An interaction effect of norm violations on causal judgment

Existing research has shown that norm violations influence causal judgments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interaction pattern in people's judgments. Specifically, it predicts that when people are judging the degree to which a particular factor is a cause, there should be an interaction between (a) the degree to which that factor violates a norm and (b) the degree to which another factor in the situation violates norms. A study of moral norms (N = 1000) and norms of proper functioning (N = 3000) revealed robust evidence for the predicted interaction effect. The implications of these patterns for existing theories of causal judgments is discussed.

Invariance Applied

This chapter applies the ideas about invariance from Chapter 5 to the analysis (both normative and descriptive) of various aspects of commonsense cause reasoning. The focus is mainly on one particular kind of invariance—invariance under changes in background conditions, here called insensitivity. This is used to cast light on causal judgments involving omissions and examples involving double prevention (in which the occurrence of c prevents the occurrence of e, which had it occurred, would have prevented the occurrence of f, with the result that f occurs). It is argued that causal claims regarding omissions and double prevention relations differ normatively depending on the invariance of the relations involved and that this is also reflected in the judgments that people make about such claims.

Certainty and Singular Causal Judgment

In this paper, I map out broad aims, challenges, predictions, and implica- tions for the resulting intersection of singular causal judgment and metacognition that I (tentatively) call causal metacognition. First, I will overview research on sin- gular causal judgment, focusing on popular counterfactual theories that provide a formal framework for evaluating dependency relationships, as well as several compet- ing definitions of singular causal strength. Next, I will provide relevant background in the literature on metacognition for perception and decision-making, discussing major computational theories of metacognitive judgments. After covering the small amount of work on uncertainty in causal judgments, I will then argue that although singular causal judgments pose a particular problem for some theories of metacognition, coun- terfactual theories of singular causal judgment already provide testable predictions for confidence in causal judgments and can be extended to account for a wide range of patterns in confidence in singular causal judgments. Finally, I will summarize why we need a study of causal metacognition, and what empirical and theoretical advancements in that field might look like.

The interplay between covariation, temporal, and mechanism information in singular causation judgments

Most psychological studies on causal cognition have focused on how people make predictions from causes to effects or how they assess causal strength for general causal relationships (e.g., “smoking causes cancer”). In the past years, there has been a surge of interest in other types of causal judgments, such as diagnostic inferences or causal selection. Our focus here is on how people assess singular causation relations between cause and effect events that occurred at a particular spatiotemporal location (e.g., “Mary’s having taking this pill caused her sickness.”). The analysis of singular causation has received much attention in philosophy, but relatively few psychological studies have investigated how lay people assess these relations. Based on the power PC model of causal attribution proposed by Cheng and Novick (2005), we have developed and tested a new computational model of singular causation judgments integrating covariation, temporal, and mechanism information. We provide an overview of this research and outline important questions for future research.

Degrading Causation

When asking if lightning caused the forest fire, one might think that the lightning is more of a cause than the dry climate (i.e., it is a graded cause) or they might instead think that the lightning strike completely caused the fire while the dry conditions did not cause it at all (i.e., it is a binary cause). Psychologists and philosophers have long debated whether such judgments are graded. To address this debate, we started by reanalyzing data from four recent studies. In this context, we provide novel evidence that causal judgments are actually multimodal: although most causal judgements were binary, there was also some gradation. We then tested two competing explanations for the gradation we observed: the confidence explanation, which states that gradation distinguishes between certain and uncertain causes, and the strength explanation, which states that gradation distinguishes between strong and weak causes. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was moderated by confidence. People tended to make graded causal judgments when they were less confident, but they tended to make discrete causal judgments when they were more confident. Experiment 2 tested the causal strength explanation and showed that although causal judgments varied with factors associated with causal strength, confidence ratings were unchanged. Overall, we found that causal judgments are multimodal and that observed gradation reflects independent effects of confidence and causal strength on causal judgments.

Can We Set Aside Previous Experience in a Familiar Causal Scenario?

Causal and predictive learning research often employs intuitive and familiar hypothetical scenarios to facilitate learning novel relationships. The allergist task, in which participants are asked to diagnose the allergies of a fictitious patient, is one example of this. In such studies, it is common practice to ask participants to ignore their existing knowledge of the scenario and make judgments based only on the relationships presented within the experiment. Causal judgments appear to be sensitive to instructions that modify assumptions about the scenario. However, the extent to which prior knowledge continues to affect competition for associative learning, even after participants are instructed to disregard it, is unknown. To answer this, we created a cue competition design that capitalized on prevailing beliefs about the allergenic properties of various foods. High and low allergenic foods were paired with foods moderately associated with allergy to create two compounds; high + moderate and low + moderate. We expected high allergenic foods to produce greater competition for associative memory than low allergenic foods. High allergenic foods may affect learning either because they generate a strong memory of allergy or because they are more salient in the context of the task. We therefore also manipulated the consistency of the high allergenic cue-outcome relationship with prior beliefs about the nature of the allergies. A high allergenic food that is paired with an inconsistent allergenic outcome should generate more prediction error and thus more competition for learning, than one that is consistent with prior beliefs. Participants were instructed to either use or ignore their knowledge of food allergies to complete the task. We found that while participants were able to set aside their prior knowledge when making causal judgments about the foods in question, associative memory was weaker for the cues paired with highly allergenic foods than cues paired with low allergenic foods regardless of instructions. The consistency manipulation had little effect on this result, suggesting that the effects in associative memory are most likely driven by selective attention to highly allergenic cues. This has implications for theories of causal learning as well as the way causal learning tasks are designed.