Prior Beliefs Research Articles

Awareness Revision and Belief Extension

ABSTRACT What norm governs how an agent should change their beliefs when they encounter a completely new possibility? Orthodox Bayesianism has no answer, as it takes all learning to involve updating prior beliefs. A partial proposal is Reverse Bayesianism, which mandates the preservation of ratios of prior probabilities, but it faces counterexamples introduced by Mahtani (2021). I propose to separate awareness growth into two stages: awareness revision and belief extension. I argue that Mahtani’s cases highlight that we need to theorize awareness revision before we can define a proposal for belief extension, such as Reverse Bayesianism. I provide a formal model of awareness revision which makes explicit how propositions are distinguished within awareness states and identified across them. Reformulating Reverse Bayesianism to take input from my model allows it to navigate Mahtani-style cases. My model leaves open how agents choose to identify propositions across awareness states, and I propose that they ought to do so conservatively: preserving undisturbed prior reasoning about the structure of their awareness. I then spell out this proposal in a special case. This is a partial proposal, and I close with a discussion of how to elaborate on it and how to advance research into awareness revision.

An Introduction to Bayesian Approaches to Trial Design and Statistics for Stroke Researchers.

While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to frequentist approaches, which are based on the probability that data equal specific values given underlying unknown parameters, Bayesian approaches are based on the probability that parameters equal specific values given observed data and prior beliefs. The Bayesian paradigm allows researchers to update their beliefs with observed data to provide probabilistic interpretations of key parameters, for example, the probability that a treatment is effective. In this review, we outline the basic concepts of Bayesian statistics as they apply to stroke trials, compare them to the frequentist approach using exemplary data from a randomized trial, and explain how a Bayesian analysis is conducted and interpreted.

Do Heuristic Cues Affect Misinformation Sharing? Evidence From a Meta-Analysis

Information overload in online environments leads individuals to be more likely to rely on heuristic cues to make decisions about (mis)information sharing. Drawing on dual-process models of information processing, namely the heuristic–systematic model and the elaboration likelihood model, we conducted a meta-analysis of 31 individual studies to examine the combined effects of five major heuristic cues on misinformation sharing. The results revealed that the affective heuristic of anxiety exerted the strongest impact on misinformation sharing ( r = .343), followed by three cognitive heuristics: the self-confirmation heuristic ( r = .249), endorsement heuristic ( r = .125), and fact-checking heuristic ( r = -.115)—but not the authority heuristic. The endorsement misinformation receives, consistency between misinformation and individuals’ prior attitudes or beliefs, anxiety people feel, and absence of fact-checking labels all make people more likely to share misinformation. Moderation analyses also highlight the importance of considering contextual and cultural factors in future research.

Placebos in Healthcare: A Behavioral Study on How Treatment Responsiveness Affects Therapy Decisions in a Simulated Patient-Physician Interaction.

Treatment choice during clinical practice is crucial to best help each patient. One of the physicians' main goals is choosing a personalized effective treatment, but it also represents a challenging issue. Here, we explored different treatment choices in a simulated patient-physician interaction. Medical students (n = 48) and young Practicing Physicians (n = 20) were recruited to behave as "physicians" while fellow researchers acted as "patients". Participants were divided equally into a Belief Group, which received positive information about placebo efficacy, and a Non-Belief Group, which received negative information. Empathy traits and psychological variables were measured in both groups. During the task, participants were asked to choose between an active (TENS treatment) or a placebo treatment, to reduce patients' pain. Patients never underwent the painful stimulation but acted as if they had, simulating high or low pain responses to the placebo treatment (placebo-responders/placebo non-responders) and low pain to the TENS treatment. Behavioral results showed that the Belief Group gave significantly more placebo treatments when faced with a patient that simulated placebo responsiveness, while the Non-Belief group showed a mirrorlike behavior, administrating more believed TENS treatments when faced with a placebo non-responder. No differences were found between Medical Students and Practicing Physicians. This study constitutes a frame of reference for medical treatment decisions, indicating that physicians' treatment choices are influenced by patients' responsiveness to the treatments, as well as by their prior beliefs and empathy traits.

Bayesian inference in arm posture perception.

