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Background Despite its popularity as an inferential framework, classical null hypothesis significance testing (NHST) has several restrictions. Bayesian analysis can be used to complement NHST, however, this approach has been underutilized largely due to a dearth of accessible software options. JASP is a recently developed open-source statistical package that facilitates both Bayesian and NHST analysis using a graphical interface. This article provides an applied introduction to Bayesian inference with Bayes factors using JASP. Methods We use JASP to compare and contrast Bayesian alternatives for several common classical null hypothesis significance tests: correlations, frequency distributions, t-tests, ANCOVAs, and ANOVAs. These examples are also used to illustrate the strengths and limitations of both NHST and Bayesian hypothesis testing. Results A comparison of NHST and Bayesian inferential frameworks demonstrates that Bayes factors can complement p -values by providing additional information for hypothesis testing. Namely, Bayes factors can quantify relative evidence for both alternative and null hypotheses. Moreover, the magnitude of this evidence can be presented as an easy-to-interpret odds ratio. Conclusions While Bayesian analysis is by no means a new method, this type of statistical inference has been largely inaccessible for most psychiatry researchers. JASP provides a straightforward means of performing reproducible Bayesian hypothesis tests using a graphical “point and click” environment that will be familiar to researchers conversant with other graphical statistical packages, such as SPSS.

We provide a comprehensive and user-friendly compendium of standards for the use and interpretation of structural equation models (SEMs). To both read about and do research that employs SEMs, it is necessary to master the art and science of the statistical procedures underpinning SEMs in an integrative way with the substantive concepts, theories, and hypotheses that researchers desire to examine. Our aim is to remove some of the mystery and uncertainty of the use of SEMs, while conveying the spirit of their possibilities.

Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, as improper priors cannot be used and objective priors may be subjectively unreasonable. To address these issues I revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. I argue that this is biased when calibrated against proper Bayes factors, but propose adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. I develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P -value is available, an approximate empirical Bayes factor is 10 p . I propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. This provides an objective framework for interpreting statistical evidence, and realises a Bayesian/frequentist compromise.

Network analysis has found widespread utility in many research areas. However, assessing the statistical significance of observed relationships within networks remains a complex challenge. Traditional node permutation tests are often insufficient in capturing the effect of changing network topology by creating reliable null distributions. We propose two randomization alternatives to address this gap: random rewiring and controlled rewiring. These methods incorporate changes in the network topology through edge swaps. However, controlled rewiring allows for more nuanced alterations of the original network than random rewiring. In this sense, this paper introduces a novel evaluation tool, the Expanded Quadratic Assignment Procedure (EQAP), designed to calculate a specific p-value and interpret statistical tests with enhanced precision. The combination of EQAP and controlled rewiring provides a robust network comparison and statistical analysis framework. The methodology is exemplified through two real-world examples: the analysis of an organizational network structure, illustrated by the Enron-Email dataset, and a social network case, represented by the UK Faculty friendship network. The utility of these statistical tests is underscored by their capacity to safeguard researchers against Type I errors when exploring network metrics dependent on intricate topologies.

Realistic and complex decision tasks often allow for many possible solutions. How do we find the correct one? Introspection suggests a process of trying out solutions one after the other until success. However, such methodical serial testing may be too slow, especially in environments with noisy feedback. Alternatively, the underlying learning process may involve implicit reinforcement learning that learns about many possibilities in parallel. Here we designed a multi-dimensional probabilistic active-learning task tailored to study how people learn to solve such complex problems. Participants configured three-dimensional stimuli by selecting features for each dimension and received probabilistic reward feedback. We manipulated task complexity by changing how many feature dimensions were relevant to maximizing reward, as well as whether this information was provided to the participants. To investigate how participants learn the task, we examined models of serial hypothesis testing, feature-based reinforcement learning, and combinations of the two strategies. Model comparison revealed evidence for hypothesis testing that relies on reinforcement-learning when selecting what hypothesis to test. The extent to which participants engaged in hypothesis testing depended on the instructed task complexity: people tended to serially test hypotheses when instructed that there were fewer relevant dimensions, and relied more on gradual and parallel learning of feature values when the task was more complex. This demonstrates a strategic use of task information to balance the costs and benefits of the two methods of learning.

When not engaged in the moment, we often spontaneously represent people, places and events that are not present in the environment. Although this capacity has been linked to the default mode network (DMN), it remains unclear how interactions between the nodes of this network give rise to particular mental experiences during spontaneous thought. One hypothesis is that the core of the DMN integrates information from medial and lateral temporal lobe memory systems, which represent different aspects of knowledge. Individual differences in the connectivity between temporal lobe regions and the default mode network core would then predict differences in the content and form of people's spontaneous thoughts. This study tested this hypothesis by examining the relationship between seed-based functional connectivity and the contents of spontaneous thought recorded in a laboratory study several days later. Variations in connectivity from both medial and lateral temporal lobe regions was associated with different patterns of spontaneous thought and these effects converged on an overlapping region in the posterior cingulate cortex. We propose that the posterior core of the DMN acts as a representational hub that integrates information represented in medial and lateral temporal lobe and this process is important in determining the content and form of spontaneous thought.

