Jonathan Albert Fugelsang’s research while affiliated with University of Waterloo and other places

Prior research has shown that people judge algorithmic errors more harshly than identical mistakes made by humans—a bias known as algorithm aversion. We explored this phenomenon across two studies (N = 1199), focusing on the often-overlooked role of conventionality when comparing human versus algorithmic errors by introducing a simple conventionality intervention. Our findings revealed significant algorithm aversion when participants were informed that the decisions described in the experimental scenarios were conventionally made by humans. However, when participants were told that the same decisions were conventionally made by algorithms, the bias was significantly reduced—or even completely offset. This intervention had a particularly strong influence on participants’ recommendations of which decision-maker should be used in the future—even revealing a bias against human error makers when algorithms were framed as the conventional choice. These results suggest that the existing status quo plays an important role in shaping people’s judgments of mistakes in human–algorithm comparisons.

Does the legal status of an action shape how morally wrong people perceive it to be? Across three experiments (N = 1,226), we find that participants judged actions to be more morally wrong when labelled as “illegal” as opposed to “not illegal.” Furthermore, we demonstrate the robustness of this effect, revealing the impact of act legality on moral evaluations across manipulations of agent intentionality and type of law-making process. Notably, the influence of act legality was not restricted to judgments of actions, but also guided perceptions of others’ moral character. Experiments 1 and 2 revealed individual differences in the extent to which people’s moral judgments were shaped by an act’s legal status, with act legality being most influential for participants viewing respect for authority as a moral good. Based on these findings, we forward an account in which individuals use legal judgments as heuristic cues when making moral evaluations.

People commonly exhibit a bias blind spot, judging themselves to be less susceptible to bias than the “average other.” However, less is known about how people attribute bias to familiar others who evoke strong affect. The present work (N = 1,980) examines the degree to which participants’ attributions of bias are sensitive to their affective impressions of others. In Experiment 1, participants viewed themselves as considerably less biased than the average survey respondent and a personally-known disliked other but not less biased than a familiar individual who they liked. Experiments 2 and 3 examined the bias blind spot in politically polarized groups of Democrats and Republicans. While participants judged themselves to be somewhat less biased than the average co-partisan, they viewed themselves as much less susceptible to various psychological biases compared to their political opponents. Notably, in all experiments, the effect of other target selection (e.g., in-group vs. out-group member) on the bias blind spot was mediated by affective evaluations of target others. We discuss the theoretical implications of people using readily available affective evaluations as heuristic cues when attributing bias to familiar others. Additionally, we consider the practical implications of people failing to attribute bias to individuals they like, while readily attributing bias to those they dislike, including the potential for such attributions to exacerbate interpersonal and intergroup conflicts.

We examine the impact of partisan language (language used to support a political agenda), both with regard to peoples’ perceptions of the speakers who use it and their evaluations of events it is used to describe. Two experiments recruited 1,121 Democrats and Republicans from the United States. Using a set of liberal-biased (expand voting rights) and conservative-biased (reduce election security) terms, we find that partisans judge speakers describing polarizing events using ideologically-congruent language as more trustworthy than those describing events in a non-partisan way (expand mail-in voting). However, when presented to rival partisans, ideologically-biased language promoted negative evaluations of opposing partisans, with speakers attributed out-group language being viewed as especially untrustworthy. Furthermore, presenting Democrats and Republicans with ideologically-congruent descriptions of political events polarized their attitudes towards the events described. Overall, the present investigation reveals how partisan language, while praised by co-partisans, can damage trust and amplify disagreement across political divides.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

Individuals often assess themselves as being less susceptible to common biases compared to others. This bias blind spot (BBS) is thought to represent a metacognitive error. In this research, we tested three explanations for the effect: The cognitive sophistication hypothesis posits that individuals who display the BBS more strongly are actually less biased than others. The introspection bias hypothesis posits that the BBS occurs because people rely on introspection more when assessing themselves compared to others. The conversational processes hypothesis posits that the effect is largely a consequence of the pragmatic aspects of the experimental situation rather than true metacognitive error. In two experiments (N = 1057) examining 18 social/motivational and cognitive biases, there was strong evidence of the BBS. Among the three hypotheses examined, the conversational processes hypothesis attracted the greatest support, thus raising questions about the extent to which the BBB is a metacognitive effect.

People often overestimate their understanding of how things work. For instance, people believe they can explain even ordinary phenomena such as the operation of zippers and speedometers in greater depth than they really can. This is called the illusion of explanatory depth. Fortunately, a person can expose the illusion by attempting to generate a causal explanation for how the phenomenon operates (e.g., how a zipper works). Researchers have assumed for two decades that explanation exposes the illusion because explanation makes salient the gaps in a person’s knowledge of that phenomenon. However, recent evidence suggests that people might be able to expose the illusion by instead explaining a different phenomenon. If true, this would challenge our fundamental understanding of how the illusion works. Across three preregistered studies we tested whether the process of explaining one phenomenon (e.g., how a zipper works) would lead someone to report knowing less about a completely different phenomenon (e.g., how snow forms). In each study we found that explaining led people to report knowing less about various phenomena, regardless of what was explained. For example, people reported knowing less about how snow forms after attempting to explain how a zipper works. We discuss alternative accounts of the illusion of explanatory depth that might better fit our results. We also consider the utility of explanation as an indirect, non-confrontational debiasing method in which a person generalizes a feeling of ignorance about one phenomenon to their knowledge base more generally.

Individuals often assess themselves as being less susceptible to common biases compared to others. This bias blind spot (BBS) is thought to represent a metacognitive error. In this research, we tested three explanations for the effect: The cognitive sophistication hypothesis posits that individuals who display the BBS more strongly are actually less biased than others. The introspection bias hypothesis posits that the BBS occurs because people rely on introspection more when assessing themselves compared to others. The conversational processes hypothesis posits that the effect is largely a consequence of the pragmatic aspects of the experimental situation rather than true metacognitive error. In two experiments (N = 1057) examining 18 social/motivational and cognitive biases, there was strong evidence of the BBS. Among the three hypotheses examined, the conversational processes hypothesis attracted the greatest support, thus raising questions about the extent to which the BBB is a metacognitive effect.