The relevance of syntactic complexity for truth judgments: A registered report.

Fluency theories predict higher truth judgments for easily processed statements. We investigated two factors relevant for processing fluency: repetition and syntactic complexity. In three online experiments, we manipulated syntactic complexity by creating simple and complex versions of trivia statements. Experiments 1 and 2 replicated the repetition-based truth effect. However, syntactic complexity did not affect truth judgments although complex statements were processed slower than simple statements. This null effect is surprising given that both studies had high statistical power and varied in the relative salience of syntactic complexity. Experiment 3 provides a preregistered test of the discounting explanation by using improved trivia statements of equal length and by manipulating the salience of complexity in a randomized design. As predicted by fluency theories, simple statements were more likely judged as true than complex ones, while this effect was small and not moderated by the salience of complexity.

Measuring the variability of personality traits with interval responses: Psychometric properties of the dual-range slider response format : Measuring variability with interval responses.

Measuring the variability in persons' behaviors and experiences using ecological momentary assessment is time-consuming and costly. We investigate whether interval responses provided through a dual-range slider (DRS) response format can be used as a simple and efficient alternative: Respondents indicate variability in their behavior in a retrospective rating by choosing a lower and an upper bound on a continuous, bounded scale. We investigate the psychometric properties of this response format as a prerequisite for further validation. First, we assess the test-retest reliability of factor-score estimates for the width of DRS intervals. Second, we test whether factor-score estimates of the visual analog scale (VAS) and the location of DRS intervals show convergent validity. Third, we investigate whether factor-score estimates for the DRS are uncorrelated between different personality scales. We present a longitudinal multitrait-multimethod study using two personality scales (Extraversion, Conscientiousness) and two response formats (VAS, DRS) at two measurement occasions (6-8 weeks apart) for which we estimate factor-score correlations in a joint item response theory model. The test-retest reliability of the width of DRS intervals was high ( ρ ^ ≥ . 73 ). Also, convergent validity between location scores of VAS and DRS was high ( ρ ^ ≥ . 88 ). Conversely, discriminant validity of the width of DRS intervals between Extraversion and Conscientiousness was poor ( ρ ^ ≥ . 94 ). In conclusion, the DRS seems to be a reliable response format that could be used to measure the central tendency of a trait equivalently to the VAS. However, it might not be well suited for measuring intra-individual variability in personality traits.

A tutorial on Bayesian model-averaged meta-analysis in JASP.

Researchers conduct meta-analyses in order to synthesize information across different studies. Compared to standard meta-analytic methods, Bayesian model-averaged meta-analysis offers several practical advantages including the ability to quantify evidence in favor of the absence of an effect, the ability to monitor evidence as individual studies accumulate indefinitely, and the ability to draw inferences based on multiple models simultaneously. This tutorial introduces the concepts and logic underlying Bayesian model-averaged meta-analysis and illustrates its application using the open-source software JASP. As a running example, we perform a Bayesian meta-analysis on language development in children. We show how to conduct a Bayesian model-averaged meta-analysis and how to interpret the results.

Cheating to benefit others? On the relation between Honesty-Humility and prosocial lies.

OBJECTIVE: Among basic personality traits, Honesty-Humility yields the most consistent, negative link with dishonest behavior. The theoretical conceptualization of Honesty-Humility, however, suggests a potential boundary condition of this relation, namely, when lying is prosocial. We therefore tested the hypothesis that the association between Honesty-Humility and dishonesty weakens once lying benefits someone else, particularly so if this other is needy. METHODS: In two online studies (Study 1: N = 775 in Germany; Study 2: N = 737 in the UK, preregistered), we measured self-reported Honesty-Humility and dishonest behavior in incentivized cheating paradigms in which the beneficiary of participants' dishonesty was either the participants themselves, a "non-needy" other (e.g., another participant), or a "needy" other (e.g., a charity). RESULTS: We found support for the robustness of the negative association between Honesty-Humility and dishonesty, even if lying was prosocial. CONCLUSION: Individuals high in Honesty-Humility largely prioritize honesty, even if there is a strong moral imperative to lie; those low in Honesty-Humility, by contrast, tend to lie habitually and thus even if they themselves do not directly profit monetarily. This suggests that (un)truthfulness may be an absolute rather than a relative aspect of Honesty-Humility, although further systematic tests of this proposition are needed.

Enabling Population Biology Studies of Stemphylium vesicarium from Onion with Microsatellites.

