Probabilistic Pattern Research Articles

Ward‐Specific Probabilistic Patterns in Temporal Dynamics of Nursing Demand in Japanese Large University Hospital: Implication for Forecasting and Resource Allocation

As global populations age, a looming nursing shortage is anticipated to become a critical issue. Charge nurses have the responsibility of optimally allocating nursing resources to ensure the quality of patient care during a shift. Therefore, an accurate estimate of nursing demand is crucial. However, the ability to forecast future nursing demand remains underdeveloped, mainly because the nature of nursing demand is highly individualized and does not follow a definitive pattern. In practice, the nursing demand is often perceived as unpredictable, leading to an ad hoc approach to staffing. The primary objective of our study is to demonstrate that longitudinal data analysis can reveal strong statistical regularities in the temporal dynamics of nursing demand. This approach not only provides new possibilities for efficient resource allocation but also paves the way for data‐driven prediction of nursing demand. Our study uses Sankey diagrams to visualize the temporal dynamics of nursing demand within each ward for each fiscal year, representing these dynamics as an overlay of trajectories from multiple individual patients. Consequently, our study reveals ward‐specific statistical regularities in the temporal dynamics of nursing demand. In one ward, approximately 25% of patients experienced an increase in nursing demand from 1 to between 6 and 9 points from the second to the third day of hospitalization, while in another, only 0.1% showed such an increase. These findings suggest that patients admitted to the wards tend to exhibit a certain probabilistic change in nursing demand. This study can predict probabilistically the temporal variation of nursing demand among patients in the coming years by analyzing data on the temporal changes in nursing demand over the past years. Our findings are expected to significantly influence the forecasting of nursing demand and the estimation of nursing resources, leading to data‐driven and more efficient nursing management.

Resolving inter-regional communication capacity in the human connectome.

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

Hippocampal network axons respond to patterned theta burst stimulation with lower activity of initially higher spike train similarity from EC to DG and later similarity of axons from CA1 to EC

Objective. Decoding memory functions for each hippocampal subregion involves extensive understanding of how each hippocampal subnetwork processes input stimuli. Theta burst stimulation (TBS) recapitulates natural brain stimuli which potentiates synapses in hippocampal circuits. TBS is typically applied to a bundle of axons to measure the immediate response in a downstream subregion like the cornu ammonis 1 (CA1). Yet little is known about network processing in response to stimulation, especially because individual axonal transmission between subregions is not accessible. Approach. To address these limitations, we reverse engineered the hippocampal network on a micro-electrode array partitioned by a MEMS four-chambered device with interconnecting microfluidic tunnels. The micro tunnels allowed monitoring single axon transmission which is inaccessible in slices or in vivo. The four chambers were plated separately with entorhinal cortex (EC), dentate gyrus (DG), CA1, and CA3 neurons. The patterned TBS was delivered to the EC hippocampal gateway. Evoked spike pattern similarity in each subregions was quantified with Jaccard distance metrics of spike timing. Main results. We found that the network subregion produced unique axonal responses to different stimulation patterns. Single site and multisite stimulations caused distinct information routing of axonal spikes in the network. The most spatially similar output at axons from CA3 to CA1 reflected the auto association within CA3 recurrent networks. Moreover, the spike pattern similarities shifted from high levels for axons to and from DG at 0.2 s repeat stimuli to greater similarity in axons to and from CA1 for repetitions at 10 s intervals. This time-dependent response suggested that CA3 encoded temporal information and axons transmitted the information to CA1. Significance. Our design and interrogation approach provide first insights into differences in information transmission between the four subregions of the structured hippocampal network and the dynamic pattern variations in response to stimulation at the subregional level to achieve probabilistic pattern separation and novelty detection.

