Rachel G. Stephens

People increasingly rely on social media for health information, despite substantial variability in information quality. We conducted a systematic review and meta-analysis examining whether people are sensitive to source expertise when evaluating and sharing online health claims, and if this depends on whether the source is an individual expert (e.g., a physician) or an expert group (e.g., a health organisation). Three searches across four databases identified 25 individual studies published across 22 papers. From these studies, we found that people were only marginally more persuaded by health experts than nonexperts, and that this effect was not moderated by expert type (individual vs. group). However, we found tentative evidence that experts are more persuasive when accompanied by congruent credibility cues, but that expertise can backfire when these cues are incongruent. Evidence regarding people’s sensitivity to source expertise when sharing was mixed, reflecting heterogeneity in study designs. This review highlights several limitations in the current literature, indicating the need for further research that accounts for how both experts and nonexperts are perceived in online health contexts. We discuss implications and propose several avenues for future research and potential interventions.

Traditional psychological models of belief revision focus on face-to-face interactions, but with the rise of social media, more effective models are needed to capture belief revision at scale, in this rich text-based online discourse. Here, we use a hybrid approach, utilizing large language models (LLMs) to develop a model that predicts successful persuasion using features derived from psychological experiments. Our approach leverages LLM generated ratings of features previously examined in the literature to build a random forest classification model that predicts whether a message will result in belief change. Of the eight features tested, \textit{epistemic emotion} and \textit{willingness to share} were the top-ranking predictors of belief change in the model. Our findings provide insights into the characteristics of persuasive messages and demonstrate how LLMs can enhance models of successful persuasion based on psychological theory. Given these insights, this work has broader applications in fields such as online influence detection and misinformation mitigation, as well as measuring the effectiveness of online narratives.

When individuals are asked to evaluate the believability of the conclusions to valid or invalid arguments, they often endorse valid conclusions at higher rates than invalid ones. This effect of validity on belief judgments—the “logic-belief effect” —is considered evidence of intuitive logic. However, recent studies challenged this interpretation by demonstrating that “pseudo-logical” arguments interfere with belief judgments in the same way as valid logical structures do. This finding suggests that a simple heuristic that relies on the matching of constituent propositions, rather than sensitivity to logical validity per se, may lead to the logic-belief effect. To further test this matching heuristic account, across two experiments, we instructed participants to evaluate the conclusions of a series of logical and pseudo-logical arguments based on logic or belief, before and after a logic training block. The results showed that whilst both judgment types were impacted by both logical and pseudo-logical structures before training, after training the effect of the latter was indeed minimized in logic judgments but not belief judgments. The results largely support the matching heuristic account, which have important implications for contemporary dual-process theories of reasoning.

Theory of mind (ToM) has been argued to be a multidimensional construct, with ToM inferences depending on distinct processes across affective and cognitive ToM tasks and across first-order cognitive and second-order cognitive ToM tasks. Behavioural evidence for a multidimensional account has primarily depended on dissociations identified via analysis of variance, a statistical approach insufficient for assessing dimensionality. Instead, state-trace analysis (STA) is a more appropriate statistical technique to uncover dimensionality. The current study first applied STA to two summary datasets that had previously identified key dissociations between cognitive and affective ToM; these reanalyses did not support a multidimensional account of ToM. Next, STA was applied to a more detailed dataset to reveal whether ToM is based on multiple processes in a sample of 115 older adults aged 60–85 years (M = 68.5, SD = 5.92, 61.7% female) with higher or lower emotion perception ability. Participants made ToM judgements about different social exchanges (e.g., sarcasm or lying). STA results supported a multidimensional account of ToM across first-order cognitive, second-order cognitive, and affective ToM subdomains. These results lay a more rigorous foundation for subsequent studies to further examine the dimensionality of ToM and to apply formal modelling, progressing the field’s understanding and measurement of the cognitive processes driving ToM judgements.

De Neys offers a welcome departure from the dual-process accounts that have dominated theorizing about reasoning. However, we see little justification for retaining the distinction between intuition and deliberation. Instead, reasoning can be treated as a case of multiple-cue decision making. Reasoning phenomena can then be explained by decision-making models that supply the processing details missing from De Neys's framework.

Much recent research and theorizing in the field of reasoning has been concerned with intuitive sensitivity to logical validity, such as the logic-brightness effect, in which logically valid arguments are judged to have a "brighter" typeface than invalid arguments. We propose and test a novel signal competition account of this phenomenon. Our account makes two assumptions: (a) as per the demands of the logic-brightness task, people attempt to find a perceptual signal to guide brightness judgments, but (b) when the perceptual signal is hard to discern, they instead attend to cues such as argument validity. Experiment 1 tested this account by manipulating the difficulty of the perceptual contrast. When contrast discrimination was relatively difficult, we replicated the logic-brightness effect. When the discrimination was easy, the effect was eliminated. Experiment 2 manipulated the ambiguity of the perceptual task, comparing discrimination performance when the perceptual contrast was labeled in terms of rating "brightness" or "darkness". When the less ambiguous darkness labeling was used, there was no evidence of a logic-brightness effect. In both experiments, individual sensitivity to the perceptual discrimination was negatively correlated with sensitivity to argument validity. Hierarchical latent mixture modeling revealed distinct individual strategies: responses based on perceptual cues, responses based on validity or guessing. Consistent with the signal competition account, the proportion of those responding to validity increased with perceptual discrimination difficulty or task ambiguity. The results challenge explanations of the logic-brightness effect based on parallel dual-process models of reasoning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Four experiments examined the claims that people can intuitively assess the logical validity of arguments, and that qualitatively different reasoning processes drive intuitive and explicit validity assessments. In each study participants evaluated arguments varying in validity and believability using either deductive criteria (logic task) or via an intuitive, affective response (liking task). Experiment 1 found that people are sensitive to argument validity on both tasks, with valid arguments receiving higher liking as well as higher deductive ratings than invalid arguments. However, the claim that this effect is driven by logical intuitions was challenged by the finding that sensitivity to validity in both liking and logic tasks was affected in similar ways by manipulations of concurrent memory load (Experiments 1 and 2) and variations in individual working memory capacity (Experiments 3 and 4). In both tasks better discrimination between valid and invalid arguments was found when more working memory resources were available. Formal signal detection models of reasoning were tested against the experimental data using signed difference analysis (Stephens, Dunn, & Hayes, 2018b). A single-process reasoning model which assumes that argument evaluation in both logic and liking tasks involves a single latent dimension for assessing argument strength but different response criteria for each task, was found to be consistent with the data from each experiment (as were some dual-process models). The experimental and modeling results confirm that people are sensitive to argument validity in both explicit logic and affect rating tasks, but that these results can be explained by a single underlying reasoning process. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

We present a state-trace analysis of sentence ratings elicited by asking participants to evaluate the overall acceptability of a sentence and those elicited by asking participants to focus on structural well-formedness only. Appealing to literature on “grammatical illusion” sentences, we anticipated that a simple instruction manipulation might prompt people to apply qualitatively different kinds of judgment in the two conditions. Although differences consistent with the subjective experience of grammatical illusion dissociations were observed, the state trace analysis of the rating data indicates that responses were still consistent with both judgment types accessing a single underlying factor. These results add to the existing comparisons between analytic and probabilistic modeling approaches to predicting rating judgments.