Stemphylium leaf blight (SLB), caused by the fungus Stemphylium vesicarium, is dominant within the foliar disease complex affecting onion production in New York (NY). The disease causes premature defoliation and significant reductions in bulb weight and quality. Foliar diseases of onion are usually managed by an intensive fungicide program, but SLB management is complicated by resistance to multiple single-site modes of action. The design of integrated disease management strategies is limited by incomplete knowledge surrounding the dominant sources of S. vesicarium inoculum. To facilitate genomic-based studies of S. vesicarium populations, nine microsatellite markers were developed. The markers were multiplexed into two PCR assays containing four and five fluorescently labeled microsatellite markers. Initial testing of the S. vesicarium isolates found the markers were highly polymorphic and reproducible with an average of 8.2 alleles per locus. The markers were used to characterize 54 S. vesicarium isolates from major NY onion production regions in 2016 (n = 27) and 2018 (n = 27). Fifty-two multilocus genotypes (MLGs) were identified between these populations. Genotypic and allelic diversities were high in both the 2016 and 2018 populations. A greater degree of genetic variation was observed within populations than between years. No distinct pattern of MLGs according to population was identified and some MLGs were closely related between 2016 and 2018. The lack of evidence for linkage among loci also was strongly suggestive of clonal populations with only minor differences between the two populations. These microsatellite markers will be a foundational resource for the testing of hypotheses surrounding the population biology of S. vesicarium and therefore informing disease management.

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.

The Population of Fusarium oxysporum f. sp. cubense in Brazil Is Not Structured by Vegetative Compatibility Group or by Geographic Origin.

Fusarium wilt, caused by the soilborne fungus Fusarium oxysporum f. sp. cubense (Foc), is considered one of the most destructive diseases of bananas in Brazil. In this study, a collection of 194 monosporic isolates from several banana-producing regions located in different climatic zones along a south-to-north transect in Brazil was formed to assess the genetic structure of the population of Foc. The isolates underwent pathogenicity tests, PCR diagnosis for the detection of tropical race 4, and screening of SIX homolog genes that produce putative effector proteins. The vegetative compatibility group (VCG) of 119 isolates was determined by pairing against 17 internationally known VCG-tester strains. A group of 158 isolates was selected for simple sequence repeat (SSR) genotyping. There was moderate diversity of Foc in Brazil. Eight VCGs were identified: 0120, 0122, 0124, 0125, 0128, 01215, 01220, and 01222, of which 78% of isolates belong to a single VCG, whereas 22% of isolates are assigned to multiple VCGs, belonging to complexes of VCGs. The distribution of VCGs is uneven and independent of the banana genotype. The isolates of a VCG shared a similar profile of SIX homologs, but there was no association with geographic region. Four SSR loci were polymorphic, and, on average, 7.5 alleles were detected per locus. Thirty-five multilocus genotypes (MLGs) were identified. There was no association between VCG and MLGs, and no genetic structure of the population of Foc in Brazil was detected.

Spatiotemporal Dynamics of Stemphylium Leaf Blight and Potential Inoculum Sources in New York Onion Fields.

Stemphylium leaf blight (SLB) caused by Stemphylium vesicarium is the dominant foliar disease affecting large-scale onion production in New York. The disease is managed by fungicides, but control failures are prevalent and are attributed to fungicide resistance. Little is known of the relative role of inoculum sources in initiation and spread of SLB epidemics. Plate testing of 28 commercially available organic onion seedlots from 2016 and 2017 did not detect S. vesicarium. This finding suggests that although S. vesicarium has been reported as seed-transmitted, this is unlikely to be a significant inoculum source in commercially available organic seed lots and even less so in fungicide-treated seed used to establish conventional fields. The spatial and spatiotemporal dynamics of SLB epidemics in six onion fields were evaluated along linear transects in 2017 and 2018. Average SLB incidence increased from 0 to 100% throughout the cropping seasons with an average final lesion length of 28.3 cm. Disease progress was typical of a polycyclic epidemic and the logistic model provided the best fit to 83.3% of the datasets. Spatial patterns were better described by the beta-binomial than binomial distribution in half of the datasets (50%) and random patterns were more frequently observed by the index of dispersion (59%). Geostatistical analyses also found a low frequency of datasets with aggregation (60%). Spatiotemporal analysis of epidemics detected that the aggregation was influenced by disease incidence. However, diseased units were not frequently associated with the previous time period according to the spatiotemporal association function of spatial analyses by distance indices. Variable spatial patterns suggested mixed inoculum sources dependent upon location, and likely an external inoculum source at the sampling scale used in this study. A small-plot replicated trial was also conducted in each of 2 years to quantify the effect of S. vesicarium-infested onion residue on SLB epidemics in a field isolated from other onion fields. SLB incidence was significantly reduced in plots without residue compared with those in which residue remained on the soil surface. Burial of infested residue also significantly reduced epidemic progress in 1 year. The effect of infested onion residue on SLB epidemics in the subsequent onion crop suggests rotation or residue management may have a substantial effect on epidemics. However, the presence of an inoculum source external to fields in onion production regions, as indicated by a lack of spatial aggregation, may reduce the efficacy of in-field management techniques.

