Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in ...psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.
Neuronal cell dysfunction plays an important role in neurodegenerative diseases. Oxidative stress can disrupt the redox balance within neuronal cells and may cause neuronal nitric oxide synthase ...(nNOS) to uncouple, contributing to the neurodegenerative processes. Experimental studies and clinical trials using nNOS cofactor tetrahydrobiopterin (BH4) and antioxidants in neuronal cell dysfunction have shown inconsistent results. A better mechanistic understanding of complex interactions of nNOS activity and oxidative stress in neuronal cell dysfunction is needed. In this study, we developed a computational model of neuronal cell using nNOS biochemical pathways to explore several key mechanisms that are known to influence neuronal cell redox homeostasis. We studied the effects of oxidative stress and BH4 synthesis on nNOS nitric oxide production and biopterin ratio (BH4/total biopterin). Results showed that nNOS remained coupled and maintained nitric oxide production for oxidative stress levels less than 230 nM/s. The results showed that neuronal oxidative stress above 230 nM/s increased the degree of nNOS uncoupling and introduced instability in the nitric oxide production. The nitric oxide production did not change irrespective of initial biopterin ratio of 0.05 - 0.99 for a given oxidative stress. Oxidative stress resulted in significant reduction in BH4 levels even when nitric oxide production was not affected. Enhancing BH4 synthesis or supplementation improved nNOS coupling, however the degree of improvement was determined by the levels of oxidative stress and BH4 synthesis. The results of our mechanistic analysis indicate that there is a potential for significant improvement in neuronal dysfunction by simultaneously increasing BH4 levels and reducing cellular oxidative stress.
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•Computational model of nNOS was developed to understand the role of oxidative stress•nNOS remained coupled & maintained NO production for oxidative stress level < 230nM/s•NO production decreased and caused nNOS to uncouple at high oxidative stress levels•BH4 deficiency contributed to uncoupling of nNOS and reduced NO production•BH4 supplementation/synthesis enhanced neuronal function by restoring nNOS coupling
For the past decade, considerable research effort has been devoted toward computationally identifying and experimentally verifying single phase, high-entropy systems. However, predicting the ...resultant crystal structure(s) “in silico” remains a major challenge. Previous studies have primarily used density functional theory to obtain correlated parameters and fit them to existing data, but this is impractical given the extensive regions of unexplored composition space and considerable computational cost. A rapidly developing area of materials science is the application of machine learning to accelerate materials discovery and reduce computational and experimental costs. Machine learning has inherent advantages over traditional modeling, owing to its flexibility as new data becomes available and its rapid ability to construct relationships between input data and target outputs. In this article, we propose a novel high-throughput approach, called “ML-HEA”, for coupling thermodynamic and chemical features with a random forest machine learning model for predicting the solid solution forming ability. The model can be a primary tool or integrated into existing alloy discovery workflows. The ML-HEA method is validated by comparing the results with reliable experimental data for binary, ternary, quaternary, and quinary systems. Comparison to other modeling approaches, including CALPHAD and the LTVC model, are also made to assess the performance of the machine learning model on labeled and unlabeled data. The uncertainty of the model in predicting the resultant phase of each composition is explored via the output of individual predictor trees. Importantly, the developed model can be immediately applied to explore material space in an unconstrained manner, and is readily updated to reflect the results of new experiments.
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Systemic vocal fold dehydration is known to increase vocal fold stiffness, which has been hypothesized to have important effect on voice production. However, it remains unclear whether the ...dehydration-induced vocal fold stiffness changes can have a noticeable impact on phonation, particularly in normal phonation conditions. The goal of this study was to investigate the impact of vocal fold stiffness changes due to vocal fold systemic dehydration and its significance in daily communication.
Parametric computational simulation using a three-dimensional vocal fold model, in which the vocal fold stiffness was varied as a function of systemic dehydration levels based on previously-reported experimental data.
The results showed that systemic dehydration had significant effects on voice production only at high levels of dehydration, at which dehydration increased the phonation threshold pressure and fundamental frequency, and decreased glottal opening area, vocal intensity and glottal efficiency. The effect depended mainly on the overall dehydration level but was also slightly affected by the dehydration distribution and muscular control. However, for dehydration levels typical of normal phonation conditions, the effect was negligible.
The results indicated that dehydration-induced vocal fold stiffness change likely is not an important mechanism through which vocal fold systemic dehydration affects voice production. Nevertheless, a large decrease in glottal efficiency implies a possible perceived increase of vocal effort under a realistic dehydration condition.
What Is the Readiness Potential? Schurger, Aaron; Hu, Pengbo 'Ben'; Pak, Joanna ...
