Normality and actual causal strength

Counterfactual simulation in causal cognition

How do people make causal judgments and assign responsibility? In this review article, I argue that counterfactual simulations are key. To simulate counterfactuals, we need three ingredients: a generative mental model of the world, the ability to perform interventions on that model, and the capacity to simulate the consequences of these interventions. The counterfactual simulation model (CSM) uses these ingredients to capture people's intuitive understanding of the physical and social world. In the physical domain, the CSM predicts people's causal judgments about dynamic collision events, complex situations that involve multiple causes, omissions as causes, and causes that sustain physical stability. In the social domain, the CSM predicts responsibility judgments in helping and hindering scenarios.

Now you see me, now you don't: an exploration of religious exnomination in DALL-E

Artificial intelligence (AI) systems are increasingly being used not only to classify and analyze but also to generate images and text. As recent work on the content produced by text and image Generative AIs has shown (e.g., Cheong et al., 2024, Acerbi & Stubbersfield, 2023), there is a risk that harms of representation and bias, already documented in prior AI and natural language processing (NLP) algorithms may also be present in generative models. These harms relate to protected categories such as gender, race, age, and religion. There are several kinds of harms of representation to consider in this context, including stereotyping, lack of recognition, denigration, under-representation, and many others (Crawford in Soundings 41:45-55, 2009; in: Barocas et al., SIGCIS Conference, 2017). Whereas the bulk of researchers' attention thus far has been given to stereotyping and denigration, in this study we examine 'exnomination', as conceived by Roland Barthes (1972), of religious groups. Our case study is DALL-E, a tool that generates images from natural language prompts. Using DALL-E mini, we generate images from generic prompts such as "religious person." We then examine whether the generated images are recognizably members of a nominated group. Thus, we assess whether the generated images normalize some religions while neglecting others. We hypothesize that Christianity will be recognizably represented more frequently than other religious groups. Our results partially support this hypothesis but introduce further complexities, which we then explore.

Looking at Mental Images: Eye-Tracking Mental Simulation During Retrospective Causal Judgment

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.

People's thinking plans adapt to the problem they're trying to solve

Much of our thinking focuses on deciding what to do in situations where the space of possible options is too large to evaluate exhaustively. Previous work has found that people do this by learning the general value of different behaviors, and prioritizing thinking about high-value options in new situations. Is this good-action bias always the best strategy, or can thinking about low-value options sometimes become more beneficial? Can people adapt their thinking accordingly based on the situation? And how do we know what to think about in novel events? Here, we developed a block-puzzle paradigm that enabled us to measure people's thinking plans and compare them to a computational model of rational thought. We used two distinct response methods to explore what people think about-a self-report method, in which we asked people explicitly to report what they thought about, and an implicit response time method, in which we used people's decision-making times to reveal what they thought about. Our results suggest that people can quickly estimate the apparent value of different options and use this to decide what to think about. Critically, we find that people can flexibly prioritize whether to think about high-value options (Experiments 1 and 2) or low-value options (Experiments 3, 4, and 5), depending on the problem. Through computational modeling, we show that these thinking strategies are broadly rational, enabling people to maximize the value of long-term decisions. Our results suggest that thinking plans are flexible: What we think about depends on the structure of the problems we are trying to solve.

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the actual effort or force (individuals are more responsible if they put forth more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N=717), we found that participants' judgments are best described by a model that considers both actual and counterfactual effort. This finding generalized to an independent validation data set (N=99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

Mutual entailment between causation and responsibility

The standard view in philosophy is that responsibility entails causation. Most philosophers treat this entailment claim as an evident insight into the ordinary concepts of responsibility and causation. Further, it is taken to be equally obvious that the reversal of this claim does not hold: causation does not entail responsibility. In contrast, Sytsma and Livengood have put forward an account of the use of ordinary causal attributions (statements like "X caused Y") that contends that they are typically used interchangeably with responsibility attributions (statements like "X is responsible for Y"). Put in terms of the concepts at play in these attributions, this account suggests that the reversal of the entailment claim may also hold, and, a fortiori, there would be mutual entailment between the ordinary concepts of responsibility and causation. Using the cancellability test, we report the results of three pre-registered studies providing empirical evidence that causation and responsibility are mutually entailed by each other.