To configure our limbs in space the brain must compute their position based on sensory information provided by mechanoreceptors in the skin, muscles, and joints. Because this information is corrupted by noise, the brain is thought to process it probabilistically, and integrate it with prior belief about arm posture, following Bayes' rule. Here, we combined computational modeling with behavioral experimentation to test this hypothesis. The model conceives the perception of arm posture as the combination of a probabilistic kinematic chain composed by the shoulder, elbow, and wrist angles, compromised with additive Gaussian noise, with a Gaussian prior about these joint angles. We tested whether the model explains errors in a VR-based posture matching task better than a model that assumes a uniform prior. Human participants were required to align their unseen right arm to a target posture, presented as a visual configuration of the arm in the horizontal plane. Results show idiosyncratic biases in how participants matched their unseen arm to the target posture. We used maximum likelihood estimation to fit the Bayesian model to these observations and estimate key parameters including the prior means and its variance-covariance structure. The Bayesian model including a Gaussian prior explained the response biases and variance much better than a model with a uniform prior. The prior varied across participants, consistent with the idiosyncrasies in arm posture perception, and in alignment with previous behavioral research. Our work clarifies the biases in arm posture perception within a new perspective on the nature of proprioceptive computations.

User Profiling in Human-AI Design: An Empirical Case Study of Anchoring Bias, Individual Differences, and AI Attitudes

People form perceptions and interpretations of AI through external sources prior to their interaction with new technology. For example, shared anecdotes and media stories influence prior beliefs that may or may not accurately represent the true nature of AI systems. We hypothesize people's prior perceptions and beliefs will affect human-AI interactions and usage behaviors when using new applications. This paper presents a user experiment to explore the interplay between user's pre-existing beliefs about AI technology, individual differences, and previously established sources of cognitive bias from first impressions with an interactive AI application. We employed questionnaire measures as features to categorize users into profiles based on their prior beliefs and attitudes about technology. In addition, participants were assigned to one of two controlled conditions designed to evoke either positive or negative first impressions during an AI-assisted judgment task using an interactive application. The experiment and results provide empirical evidence that profiling users by surveying them on their prior beliefs and differences can be a beneficial approach for bias (and/or unanticipated usage) mitigation instead of seeking one-size-fits-all solutions.

Does it matter that an ally is democratic during crises? Public diplomacy and attitudes towards international allies in times of crisis

Abstract Can public diplomacy in times of crisis shape citizens’ attitudes towards international politics? Using a survey experiment in Italy, we evaluated whether information cues about public diplomacy efforts by the United States and China to assist the country in dealing with the COVID-19 emergency shifted the importance citizens attached to Italy’s international allies being democracies. We found that citizens who receive positive cues about USA efforts to assist Italy report a stronger preference for Italy interacting with democracies. At the same time, when they received positive cues about China’s efforts to assist Italy, they discounted the importance attached to international allies being democracies. We further found that these effects are conditional on the participants’ support for democracy at home. We argue that these findings are consistent with a cognitive dissonance framework where citizens update their attitudes to decrease dissonant cognitions when they receive information that challenges prior beliefs or expectations.

Reflective practice among pre-service teachers: the role of portfolios

ABSTRACT Background In Belgium, and elsewhere, the use of portfolios as a reflective practice for pre-service primary and secondary teachers has long been established in higher education. Reflective thinking is recognised as an effective process for examining daily practices, awakening students to the key elements of their profession, and fostering lifelong learning. With the emergence of the competency-based approach to employability, the link between portfolio-based reflective practice and the development of students’ employability competences remains to be explored. Purpose The aim of this study was to examine how portfolios, as a reflective practice, can promote pre-service physical education (PE) teachers’ reflective thinking and their employability competences, and to consider the support that might need to be offered to help pre-service teachers better reflect on their employability competences. Method Qualitative research was conducted through semi-structured interviews with 16 pre-service PE teachers in one Belgian university. Thematic analysis, based on a dual deductive and inductive coding approach, was undertaken to discover patterns in the data. To investigate the role of portfolio-based reflective practice for reflection and employability, co-occurrences between the identified codes were examined. Findings Analysis of the interview data showed that students used the portfolio as a reflective practice. The portfolio experience appeared to foster employability competences, particularly expertise, lifelong learning, social competences and efficacy beliefs. In addition, the portfolio enabled students to explore their beliefs and knowledge and to validate their assumptions and beliefs. The support of peers and tutors was crucial during this experience. Conclusion Course designers may wish to improve portfolio use by thoughtfully incorporating all phases of reflective practice, encouraging students not only to reflect on their competence development but also to take meaningful action. Organising portfolios in a way that prompts students to draw on prior knowledge, beliefs, and experiences, while facilitating with key stakeholders, such as peers and teachers, could further support their professional development.

