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Open Access Publications from the University of California

Individual Differences in Causal Learning


Causal inference from observed cases is a central cognitive challenge. There has been some evidence for individual differences in causal learning strategies, but prior work has not examined fine-grained sequences of judgments. In this paper, we report a large-scale model-fitting effort to determine the best-fitting causal inference models for individual participants. We fit a range of different model-types against multiple judgment sequences from each participant, thereby enabling comparisons of learning strategy both between- and within-participant. The model-fitting effort revealed some diversity in learning strategy along both dimensions, though individuals did exhibit some stability. Overall, however, the model fits were worse than expected, particularly when compared to the high accuracy reported for many of the models when used to predict group-level causal judgments. These results thus call into question whether these models might accurately describe the average behavior without accurately describing many (or any) individual’s behaviors.

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