCR signatures and multi-clonal neutralizing responses that are relatively frequent in the population. Moreover, our data support the use of combination antibody therapy to prevent and treat COVID-19