Factors influencing the update of beliefs regarding controversial political issues

When updating beliefs in light of new information, people preferentially integrate information that is consistent with their prior beliefs and helps them construe a coherent view of the world. Such a selective integration of new information likely contributes to belief polarisation and compromises public discourse. Therefore, it is crucial to understand the factors that underlie biased belief updating. To this end, I conducted three pre-registered experiments covering different controversial political issues (i.e., Experiment 1: climate change, Experiment 2: speed limit on highways, Experiment 3: immigration in relation to violent crime). The main hypothesis was that negative reappraisal of new information (referred to as “cognitive immunisation”) hinders belief updating. Support for this hypothesis was found only in Experiment 2. In all experiments, the magnitude of the prediction error (i.e., the discrepancy between prior beliefs and new information) was strongly related to belief updating. Across experiments, participants’ general attitudes regarding the respective issue influenced the strength of beliefs, but not their update. The present findings provide some indication that the engagement in cognitive immunisation can lead to the maintenance of beliefs despite disconfirming information. However, by far the largest association with belief updating was with the magnitude of the prediction error.

Instability and Uncertainty Are Critical for Psychotherapy: How the Therapeutic Alliance Opens Us Up

Tschacher and Haken have recently applied a systems-based approach to modeling psychotherapy process in terms of potentially beneficial tendencies toward deterministic as well as chaotic forms of change in the client’s behavioral, cognitive and affective experience during the course of therapy. A chaotic change process refers to a greater exploration of the states that a client can be in, and it may have a potential positive role to play in their development. A distinction is made between on the one hand, specific instances of instability which are due to techniques employed by the therapist, and on the other, a more general instability which is due to the therapeutic relationship, and a key, necessary result of a successful therapeutic alliance. Drawing on Friston’s systems-based model of free energy minimization and predictive coding, it is proposed here that the increase in the instability of a client’s functioning due to therapy can be conceptualized as a reduction in the precisions (certainty) with which the client’s prior beliefs about themselves and their world, are held. It is shown how a good therapeutic alliance (characterized by successful interpersonal synchrony of the sort described by Friston and Frith) results in the emergence of a new hierarchical level in the client’s generative model of themselves and their relationship with the world. The emergence of this new level of functioning permits the reduction of the precisions of the client’s priors, which allows the client to ‘open up’: to experience thoughts, emotions and experiences they did not have before. It is proposed that this process is a necessary precursor to change due to psychotherapy. A good consilience can be found between this approach to understanding the role of the therapeutic alliance, and the role of epistemic trust in psychotherapy as described by Fonagy and Allison. It is suggested that beneficial forms of instability in clients are an underappreciated influence on psychotherapy process, and thoughts about the implications, as well as situations in which instability may not be beneficial (or potentially harmful) for therapy, are considered.

Belief Updating and Paranoia in Individuals with Schizophrenia

Background and Hypothesis: Persecutory delusions are among the most common delusions in schizophrenia and represent the extreme end of the paranoia continuum. Paranoia is accompanied by significant worry and distress. Identifying cognitive mechanisms underlying paranoia is critical for advancing treatment. We hypothesized that aberrant belief updating, which is related to paranoia in human and animal models, would also contribute to persecutory beliefs in individuals with schizophrenia. Study Design: Belief updating was assessed in 42 schizophrenia and 44 healthy participants, using a 3-option probabilistic reversal learning (3-PRL) task. Hierarchical Gaussian filter (HGF) was used to estimate computational parameters of belief updating. Paranoia was measured using the Positive and Negative Syndrome Scale (PANSS) and the revised Green et al. Paranoid Thoughts Scale (R-GPTS). Unusual thought content was measured with the Psychosis Symptom Rating Scale (PSYRATS) and the Peters et al. Delusions Inventory (PDI-21). Worry was measured using the Dunn Worry Questionnaire. Results: Consistent with prior work, paranoia was significantly associated with elevated win-switch rate, prior on volatility and sensitivity to volatility in both schizophrenia and across the whole sample. These relationships were specific to paranoia and did not extend to unusual thought content or measures of anxiety. We did, however, find a significant indirect effect of paranoia on the relationship between prior beliefs about volatility and worry. Conclusions: This work provides evidence that relationships between belief updating parameters and paranoia extend to schizophrenia, may be specific to persecutory beliefs, and contribute to theoretical models implicating worry in the maintenance of persecutory delusions.

US Media, Selective Exposure, and the Promotion of COVID-19 Vaccinations in Black and Latino Communities

Through selective exposure, this study examined the role the US news media played in encouraging or discouraging minority races from getting vaccinated. Through content analysis and focus groups, we were able to demonstrate that most media messages focused on prior beliefs in their reporting, therefore, discouraging the black and Latino minorities from getting the COVID-19 vaccinations. Further, while blacks and Latinos based their fears of the vaccines on health effects, white respondents were more concerned about government surveillance and the desire to go back to “normal” life after the quarantine. Ultimately, white respondents were more positive about vaccination arguing that they were tired of the quarantine and wanted normal life back.

