Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures.

Within predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However, this learning mechanism cannot explain how new parameters are added to a model. Structure learning, unlike parameter learning, makes structural changes to a generative model by altering its causal connections or adding or removing parameters. Whilst these two types of learning have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between parameter learning and structure learning on the basis of how they affect pupil dilation. Participants took part in a within-subject computer-based learning experiment with two phases. In the first phase, participants had to learn the relationship between cues and target stimuli. In the second phase, they had to learn a conditional change in this relationship. Our results show that the learning dynamics were indeed qualitatively different between the two experimental phases, but in the opposite direction as we originally expected. Participants were learning more gradually in the second phase compared to the first phase. This might imply that participants built multiple models from scratch in the first phase (structure learning) before settling on one of these models. In the second phase, participants possibly just needed to update the probability distribution over the model parameters (parameter learning).

An optical atomic clock based on a highly charged ion.

Optical atomic clocks are the most accurate measurement devices ever constructed and have found many applications in fundamental science and technology1-3. The use of highly charged ions (HCI) as a new class of references for highest-accuracy clocks and precision tests of fundamental physics4-11 has long been motivated by their extreme atomic properties and reduced sensitivity to perturbations from external electric and magnetic fields compared with singly charged ions or neutral atoms. Here we present the realization of this new class of clocks, based on an optical magnetic-dipole transition in Ar13+. Its comprehensively evaluated systematic frequency uncertainty of 2.2 × 10-17 is comparable with that of many optical clocks in operation. From clock comparisons, we improve by eight and nine orders of magnitude on the uncertainties for the absolute transition frequency12 and isotope shift (40Ar versus 36Ar) (ref. 13), respectively. These measurements allow us to investigate the largely unexplored quantum electrodynamic (QED) nuclear recoil, presented as part of improved calculations of the isotope shift, which reduce the uncertainty of previous theory14 by a factor of three. This work establishes forbidden optical transitions in HCI as references for cutting-edge optical clocks and future high-sensitivity searches for physics beyond the standard model.

No evidence for modulation of facial mimicry by attachment tendencies in adulthood: an EMG investigation.

Mimicking another individual functions as a social glue: it smoothens the interaction and fosters affiliation. Here, we investigated whether the intrinsic motivation to affiliate with others, stemming from attachment relationships, modulates individuals' engagement in facial mimicry (FM). Participants (N = 100; MAge = 24.54 years, SDAge = 3.90 years) observed faces with happy, sad, and neutral expressions, while their facial muscle activity was recorded with electromyography. Attachment was measured with the Attachment Styles Questionnaire, which provides a multidimensional profile for preoccupied and dismissing styles. It was proposed that the preoccupied and dismissing styles are characterized by high and low intrinsic affiliation motivation, respectively, and these were hypothesized to manifest in enhanced and diminished FM. Participants showed happy and sad FM, yet attachment styles did not significantly predict FM. Bayes Factor analyses lend evidence favoring the null hypothesis, suggesting that adult attachment do not contribute to FM.

Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms.

Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis.

Predicting Choice Behavior of Group Members.

Meaningful social interactions rest upon our ability to accurately infer and predict other people's preferences. Ireferen doing so, we can separate two sources of information: knowledge we have about the particular individual (individual knowledge) and knowledge we have about the social group to which that individual belongs (categorical knowledge). However, it is yet unclear how these two types of knowledge contribute to making predictions about other people's choice behavior. To fill this gap, we had participants learn probabilistic preferences by predicting object choices of agents with and without a common logo printed on their shirt. The logo thereby served as a visual cue to increase perceptions of groupness. We quantified how similar predictions for a specific agent are relative to the objective individual-level preferences of that agent and how close these predictions are relative to the objective group-level preferences to which that agent belongs. We found that the logo influenced how close participants' predictions were to the individual-level preferences of an agent relative to the preferences of the group the agent belongs to. We interpret this pattern of results as indicative of a differential weighting of individual and categorical group knowledge when making predictions about individuals that are perceived as forming a social group. The results are interpreted in an assimilation account of categorization and stress the importance of group knowledge during daily social interactions.