Lucky or clever? From expectations to responsibility judgments

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.

Optimizing competence in the service of collaboration

In order to efficiently divide labor with others, it is important to understand what our collaborators can do (i.e., their competence). However, competence is not static-people get better at particular jobs the more often they perform them. This plasticity of competence creates a challenge for collaboration: For example, is it better to assign tasks to whoever is most competent now, or to the person who can be trained most efficiently "on-the-job"? We conducted four experiments (N=396) that examine how people make decisions about whom to train (Experiments 1 and 3) and whom to recruit (Experiments 2 and 4) to a collaborative task, based on the simulated collaborators' starting expertise, the training opportunities available, and the goal of the task. We found that participants' decisions were best captured by a planning model that attempts to maximize the returns from collaboration while minimizing the costs of hiring and training individual collaborators. This planning model outperformed alternative models that based these decisions on the agents' current competence, or on how much agents stood to improve in a single training step, without considering whether this training would enable agents to succeed at the task in the long run. Our findings suggest that people do not recruit and train collaborators based solely on their current competence, nor solely on the opportunities for their collaborators to improve. Instead, people use an intuitive theory of competence to balance the costs of hiring and training others against the benefits to the collaboration.

The importance of epistemic intentions in ascription of responsibility

We investigate how people ascribe responsibility to an agent who caused a bad outcome but did not know he would. The psychological processes for making such judgments, we argue, involve finding a counterfactual in which some minimally benevolent intention initiates a course of events that leads to a better outcome than the actual one. We hypothesize that such counterfactuals can include, when relevant, epistemic intentions. With four vignette studies, we show that people consider epistemic intentions when ascribing responsibility for a bad outcome. We further investigate which epistemic intentions people are likely to consider when building counterfactuals for responsibility ascription. We find that, when an agent did not predict a bad outcome, people ascribe responsibility depending on the reasons behind the agents' lack of knowledge. People judge agents responsible for the bad outcome they caused when they could have easily predicted the consequences of their actions but did not care to acquire the relevant information. However, when this information was hard to acquire, people are less likely to judge them responsible.

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.

When guilt works: a comprehensive meta-analysis of guilt appeals

Introduction

Guilt appeals are widely used as a persuasive approach in various areas of practice. However, the strength and direction of the persuasive effects of guilt appeals are mixed, which could be influenced by theoretical and methodological factors.

Method

The present study is a comprehensive meta-analysis of 26 studies using a random-effects model to assess the persuasive effects of guilt appeals. In total, 127 effect sizes from seven types of persuasive outcomes (i.e., guilt, attitude, behavior, behavioral intention, non-guilt emotions, motivation, and cognition) were calculated based on 7,512 participants.

Results

The analysis showed a small effect size of guilt appeals [g = 0.19, 95% CI (0.10, 0.28)]. The effect of guilt appeals was moderated by the theoretical factors related to appraisal and coping of guilt arousal, including attributed responsibility, controllability and stability of the causal factors, the proximity of perceiver-victim relationship, recommendation of reparative behaviors, and different outcome types. The effect was also associated with methods used in different studies.

Discussion

Overall, the findings demonstrated the persuasive effects of guilt appeals, but theoretical and methodological factors should be considered in the design and testing of guilt appeals. We also discussed the practical implications of the findings.

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.

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'.

Exploring the Representational Structure of Trait Knowledge Using Perceived Similarity Judgments

A large body of past work has sought to identify the underlying dimensions that capture our trait knowledge of other people. However, the importance of particular traits in determining our overall impressions of others is not well understood, and different traits may be fundamental for impressions of famous versus unfamiliar people. For instance, we may focus on competence when evaluating a famous person, but on trustworthiness when evaluating a stranger. To examine the structure of overall impressions of famous people and of unfamiliar people, we probed the contributions of 13 different trait judgments to perceived similarity judgments. We found that different sets of traits best predicted perceived similarity between famous people versus between unfamiliar people; however, the relationship between each trait and perceived similarity generalized to some extent from famous people to unfamiliar people, suggesting a degree of overlap in the structure of overall impressions.

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.

Plans or Outcomes: How Do We Attribute Intelligence to Others?

