Humans create more novelty than ChatGPT when asked to retell a story.

We compare how humans retell stories to how ChatGPT retells stories in chains of three retellings by different people or different accounts on ChatGPT. ChatGPT provides competent summaries of the original narrative texts in one step of retelling. In subsequent retellings few additional changes occur. Human retellers, by contrast, reduce the original text incrementally and by creating 55-60% of novel words and concepts (synsets) at each iteration. The retellings by both ChatGPT and humans show very stable emotion ratings, which is a puzzle for human retellers given the high degree of novel inventions across retellings. ChatGPT maintains more nouns, adjectives, and prepositions and also uses language later acquired in life, while humans use more verbs, adverbs, and negations and use language acquired at a younger age. The results reveal that spontaneous retelling by humans involves ongoing creativity, anchored by emotions, beyond the default probabilistic wording of large language models such as ChatGPT.

Serial reproduction of narratives preserves emotional appraisals.

We conducted the largest multiple-iteration retelling study to date (12,840 participants and 19,086 retellings) with two different studies that test how emotional appraisals are transmitted across retellings. We use a novel Bayesian model that tracks changes across retellings. Study 1 examines the preservation of appraisals of happy and sad stories and finds that retellings preserve the story's degree of happiness and sadness even when length shrinks and aspects of story coherence and rationalisation deteriorate. Study 2 compared the transmission of appraisals of happiness and sadness with embarrassment, disgust, and risk. Appraisals of happiness, sadness, and also embarrassment showed high appraisal preservation, while disgust and risk were not well preserved. We conclude that participants in our studies encoded happy and sad stories by encapsulating the events and details into an overall emotional appraisal of the story and that this processing strategy might also apply to stories involving other emotions like embarrassment. The emotional appraisal played a key role in retelling by helping to guide the selection, invention, and ordering of the story elements. Hence, we posit that emotion appraisals can operate as anchors for remembering and retelling stories, thus playing an important role in narrative communication.

Bayesian Analysis Reporting Guidelines.

Previous surveys of the literature have shown that reports of statistical analyses often lack important information, causing lack of transparency and failure of reproducibility. Editors and authors agree that guidelines for reporting should be encouraged. This Review presents a set of Bayesian analysis reporting guidelines (BARG). The BARG encompass the features of previous guidelines, while including many additional details for contemporary Bayesian analyses, with explanations. An extensive example of applying the BARG is presented. The BARG should be useful to researchers, authors, reviewers, editors, educators and students. Utilization, endorsement and promotion of the BARG may improve the quality, transparency and reproducibility of Bayesian analyses.

Lay evaluations of police and civilian use of force: Action severity scales.

In modern societies, citizens cede the legitimate use of violence to law enforcement agents who act on their behalf. However, little is known about the extent to which lay evaluations of forceful actions align with or diverge from official use-of-force policies and heuristics that officers use to choose appropriate levels of responsive force. Moreover, it is impossible to accurately compare official policies and lay intuitions without first measuring the perceived severity of a set of representative actions. To map these psychometric scale values precisely, we presented participants (N = 411 Amazon Mechanical Turk workers, N = 395 undergraduates) with minimal vignettes describing officer and civilian actions that span the entire range of force options (from polite dialogue to lethal force), and asked them to rate physical magnitude and moral appropriateness. We used Bayesian methods to model the ratings as functions of simultaneously estimated scale values of the actions. Results indicated that the perceived severity of actions across all physical but nonlethal categories clustered tightly together, while actions at the extreme levels were relatively spread out. Moreover, less normative officer actions were perceived as especially morally severe. Broadly, our findings reveal divergence between lay perceptions of force severity and official law enforcement policies, and they imply that the groundwork for disagreement about the legitimacy of police and civilian actions may be partially rooted in the differential way that action severity is perceived by law enforcement relative to civilian observers. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Bayesian data analysis for newcomers.

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

An Evolutionary Analysis of Learned Attention.

Humans and many other species selectively attend to stimuli or stimulus dimensions-but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.

Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users.