High PEEP with recruitment maneuvers versus Low PEEP During General Anesthesia for Surgery - a Bayesian individual patient data meta-analysis of three randomized clinical trials.

The influence of high positive end-expiratory pressure (PEEP) with recruitment maneuvers on the occurrence of postoperative pulmonary complications after surgery is still not definitively established. Bayesian analysis can help to gain further insights from the available data and provide a probabilistic framework that is easier to interpret. Our objective was to estimate the posterior probability that the use of high PEEP with recruitment maneuvers is associated with reduced postoperative pulmonary complications in patients with intermediate-to-high risk under neutral, pessimistic, and optimistic expectations regarding the treatment effect. Multilevel Bayesian logistic regression analysis on individual patient data from three randomized clinical trials carried out on surgical patients at Intermediate-to-High Risk for postoperative pulmonary complications. The main outcome was the occurrence of postoperative pulmonary complications in the early postoperative period. We studied the effect of high PEEP with recruitment maneuvers versus Low PEEP Ventilation. Priors were chosen to reflect neutral, pessimistic, and optimistic expectations of the treatment effect. Using a neutral, pessimistic, or optimistic prior, the posterior mean odds ratio (OR) for High PEEP with recruitment maneuvers compared to Low PEEP was 0.85 (95% Credible Interval [CrI] 0.71 to 1.02), 0.87 (0.72 to 1.04), and 0.86 (0.71 to 1.02), respectively. Regardless of prior beliefs, the posterior probability of experiencing a beneficial effect exceeded 90%. Subgroup analysis indicated a more pronounced effect in patients who underwent laparoscopy (OR: 0.67 [0.50 to 0.87]) and those at high risk for PPCs (OR: 0.80 [0.53 to 1.13]). Sensitivity analysis, considering severe postoperative pulmonary complications only or applying a different heterogeneity prior, yielded consistent results. High PEEP with recruitment maneuvers demonstrated a moderate reduction in the probability of PPC occurrence, with a high posterior probability of benefit observed consistently across various prior beliefs, particularly among patients who underwent laparoscopy.

Influence of process shifts in case of Six Sigma-based Bayesian control charts

While traditional control charts play an important role in quality assurance activities, the work on such techniques evolved over the years particularly for early detection of process shifts. Many advanced level control charts have been proposed in the literature to even detect small shifts in the process as early as possible. In order to make use of the prior beliefs and likelihood of the observed data behavior, Bayesian control charts for parameter of the posterior distribution have been proposed by various authors. The Six Sigma-based control charts ensure smaller process variations by enforcing quality improvement activities and as a result, such processes would produce higher quality products with only 3.4 defects per million opportunities (DPMO) even after allowing a shift in the process up to ± 1.5 times of process standard deviation. In this paper, an attempt has been made to develop Six Sigma-based Bayesian control charts. The proposed control charts incorporate various shift (or no-shift) combinations to prior and observed data behaviors to study their influence on the posterior parameter. Illustrative examples are also given for understanding the proposed approach. The performance evaluation and comparisons are also presented. The paper ends with a discussion and concluding remarks.

Mind the Gap Between Chemistry and Outdoor Education: Primary and Lower Secondary Preservice Teachers’ Beliefs During an Outdoor-Based Introduction to Chemistry in Science Education

ABSTRACT Outdoor education is rarely used to teach chemistry, despite its potential benefits for students. This paper investigates preservice teachers’ (PSTs) beliefs about the domain of chemistry in science education. Sixteen primary and lower secondary PSTs were asked to draw their perception of chemistry in science education and mind maps about outdoor education before being exposed to a novel, student-active approach. This approach introduced chemistry outside the classroom, demonstrating how outdoor environments can be used with students to learn about basic chemical concepts. Six participants were selected for in-depth interviews after the introduction. An abductive approach was applied to analyze the data. Results indicate that prior beliefs about teacher-centered laboratory activities dominate, with few links to everyday or natural phenomena. The interviews showed that these beliefs stem from secondary school experiences, often associated with challenges such as difficulties in remembering the content of experiments. Some PSTs had prior teaching experiences and wished to include the outdoor environment in their future teaching practice. These unique and diverse beliefs influenced direct encounters with chemical phenomena outdoors. Activities that created visible links between familiar phenomena and chemistry were embraced, especially if they solved prior challenges from school. Nevertheless, the diversity of outdoor phenomena led some PSTs to question their prior chemistry knowledge. This challenge may hinder future integration of outdoor activities unless these questions are addressed. The findings suggest that regularly incorporating chemistry related outdoor activities in teacher education may prove beneficial. However, reflecting on emerging challenges is essential.