Humans routinely make inferences about both the contents and the workings of other minds based on observed actions. People consider what others want or know, but also how intelligent, rational, or attentive they might be. Here, we introduce a new methodology for quantitatively studying the mechanisms people use to attribute intelligence to others based on their behavior. We focus on two key judgments previously proposed in the literature: judgments based on observed outcomes (you're smart if you won the game) and judgments based on evaluating the quality of an agent's planning that led to their outcomes (you're smart if you made the right choice, even if you didn't succeed). We present a novel task, the maze search task (MST), in which participants rate the intelligence of agents searching a maze for a hidden goal. We model outcome-based attributions based on the observed utility of the agent upon achieving a goal, with higher utilities indicating higher intelligence, and model planning-based attributions by measuring the proximity of the observed actions to an ideal planner, such that agents who produce closer approximations of optimal plans are seen as more intelligent. We examine human attributions of intelligence in three experiments that use MST and find that participants used both outcome and planning as indicators of intelligence. However, observing the outcome was not necessary, and participants still made planning-based attributions of intelligence when the outcome was not observed. We also found that the weights individuals placed on plans and on outcome correlated with an individual's ability to engage in cognitive reflection. Our results suggest that people attribute intelligence based on plans given sufficient context and cognitive resources and rely on the outcome when computational resources or context are limited.

Moral dynamics: Grounding moral judgment in intuitive physics and intuitive psychology

When holding others morally responsible, we care about what they did, and what they thought. Traditionally, research in moral psychology has relied on vignette studies, in which a protagonist's actions and thoughts are explicitly communicated. While this research has revealed what variables are important for moral judgment, such as actions and intentions, it is limited in providing a more detailed understanding of exactly how these variables affect moral judgment. Using dynamic visual stimuli that allow for a more fine-grained experimental control, recent studies have proposed a direct mapping from visual features to moral judgments. We embrace the use of visual stimuli in moral psychology, but question the plausibility of a feature-based theory of moral judgment. We propose that the connection from visual features to moral judgments is mediated by an inference about what the observed action reveals about the agent's mental states, and what causal role the agent's action played in bringing about the outcome. We present a computational model that formalizes moral judgments of agents in visual scenes as computations over an intuitive theory of physics combined with an intuitive theory of mind. We test the model's quantitative predictions in three experiments across a wide variety of dynamic interactions.

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.

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.

Blaming automated vehicles in difficult situations

Automated vehicles (AVs) have made huge strides toward large-scale deployment. Despite this progress, AVs continue to make mistakes, some resulting in death. Although some mistakes are avoidable, others are hard to avoid even by highly skilled drivers. As these mistakes continue to shape attitudes toward AVs, we need to understand whether people differentiate between them. We ask the following two questions. When an AV makes a mistake, does the perceived difficulty or novelty of the situation predict blame attributed to it? How does that blame attribution compare to a human driving a car? Through two studies, we find that the amount of blame people attribute to AVs and human drivers is sensitive to situation difficulty. However, while some situations could be more difficult for AVs and others for human drivers, people blamed AVs more, regardless. Our results provide novel insights in understanding psychological barriers influencing the public's view of AVs.

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Attributed blame to machine and human drivers is sensitive to situation difficulty

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Mistakes in simple situations receive more blame than in novel or complex situations

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Machine drivers receive more blame, across different situations

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.

Witnessing, Remembering, and Testifying: Why the Past Is Special for Human Beings

The past is undeniably special for human beings. To a large extent, both individuals and collectives define themselves through history. Moreover, humans seem to have a special way of cognitively representing the past: episodic memory. As opposed to other ways of representing knowledge, remembering the past in episodic memory brings with it the ability to become a witness. Episodic memory allows us to determine what of our knowledge about the past comes from our own experience and thereby what parts of the past we can give testimony about. In this article, we aim to give an account of the special status of the past by asking why humans have developed the ability to give testimony about it. We argue that the past is special for human beings because it is regularly, and often principally, the only thing that can determine present social realities such as commitments, entitlements, and obligations. Because the social effects of the past often do not leave physical traces behind, remembering the past and the ability to bear testimony it brings is necessary for coordinating social realities with other individuals.

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.

Optimality and heuristics in perceptual neuroscience

The foundation for modern understanding of how we make perceptual decisions about what we see or where to look comes from considering the optimal way to perform these behaviors. While statistical computation is useful for deriving the optimal solution to a perceptual problem, optimality requires perfect knowledge of priors and often complex computation. Accumulating evidence, however, suggests that optimal perceptual goals can be achieved or approximated more simply by human observers using heuristic approaches. Perceptual neuroscientists captivated by optimal explanations of sensory behaviors will fail in their search for the neural circuits and cortical processes that implement an optimal computation whenever that behavior is actually achieved through heuristics. This article provides a cross-disciplinary review of decision-making with the aim of building perceptual theory that uses optimality to set the computational goals for perceptual behavior but, through consideration of ecological, computational, and energetic constraints, incorporates how these optimal goals can be achieved through heuristic approximation.