Making a positive difference: Criticality in groups

How critical are individual members perceived to be for their group's performance? In this paper, we show that judgments of criticality are intimately linked to considering responsibility. Prospective responsibility attributions in groups are relevant across many domains and situations, and have the potential to influence motivation, performance, and allocation of resources. We develop various models that differ in how the relationship between criticality and responsibility is conceptualized. To test our models, we experimentally vary the task structure (disjunctive, conjunctive, and mixed) and the abilities of the group members (which affects their probability of success). We show that both factors influence criticality judgments, and that a model which construes criticality as anticipated credit best explains participants' judgments. Unlike prior work that has defined criticality as anticipated responsibility for both success and failures, our results suggest that people only consider the possible outcomes in which an individual contributed to a group success, but disregard group failure.

The pervasive impact of ignorance

Norm violations have been demonstrated to impact a wide range of seemingly non-normative judgments. Among other things, when agents' actions violate prescriptive norms they tend to be seen as having done those actions more freely, as having acted more intentionally, as being more of a cause of subsequent outcomes, and even as being less happy. The explanation of this effect continue to be debated, with some researchers appealing to features of actions that violate norms, and other researcher emphasizing the importance of agents' mental states when acting. Here, we report the results of two large-scale experiments that replicate and extend twelve of the studies that originally demonstrated the pervasive impact of norm violations. In each case, we build on the pre-existing experimental paradigms to additionally manipulate whether the agents knew that they were violating a norm while holding fixed the action done. We find evidence for a pervasive impact of ignorance: the impact of norm violations on non-normative judgments depends largely on the agent knowing that they were violating a norm when acting. Moreover, we find evidence that the reduction in the impact of normality is underpinned by people's counterfactual reasoning: people are less likely to consider an alternative to the agent's action if the agent is ignorant. We situate our findings in the wider debate around the role or normality in people's reasoning.

What would have happened? Counterfactuals, hypotheticals and causal judgements

How do people make causal judgements? In this paper, I show that counterfactual simulations are necessary for explaining causal judgements about events, and that hypotheticals do not suffice. In two experiments, participants viewed video clips of dynamic interactions between billiard balls. In Experiment 1, participants either made hypothetical judgements about whether ball B would go through the gate if ball A were not present in the scene, or counterfactual judgements about whether ball B would have gone through the gate if ball A had not been present. Because the clips featured a block in front of the gate that sometimes moved and sometimes stayed put, hypothetical and counterfactual judgements came apart. A computational model that evaluates hypotheticals and counterfactuals by running noisy physical simulations accurately captured participants' judgements. In Experiment 2, participants judged whether ball A caused ball B to go through the gate. The results showed a tight fit between counterfactual and causal judgements, whereas hypotheticals did not predict causal judgements. I discuss the implications of this work for theories of causality, and for studying the development of counterfactual thinking in children. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

It's not what you did, it's what you could have done

We are more likely to judge agents as morally culpable after we learn they acted freely rather than under duress or coercion. Interestingly, the reverse is also true: Individuals are more likely to be judged to have acted freely after we learn that they committed a moral violation. Researchers have argued that morality affects judgments of force by making the alternative actions the agent could have done instead appear comparatively normal, which then increases the perceived availability of relevant alternative actions. Across five studies, we test the novel predictions of this account. We find that the degree to which participants view possible alternative actions as normal strongly predicts their perceptions that an agent acted freely. This pattern holds both for perceptions of the prescriptive normality of the alternatives (whether the actions are good) and descriptive normality of the alternatives (whether the actions are unusual). We also find that manipulating the prudential value of alternative actions or the degree to which alternatives adhere to social norms, has a similar effect to manipulating whether the actions or their alternatives violate moral norms. This pattern persists even when what is actually done is held constant, and these effects are explained by changes in the perceived normality of the alternatives. Together, these results suggest that across contexts, participants' force judgments depend not on the morality of the actual action taken, but on the normality of possible alternatives. More broadly, our results build on prior work that suggests a unifying role of normality and counterfactuals across many areas of high-level human cognition.

An interaction effect of norm violations on causal judgment

Existing research has shown that norm violations influence causal judg- ments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interac- tion 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.

Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Pre-schoolers

The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of causal-invariance decomposition functions is essential for this process. Such knowledge specifies the decomposition of an observed outcome into contributions from constituent causes under the default assumption that the empirical knowledge acquired is invariant across contextual/background causes. The paper reports two psychological experiments (and replication studies) with pre-school-age children on generalization across contexts involving binary cause and effect variables. The critical role of causal invariance for constructing useable causal knowledge predicts that even young children should (tacitly) use the causal-invariance decomposition function for such variables rather than a non-causal-invariance decomposition function common in statistical practice in research involving binary outcomes. The findings support the rational shaping of empirical causal knowledge by the causal-invariance constraint, ruling out alternative explanations in terms of non-causal-invariance decomposition functions, heuristics, and biases. For the same causal structure involving candidate causes and outcomes that are binary variables with a "present" value and an "absent" value, the paper argues against the possibility of multiple rational characterizations of the "sameness of causal influence" that justifies generalization across contexts.

Double Prevention, Causal Judgments, and Counterfactuals

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.

Causation comes in degrees

Which country, politician, or policy is more of a cause of the Covid-19 pandemic death toll? Which of the two factories causally contributed more to the pollution of the nearby river? A wide-ranging portion of our everyday thought and talk, and attitudes rely on a graded notion of causation. However, it is sometimes highlighted that on most contemporary accounts, causation is on-off. Some philosophers further question the legitimacy of talk of degrees of causation and suggest that we avoid it. Some hold that the notion of degrees of causation is an illusion. In this paper, I'll argue that causation does come in degrees.

Causal Judgment in the Wild: Evidence from the 2020 U.S. Presidential Election

When explaining why an event occurred, people intuitively highlight some causes while ignoring others. How do people decide which causes to select? Models of causal judgment have been evaluated in simple and controlled laboratory experiments, but they have yet to be tested in a complex real-world setting. Here, we provide such a test, in the context of the 2020 U.S. presidential election. Across tens of thousands of simulations of possible election outcomes, we computed, for each state, an adjusted measure of the correlation between a Biden victory in that state and a Biden election victory. These effect size measures accurately predicted the extent to which U.S. participants (N = 207, preregistered) viewed victory in a given state as having caused Biden to win the presidency. Our findings support the theory that people intuitively select as causes of an outcome the factors with the largest standardized causal effect on that outcome across possible counterfactual worlds.

Predicting responsibility judgments from dispositional inferences and causal attributions

The question of how people hold others responsible has motivated decades of theorizing and empirical work. In this paper, we develop and test a computational model that bridges the gap between broad but qualitative framework theories, and quantitative but narrow models. In our model, responsibility judgments are the result of two cognitive processes: a dispositional inference about a person's character from their action, and a causal attribution about the person's role in bringing about the outcome. We test the model in a group setting in which political committee members vote on whether or not a policy should be passed. We assessed participants' dispositional inferences and causal attributions by asking how surprising and important a committee member's vote was. Participants' answers to these questions in Experiment 1 accurately predicted responsibility judgments in Experiment 2. In Experiments 3 and 4, we show that the model also predicts moral responsibility judgments, and that importance matters more for responsibility, while surprise matters more for judgments of wrongfulness.

A counterfactual simulation model of causation by omission

When do people say that an event that did not happen was a cause? We extend the counterfactual simulation model (CSM) of causal judgment (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2021) and test it in a series of three experiments that look at people's causal judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of questions that need to be answered in order to give an adequate causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative causal relationships evaluated, and how is the causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make causal judgments about omissions in physical interactions by using their intuitive understanding of physics to mentally simulate what would have happened in relevant counterfactual situations. Prior work has argued that normative expectations affect judgments of omissive causation. Here we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's judgments.

A simple definition of 'intentionally'

Cognitive scientists have been debating how the folk concept of intentional action works. We suggest a simple account: people consider that an agent did X intentionally to the extent that X was causally dependent on how much the agent wanted X to happen (or not to happen). Combined with recent models of human causal cognition, this definition provides a good account of the way people use the concept of intentional action, and offers natural explanations for puzzling phenomena such as the side-effect effect. We provide empirical support for our theory, in studies where we show that people's causation and intentionality judgments track each other closely, in everyday situations as well as in scenarios with unusual causal structures. Study 5 additionally shows that the effect of norm violations on intentionality judgments depends on the causal structure of the situation, in a way uniquely predicted by our theory. Taken together, these results suggest that the folk concept of intentional action has been difficult to define because it is made of cognitive building blocks, such as our intuitive concept of causation, whose logic cognitive scientists are just starting to understand.