Model identification, migration, and investment-specific shocks: Three essays

<p>This thesis consists of an introduction and three substantive chapters. Chapter 2 explores the identification of a small open economy model. Chapter 3 focuses on the business cycle consequences of migration. And chapter 4 investigates the contribution of investment-specific technology shocks to business cycle fluctuations in the presence of financial frictions. Chapter 2 takes a conventional new open economy macro model for a small open economy and addresses three questions: what data series should be used to identify the parameters of such a model? Are foreign data important for the identification of domestic parameters? And lastly, which structural parameters are interdependent? The chapter illustrates an applied methodology that enables an investigator to understand which data series are informative about parameters. The methodology can also be used to learn about the properties of the model. In particular, the methodology highlights which parameters are connected to which data series. Identification of business cycle models matters because our ability to recover structural parameters is influenced by the data series that are used to inform the estimation. Structural parameters determine both the specification of household preferences and the constraints that affect business cycle volatility, which together determine welfare. Consequently, identification analysis can provide insights into household welfare, which in turn has ramifications for the specification of monetary policy rules. If parameters are identified then the likelihood will eventually outweigh any prior beliefs as the sample size becomes large (Gelman et al., 2004, p. 107). The approach discussed here thus shows whether data will eventually dominate prior beliefs about parameters, determining whether analysis can – in the limit – resolve conflicting prior beliefs, and therefore usefully inform the design of policy rules. Chapter 3 of this thesis examines the business cycle effects that arise from an expansion of the population due to migration. In recent years, migration flows have become a highly politicised topic, both in New Zealand and abroad. While the debate on migration has become heated, comparatively little is known about the business cycle consequences of migration flows. This chapter contributes to the macroeconomic literature by illustrating the contribution that migration shocks make to cyclical fluctuations in New Zealand, and illustrates their dynamic impact. Using an estimated dynamic stochastic general equilibrium (DSGE) model of a small open economy and a structural vector autoregression, the chapter shows that migration shocks account for a considerable portion of the variability of per capita gross domestic product (GDP). While migration shocks matter for the capital investment and consumption components of per capita GDP, other shocks are more important drivers of cyclical fluctuations in these aggregates. Migration shocks also make some contribution to residential investment and real house prices, but other shocks play a more substantial role in driving housing market volatility. In the DSGE model, the level of human capital possessed by migrants relative to that of locals materially affects the business cycle impact of migration. The impact of migration shocks is larger when migrants have substantially different – larger or smaller – levels of human capital relative to locals. When the average migrant has higher levels of human capital than locals, as seems to be common for migrants into most OECD¹ economies, a migration shock has an expansionary effect on per capita GDP and its components, which also accords with the evidence from a structural vector autoregression. Chapter 4 of this thesis investigates the contribution of investment-specific technology (IST) shocks in driving cyclical fluctuations in a closed economy model when a borrowing constraint is introduced à la Kiyotaki and Moore (1997). IST shocks have been identified as a major driver of the business cycle, eg see Greenwood et al. (2000), and Justiniano et al. (2010, 2011). These shocks affect the rate at which investment goods are transformed into capital stock, and have been linked to frictions in financial markets, because financial intermediation is instrumental in facilitating investment. The third chapter shows that the importance of these investment shocks is in fact substantially diminished when collateral constraints on firms are introduced into an estimated dynamic stochastic general equilibrium model. In the presence of binding collateral constraints, risk premium shocks, which perturb interest rates and affect intertemporal substitution, supplant IST shocks as important drivers of the business cycle. ¹ Organisation for Economic Cooperation and Development.</p>

