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Individual cell sensing of external cues has evolved through the temporal patterns in signaling. Since nuclear factor κB (NF-κB) signaling dynamics have been examined using a single subunit, RelA, it remains unclear whether more information might be transmitted via other subunits. Using NF-κB double-knockin reporter mice, we monitored both canonical NF-κB subunits, RelA and c-Rel, simultaneously in single macrophages by quantitative live-cell imaging. We show that signaling features of RelA and c-Rel convey more information about the stimuli than those of either subunit alone. Machine learning is used to predict the ligand identity accurately based on RelA and c-Rel signaling features without considering the co-activated factors. Ligand discrimination is achieved through selective non-redundancy of RelA and c-Rel signaling dynamics, as well as their temporal coordination. These results suggest a potential role of c-Rel in fine-tuning immune responses and highlight the need for approaches that will elucidate the mechanisms regulating NF-κB subunit specificity.

The endoplasmic reticulum (ER) forms an interconnected network of tubules stretching throughout the cell. Understanding how ER functionality relies on its structural organization is crucial for elucidating cellular vulnerability to ER perturbations, which have been implicated in several neuronal pathologies. One of the key functions of the ER is enabling Ca[Formula: see text] signaling by storing large quantities of this ion and releasing it into the cytoplasm in a spatiotemporally controlled manner. Through a combination of physical modeling and live-cell imaging, we demonstrate that alterations in ER shape significantly impact its ability to support efficient local Ca[Formula: see text] releases, due to hindered transport of luminal content within the ER. Our model reveals that rapid Ca[Formula: see text] release necessitates mobile luminal buffer proteins with moderate binding strength, moving through a well-connected network of ER tubules. These findings provide insight into the functional advantages of normal ER architecture, emphasizing its importance as a kinetically efficient intracellular Ca[Formula: see text] delivery system.

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.

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. What drives people to disrupt the existing conceptual landscape and create new things? Here, we examine the decision to create new things under different levels of expected returns. We formalize innovation as stochastically recombining existing ideas, where successful and more complex combinations generate higher returns. This formalization allows us to cast innovation-seeking as a Markov decision process, and derive optimal policies under different settings. Data collected through an online behavioral experiment confirm our prediction that people should invest more time and effort in seeking innovations when they know the chances of success are high and the potential new ideas would be rewarding. However, people also deviate from being optimal, both innovating more and less than they should in different settings.

In this paper, we test the effect of manipulating discrimination difficulty on subsequent generalization of learning and in particular, on the peak shift effect. Participants learned a discrimination where one stimulus led to an outcome (S+) and another stimulus led to no outcome (S-). Difficulty was manipulated by varying the degree of similarity between the S+ and S- across groups (easy/medium/hard). In contrast to similar studies in animals, we found that increasing the difficulty of the discrimination resulted in less peak shift. Using a hierarchical mixture model, we characterize the effects of discrimination difficulty on relational- and similarity based responding, and show for the first time, a similar mixture of responding on stimulus identification gradients. We conclude that peak shift on generalization and identification measures can be explained by mixtures of participants responding in different ways.

Choice deadlines are commonly imposed in decision-making research to incentivize speedy responses and sustained attention to the task settings. However, computational models of choice and response times routinely overlook this deadline, instead simply omitting trials past the deadline from further analysis. This choice is made under the implicit assumption that parameter estimation is not significantly affected by ignoring these omissions. Using new tools from likelihood-free inference, here we elucidate the degree to which omitting omissions, even in seemingly benign settings, can lead researchers astray. We explore the phenomenon using a Sequential Sampling Model (SSM) with collapsing boundaries as a test-bed.

Decisions about extinction risks are ubiquitous in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: a Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal solutions for both variants and compare them to behavioural data. Our findings suggest that people understand the difference between the two variants and their behaviour is qualitatively in line with the optimal solution. Further, we find evidence for risk-aversion in the Keep condition but not in the Lose condition. We hope that this task can facilitate further research on this vital topic.

