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The modeling of fibrotic effects on fluid flow and metabolism in the liver can be computationally challenging. This paper combines innovative concepts based on fundamental physics to address such issues at the level of the liver functional unit, the lobule, and upscales and extends this to a multi-lobule tissue scale analysis. Fibrosis effects on fluid flow and metabolism are introduced using percolation theory and its consequences are explored for single lobule and multi-lobule patterns, without and with distortion. [ABSTRACT FROM AUTHOR]

Optimizing open-end piles is crucial for sustainability as it minimizes material consumption and reduces environmental impact. By improving construction efficiency, less steel is needed, reducing the carbon footprint associated with production and transportation. Improved pile performance also results in more durable structures that require less frequent replacement and maintenance, which in turn saves resources and energy. This paper presents a parametric study on optimal designs for open-ended piles in sand, presenting a novel approach to directly compute optimal pile designs using CPT results. It addresses challenges posed by soil variability and layered conditions, with the optimization model accounting for interdependencies among pile length, diameter, wall thickness and soil properties, including the pile–soil plug system. A mixed-integer optimization model OPEN-Pile was developed, consisting of an objective function for pile mass and CO2 emissions. The objective function was constrained by a set of design and geotechnical conditions that corresponded to current codes of practice and recommendations. The efficiency of the developed optimization model is illustrated by two case studies. In the case of Blessington sand, the calculation results show that it is more economical and environmentally friendly to increase the pile diameter and pile wall thickness than the pile length. In efficient design, the ratio between diameter and wall thickness is calculated at the upper limit. For the optimum design of piles in Blessington sand, the optimum ratios of pile length to diameter, diameter to wall thickness and length to wall thickness are 5, 50 and 250, respectively. In a layered soil profile, the decision of where to place the pile base depends on the resistance of the cone tip and the thickness of the individual layers. To determine in which layer the pile base should be placed, we need to perform an optimization for the given design data. [ABSTRACT FROM AUTHOR]

Simple Summary: Bacillus subtilis is a model organism used to study molecular processes in Gram-positive bacteria. Sigma factor B (σB, SigB) is a major regulator of gene expression in response to various stresses. SigB itself is controlled by a network involving several other factors. In this paper, I focused on computational modeling of their interactions and analyzed how these interactions influence the production of SigB and other components of the network. Understanding the detailed functioning of such a network and the concept of its analysis helps to comprehend molecular processes occurring in the cell. Bacillus subtilis is a model organism used to study molecular processes in Gram-positive bacteria. Sigma factor B, which associates with RNA polymerase, is one of the transcriptional regulators involved in the cell's response to environmental stress. Experiments have proven that the amounts of free σB (SigB) are controlled by a system of anti- (RsbW) and anti-anti-sigma (RsbV) factors expressed from the same operon as SigB. Moreover, the phosphorylation state of RsbV is controlled by phosphatases RsbP and RsbU, which directly dephosphorylate RsbV. A set of chemical equations describing the network controlling the levels of free SigB was converted to a set of differential equations quantifying the dynamics of the network. The solution of these equations allowed the simulation of the kinetic behavior of the network and its components under real conditions reflected in the time series of protein expression. In this study, the time series of protein expression measured by mass spectrometry were utilized to investigate the role of phosphatases RsbU/RsbP in transmitting the environmental signal. Additionally, the influence of kinetic constants and the amounts of other network components on the functioning of the network was investigated. A comparison with the same simulation performed using a transcriptomic dataset showed that while the time series between the proteomic and transcriptomic datasets are not correlated, the results are the same. This indicates that when modeling is performed within one dataset, it does not matter whether the data come from the mRNA or protein level. In summary, the computational results based on experimental data provide a quantitative insight into the functioning of the SigB-dependent circuit and offer a template for the quantitative study of similar systems. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing/planarization (CMP) combines physical grinding and chemical reactions to planarize the wafer surface. The complex mechanism of CMP brings great challenges to the mechanism-based modeling process. The data-driven CMP modeling process is limited by insufficient datasets. At the same time, these two types of models generally have high computational complexity. In this paper, we introduce the group method of data handling (GMDH)-type polynomial network to build the CMP model to address the above challenges. We designed and manufactured the test chip using a 28nm process. The measurement data from the test chip shows that compared with the mechanism-based CMP model, the trained CMP model based on GMDH-type polynomial network has higher accuracy and lower computational complexity, with the average simulation speed being 115x faster. Experiments based on silicon data show that this modeling method has a small demand for data, and 20 randomly selected sets of data can meet the needs for modeling the current CMP process. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of natural fiber-reinforced composites (NFRCs) for lower-limb prosthetic designs. It covers the characteristics, types, and properties of natural fiber-reinforced composites as well as their advantages and drawbacks in prosthetic designs. This review also discusses successful prosthetic designs that incorporate NFRCs and the factors that make them effective. Additionally, this study explores the use of computational biomechanical models to evaluate the effectiveness of prosthetic devices and the key factors that are considered. Overall, this document provides a valuable resource for anyone interested in using NFRCs for lower-limb prosthetic designs. [ABSTRACT FROM AUTHOR]