Sensitive attitudes and adherence to recommendations during the COVID-19 pandemic: Comparing direct and indirect questioning techniques.

During the COVID-19 pandemic, various behavioral measures were imposed to curb the spread of the virus. In a preregistered study based on a quota-representative sample of adult Danish citizens (N = 1031), we compared the prevalence estimates of self-reported handwashing, physical distancing, and attitudes toward the behavioral measures between people surveyed with a direct and an indirect questioning approach (i.e., the crosswise model). Moreover, we investigated two possible predictors of sensitive behaviors and attitudes, namely empathy for people vulnerable to the virus and Honesty-Humility from the HEXACO Model of Personality. We also examined the interaction of both predictors with the questioning format. Survey participants reported more violation of guidelines regarding handwashing and physical distancing when asked indirectly rather than directly, whereas attitudes regarding the behavioral measures did not differ between the two questioning formats. Respondents with less empathy for people vulnerable to COVID-19 reported more violations of handwashing and physical-distancing, and those low on Honesty-Humility reported more violations of physical distancing.

Development of a Sequential Sampling Plan using Spatial Attributes of Cercospora Leaf Spot Epidemics of Table Beet in New York.

Sampling strategies that effectively assess disease intensity in the field are important to underpin management decisions. To develop a sequential sampling plan for the incidence of Cercospora leaf spot (CLS), caused by Cercospora beticola, 31 table beet fields were assessed in the state of New York. Assessments of CLS incidence were performed in six leaves arbitrarily selected in 51 sampling locations along each of three to six linear transects per field. Spatial pattern analyses were performed, and results were used to develop sequential sampling estimation and classification models. CLS incidence (p) ranged from 0.13 to 0.92 with a median of 0.31, and beta-binomial distribution, which is reflective of aggregation, best described the spatial patterns observed. Aggregation was commonly detected (>95%) by methods using the point-process approach, runs analyses, and autocorrelation up to the fourth spatial lag. For Spatial Analysis by Distance Indices, or SADIE, 45% of the datasets were classified as a random pattern. In the sequential sampling estimation and classification models, disease units are sampled until a prespecified target is achieved. For estimation, the goal was sampling CLS incidence with a preselected coefficient of variation (C). Achieving the C = 0.1 was challenging with <51 sampling units, and only observed on datasets with incidence >0.3. Reducing the level of precision, i.e., increasing C to 0.2, allowed the preselected C to be achieved with a lower number of sampling units and with an estimated incidence ([Formula: see text]) close to the true value of p. For classification, the goal was to classify the datasets above or below prespecified thresholds (pt) used for CLS management. The average sample number, or ASN, was determined by Monte Carlo simulations, and was between 20 and 45 at disease incidence values close to pt, and approximately 11 when far from pt. Correct decisions occurred in >76% of the validation datasets. Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.

A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology.

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt ( 2012a ) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt's model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.

Detecting nonadherence without loss in efficiency: A simple extension of the crosswise model.

In surveys concerning sensitive behavior or attitudes, respondents often do not answer truthfully, because of social desirability bias. To elicit more honest responding, the randomized-response (RR) technique aims at increasing perceived and actual anonymity by prompting respondents to answer with a randomly modified and thus uninformative response. In the crosswise model, as a particularly promising variant of the RR, this is achieved by adding a second, nonsensitive question and by prompting respondents to answer both questions jointly. Despite increased privacy protection and empirically higher prevalence estimates of socially undesirable behaviors, evidence also suggests that some respondents might still not adhere to the instructions, in turn leading to questionable results. Herein we propose an extension of the crosswise model (ECWM) that makes it possible to detect several types of response biases with adequate power in realistic sample sizes. Importantly, the ECWM allows for testing the validity of the model's assumptions without any loss in statistical efficiency. Finally, we provide an empirical example supporting the usefulness of the ECWM.

TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling.

Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution. We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity-the beta-MPT approach (Smith & Batchelder, Journal of Mathematical Psychology 54:167-183, 2010) and the latent-trait MPT approach (Klauer, Psychometrika 75:70-98, 2010). TreeBUGS reads standard MPT model files and obtains Markov-chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within- and between-subjects comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent-trait MPT model.

From information processing to decisions: Formalizing and comparing psychologically plausible choice models.

Decision strategies explain how people integrate multiple sources of information to make probabilistic inferences. In the past decade, increasingly sophisticated methods have been developed to determine which strategy explains decision behavior best. We extend these efforts to test psychologically more plausible models (i.e., strategies), including a new, probabilistic version of the take-the-best (TTB) heuristic that implements a rank order of error probabilities based on sequential processing. Within a coherent statistical framework, deterministic and probabilistic versions of TTB and other strategies can directly be compared using model selection by minimum description length or the Bayes factor. In an experiment with inferences from given information, only three of 104 participants were best described by the psychologically plausible, probabilistic version of TTB. Similar as in previous studies, most participants were classified as users of weighted-additive, a strategy that integrates all available information and approximates rational decisions.