We conduct tests of a hybrid-similarity exemplar model on its ability to account for the context-dependent memorability of items embedded in high-dimensional category spaces. According to the model, recognition judgments are based on the summed similarity of test items to studied exemplars. The model allows for the idea that “self-similarity” among objects differs due to matching on highly salient distinctive features. Participants viewed a study list of rock images belonging to geologically defined categories where the number of studied items from each category was manipulated, and their old-new recognition performance was then tested. With a minimum of parameter estimation, the model provided good accounts of changing levels of memorability due to contextual effects of category size, within- and between-category similarity, and the presence of distinctive features. We discuss future directions for improving upon the current predictions from the model.

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

We propose a neural dynamic architecture that models negation processing. The architecture receives a visual scene and a relational phrase like ``The blue object is not to the right of the yellow object'' or ``The blue object is to the right of the green object'' as input, and autonomously determines whether the phrase correctly describes the visual scene. The model is built out of empirically founded components for perceptually grounded cognition and constrained by neural principles. We demonstrate that the model can explain two commonly found reaction time effects: the negation effect in which reaction times are higher for negated than for affirmative phrases, and the polarity-by-truth-value interaction effect in which reaction times for false negated phrases are faster than those for true negated phrases whereas the opposite is true for affirmative phrases. The model is consistent with some aspects of the two-step simulation theory.

Emerging folklore in the cognitive sciences suggests that interpretability techniques to reverse-engineer artificial neural networks (ANNs) could speed up discovery and theory-building. For many researchers in psychology, linguistics, neuroscience, and artificial intelligence (AI), the full observability and perturbability of ANNs trained on complex tasks affords a shortcut to domain insights, cognitive theories, neurocognitive models, application improvement, and user safety. Folklore intuitions, however, are typically disconnected from other relevant knowledge. Here we examine these intuitions formally by drawing relevant connections to computational complexity theory. We model interpretability queries computationally and analyze their resource demands for biological/artificial high-level cognition. We prove mathematically that, contrary to folklore, basic circuit-finding queries in classic ANNs are already infeasibly demanding to answer even approximately. We discuss how interdisciplinary integration can mitigate this disconnect and situate the broader implications for the cognitive sciences, the philosophy of AI-fueled discovery, and AI ethics.

Sensation-seeking (SS) is characterised by a proclivity for intense experiences and disregard for potential aversive consequences. While SS is implicated as a vulnerability factor in various mental disorders, the underlying mechanisms remain elusive. Recent approaches propose an alternative perspective, suggesting that SS may be linked to highly explorative, and therefore risky, behaviours driven by a preference for informative environments. To probe this hypothesis, we reanalysed a dataset where participants chose to self-administer or avoid mild electric stimulation (MES) in an economic decision-making task. Contrary to previous interpretations associating higher sensation-seeking with the positive economic value of experiencing MES, Bayesian models of learning reveal an alternative account: sensation-seekers are more attuned to information about stimuli-shock contingencies. Specifically, high sensation-seeking individuals are less avoidant of information about the possibility of a shock, supporting the idea that sensation-seeking is linked to a preference for informative environments.

High-dimensional deep neural network representations of images and concepts can be aligned to predict human annotations of diverse stimuli. However, such alignment requires the costly collection of behavioral responses, such that, in practice, the deep-feature spaces are only ever sparsely sampled. Here, we propose an active learning approach to adaptively sample experimental stimuli to efficiently learn a Bayesian matrix factorization model with deep side information. We observe a significant efficiency gain over a passive baseline. Furthermore, with a sequential batched sampling strategy, the algorithm is applicable not only to small datasets collected from traditional laboratory experiments but also to settings where large-scale crowdsourced data collection is needed to accurately align the high-dimensional deep feature representations derived from pre-trained networks. This provides cost-effective solutions for collecting behavioral data and generating high-quality predictions in large-scale behavioral and cognitive studies.

Recent research has identified substantial individual differences in how people solve value-based tasks. Here, we examine such differences in the motivational Go/NoGo task, which orthogonalizes action and valence, using open-source data from 817 participants. Using computational modeling and behavioral analysis, we identified four distinct clusters of people. Three clusters corresponded to previous models of the task, including people with different learning rates for cues that signal rewarding and punishing states and with different sensitives for rewards and punishments. The fourth cluster of people acted like naïve reinforcement learners, with their responses shaped by outcomes in a manner that was independent of the state information provided by the cues. In addition to providing evidence that state-independent learning is a common disposition, we show that not considering such learning can dramatically affect the results of computational modeling. We discuss the implications for the modeling of data from heterogeneous populations.