This paper presents an initial study of the computational modeling for determining the stress–strain behavior of the cord–rubber segment, tailored to be used as a flexible member in the construction of torque transfer coupling in railway vehicles. The presented computational models use the multibody simulation approach for the assessment of segment deformation under diverse loading characteristics. The results are then used in the finite element computational models of the cord–rubber segment. The computational model is validated through tension tests of cord–rubber segments. The homogenized and microstructural models show their ability to simulate the overall stiffness of the segment relatively accurately in tension, but they are limited in not being able to include the influence of the segment's manufacturing technology. The micro-scale computational model then incorporates realistic representations of cord bundles, considering various cross-sectional shapes, whereas a very important factor significantly influencing the correlation of the results of the technical experiment and simulation is the consideration of the manufacturing technology in the process of building the computational model. The results show that the computational modeling approach in this work can be used for the determination of the optimal manufacturing technology conditions with regard to the stress–strain behavior of the given segment. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing that are grounded in macro- and mesoscopic anatomical properties of the network's biological counterpart. Whereas, goal-driven modeling is already becoming prevalent in the neuroscience of perception, its application to the sensorimotor domain is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. This paper describes AngoraPy, a Python library that mitigates this obstacle by providing researchers with the tools necessary to train complex recurrent convolutional neural networks that model the human sensorimotor system. To make the technical details of this toolkit more approachable, an illustrative example that trains a recurrent toy model on in-hand object manipulation accompanies the theoretical remarks. An extensive benchmark on various classical, 3D robotic, and anthropomorphic control tasks demonstrates AngoraPy's general applicability to a wide range of tasks. Together with its ability to adaptively handle custom architectures, the flexibility of this toolkit demonstrates its power for goal-driven sensorimotor modeling. [ABSTRACT FROM AUTHOR]

Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms. [ABSTRACT FROM AUTHOR]

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]

This paper explores the use of linguistic strategies, specifically discourse markers like 'but', to express contrasts between expectations and reality when faced with unexpected events. The study concentrates on Denial of Expectation (DofE), themost powerful form of contrast, which arises when the expected value based on background assumptions is not met. The main focus of this paper is to model DofE as a weighted homogeneous relationship between object properties. The aim is to predict DofE for numerical properties in specific contexts. I aim to address a gap in previous models by considering the role of context. This is achieved by analyzing contrastive sentences from German car and motorcycle reviews. The research presents the concept of expectation intervals for scalar properties. These intervals align with expectations and exceeding them triggers a potential contrast. The study incorporates causality, expected behavior, and a shift function in selecting contrastive pairs, transforming the conditions into an algorithm.Keywords: contrast; computational and cognitive modeling; discourse analysis

To conceptualise biological and mental processes, often a dynamical systems perspective is suggested. In addition to dynamics, the structure of the contextual makeup or world configuration (of an organism or brain) plays a crucial role too, as well as adaptivity of the processes. This paper provides a conceptual perspective where the structure, dynamics, and adaptivity of these processes are distinguished and related to each other via adaptive dynamical systems. Moreover, it is shown how networks can be used to represent this conceptual perspective. Here an adaptive dynamical system of any order of adaptivity can be covered where any level can exert control over the level below. The approach is illustrated by case studies for higher-order adaptive evolutionary processes. One of these case studies shows a fifth-order adaptive dynamical system that models how due to bad environmental influences at a young age, epigenetic effects can lead to a lifelong mental disorder.

Cognitive processes underlie economic relations. In this paper, we develop a conceptual, mathematical, and computational framework for modeling market exchange as a series of dynamically interacting cognitive processes. Specifically, we show how advertisers can build trust and gain confidence in their pricing power to the point that they erode trust and undermine the efficacy of their advertising. Customers conversely orient towards advertisers seeking information or turn away from them as unreliable communicators. These behaviors and the patterns they generate occur inside a state space of unallocated perceived value. They constitute a small subset of the full range of possible strategic and adaptive responses that define cognitive microeconomics.

Computational psycholinguistics has traditionally employed a complexity metric called Node Count, which counts the number of syntactic nodes representing syntactic structures and predicts processing costs in human sentence processing. However, Node Count does not dissociate distinct syntactic operations deriving those syntactic structures, so that how much processing cost each syntactic operation induces remains to be investigated. In this paper, we introduce a novel complexity metric dubbed Composition Count, which counts the number of syntactic operations deriving syntactic structures, allowing us to understand the computational system of human sentence processing from the derivational, not representational, perspective. Specifically, employing Combinatory Categorial Grammar (CCG) which is equipped with multiple syntactic operations and thus suitable for the purpose here, we investigate (i) how much distinct syntactic operations of CCG contribute to predicting human reading times, and (ii) whether the same holds across languages. The results demonstrate that distinct syntactic operations of CCG have independent and cross-linguistic contributions to predicting human reading times, while Node Count turns out not to be robust cross-linguistically. In conclusion, these results strongly suggest the importance of Composition Count to dissociate distinct syntactic operations, not whole syntactic representations, and understand the computational system of human sentence processing.