Trends in cognitive sciences,
07/2021, Letnik:
25, Številka:
7
Journal Article
Recenzirano
Odprti dostop
The readiness potential (RP), a slow buildup of electrical potential recorded at the scalp using electroencephalography, has been associated with neural activity involved in movement preparation. It ...became famous thanks to Benjamin Libet (Brain 1983;106:623–642), who used the time difference between the RP and self-reported time of conscious intention to move to argue that we lack free will. The RP’s informativeness about self-generated action and derivatively about free will has prompted continued research on this neural phenomenon. Here, we argue that recent advances in our understanding of the RP, including computational modeling of the phenomenon, call for a reassessment of its relevance for understanding volition and the philosophical problem of free will.
The readiness potential (RP) has been widely interpreted to indicate preparation for movement and is used to argue that our brains decide before we do. It thus has been a fulcrum for discussion about the neuroscience of free will.Recent computational models provide an alternative framework for thinking about the significance of the RP, suggesting instead that the RP is a natural result of the operation of a stochastic accumulator process of decision-making, analyzed by time-locking to threshold-crossing events.These models call for a reevaluation of: (i) the ontological standing of the RP as reflecting a real, causally efficacious signal in the brain; (ii) the meaningfulness of temporal comparisons between the ‘onset’ of the RP and the timing of other phenomena; (iii) the moment at which we, as experimenters, identify that a decision to act has been made; and (iv) the relevance of the RP for discussions of free will.
Computationally efficient structure-property (S-P) linkages (i.e., reduced order models) are a necessary key ingredient in accelerating the rate of development and deployment of structural materials. ...This need represents a major challenge for polycrystalline materials, which exhibit rich heterogeneous microstructure at multiple structure/length scales, and exhibit a wide range of properties. In this study, a novel framework is described for extracting S-P linkages in polycrystalline microstructures that are obtained using 2-point spatial correlations (also called 2-point statistics) to quantify the material's microstructure, and principal component analysis (PCA) to represent this information in a reduced dimensional space. Additionally, it is demonstrated that the use of generalized spherical harmonics (GSH) as a Fourier basis for functions defined on the orientation space leads to a compact and computationally efficient representation of the desired S-P linkages. In this study, these novel protocols are developed and demonstrated for elastic stiffness and yield strength predictions for α− Ti microstructures using a dataset produced through microscale finite element simulations.
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•Classification and various thickening mechanisms of shear thickening fluids (STF) are reviewed.•The advantages and disadvantages of STF’s computational models are first explored.•STF ...applied in vibration control, adaptive structures and industrial polishing are also discussed.•The perspective of this review on STF application is from mechanics.
Shear thickening fluids (STFs) are a new type of nanosuspension, which are formed by dispersing micro and nanoparticles in a dispersant. STFs are easily deformed under the action of a low shear rate. However, they instantly transform into a hard solid-like state at a high shear rate. After the removal of the impact force, STFs revert to their original liquid state. During this process, STFs absorb a significant amount of impact energy. Hence, they can be employed as a buffer and for vibration reduction. In this study, a comprehensive review of existing literature on STFs is presented. First, the basic properties, classification, and rheological mechanism evolution of STFs are discussed. The factors influencing the shear thickening behavior of these fluids are then reviewed. Subsequently, several computational models of the STF are discussed because the underlying mechanism of STF is still unclear, and to date, there is a paucity of good computational models. Finally, the research progress of composites based on STF in the fields of stab and spike resistance and low- and high-velocity impacts, and the use of STF as a new energy dissipation medium in the fields of explosion resistance, vibration control, adaptive structure, and industrial polishing are summarized.
Making rapid decisions on the basis of sensory information is essential to everyday behaviors. Why, then, are perceptual decisions so variable despite unchanging inputs? Spontaneous neural ...oscillations have emerged as a key predictor of trial-to-trial perceptual variability. New work casting these effects in the framework of models of perceptual decision-making has driven novel insight into how the amplitude of spontaneous oscillations impact decision-making. This synthesis reveals that the amplitude of ongoing low-frequency oscillations (<30 Hz), particularly in the alpha-band (8–13 Hz), bias sensory responses and change conscious perception but not, surprisingly, the underlying sensitivity of perception. A key model-based insight is that various decision thresholds do not adapt to alpha-related changes in sensory activity, demonstrating a seeming suboptimality of decision mechanisms in tracking endogenous changes in sensory responses.
Spontaneous changes in low-frequency oscillatory amplitude bias moment-to-moment perceptual decisions through criterion effects, not sensitivity changes.Models of perceptual decision-making combined with neurophysiological evidence suggest that spontaneous alpha-band oscillations modulate sensory responses without changing the fidelity of stimulus representations.Criteria underlying subjective measures of perception, such as detection, visibility, and confidence, do not adapt to alpha-related changes in sensory processing, leading to dissociations between objective and subjective aspects of perception.There is emerging support for a domain-general effect of alpha on perceptual decisions across sensory modalities.
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