Context effects in multi-alternative, multi-attribute choice are pervasive and yet, paradoxically, elusive at the same time. For example, simple changes to the spatial layout of alternatives on the screen can nullify or reverse the effects. Despite the success of dynamic decision models in explaining the occurrence of context effects, a coherent theory for understanding their elusiveness is currently lacking. We introduce a novel theoretical framework that relies on attention modulated comparisons to explain the elusive nature of context effects. We show via simulation that our model produces the attraction, compromise, and similarity effects simply by assuming that more time is spent comparing alternatives that are more similar. However, when more time is spent comparing dissimilar alternatives, model simulations reveal a reversal of the attraction and compromise effects. The empirical support for this model-based prediction is assessed by manipulating similarity-based attention in separate experiments for the three context effects (total N = 887). Further, by allowing the spatial organization of information to constrain the attention process, the model can explain changes in context effects induced by display layout. We show that the model's spatial attention mechanism allows it to capture presentation order effects in a reanalysis of previously published data. Finally, we develop a continuous approximation of the full model that permits fitting of choices and response times. In summary, the proposed framework provides a new tool for understanding not only the existence of context effects in choice, but also the attentional factors that lead to null or reversed context effects.

In this study, we integrate the established obstacle problem formulation from ice sheet modeling with cutting-edge deep learning methodologies to enhance ice thickness predictions, specifically targeting the Greenland ice sheet. By harmonizing the mathematical structure with an energy minimization framework tailored for neural network approximations, our method's efficacy is confirmed through both 1D and 2D numerical simulations. Utilizing the NSIDC-0092 dataset for Greenland and incorporating bedrock topography for model pre-training, we register notable advances in prediction accuracy. Our research underscores the potent combination of traditional mathematical models and advanced computational techniques in delivering precise ice thickness estimations.

Glucose-stimulated insulin secretion (GSIS) in pancreatic β cells is vital to metabolic homeostasis. Recent evidence has highlighted the critical role of the cells' microtubule (MT) cytoskeleton in regulating transport and availability of insulin containing vesicles. How these vesicles move within the cell and how that mobility is influenced by the MT network is however not well understood. The MT network in these cells is dense and randomly oriented. Further insulin vesicles are relatively large compared to the spaces in this dense meshwork. Here we develop a 3D computational model that simulates vesicle motions in the dense MT network of the β cell. The structure of this MT network, along with the dynamics of vesicle motions, are calibrated to microscopy data from β cells to ensure physiological relevance. Our results reveal a number of key observations. 1) The MT network in β cells likely impairs motion of larger vesicles (200 - 300nm in diameter). 2) This is in part a consequence of their "caging" by the MT network. 3) This results in a substantial reduction in the likelihood of vesicles transiting from the cells interior to the plasma membrane, a pre-cursor to GSIS. 4) Dynamic remodeling of the MT network reduces the strength of these effects. 5) That same remodeling however introduces anomalous (sub-diffusion) motion characteristics. Taken together, these results indicate that the dense MT network of the β cell substantially inhibits mobility and availability (for GSIS) of insulin. It further sheds light on how the complex filament network in cells leads to statistically anomalous motions. Finally, this modeling further provides a test-bed for determining how potential manipulations of the structure and dynamics of this network would tune GSIS.

Context effects in multi-alternative, multi-attribute choice are widely documented, but often elusive. We show that this elusiveness can arise in part from the way that choices are presented. To illustrate this, we use a modeling framework to predict how changes to the format of attribute values, specifically the commensurability of attribute values, influences attention allocation and consequently context effects. Guided by this framework, we show in two online choice experiments (total N = 954 adults) that manipulating the commensurability of attributes leads to different patterns of context effects. Robust attraction and compromise effects are found when attributes are incommensurable (e.g., CPU speed in GHz and RAM memory in GB, or quality ratings on different scales), and mostly null effects occur when attributes are commensurable (e.g., quality ratings on the same scale). Our findings show how the format of choice information can substantially alter the integration of that information and resulting choice patterns.

In early March 2020, two crises emerged: the COVID-19 public health crisis and a corresponding economic crisis resulting from business closures and skyrocketing job losses. While the link between socioeconomic status and infectious disease is well-documented, the psychological relationships among economic considerations, such as financial constraint and economic anxiety, and health considerations, such as perceptions of disease spread and preventative actions, is not well understood. Despite past research illustrating the strong link between financial fragility and a wide range of behaviors, surprisingly little research has examined the psychological relationship between the economic crisis and beliefs and behaviors related to the co-occurring health crisis. We show that financial constraint predicts people’s beliefs about both their personal risk of infection and the national spread of the virus as well as their social distancing behavior. In addition, we compare the predictive utility of financial constraint to two other commonly studied factors: political partisanship and local disease severity. We also show that negative affect partially mediates the relationship between financial constraint and COVID-19 beliefs and social distancing behaviors. These results suggest the economic crisis created by COVID-19 spilled over into people’s beliefs about the health crisis and their behaviors.