Advancements in deep neural networks have led to significant progress in computer vision and natural language processing. These networks, trained on real-world stimuli, develop high-level feature representations of stimuli. It is hypothesized that these representations, stemming from different inputs, should converge into similar conceptual systems, as they reflect various perspectives of the same underlying reality. This paper examines the degree to which different conceptual systems can be aligned in an unsupervised manner, using feature-based representations from deep neural networks. Our investigation centers on the alignment between the image and word representations produced by diverse neural networks, emphasizing those trained via self-supervised learning methods. Subsequently, to probe comparable alignment patterns in human learning, we extend this examination to models trained on developmental headcam data from children. Our findings reveal a more pronounced alignment in models trained through self-supervised learning compared to supervised learning, effectively uncovering higher-level structural connections among categories. However, this alignment was notably absent in models trained with limited developmental headcam data, suggesting more data, more inductive biases, or more supervision are needed to establish alignment from realistic input.

Studies of meaning in human and primate communication face, in principle, similar methodological problems. In both cases, meaning is not observable directly, but must be inferred from more indirect sources, such as directly observable behavior. Recent work in probabilistic cognitive modeling of language use has therefore developed methods of inferring latent se- mantic meaning through the lens of a probabilistic model of language use. In this paper, we explore how to adapt such an approach for insightful investigations of primate communication. Towards this end, we develop a suitable probabilistic model of processes that generate communicative behavior by making use of functionally specified latent meaning representations. As a proof of concept, we apply this model to a rich, annotated data set of orangutan communicative dyadic interaction and conclude that explicit probabilistic modeling can provide additional insights for the study of animal communication pertaining to the context-dependent nature of signals and the gradual evolution of human communication systems.

This paper uses phylogenetic modeling to investigate the evolutionary mechanisms responsible for the maintenance of sound symbolism in the world's languages. Applying our model to sound-meaning correspondences reported in the literature, we find that many previously established associations are weaker than expected when analyzed using our framework. This is possibly because certain sound-meaning associations are artifacts of slow-changing vocabulary items rather than specific preferences for certain sounds in words with certain meanings. For sound-meaning associations for which we find evidence, the maintenance of sound symbolism appears to be due to a tendency to preserve words in certain meanings if certain sounds are present.

In this paper, we report three reading time (RT) experiments (one using self-paced reading and two using A-Maze) that tested the cognitive mechanisms underlying the processing of classifier-noun dependencies in Mandarin Chinese (MC). We leveraged prenominal relative clauses and the contrast between general and specific classifiers in MC, which offered a good testing ground for existing theories of sentence processing. Results from the A-Maze experiments showed both locality and expectation effects. More importantly, we observed an interaction between locality and expectation in the way of Information Locality (Futrell, 2019; Futrell, Gibson, & Levy, 2020): Expectation-driven facilitation was highly constrained by locality effects. To capture the results, we implemented a resource-rational Lossy-Context Surprisal model (Hahn et al., 2022) for MC, which successfully replicated the key patterns in the A-Maze experiments.

This study investigates cue-based memory retrieval during sentence processing. Cue-based retrieval theories argue that the parser uses lexical and structural information as retrieval cues to retrieve items from memory. Evidence for cue-based memory retrieval comes from research showing that non-target representations matching retrieval cues interfere with target retrieval. However, the degree of susceptibility to this similarity-based interference has been debated, having led to the development of different computational models. This study focuses on two cue-based models and tests their predictions in two experiments. The results suggested similarity-based interference, but its patterns were not fully compatible with these models. To reconcile these findings within the framework of cue-based memory retrieval, this paper presents a model that assigns substantial weight to the structure-based cue and incorporates the notions of initial retrieval and revision. Results from simulations indicate that the model incorporating these assumptions provides a better fit to the observed data.

To date, no account of lie-truth judgement formation has been capable of explaining how core cognitive mechanisms such as memory encoding and retrieval are employed to reach a judgement of either truth or lie. One account, the Adaptive Lie Detector theory (ALIED: Street, Bischof, Vadillo, & Kingstone, 2016) is sufficiently well defined that its assumptions may be implemented in a computational model. In this paper we describe our attempt to ground ALIED in the representations and mechanisms of the ACT-R cognitive architecture and then test the model by comparing it to human data from an experiment conducted by Street et al. (2016). The model provides a close fit to the human data and a plausible mechanistic account of how specific and general information are integrated in the formation of truth-lie judgements.

Humans learn and generate languages, music, games, and seemingly limitless varieties of other structures across domains. Unlike many AI systems, we often do so from little data. How do we learn such large varieties of richly structured representations so efficiently? One possibility is that people ``learn by programming,'' synthesizing data-generating algorithms to explain what we observe. We examine the nature and origins of this learning mechanism in adults, children, and nonhuman primates (macaque monkeys), using a highly unconstrained sequence prediction task. Although adults and children quickly learn many richly structured sequences, monkeys learn only the simplest sequences (e.g. lines). We test multiple learning models, finding that adults are best explained by a ``Language of Thought''-like program-learning model and monkeys by a simpler extrapolation strategy. Children exhibit varied learning strategies but are best fit in aggregate by an intermediately expressive model. Paper available at https://sites.google.com/view/patternlearning.