People sometimes display moral flexibility by deciding that a commonly accepted moral norm ought not to apply in particular circumstances. But how? We explore this question in the context of queuing. We show that people's judgements about the moral permissibility of queue-cutting can be explained through cognitive processes related to moral contractualism: universalization, virtual bargaining, and functional thinking. Participants were presented vignettes depicting prospective queue-cutters, and asked whether it was morally permissible to queue-cut in those circumstances. We model these judgements with reference to the existence of a game-theoretic equilibrium supporting queue cutting in a repeated game, and to considerations of whether queue cutting would subvert or enact the function of a queue: if you pay the waiting cost, you should get the reward. These results support the notion that moral flexibility is in part related to contractualist moral principles.

What does a social learner learn? Research has explored imitation-based social learning strategies as well as inverse reinforcement algorithms that estimate others' true reward function. In the current study, we propose that social learning may be more elaborate and develop a model of social learning using Bayesian inference that seeks to understand both the task an observed demonstrator is performing and the demonstrator itself. Using simulations, we show that the model is able to learn about the demonstrator when provided with full and partial information. We strengthen this point by asking the model to make inferences about missing choice and reward information. Last, we show that the model is able to represent one set of beliefs about the environment while attributing a distinct set of beliefs to the demonstrator. Thus, we move away from simple models of social learning, investigating inference-making as a core mechanism of social learning.

Literary and aesthetic studies have suggested that readers can stay in indeterminate states where they hold multiple interpretations, and that this indeterminate interpretation state causes an aesthetic feeling, involvement, and understanding of art. To explore the indeterminate interpretation state, we employed a quantum cognition framework and conducted a reading experiment using a short story. The results suggest that readers' interpretations can be regarded as a superposition state corresponding to the indeterminate and polysemous interpretation of the story. We also estimated a time series of quantum indeterminacy and discussed the text features related to quantum indeterminacy.

Understanding how humans learn by themselves is crucial to develop interventions to prevent dropout and improve learner engagement. Classical learning curves were proposed to fit and describe experimental data involving enforced learning. However in real-world learning contexts such as MOOCs and hobbies, learners may quit - and often do. Even in formal settings such as college success typically requires intensive self-study outside lectures. Previous research in educational psychology supports a positive reciprocal relationship between motivation and achievement. Integrating insights from learning curves, forgetting curves and motivation-achievement cycles, we propose a formal Reciprocal-Practice-Success (RPS) model of learning ‘in the wild'. First, we describe the different components of the basic RPS model. Using simulations, we then show how long term learning outcomes critically depend on the shape of the learning curve. Concave curves lead to more consistent learning outcomes whereas S-shaped curves lead to either expertise or dropout. We also provide a dynamical systems version for the RPS model which shows similar qualitative behaviour. Through a bifurcation analysis of two controllable learning parameters - minimum practice rate and success sensitivity, we show which learner-specific interventions may be effective to preventing dropout. We also discuss theorized mechanisms which affect the inflection point of S-shaped learning curves such as task-complexity and relative feedback from failures vs. successes. These provide more task-specific interventions to lower quitting rates. Finally, we discuss possible extensions to the basic RPS model which will allow capturing spacing effects and insights from other motivation theories.

We propose two novel benchmarks for assessing models of probability judgments: the impact of Cognitive Reflection Test (CRT) on probability judgment expressions and 16 ``normality identities" expected to sum to 1 under classical probability theory. We compared three models on these benchmarks – the Probability Plus Noise Model (PPN), the Bayesian Sampler (BS), and the Quantum Sequential Sampler (QSS) – using the largest dataset to date for probability judgments. Our results reveal that higher CRT scores are associated with fewer probabilistic fallacies and identity violations, a trend most accurately captured by the QSS, although we also identified QSS limitations. Regarding the normality identities, the QSS outperformed the PPN and the BS, which had difficulty with both the average values of the normality identities and their dependence on CRT scores. Additionally, we uncovered a unique ``1 crossing" effect for normality identities N8 and N11, an effect PPN and BS cannot capture.