Many decision-making theories are encoded in a class of processes known as evidence accumulation models (EAM). These assume that noisy evidence stochastically accumulates until a set threshold is reached, triggering a decision. One of the most successful and widely used of this class is the Diffusion Decision Model (DDM). The DDM however is limited in scope and does not account for processes such as evidence leakage, changes of evidence, or time varying caution. More complex EAMs can encode a wider array of hypotheses, but are currently limited by computational challenges. In this work, we develop the Python package PyBEAM (Bayesian Evidence Accumulation Models) to fill this gap. Toward this end, we develop a general probabilistic framework for predicting the choice and response time distributions for a general class of binary decision models. In addition, we have heavily computationally optimized this modeling process and integrated it with PyMC, a widely used Python package for Bayesian parameter estimation. This 1) substantially expands the class of EAM models to which Bayesian methods can be applied, 2) reduces the computational time to do so, and 3) lowers the entry fee for working with these models. Here we demonstrate the concepts behind this methodology, its application to parameter recovery for a variety of models, and apply it to a recently published data set to demonstrate its practical use.

The clustering of membrane-bound proteins facilitates their transport by cortical actin flow in early Caenorhabditis elegans embryo cell polarity. PAR-3 clustering is critical for this process, yet the biophysical processes that couple protein clusters to cortical flow remain unknown. We develop a discrete, stochastic agent-based model of protein clustering and test four hypothetical models for how clusters may interact with the flow. Results show that the canonical way to assess transport characteristics from single-particle tracking data used thus far in this area, the Péclet number, is insufficient to distinguish these hypotheses and that all models can account for transport characteristics quantified by this measure. However, using this model, we demonstrate that these different cluster–cortex interactions may be distinguished using a different metric, namely the scalar projection of cluster displacement on to the flow displacement vector. Our results thus provide a testable way to use existing single-particle tracking data to test how endogenous protein clusters may interact with the cortical flow to localize during polarity establishment. To facilitate this investigation, we also develop both improved simulation and semi-analytic methodologies to quantify motion summary statistics (e.g., Péclet number and scalar projection) for these stochastic models as a function of biophysical parameters.

Adherens junctions physically link two cells at their contact interface via extracellular binding between cadherin molecules and intracellular interactions between cadherins and the actin cytoskeleton. Cadherin and actomyosin cytoskeletal dynamics are regulated reciprocally by mechanical and chemical signals, which subsequently determine the strength of cell-cell adhesions and the emergent organization and stiffness of the tissues they form. However, an understanding of the integrated system is lacking. We present a new mechanistic computational model of intercellular junction maturation in a cell doublet to investigate the mechanochemical cross talk that regulates adherens junction formation and homeostasis. The model couples a two-dimensional lattice-based simulation of cadherin dynamics with a reaction-diffusion representation of the reorganising actomyosin network through its regulation by Rho signalling at the intracellular junction. We demonstrate that local immobilization of cadherin induces cluster formation in a cis-less-dependent manner. We then recapitulate the process of cell-cell contact formation. Our model suggests that cortical tension applied on the contact rim can explain the ring distribution of cadherin and actin filaments (F-actin) on the cell-cell contact of the cell doublet. Furthermore, we propose and test the hypothesis that cadherin and F-actin interact like a positive feedback loop, which is necessary for formation of the ring structure. Different patterns of cadherin distribution were observed as an emergent property of disturbances of this positive feedback loop. We discuss these findings in light of available experimental observations on underlying mechanisms related to cadherin/F-actin binding and the mechanical environment.