Examination of occurrence probability of vortex-induced vibration of long-span bridge decks by Fokker–Planck–Kolmogorov equation

Vortex-induced vibration (VIV) is a major concern for long-span bridge decks, which may happen especially for closed-box girders under certain environmental wind conditions. Traditionally, sustained VIV is observed as long as the wind speed falls within the lock-in region in wind tunnel tests. However, structural vibration monitoring of long-span bridges has indicated that frequency of VIV events may exhibit a probabilistic pattern. This paper proposes an analytical framework to evaluate the probability of VIV occurrence. First, a nonlinear aeroelastic model with multi-stability limit cycles is used to simulate the bridge deck VIV response within the VIV-triggering wind conditions. The first structural dynamic equilibrium point is usually unstable; the bridge deck instantaneous oscillation amplitude, which is excited by external environmental loads, must exceed this fixed point to trigger the VIV event. The external environmental excitation applied on bridge can be identified from the deck vibration without VIV occurring, then the VIV occurrence probability can be evaluated by Fokker–Planck–Kolmogorov equation. This study utilizes the field measurement data from the Humen Bridge, on which VIV event occurred the first time after 23-year servicing period, because water-filled barriers were placed along the two edges of bridge deck. Afterwards, VIV occurred frequently for over one month. The bridge deck VIV occurrence probability is evaluated by combining environmental wind information, stochastic deck excitation magnitude and a nonlinear aeroelastic VIV model. The results suggest that the proposed methods can approximate the VIV occurrence probability in comparison with empirical estimations using field measurements.

Market Crashes and Time-Translation Invariance

The general framework for quantitative technical analysis of market prices is revisited and extended. The concept of a global time-translation invariance and its spontaneous violation and restoration is introduced and discussed. We find that different temporal patterns leading to some famous crashes (e.g., bubbles, hockey sticks, etc.) exhibit analogous probabilistic distributions found only in the time series for the stock market indices. A number of examples of crashes are presented. We stress that our goal here is to study the crash as a particular phenomenon created by spontaneous time-translation symmetry breaking/restoration. We ask only “how to calculate and interpret the probabilistic pattern which we encounter in the day preceding crash, and how to calculate the typical market reactions to shock?”.

Molecular sensitization pattern to house dust mites is formed from the first years of life and includes group 1, 2, Der p 23, Der p 5, Der p 7 and Der p 21 allergens

BackgroundAs the process and nature of developing sensitivity to house dust mites (HDMs) remain not fully studied, our goal was to establish the pattern, nature and timeframe of house dust mite (HDM) sensitization development in patients in Ukraine as well as the period when treatment of such patients would be most effective.MethodsThe data of the multiplex allergy test Alex2 was collected from 20,033 patients. To determine age specifics of sensitization, descriptive statistics were used. Bayesian Network analysis was used to build probabilistic patterns of individual sensitization.ResultsPatients from Ukraine were most often sensitized to HDM allergens of group 1 (Der p 1, Der f 1) and group 2 (Der p 2, Der f 2) as well as to Der p 23 (55%). A considerable sensitivity to Der p 5, Der p 7 and Der p 21 allergens was also observed. The overall nature of sensitization to HDM allergens among the population of Ukraine is formed within the first year of life. By this time, there is a pronounced sensitization to HDM allergens of groups 1 and 2 as well as to Der p 23. Significance of sensitization to Der p 5, Der p 7 and Der p 21 allergens grows starting from the age of 3–6. Bayesian Network data analysis indicated the leading role of sensitization to Der p 1 and Der f 2. While developing the sensitivity to group 5 allergens, the leading role may belong to Der p 21 allergen.ConclusionThe results obtained indicate the importance of determining the sensitization profile using the multi-component approach. A more detailed study of the optimal age for AIT prescription is required as the pattern of sensitization to HDMs is formed during the first year of life.

Estimation of probability distribution of long-term fatigue damage on wind turbine tower using residual neural network

