Temporal stability in measurements matters only for stable constructs
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A systematic review and meta-analyses of the temporal stability and convergent validity of risk preference measures
Understanding whether risk preference represents a stable, coherent trait is central to efforts aimed at explaining, predicting and preventing risk-related behaviours. We help characterize the nature of the construct by adopting a systematic review and individual participant data meta-analytic approach to summarize the temporal stability of 358 risk preference measures (33 panels, 57 samples, 579,114 respondents). Our findings reveal noteworthy heterogeneity across and within measure categories (propensity, frequency and behaviour), domains (for example, investment, occupational and alcohol consumption) and sample characteristics (for example, age). Specifically, while self-reported propensity and frequency measures of risk preference show a higher degree of stability than behavioural measures, these patterns are moderated by domain and age. Crucially, an analysis of convergent validity reveals a low agreement across measures, questioning the idea that they capture the same underlying phenomena. Our results raise concerns about the coherence and measurement of the risk preference construct.
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement
Taxonomic incommensurability denotes the difficulty in comparing scientific theories due to different uses of concepts and operationalizations. To tackle this problem in psychology, here we use language models to obtain semantic embeddings representing psychometric items, scales and construct labels in a vector space. This approach allows us to analyse different datasets (for example, the International Personality Item Pool) spanning thousands of items and hundreds of scales and constructs and show that embeddings can be used to predict empirical relations between measures, automatically detect taxonomic fallacies and suggest more parsimonious taxonomies. These findings suggest that semantic embeddings constitute a powerful tool for tackling taxonomic incommensurability in the psychological sciences.
Bayesian stability and force modeling for uncertain machining processes
Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation
Understanding how visual information is encoded in biological and artificial systems often requires the generation of appropriate stimuli to test specific hypotheses, but available methods for video generation are scarce. Here we introduce the spatiotemporal style transfer (STST) algorithm, a dynamic visual stimulus generation framework that allows the manipulation and synthesis of video stimuli for vision research. We show how stimuli can be generated that match the low-level spatiotemporal features of their natural counterparts, but lack their high-level semantic features, providing a useful tool to study object recognition. We used these stimuli to probe PredNet, a predictive coding deep network, and found that its next-frame predictions were not disrupted by the omission of high-level information, with human observers also confirming the preservation of low-level features and lack of high-level information in the generated stimuli. We also introduce a procedure for the independent spatiotemporal factorization of dynamic stimuli. Testing such factorized stimuli on humans and deep vision models suggests a spatial bias in how humans and deep vision models encode dynamic visual information. These results showcase potential applications of the STST algorithm as a versatile tool for dynamic stimulus generation in vision science.
Terminal differentiation and persistence of effector regulatory T cells essential for preventing intestinal inflammation
Regulatory T (Treg) cells are a specialized CD4+ T cell lineage with essential anti-inflammatory functions. Analysis of Treg cell adaptations to non-lymphoid tissues that enable their specialized immunosuppressive and tissue-supportive functions raises questions about the underlying mechanisms of these adaptations and whether they represent stable differentiation or reversible activation states. Here, we characterize distinct colonic effector Treg cell transcriptional programs. Attenuated T cell receptor (TCR) signaling and acquisition of substantial TCR-independent functionality seems to facilitate the terminal differentiation of a population of colonic effector Treg cells that are distinguished by stable expression of the immunomodulatory cytokine IL-10. Functional studies show that this subset of effector Treg cells, but not their expression of IL-10, is indispensable for colonic health. These findings identify core features of the terminal differentiation of effector Treg cells in non-lymphoid tissues and their function.
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