Research in behavioral decision-making has produced many models of decision under risk. To improve our understanding of choice under risk, it is essential to perform rigorous model comparisons over large sets of decision settings to find which models are most useful. Recently, such large-scale comparisons have produced conflicting conclusions: A variant of cumulative prospect theory (CPT) was the best model in a study by He, Analytis, and Bhatia (2022), whereas variants of the model BEAST were the best in two choice prediction competitions. This study delves into these contradictions to identify and explore the underlying reasons. We replicate and extend the analysis by He et al., this time incorporating BEAST, which was previously excluded because it cannot be analytically estimated. Our results show that while CPT excels in systematically hand-crafted tasks, BEAST—originally designed for broader decision-making contexts—matches or even surpasses CPT’s performance when choice tasks are randomly selected, and predictions are made for new, unknown decision makers. This success of BEAST, very different from classical decision models—as it does not assume, for example, subjective transformations of outcomes and probabilities—puts into question previous conclusions concerning the underlying psychological mechanisms of choice under risk. Our results challenge the field to expand beyond established evaluating techniques and highlight the importance of an inclusive approach toward nonanalytic models, like BEAST, to achieve more objective insights into decision-making behavior.

The constructs of motivation (or needs, motives, etc.) to explain higher-order behavior have burgeoned in psychology. In this article, we critically evaluate such high-level motivation constructs that many researchers define as causal determinants of behavior. We identify a fundamental issue with this predominant view of motivation, which we called the black-box problem. Specifically, high-level motivation constructs have been considered as causally instigating a wide range of higher-order behavior, but this does not explain what they actually are or how behavioral tendencies are generated. The black box problem inevitably makes the construct ill-defined and jeopardizes its theoretical status. To address the problem, we discuss the importance of mental computational processes underlying motivated behavior. Critically, from this perspective, motivation is not a unitary construct that causes a wide range of higher-order behavior --- it is an emergent property that people construe through the regularities of subjective experiences and behavior. The proposed perspective opens new avenues for future theoretical development, i.e., the examination of how motivated behavior is realized through mental computational processes.

The structure of trimethylsulfoxonium-exchanged vermiculite has been examined to compare it with other onium-exchanged structures, such as tetramethylammonium- and tetramethylphosphonium-exchanged vermiculite. The three organic cations are tetrahedral in shape, but trimethylsulfoxonium [(CH3)3SO+] has an oxygen atom replacing a methyl group at one apex. This study describes the effect this substitution and the larger S atom have on the site location in the interlayer and the effect on the vermiculite 2:1 layer. These clay minerals may be commercially useful as adsorbents.

Na-exchanged crystals of vermiculite from Santa Olalla, Spain, were intercalated with trimethylsulfoxonium [Me3SO+ = (CH3)3SO+] molecules by refluxing in an aqueous 0.25 M trimethylsulfoxonium iodide solution at 80°C for 14 days. The resulting Me3SO+-exchanged vermiculite crystals were studied by single-crystal, X-ray diffraction methods and by computer modeling (density functional theory). Cell parameters are a = 5.349(2), b = 9.270(3), c = 13.825(8) Å, and β = 97.40(4)°, the space group is C2/m, and the polytype is 1M. Refinement results (R = 0.073, wR = 0.080) show that in the average structure of C2/m, the S atoms of the Me3SO+ molecules form two partially occupied planes [2.066(2) Å from each basal oxygen plane] between the 2:1 layers, and the S atoms show considerable positional disorder. The O atom of the Me3SO+ molecule occurs in the central plane of the interlayer, as far away from each 2:1 layer as possible. In projection down the c* axis, the Me3SO+ molecule resides within the center of the silicate rings from each adjacent 2:1 layer. In the ideal (static) model of the Me3SO+-exchanged vermiculite structure, the Me3SO+ molecule is oriented such that two methyl groups point toward charge-deficient bridging oxygen atoms of the basal plane; thus, the organic pillars charge compensate the bridging oxygen atoms of the 2:1 layer that are charge deficient. In projection, the oxygen atom of the Me3SO+ molecule projects over a tetrahedron containing Si. Computer modeling showed that if H2O is not included in the model, the Me3SO+ molecule (S and O atoms) is in the center of the interlayer, but with the addition of randomly placed H2O, two partially occupied planes similar to the X-ray derived model are formed.

Sorption of U(VI) on clay and related minerals has been inspected experimentally and computationally because of its central role in safety considerations of geological repositories for highly radioactive waste. Np(V), which also has long half-life isotopes, has received considerably less attention. The purpose of the present study was to investigate computationally the adsorption of Np(V) on a clay-mineral surface and to compare it to adsorption of U(VI). As a sample case study, adsorption of Np(V) at the (110) edge surface of the common clay mineral montmorillonite was modeled. The density functional approach and periodic supercell models were applied. Mono- and bidentate adsorption complexes with coordination numbers 4 and 5 were inspected and compared to corresponding U(VI) species. While U(VI) prefers bidentate adsorption complexes with varying coordination numbers, Np(V) is more stable when monodentate-coordinated with a coordination number of four. In line with its smaller hydrolysis constant in aqueous solution, Np(V) shows a lower tendency to form monohydroxides on the mineral surface compared to U(VI). As no experimental geometry parameters are available for Np(V) adsorbed on montmorillonite, the results were compared tentatively to EXAFS data for adsorption at kaolinite and good agreement for the geometry changes due to adsorption was found for the more preferred adsorbed species.