Deciding with the eye: how the visually manipulated accessibility of information in memory influences decision behavior.

Decision situations are typically characterized by uncertainty: Individuals do not know the values of different options on a criterion dimension. For example, consumers do not know which is the healthiest of several products. To make a decision, individuals can use information about cues that are probabilistically related to the criterion dimension, such as sugar content or the concentration of natural vitamins. In two experiments, we investigated how the accessibility of cue information in memory affects which decision strategy individuals rely on. The accessibility of cue information was manipulated by means of a newly developed paradigm, the spatial-memory-cueing paradigm, which is based on a combination of the looking-at-nothing phenomenon and the spatial-cueing paradigm. The results indicated that people use different decision strategies, depending on the validity of easily accessible information. If the easily accessible information is valid, people stop information search and decide according to a simple take-the-best heuristic. If, however, information that comes to mind easily has a low predictive validity, people are more likely to integrate all available cue information in a compensatory manner.

Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methods.

Researchers have become increasingly aware that data-analysis decisions affect results. Here, we examine this issue systematically for multinomial processing tree (MPT) models, a popular class of cognitive models for categorical data. Specifically, we examine the robustness of MPT model parameter estimates that arise from two important decisions: the level of data aggregation (complete-pooling, no-pooling, or partial-pooling) and the statistical framework (frequentist or Bayesian). These decisions span a multiverse of estimation methods. We synthesized the data from 13,956 participants (164 published data sets) with a meta-analytic strategy and analyzed the magnitude of divergence between estimation methods for the parameters of nine popular MPT models in psychology (e.g., process-dissociation, source monitoring). We further examined moderators as potential sources of divergence. We found that the absolute divergence between estimation methods was small on average (<.04; with MPT parameters ranging between 0 and 1); in some cases, however, divergence amounted to nearly the maximum possible range (.97). Divergence was partly explained by few moderators (e.g., the specific MPT model parameter, uncertainty in parameter estimation), but not by other plausible candidate moderators (e.g., parameter trade-offs, parameter correlations) or their interactions. Partial-pooling methods showed the smallest divergence within and across levels of pooling and thus seem to be an appropriate default method. Using MPT models as an example, we show how transparency and robustness can be increased in the field of cognitive modeling. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses.

Standard response formats such as rating or visual analogue scales require respondents to condense distributions of latent states or behaviors into a single value. Whereas this is suitable to measure central tendency, it neglects the variance of distributions. As a remedy, variability may be measured using interval-response formats, more specifically the dual-range slider (RS2). Given the lack of an appropriate item response model for the RS2, we develop the Dirichlet dual response model (DDRM), an extension of the beta response model (BRM; Noel & Dauvier in Appl Psychol Meas, 31:47-73, 2007). We evaluate the DDRM's performance by assessing parameter recovery in a simulation study. Results indicate overall good parameter recovery, although parameters concerning interval width (which reflect variability in behavior or states) perform worse than parameters concerning central tendency. We also test the model empirically by jointly fitting the BRM and the DDRM to single-range slider (RS1) and RS2 responses for two Extraversion scales. While the DDRM has an acceptable fit, it shows some misfit regarding the RS2 interval widths. Nonetheless, the model indicates substantial differences between respondents concerning variability in behavior. High correlations between person parameters of the BRM and DDRM suggest convergent validity between the RS1 and the RS2 interval location. Both the simulation and the empirical study demonstrate that the latent parameter space of the DDRM addresses an important issue of the RS2 response format, namely, the scale-inherent interdependence of interval location and interval width (i.e., intervals at the boundaries are necessarily smaller).

How to develop, test, and extend multinomial processing tree models: A tutorial.

Many psychological theories assume that observable responses are determined by multiple latent processes. Multinomial processing tree (MPT) models are a class of cognitive models for discrete responses that allow researchers to disentangle and measure such processes. Before applying MPT models to specific psychological theories, it is necessary to tailor a model to specific experimental designs. In this tutorial, we explain how to develop, fit, and test MPT models using the classical pair-clustering model as a running example. The first part covers the required data structures, model equations, identifiability, model validation, maximum-likelihood estimation, hypothesis tests, and power analyses using the software multiTree. The second part introduces hierarchical MPT modeling which allows researchers to account for individual differences and to estimate the correlations of latent processes among each other and with additional covariates using the TreeBUGS package in R. All examples including data and annotated analysis scripts are provided at the Open Science Framework (https://osf.io/24pbm/). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A review of applications of the Bayes factor in psychological research.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).