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Ventral stress fibers (VSFs) are contractile actin fibers dynamically attached to cell-matrix focal adhesions. VSFs are critical in cellular traction force production and migration. VSFs vary from randomly oriented short, thinner fibers to long, thick fibers that span along the whole long axis of a cell. De novo VSF formation was shown to occur by cortical actin mesh condensation or by crosslinking of dorsal stress fibers and transverse arcs at the cell front. However, the formation of long VSFs that extend across the whole cell axis is not well understood. Here, we report a novel phenomenon of VSF merging in migratory fibroblast cells, which is guided by mechanical force balance and contributes to VSF alignment along the long cell axis. The mechanism of VSF merging involves two steps: connection of two ventral fibers by an emerging myosin II bridge at an intervening adhesion and intervening adhesion dissolution. Our data indicate that these two steps are interdependent: slow adhesion disassembly leads to the slowing of the myosin bridge formation. Cellular data and computational modeling show that the contact angle between merging fibers decides successful merging, with shallow angles leading to merge failure. Our data and modeling further show that merging increases the share of uniformly aligned long VSFs, likely contributing to directional traction force production. Thus, we characterize merging as a process for dynamic reorganization of VSFs with functional significance for directional cell migration.

One of the most interesting findings in multialternative, multiattribute choice is the occurrence of "context effects," where the relative preference between two alternatives is influenced by a third, decoy alternative. Although context effects have been well documented, the circumstances necessary for them to occur is a puzzle. Sometimes, the effects disappear or reverse, suggesting they are fragile. We hypothesize this fragility is partly due to a sequential comparison process where subsets of options are compared and evaluated during deliberation. Thus, manipulating the comparison of options (by manipulating presentation order of options) should influence the manifestation of these effect. We test this hypothesis in two ways for the attraction effect. First, we develop a temporally sequential version of a well-established perceptual context effects paradigm, where participants only see 1 alternative at a time. Results show that presentation order strongly influences choices. In some orders, a standard attraction effect occurs and in others, a reversal (or "repulsion") effect occurs. A piecewise extension of the Multiattribute Linear Ballistic Accumulator model explains our results by showing how forgetting (due to the sequential presentation) influences the comparison of options. Quantitative fitting demonstrates that the model explains well the data, outperforming alternative heuristic models. Second, we reanalyze attraction effect data from Trueblood et al. (2015) where the spatial order of options was manipulated. Similar to the sequential task, results show a complex pattern of attraction and repulsion effects. Taken together, these results indicate that a malleable comparison process contributes to the fragility of context effects.

Heterogeneity of glucose-stimulated insulin secretion (GSIS) in pancreatic islets is physiologically important but poorly understood. Here, we utilize mouse islets to determine how microtubules (MTs) affect secretion toward the vascular extracellular matrix at single cell and subcellular levels. Our data indicate that MT stability in the β-cell population is heterogenous, and that GSIS is suppressed in cells with highly stable MTs. Consistently, MT hyper-stabilization prevents, and MT depolymerization promotes the capacity of single β-cell for GSIS. Analysis of spatiotemporal patterns of secretion events shows that MT depolymerization activates otherwise dormant β-cells via initiation of secretion clusters (hot spots). MT depolymerization also enhances secretion from individual cells, introducing both additional clusters and scattered events. Interestingly, without MTs, the timing of clustered secretion is dysregulated, extending the first phase of GSIS and causing oversecretion. In contrast, glucose-induced Ca2+ influx was not affected by MT depolymerization yet required for secretion under these conditions, indicating that MT-dependent regulation of secretion hot spots acts in parallel with Ca2+ signaling. Our findings uncover a novel MT function in tuning insulin secretion hot spots, which leads to accurately measured and timed response to glucose stimuli and promotes functional β-cell heterogeneity.

Rho GTPases such as Rho, Rac, and Cdc42 are important regulators of the cortical cytoskeleton in processes including cell division, locomotion, and repair. In these processes, Rho GTPases assume characteristic patterns wherein the active GTPases occupy mutually exclusive "zones" in the cell cortex. During cell wound repair, for example, a Rho zone encircles the wound edge and is in turn encircled by a Cdc42 zone. Here we evaluated the contributions of cross-talk between Rho and Cdc42 to the patterning of their respective zones in wounded Xenopus oocytes using experimental manipulations in combination with mathematical modeling. The results show that the position of the Cdc42 zone relative to the Rho zone and relative to the wound edge is controlled by the level of Rho activity. In contrast, the outer boundary of the Rho zone is limited by the level of Cdc42 activity. Models based on positive feedback within zones and negative feedback from Rho to the GEF-GAP Abr to Cdc42 capture some, but not all, of the observed behaviors. We conclude that GTPase zone positioning is controlled at the level of Rho activity and we speculate that the Cdc42 zone or something associated with it limits the spread of Rho activity.