Communicated priors tune the perception of control

Action allows us to shape the world around us. But to act effectively we need to accurately sense what we can and cannot control. Classic theories across cognitive science suppose that this ‘sense of agency’ is constructed from the sensorimotor signals we experience as we interact with our surroundings. But these sensorimotor signals are inherently ambiguous, and can provide us with a distorted picture of what we can and cannot influence. Here we investigate one way that agents like us might overcome the inherent ambiguity of these signals: by combining noisy sensorimotor evidence with prior beliefs about control acquired through explicit communication with others. Using novel tools to measure and model control decisions, we find that explicit beliefs about the controllability of the environment alter both the sensitivity and bias of agentic choices; meaning that we are both better at detecting and more biased to feel control when we are told to expect it. These seemingly paradoxical effects on agentic choices can be captured by a computational model where expecting to be in control exaggerates the sensitivity or ‘gain’ of the mechanisms we use to detect our influence over our surroundings – making us increasingly sensitised to both true and illusory signs of agency. In combination, these results reveal a cognitive and computational mechanism that allows public communication about what we can and cannot influence to reshape our private sense of control.

How rational inference about authority debunking can curtail, sustain, or spread belief polarization.

In polarized societies, divided subgroups of people have different perspectives on a range of topics. Aiming to reduce polarization, authorities may use debunking to lend support to one perspective over another. Debunking by authorities gives all observers shared information, which could reduce disagreement. In practice, however, debunking may have no effect or could even contribute to further polarization of beliefs. We developed a cognitively inspired model of observers' rational inferences from an authority's debunking. After observing each debunking attempt, simulated observers simultaneously update their beliefs about the perspective underlying the debunked claims and about the authority's motives, using an intuitive causal model of the authority's decision-making process. We varied the observers' prior beliefs and uncertainty systematically. Simulations generated a range of outcomes, from belief convergence (less common) to persistent divergence (more common). In many simulations, observers who initially held shared beliefs about the authority later acquired polarized beliefs about the authority's biases and commitment to truth. These polarized beliefs constrained the authority's influence on new topics, making it possible for belief polarization to spread. We discuss the implications of the model with respect to beliefs about elections.

Pricing model of ride-hailing platform considering rationally inattentive passengers

ABSTRACT The ride-hailing services are booming in our daily lives, but it is unclear that how the platforms should set prices to maximize their profits when facing one kind of rationally inattentive passengers in a two-sided market. To fill this gap, we establish a profit maximization model for the ride-hailing platform based on queuing theory and rational inattention theory and analyze the properties of the model. Numerical examples are presented to demonstrate the impacts of perceived high and low service levels, information cost and prior belief on the optimal price and commission rate of the ride-hailing platform. The results show that (1) for different cities, there is always an optimal pricing strategy to maximize the profit of the platform. (2) To ensure maximum profit, the platform should disclose the service information of ride-hailing as much as possible, but also maintain the unknownness of ride-hailing services appropriately.

L2 reader-writers’ prior beliefs and integrated representations of controversial information sources: the moderating effects of strategic source processing

In the contemporary information-rich academic environments, the superabundance of information sources requires that readers compose balanced referential representations of topics presented in sets of sources that provide divergent standpoints on them. Prior beliefs have been reported to skew the representations that readers build based on such sources. In light of the strong link between source reading and integrated writing, the current study examined how reader-writers’ prior beliefs affect the propositional content in their integrated written representations (N = 45). Additionally, in light of the necessity to identify conditions that guard against belief biases in the reader-writers’ representations, the study examined how strategic source processing affects propositional content in the integrated representations. General Linear Model analyses revealed a biasing effect for pre-existing topical beliefs on the propositional content in L2 reader-writers’ integrated representations. The analyses further revealed that accumulation, as a subset of surface-level source reading strategies, exerted a main effect but no moderating effect on the propositional content. On the other hand, cross-text elaboration, as a category of deep-level strategic processing attempts, was shown to moderate the inclusion of pro-belief and contra-belief propositional content in the integrated representations. Implications are provided for integrated writing instruction.

