Additive partitioning of multispecies distributional aggregation of local assemblages.

The distribution of species is not random in space. At the finest-resolution spatial scale, that is, field sampling locations, distributional aggregation level of different species would be determined by various factors, for example spatial autocorrelation or environmental filtering. However, few studies have quantitatively measured the importance of these factors. In this study, inspired by the statistical properties of a Markov transition model, we propose a novel additive framework to partition local multispecies distributional aggregation levels for sequential sampling-derived field biodiversity data. The framework partitions the spatial distributional aggregation of different species into two independent components: regional abundance variability and the local spatial inertia effect. Empirical studies from field amphibian surveys through line-transect sampling in southwestern China (Minya Konka) and central-southern Vietnam showed that local spatial inertia was always the dominant mechanism structuring the local occurrence and distributional aggregation of amphibians in the two regions with a latitudinal gradient from 1200 to nearly 4000 m. However, regional abundance variability is still nonnegligible in highly diverse tropical regions (i.e. Vietnam) where the altitude is not higher than 2000 m. In summary, we propose a novel framework that shows that the multispecies distributional aggregation level can be structured by two additive components. The two partitioned components could be theoretically independent. These findings are expected to deepen our understanding of the local community structure from the perspective of both spatial distribution and regional diversity patterns. The partitioning framework might have potential applications in field ecology and macroecology research.

Negative self-referent cognition predicts future depression symptom change: an intensive sampling approach.

Cognitive theories of depression assert that negative self-referent cognition has a causal role in the development and maintenance of depression symptoms, but few studies have examined temporal associations between these constructs using intensive, longitudinal sampling strategies. In three samples of undergraduate students, we examined associations between change in self-referent processing and depression across 5 daily assessments (Sample 1, N = 303, 1,194 measurements, 79% adherence), 7 daily assessments (Sample 2, N = 313, 1,784 measurements, 81% adherence), and 7 weekly assessments (Sample 3; N = 155, 833 measurements, 81% adherence). Random intercept cross-lagged panel models indicated large cross-lagged effects in two of the three samples (Samples 1 and 3 but not Sample 2), such that more negative self-referent thinking than usual was significantly associated with a subsequent increase in depression symptoms at the next time lag. Notably, change in depression from usual was not associated with increases in negative self-referent processing at the next time point in any sample. These findings suggest that change in negative self-referent processing may be causally linked to future increases in depression on a day-to-day and week-to-week basis, although confidence in this conclusion is tempered somewhat by a lack of replication in Sample 2.

Associative memory retrieval modulates upcoming perceptual decisions.

Expectations can inform fast, accurate decisions. But what informs expectations? Here we test the hypothesis that expectations are set by dynamic inference from memory. Participants performed a cue-guided perceptual decision task with independently-varying memory and sensory evidence. Cues established expectations by reminding participants of past stimulus-stimulus pairings, which predicted the likely target in a subsequent noisy image stream. Participant's responses used both memory and sensory information, in accordance to their relative reliability. Formal model comparison showed that the sensory inference was best explained when its parameters were set dynamically at each trial by evidence sampled from memory. Supporting this model, neural pattern analysis revealed that responses to the probe were modulated by the specific content and fidelity of memory reinstatement that occurred before the probe appeared. Together, these results suggest that perceptual decisions arise from the continuous sampling of memory and sensory evidence.

Sampling, a New iOS Application for Assessment of Damage by Diseases and Insect Pests Using Sequential Sampling Plans.