Existing models of the basal ganglia assume the existence of separate channels of neuron populations for representing each available action. This type of localist mapping limits models to small, discrete action spaces, since additional actions require additional channels, costing neural resources and imposing new connective tracts. In contrast, evidence suggests that the basal ganglia plays a role in the selection of both discrete action units, and continuously-valued action kinematics. In this work, we model the basal ganglia with distributed action representations, using high-dimensional vectors. This method lends itself to representing both discrete and continuous action spaces. Vectors that represent actions are weighted by a scalar value (their salience to the current task), and bundled together to form a single input vector. This paper provides an overview of the encoding method and network structure, as well as a demonstration of the model solving an action selection task using spiking neurons.

The mechanisms of learning stimulus-stimulus relationships are a longstanding research subject in psychology and neuroscience. Although traditional computational models provide valuable insights into learning processes, they often focus on the average behavior of a population. Individual learning trajectories, however, exhibit a diverse range of behaviors not captured by these models. In this paper, we compare sampling-based process-level models (i.e., particle filters) to representative associative and causal models (i.e., augmented Rescorla-Wagner and PowerPC) in their ability to capture individual learning behavior. We use likelihood-free inference incorporating machine-learned summary statistics for model estimation. We conduct a simulation study to demonstrate high model identifiability and test the models on an existing dataset and a newly conducted experiment which replicates and extends previous studies. We find that most participants are best explained by a particle filtering account, but more targeted experimental designs are required to estimate the best-fitting sub-type of these particle filter models.

The human cognitive system exhibits remarkable flexibility and generalization capabilities, partly due to its ability to form low-dimensional, compositional representations of the environment. In contrast, standard neural network architectures often struggle with abstract reasoning tasks, overfitting, and requiring extensive data for training. This paper investigates the impact of the relational bottleneck—a mechanism that focuses processing on relations among inputs—on the learning of factorized representations conducive to compositional coding and the attendant flexibility of processing. We demonstrate that such a bottleneck not only improves generalization and learning efficiency, but also aligns network performance with human-like behavioral biases. Networks trained with the relational bottleneck developed orthogonal representations of feature dimensions latent in the dataset, reflecting the factorized structure thought to underlie human cognitive flexibility. Moreover, the relational network mimics human biases towards regularity without pre-specified symbolic primitives, suggesting that the bottleneck fosters the emergence of abstract representations that confer flexibility akin to symbols.

Language often has different ways to express the same or similar meanings. Despite this, however, people seem to have preferences for some ways over others. For example, people overwhelmingly prefer bread and butter to butter and bread. Previous research has demonstrated that these ordering preferences grow stronger with frequency (i.e., frequency-dependent preference extremity). In this paper we demonstrate that this frequency-dependent preference extremity can be accounted for by noisy-channel processing models (e.g., Gibson, Bergen, & Piantadosi, 2013; Levy, 2008). We also show that this preference extremity can only be accounted for if the listener infers more noise than the speaker produces. Finally, we show that the model can account for the language-wide distribution of binomial ordering preferences.

In English binomial expressions, “bread and butter” is preferred over “butter and bread”. Morgan & Levy (2015) show that for these types of expressions, frequently used phrases tend to have stronger, more extreme preferences. In contrast, there is roughly an equal preference for “bishops and rooks” versus “rooks and bishops”, a much less common pairing. This paper extends this research to the concept of Mandarin elastic word length, a phenomenon in which most Mandarin words have long and short forms. We find evidence for frequency-dependent regularization in the elastic length of Noun-Noun compounds in Chinese, demonstrating that frequency-dependent regularization extends to structures with more than two alternations and to languages other than English.

Index systems are central to our everyday and intellectual lives. Their ubiquity and diversity make them an important class of cognitive artifacts, the study of which has implications for our understanding of representational systems in general. This paper builds schema-theoretic network models of the nature of the memory structures, that underpin the interpretation of indexing systems. We identify four common classes of index systems. Using Representation Interpretation Structure Theory, we ex-plain how the four basic classes can be responsible for the substantial diversity among index systems.

The Wisconsin Card Sorting Test (WCST) is commonly used to assess executive (dys-)function, particularly in neuropsychological patients. Performance on the test typically yields two types of error: perseverative errors, where participants persist in applying an inferred rule despite negative feedback, and set-loss errors, were participants cease applying an inferred rule despite positive feedback. The two types of error are known to dissociate. In this paper we apply an existing model of the WCST -- the model of Bishara et al. (2010) -- to a novel dataset, focussing specifically on the distribution of the two types of error over the duration of the task. Using Maximum Likelihood Estimation to fit the model to the data, we argue that the model provides a good account of the performance of some participants, but a poor account of individual differences. It is argued that this is because the model is essentially a competence model which fails to incorporate performance factors, and that accounting for the different types of error, and in particular the error distribution during the task, requires incorporating performance factors into the model. Some consequences of this for the broader enterprise of developing normative competence models are discussed.

