Li Qian Tay

Misinformation can negatively impact individual cognition and societal outcomes, but debates persist regarding its definition and scope. We argue that further theoretical and practical progress requires addressing three key challenges: (1) some claims are inherently difficult to identify as objectively false, (2) researchers and the public may not always share a common epistemology, and (3) each of these creates challenges in developing social consensus about what is and is not misinformation. To this end, we introduce a framework grounded in critical realism, which assumes that a mind-independent reality determines the consequences of (mis)information belief, but that evidence can be partial, contestable, and accessed through different psychological and epistemic lenses. Within this framework, we define evidence retrievability as a time-indexed property of claims and distinguish objective misinformation (claims that contradict reality) from attributional misinformation (claims labeled false within a given epistemology). We then present an agent-based model that assigns agents trust weights over different evidence channels, such that epistemic conflict arises when agents discount others’ evidential standards, and epistemic bridging occurs when they adopt them. Our simulations provide an initial illustration of how objective and attributional misinformation can diverge, and that bridging may produce a socially cohesive but objectively false consensus when evidence retrievability is low. We discuss implications for research and interventions.

Understanding causality and the mechanisms underlying psychological phenomena has been a cornerstone of psychological research with significant implications for theory development and intervention design. While traditional methods such as experimental manipulations or structural equation modelling have been extensively used to explore causal relationships, recent advances in computational techniques have introduced causal discovery methods as a powerful alternative. These methods can uncover complex causal network structures from observational or interventional data, making it possible to identify causal directions in complex interdependencies involving numerous variables. Building on a growing body of literature, this paper offers a broad survey of core causal discovery algorithms and their recent applications across disciplines, with particular attention to their use in uncovering psychological mechanisms. To complement this overview, we provide a tutorial using data from the Health Behavior in School-Aged Children (HBSC) study. This case study demonstrates how causal discovery can be applied to examine gender-specific mechanisms underlying bullying-related outcomes. We also discuss the opportunities and challenges of integrating causal discovery into psychological research.

Recent academic debate has seen the emergence of the claim that misinformation is not a significant societal problem. We argue that the arguments used to support this minimizing position are flawed, particularly if interpreted (e.g., by policymakers or the public) as suggesting that misinformation can be safely ignored. Here, we rebut the two main claims, namely that misinformation is not of substantive concern (a) due to its low incidence and (b) because it has no causal influence on notable political or behavioral outcomes. Through a critical review of the current literature, we demonstrate that (a) the prevalence of misinformation is nonnegligible if reasonably inclusive definitions are applied and that (b) misinformation has causal impacts on important beliefs and behaviors. Both scholars and policymakers should therefore continue to take misinformation seriously. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

In their impactful pre-registered meta-analysis, Chan and Albarracín aimed to determine the degree to which the public updates science-relevant misinformation following a correction (e.g., p.1514, paragraph 3). Based on an impressive 74 studies and 205 effect sizes, the authors concluded that “attempts to debunk science-relevant misinformation were, on average, not successful (d=0.19, P=0.131, 95% CI −0.06 to 0.43)”; p.1514), with the effect of corrections “smaller than those identified in all other areas” (p.15171; e.g., politics and health). In this commentary we show that the reported null effect was due to the inappropriate pooling of two distinct effect types into a single estimate. This clarification is necessary because meta-analyses are often perceived as the ‘gold standard’ of evidence, and numerous papers have cited Chan and Albarracín1 as evidence that corrections have little-to-no effect on science-relevant misinformation (35% by our estimates; see Supplementary Table 1).

In complex social systems, efforts to optimize proxies that supposedly reflect system goals can paradoxically lead to outcomes that undermine those very goals. This phenomenon is known as proxy failure. Proxy failure stems from the fact that certain shortcuts can enable the optimization of proxies without truly advancing the system's goals. Building on recent attempts to provide a unifying framework for proxy failures across domains, the current study investigates a central question: Why do shortcuts that undermine system goals repeatedly emerge and spread in complex social systems? Using an agent-based model with reinforcement learning agents, this study simulates 7 experiments to investigate the individual, social, and network-level factors that contribute to the discovery and diffusion of shortcuts. Results revealed that, (1) at the individual level, agents with weaker prior knowledge, higher exploration tendencies, and greater planning depth were more likely to discover shortcuts; (2) at the social level, frequency-biased learners tended to abandon shortcuts when surrounded by many neighbors, and the early success (or failure) of a pioneer agent (those who discovered shortcuts in early stages) had cascading effects on shortcut adoption across layers of agents; and (3) at the network level, shortcuts diffused more readily in dense and highly modular networks, while clustering showed no significant impact. Crucially, when hub nodes acted as pioneers, shortcut adoption was both faster and more persistent. These results together suggest that proxy failure is not solely a consequence of flawed proxy design, but also a product of the cognitive, social, and structural dynamics. The findings, along with the computational framework developed, offer a foundation for the design of more robust and resilient social systems against proxy failure.

Psychological research has provided important insights into the processing of misinformation and conspiracy theories. Traditionally, this research has focused on randomized laboratory experiments and observational (non-experimental) studies seeking to establish causality via third-variable adjustment. However, laboratory experiments will always be constrained by feasibility and ethical considerations, and observational studies can often lead to unjustified causal conclusions or confused analysis goals. We argue that research in this field could therefore benefit from clearer thinking about causality and an expanded methodological toolset that includes natural experiments. Using both real and hypothetical examples, we offer an accessible introduction to the counterfactual framework of causality and highlight the potential of instrumental variable analysis, regression discontinuity design, difference-in-differences, and synthetic control for drawing causal inferences. We hope that such an approach to causality will contribute to greater integration amongst the various misinformation- and conspiracy- adjacent disciplines, thereby leading to more complete theories and better applied interventions.

Misinformation can negatively influence belief formation and reasoning. To counter these influences, a range of countermeasures have been developed, the two dominant approaches being (proactive) prebunking and (reactive) debunking. While psychological research has revealed important insights into the efficacy of these interventions, the literature (1) lacks comparative analysis contrasting the two approaches, (2) has mostly used time-constrained designs that provide conducive intervention contexts, and (3) has focussed predominantly on cognitive measures, neglecting behavioural outcomes. We addressed these limitations in an experiment (N = 793) that provided two misleading articles on separate topics. We contrasted prebunking and debunking interventions that targeted the initial misinformation (article 1), and tested to what extent the interventions conferred protection against novel misinformation (article 2) either immediately or after a one-week delay. We included standard questionnaire measures of inferential reasoning as well as a text-generation task (writing a social-media post). With no delay, prebunking effectively protected against novel misinformation on both measures, while debunking effects were statistically significant only on the cognitive measure. Neither intervention was effective after a seven-day delay.

Recent academic debate has seen the emergence of the claim that misinformation is not a significant problem. We believe that the arguments used to support this minimizing position are flawed, particularly if interpreted (e.g., by policymakers or the public) as suggesting that misinformation can be safely ignored. Here, we rebut the two main claims, namely that misinformation is not of substantive concern (1) due to its low incidence and (2) because it has no causal influence on notable political or behavioral outcomes. Through a critical review of current literature, we demonstrate that (1) the prevalence of misinformation is non-negligible if reasonably inclusive definitions are applied and that (2) misinformation has causal impacts on important beliefs and behaviors. Both scholars and policymakers should therefore continue to take misinformation seriously.