Regular scouting for plant diseases and insect pests by growers, crop consultants, extension educators, and researchers (herein defined as stakeholders) is the cornerstone of integrated pest management practices. Sequential sampling plans have the potential to save time and labor in field scouting and reduce the frequency of errors surrounding decision-making. The incorporation of the algorithms behind sequential sampling plans into mobile devices can make scouting for diseases and insect pests more straightforward, practical, and enjoyable. Here, we introduce an iOS application called Sampling. The application was designed for stakeholders to use on a mobile device for assessing disease and insect pest incidence in the field using sequential sampling plans. The application allows users to select a disease or insect pest from a prepopulated list and specify the objective of sampling: Estimation or classification. Conducting sequential sampling depends upon different precision levels and action thresholds within each objective. Detailed instructions for each sequential sampling plan are available as a guide. When sampling begins, users enter the number of diseased individuals at each sampling unit. The specific algorithm developed for the disease or insect pest will inform the user when to stop sampling for the desired goal and return the final incidence and precision or threshold achieved. Results are automatically saved in the application, and the user can inspect and share results by exporting them to a range of compatible programs. The initial version of Sampling (1.1) was released with the sequential sampling plans for Cercospora leaf spot of table beet. Sequential sampling plans for additional diseases or pests will be added to Sampling in subsequent versions. Sampling is available as a free download from the Apple Store (https://apple.co/3pUiYKy) and is compatible with iOS 14.0 or greater on the iPhone or iPad.

Under Threat, Weaker Evidence Is Required to Reach Undesirable Conclusions.

Critical decisions, such as in domains ranging from medicine to finance, are often made under threatening circumstances that elicit stress and anxiety. The negative effects of such reactions on learning and decision-making have been repeatedly underscored. In contrast, here we show that perceived threat alters the process by which evidence is accumulated in a way that may be adaptive. Participants (n = 91) completed a sequential evidence sampling task in which they were incentivized to accurately judge whether they were in a desirable state, which was associated with greater rewards than losses, or an undesirable state, which was associated with greater losses than rewards. Before the task participants in the "threat group" experienced a social-threat manipulation. Results show that perceived threat led to a reduction in the strength of evidence required to reach an undesirable judgment. Computational modeling revealed this was because of an increase in the relative rate by which negative information was accumulated. The effect of the threat manipulation was global, as the alteration to evidence accumulation was observed for information which was not directly related to the cause of the threat. Requiring weaker evidence to reach undesirable conclusions in threatening environments may be adaptive as it can lead to increased precautionary action.SIGNIFICANCE STATEMENT To make good judgments, people gather information. As information is often unlimited, a decision has to be made as to when the data are sufficiently strong to reach a conclusion. Here, we show that this decision is significantly influenced by perceived threat. In particular, under threat, the rate of negative information accumulation increased, such that weaker evidence was required to reach an undesirable conclusion. Such modulation could be adaptive as it can result in enhanced cautious behavior in dangerous environments.

Selective Integration during Sequential Sampling in Posterior Neural Signals.

Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as "selective integration" can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers' decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.

Sequential Sampling and Estimation of Approximately Bandlimited Graph Signals.

Graph signal sampling has been widely studied in recent years, but the accurate signal models required by most of the existing sampling methods are usually unavailable prior to any observations made in a practical environment. In this paper, a sequential sampling and estimation algorithm is proposed for approximately bandlimited graph signals, in the absence of prior knowledge concerning signal properties. We approach the problem from a Bayesian perspective in which we formulate the signal prior by a multivariate Gaussian distribution with unknown hyperparameters. To overcome the interconnected problems associated with the parameter estimation, in the proposed algorithm, hyperparameter estimation and sample selection are performed in an alternating way. At each step, the unknown hyperparameters are updated by an expectation maximization procedure based on historical observations, and then the next node in the sampling operation is chosen by uncertainty sampling with the latest hyperparameters. We prove that under some specific conditions, signal estimation in the proposed algorithm is consistent. Subsequent validation of the approach through simulations shows that the proposed procedure yields performances which are significantly better than existing state-of-the-art approaches notwithstanding the additional attribute of robustness in the presence of a broad range of signal attributes.

Generation of digital patients for the simulation of tuberculosis with UISS-TB.

BACKGROUND: The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial. RESULTS: One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject. CONCLUSIONS: We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration.

Modeling choice paradoxes under risk: From prospect theories to sampling-based accounts.