Causal judgments about atypical actions are influenced by agents' epistemic states

A prominent finding in causal cognition research is people's tendency to attribute increased causality to atypical actions. If two agents jointly cause an outcome (conjunctive causation), but differ in how frequently they have performed the causal action before, people judge the atypically acting agent to have caused the outcome to a greater extent. In this paper, we argue that it is the epistemic state of an abnormally acting agent, rather than the abnormality of their action, that is driving people's causal judgments. Given the predictability of the normally acting agent's behaviour, the abnormal agent is in a better position to foresee the consequences of their action. We put this hypothesis to test in four experiments. In Experiment 1, we show that people judge the atypical agent as more causal than the normally acting agent, but also judge the atypical agent to have an epistemic advantage. In Experiment 2, we find that people do not judge a causal difference if no epistemic advantage for the abnormal agent arises. In Experiment 3, we replicate these findings in a scenario in which the abnormal agent's epistemic advantage generalises to a novel context. In Experiment 4, we extend these findings to mental states more broadly construed and develop a Bayesian network model that predicts the degree of outcome-oriented mental states based on action normality and epistemic states. We find that people infer mental states like desire and intention to a greater extent from abnormal behaviour when this behaviour is accompanied by an epistemic advantage. We discuss these results in light of current theories and research on people's preference for abnormal causes.

Counterfactual thinking and recency effects in causal judgment

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (N = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, we argue that an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of our extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one. Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure. We discuss these results in relation to work on counterfactual thinking, causal modeling, and late-preemption.

How pills undermine skills: Moralization of cognitive enhancement and causal selection

Despite the promise to boost human potential and wellbeing, enhancement drugs face recurring ethical scrutiny. The present studies examined attitudes toward cognitive enhancement in order to learn more about these ethical concerns, who has them, and the circumstances in which they arise. Fairness-based concerns underlay opposition to competitive use-even though enhancement drugs were described as legal, accessible and affordable. Moral values also influenced how subsequent rewards were causally explained: Opposition to competitive use reduced the causal contribution of the enhanced winner's skill, particularly among fairness-minded individuals. In a follow-up study, we asked: Would the normalization of enhancement practices alleviate concerns about their unfairness? Indeed, proliferation of competitive cognitive enhancement eradicated fairness-based concerns, and boosted the perceived causal role of the winner's skill. In contrast, purity-based concerns emerged in both recreational and competitive contexts, and were not assuaged by normalization.

The trajectory of counterfactual simulation in development

Young children often struggle to answer the question "what would have happened?" particularly in cases where the adult-like "correct" answer has the same outcome as the event that actually occurred. Previous work has assumed that children fail because they cannot engage in accurate counterfactual simulations. Children have trouble considering what to change and what to keep fixed when comparing counterfactual alternatives to reality. However, most developmental studies on counterfactual reasoning have relied on binary yes/no responses to counterfactual questions about complex narratives and so have only been able to document when these failures occur but not why and how. Here, we investigate counterfactual reasoning in a domain in which specific counterfactual possibilities are very concrete: simple collision interactions. In Experiment 1, we show that 5- to 10-year-old children (recruited from schools and museums in Connecticut) succeed in making predictions but struggle to answer binary counterfactual questions. In Experiment 2, we use a multiple-choice method to allow children to select a specific counterfactual possibility. We find evidence that 4- to 6-year-old children (recruited online from across the United States) do conduct counterfactual simulations, but the counterfactual possibilities younger children consider differ from adult-like reasoning in systematic ways. Experiment 3 provides further evidence that young children engage in simulation rather than using a simpler visual matching strategy. Together, these experiments show that the developmental changes in counterfactual reasoning are not simply a matter of whether children engage in counterfactual simulation but also how they do so. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Norms Affect Prospective Causal Judgments

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-that is, 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.

