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In psychological research, noise is often considered a nuisance that obscures rather than contributes information. This simplification overlooks that noise can be informative and that by exploring the nature of the noise one can often draw additional conclusions concerning the underlying psychological processes. It is arguably only in recent years that the mainstream of researchers has taken this idea to heart and demonstrated that it can lead to breakthroughs in the understanding of human behavior. The aim of this special section is to showcase some of the ways in which systematic exploration of noise can be achieved and how it can enrich psychological research. In this introductory article, we introduce the idea of treating noise as endogenous as opposed to exogenous to the theoretical and statistical models of psychological phenomena. We then contribute a historical review of the role of noise in psychological research, including discussions of previous endogenous treatments of noise in the literature. As an illustration, we describe our own research on the precise/not precise model and show how noise distributions can be used to delineate analytic and intuitive modes of reasoning. Finally, we briefly introduce the other contributions to this special section.

Arriving at a clinical diagnosis using diagnostic criteria is fundamental to mental health practice. Here we argue that high diagnostic quality can only be achieved by aligning diagnostic frameworks with the cognitive capabilities of clinicians, which may require redefining not only the diagnostic procedures but also the criteria themselves.

BackgroundImplementing evidence-based parenting programs often involves navigating fidelity-adaptation decisions. While research has explored various aspects of this dilemma, little is known about how practitioners' outcome preferences influence their decisions in real-world scenarios.MethodsThis study employed a discrete choice experiment (DCE) to investigate the relative importance of five outcomes (Relationship Quality, Satisfaction, Workload Strain, Value Conflict, and Reach) in fidelity-adaptation decisions among 209 practitioners delivering evidence-based parenting programs in Sweden. The DCE presented 25 choice sets across five contextual scenarios, analyzed using Bayesian hierarchical logistic regression.ResultsAll five outcomes significantly influenced practitioners' choices, with Relationship Quality emerging as the most impactful (log-odds: 4.56, 95% CI [4.16, 4.91]). Satisfaction and minimizing Value Conflict showed similar importance (log odds: 2.45 and -2.40, respectively), while Workload Strain and Reach had slightly less impact (log odds: -2.10 and 1.96, respectively).ConclusionsThis study offers a novel perspective on the role of outcome preference in navigating fidelity-adaptation decisions. The strong preference for improving parent-child relationships aligns with core parenting program goals, while consideration of other outcomes reflects practitioners' holistic approach to implementation. These findings can inform the design of interventions and implementation strategies that balance effectiveness with real-world constraints, potentially enhancing parenting programs' adoption, sustainability, and impact.

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models.

Intensive care unit (ICU) staff continue to face recurrent work-related traumatic events throughout the COVID-19 pandemic. Intrusive memories (IMs) of such traumatic events comprise sensory image-based memories. Harnessing research on preventing IMs with a novel behavioural intervention on the day of trauma, here we take critical next steps in developing this approach as a treatment for ICU staff who are already experiencing IMs days, weeks, or months post-trauma. To address the urgent need to develop novel mental health interventions, we used Bayesian statistical approaches to optimise a brief imagery-competing task intervention to reduce the number of IMs. We evaluated a digitised version of the intervention for remote, scalable delivery. We conducted a two-arm, parallel-group, randomised, adaptive Bayesian optimisation trial. Eligible participants worked clinically in a UK NHS ICU during the pandemic, experienced at least one work-related traumatic event, and at least three IMs in the week prior to recruitment. Participants were randomised to receive immediate or delayed (after 4 weeks) access to the intervention. Primary outcome was the number of IMs of trauma during week 4, controlling for baseline week. Analyses were conducted on an intention-to-treat basis as a between-group comparison. Prior to final analysis, sequential Bayesian analyses were conducted (n = 20, 23, 29, 37, 41, 45) to inform early stopping of the trial prior to the planned maximum recruitment (n = 150). Final analysis (n = 75) showed strong evidence for a positive treatment effect (Bayes factor, BF = 1.25 × 106): the immediate arm reported fewer IMs (median = 1, IQR = 0–3) than the delayed arm (median = 10, IQR = 6–16.5). With further digital enhancements, the intervention (n = 28) also showed a positive treatment effect (BF = 7.31). Sequential Bayesian analyses provided evidence for reducing IMs of work-related trauma for healthcare workers. This methodology also allowed us to rule out negative effects early, reduced the planned maximum sample size, and allowed evaluation of enhancements. Trial Registration NCT04992390 (www.clinicaltrials.gov).

Research points to the limitations of approaches to decision-making, that rest on general ‘Newtonian principles’ derived from unitary a priori conceptions of rationality. To understand how the mind exploits environments, we instead propose a process of more open-ended discovery and systematization in the mold of Linnaeus’s famous taxonomy of plants.