This study extends the Retrieving Effectively from Memory model, a prominent computational model of episodic memory, to the domain of categorization. Our modeling approach begins with the assumption that same-category items share common features representing defining characteristics of their category, and that they are encoded in the same category list context. We then assumed that category judgments occur based on the comparison of an item's averaged similarity to the exemplars from each category. We use this model to explore how the learning modes of observation and classification might influence category learning and consider several strategies that may emerge during the classification mode. Model simulation results indicate that different strategies which people might adopt during classification can either confer an advantage or pose a disadvantage in category learning. These findings suggest potential avenues for future research, particularly in exploring diverse strategies employed during learning.

Connectives like ‘because' are referred to as ‘processing instructions' as they facilitate processing of linguistic material directly following the connective. In an expectation-driven account of discourse processing, this can be attributed to predictions that readers make about the upcoming discourse relation, but also to predictions about upcoming discourse content. By modeling these two accounts, termed the relation prediction account and the content prediction account respectively, we show that they make different predictions about when the presence of a connective is most beneficial. In a self-paced reading study, we replicate the facilitative effect of the connective on processing, but do not find any evidence that this effect can be explained by a strong or weak version of either of the two accounts. This suggests that the role of the connective goes above and beyond informing the reader about the upcoming relation and content and possibly triggers a different processing strategy.

Regret Theory (Loomes & Sugden, 1982) is a theory of decision making based on the idea that people consider not only outcome utility, but also future regret or rejoicing, which depends on both the chosen option and foregone options. Regret theory was originally proposed as a theory of choice under uncertainty. Here, we demonstrate that Regret Theory also predicts the widely studied attraction, compromise, and similarity context effects. First, we show that it predicts attraction effects in choice among gamble triples. Second, we apply Regret Theory to non-gamble multi-attribute choice settings and show that both predicts these context effects and predicts a within-subject dissociation between the compromise and similarity effects previously observed in empirical studies. Regret Theory provides a foundation for a unified account of risky and multi-attribute choice, and we believe the form we present here provides the simplest account to date that explains phenomena in both domains.

Texts available on the Web have been generated by human minds. We observe that simple patterns are over-represented: abcdef is more frequent than arfbxg and 1000 appears more often than 1282. We suggest that word frequency patterns can be predicted by cognitive models based on complexity minimization. Conversely, the observation of word frequencies offers an opportunity to infer particular cognitive mechanisms involved in their generation.

There is no guarantee that the set of possible theories that boundedly rational agents consider contains the true theory. And yet, these agents update their beliefs as new evidence comes in, leading to a conclusion about a particular domain. In this paper, we investigate under which conditions such agents arrive at sufficiently accurate beliefs compared to ideal agents. In doing so, we work within the framework of objective Bayesianism and draw on the literature on novel predictions in philosophy of science.

In curriculum learning, teaching involves cooperative selection of sequences of data via plans to facilitate efficient and effective learning.One-off cooperative selection of data has been mathematically formalized as entropy-regularized optimal transport and the limiting behavior of myopic sequential interactions has been analyzed, both yielding theoretical and practical guarantees.We recast sequential cooperation with curriculum planning in a reinforcement learning framework and analyze performance mathematically and by simulation.We prove that infinite length plans are equivalent to not planning under certain assumptions on the method of planning, and isolate instances where monotonicity and hence convergence in the limit hold, as well as cases where it does not. We also demonstrate through simulations that argmax data selection is the same across planning horizons and demonstrate problem-dependent sensitivity of learning to the teacher's planning horizon. Thus, we find that planning ahead yields efficiency at the cost of effectiveness.

Searching through memory is mediated by complex interactions between the underlying mental lexicon and the processes that operate on this lexicon. However, these interactions are difficult to study due to the effortless manner in which neurotypical individuals perform cognitive tasks. In this work, we examine these interactions within a sample of prelingually deaf individuals with cochlear implants and normal hearing individuals who were administered the verbal fluency task for the "animals" category. Specifically, we tested how different candidates for underlying mental lexicons and processes account for search behavior within the verbal fluency task across the two groups. The models learned semantic representations from different combinations of textual (word2vec) and speech-based (speech2vec) information. The representations were then combined with process models of memory search based on optimal foraging theory that incorporate different lexical sources for transitions within and between clusters of items produced in the fluency task. Our findings show that semantic, word frequency, and phonological information jointly influence search behavior and highlight the delicate balance of different lexical sources that produces successful search outcomes.

