Reading focus
Syllabus: How Do We Build and Test Psychological Theories?
Track how the combined prediction of cues determines learning about each cue.
Why you’re reading it. Work through a theory that makes related effects follow from a common rule.
A question to consider. When a well-learned cue is joined by a new cue, what changes in the outcome would alter the model’s prediction for learning about the newcomer?
Why this reading
This chapter turns a verbal idea about surprise into a model with explicit predictions. Its central move is to make learning about one cue depend on the combined prediction supplied by the cues present, allowing the same rule to address several forms of cue competition.
Before you read
A conditioned stimulus (CS) is a cue; the unconditioned stimulus (US) is the event it signals. Associative strength is a model quantity, not a directly observed response. Prediction error is the difference between the outcome supported by the US and the current combined prediction. The learning-rate parameters regulate the size of an update.
Read for
- Translate the update rule into words and distinguish a cue’s own associative strength from the compound’s summed strength.
- Trace why prior learning about A can reduce learning about X when AX is followed by the same outcome, and why a larger-than-expected outcome changes the prediction.
- Notice that even a reinforced trial can reduce associative strength when the combined prediction exceeds the outcome; examine the assumptions needed to connect strength with behavior.
Consider afterwards
- Two separately trained cues each predict one outcome. What does the model predict when they are combined but the outcome remains unchanged? State which assumptions your answer needs.
Editorial context
The chapter proposes a formal account, not a complete description of learning or a demonstrated neural mechanism. It leaves parts of the mapping from associative strength to responding unspecified. Soto and colleagues’ later appraisal discusses its continuing influence alongside its limitations. Explaining a familiar effect is one achievement; specifying conditions under which the effect should change is another.