Important developments in the study of decision making have been based on the establishment and testing of choice paradoxes (e.g., Allais') that reject different theories (e.g., Expected Utility Theory). One of the most popular and celebrated models in the literature, Cumulative Prospect Theory (CPT), has managed to retain its status despite a growing body of empirical evidence stemming from a collection of choice paradoxes that reject it. Two alternative models, Transfer of Attention Exchange (TAX) and an extension of Decision Field Theory (DFTe), have been proposed as possible alternatives to CPT. To date, no study has directly compared these three models within the context of a large set of lottery problems that tests different choice paradoxes. The present study accomplishes this by using a large and diverse set of lottery problems, involving both potential gains and losses. Our results support the presence and robustness of a set of 'strong' choice paradoxes that reject CPT irrespective of its parametric form. Model comparison results show that DFTe provides the best account for the present set of lottery problems, as it is able to accommodate the choice data at large in a parsimonious fashion. The success of DFTe shows that many behavioral phenomena, including paradoxes that CPT cannot account for, can be successfully captured by a simple noisy-sampling process. Overall, our results suggest that researchers should move away from CPT, and focus their efforts on alternative models such as DFTe.

ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling.

Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model's accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

Methodological evaluation of gingival crevicular fluid volume and neutrophil elastase levels: sequential sampling, length of sampling time and two different sampling methods.

Objectives: The mechanisms underlying the formation and composition of gingival crevicular fluid (GCF) and its flow into and from periodontal pockets are not understood very well. The aim of this study was to evaluate the length of sampling time and sequential sampling of GCF neutrophil elastase (NE) enzyme levels by using intracrevicular and orifice methods.Material and methods: Twenty adults (mean age of 41.8 years, ranged 31-60 years, 18 males and 2 females) with chronic periodontitis were enrolled and all completed the 3-d study. GCF was collected by both intracrevicular and intrasulcular methods, 720 samples of GCF were collected. In first, second and third day, the length of sampling time in seconds (s) and order were '5- 10-30-s'; '10- 30- 5-s' and '30- 5- 10-s,' respectively. GCF elastase levels were determined by hydrolysis of neutrophil specific substrate N-methoxysuccinyl-Ala-Ala-Pro-Val-p-nitroanilide.Results: NE activity (µU) and NE activity/volume (µU/µl) were significantly different for order of sampling (p < .05), but not for the length of sampling time (p>.05).Conclusions: Within the limits of this study, the choice of sampling technique in GCF-profile studies seems to be a critical decision as it has the potential to affect the GCF volume and NE activity.

Multi-factor analysis in language production: Sequential sampling models mimic and extend regression results.

For multi-factor analyses of response times, descriptive models (e.g., linear regression) arguably constitute the dominant approach in psycholinguistics. In contrast empirical cognitive models (e.g., sequential sampling models, SSMs) may fit fewer factors simultaneously, but decompose the data into several dependent variables (a multivariate result), offering more information to analyze. While SSMs are notably popular in the behavioural sciences, they are not significantly developed in language production research. To contribute to the development of this modelling in language, we (i) examine SSMs as a measurement modelling approach for spoken word activation dynamics, and (ii) formally compare SSMs to the default method, regression. SSMs model response activation or selection mechanisms in time, and calculate how they are affected by conditions, persons, and items. While regression procedures also model condition effects, it is only in respect to the mean RT, and little work has been previously done to compare these approaches. Through analyses of two language production experiments, we show that SSMs reproduce regression predictors, and further extend these effects through a multivariate decomposition (cognitive parameters). We also examine a combined regression-SSM approach that is hierarchical Bayesian, which can jointly model more conditions than classic SSMs, and importantly, achieve by-item modelling with other conditions. In this analysis, we found that spoken words principally differed from one another by their activation rates and production times, but not their thresholds to be activated.

Preference accumulation as a process model of desirability ratings.

In desirability rating tasks, decision makers evaluate objects on a continuous response scale. Despite their prominence, full process models of these rating tasks have not been developed. We investigated whether a preference accumulation process, a process often used to model discrete choice, might explain ratings as well. According to our model, attributes from each option are sampled and evaluated stochastically. The evaluations are integrated over time, forming a preference. Preferences for options compete with each other, and accumulated preferences can decay. The model makes precise predictions regarding the statistical distribution of desirability ratings, as well as their dependence on deliberation time and on context. We test and confirm these predictions in two experimental studies. Additionally, quantitative model fits indicate that participants are better described by our proposed model, relative to a model without dynamism, competition, or stochastic attribute sampling. Our results show that the descriptive power of models of preference accumulation extends beyond discrete choice, and that the assumptions of this framework accurately characterize the core cognitive processes at play in the construction of preference and the evaluation of objects.