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.

We introduce a dimension reduction framework (CDR) that sheds light on how individuals simplify the multidimensional world to guide decision-making and comprehension. Our proposal posits that cognitive limitations prompt the adoption of simplified models, reducing the environment to a subset of dimensions. Within these limitations, we propose that individuals exploit both environment structure and goal relevance. Employing information theory, we formalize these principles and develop a model that explains how environmental and cognitive factors influence dimension reduction. Furthermore, we present an experimental method for the model's assessment and initial findings that support it.

While subjective visual experiences are remarkably stable and coherent, their underlying data is incomplete and heavily influenced by the eyes' saccadic rhythm. In this work, we show that a deep and recurrent neural network can effectively reconstruct vibrant images from restricted retinal inputs during active vision. Our method includes the creation of a dataset for synthetic retinal inputs, containing intensity, color, and event-camera-generated motion data. We demonstrate the importance of both long-short-term memory and corollary discharge signals to image stabilization and the system's sensitivity to noise, corresponding to recent experimental findings. Our study contributes to the advancement of realistic and dynamic models for image reconstruction, providing insights into the complexities of active visual perception.

People judge an object's criterion value by relying on its similarity to previously experienced objects, the so-called exemplars. This work investigates exemplar-based processes in quantitative judgments by applying cognitive modeling to data from an eye-tracking experiment. Participants (N = 49) first learned the criterion value and location on the screen of each of four exemplars. Then, they assessed the criterion value of briefly presented test stimuli, and eye-tracking measured the gaze proportion to the now blank exemplar locations (looking-at-nothing). Participants who showed more looking-at-nothing also relied more on exemplars according to cognitive modeling of the test phase responses in the RulEx-J framework. Furthermore, looking-at-nothing was directed in particular at locations of exemplars similar to the test stimulus. Our multi-method approach thus suggests tight links between eye-tracking and cognitive modeling. The insights from process-tracing methods might be particularly valuable, when cognitive modeling cannot distinguish between alternative processes to perform quantitative judgments.

In this paper, we explore the objective-Bayesian principle of minimum information and Maximum Entropy as a solution to the problem of awareness growth: how should rational agents adjust their beliefs upon becoming aware of new possibilities? We introduce the Maximum Entropy principle as a theoretical solution to the problem of awareness growth and present the results of two experiments conducted to compare human reasoners' responses with the theoretical prescriptions of the Maximum Entropy approach. We discover that, although the MaxEnt method may appear computationally demanding, participants' responses are largely consistent with the theoretical prescription.

The paper presents a model and a discussion of the computational representation of virtue ethics in autonomous devices. One of the key problems in formal modeling of virtue ethics is the computational representation of the concept of virtue. In our model, the virtue is represented by a set of minimal extents to which a set of values, relevant to the virtue, should be satisfied. A device will be moral if any decision made satisfies all relevant values above the declared thresholds.

The classical (symbol system) theory of cognition is supposed to explain systematicity---the coexistence of cognitive abilities. However, the classical theory does not explain why cognitive systems should be symbolic, nor why cognition sometimes fails to be systematic, so the symbol system assumption is seen by some as ad hoc: motivated only to fit the data. A mathematical theory is presented as a framework towards addressing these questions in terms of the available cognitive resources and the intended goals. A cognitive system is supposed to be resource-dependent and goal-driven. Accordingly, systematicity, or lack thereof follows from a universal construction principle (in a category theory sense) in this context---systems of symbols arise (or, fail to arise) as the "best" possible mapping given the available resources and the intended goal.

Pupil dilation has been associated with increased cognitive load or mental effort requirements, which can be modulated by external sensory stimulation as well as internal cognition. Underlying a cognitive task are multiple interplaying processes which can be stimulus-driven, goal-driven or spontaneous. However, what remains unknown is how these multiple processes correlate with pupil size modulations and whether it is possible to dissociate their individual effects. To answer this, we employed behavioural and pupil data from two cognitive tasks performed in internal and external attention conditions, where stimulus-driven attention demands were manipulated for the same set of tasks. Using model-based analysis, we were able to dissociate within conditions, how individual processes affect pupil and also compare their effects between conditions. We made two important and novel findings – first, within both the conditions we were able to dissociate stimulus-driven and goal-driven effects. Second, when compared between the two attention conditions, we found distinct stimulus-driven attention-based effects but similar goal-directed task-based effects. Our results indicate that pupil can be used as a reliable tool to study cognition.