Fatigue is one of the most significant failure modes in structural and mechanical design. As for wind engineering, the fatigue issue on wind turbine towers is more critical since it concerns not only the structural safety but also the power production of the wind turbine. On the one hand, due to the complex fluid–solid interaction and the sophisticated control system of the wind turbine, it is impossible to predict the probability of fatigue-induced failure on wind turbine towers by analytical method. On the other hand, the structural reliability methods based on a numerical approach are usually time-consuming. To overcome these drawbacks, this work firstly proposes a probabilistic fatigue analysis framework to estimate the fatigue damage of wind turbine tower based on numerical simulations. Then, to reduce the computational cost of numerical approach, a residual neural network (ResNet)-assisted fatigue estimation approach is designed for the assessment of long-term fatigue loads under the proposed probabilistic fatigue analysis framework. The proposed probabilistic fatigue analysis framework estimates the cumulative fatigue damage on the cross-section of wind towers in a probabilistic pattern. The designed surrogate-assisted approach learns a model to approximate the relationship between wind speed and the fatigue damage. Then, this surrogate model can be used to predict fatigue damage under different wind speed so that a large number of simulation can be replaced by model prediction. Consequently, the efficiency of the proposed probabilistic fatigue analysis method can be significantly improved. Our proposed method is validated by numerical studies with a state-of-the-art wind turbine and has been applied in a wind turbine design with real-world wind loads.

BAYESIAN NETWORK ANALYSIS INDICATES A HIGH PROBABILITY OF SIMULTANEOUS SENSITIZATION TO MAJOR GRASS ALLERGENS

<h3>Introduction</h3> Grass pollen allergy impacts many patients characterized by high allergen cross-reactivity. The primary elicitor of grass allergy is Phl p 1. However, IgE to Phl p 5 or Phl p 2 also serve as markers of true grass pollen sensitization. Understanding the patterns of sensitization to grasses in certain population remains important for accurate prevention and treatment of allergy to Poaceae pollen. <h3>Methods</h3> Data obtained by multiplex allergy testing Alex² was collected from 20,333 patients. Bayesian Network analysis was used to build probabilistic patterns of patient sensitization to majpr grass pollen allergens. <h3>Results</h3> Grass pollen sensitive individuals constituted 6170 patients or 30.34 % of the entire database; 3935 (19.35 %) were sensitive to Phl p 1; 3460 (17.02 %) – to Lol p 1; 2842 (13.98 %) – to Cyn d 1; and 1772 (8.71 %) – to Phl p 2. Bayesian Network analysis indicated that sensitization to Phl p 1 was associated with sensitivity to Cyn d 1, Lol p 1, Phl p 2 and Sec c_pollen with 94.70 % probability. Sensitization to Lol p 1 was connected with sensitivity to Cyn d 1, Pas n extract, Phl p 2, Phl p 5 with 91.96 % probability. Cyn d 1 sensitization could be seen together with Cyn d, Sec c_pollen, Phr c extracts and Phl p 6 sensitization in 86.58 % of cases. <h3>Conclusion</h3> Bayesian Network analysis suggests that group I allergens are highly cross-reactive and can be a good target for AIT in the Ukrainian population sensitive to grass pollen.

Adults regularize variation when linguistic cues suggest low input reliability

Children regularize inconsistent probabilistic patterns in linguistic input, yet they also acquire and match probabilistic sociolinguistic variation. What factors in the language input contribute to whether children will regularize or match the probabilistic patterns they are exposed to? Here, we test the hypothesis that low input reliability facilitates regularization. As a first step, we asked adult participants to acquire a variable plural marking pattern from a written (Exp 1) and a spoken (Exp 2) artificial language under different conditions, where they were led to believe input was more, or less, reliable. In both experiments, input reliability was manipulated through both information about the speaker (e.g., whether the speaker was likely to make mistakes) and linguistic cues (e.g., typos or pronunciation errors). Results showed that adults regularized the written language more only when they were told the speaker would make mistakes and the plural variants resembled typos (Exp 1), whereas they regularized the spoken language more when the plural variants resembled pronunciation errors regardless of the speaker’s said reliability in the spoken language. We conclude that input reliability is an important factor that can modulate learners’ regularization of probabilistic linguistic input, and that linguistic cues may play a more critical role than top-down knowledge about the speaker. The current study lays down an important foundation for future work exploring whether children are able to incorporate input reliability cues when learning probabilistic linguistic variation.