Substance dependent individuals (SDI) often exhibit decision-making deficits; however, it remains unclear whether the nature of the underlying decision-making processes is the same in users of different classes of drugs and whether these deficits persist after discontinuation of drug use. We used computational modeling to address these questions in a unique sample of relatively "pure" amphetamine-dependent (N = 38) and heroin-dependent individuals (N = 43) who were currently in protracted abstinence, and in 48 healthy controls (HC). A Bayesian model comparison technique, a simulation method, and parameter recovery tests were used to compare three cognitive models: (1) Prospect Valence Learning with decay reinforcement learning rule (PVL-DecayRI), (2) PVL with delta learning rule (PVL-Delta), and (3) Value-Plus-Perseverance (VPP) model based on Win-Stay-Lose-Switch (WSLS) strategy. The model comparison results indicated that the VPP model, a hybrid model of reinforcement learning (RL) and a heuristic strategy of perseverance had the best post-hoc model fit, but the two PVL models showed better simulation and parameter recovery performance. Computational modeling results suggested that overall all three groups relied more on RL than on a WSLS strategy. Heroin users displayed reduced loss aversion relative to HC across all three models, which suggests that their decision-making deficits are longstanding (or pre-existing) and may be driven by reduced sensitivity to loss. In contrast, amphetamine users showed comparable cognitive functions to HC with the VPP model, whereas the second best-fitting model with relatively good simulation performance (PVL-DecayRI) revealed increased reward sensitivity relative to HC. These results suggest that some decision-making deficits persist in protracted abstinence and may be mediated by different mechanisms in opiate and stimulant users.

Dissociating stimulus-set and response-set in the context of task-set switching.

The primary aim of the present research was to determine how stimulus-set and response-set components of task-set contribute to switch costs and conflict processing. Three experiments are described wherein participants completed an explicitly cued task-switching procedure. Experiment 1 established that task switches requiring a reconfiguration of both stimulus- and response-set incurred larger residual switch costs than task switches requiring the reconfiguration of stimulus-set alone. Between-task interference was also drastically reduced for response-set conflict compared with stimulus-set conflict. A second experiment replicated these findings and demonstrated that stimulus- and response-conflict have dissociable effects on the "decision time" and "motor time" components of total response time. Finally, a third experiment replicated Experiment 2 and demonstrated that the stimulus- and response- components of task switching and conflict processing elicit dissociable neural activity as evidence by event-related brain potentials.

Posterior predictive checks can and should be Bayesian: comment on Gelman and Shalizi, 'Philosophy and the practice of Bayesian statistics'.

Bayesian inference is conditional on the space of models assumed by the analyst. The posterior distribution indicates only which of the available parameter values are less bad than the others, without indicating whether the best available parameter values really fit the data well. A posterior predictive check is important to assess whether the posterior predictions of the least bad parameters are discrepant from the actual data in systematic ways. Gelman and Shalizi (2012a) assert that the posterior predictive check, whether done qualitatively or quantitatively, is non-Bayesian. I suggest that the qualitative posterior predictive check might be Bayesian, and the quantitative posterior predictive check should be Bayesian. In particular, I show that the 'Bayesian p-value', from which an analyst attempts to reject a model without recourse to an alternative model, is ambiguous and inconclusive. Instead, the posterior predictive check, whether qualitative or quantitative, should be consummated with Bayesian estimation of an expanded model. The conclusion agrees with Gelman and Shalizi regarding the importance of the posterior predictive check for breaking out of an initially assumed space of models. Philosophically, the conclusion allows the liberation to be completely Bayesian instead of relying on a non-Bayesian deus ex machina. Practically, the conclusion cautions against use of the Bayesian p-value in favour of direct model expansion and Bayesian evaluation.

Bayesian estimation supersedes the t test.

Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms.

Contextual modulation of attention in human category learning.