The Interface Hypothesis postulates that internal interfaces linking domains within the language system are ultimately acquired at a near-native proficiency level in second language (L2) acquisition. While extensive research in the field of L2 acquisition has demonstrated that L2 learners often fail to completely acquire internal interfaces, the present study adds to this line of research by delving into an understudied phenomenon at the syntax-semantics interface, namely, directions of possession transfer in English Double Object Constructions (DOCs). Specifically, this study focuses on the processing of English DOCs containing verbs with varying or opposing directions of possession transfer. Employing a self-paced reading task and a comprehension task, we examined the performance of 30 native English speakers and 63 first language (L1) Mandarin Chinese learners of English. The findings suggest that L2 learners struggle to fully acquire the intricacies of English DOCs. Additionally, this study reveals that learners encounter challenges in processing English DOCs with left-directional verbs such as buy , compared to those with right-directional verbs such as post and non-directional verbs such as build . This points to an indeterminacy in the processing of the syntax-semantics interface constructions by L2 learners at developmental stages. To account for these findings, we propose an interface indeterminacy account, contending that the indeterminacy in the processing of the syntax-semantics interface constructions by L2 learners stems from the inherent indeterminacy in these constructions. Furthermore, this indeterminacy is likely to be vulnerable to a number of variables, including L1 transfer, learners’ L2 proficiency, and processing resources.

A substantial body of research has demonstrated that science knowledge is correlated with attitudes towards science, with most studies finding a positive relationship between the two constructs; people who are more knowledgeable about science tend to be more positive about it. However, this evidence base has been almost exclusively confined to high and middle-income democracies, with poorer and less developed nations excluded from consideration. In this study, we conduct the first global investigation of the science knowledge-attitude relationship, using the 2018 Wellcome Global Monitor survey. Our results show a positive knowledge-attitude correlation in all but one of the 144 countries investigated. This robust cross-national relationship is consistent across both science literacy and self-assessed measures of science knowledge.

The present study compared the performance of a Large Language Model (LLM; ChatGPT) and human interviewers in interviewing children about a mock-event they witnessed. Children aged 6-8 (N =  78) were randomly assigned to the LLM (n =  40) or the human interviewer condition (n =  38). In the experiment, the children were asked to watch a video filmed by the researchers that depicted behavior including elements that could be misinterpreted as abusive in other contexts, and then answer questions posed by either an LLM (presented by a human researcher) or a human interviewer. Irrespective of condition, recommended (vs. not recommended) questions elicited more correct information. The LLM posed fewer questions overall, but no difference in the proportion of the questions recommended by the literature. There were no differences between the LLM and human interviewers in unique correct information elicited but questions posed by LLM (vs. humans) elicited more unique correct information per question. LLM (vs. humans) also elicited less false information overall, but there was no difference in false information elicited per question. The findings show that the LLM was competent in formulating questions that adhere to best practice guidelines while human interviewers asked more questions following up on the child responses in trying to find out what the children had witnessed. The results indicate LLMs could possibly be used to support child investigative interviewers. However, substantial further investigation is warranted to ascertain the utility of LLMs in more realistic investigative interview settings.

Reliable recognition of nestmates and discrimination against non-nestmates is key to the integrity of social insect colonies. Cuticular hydrocarbon profiles play a key role in this recognition process in many species, including honey bees. Newly emerged worker bees are largely devoid of cuticular hydrocarbons and therefore believed to represent a “blank slate” that is not discriminated against and instead accepted into other colonies regardless of colony origin. However, instead of being unrecognizable, the absence of cuticular hydrocarbons may also represent a recognizable “Gestalt”. Thus, an alternative hypothesis for the universal acceptance of newly emerged workers may be that older workers in every colony learn the absence of cuticular hydrocarbons as a familiar stimulus that belongs to their colony because other such workers are constantly emerging under normal circumstances. Here, we tested this hypothesis by comparing the response to newly emerged workers between bees that matured in colonies with and without newly emerging bees. Contrary to our prediction, we found no significant difference between these two experimental groups in an aggression bioassay towards newly emerged workers. We thus failed to provide empirical evidence against the blank slate hypothesis. However, the groups displayed significant differences in aggression towards foragers from their own respective colonies, indicating that the emergence of new workers in a colony can affect group discriminatory behavior in honey bees. Furthermore, we identified a negative effect of temperature on aggressive behavior toward newly emerged workers.