Spatial and temporal variation in drought types for wheat in Argentina and its association with actual yield and fertilization rate

Wheat annual acreage in Argentina is 3.0–6.8 Mha and spans across latitudes between − 24° to − 38° in regions with diverse climate, soil, and management practices. Identifying regional limitations is critical for both breeding and crop management. The objectives of this work were to (1) determine the environment types of rainfed wheat in Argentina as sets of spatial, probabilistic patterns of water stress across the region, accounting for crop phenology, soil depth and plant available water at sowing, and (2) associate the environment types with actual yield and fertilizer rate in farmers’ fields. Water stress was quantified as the simulated daily ratio between actual and potential crop transpiration (WSI) using the CERES-Wheat model and climate series (1983–2015) in 15 locations and classified the WSI trajectories along crop phenology into environment types. Environment types revealed two contrasting regions, one in the southwest and east, and the other in the center and north of the country. The southwestern-eastern region showed a higher synchrony between rainfall and crop cycle, higher frequency of non-stressful environments, higher actual yield and higher fertilization rates in farmers’ fields. The central-northern region showed lower synchrony between rainfall and crop cycle, a higher frequency of stressful environments, lower actual yield, and lower fertilization rates. This work highlights (i) the diverse environmental conditions for wheat in Argentina, hence providing a quantitative framework for matching adaptive traits and crop management to specific environments, and (ii) the two causal links between drought and crop yield reduction: a direct biological link whereby dry soil and elevated evaporative demand reduce growth and yield, and an indirect link mediated by a more conservative cropping approach with lower inputs in drier, riskier environments.

Pseudo and resistant hypertension: A chaotic perspective.

Systemic blood pressure (BP) may oscillate for homeostatic needs (equilibrium by constancy) or just as shifts in other intrinsic and extrinsic variables known as allostatic changes. This transitory pressure often rises alerts physicians to out‐of‐control hypertension or even hypertensive crisis. There is a very complex theory underlying these stochastic phenomena, which physicists and mathematicians translate into a single word: chaos. These changes happen according to a stochastic probabilistic pattern that presumes chaotic but somewhat predictable and nonlinear modeling of BP‐related dynamics as a mathematical approach. Based on the chaos theory, small changes at the initial BP (baseline overtime) values could disturb the homeostasis leading to extreme BP chaotic shifts. These almost insignificant oscillations may also affect other variables and systems, leading to the misdiagnosis of hypertension, “out‐of‐control” BP levels, and resistant hypertension (RHT). Thus, these unpredictable and transient increases in BP values may be improperly diagnosed as the white coat and masked or resistant hypertension. Indeed, the interference of the chaos in any phenotype of (true or false) hard to control BP is not considered in clinical settings. This review provides some basic concepts on chaos theory and BP regulation. Besides pseudoresistant hypertension (lack of adherence, circadian variations, and others (white‐coat, masked, early morning effects or hypertension), chaotic changes can be responsible for out‐of‐control hypertension.

Acquisition of cumulative conditioning effects on words: Spanish-speaking children’s [subject pronoun + verb] usage

Child language acquisition research has provided ample evidence of lexical frequency effects. This corpus-based analysis introduces a novel frequency measure shown to significantly constrain adult language variation, but heretofore unexplored in child language acquisition research. Among adults, frequent occurrence of a form in a particular discourse context that conditions usage accumulates in memory over time and shapes the lexical representation of that form. This study contributes to the body of research on frequency effects in child language acquisition by testing whether such cumulative conditioning effects are also found among children, and, if so, at what age such effects appear. Specifically, the study investigates the influence of a distributional frequency measure (each verb form’s likelihood of use in a switch vs same-reference discourse context) on variable subject personal pronoun (SPP) expression ( N = 2227) in Spanish (e.g. yo voy ~ voy, both meaning ‘I go’) in the speech of 65 monolingual children in two age cohorts. Results reveal sensitivity to the contextual conditioning of discourse continuity (switch reference) among both the younger (6- and 7-year-olds) and older (8- and 9-year-olds) children in support of previous research. In addition, each verb’s likelihood of use in a switch-reference context significantly predicted the SPP use among the older children, but not the younger ones, suggesting that the cumulative effect of a probabilistic pattern takes time to emerge during childhood. The lexically specific accumulation in memory of contextual conditioning effects supports exemplar models of child language acquisition: each instance of use in discourse contributes to the lexical representation of that form and, over time, plays a role in the creation of morphosyntactic patterns during language development.

