Previous research indicates that political conservatism is associated with epistemic needs for structure and certainty (Jost et al., 2003) and that nouns elicit clearer and more definite perceptions of reality than other parts of speech (Carnaghi et al., 2008). We therefore hypothesized that conservatives would exhibit preferences for nouns (vs. verbs and adjectives), insofar as nouns are better suited to satisfy epistemic needs. In Study 1, we observed that social conservatism was associated with noun preferences in Polish and that personal need for structure accounted for the association between ideology and grammatical preferences. In Study 2, conducted in Arabic, social conservatism was associated with a preference for the use of nominal sentences (composed of nouns only) over verbal sentences (which included verbs and adjectives). In Study 3, we found that more conservative U.S. presidents used greater proportions of nouns in major speeches, and this effect was related to integrative complexity. We discuss the possibility that conservative ideology is linked to grammatical preferences that foster feelings of stability and predictability.

Individual studies of health psychology are samples taken in particular places at particular times. The results of such studies manifest multiple processes, including those associated with individual, sample, intervention, and study design characteristics. Although extant meta-analyses of health phenomena have routinely considered these factors to explain heterogeneity, they have tended to neglect the environments where studies are conducted, which is ironic, as health phenomena cluster in space and times (e.g., epidemics). The settings in which study participants live, work, and recreate can be characterised by such environmental factors such as disease, weather, local and broad economic trends, the level of stigmatisation of minority groups, and allostatic load due to all causes. We introduce spatiotemporal meta-analysis, designed to address heterogeneity in study environments. We list potential challenges in developing spatiotemporal meta-analyses, and discuss future directions for this form of systematic reviewing methodology. Logically, to the extent that relevant spatiotemporal information on environmental conditions is available and varies widely, it can help to explain variability in study results that is not explained by individual, sample, study, or intervention features.

This article presents a novel, nonlinear, data-driven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal dynamics. This approach thus provides enhanced capabilities over conventional computational neuroscience tools restricted to analyzing signals in either the time or space domains. This is achieved via machine learning and kernel methods for data-driven approximation of skew-product dynamical systems. The approximations successfully converge to theoretical values in the limit of long embedding windows. First, we describe the method, then using electrocorticographic (ECoG) data from a mismatch negativity experiment, we extract time-separable frequencies without bandpass filtering or prior selection of wavelet features. Finally, we discuss in detail two of the extracted components, Beta (∼ 13 Hz) and high Gamma (∼ 50 Hz) frequencies, and explore the spatiotemporal dynamics of high-and low-frequency components.

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This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

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