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.

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.

Even subtle forms of hemispatial neglect after stroke negatively affect the performance of daily life tasks, increases the risk of injury, and are associated with poor rehabilitation outcomes. Conventional paper-and-pencil tests, however, often underestimate the symptoms. We aimed to identify relevant neglect-specific measures and clinical decision rules based on machine learning techniques on behavioral data generated in a new Virtual Reality (VR) application, the immersive virtual road-crossing task. In total, 59 participants were included in our study: Two right-hemispheric stroke groups with left neglect (n = 20) or no neglect (n = 19), classified based on conventional tests and medical diagnosis, and healthy controls (n = 20). A neuropsychological test battery and the VR task were administered to all participants. We applied decision trees and random forest models to predict the respective groups based on the results of the VR task. Our feature selection procedure yielded six features as suitable predictors - most of which involved lateral time-related measures, particularly reaction times, and head movements. Our model achieved a high training accuracy of 96.6 % and, after crossvalidation, an accuracy of 76.8%. These results confirm previous reports that temporal behavioral patterns are key to detecting subtle neglect in patients with chronic stroke. Our results indicate that VR combined with machine learning has the potential to achieve higher test accuracies while being highly applicable to clinical practice.

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