A key task in natural language generation (NLG) is Referring Expression Generation (REG), in which a set of properties are selected to describe a target referent. Computational cognitive models of REG typically focus on REG-in-context, where the referring expressions are designed to take into account the conversational context into which they are to be generated. However, in practice, these methods only focus on linguistic context of the text into which they are to be inserted. We argue that to develop robust models of naturalistic human referring, REG will need to move beyond linguistic context, and account for cognitive and environmental context as well. That is, we propose that a cognitivist, interactionist, and situated approach to modeling REG is needed. In this paper, we present GAIA, a Givenness Hierarchy theoretic model of REG, and demonstrate the immediate qualitative benefits of this model over the traditional REG model which it extends.

Extensive evidence from diverse areas of the cognitive sciences suggests that iconicity—the resemblance between form and its meaning—is pervasive and plays a pivotal role in the processing, memory, and evolution of human language. However, despite its acknowledged importance, iconicity in language models remains notably underexplored. This paper examines whether Japanese language models learn iconic associations between shape and sound, known as the bouba/kiki (or maluma/takete) effect, which has been widely observed in human language as well as English and multilingual vision-and-language models, including Finnish, Indonesian, Hungarian, and Lithuanian models in previous studies. A comparison between the current results and the previous studies revealed that Japanese models learn language-specific aspects of iconicity, such as the associations between /p/ and roundness, and /ɡ/ and hardness, reflecting the sound symbolic system in Japanese.

Self models contribute to key functional domains of human intelligence that are not yet presented in today's artificial intelligence. One important aspect of human problem-solving involves the use of conceptual self-knowledge to detect self-relevant information presented in the environment, which guides the subsequent retrieval of autobiographical memories that are relevant to the task at hand. This process enables each human to behave self-consistently in our own way across complex situations, manifested as self-interest and trait-like characteristics. In this paper, we outline a computational framework that implements the conceptual aspect of human self models through a modified version of the joint-embedding predictive architecture. We propose that through the incorporation of human-like autobiographical memory retrieval and self-importance evaluation, the modified architecture could support machine agents with significantly enhanced self-consistency, which could be applied to deliver more believable simulations of human behaviors.

In the field of medical decision-making, understanding the treatment intentions of clinicians is crucial for effective treatment strategies. However, these intentions are often implicit and challenging to quantify. In this paper, we propose a novel two-module model to infer and implement clinicians' treatment intentions through treatment records. We construct the InfCTI module, which infers intentions and quantifies them numerically, and the ImpCTI module, which generates treatment strategies based on inferred intentions. Our experiments demonstrate that the treatment strategies obtained by ImpCTI reflect clinicians' intentions and the intention values obtained by InfCTI are reasonable. This model has the potential to improve the quality of care provided to patients.

Human cognition significantly influences expressed behavior and is intrinsically tied to authentic personality traits. Personality assessment plays a pivotal role in various fields, including psychology, education, social media, etc. However, traditional self-report questionnaires can only provide data based on what individuals are willing and able to disclose, thereby lacking objective. Moreover, automated measurements and peer assessments demand significant human effort and resources. In this paper, given the advantages of the Virtual Reality (VR) technique, we develop a VR simulator --- PersonalityScanner, to stimulate cognitive processes and simulate daily behaviors based on an immersive and interactive simulation environment, in which participants carry out a battery of engaging tasks that formulate a natural story of first-day at work. Through this simulator, we collect a synchronous multi-modal dataset with ten modalities, including first/third-person video, audio, text, eye tracking, facial microexpression, pose, depth data, log, and inertial measurement unit. By systematically examining the contributions of different modalities on revealing personality, we demonstrate the superior performance and effectiveness of PersonalityScanner.

Computational models have long been used in Cognitive Science, but to date most research has used language models trained on text. With recent advances in Computer Vision, new research is expanding to visually informed models. In this paper, we explore the potential of such models to account for human naming behavior as recorded in naming norms (where subjects are asked to name visually presented objects). We compare the performance of three representative models on a set of norms that include stimuli in the form of line drawings, colored drawings, and realistic photos. The state-of-the-art Language and Vision model CLIP, trained on both text and images, performs best. It generalizes well across different types of stimuli and achieves good overall accuracy. CLIP affords both linguistic (text-based) and visual (image-based) representations for names, and we find that textual representations outperform visual representations. This is good news, as textual representations are easier to obtain than visual representations. All in all, our results show promise for the use of Computer Vision and Language and Vision models in Cognitive Science.

Despite the fact that beliefs are mental states that cannot be directly observed, humans talk about each others' beliefs on a regular basis, often using rich compositional language to describe what others think and know. What explains this capacity to interpret the hidden epistemic content of other minds? In this paper, we take a step towards an answer by grounding the semantics of belief statements in a Bayesian theory-of-mind: By modeling how humans jointly infer coherent sets of goals, beliefs, and plans that explain an agent's actions, then evaluating statements about the agent's beliefs against these inferences via epistemic logic, our framework provides a functional role semantics for belief, explaining the gradedness and compositionality of human belief attributions, as well as their intimate connection with goals and plans. We evaluate this framework by studying how humans attribute goals and evaluate belief sentences while watching an agent solve a doors-and-keys gridworld puzzle that requires instrumental reasoning about hidden objects. In contrast to pure logical deduction, non-mentalizing baselines, and mentalizing that ignores the role of instrumental plans, our model provides a much better fit to human goal and belief attributions, demonstrating the importance of theory-of-mind for modeling how humans understand language about beliefs.

