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This work attempts to bridge the divide between accounts of causal reasoning with respect to agents and objects. We begin by examining the influence of animacy. In a collision-based context, we vary the animacy status of an object using 3D animations. By holding the fine-grained kinematics of the actual and counterfactual outcomes fixed across animate and inanimate conditions, we find that animacy, itself, has no effect on causal attribution judgments. Next, we test if causal judgments for animate and inanimate objects differ as a function of the counterfactuals they respectively afford in a disjunctive causal structure. Here, we find that the effect of perceived animacy on causal attribution is mediated by differences in counterfactual judgments. Finally, we introduce the known effect of prescriptive norm violations to this paradigm. Our findings collectively highlight how normative expectations specify the counterfactual considerations that guide causal reasoning for both agents and objects.

CogSci 2023

Causal Reasoning Across Agents and Objects

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The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Humans excel at causal reasoning, yet at the same time consistently fail to respect its basic axioms. They seemingly fail to recognize, for instance, that only the direct causes of an event can affect its probability (the Markov condition). How can one explain this paradox? Here we argue that standard normative analyses of causal reasoning mostly apply to the idealized case where the reasoner has perfect confidence in her knowledge of the underlying causal model. Given uncertainty about the correct representation of a causal system, it is not always rational for a reasoner to respect the Markov condition and other ‘normative’ principles. To test whether uncertainty can account for the apparent fallibility of human judgments, we formulate a simple computational model of a rational-but-uncertain causal reasoner. In a re-analysis of a recent causal reasoning study, the model fits the data significantly better than its standard normative counterpart.

Uncertainty can explain apparent mistakes in causal reasoning

Gathering information via question asking is an essential and effective tool for learning. However, it also requires learners to select from a near infinite space of possible queries. Here, we investigate a potentially powerful guide for question asking in young learners: the relationship between cause and effect. Children (5- and 7-year-olds) read a storybook about an event with an unknown cause and made several choices between two questions to ask about possible candidate causes. Both questions revealed similar information, but only one had the potential to determine whether a candidate was capable of causing the event described. Participants overwhelmingly selected causally relevant over irrelevant questions, with strong performance in both age-groups and for all types of information. These results suggest that young learners employ their understanding of the casual connections between events to identify and impose useful constraints on information search.

Children Use Causality to Guide Question Asking

Causal perception is a cornerstone of early cognitive development. A large database of research attests to the fact that infant causal perception emerges between 6 and 10 months of age. However, this research has tended to use artificial stimuli, and it remains unknown how infants learn about the causal properties of more realistic categories such as people and objects. For example, how do infants learn that people can cause other people to act and behave at a distance, whereas inanimate objects require contact to move and act? One answer to this question is that this knowledge is present from birth or shortly thereafter and is underpinned by core knowledge systems. An alternative perspective maintains that infants acquire this knowledge via domain-general associative learning. The goal of the present paper is to demonstrate that this alternative perspective—implemented in a connectionist computational model—is sufficient to explain infants’ developing knowledge about people and object causal action.

How infants learn about people and object causal action: An associative account

Group disparities, such as the gender wage or racial achievement gap, pervade our society. Unfortunately, from a young age, we often attribute these disparities to inherent features of groups, leading to problematic outcomes (e.g. overgeneralizing, justifying group disparities as the norm). Structural thinking, which attributes disparities to a stable external structure, could be an alternative causal framework, although little work has explored its outcomes. Could structural thinking lead to more positive social outcomes than inherent thinking? Adults who learned about the structural (versus biological or cultural) origins of an occupational disparity were more context-sensitive in their generalizations, less likely to endorse the disparity, and provided more structural interventions. 7- to 9-year-olds showed hints of the former two as well. Cultural thinking showed an intermediate pattern between biological and structural thinking. Overall, structural thinking could be a more socially fruitful way of thinking that mobilizes progress on social disparities.