In a category-learning experiment, we assessed whether participants were able to selectively attend to different components of a compound stimulus in two distinct contexts. The participants were presented with stimulus compounds for which they had to learn categorical labels. Each compound comprised one feature from each of two dimensions, and on different trials the compound was presented in two contexts, X and Y. In Context X, Dimension A was relevant to the solution of the categorization task and Dimension B was irrelevant, whereas in Context Y, Dimension A was irrelevant and Dimension B was relevant. The results of transfer tests to novel stimuli suggested that people learned to attend selectively to Dimension A in Context X and Dimension B in Context Y. These findings contribute to the growing body of evidence that people can learn to selectively attend to particular dimensions of stimuli dependent on the context in which the stimuli are presented. Furthermore, the findings demonstrate that context-dependent changes in attention transfer to other categorization tasks involving novel stimuli.

Integrating advice and experience: learning and decision making with social and nonsocial cues.

When making decisions, people typically gather information from both social and nonsocial sources, such as advice from others and direct experience. This research adapted a cognitive learning paradigm to examine the process by which people learn what sources of information are credible. When participants relied on advice alone to make decisions, their learning of source reliability proceeded in a manner analogous to traditional cue learning processes and replicated the established learning phenomena. However, when advice and nonsocial cues were encountered together as an established phenomenon, blocking (ignoring redundant information) did not occur. Our results suggest that extant cognitive learning models can accommodate either advice or nonsocial cues in isolation. However, the combination of advice and nonsocial cues (a context more typically encountered in daily life) leads to different patterns of learning, in which mutually supportive information from different types of sources is not regarded as redundant and may be particularly compelling. For these situations, cognitive learning models still constitute a promising explanatory tool but one that must be expanded. As such, these findings have important implications for social psychological theory and for cognitive models of learning.

Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison.

Psychologists have been trained to do data analysis by asking whether null values can be rejected. Is the difference between groups nonzero? Is choice accuracy not at chance level? These questions have been traditionally addressed by null hypothesis significance testing (NHST). NHST has deep problems that are solved by Bayesian data analysis. As psychologists transition to Bayesian data analysis, it is natural to ask how Bayesian analysis assesses null values. The article explains and evaluates two different Bayesian approaches. One method involves Bayesian model comparison (and uses Bayes factors). The second method involves Bayesian parameter estimation and assesses whether the null value falls among the most credible values. Which method to use depends on the specific question that the analyst wants to answer, but typically the estimation approach (not using Bayes factors) provides richer information than the model comparison approach.

Bayesian learning theory applied to human cognition.

Probabilistic models based on Bayes' rule are an increasingly popular approach to understanding human cognition. Bayesian models allow immense representational latitude and complexity. Because they use normative Bayesian mathematics to process those representations, they define optimal performance on a given task. This article focuses on key mechanisms of Bayesian information processing, and provides numerous examples illustrating Bayesian approaches to the study of human cognition. We start by providing an overview of Bayesian modeling and Bayesian networks. We then describe three types of information processing operations-inference, parameter learning, and structure learning-in both Bayesian networks and human cognition. This is followed by a discussion of the important roles of prior knowledge and of active learning. We conclude by outlining some challenges for Bayesian models of human cognition that will need to be addressed by future research. WIREs Cogn Sci 2011 2 8-21 DOI: 10.1002/wcs.80 For further resources related to this article, please visit the WIREs website.

What to believe: Bayesian methods for data analysis.

Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal.

Sequential Learning Models for the Wisconsin Card Sort Task: Assessing Processes in Substance Dependent Individuals.

The Wisconsin Card Sort Task (WCST) is a commonly used neuropsychological test of executive or frontal lobe functioning. Traditional behavioral measures from the task (e.g., perseverative errors) distinguish healthy controls from clinical populations, but such measures can be difficult to interpret. In an attempt to supplement traditional measures, we developed and tested a family of sequential learning models that allowed for estimation of processes at the individual subject level in the WCST. Testing the model with substance dependent individuals and healthy controls, the model parameters significantly predicted group membership even when controlling for traditional behavioral measures from the task. Substance dependence was associated with a) slower attention shifting following punished trials and b) reduced decision consistency. Results suggest that model parameters may offer both incremental content validity and incremental predictive validity.