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Extending counterfactual reasoning models to capture unconstrained social explanations

Stephanie Droop
University of Edinburgh ~ Institute for Language, Cognition and Computation

In contrast to rationalist accounts, people do not always have consistent goals nor do they always explain other people's behaviour as driven by rational goal pursuit. Elsewhere, counterfactual accounts have shown how a situation model can be perturbed to measure the explanatory power of different causes. We take this approach to explore how people explain others' behaviour in two online experiments and a computational model. First, 90 UK-based adults rated the likelihood of various scenarios combining short biographies with trajectories through a gridworld. Then 49 others saw each scenario and outcome, and verbally gave their best explanations for why the character moved the way they did. Participants generated a range of explanations for even the most 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.



causal model
mixed methods

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Cite this as:

Droop, S. (2023, July). Extending counterfactual reasoning models to capture unconstrained social explanations. Abstract published at MathPsych/ICCM/EMPG 2023. Via