Let's talk structure: the positive outcomes of structural thinking

Prior knowledge can affect causal judgments by inducing expectations in learners. One important type of prior knowledge is about domains; for example, physical systems are typically believed to be more deterministic than psychological ones. We examine the role of these types of determinism expectations in shaping subsequent causal judgments, and we argue that they mediate effects of abstract domain-wide expectations. Study 1 shows that expectations of determinism—in particular, of sufficiency—positively predict judgments of strength and sufficiency ratings, even when holding statistical data constant. Study 2 shows that abstract expectations at the domain level have an effect on causal judgments, but these effects are mediated (differently between conditions) by vignette-level expectations of determinism. These results jointly suggest a productive way to conceptualize domain effects on causal judgments, namely as effects of expectations about the causal relations being investigated.

Expectations of Determinism Underlie Domain Effects on Adult Causal Learning

In contrast to rationalist models, people do not always have singular goals or even explain other people's behaviour as goal pursuit. Elsewhere, counterfactual accounts determine ways an event may be modified to measure the effects of different causes. We explore how people explain surprising behaviour in two online experiments, with the help of computational modelling. First, 90 UK-based adults rated the likelihood of various outcomes given various situations. Then 49 others saw each situation and outcome, and verbally gave their best explanations for what happened. People generate a range of explanations for even incongruous behaviour. We present an expanded version of a counterfactual effect size model which uses innovative features (crowdsourced parameters and free text responses) that not only can generalise to human situations and handle a range of surprising behaviours, but also performs better than the existing model it is based on.

Extending counterfactual reasoning models to capture unconstrained social explanations

We investigate the role of causal stability in guiding children’s exploration. To study this, we developed a computerized version of an information-search paradigm to study how children dynamically explore different agents and backgrounds to learn more about their causal stability. We presented 5- to 7- year old children (n = 60) with stable and unstable causes and assessed children’s causal attributions of outcomes and their exploratory behavior as they tried out previously observed and novel causes across previously observed and novel backgrounds. We find that children acknowledge causal instability in their causal attributions. They become increasingly adept at tracking causal efficacy across multiple factors simultaneously, but this does not translate into a blanket preference for exploring stable or unstable causes. We suggest a possibility that causal (in)stability guides exploration in more subtle and indirect ways and discuss the implications of our findings for the development of active exploration.

The Role of Causal Stability in Children's Active Exploration

There are at least three ways of learning how the world works: learning from observations, from interventions, and from explanations. Prior work on causal inference focused on how people learn the underlying causal structure of a model through observation and intervention. Our study is the first to look at what people can learn from explanations in the context of a causal structure learning paradigm. We develop a normative inference model that learns from observations and explanations, and compare the model's predictions to participants' performance. The task is to infer the causal connections in 3-node graphs, based on information about their co-activation, and explanations of the kind "B activated because A activated". We find that participants learn better from explanations than from observations. However, while the normative model benefits from having observations in addition to explanations, participants did not.

Show and tell: Learning causal structures from observations and explanations

Children exhibit a sophisticated explanatory judgment: they expect, generate, and judge explanations. Do children also exhibit a metaphysical explanatory judgment, such that everything must have an explanation? If they do, this would conform to what philosophers have called the Principle of Sufficient Reason (PSR). In this study, 6-9-year-old children (N = 55, Mage = 8.07, SDage = 1.32) were shown a number of statements across domains (Psychology, Biology, Nature, Physics, Religion, and Supernatural) and were asked if they agree or disagree that each statement has to have an explanation. As a comparison, children were also asked about coincidences, which, metaphysically, do not have an explanation. We found that children conform to the PSR: of the statements that children thought were true, children of all ages believed that the statements must have an explanation, except for coincidences. This is the first step at uncovering the development of our metaphysical explanatory judgment.

Must there be an explanation? Children and the Principle of Sufficient Reason

In science, we use common graphical representations to indicate changes in events over time, independent of domain. Are children also sensitive to abstract patterns in the ways events change over time? In a series of four experiments, we show that young children (range: 48-84 months) distinguish different function families (Exp 1). Children can also distinguish specific function types within function families (e.g., between linear and sigmoid monotonic functions; Exp 2); nonetheless, they will group different function types within a family together, rather than with functions in a different family (Exp 3). Finally, we show that children’s sensitivity to functions is abstract, allowing them to match observable causes to verbal descriptions of their effects (Exp 4). These results suggest that although some aspects of function understanding, like learning how to interpret graphs, requires formal education, the ability to identify abstract functional relationships is intuitive and early emerging.

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Uncertainty can explain apparent mistakes in causal reasoning

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