counterfactual reasoning
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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 continues to be debated, with some researchers appealing to features of actions that violate norms, and other researchers emphasising 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 of normality in people's reasoning.

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target’s location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants’ behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

We present a summary of research that we have conducted employing AI to better understand human morality. This summary adumbrates theoretical fundamentals and considers how to regulate development of powerful new AI technologies. The latter research aim is benevolent AI, with fair distribution of benefits associated with the development of these and related technologies, avoiding disparities of power and wealth due to unregulated competition. Our approach avoids statistical models employed in other approaches to solve moral dilemmas, because these are “blind” to natural constraints on moral agents, and risk perpetuating mistakes. Instead, our approach employs, for instance, psychologically realistic counterfactual reasoning in group dynamics. The present paper reviews studies involving factors fundamental to human moral motivation, including egoism vs. altruism, commitment vs. defaulting, guilt vs. non-guilt, apology plus forgiveness, counterfactual collaboration, among other factors fundamental in the motivation of moral action. These being basic elements in most moral systems, our studies deliver generalizable conclusions that inform efforts to achieve greater sustainability and global benefit, regardless of cultural specificities in constituents.

This study investigated the relation between pretense, counterfactual reasoning (CFR), and executive functions (EFs) based on the ‘Unifying Theory of Imaginative Processes’ proposed by Weisberg & Gopnik (2013). An observational study tested a hypothetical model of the structural relation between pretense and CFR and whether a second-order factor explains their shared associations. 189 typically developing children (Mean age = 58 months, SD = 4 months; males = 101, females = 88) were recruited from Cambridgeshire, UK and completed pretend-play, CFR, EFs, and language tasks. Pretense and CFR constructs were significantly correlated (r = 0.57, p = .001) and the hypothetical model was a good fit of the data. The empirical evidence provides initial support to the unifying theory of imaginative processes.

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.

Did Tom’s use of nuts in the dish cause Billy’s allergic reaction? According to counterfactual theories of causation, an agent is judged a cause to the extent that their action made a difference to the outcome (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2020; Gerstenberg, Halpern, & Tenenbaum, 2015; Halpern, 2016; Hitchcock & Knobe, 2009). In this paper, we argue for the integration of epistemic states into current counterfactual accounts of causation. In the case of ignorant causal agents, we demonstrate that people’s counterfactual reasoning primarily targets the agent’s epistemic state – what the agent doesn’t know –, and their epistemic actions – what they could have done to know – rather than the agent’s actual causal action. In four experiments, we show that people’s causal judgment as well as their reasoning about alternatives is sensitive to the epistemic conditions of a causal agent: Knowledge vs. ignorance (Experiment 1), self-caused vs. externally caused ignorance (Experiment 2), the number of epistemic actions (Experiment 3), and the epistemic context (Experiment 4). We see two advantages in integrating epistemic states into causal models and counterfactual frameworks. First, assuming the intervention on indirect, epistemic causes might allow us to explain why people attribute decreased causality to ignorant vs. knowing causal agents. Moreover, causal agents’ epistemic states pick out those factors that can be controlled or manipulated in order to achieve desirable future outcomes, reflecting the forward-looking dimension of causality. We discuss our findings in the broader context of moral and causal cognition.

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target's location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with $10^7$ latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants' behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

Aims: No recent studies have investigated language effects on counterfactual reasoning in bilinguals. This paper investigates the impact of bilinguals’ native language and language of testing on counterfactual reasoning, addressing two questions: (1) Do older Chinese reasoners, educated before English became a school subject, draw different inferences, or use different cues to draw inferences, compared with English peers and younger ChineseL1 reasoners? Does knowing English affect their reasoning? and (2) Do Chinese reasoners draw different inferences, or use different cues, when tested in Chinese and when tested in English? Design: Experiment 1: The explanatory variables are first language (between-group: Chinese, English), age cohort (between-group: young, older), inferential chain length (within-group: short, long). Experiment 2: The explanatory variables are language of testing (between-group: Chinese, English) and inferential chain length (within-group: short, long). The outcome is the consequent probability rating. Open questions investigate cues used to draw inferences. Analysis: The sample comprised 188 participants. Generalised linear mixed-effects models were used for quantitative data, thematic analysis for qualitative data. Findings: Older Chinese speakers rate long-chain consequents as more probable than English peers. Chinese and English reasoners use different cues to make inferences, as do Chinese reasoners tested in Chinese L1 or English L2. Originality: This is the first paper to compare Chinese reasoners educated before and after English entered the school curriculum, and to investigate inferential chain length effects on Chinese counterfactual reasoning. It introduces a novel task (consequent evaluation), and adopts a mixed-method approach to investigate both the product and process of reasoning, using quantitative and qualitative data respectively. Significance: The study provides new evidence and interpretation for the old debate about language effects on counterfactual reasoning in cognitive psychology; shows that conditional reasoning is a fruitful topic for linguistic relativity and bilingual cognition research; and testifies that qualitative data allows detection of differences in thinking processes.