In decision making scenarios, reasoning can be viewed as an agent executing an algorithm p ‚àà P that selects an action a ‚àà A, aiming to optimize some outcome. Metareasoning extends this by selecting p itself through a meta-algorithm p^{meta}. Previous approaches to study metareasoning in humans have required that the transition/reward distributions are known by the agent, but the value function isn't. We extend these efforts to study metareasoning for agents acting in unknown environments by formalizing the meta problem as a meta Bayes adaptive Markov decision problem (meta-BAMDP). We formally investigate the theoretical consequences of this framework within the context of two-armed Bernoulli bandit (TABB) tasks. Not only do we make theoretical progress in making the (usually intractable) metareasoning problem tractable, but we also generate predictions for a resource rational account of human exploration in TABB tasks.

Languages across the world organize semantic categories in many ways. Research in semantic typology and efficient communication has shown that languages tend to be shaped by pressures for communicative efficiency. It was recently proposed, in addition to this principle of efficiency, that the cross-linguistic prevalence of a system is explained by considering and formalizing the Typological Prevalence Hypothesis. This recent research found that the interaction between communicative and developmental pressures infers the prevalence of color-naming systems across the world better than phylogenetic relatedness alone. However, it is not yet clear whether the information-theoretic framework developed by the authors can explain the typological prevalence of non-perceptual categories. Therefore, we extend this model to kinship systems to test if this formalization of the Typological Prevalence Hypothesis can generalize to other semantic domains.

The impact of visual attention on choice processes has been established over the last decades. Several studies are consistent with the view that visual attention increases the subjective value of the attended option. However, a few computational models have been proposed to investigate how attention and subjective values interact in multi-attribute choices. Moreover, these models disagree in terms of whether value is modulated by attention additively or multiplicatively. The additive theory states that the boost up subjective value depends only on gaze duration, and gaze on an option magnifies the subjective value at a constant rate. On the other hand, the multiplicative theory assumes that the magnitude of the attention-driven boost is value-dependent, and gazing at a high-value option yields a more significant boost in subjective value. Although there is a long debate on these two theories, recent studies have shown that both additive and multiplicative interactions between subjective value and gaze time may be essential for explaining empirical data and have suggested hybrid theories. For multi-attribute decisions, however, extant attentional models only consider the multiplicative interaction. This work introduces a new computational framework to account for attention in multi-attribute decisions. Our model assumes a hybrid attentional mechanism for the interaction between subjective values and gaze duration. We have tested the model on four datasets from various domains (e.g., clothing/brand, food/nutrition, food bundle, and money risk tasks). The results from the nested model comparison show that the proposed hybrid model works better than the other computational models.

How does experiencing a rare event, like a car accident or a lottery win, influence decision-makers consecutive decisions? Studying these so-called recency effects holds a long tradition in research on experience-based decision-making. Previous work suggests opposite behavioral patterns after experiencing a positive rare event: People have been found to be more likely to either repeat their choice (positive recency) or to avoid it (negative recency). The effect is thought to persevere for multiple choices and decrease over time. In this study, we provide new insights into recency effects by analyzing people's repeated choices from an extensive database—consisting of 3 million choices by 8,000 participants across 12 different decision-from-experience paradigms collected from 139 studies. We provide a conceptual framework clarifying patterns of positive and negative recency, including how the direction and magnitude of impact change over time.

Drift-Diffusion Models (DDMs) are a widely-used class of models that assume an accumulation of evidence during a quick decision. These models are often used as measurement models to assess individual differences in cognitive processes, such as an individual's evidence accumulation rate and response caution. An additional underlying assumption of these models is that there is internal noise in the evidence accumulation process. However, fitting DDMs to experimental choice-response time data alone cannot yield estimates of an individual's evidence accumulation rate, caution, and internal noise at the same time. This is due to an intrinsic joint-unidentifiability of these parameters when fitting DDMs to behavioral data. We introduce methods of estimating these parameters at the same time with additional data types. The methods to estimate model parameters rely on Bayesian inference and simulation-based Bayesian inference. We show why these methods are useful without making strong assumptions.