Immoral Professors and Malfunctioning Tools: Counterfactual Relevance Accounts Explain the Effect of Norm Violations on Causal Selection

Causal judgments are widely known to be sensitive to violations of both prescriptive norms (e.g., immoral events) and statistical norms (e.g., improbable events). There is ongoing discussion as to whether both effects are best explained in a unified way through changes in the relevance of counterfactual possibilities, or whether these two effects arise from unrelated cognitive mechanisms. Recent work has shown that moral norm violations affect causal judgments of agents, but not inanimate artifacts used by those agents. These results have been interpreted as showing that prescriptive norm violations only affect causal reasoning about intentional agents, but not the use of inanimate artifacts, thereby providing evidence that the effect of prescriptive norm violations arises from mechanisms specific to reasoning about intentional agents, and thus casting doubt on a unified counterfactual analysis of causal reasoning. Four experiments explore this recent finding and provide clear support for a unified counterfactual analysis. Experiment 1 demonstrates that these newly observed patterns in causal judgments are closely mirrored by judgments of counterfactual relevance. Experiment 2 shows that the relationship between causal and counterfactual judgments is moderated by causal structure, as uniquely predicted by counterfactual accounts. Experiment 3 directly manipulates the relevance of counterfactual alternatives and finds that causal judgments of intentional agents and inanimate artifacts are similarly affected. Finally, Experiment 4 shows that prescriptive norm violations (in which artifacts malfunction) affect causal judgments of inanimate artifacts in much the same way that prescriptive norm violations (in which agents act immorally) affect causal judgments of intentional agents.

Causal Responsibility and Robust Causation

How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor – the robustness (or stability) of the causal chain linking the agent’s action and the outcome – influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments increase with robustness. In the first experiment, the robustness manipulation also raises the probability of the effect given the action. Experiments 2 and 3 control for probability-raising, and show that robustness still affects judgments of causal responsibility. In particular, Experiment 3 introduces an Ellsberg type of scenario to manipulate robustness, while keeping the conditional probability and the skill deployed in the action fixed. Experiment 4, replicates the results of Experiment 3, while contrasting between judgments of causal strength and of causal responsibility. The results show that in all cases, the perceived degree of responsibility (but not of causal strength) increases with the robustness of the action-outcome causal chain.

On the Irrationality of Being in Two Minds

This article presents a general framework that allows irrational decision making to be theoretically investigated and simulated. Rationality in human decision making under uncertainty is normatively prescribed by the axioms of probability theory in order to maximize utility. However, substantial literature from psychology and cognitive science shows that human decisions regularly deviate from these axioms. Bistable probabilities are proposed as a principled and straight forward means for modeling (ir)rational decision making, which occurs when a decision maker is in "two minds". We show that bistable probabilities can be formalized by positive-operator-valued projections in quantum mechanics. We found that (1) irrational decision making necessarily involves a wider spectrum of causal relationships than rational decision making, (2) the accessible information turns out to be greater in irrational decision making when compared to rational decision making, and (3) irrational decision making is quantum-like because it violates the Bell-Wigner polytope.

How We Know What Not To Think

Humans often represent and reason about unrealized possible actions - the vast infinity of things that were not (or have not yet been) chosen. This capacity is central to the most impressive of human abilities: causal reasoning, planning, linguistic communication, moral judgment, etc. Nevertheless, how do we select possible actions that are worth considering from the infinity of unrealized actions that are better left ignored? We review research across the cognitive sciences, and find that the possible actions considered by default are those that are both likely to occur and generally valuable. We then offer a unified theory of why. We propose that (i) across diverse cognitive tasks, the possible actions we consider are biased towards those of general practical utility, and (ii) a plausible primary function for this mechanism resides in decision making.

Quantitative causal selection patterns in token causation

When many events contributed to an outcome, people consistently judge some more causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air—in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people’s causal judgments are on a continuum—and relatively little is known about how these judgments vary quantitatively as the prior probabilities change. In this paper, we measure people’s causal judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories.

Statistical reporting inconsistencies in experimental philosophy

Experimental philosophy (x-phi) is a young field of research in the intersection of philosophy and psychology. It aims to make progress on philosophical questions by using experimental methods traditionally associated with the psychological and behavioral sciences, such as null hypothesis significance testing (NHST). Motivated by recent discussions about a methodological crisis in the behavioral sciences, questions have been raised about the methodological standards of x-phi. Here, we focus on one aspect of this question, namely the rate of inconsistencies in statistical reporting. Previous research has examined the extent to which published articles in psychology and other behavioral sciences present statistical inconsistencies in reporting the results of NHST. In this study, we used the R package statcheck to detect statistical inconsistencies in x-phi, and compared rates of inconsistencies in psychology and philosophy. We found that rates of inconsistencies in x-phi are lower than in the psychological and behavioral sciences. From the point of view of statistical reporting consistency, x-phi seems to do no worse, and perhaps even better, than psychological science.