Assessing performance of sequential analysis methods for active drug safety surveillance using observational data.

The routine use of sequential methods is well established in clinical studies. Recently, there has been increasing interest in applying these methods to prospectively monitor the safety of newly approved drugs through accrual of real-world data. However, the application to marketed drugs using real-world data has been limited and work is needed to determine which sequential approaches are most suited to such data. In this study, the conditional sequential sampling procedure (CSSP), a group sequential method, was compared with a log-linear model with Poisson distribution (LLMP) through a SAS procedure (PROC GENMOD) combined with an alpha-spending function on two large longitudinal US administrative health claims databases. Relative performance in identifying known drug-outcome associations was examined using a set of 50 well-studied drug-outcome pairs. The study finds that neither method correctly identified all pairs but that LLMP often provides better ability and shorter time for identifying the known drug-outcome associations with superior computational performance when compared with CSSP, albeit with more false positives. With the features of flexible confounding control and ease of implementation, LLMP may be a good alternative or complement to CSSP.

Learning to allocate limited time to decisions with different expected outcomes.

The goal of this article is to investigate how human participants allocate their limited time to decisions with different properties. We report the results of two behavioral experiments. In each trial of the experiments, the participant must accumulate noisy information to make a decision. The participants received positive and negative rewards for their correct and incorrect decisions, respectively. The stimulus was designed such that decisions based on more accumulated information were more accurate but took longer. Therefore, the total outcome that a participant could achieve during the limited experiments' time depended on her "decision threshold", the amount of information she needed to make a decision. In the first experiment, two types of trials were intermixed randomly: hard and easy. Crucially, the hard trials were associated with smaller positive and negative rewards than the easy trials. A cue presented at the beginning of each trial would indicate the type of the upcoming trial. The optimal strategy was to adopt a small decision threshold for hard trials. The results showed that several of the participants did not learn this simple strategy. We then investigated how the participants adjusted their decision threshold based on the feedback they received in each trial. To this end, we developed and compared 10 computational models for adjusting the decision threshold. The models differ in their assumptions on the shape of the decision thresholds and the way the feedback is used to adjust the decision thresholds. The results of Bayesian model comparison showed that a model with time-varying thresholds whose parameters are updated by a reinforcement learning algorithm is the most likely model. In the second experiment, the cues were not presented. We showed that the optimal strategy is to use a single time-decreasing decision threshold for all trials. The results of the computational modeling showed that the participants did not use this optimal strategy. Instead, they attempted to detect the difficulty of the trial first and then set their decision threshold accordingly.

Outcomes with a posterior reduced dental arch: a randomised controlled trial.

To compare function, patient satisfaction and quality of life of patients with a posterior reduced mandibular arch with those who had all missing teeth replaced with removable partial dentures. Patients with at least three and not more than six posterior occluding pairs of teeth were enrolled sequentially and randomised into one of two treatment groups: a denture and no-denture group. A research assistant allocated interventions; concealment was ensured using opaque-sealed envelopes. Analysis of data was performed in stages, adding samples of 10 incrementally, and stopping when the relevant statistical tests indicated a clear conclusion as judged by the power set at 80% or above. Study outcomes included patient satisfaction, function and survival of remaining teeth at 3 and 12 months post-intervention, using a visual analogue scale and the Oral Impacts on Daily Performance). Statistical analysis was performed by the 'intention-to-treat' principle. Age range of included patients was 23-55 years (mean = 42·3; s.d. = 9·2), with 78% being females. Most patients (70%) belonged to the low- or no-income group. Nine patients left the study, for different reasons. Primary outcomes for the denture group: 10% of the patients were not satisfied and 20% were unhappy with their function; for the no-denture group: 85% of the patients (with 15% having left the study) were satisfied with both their function and their non-denture status. Patients with posterior reduced mandibular dental arches reported greater perceived satisfaction, function and quality of life compared to those who had received a cobalt-chrome clasp-retained partial removable prosthesis.