Click Fraud Detection Approaches to analyze the Ad Clicks Performed by Malicious Code

Mobile PR is an important component of the mobile app ecosystem. A major threat to this ecosystem’s long-term health is click fraud, which involves clicking on ads while infected with malware or using an automated bot to do it for you. The methods used to identify click fraud now focus on looking at server requests. Although these methods have the potential to produce huge numbers of false negatives, they may easily be avoided if clicks are hidden behind proxies or distributed globally. AdSherlock is a customer-side (inside the app) efficient and deployable click fraud detection system for mobile applications that we provide in this work. AdSherlock separates the computationally expensive click request identification procedures into an offline and online approach. AdSherlock uses URL (Uniform Resource Locator) tokenization in the Offline phase to create accurate and probabilistic patterns. These models are used to identify click requests online, and an ad request tree model is used to detect click fraud after that. In order to develop and evaluate the AdSherlock prototype, we utilise actual applications. It injects the online detector directly into an executable software package using binary instrumentation technology (BIT). The findings show that AdSherlock outperforms current state-of-the-art methods for detecting click fraud with little false positives. Advertisement requests identification, mobile advertising fraud detection are some of the keywords used in this article.

(Invited) Detection of Dislocations Using X-Ray Diffraction Imaging (Topography) KOH Etching and Their Evolution after Epitaxial Growth in 4H-SiC

This work reports the characterization and analysis of various types of SiC substrate wafers by a high throughput (2 wafers/hour) X-ray diffraction imaging (XRDI) system. Quantitative maps of various dislocations are obtained using Automated Defect Classification (ADC) and correlated to other defect characterization methods including molten KOH etching and Photoluminescence (PL) scans. Different and often complementary views of the wafer were obtained by integrating the data. Epitaxial layers grown on the substrates show the probabilistic pattern of nucleation for various types of extended defects from defects in the underlying crystal structure. Nucleated Stacking Faults, especially, were well correlated with distributions of Basal Plane dislocations in the substrates.

Developmental Dyslexia, Reading Acquisition, and Statistical Learning: A Sceptic's Guide.

Many theories have been put forward that propose that developmental dyslexia is caused by low-level neural, cognitive, or perceptual deficits. For example, statistical learning is a cognitive mechanism that allows the learner to detect a probabilistic pattern in a stream of stimuli and to generalise the knowledge of this pattern to similar stimuli. The link between statistical learning and reading ability is indirect, with intermediate skills, such as knowledge of frequently co-occurring letters, likely being causally dependent on statistical learning skills and, in turn, causing individual variation in reading ability. We discuss theoretical issues regarding what a link between statistical learning and reading ability actually means and review the evidence for such a deficit. We then describe and simulate the “noisy chain hypothesis”, where each intermediary link between a proposed cause and the end-state of reading ability reduces the correlation coefficient between the low-level deficit and the end-state outcome of reading. We draw the following conclusions: (1) Empirically, there is evidence for a correlation between statistical learning ability and reading ability, but there is no evidence to suggest that this relationship is causal, (2) theoretically, focussing on a complete causal chain between a distal cause and developmental dyslexia, rather than the two endpoints of the distal cause and reading ability only, is necessary for understanding the underlying processes, (3) statistically, the indirect nature of the link between statistical learning and reading ability means that the magnitude of the correlation is diluted by other influencing variables, yielding most studies to date underpowered, and (4) practically, it is unclear what can be gained from invoking the concept of statistical learning in teaching children to read.

Neural network model of the wells' drilling speed and modes predicting in complex reservoirs

The article considers the problem connected with the study of well drilling rates in complex reservoirs. Its solution is presented in the form of a neural network model that takes into account the structural, geomechanical and technological features of the «rock mass — well» system.The possibility of predicting the well drilling method with different strength and structural-lithological characteristics of the massif, based on neural network modeling, is presented.The purpose of this study is to obtain mathematical models for analysis of the probabilistic and statistical patterns of well drilling processes in conditions of uncertainty.The scientific novelty of the work performed is the qualitative and quantitative assessment of the mutual influence of geological and technological factors on the well drilling rate; search for optimal well drilling modes in complex reservoirs on the basis of mathematical modeling.

Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex

Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed five images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items (plus 100 ms per item). The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.