In the face of the COVID-19 pandemic it is important to identify factors that make people particularly vulnerable of developing mental-health issues in order to provide case-specific treatments. In this article, we examine the roles of two psychological constructs - originally put forth in the behavioral decision sciences - in predicting interindividual differences in fear responses: general risk aversion (GRA) and intolerance of uncertainty (IU). We first provide a review of these constructs and illustrate why they may play important roles in shaping anxietyrelated disorders. Thereafter we present an empirical study that collected survey data from 550 U.S. residents, comprising self-assessments of dispositions towards risk and uncertainty, anxiety- and depression levels, as well as demographic variables - to thus test the extent to which these psychological constructs are predictive of strong fear responses related to COVID-19 (i.e., mortal fear, racing heart). The results from Bayesian multi-model inference analyses showed that GRA and IU were more powerful predictors of fear responses than demographic variables. Moreover, the predictive power of these constructs was independent of general anxiety- and depression levels. Subsequent mediation analyses showed that the effects of GRA and IU were both direct and indirect via anxiety. We conclude by discussing possible treatment options, but also highlight that future research needs to further examine causal pathways and conceptual overlaps.

In 1956, Brunswik proposed a definition of what he called intuitive and analytic cognitive processes, not in terms of verbally specified properties, but operationally based on the observable error distributions. In the decades since, the diagnostic value of error distributions has generally been overlooked, arguably because of a long tradition to consider the error as exogenous (and irrelevant) to the process. Based on Brunswik’s ideas, we develop the precise/not precise (PNP) model, using a mixture distribution to model the proportion of error-perturbed versus error-free executions of an algorithm, to determine if Brunswik’s claims can be replicated and extended. In Experiment 1, we demonstrate that the PNP model recovers Brunswik’s distinction between perceptual and conceptual tasks. In Experiment 2, we show that also in symbolic tasks that involve no perceptual noise, the PNP model identifies both types of processes based on the error distributions. In Experiment 3, we apply the PNP model to confirm the often-assumed “quasi-rational” nature of the rule-based processes involved in multiple-cue judgment. The results demonstrate that the PNP model reliably identifies the two cognitive processes proposed by Brunswik, and often recovers the parameters of the process more effectively than a standard regression model with homogeneous Gaussian error, suggesting that the standard Gaussian assumption incorrectly specifies the error distribution in many tasks. We discuss the untapped potentials of using error distributions to identify cognitive processes and how the PNP model relates to, and can enlighten, debates on intuition and analysis in dual-systems theories.

One of the most influential working hypotheses in psychology, to this day, is that human information processing in higher-order cognition (e.g., judgment and decision-making) is constrained by having to process objects serially, one at a time. However, a rather large body of research, accumulated over the past 50 years, has demonstrated that serial-processing models provide a poor descriptive account of human information processing. An alternate to the serial-processing view is that people can process information concurrently; many cognitive processes can advance independently of each other even if the system involves only a single central informationprocessing unit (i.e., central executive). Perhaps this general idea can be advanced beyond its present standing and is to provide new powerful tools in the pursuit of understanding human behavior. To this end, the article provides a review of (i) the conceptual differences between different types of processing (serial, concurrent, parallel), (ii) recent advancements in the field of computer science, and (iii) existing research on human information-processing, which is in line with the advancements in computer science. Finally, the article provides a discussion of outstanding research questions gleaned from these reviews-questions that could stimulate entirely new research programs.

Many instances in human affairs involve considering the value of different outcomes and the probability (or risk) of these outcomes occuring (e.g., gambling, financial decision-making, medical decision-making, criminal behavior). The point of departure for present thesis is that descriptive theories of judgment- and decision making under risk have yet to fully utilize explanations grounded in accounts of how people integrate outcomes with their adherent probabilities. The most widely embraced accounts are positioned on opposite ends of a spectrum, holding either (i) that people consistently and effortlessly engage in the normative principle of multiplicatively integrating the value or utility of possible outcomes with their adherent probabilities (i.e., weighting), or (ii) that people only have the ability to engage in simple heuristics or context-dependent sampling strategies. The present thesis proposes that the field should consider positions between these extreme positions. To this end, three empirical studies were conducted in which people evaluated risky prospects in the form of numerically described monetary lotteries.

The studies show that use of weighting was robust to increases of cognitive demands, as when (i) other evaluations are not available as reference points (Study I), (ii) outcomes and probabilities are presented sequentially before the evaluation (Study II), and (iii) the prospect structure involves two independent outcomes (Study III). The results suggest that - even if people can turn to heuristics when they are more efficient, for specific stages in the decision process, or for very complex problems - people indeed have both the inclination and ability to weight the outcomes by their probabilities in the evaluation of individual prospects, or for a subset of decision alternatives.

In contrast to popular weighting models, however, the cognitive-modeling efforts throughout the studies speak against the notion that the weighting process can be assumed to be consistent and effortless. Instead, the cognitive process of weighting outcomes and probabilities is better characterized as an anchoring-and-adjustment strategy: people anchor on the value of the outcome and make linear adjustments downwards to account for probability. The studies show that these adjustments are often insufficient or noise-prone when the cognitive demands increase due to (i) properties of the task environment (Study I and Study II), or (ii) lack of domain-specific knowledge (i.e., numeracy and financial literacy, Study III). In conclusion, the thesis has highlighted the important, but previously neglected, nuances of human cognition in judgment and decision-making under risk - nuances found between previously conflicting standpoints. Future research exploring these nuances should make a necessary distinction between people’s underlying competence and the performance they exhibit at a given moment.