The space of human goals is tremendously vast; and yet, from just a few moments of watching a scene or reading a story, we seem to spontaneously infer a range of plausible motivations for the people and characters involved. What explains this remarkable capacity for intuiting other agents' goals, despite the infinitude of ends they might pursue? And how does this cohere with our understanding of other people as approximately rational agents? In this paper, we introduce a sequential Monte Carlo model of open-ended goal inference, which combines top-down Bayesian inverse planning with bottom-up sampling based on the statistics of co-occurring subgoals. By proposing goal hypotheses related to the subgoals achieved by an agent, our model rapidly generates plausible goals without exhaustive search, then filters out goals that would be irrational given the actions taken so far. We validate this model in a goal inference task called Block Words, where participants try to guess the word that someone is stacking out of lettered blocks. In comparison to both heuristic bottom-up guessing and exact Bayesian inference over hundreds of goals, our model better predicts the mean, variance, efficiency, and resource rationality of human goal inferences, achieving similar accuracy to the exact model at a fraction of the cognitive cost, while also explaining garden-path effects that arise from misleading bottom-up cues. Our experiments thus highlight the importance of uniting top-down and bottom-up models for explaining the speed, accuracy, and generality of human theory-of-mind.

This paper explores the cognitive processes underlying how and why trust in informational sources fluctuates. If information from experts and mainstream media is broadly more accurate than peer networks', why do we sometimes lose trust in experts? Counterintuitively, we often prefer information from authoritative sources, even if they become distrusted. We built a computational model of these dynamics. It includes a decision process sensitive to information processing costs and a learning process driven by prediction error minimization. We hypothesized that human information-processing biases could explain why experts are preferred as default sources of information and why their legitimacy is less resilient than peer networks' when both provide inaccurate information. We ran simulations over a wide range of parameters and found that the processing advantages of following experts can be outweighed by overreacting to their mistakes. This effect is higher when the environment is unstable and the epistemic authorities are biased.

Purpose: The objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality. Methods: There is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. A network model was constructed using the Bayesian network method, utilizing a dataset consisting of three rats' local field potentials. The model took into consideration the time delay of communication among brain areas, as recorded in this study. In pursuit of this objective, the delayed mutual information method was employed to ascertain the temporal delay between local field potentials and K2 score for the purpose of model comparison. Results: Bayesian network depicted the probabilistic relationship among rat's brain areas. Delayed mutual information captured the lag among brain areas, and after its appliance on the Bayesian network model, posed better results. Conclusion: The primary novelty of this research lies in its integration of minor delays within the Bayesian network approach, accomplished through the utilization of the delayed mutual information technique prior to its implementation. The suggested methodology incorporates an essential feature that supports the analysis of functional connectivity among brain areas, thereby providing support for the dynamics of neurophysiology. [ABSTRACT FROM AUTHOR]

For better decisions in social interactions, humans often must understand the thinking of others and predict their actions. Since such predictions are uncertain, multiple predictions may be necessary for better decision-making. However, the neural processes and computations underlying such social decision-making remain unclear. We investigated this issue by developing a behavioral paradigm and performing functional magnetic resonance imaging and computational modeling. In our task, female and male participants were required to predict others' choices in order to make their own value-based decisions, as the outcome depended on others' choices. Results showed, to make choices, the participants mostly relied on a value difference (primary) generated from the case where others would make a likely choice, but sometimes they additionally used another value difference (secondary) from the opposite case where others make an unlikely choice. We found that the activations in the posterior cingulate cortex (PCC) correlated with the primary difference while the activations in the right dorsolateral prefrontal cortex (rdlPFC) correlated with the secondary difference. Analysis of neural coupling and temporal dynamics suggested a three-step processing network, beginning with the left amygdala signals for predictions of others' choices. Modulated by these signals, the PCC and rdlPFC reflect the respective value differences for self-decisions. Finally, the medial prefrontal cortex integrated these decision signals for a final decision. Our findings elucidate the neural process of constructing value-based decisions by predicting others and illuminate their key variables with social modulations, providing insight into the differential functional roles of these brain regions in this process. [ABSTRACT FROM AUTHOR]

Significance: Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. Aim: To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Approach: Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. Results: When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. Conclusions: This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression. [ABSTRACT FROM AUTHOR]

This quasi-experimental study investigated the impact of computational learning activities on high school students' computational thinking (CT) and computational modeling (CM) skills. High school students (n = 90) aged 16 to 19 engaged in activities using computer models versus textbook-based models in mathematics and social science. The results indicated that students using computer models showed significant improvements in CT and CM skills compared to their peers in conventional learning settings. However, a potential ceiling effect in the CT assessments suggests that the test may not fully capture the extent of skill development. These findings highlight the importance of integrating computational learning activities in education, as they enhance students' abilities to apply these skills beyond the classroom. [ABSTRACT FROM AUTHOR]

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements. [ABSTRACT FROM AUTHOR]