Many important real-world decision tasks involve the detection of rarely occurring targets (e.g., weapons in luggage, potentially cancerous abnormalities in radiographs). Over the past decade, it has been repeatedly demonstrated that extreme prevalence (both high and low) leads to an increase in errors. While this "prevalence effect" is well established, the cognitive and/or perceptual mechanisms responsible for it are not. One reason for this is that the most common tool for analyzing prevalence effects, Signal Detection Theory, cannot distinguish between different biases that might be present. Through an application to pathology image-based decision-making, we illustrate that an evidence accumulation modeling framework can be used to disentangle different types of biases. Importantly, our results show that prevalence influences both response expectancy and stimulus evaluation biases, with novices (students, N = 96) showing a more pronounced response expectancy bias and experts (medical laboratory professionals, N = 19) showing a more pronounced stimulus evaluation bias.

The chemical master equation (CME), which describes the discrete and stochastic molecule number dynamics associated with biological processes like transcription, is difficult to solve analytically. It is particularly hard to solve for models involving bursting/gene switching, a biological feature that tends to produce heavy-tailed single cell RNA counts distributions. In this paper, we present a novel method for computing exact and analytic solutions to the CME in such cases, and use these results to explore approximate solutions valid in different parameter regimes, and to compute observables of interest. Our method leverages tools inspired by quantum mechanics, including ladder operators and Feynman-like diagrams, and establishes close formal parallels between the dynamics of bursty transcription, and the dynamics of bosons interacting with a single fermion. We focus on two problems: (i) the chemical birth-death process coupled to a switching gene/the telegraph model, and (ii) a model of transcription and multistep splicing involving a switching gene and an arbitrary number of downstream splicing steps. We work out many special cases, and exhaustively explore the special functionology associated with these problems. This is Part I in a two-part series of papers; in Part II, we explore an alternative solution approach that is more useful for numerically solving these problems, and apply it to parameter inference on simulated RNA counts data.

Migratory cells are known to adapt to environments that contain wide-ranging levels of chemoattractant. Although biochemical models of adaptation have been previously proposed, here, we discuss a different mechanism based on mechanosensing, in which the interaction between biochemical signaling and cell tension facilitates adaptation. We describe and analyze a model of mechanochemical-based adaptation coupling a mechanics-based physical model of cell tension coupled with the wave-pinning reaction-diffusion model for Rac GTPase activity. The mathematical analysis of this model, simulations of a simplified one-dimensional cell geometry, and two-dimensional finite element simulations of deforming cells reveal that as a cell protrudes under the influence of high stimulation levels, tension-mediated inhibition of Rac signaling causes the cell to polarize even when initially overstimulated. Specifically, tension-mediated inhibition of Rac activation, which has been experimentally observed in recent years, facilitates this adaptation by countering the high levels of environmental stimulation. These results demonstrate how tension-related mechanosensing may provide an alternative (and potentially complementary) mechanism for cell adaptation.

In 2000, Gillespie rehabilitated the chemical Langevin equation (CLE) by describing two conditions that must be satisfied for it to yield a valid approximation of the chemical master equation (CME). In this work, we construct an original path-integral description of the CME and show how applying Gillespie's two conditions to it directly leads to a path-integral equivalent to the CLE. We compare this approach to the path-integral equivalent of a large system size derivation and show that they are qualitatively different. In particular, both approaches involve converting many sums into many integrals, and the difference between the two methods is essentially the difference between using the Euler-Maclaurin formula and using Riemann sums. Our results shed light on how path integrals can be used to conceptualize coarse-graining biochemical systems and are readily generalizable.

Over the last decade, there has been a robust debate in decision neuroscience and psychology about what mechanism governs the time course of decision-making. Historically, the most prominent hypothesis is that neural architectures accumulate information over time until some threshold is met, the so-called Evidence Accumulation hypothesis. However, most applications of this theory rely on simplifying assumptions, belying a number of potential complexities. Is changing stimulus information perceived and processed in an independent manner or is there a relative component? Does urgency play a role? What about evidence leakage? Although the latter questions have been the subject of recent investigations, most studies to date have been piecemeal in nature, addressing one aspect of the decision process or another. Here we develop a modeling framework, an extension of the Urgency Gating Model, in conjunction with a changing information experimental paradigm to simultaneously probe these aspects of the decision process. Using state-of-the-art Bayesian methods to perform parameter-based inference, we find that (a) information processing is relative with early information influencing the perception of late information, (b) time varying urgency and evidence accumulation are of roughly equal strength in the decision process, and (c) leakage is present with a time scale of ∼200-250 ms. We also show that these effects can only be identified in a changing information paradigm. To our knowledge, this is the first comprehensive study to utilize a changing information paradigm to jointly and quantitatively estimate the temporal dynamics of human decision-making. (PsycInfo Database Record (c) 2020 APA, all rights reserved).