Slope and Aspect Effects on Seedbed Microclimate and Germination Timing of Fall-Planted Seeds

Rangeland vegetation in the Great Basin, United States, is frequently disturbed by natural- and human-caused wildfires that facilitate the establishment and dominance of introduced annual weeds such as cheatgrass (Bromus tectorum) and medusahead wildrye (Taeniatherum caput-medusae [L.] Nevski). Ecological resilience and resistance of native and seeded-non-native plant communities in this region, however, appear to follow topographic patterns associated with slope, aspect, and elevation. Currently, resistance and resilience concepts are being used to prioritize rangeland restoration efforts based on soil-climate classification. We hypothesized that probabilistic patterns of shorter-term weather effects on seedbed microclimate might also be correlated with these perceived spatial patterns of resistance and resilience over space. We used a 39-yr gridded weather dataset to estimate seedbed temperature and water potential at seeding depth as a function of slope and aspect using the Simultaneous Heat and Water (SHAW) model. Seedbed temperature and water potential were then used as input to hydrothermal germination response models to generate indices of seedbed favorability for initial germination and emergence and to estimate cumulative germination response as a function of topography and planting date for the very-fast-germinating cheatgrass, fast-germinating bottlebrush squirreltail (Elymus elymoides [Raf] Swezey), and slower-germinating Idaho fescue (Festuca idahoensis Elmer). Topographic mapping of seedbed favorability showed distinct seasonal patterns associated with both slope and aspect. Southern exposures are likely to facilitate both prewinter germination for early-fall-planted seeds and relatively more midwinter germination for seeds planted later in the fall, but these exposures are also less subject to midwinter frost effects. Northern exposures were likely to delay germination into later winter and early spring and thus avoid potential exposure to a higher probability of winter frost mortality. Microclimatic simulations of this type may provide new metrics for improving our understanding of the mechanistic causes of observed patterns of ecological resistance and resilience over space.

A Linguistic Analysis Metric in Detecting Ransomware Cyber-attacks

Originating and striking from anywhere, cyber-attacks have become ever more sophisticated in our modern society and users are forced to adopt increasingly good and vigilant practices to protect from them. Among these, ransomware remains a major cyber-attack whose major threat to end users (disrupted operations, restricted files, scrambled sensitive data, financial demands, etc.) does not particularly lie in number but in severity. In this study we explore the possibility of real-time detection of ransomware source through a linguistic analysis that examines machine translation relative to the Levenshtein Distance and may thereby provide important indications as to attacker’s language of origin. Specifically, the aim of our research is to advance a metric to assist in determining whether an external ransom text is an indicator of either a human- or a machine-generated cyber-attack. Our proposed method works its argument on a set of Eastern European languages but is applicable to a large(r) range of languages and/or probabilistic patterns, being characterized by usage of limited resources and scalability properties.

Testing MaxEnt with sound symbolism: A stripy wug-shaped curve in Japanese Pokémon names

An issue that is actively explored in the contemporary linguistics literature is how to account for probabilistic generalizations, for which there are currently various competing theories. To bear on this debate, Hayes (2021) proposes that we examine a grammatical framework in terms of its quantitative signature, typical probabilistic patterns that the framework is predicted to generate. In this research report, I zoom in on the quantitative signature of maximum entropy harmonic grammar (MaxEnt HG), because this framework has proven useful in modeling probabilistic generalizations across different linguistic domains. Given a linear scale of violations of one constraint, MaxEnt yields a sigmoid curve. When another constraint is relevant and can be violated multiple times, this sigmoid curve can be shifted, yielding multiple sigmoid curves, which results in a stripy wug-shaped curve. Expanding upon a previous study (Kawahara 2020b), the current experiment demonstrates that we observe a stripy wug-shaped curve in a particularly clear manner in patterns of sound symbolism, systematic associations between sounds and meanings. Concretely, the experiment with Japanese speakers shows that the judgment of Pokémons' evolution status is affected by the mora counts of nonce names, resulting in a sigmoid curve, and that this sigmoid curve is shifted according to the number of voiced obstruents contained in the names. The overall results suggest that MaxEnt is a useful tool for modeling systematic sound-meaning correspondences.