The curse of dimensionality confounds the comprehensive evaluation of computational structural mechanics problems. Adequately capturing complex material behavior and interacting physics phenomenon in models can lead to long run times and memory requirements resulting in the need for substantial computational resources to analyze one scenario for a single set of input parameters. The computational requirements are then compounded when considering the number and range of input parameters spanning material properties, loading, boundary conditions, and model geometry that must be evaluated to characterize behavior, identify dominant parameters, perform uncertainty quantification, and optimize performance. To reduce model dimensionality, global sensitivity analysis (GSA) enables the identification of dominant input parameters for a specific structural performance output. However, many distinct types of GSA methods are available, presenting a challenge when selecting the optimal approach for a specific problem. While substantial documentation is available in the literature providing details on the methodology and derivation of GSA methods, application-based case studies focus on fields such as finance, chemistry, and environmental science. To inform the selection and implementation of a GSA method for structural mechanics problems for a nonexpert user, this article investigates five of the most widespread GSA methods with commonly used structural mechanics methods and models of varying dimensionality and complexity. It is concluded that all methods can identify the most dominant parameters, although with significantly different computational costs and quantitative capabilities. Therefore, method selection is dependent on computational resources, information required from the GSA, and available data.

Finnish nonfinite clauses constitute a complex grammatical class with a seemingly chaotic mix of verbal and nominal properties. Thirteen nonfinite constructions, their selection, control, thematic role assignment, nonfinite agreement, embedded subjects, and syntactic status were targeted for analysis. An analysis is proposed which derives their syntactic and semantic properties by relying on a computational model of human information processing. The model analyzes Finnish nonfinite constructions as truncated clauses with one functional layer above the verb phrase. Research methods from naturalistic cognitive science and computational linguistics are considered as potentially useful tools for linguistics.

Computational cognitive modeling is a tool we can use to evaluate theories of syntactic acquisition. Here, I review several models implementing theories that integrate information from both linguistic and non-linguistic sources to learn different types of syntactic knowledge. Some of these models additionally consider the impact of factors coming from children’s developing non-linguistic cognition. I discuss some existing child behavioral work that can inspire future model-building, and conclude by considering more specifically how to build better models of syntactic acquisition.

Some epistemic agents will not change their position on a claim. These are dogmatists, common creatures in our epistemic communities. This paper discusses the population-level epistemic effects of increasing numbers of dogmatists. All agents in the model are assigned a degree of belief (using a Likert-type scale) and adopt the beliefs of others in interactions. Subsets of agents are dogmatists. Analysis of model results suggests that even a modest increase in a group's dogmatists can have substantial effects on belief spread. I conclude by arguing that the model (a) helps identify two kinds of dogmatists and (b) suggests another way epistemic bubbles can form.

The cardiac sarcomere is a cellular structure in the heart that enables muscle cells to contract. Dozens of proteins belong to the cardiac sarcomere, which work in tandem to generate force and adapt to demands on cardiac output. Intriguingly, the majority of these proteins have significant intrinsic disorder that contributes to their functions, yet the biophysics of these intrinsically disordered regions (IDRs) have been characterized in limited detail. In this review, we first enumerate these myofilament-associated proteins with intrinsic disorder (MAPIDs) and recent biophysical studies to characterize their IDRs. We secondly summarize the biophysics governing IDR properties and the state-of-the-art in computational tools toward MAPID identification and characterization of their conformation ensembles. We conclude with an overview of future computational approaches toward broadening the understanding of intrinsic disorder in the cardiac sarcomere.

It is a widely held view that people rely on incomplete information to find a relationship partner, resulting in non-compensatory choice heuristics. However, recent experimental work typically finds that partner choice follows compensatory choice strategies. To bridge this gap between theory and experimental evidence, we characterize the mate choice problem by distinguishing the information search process from the evaluation process. In an eye-tracking experiment and a MouseLab experiment, we show that people display strong value-directed search heuristics in response to all types of cues and that the magnitude of value-directed searches increases with cue primacy. Cue primacy also explains the interaction effect of cue type and participant sex on the extent of valued-directed search. We further argue that value-directed searching does not necessarily lead to non-compensatory choice rules but may serve compensatory decision-making. Our results demonstrate that people may adopt remarkably smart search heuristics to find an ideal partner efficiently.

In this paper, I create a simulation model that predicts the portfolio of judges the president chooses to fill vacancies in the judiciary. I find that the president’s strategy in terms of appointments depends on constraint from the Senate, the talent pool of possible judges to appoint, the ideology of the courts in the judiciary, and the number of vacancies to be filled. The model is successful in replicating results that have been found in previous research, while also generating new hypotheses about previously unexplored aspects of the appointment process.