Counterfactual models perturb a situation model to measure the explanatory power of different causes. We modify one to explore how people explain others' behaviour in two online experiments. First, 90 UK-based adults rated the likelihood of various scenarios combining short biographies with characters' trajectories through a gridworld. Then 49 others saw each scenario and outcome, and verbally gave their best explanations for why the character went the way they did. Participants generated a range of explanations for even the most incongruous behaviour, citing factors outside the given information. Using the innovative features of crowdsourced parameters and free text responses, we show people anchor to real world dependency c.75% of the time, and stochastically sample the rest. We present an expanded version of the counterfactual effect size model (Quillien & Lucas 2022) which handle surprising behaviours and performs better than the existing model it is based on.

Optimal decision making depends on people’s ability to generate and test hypotheses of their environments. To understand how hypotheses are tested, researchers often focus on uncovering and documenting the typical testing strategies that people spontaneously use (e.g., a positive or confirmatory testing strategy). Under this approach, it is difficult to account for people’s capacity to overcome the limitations of their existing hypothesis testing strategies and/or to invent completely new strategies. Here we sketch a model for how hypothesis testing strategies can themselves be generated and evaluated and discuss its implications for existing models of hypothesis testing.

We explore whether people can recognise the epistemic goal of active learners. In a novel online experiment, 110 adults watched screen recordings of other adults (``players'') manipulating objects in a 2D simulated physical microworld. Players had the goal of either identifying the magnet-like force connecting two of the objects, or their relative masses. Observers were asked to identify the learning goal of the player. By drawing from a previously collected dataset of active physical learning interactions and an ideal observer analysis, we manipulated how informative the players' actions are about the target property, and observers' level of access to the players' micro-control actions. We found observers were better at identifying the goals of successful players and of players trying to identify force, while the micro-dynamic evidence improved accuracy on identifying the mass goal. We use mixed methods to explore what cues observers used to make these judgments.

Many of the real world phenomena that cognizers must grapple with are continuous, not only in the values they can take, but also in how these values change over time. The mind must somehow abstract from these inputs to extract useful discrete concepts such as objects, events and causal relationships. We investigate several factors that affect basic inferences about causal relationships between continuous variables based on observations in continuous time. In a novel experiment, we explore the ways in which causal judgments are sensitive to factors that relate to causal inductive biases (e.g. causal lags, the direction of variation) and causal perception (e.g. the range and rapidity of variation). We argue standard statistical time-series models have limited utility in accounting for human sensitivity to these factors. We suggest further work is needed to fully understand the cognitive processes that underlie causal induction from time-series information.

How do people use temporal information to make causal judg-ments? A number of studies have investigated the role of timein inferring generative causal structure, while few have exam-ined prevention. Here, we focus on a challenging task in whichparticipants learn the structure of several causal “devices” bywatching the devices’ patterns of activation over time. Eachdevice potentially includes both generative (producing an acti-vation of its effect) and preventative (blocking any effect acti-vations within a short time window) causal relationships. Weexamine judgment patterns through the lens of a normativemodel which incorporates actual causation with considerationsof prevention. We contrast this with a more computationallytractable feature-based approximation. Participants’ perfor-mance was substantially above chance in all conditions. Themajority of participants’ causal judgments were best fit by thefeature-based approximation based on delay and count heuris-tic cues.

Human learning and generalization benefit from bootstrapping: we arrive at complex concepts by starting small and building upon past successes. In this paper, we examine a computational account of causal conceptual bootstrapping, and describe a novel experiment in which the sequence of training data results in a dramatic order effect: participants succeed in identifying a compound concept only after experiencing training data in a “helpful” order. Our computational model represents causal relations as reusable, modular programs, which can themselves be “chunked” and flexibly reused to tackle more complex tasks. Our specific approach is based in combinatory logic and adaptor grammars, building on previous theories that posit a “language of thought” for concept representation, but making the learning process more sensitive to a learner’s experiences than any particular choice of conceptual primitives. Crucially, we demonstrate that a caching mechanism like that used in adaptor grammars is key to explain human-like bootstrapping patterns in causal generalization.

Like scientists, children have a sharp sense of when and howto seek evidence, but when it comes to generating causal in-terventions, their performance often falls short of normativeinformation-theoretic metrics such as the expected informationgain (EIG). We looked at whether this deviation resulted frommixing discriminatory strategies such as maximizing EIG withconfirmatory strategies such as the positive test strategy (PTS).Thirty-nine 5- to -7-year-olds solved 6 puzzles where they hadone opportunity to intervene on a three-node causal system toidentify the correct structure from two possibilities. Children’sintervention choices were better fit by a Bayesian model thatincorporated EIG and PTS compared to alternative models thatonly considered a single strategy or selected interventions atrandom. Our findings suggest that children’s intervention strat-egy may be a combination of discrimination and confirmation.

Research in lexical acquisition has frequently focused on children's ability to make rapid, context-informed guesses about the meaning of newly encountered words, known as ‘fast mapping'. However, there is a gap in research examining how children and adults revise and adjust these guesses about word meanings as they encounter words repeatedly applied to different referents. We propose, on computational grounds, that learners adjust word meanings incrementally to accommodate new evidence. To begin to test this proposal, we lay out a new research program probing how word meanings evolve. In a pilot experiment, adults learn the meaning of novel kinship terms and we probe their beliefs by repeatedly eliciting generalizations. We manipulate the order in which participants observe the same word used to refer to different members of a family tree. We find a mixed pattern of order effects but our inspection of individual trajectories suggestive of a syntax-level relationship between the current and previous hypothesis. This relationship was supported by a computational model based analysis of lexical meaning generation via a probabilistic language of thought.

Causal models are key to flexible and efficient exploitation of the environment. However, learning causal structure is hard, with massive spaces of possible models, hard-to-compute marginals and the need to integrate diverse evidence over many instances. We report on two experiments in which participants learnt about probabilistic causal systems involving three and four variables from sequences of interventions. Participants were broadly successful, albeit exhibiting sequential dependence and floundering under high background noise. We capture their behavior with a simple model, based on the “Neurath’s ship” metaphor for scientific progress, that neither maintains a probability distribution, nor computes exact likelihoods.

In this study, we investigated human causal learning in a continuous time and space setting. Our experiments revealed that people are capable causal learners in such contexts, and that standard Bayesian updating of prior beliefs partially explains how priors impact their judgments. Specifically, individuals with strong prior beliefs consistent with the ground truth are less likely to misinterpret indirect effects as direct in structures like causal chains. When priors were inconsistent with the ground truth however, participants performed worse on average, choosing less informative interventions. Computational modelling revealed that to deal with the abundance of data in this setting, participants use interventions to mark informative data and focus on direct outgoing links from the variable they intervene on. This task-decomposition strategy, when paired with participants' interventions, achieves comparable accuracy to using the entirety of the dataset, despite ignoring almost half of the available data. These findings are in line with the resource rational framework, where discarding data outside of interventions saves computational costs and the full inference problem about the graph is broken down into sub-problems of inferring individual links between variables. Overall, our study reinforces the idea that humans are frugal and intuitive active learners who combine actions and inference to optimize learning while minimizing effort.