Pupil dilation is assumed to be a slow and indirect reflection of latent cognitive events. Deconvolution approaches promise a more precise study of these events, assuming that they all trigger a delayed pupil response.However, conventional deconvolution approaches neglect the possibility that between-event timings and the shape of the pupil responses differ between subjects, trials, and cognitive events. Accounting for this variability however is crucial to 1) achieve precise recovery of latent events and 2) to investigate how trial-level predictors influence cognitive processes.We present a new method that performs trial-level deconvolution by combining generalized additive mixed models with Hidden semi-Markov models. We tested this method on synthetic data and subsequently applied it to data from a lexical decision experiment (N=24) and recovered six processing events. Investigating the trial-level durations of the recovered events revealed that early visual and late decision-related processing were influenced differently by frequency and word-type.

I formalize the problem of care in the mathematical language of sequential decision-making. Drawing upon insights from developmental psychology, robotics, and computational cognitive modeling, I conceptualize care as a dynamic interplay between the caregiver ('one-caring') and the care recipient ('cared-for'). Caring actions maximize the utility of the cared-for at a future point when they are required to act autonomously. Since this quantity cannot be directly optimized, the focus is on enabling increasing levels of autonomy through environmental shaping, risk reduction, and safe exploration. I distinguish caregiving from helping and teaching by care's focus on exploration and autonomy that increase capacity over time. In the context of elderly care, the emphasis shifts towards preserving rather than enhancing capacity. Finally, I consider the role of caregiving in the development of moral values and the possibility of artificially intelligent agents that might someday care for us.

Stochastic events in our daily environments, such as a missed bus or forgotten keys, require adaptive understanding for efficient exploitation of the environment. In this study we tested how humans acquire and use such understanding if the either the type or probability of events change. We asked 281 participants to predict the location of an animated fly, which hid from the observers. Over the first 10 trials we induced a strong prior model of the task environment and subsequently introduced stochastic changes and new content to manipulate the rate of model violations. Prediction errors derived from a specific world model drove information seeking actions, leading to new explanations and associated probability estimates. Current world model constrained possible updates, often only leading to partial investigation and suboptimal strategies, especially when behavior had positive utility. Additionally, evidence for accurate understanding but failure to identify and exploit ideal behavior was a characteristic result.

Atmospheric freeze drying (AFD) is a promising alternative to conventional vacuum freeze-drying (VFD), operating under atmospheric conditions with lower energy consumption, continuous processing, and cost-effectiveness, especially in cold climates. However, AFD faces challenges such as prolonged drying time, product shrinkage, and ice thawing. These issues are addressed through hybrid techniques incorporating thermal or mechanical energy to enhance drying efficiency. This review paper presentes recent advancements in AFD by examining its fundamental principles underlying the process and innovative approaches designed to improve its efficiency. The application of differential scanning calorimetry (DSC) and the development of state diagrams have been discussed as tools for analyzing thermal characteristics and designing efficient drying regimes. The review also explores the influence of process parameters such as drying temperature, air velocity, and sample characteristics on drying kinetics and product attributes, offering insights into optimal conditions. Hybrid approaches, including heat pumps, vortex tubes, expanders, ultrasonic and microwave assistance, adsorbent usage, and fluidization, have shown significant energy savings and product quality improvements. Finally, the predominant modeling approaches employed in AFD have been explored to provide a comprehensive understanding of drying kinetics. Despite advancements, ongoing research is needed to overcome technical barriers and extend AFD’s applicability across various industries. [ABSTRACT FROM AUTHOR]

