Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities.

In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation.

The Cognitive Underpinnings of Option Generation in Everyday Life Decision-Making: A Latent Variable Analysis.

The ability to generate options for action is crucial for everyday life decision-making. In this article, we propose and test a model of the cognitive underpinnings of option generation in everyday life situations. We carried out a laboratory study using measures of a wide range of cognitive functions and asked participants (N = 157) to generate options for actions for different everyday life decision-making scenarios. The results of a latent variable analysis show that the cognitive underpinnings of option generation are consistent across different everyday life situations and, hence, option generation can be conceptualized as a general construct. Moreover, the results of a confirmatory factor analysis reveal that, when controlling for the shared variance among the cognitive processes assessed, verbal fluency, working memory capacity, ideation fluency, and processing speed predicted option generation. These findings suggest that option generation in everyday life situations can be distinguished from other cognitive constructs, such as divergent thinking (in terms of ideas' originality) and long-term memory.

Quantifying spatiotemporal complexity of cardiac dynamics using ordinal patterns.

Analyzing the dynamics of complex excitation wave patterns in cardiac tissue plays a key role for understanding the origin of life-threatening arrhythmias and for devising novel approaches to control them. The quantification of spatiotemporal complexity, however, remains a challenging task. This holds in particular for the analysis of data from fluorescence imaging (optical mapping), which allows for the measurement of membrane potential and intracellular calcium at high spatial and temporal resolution. Hitherto methods, like dominant frequency maps and the analysis of phase singularities, address important aspects of cardiac dynamics, but they consider very specific properties of excitable media, only. This article focuses on the benchmark of spatial complexity measures over time in the context of cardiac cell cultures. Standard Shannon Entropy and Spatial Permutation Entropy, an adaption of [1], have been implemented and applied to optical mapping data from embryonic chicken cell culture experiments. We introduce spatial separation of samples when generating ordinal patterns and show its importance for Spatial Permutation Entropy. Results suggest that Spatial Permutation Entropies provide a robust and interpretable measure for detecting qualitative changes in the dynamics of this excitable medium.

The effects of caffeine on option generation and subsequent choice.

RATIONALE: Although the effects of caffeine on basic cognitive functions are well-known, its effects on more complex decision making, particularly on option generation, is yet to be explored. OBJECTIVE: We examined the effects of caffeine on option generation in decision making using everyday life decisional situations. METHODS: In a double-blind placebo-controlled experiment, participants (N = 47) either received 300 mg of caffeine or a placebo. Participants had to generate choice options (things they could do) for a series of high and low familiar real-world scenarios and, subsequently, to decide among these options. RESULTS: Analyses revealed that participants in the caffeine condition generated significantly fewer options than participants in the placebo condition. Moreover, caffeine significantly reduced the option generation onset time, that is, participants in the caffeine condition generated their first option significantly faster than participants in the placebo condition. Regarding subsequent choice, we found evidence supporting the "take-the-first" heuristic, that is, the tendency to select the first generated option. This tendency was neither affected by caffeine nor by the familiarity of the scenarios. CONCLUSIONS: Caffeine results in fewer options generated in unconstrained real-life decision-making situations and decreases generation onset times.

Social influence and perceptual decision making: a diffusion model analysis.

Classic studies on social influence used simple perceptual decision-making tasks to examine how the opinions of others change individuals' judgments. Since then, one of the most fundamental questions in social psychology has been whether social influence can alter basic perceptual processes. To address this issue, we used a diffusion model analysis. Diffusion models provide a stochastic approach for separating the cognitive processes underlying speeded binary decisions. Following this approach, our study is the first to disentangle whether social influence on decision making is due to altering the uptake of available sensory information or due to shifting the decision criteria. In two experiments, we found consistent evidence for the idea that social influence alters the uptake of available sensory evidence. By contrast, participants did not adjust their decision criteria.

Synchronization patterns in transient spiral wave dynamics.

Transient dynamics of spiral waves in a two-dimensional Barkley model is shown to be governed by pattern formation processes resulting in regions of synchronized activity separated by moving interfaces. During the transient the number of internally synchronized regions decreases as synchronization fronts move to the boundary of the simulated area. This spatiotemporal transient dynamics in an excitable medium is detected and visualized by means of an analysis of the local periodicity and by evaluation of prediction errors across the spatial domain. During the (long) transient both analyses show patterns that must not be misinterpreted as any information about (spatial) structure of the underlying (completely homogeneous) system.