Model identification, migration, and investment-specific shocks: Three essays

<p>This thesis consists of an introduction and three substantive chapters. Chapter 2 explores the identification of a small open economy model. Chapter 3 focuses on the business cycle consequences of migration. And chapter 4 investigates the contribution of investment-specific technology shocks to business cycle fluctuations in the presence of financial frictions. Chapter 2 takes a conventional new open economy macro model for a small open economy and addresses three questions: what data series should be used to identify the parameters of such a model? Are foreign data important for the identification of domestic parameters? And lastly, which structural parameters are interdependent? The chapter illustrates an applied methodology that enables an investigator to understand which data series are informative about parameters. The methodology can also be used to learn about the properties of the model. In particular, the methodology highlights which parameters are connected to which data series. Identification of business cycle models matters because our ability to recover structural parameters is influenced by the data series that are used to inform the estimation. Structural parameters determine both the specification of household preferences and the constraints that affect business cycle volatility, which together determine welfare. Consequently, identification analysis can provide insights into household welfare, which in turn has ramifications for the specification of monetary policy rules. If parameters are identified then the likelihood will eventually outweigh any prior beliefs as the sample size becomes large (Gelman et al., 2004, p. 107). The approach discussed here thus shows whether data will eventually dominate prior beliefs about parameters, determining whether analysis can – in the limit – resolve conflicting prior beliefs, and therefore usefully inform the design of policy rules. Chapter 3 of this thesis examines the business cycle effects that arise from an expansion of the population due to migration. In recent years, migration flows have become a highly politicised topic, both in New Zealand and abroad. While the debate on migration has become heated, comparatively little is known about the business cycle consequences of migration flows. This chapter contributes to the macroeconomic literature by illustrating the contribution that migration shocks make to cyclical fluctuations in New Zealand, and illustrates their dynamic impact. Using an estimated dynamic stochastic general equilibrium (DSGE) model of a small open economy and a structural vector autoregression, the chapter shows that migration shocks account for a considerable portion of the variability of per capita gross domestic product (GDP). While migration shocks matter for the capital investment and consumption components of per capita GDP, other shocks are more important drivers of cyclical fluctuations in these aggregates. Migration shocks also make some contribution to residential investment and real house prices, but other shocks play a more substantial role in driving housing market volatility. In the DSGE model, the level of human capital possessed by migrants relative to that of locals materially affects the business cycle impact of migration. The impact of migration shocks is larger when migrants have substantially different – larger or smaller – levels of human capital relative to locals. When the average migrant has higher levels of human capital than locals, as seems to be common for migrants into most OECD¹ economies, a migration shock has an expansionary effect on per capita GDP and its components, which also accords with the evidence from a structural vector autoregression. Chapter 4 of this thesis investigates the contribution of investment-specific technology (IST) shocks in driving cyclical fluctuations in a closed economy model when a borrowing constraint is introduced à la Kiyotaki and Moore (1997). IST shocks have been identified as a major driver of the business cycle, eg see Greenwood et al. (2000), and Justiniano et al. (2010, 2011). These shocks affect the rate at which investment goods are transformed into capital stock, and have been linked to frictions in financial markets, because financial intermediation is instrumental in facilitating investment. The third chapter shows that the importance of these investment shocks is in fact substantially diminished when collateral constraints on firms are introduced into an estimated dynamic stochastic general equilibrium model. In the presence of binding collateral constraints, risk premium shocks, which perturb interest rates and affect intertemporal substitution, supplant IST shocks as important drivers of the business cycle. ¹ Organisation for Economic Cooperation and Development.</p>

Characterizing the Influence of Confirmation Bias on Web Search Behavior

In this study, we analyzed the relationship between confirmation bias, which causes people to preferentially view information that supports their opinions and beliefs, and web search behavior. In an online user study, we controlled confirmation bias by presenting prior information to participants that manipulated their impressions of health search topics and analyzed their behavioral logs during web search tasks. We found that web search users with poor health literacy and negative prior beliefs about the health search topic did not spend time examining the list of web search results, and these users demonstrated bias in webpage selection. In contrast, web search users with high health literacy and negative prior beliefs about the search topic spent more time examining the list of web search results. In addition, these users attempted to browse webpages that present different opinions. No significant difference in web search behavior was observed between users with positive prior beliefs about the search topic and those with neutral belief.

Refuting student teachers’ misconceptions about multimedia learning

Prior research indicates that student teachers frequently have misconceptions about multimedia learning. Our experiment with N = 96 student teachers revealed that, in contrast to standard texts, refutation texts are effective to address misconceptions about multimedia learning. However, there seems to be no added benefit of making “concessions” to student teachers’ prior beliefs (i.e., two-sided argumentation) in refutation texts. Moreover, refutation texts did not promote the selection of appropriate multimedia material. This study suggests that refutation texts addressing multimedia-learning misconceptions should be applied in teacher education. Yet, further support seems needed to aid the application of the corrected knowledge.

Fraud Beliefs Following the 2020 U.S. Presidential Election: A Belief Systems Analysis

Beliefs that the 2020 Presidential election was fraudulent are prevalent across the U.S. despite substantial contradictory evidence. We surveyed 1642 Americans during the U.S. Presidential vote count on November 4-5, assessing fraud beliefs and presenting hypothetical election outcomes before key states were decided. Participants’ fraud beliefs increased when their preferred candidate lost and decreased when he won, and this effect scaled with preference strength. A Bayesian model accounts for this bias as reflecting a rational attribution process operating on biased prior beliefs about the true election winner and beneficiary of fraud. Our findings suggest that a systems approach targeting multiple beliefs simultaneously may be more fruitful in combating false beliefs than direct “debunking” attempts.