ABSTRACT Doppler ultrasound is a commonly used method to assess hemodynamics of the fetal cardiovascular system and to monitor the well‐being of the fetus. Indices based on the velocity profile are often used for diagnosis. However, precisely linking these indices to specific underlying physiology factors is challenging. Several influences, including wave reflections, fetal growth, vessel stiffness, and resistance distal to the vessel, contribute to these indices. Understanding these data is essential for making informed clinical decisions. Mathematical models can be used to investigate the relation between velocity profiles and physiological properties. This study presents a mathematical model designed to simulate velocity wave propagation throughout the fetal cardiovascular system, facilitating the assessment of factors influencing velocity‐based indices. The model combines a one‐fiber model of the heart with a 1D wave propagation model describing the larger vessels of the circulatory system and a lumped parameter model for the microcirculation. Fetal growth from 20 to 40 weeks of gestational age is incorporated by adjusting cardiac and circulatory parameter settings according to scaling laws. The model's results, including cardiac function, cardiac output distribution, and volume distribution, show a good agreement with literature studies for a growing healthy fetus from 20 to 40 weeks. In addition, Doppler indices are simulated in various vessels and agree with literature as well. In conclusion, this study introduces a novel closed‐loop 0D‐1D mathematical model that has been verified against literature studies. This model offers a valuable platform for analyzing factors influencing velocity‐based indices in the fetal cardiovascular system. [ABSTRACT FROM AUTHOR]

AbstractIn this study, we investigated the influence of temperature and air velocity on the drying behavior of Mentha spicata L. leaves using a forced convection dryer. This drying method was chosen for its cost-effectiveness and its ability to facilitate fast and efficient drying while preserving the quality of aromatic herbs. Our aim was to identify the most suitable mathematical model for accurately representing the drying process. We analyzed experimental data on moisture content obtained during thin-layer drying of Mentha spicata L. using various established empirical models from existing literature. Thirteen empirical models were applied to fit the drying curves obtained. Statistical analysis, employing key parameters such as the coefficient of determination (R2), reduced chi-square (χ2), and root mean square error (RMSE), indicated that the Midilli and Kucuk model provided the most accurate fit for the observed drying curves. This was evidenced by the highest R2 values (0.9994, 1.0000), lowest χ2 values (6.07 × 10−6, 6.28 × 10−5), and lowest RMSE values (2.17 × 10−3, 6.86 × 10−3). We observed an increasing trend in moisture diffusivity with temperature variations between 32 °C and 60 °C, while maintaining a constant airflow velocity of 1.2 m/s. Additionally, an increase in mean air velocity from 0.7 to 1.7 m/s correlated with higher moisture diffusivity. The effectiveness of the Arrhenius equation in representing the temperature dependence of diffusivity was confirmed, with an activation energy of 72 kJ/mol, consistent with previous literature findings. [ABSTRACT FROM AUTHOR]

The high turgor pressure across the plasma membrane of yeasts creates a requirement for substantial force production by actin polymerization and myosin motor activity for clathrin-mediated endocytosis (CME). Endocytic internalization is severely impeded in the absence of fimbrin, an actin filament crosslinking protein called Sac6 in budding yeast. Here, we combine live-cell imaging and mathematical modeling to gain insights into the role of actin filament crosslinking proteins in force generation. Genetic manipulation showed that CME sites with more crosslinking proteins are more effective at internalization under high load. Simulations of an experimentally constrained, agent-based mathematical model recapitulate the result that endocytic networks with more double-bound fimbrin molecules internalize the plasma membrane against elevated turgor pressure more effectively. Networks with large numbers of crosslinks also have more growing actin filament barbed ends at the plasma membrane, where the addition of new actin monomers contributes to force generation and vesicle internalization. Our results provide a richer understanding of the crucial role played by actin filament crosslinking proteins during actin network force generation, highlighting the contribution of these proteins to the self-organization of the actin filament network and force generation under increased load. [ABSTRACT FROM AUTHOR]

Mumps outbreaks among fully vaccinated young adults have raised questions about potential waning of immunity over time and need for a third dose of the measles, mumps, rubella (MMR) vaccine. However, there are currently limited data on real-life effectiveness of the third-dose MMR vaccine in preventing mumps. Here, we used a deterministic compartmental model to infer the effectiveness of the third-dose MMR vaccine in preventing mumps cases by analyzing the mumps outbreak that occurred at the University of Iowa between August 24, 2015, and May 13, 2016. The modeling approach further allowed us to evaluate the population-level impact of vaccination by different timing in relation to the start of the outbreak and varied coverage levels, and to account for potential sources of bias in estimating vaccine effectiveness. We found large uncertainty in vaccine effectiveness estimates; however, our models showed that early introduction of a third dose of MMR vaccine during a mumps outbreak can be effective in preventing transmission. School holidays, such as the winter break, likely played important roles in preventing mumps transmission. [ABSTRACT FROM AUTHOR]