The effects of social feedback on private opinions. Empirical evidence from the laboratory.

The question of how people change their opinions through social interactions has been on the agenda of social scientific research for many decades. Now that the Internet has led to an ever greater interconnectedness and new forms of exchange that seem to go hand in hand with increasing political polarization, it is once again gaining in relevance. Most recently, the field of opinion dynamics has been complemented by social feedback theory, which explains opinion polarization phenomena by means of a reinforcement learning mechanism. According to the assumptions, individuals not only evaluate the opinion alternatives available to them based on the social feedback received as a result of expressing an opinion within a certain social environment. Rather, they also internalize the expected and thus rewarded opinion to the point where it becomes their actual private opinion. In order to put the implications of social feedback theory to a test, we conducted a randomized controlled laboratory experiment. The study combined preceding and follow-up opinion measurements via online surveys with a laboratory treatment. Social feedback was found to have longer-term effects on private opinions, even when received in an anonymous and sanction free setting. Interestingly and contrary to our expectations, however, it was the mixture of supportive and rejective social feedback that resulted in the strongest influence. In addition, we observed a high degree of opinion volatility, highlighting the need for further research to help identify additional internal and external factors that might influence whether and how social feedback affects private opinions.

Rapidly Evolving Genes Are Key Players in Host Specialization and Virulence of the Fungal Wheat Pathogen Zymoseptoria tritici (Mycosphaerella graminicola).

The speciation of pathogens can be driven by divergent host specialization. Specialization to a new host is possible via the acquisition of advantageous mutations fixed by positive selection. Comparative genome analyses of closely related species allows for the identification of such key substitutions via inference of genome-wide signatures of positive selection. We previously used a comparative genomics framework to identify genes that have evolved under positive selection during speciation of the prominent wheat pathogen Zymoseptoria tritici (synonym Mycosphaerella graminicola). In this study, we conducted functional analyses of four genes exhibiting strong signatures of positive selection in Z. tritici. We deleted the four genes in Z. tritici and confirm a virulence-related role of three of the four genes ΔZt80707, ΔZt89160 and ΔZt103264. The two mutants ΔZt80707 and ΔZt103264 show a significant reduction in virulence during infection of wheat; the ΔZt89160 mutant causes a hypervirulent phenotype in wheat. Mutant phenotypes of ΔZt80707, ΔZt89160 and ΔZt103264 can be restored by insertion of the wild-type genes. However, the insertion of the Zt80707 and Zt89160 orthologs from Z. pseudotritici and Z. ardabiliae do not restore wild-type levels of virulence, suggesting that positively selected substitutions in Z. tritici may relate to divergent host specialization. Interestingly, the gene Zt80707 encodes also a secretion signal that targets the protein for cell secretion. This secretion signal is however only transcribed in Z. tritici, suggesting that Z. tritici-specific substitutions relate to a new function of the protein in the extracellular space of the wheat-Z. tritici interaction. Together, the results presented here highlight that Zt80707, Zt103264 and Zt89160 represent key genes involved in virulence and host-specific disease development of Z. tritici. Our findings illustrate that evolutionary predictions provide a powerful tool for the identification of novel traits crucial for host adaptation and pathogen evolution.

Expression profiling of the wheat pathogen Zymoseptoria tritici reveals genomic patterns of transcription and host-specific regulatory programs.

Host specialization by pathogens requires a repertoire of virulence factors as well as fine-tuned regulation of gene expression. The fungal wheat pathogen Zymoseptoria tritici (synonym Mycosphaerella graminicola) is a powerful model system for the discovery of genetic elements that underlie virulence and host specialization. We transcriptionally profiled the early stages of Z. tritici infection of a compatible host (wheat) and a noncompatible host (Brachypodium distachyon). The results revealed infection regulatory programs common to both hosts and genes with striking wheat-specific expression, with many of the latter showing sequence signatures of positive selection along the Z. tritici lineage. Genes specifically regulated during infection of wheat populated two large clusters of coregulated genes that may represent candidate pathogenicity islands. On evolutionarily labile, repeat-rich accessory chromosomes (ACs), we identified hundreds of highly expressed genes with signatures of evolutionary constraint and putative biological function. Phylogenetic analyses suggested that gene duplication events on these ACs were rare and largely preceded the diversification of Zymoseptoria species. Together, our data highlight the likely relevance for fungal growth and virulence of hundreds of Z. tritici genes, deepening the annotation and functional inference of the genes of this model pathogen.

A Predictive Approach to Nonparametric Inference for Adaptive Sequential Sampling of Psychophysical Experiments.

We present a predictive account on adaptive sequential sampling of stimulus-response relations in psychophysical experiments. Our discussion applies to experimental situations with ordinal stimuli when there is only weak structural knowledge available such that parametric modeling is no option. By introducing a certain form of partial exchangeability, we successively develop a hierarchical Bayesian model based on a mixture of Pólya urn processes. Suitable utility measures permit us to optimize the overall experimental sampling process. We provide several measures that are either based on simple count statistics or more elaborate information theoretic quantities. The actual computation of information theoretic utilities often turns out to be infeasible. This is not the case with our sampling method, which relies on an efficient algorithm to compute exact solutions of our posterior predictions and utility measures. Finally, we demonstrate the advantages of our framework on a hypothetical sampling problem.