It is common to use very high electric fields (E) to defibrillate hearts, which can damage cardiomyocytes, showing the importance of developing methods that can decrease the E applied in defibrillation protocols. We used COMSOL Multiphysics software to model, simulate, and analyze the application of an E in an isolated heart with monodirectional and multidirectional stimuli, using 2 pairs of electrodes and the superposition principle as a way to decrease the E applied and reduce the required number of stimulation electrodes. When applying an E of 3 V/cm in the 0°, 30°, and 60° directions, individually in the stimulation chamber, E was equal to 3.2 V/cm and its phases were equal to 0°, 30°, and 60°, respectively, validating the superposition principle. We observed a reduction up to 31.8% in | E | when applying a multidirectional stimulus when comparing to the monodirectional stimulus, in order to stimulate the same area of the heart. For the same amount of stimuli directions, we obtained up to 11.7% reduction of | E |, just by modifying the directions of the applied stimuli. In the simulation developed in this work, the superposition principle was validated in a stimulation chamber. For the same stimulated area, the multidirectional E has been always lower than the monodirectional. We observed that, in order to decrease the intensity of E, both the number of applied stimuli and their directions were relevant issues. [ABSTRACT FROM AUTHOR]

Background: This study posits that scaffolded team-based computational modeling and simulation projects can support model-based learning that can result in evidence of representational competence and regulatory skills. The study involved 116 students from a second-year thermodynamics undergraduate course organized into 24 teams, who worked on three two-week-long team-based computational modeling and simulation projects and reflected upon their experience. Results: Results characterized different levels of engagement with computational model-based learning in the form of problem formulation and model planning, implementation and use of the computational model, evaluation, and interpretation of the outputs of the model, as well as reflection on the process. Results report on students' levels of representational competence as related to the computational model, meaning-making of the underlying code of the computational model, graphical representations generated by the model, and explanations and interpretations of the output representations. Results also described regulatory skills as challenges and strategies related to programming skills, challenges and strategies related to meaning-making skills for understanding and connecting the science to the code and the results, and challenges and strategies related to process management mainly focused on project management skills. Conclusion: Characterizing dimensions of computational model-based reasoning provides insights that showcase students' learning, benefits, and challenges when engaging in team-based computational modeling and simulation projects. This study also contributes to evidence-based scaffolding strategies that can support undergraduate students' engagement in the context of computational modeling and simulation. [ABSTRACT FROM AUTHOR]

Bioelectric signals possess the ability to robustly control and manipulate patterning during embryogenesis and tissue-level regeneration. Endogenous local and global electric fields function as a spatial 'pre-pattern', controlling cell fates and tissue-scale anatomical boundaries; however, the mechanisms facilitating these robust multiscale outcomes are poorly characterized. Computational modeling addresses the need to predict in vitro patterning behavior and further elucidate the roles of cellular bioelectric signaling components in patterning outcomes. Here, we modified a previously designed image pattern recognition algorithm to distinguish unique spatial features of simulated non-excitable bioelectric patterns under distinct cell culture conditions. This algorithm was applied to comparisons between simulated patterns and experimental microscopy images of membrane potential (Vmem) across cultured human iPSC colonies. Furthermore, we extended the prediction to a novel co-culture condition in which cell sub-populations possessing different ionic fluxes were simulated; the defining spatial features were recapitulated in vitro with genetically modified colonies. These results collectively inform strategies for modeling multiscale spatial characteristics that emerge in multicellular systems, characterizing the molecular contributions to heterogeneity of membrane potential in non-excitable cells, and enabling downstream engineered bioelectrical tissue design. [ABSTRACT FROM AUTHOR]

This work identifies the most effective machine learning techniques and supervised learning models to estimate power output from photovoltaic (PV) plants precisely. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest regressor, Support Vector regression (SVR), Multi-layer Perceptron regressor (MLP), Linear regressor (LR), Gradient Boosting, k-Nearest Neighbors regressor (KNN), Ridge regressor (Rr), Lasso regressor (Lsr), Polynomial regressor (Plr) and XGBoost regressor (XGB). The methodology applied starts with meticulous data preprocessing steps to ensure dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root-Mean-Squared Error (RMSE), Normalized Root-Mean-Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2), Integral Absolute Error (IAE), and Standard Deviation of the Difference (SDD). Among the models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048%, and an R2 score of 0.968. Furthermore, the Random Forest regressor (the best-performing model) is integrated into a MATLAB application for real-time predictions, enhancing its usability and accessibility for a wide range of applications in renewable energy. [ABSTRACT FROM AUTHOR]

