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Publishing graphs that depict culture change has long been a means to illustrate what is known of the culture history of an area, including the Plateau culture area. Examination of 471 pieces of literature on Plateau archaeology published between 1895 and 2019 reveals aspects of the history of graphing culture change in the area. Different types of graphs, including tables of data, have been used across both North America and the Plateau. Graphs appear later in the twentieth century Plateau literature than for North America in general. Whereas spindle graphs (temporal frequency distributions) were popular elsewhere on the continent, they are rare in the Plateau literature. The richness of graph types used by Plateau archaeologists over time is not correlated with that evident among North American archaeologists. Plateau archaeologists have used line graphs and bar graphs to depict culture change. Excluding tables, the most popular graph type appearing in the Plateau literature consists of spatio-temporal rectangles bounding names of culture units. Graphs of temporally diagnostic projectile point forms plotted against time appear first in the 1960s, likely at the hands of Roald Fryxell. Although this graph type explicitly shows culture change, it is used less frequently by Plateau archaeologists than culture unit names within spatio-temporal rectangles. [ABSTRACT FROM AUTHOR]

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Although there have been major strides toward uncovering the neurobehavioral mechanisms involved in cognitive functions like memory and decision making, methods for measuring behavior and accessing latent processes through computational means remain limited. To this end, we have created SUPREME (Sensing to Understanding and Prediction Realized via an Experiment and Modeling Ecosystem): a toolbox for comprehensive cognitive assessment, provided by a combination of construct-targeted tasks and corresponding computational models. SUPREME includes four tasks, each developed symbiotically with a mechanistic model, which together provide quantified assessments of perception, cognitive control, declarative memory, reward valuation, and frustrative nonreward. In this study, we provide validation analyses for each task using two sessions of data from a cohort of cognitively normal participants (N = 65). Measures of test-retest reliability (r: 0.58–0.75), stability of individual differences (ρ: 0.56–0.70), and internal consistency (α: 0.80–0.86) support the validity of our tasks. After fitting the models to data from individual subjects, we demonstrate each model's ability to capture observed patterns of behavioral results across task conditions. Our computational approaches allow us to decompose behavior into cognitively interpretable subprocesses, which we can compare both within and between participants. We discuss potential future applications of SUPREME, including clinical assessments, longitudinal tracking of cognitive functions, and insight into compensatory mechanisms. [ABSTRACT FROM AUTHOR]

Despite the success of modern neuroimaging techniques in furthering our understanding of cognitive and pathophysiological processes, translation of these advances into clinically relevant tools has been virtually absent until now. Neuromodeling represents a powerful framework for overcoming this translational deadlock, and the development of computational models to solve clinical problems has become a major scientific goal over the last decade, as reflected by the emergence of clinically oriented neuromodeling fields like Computational Psychiatry, Computational Neurology, and Computational Psychosomatics. Generative models of brain physiology and connectivity in the human brain play a key role in this endeavor, striving for computational assays that can be applied to neuroimaging data from individual patients for differential diagnosis and treatment prediction. In this review, we focus on dynamic causal modeling (DCM) and its use for Computational Psychiatry. DCM is a widely used generative modeling framework for functional magnetic resonance imaging (fMRI) and magneto‐/electroencephalography (M/EEG) data. This article reviews the basic concepts of DCM, revisits examples where it has proven valuable for addressing clinically relevant questions, and critically discusses methodological challenges and recent methodological advances. We conclude this review with a more general discussion of the promises and pitfalls of generative models in Computational Psychiatry and highlight the path that lies ahead of us. This article is categorized under: Neuroscience > Computation Neuroscience > Clinical Neuroscience [ABSTRACT FROM AUTHOR]

The present article aims to illustrate and review evidence on how associative learning involving interoceptive stimuli (interoceptive conditioning) can lead to changes in physiological, emotional, and perceptual outcomes. We first outline a functional perspective on Pavlovian conditioning and provide a general introduction and historical background on interoceptive conditioning as a special instance of Pavlovian conditioning. Next, biological and learning accounts of panic disorder are discussed, followed by an analysis of which stimuli and responses may be most promising to model learning that is relevant to panic disorder. Finally, studies on interoceptive fear conditioning with respiratory stimuli are reviewed and discussed, and future directions are outlined. Associative fear learning to interoceptive stimuli features in theoretical accounts of panic disorder. Likewise, interoceptive exposure therapy is based on the assumption that interoceptive fear is learned and can be extinguished again. Nonetheless, fear conditioning research is dominated by exteroceptive paradigms that score low on their ecological validity for panic disorder. From a functional perspective, panic‐relevant conditioning paradigms ought to use panicogenic unconditional stimuli (US), conditioned stimuli with a natural relationship to such US, and study panic‐relevant response systems. We review the empirical work with respiratory US, discuss it from such functional perspective, and outline potential avenues for future research. [ABSTRACT FROM AUTHOR]