Accurate prediction of response to endocrine therapy in breast cancer patients: current and future biomarkers.

Approximately 70% of patients have breast cancers that are oestrogen receptor alpha positive (ER+) and are therefore candidates for endocrine treatment. Many of these patients relapse in the years during or following completion of adjuvant endocrine therapy. Thus, many ER+ cancers have primary resistance or develop resistance to endocrine therapy during treatment. Recent improvements in our understanding of how tumours evolve during treatment with endocrine agents have identified both changes in gene expression and mutational profiles, in the primary cancer as well as in circulating tumour cells. Analysing these changes has the potential to improve the prediction of which specific patients will respond to endocrine treatment. Serially profiled biopsies during treatment in the neoadjuvant setting offer promise for accurate and early prediction of response to both current and novel drugs and allow investigation of mechanisms of resistance. In addition, recent advances in monitoring tumour evolution through non-invasive (liquid) sampling of circulating tumour cells and cell-free tumour DNA may provide a method to detect resistant clones and allow implementation of personalized treatments for metastatic breast cancer patients. This review summarises current and future biomarkers and signatures for predicting response to endocrine treatment, and discusses the potential for using approved drugs and novel agents to improve outcomes. Increased prediction accuracy is likely to require sequential sampling, utilising preoperative or neoadjuvant treatment and/or liquid biopsies and an improved understanding of both the dynamics and heterogeneity of breast cancer.

The importance of decision onset.

The neural mechanisms of decision making are thought to require the integration of evidence over time until a response threshold is reached. Much work suggests that response threshold can be adjusted via top-down control as a function of speed or accuracy requirements. In contrast, the time of integration onset has received less attention and is believed to be determined mostly by afferent or preprocessing delays. However, a number of influential studies over the past decade challenge this assumption and begin to paint a multifaceted view of the phenomenology of decision onset. This review highlights the challenges involved in initiating the integration of evidence at the optimal time and the potential benefits of adjusting integration onset to task demands. The review outlines behavioral and electrophysiolgical studies suggesting that the onset of the integration process may depend on properties of the stimulus, the task, attention, and response strategy. Most importantly, the aggregate findings in the literature suggest that integration onset may be amenable to top-down regulation, and may be adjusted much like response threshold to exert cognitive control and strategically optimize the decision process to fit immediate behavioral requirements.

Irrational time allocation in decision-making.

Time is an extremely valuable resource but little is known about the efficiency of time allocation in decision-making. Empirical evidence suggests that in many ecologically relevant situations, decision difficulty and the relative reward from making a correct choice, compared to an incorrect one, are inversely linked, implying that it is optimal to use relatively less time for difficult choice problems. This applies, in particular, to value-based choices, in which the relative reward from choosing the higher valued item shrinks as the values of the other options get closer to the best option and are thus more difficult to discriminate. Here, we experimentally show that people behave sub-optimally in such contexts. They do not respond to incentives that favour the allocation of time to choice problems in which the relative reward for choosing the best option is high; instead they spend too much time on problems in which the reward difference between the options is low. We demonstrate this by showing that it is possible to improve subjects' time allocation with a simple intervention that cuts them off when their decisions take too long. Thus, we provide a novel form of evidence that organisms systematically spend their valuable time in an inefficient way, and simultaneously offer a potential solution to the problem.

The dynamics of deferred decision.

Decision makers are often unable to choose between the options that they are offered. In these settings they typically defer their decision, that is, delay the decision to a later point in time or avoid the decision altogether. In this paper, we outline eight behavioral findings regarding the causes and consequences of choice deferral that cognitive theories of decision making should be able to capture. We show that these findings can be accounted for by a deferral-based time limit applied to existing sequential sampling models of preferential choice. Our approach to modeling deferral as a time limit in a sequential sampling model also makes a number of novel predictions regarding the interactions between choice probabilities, deferral probabilities, and decision times, and we confirm these predictions in an experiment. Choice deferral is a key feature of everyday decision making, and our paper illustrates how established theoretical approaches can be used to understand the cognitive underpinnings of this important behavioral phenomenon.