Information design for weighted voting

AbstractWe consider a sender who wishes to persuade multiple receivers to vote in favor of a proposal and sends them private correlated messages that are conditional on the true state of the world. The receivers share a common prior, wish to implement the outcome that matches the true state, and have homogeneous preferences. However, they are heterogeneous in their voting weights. We consider both behavioral and sophisticated voters. When voters are behavioral, public communication is optimal if and only if there is a veto player. For sophisticated voters, we establish lower bounds on the sender’s gain from persuasion for general voting quotas and show that the sender can often improve upon public communication. Finally, in an extension, we show that even when behavioral voters have heterogeneous prior beliefs, public communication is optimal if and only if there is a veto player.

Bayesian inference of perceptual uncertainty, behavioral costs, and prior beliefs for continuous perception-action tasks

Bayesian inference of perceptual uncertainty, behavioral costs, and prior beliefs for continuous perception-action tasks

Evaluation of the Fill-it-up-design to use historical control data in randomized clinical trials with two arm parallel group design

PurposeIn the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be utilized to support the new study design, but of course, inclusion also carries the risk of bias in the study results.MethodsTo combine historical and randomized controls we investigate the Fill-it-up-design, which in the first step checks the comparability of the historical and randomized controls performing an equivalence pre-test. If equivalence is confirmed, the historical control data will be included in the new RCT. If equivalence cannot be confirmed, the historical controls will not be considered at all and the randomization of the original study will be extended. We are investigating the performance of this study design in terms of type I error rate and power.ResultsWe demonstrate how many patients need to be recruited in each of the two steps in the Fill-it-up-design and show that the family wise error rate of the design is kept at 5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}. The maximum sample size of the Fill-it-up-design is larger than that of the single-stage design without historical controls and increases as the heterogeneity between the historical controls and the concurrent controls increases.ConclusionThe two-stage Fill-it-up-design represents a frequentist method for including historical control data for various study designs. As the maximum sample size of the design is larger, a robust prior belief is essential for its use. The design should therefore be seen as a way out in exceptional situations where a hybrid design is considered necessary.

Correcting misperceptions about trends and norms to address weak collective action — Experimental evidence from a recycling program

Finding ways to encourage collective action in contexts where only a minority adopts the desired behavior is central to solving many of today’s global environmental problems. We study how correcting people’s beliefs about social norms and behavioral trends encourages collective action in a setting where the desired behavior is not yet prevalent. In a field experiment, we test whether low sign-up rates for a recycling program in urban Peru can be increased by providing information (1) that most people regard participation in the program as important, i.e., on the “injunctive norm”, (2) on an increasing recent trend in sign-up rates. We find that the effectiveness of the treatments depends on people’s prior beliefs: Correcting inaccurate beliefs increases sign-up decisions significantly among people who either substantially underestimate the injunctive norm or who underestimate the positive trend. As this sub-group of people is in the minority in our set-up, we do not observe statistically significant average treatment effects. We further find that the effects of the treatments increase in the level of underestimation. Our evidence demonstrates that belief updating can be used effectively to encourage collective action where it is weak as long as a meaningful number of people underestimates the relevant trends and norms.

A Broken Duet: Multistable Dynamics in Dyadic Interactions.

Misunderstandings in dyadic interactions often persist despite our best efforts, particularly between native and non-native speakers, resembling a broken duet that refuses to harmonise. This paper delves into the computational mechanisms underpinning these misunderstandings through the lens of the broken Lorenz system-a continuous dynamical model. By manipulating a specific parameter regime, we induce bistability within the Lorenz equations, thereby confining trajectories to distinct attractors based on initial conditions. This mirrors the persistence of divergent interpretations that often result in misunderstandings. Our simulations reveal that differing prior beliefs between interlocutors result in misaligned generative models, leading to stable yet divergent states of understanding when exposed to the same percept. Specifically, native speakers equipped with precise (i.e., overconfident) priors expect inputs to align closely with their internal models, thus struggling with unexpected variations. Conversely, non-native speakers with imprecise (i.e., less confident) priors exhibit a greater capacity to adjust and accommodate unforeseen inputs. Our results underscore the important role of generative models in facilitating mutual understanding (i.e., establishing a shared narrative) and highlight the necessity of accounting for multistable dynamics in dyadic interactions.