Reading focus
Syllabus: How Do We Learn What Predicts What?
Compare predictive and diagnostic instructions while keeping presentation order separate from causal direction. Identify the cue-competition pattern that each account expects.
Why you’re reading it. Test whether a model built around cue–outcome statistics needs additional assumptions about the learner’s causal interpretation.
A question to consider. Keep a training sequence constant but describe its cues as causes in one condition and effects in another. What judgment difference would challenge an account that represents only that sequence?
Why this reading
The same sequence of observations can be understood as causes predicting an effect or as effects indicating a cause. These experiments ask whether that interpretation changes cue competition, challenging accounts that ignore what the cues mean to the learner.
Before you read
Predictive reasoning moves from a proposed cause to its effects; diagnostic reasoning moves from effects to a possible cause. Neither direction of reasoning changes the assumed direction of causation. A causal model represents that structure; an observed correlation alone does not establish it.
Read for
- Identify how instructions establish the causal roles of cues, while distinguishing those roles from their order of presentation.
- Compare the blocking and later cue-rating patterns across predictive and diagnostic conditions.
- Ask which assumptions of the associative models under comparison generate symmetrical predictions and how a causal-model account differs.
Consider afterwards
- Two indicator lights could be competing causes of a machine failure or separate consequences of one failure. How might learning about the first light change judgments about the second in each case?
Editorial context
The three experiments support the importance of causal interpretation in the tasks examined. They do not establish that all associative models ignore meaning or that people invariably infer the correct causal structure. A result that challenges a specified model is narrower than a verdict on every possible associative explanation.