Kevin O'Neill

People more frequently select norm-violating factors, relative to norm- conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Four experiments show that people more frequently select norm- violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment.

How does character information inform judgments of blame? Some argue that character information is indirectly relevant to blame because it enriches judgments about the mental states of a wrongdoer. Others argue that character information is directly relevant to blame, even when character traits are causally irrelevant to the wrongdoing. We propose an empirical synthesis of these views: a Two Channel Model of blame. The model predicts that character information directly affects blame when this information is relevant to the wrongdoing that elicits blame. Further, the effect of character information on blame depends on judgments about the true self that are independent of judgments of intentionality. Across three pre-registered studies (N = 662), we found support for all three predictions of the Two Channel Model. We propose that this reflects two distinct functions of blame: a social regulatory function that encourages norm compliance and a pedagogical function that encourages personal improvement.

Mike accidentally knocked against a bottle. Seeing that the bottle was about to fall, Jack was just about to catch it when Peter accidentally knocked against him, making Jack unable to catch it. Jack did not grab the bottle, and it fell to the ground and spilled. In double-prevention cases like these, philosophers and nonphilosophers alike tend to judge that Mike knocking into the bottle caused the beer to spill and that Peter knocking into Jack did not cause the beer to spill. This difference in causal judgment is a difficult puzzle for counterfactual theories of causal judgment; if each event had not happened, the outcome would not have, yet there is a difference in people’s causal judgments. In four experiments and three supplemental experiments, we confirm this difference in causal judgments. We also show that differences in people’s counterfactual thinking can explain this difference in their causal judgments and that recent counterfactual models of causal judgment can account for these patterns. We discuss these results in relation to work on counterfactual thinking and causal modeling.

A well-ingrained and recommended engineering practice in safety-critical software systems is to separate safety concerns from other aspects of the system. Along these lines, there have been calls for operating systems that implement ethical controls in an ethical layer separate from, and not amenable to tampering by, developers and modules in higher-level intelligence or cognition layers. There have been no implementations that demonstrate such a marshalling of ethical principles into an ethical layer. To address this, we present three different tracks for implementing such systems, and offer a prototype implementation of the third track. We end by addressing objections to our approach.

This article examines methodological and ethical issues of ethnographic research through the lens of Emmanuel Levinas's philosophy. Levinas is relevant to a critical analysis of ethnographic methods because his philosophy turns on the problematic relationship between self and other, among other important problems that define and guide contemporary anthropological research, including questions of responsibility, justice, and solidarity. This article utilizes Levinas's philosophy to outline a phenomenology of the “doing” of fieldwork, emphasizing the contingency of face-to-face encounters over controlled research design. This account provides a basis for going beyond the polarized opposition between objective and subjective ethnographic approaches. Levinas allows for an ethically informed ethnography premised upon an acknowledgement of risk and uncertainity over researcher control or reflexivity. Providing a handful of concrete examples, the article argues that critical self-reflection about the fundamental face-to-face dimension of fieldwork is central to ethnography's ethical possibilities.

How do people evaluate causal relationships? Do they just consider what actually happened, or do they also consider what could have counterfactually happened? Using eye tracking and Gaussian process modeling, we investigated how people mentally simulated past events to judge what caused the outcomes to occur. Participants played a virtual ball‐shooting game and then—while looking at a blank screen—mentally simulated (a) what actually happened, (b) what counterfactually could have happened, or (c) what caused the outcome to happen. Our findings showed that participants moved their eyes in patterns consistent with the actual or counterfactual events that they mentally simulated. When simulating what caused the outcome to occur, participants moved their eyes consistent with simulations of counterfactual possibilities. These results favor counterfactual theories of causal reasoning, demonstrate how eye movements can reflect simulation during this reasoning and provide a novel approach for investigating retrospective causal reasoning and counterfactual thinking.

When comparing the roles of the lightning strike and the dry climate in causing the forest fire, one might think that the lightning strike is more of a cause than the dry climate, or one might think that the lightning strike completely caused the fire while the dry conditions did not cause it at all. Psychologists and philosophers have long debated whether such causal judgments are graded; that is, whether people treat some causes as stronger than others. To address this debate, we first reanalyzed data from four recent studies. We found that causal judgments were actually multimodal: although most causal judgments made on a continuous scale were categorical, there was also some gradation. We then tested two competing explanations for this gradation: the confidence explanation, which states that people make graded causal judgments because they have varying degrees of belief in causal relations, and the strength explanation, which states that people make graded causal judgments because they believe that causation itself is graded. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was indeed moderated by confidence: people tended to make graded causal judgments when they were unconfident, but they tended to make more categorical causal judgments when they were confident. Experiment 2 tested the causal strength explanation and showed that although confidence still explained variation in causal judgments, it did not explain away the effects of normality, causal structure, or the number of candidate causes. Overall, we found that causal judgments were multimodal and that people make graded judgments both when they think a cause is weak and when they are uncertain about its causal role.

Abstract:This visual essay invites renewed reflection on the iconography of the people. In the spring of 2020, Guatemala's President Alejandro Giammattei prohibited citizens from leaving their homes to help contain the spread of the novel coronavirus known as Covid-19. Doing little to manage the spread of the virus, these curfew events gave new aesthetic and political meaning to a familiar visual genre: photographs of empty streets. For more than a century, and especially in the summer of 2020, images of crowds and mass protests have provided both governments on the one hand, and protesting multitudes on the other with an aesthetic representation of the people. But this interest in collective assemblies has tended to engage only one side of the equation. To fully appreciate the visual power of the people, it is also necessary to understand those images from which people are strikingly absent.

Counterfactual theories propose that people’s capacity for causal judgment depends on their ability to consider alternative possibilities: The lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate a variety of psychological effects on causal judgment, a range of recent accounts have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded measure of causal strength. While such models successfully describe the influence of the statistical normality (i.e., the base rate) of the candidate and alternate causes on causal judgments, we show that they make further untested predictions about how normality influences people’s confidence in their causal judgments. In a large (N = 3,020) sample of participants in a causal judgment task, we found that normality indeed influences people’s confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model in which people are more confident in a causal relationship when the effect of the cause is less variable among imagined counterfactual possibilities.