One of the cognitive abilities most affected by substance abuse is decisionmaking. Behavioral tasks such as the Iowa Gambling Task (IGT) provide a means to measure the learning process involved in decision-making. To comprehend this process, three hypotheses have emerged: (1) participants prioritize gains over losses, (2) they exhibit insensitivity to losses, and (3) the capacity of operational storage or working memory comes into play. A dynamic model was developed to examine these hypotheses, simulating sensitivity to gains and losses. The Linear Operator model served as the learning rule, wherein net gains depend on the ratio of gains to losses, weighted by the sensitivity to both. The study further proposes a comparison between the performance of simulated agents and that of substance abusers (n = 20) and control adults (n = 20). The findings indicate that as the memory factor increases, along with high sensitivity to losses and low sensitivity to gains, agents prefer advantageous alternatives, particularly those with a lower frequency of punishments. Conversely, when sensitivity to gains increases and the memory factor decreases, agents prefer disadvantageous alternatives, especially those that result in larger losses. Human participants confirmed the agents' performance, particularly when contrasting optimal and sub-optimal outcomes. In conclusion, we emphasize the importance of evaluating the parameters of the linear operator model across diverse clinical and community samples. [ABSTRACT FROM AUTHOR]

Introduction: There is a need to develop a comprehensive account of time-on-task fatigue effects on performance (i.e., the vigilance decrement) to increase predictive accuracy. We address this need by integrating three independent accounts into a novel hybrid framework. This framework unites (1) a motivational system balancing goal and comfort drives as described by an influential cognitive-energetic theory with (2) accumulating microlapses from a recent computational model of fatigue, and (3) frontal gamma oscillations indexing fluctuations in motivational control. Moreover, the hybrid framework formally links brief lapses (occurring over milliseconds) to the dynamics of the motivational system at a temporal scale not otherwise described in the fatigue literature. Methods: EEG and behavioral data was collected from a brief vigilance task. High frequency gamma oscillations were assayed, indexing effortful controlled processes with motivation as a latent factor. Binned and single-trial gamma power was evaluated for changes in real- and lagged-time and correlated with behavior. Functional connectivity analyses assessed the directionality of gamma power in frontal-parietal communication across time-on-task. As a high-resolution representation of latent motivation, gamma power was scaled by fatigue moderators in two computational models. Microlapses modulated transitions from an effortful controlled state to a minimal-effort default state. The hybrid models were compared to a computational microlapse-only model for goodness-of-fit with simulated data. Results: Findings suggested real-time high gamma power exhibited properties consistent with effortful motivational control. However, gamma power failed to correlate with increases in response times over time, indicating electrophysiology and behavior relations are insufficient in capturing the full range of fatigue effects. Directional connectivity affirmed the dominance of frontal gamma activity in controlled processes in the frontal-parietal network. Parameterizing high frontal gamma power, as an index of fluctuating relative motivational control, produced results that are as accurate or superior to a previous microlapse-only computational model. Discussion: The hybrid framework views fatigue as a function of a energetical motivational system, managing the trade-space between controlled processes and competing wellbeing needs. Two gamma computational models provided compelling and parsimonious support for this framework, which can potentially be applied to fatigue intervention technologies and related effectiveness measures. [ABSTRACT FROM AUTHOR]

Reinforcement learning is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g., money, points) that are later exchanged for primary reinforcers (e.g., food, drink). Although symbolic reinforcers are ubiquitous in our daily lives, widely used in laboratory tasks because they can be motivating, mechanisms by which they become motivating are less understood. In the present study, we examined how monkeys learn to make choices that maximize fluid rewards through reinforcement with tokens. The question addressed here is how the value of a state, which is a function of multiple task features (e.g., the current number of accumulated tokens, choice options, task epoch, trials since the last delivery of primary reinforcer, etc.), drives value and affects motivation. We constructed a Markov decision process model that computes the value of task states given task features to then correlate with the motivational state of the animal. Fixation times, choice reaction times, and abort frequency were all significantly related to values of task states during the tokens task (n = 5 monkeys, three males and two females). Furthermore, the model makes predictions for how neural responses could change on a moment-by-moment basis relative to changes in the state value. Together, this task and model allow us to capture learning and behavior related to symbolic reinforcement. [ABSTRACT FROM AUTHOR]

Introduction: Money source influences risk-taking behaviors. Although studies consistently indicated that individuals demonstrate a higher propensity to make risky investments when utilizing non-labor income as opposed to labor income, explanations as to why non-labor income leads to continuously blowing money into risky investments are scarce. Methods: The current study leverages a computational modeling approach to compare the differences in the dynamic risk investment process among individuals endowed with income from different sources (ie, non-labor income vs labor income) to understand the shaping force of higher risk-taking propensity in individuals with non-labor income. A total of 103 participants were recruited and completed the Balloon Analogue Risk Task (BART) with an equal monetary endowment, either as a token for completion of survey questionnaires (representing labor income) or as a prize from a lucky draw game (representing non-labor income). Results: We found that individuals endowed with non-labor income made more risky investments in BART compared to those with labor income. With computational modeling, we further identified two key differences in the dynamic risk investment processes between individuals endowed with labor and those with non-labor income. Specifically, individuals endowed with non-labor income had a higher preset expectation for risk-taking and displayed desensitization towards losses during risk investments, in contrast to individuals with labor income. Discussion: This study contributed to a better understanding of the psychological mechanisms of why individuals make more risk-taking behaviors with non-labor income, namely higher preset expectations of risk-taking and desensitization towards losses. Future research could validate these findings across diverse samples with varying backgrounds and adopt different manipulations of labor and non-labor income to enhance the external validity of our study. [ABSTRACT FROM AUTHOR]