Benjamin M. Rottman

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Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent - the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal structures(More)
We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback(More)
Previous work has found that one way people infer the direction of causal relationships involves identifying an asymmetry in how causes and effects change over time. In the current research we test the generalizability of this reasoning strategy in more complex environments involving ordinal and continuous variables and with noise. Participants were still(More)
When testing which of multiple causes (e.g., medicines) works best, the testing sequence has important implications for the validity of the final judgment. Trying each cause for a period of time before switching to the other is important if the causes have tolerance, sensitization, delay, or carryover (TSDC) effects. In contrast, if the outcome variable is(More)
When estimating the strength of the relation between a cause (X) and effect (Y), there are two main statistical approaches that can be used. The first is using a simple correlation. The second approach, appropriate for situations in which the variables are observed unfolding over time, is to take a correlation of the change scores – whether the variables(More)